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Abstract

The integration of information and communication 1 technologies in traditional grid brings about a smart grid. 2 Energy management plays a vital role in maintaining the 3 sustainability and reliability of a smart grid which in turn helps 4 to prevent blackouts. Energy management at consumers side 5 is a complex task, it requires efficient scheduling of appliances 6 with minimum delay to reduce peak-to-average ratio (PAR) 7 and energy consumption cost. In this paper, the classification 8 of appliances is introduced based on their energy consumption 9 pattern. An energy management controller is developed for 10 demand side management. We have used fuzzy logic and 11 heuristic optimization techniques for cost, energy consump-12 tion and PAR reduction. Fuzzy logic is used to control the 13 throttleable and interruptible appliances. On the other hand, 14 the heuristic optimization algorithms, BAT inspired and flower 15 pollination, are employed for scheduling of shiftable appliances. 16 We have also proposed a hybrid optimization algorithm for 17 the scheduling of home appliances, named as hybrid BAT 18 pollination optimization algorithm. Simulation results show a 19 significant reduction in energy consumption, cost and PAR. 20

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... A bio-inspired research work proposed in [20] employed Bacterial Foraging Optimization Algorithm (BFA), BAT algorithm, and a proposed Hybrid BFA and BAT (HBB) to produce an optimized scheduling plan for the appliances of residential consumers. Khalid et al. in [21] introduced an EMC that puts customer's priority ahead Game theory based approaches like [22] are implemented to solve the problem of HEMS. Peak saver PLUS program for Ontario, Canada is introduced in [22]. ...
... Eq. (4) holds that the sum of power of all devices at a time slot t i must always be less than p max . The concept of p max is inspired by the work in [21]. ...
... The R of a device is the average number of time slots for which the device was turned on (power > 0) every day. For the q and x, the day is divided into 10 sections namely late-night (time slot (1-10)), early-morning (11)(12)(13)(14)(15)(16), morning (17)(18)(19)(20), late-morning (21)(22)(23)(24), early-afternoon (25)(26)(27)(28), afternoon (29)(30)(31)(32), lateafternoon (33)(34), early-evening (35)(36)(37)(38), evening (39)(40)(41)(42), night (43)(44)(45)(46)(47)(48). The section of the day for which the frequency of the device usage is maximum gives the q (first time slot of the section) and the x (last time slot of the section). ...
Article
The amount of electricity consumed grows exponentially with each passing year. Energy management (EM) has the potential to help satisfy electricity demands more efficiently. It implements strategies to reduce energy consumption during peak time or shift its usage to off-peak hours. This research introduces SPEMS (Sustainable Parasitic EM System), a competitive yet cooperative Multi-agent System (MAS) based framework that optimizes energy cost, consumption, Peak-to-Average Ratio (PAR), and user’s discomfort unobtrusively. The framework devises an optimized load scheduling plan for smart appliances using the Host-Parasite Model where the agents with conflicting objectives need to have a symbiotic relationship. Our work follows a quantitative approach towards evaluating user comfort. It takes into account environmental aspects like user presence and weather elements including user temperature, apparent temperature, and visibility. SPEMS adapts itself to user’s behavior patterns without requiring the user to enter their everyday feedback. The concept of sampled Hall of Fame is incorporated to preserve and use the best schedules from previous days ensuring progress towards finding optimal schedules. Experiments have been performed on real-time data collected from a real home for about one and a half years. Results show that our model is computationally inexpensive, sustainable, and minimizes cost, consumption, and PAR with reduced discomfort of the user.
... The smart home (utilizing home automation, or domotics), one of the key components of a smart grid, is a dwelling that serves the residents with security, healthcare, comfort, and remote control of the home appliances through smart technology [6,7]. Smart home energy management plays an important role in Demand Side Management (DSM), one of the aspects of the smart grid [8], which deals with controlling and optimizing the various smart home appliances according to the user needs and preferences to reduce the electricity consumption and therefore the cost, enhancing energy efficiency, and maintaining a clean and green environment [9]. Although various researchers have been working in this field for years in achieving said objectives, still there is a need for state-of-the-art technologies and developments to provide optimal solutions in maximizing user comfort levels and assisted living as well as energy consumption and wastage reduction. ...
... It is concluded that the proposed method can reduce energy consumption by up to 50%. The study in [8] classified the appliances based on their energy consumption pattern [89] and accordingly designed fuzzy controllers to control the HVAC and the illumination system. ...
... A recent study [151] used MILP with normalized weighted sum and compromise programming for solving scheduling problems considering the TOU pricing scheme. The work in [8] scheduled the appliances using the Bat algorithm [152], Flower pollination, and hybrid Bat Flower pollination optimization techniques, respectively. A novel appliance scheduling optimization for a flexible and comfortable environment contributed to peak load reduction while considering socio-technical factors [153]. ...
Article
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The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.
... Optimal load scheduling enables inter and intra neighborhood scheduling of the load with efficiency near to optimal scheduling [30]. Demand side management (DSM) optimize energy consumption pattern that is presented in [31]. Three different types of optimizations (BAT, HFBA and FP) are used for scheduling of devices which reduce Peak to Average Ratio and consumption cost. ...
... Mathematical formulation of encircling and hunting behavior of this technique is given below. Equation from (28) to (31) are the equations, which describe the encircling behavior while (32) to (38) are describing hunting mechanism of grey wolf pack. ...
... Position vector of the prey and position vector of the grey wolf are described by X p and X respectively. Equation (30) and (31)are used to calculate the value of vector A and C ...
Article
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The increasing demand of energy in the traditional grids is getting more complex, less feasible, harmful, uneconomical and high in power losses. This paper presents an efficient energy management approach to mitigate such issues with smart micro grid (SMG) and aims at a solution that is both cost effective and eco-friendly, within energy market paradigm. Goals are achieved with the help of Home Energy Management Controller (HEMC), Energy Market Management Controller (EMMC) and Control Agent (CA). The individual load is managed in the presence of local generation, storage system, user comfort, DGs and Utility within energy market paradigm. Two level energy management approach is proposed to achieve concerned goals. First is to manage load and schedule storage with respect to individual local generation and market pricing. Second is to manage energy market with the help of four different types of priorities and control agent input. The problem is solved with a variant of meta-heuristic method, Multi Objective Grey Wolf Optimization (MOGWO), which gives more comprehensive solution by comparing with Particle Swarm Optimization (PSO). The proposed methodology is implemented on a SMG based-community test system. Homes within that community have different economic conditions and personal priorities. Simulation results demonstrates achievement of aimed goals in presented work.
... Optimal load scheduling enables inter and intra neighborhood scheduling of the load with efficiency near to optimal scheduling [30]. Demand side management (DSM) optimize energy consumption pattern that is presented in [31]. Three different types of optimizations (BAT, HFBA and FP) are used for scheduling of devices which reduce Peak to Average Ratio and consumption cost. ...
... Mathematical formulation of encircling and hunting behavior of this technique is given below. Equation from (28) to (31) are the equations, which describe the encircling behavior while (32) to (38) are describing hunting mechanism of grey wolf pack. ...
... Position vector of the prey and position vector of the grey wolf are described by X p and X respectively. Equation (30) and (31)are used to calculate the value of vector A and C ...
Article
Full-text available
The increasing demand of energy in the traditional grids is getting more complex, less feasible, harmful, uneconomical and high in power losses. This paper presents an efficient energy management approach to mitigate such issues with smart micro grid (SMG) and aims at a solution that is both cost effective and eco-friendly, within energy market paradigm. Goals are achieved with the help of Home Energy Management Controller (HEMC), Energy Market Management Controller (EMMC) and Control Agent (CA). The individual load is managed in the presence of local generation, storage system, user comfort, DGs and Utility within energy market paradigm. Two level energy management approach is proposed to achieve concerned goals. First is to manage load and schedule storage with respect to individual local generation and market pricing. Second is to manage energy market with the help of four different types of priorities and control agent input. The problem is solved with a variant of meta-heuristic method, Multi Objective Grey Wolf Optimization (MOGWO), which gives more comprehensive solution by comparing with Particle Swarm Optimization (PSO). The proposed methodology is implemented on a SMG based-community test system. Homes within that community have different economic conditions and personal priorities. Simulation results demonstrates achievement of aimed goals in presented work. INDEX TERMS Control agent, energy market management controller, multi objective grey wolf optimization , home energy management controller, local generation, market management, micro-grid, particle swarm optimization, renewable energy resources.
... Khalid et al. proposed a home energy management system that schedules and manages different classes of home appliances and load [21]. Flexible load like HVAC is controlled using the fuzzy logic methodology along with the hybrid optimization technique of bat and pollination algorithms used to schedule the shiftable appliances. ...
... As compared to classical control theory, an intelligent fuzzy logic controller does not require the specific mathematical formula for design, rather a practical understanding of the system under consideration is required. Kiyak et al. [18] Fayaz and Kim [19] Khalid et al. [21] Our proposed FIS Fuzzy controllers have been considered the most suitable choice among researchers for systems where analysis is very complex with existing linear controllers. Deployment of the fuzzy controller has been found in various domains, such as aerospace, medical imaging, data mining, classification, etc. [29] to name a few. ...
... For the rest of the text, this method will be referred to as "Model A". The third method used for the evaluation and comparison of the proposed fuzzy logic controller considers occupancy and electricity prices in addition to outdoor and indoor lighting for the selection of the illuminance setpoint in autonomous mode [21]. In this article, "Model B" will be used when discussing the simulation results using fuzzy controller proposed in [21]. ...
Article
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Internet of things is providing us numerous ways to improve our quality of experience by using smart cyber-physical infrastructure systems. Also, due to arrival of LED lighting systems, there is the possibility to improve user’s visual comfort at less cost. In our proposed model, by using a fuzzy inference system, used in cyber-physical infrastructure system, we save energy from the heating, ventilation and air conditioning system. This saved energy is used to improve the visual comfort of the user. Simulation results show that considering the visual comfort standard of 500 lux instead of 250 lux results in energy savings and ensures visual comfort. Together with the preservation of thermal comfort increases the overall users’ comfort. Since research confirms that users’ improved comfort results in up to 14% of increased productivity. Our model is unique in the sense that using fuzzy logic, indirectly improved the users’ productivity. By using our fuzzy logic controller on electric equipment, we can achieve improved users’ performance without paying any extra cost.
... The proposed controller considered the home appliance scheduling problem to reduce total energy consumption and resulted in an 18.33% overall reduction. Moreover, both fuzzy logic-based and heuristic optimization methods have been employed in (Khalid et al., 2019) for controlling the interruptible appliances and scheduling the shift table ones, respectively. The obtained results indicated the efficiency of the proposed algorithms in reducing the total electricity cost. ...
... Each input has a membership function which describes the associated ranges of this input. The proposed membership functions have been designed based on preliminary experiments and literature (Khalid et al., 2019;Atef & Eltawil, 2019c). Figures 3 and 4 represent the membership degree of EP and LC, respectively, with three ranges of low, medium, and high. ...
Article
Smart Home Energy Management Systems (HEMS) constitute a vital necessity for optimizing electricity usage and saving energy in smart grids. However, these systems rely on dynamic factors that are stochastic and difficult to predict, such as the load consumption and electricity prices. Therefore, constructing an efficient control system for residential buildings requires an accurate prediction process of the associated parameters. This paper proposes an integrated predictive control system that consists of both predictive model and Demand Response (DR) scheme to predict and control the daily electricity usage in the residential sector. First, a Long Short-Term Memory-based (LSTM) optimized predictive model is implemented for predicting both the hourly load consumption and electricity price for a typical smart home. Then, the predicted data are transmitted to a DR fuzzy logic-based controller that can optimally schedule the home appliances usage. In comparison with the state-of-the-art prediction techniques for the residential load consumption and electricity price, the proposed LSTM predictive model outperforms Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), and Ensembled Boosted Trees (EBT). Moreover, the proposed DR-FIS controller has shown good results in terms of reducing the electricity cost by selecting the optimal time schedule.
... The aim was to provide consumer comfort and optimize electricity consumption based on hourly-to-use tariff (HTOU). Fuzzy logic and heuristic optimization techniques are suggested in [15] for studies on a peak to average ratio (PAR) minimization and UC. The authors categorize home appliances according to the details of the power and performance of each appliance in energy consumption. ...
... Techniques and methods Objectives Pricing tariffs SEM [13] IOT Energy management and monitoring, recording, and controlling the energy consumption -HEMS [12] MBO algorithm, IOT Energy consumption and user comfort -MHEMS [14] SL algorithm Energy consumption and user comfort HTOU EMC [15] Fuzzy logic and BAT, FP, HFBA algorithms ...
Article
The home energy management system (HEMS) based on advanced internet of things (IoT) technology has attracted the special attention of engineers in the field of smart grid (SG), which has the task of the demand side management (DSM) and helps to control the equality between demand and electricity supply. The main performance of HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling the loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic, called Arithmetic Optimization Algorithm (AOA) to discover optimal scheduling of the home appliances, which is called Multi-Objective Arithmetic Optimization Algorithm (MOAOA). Furthermore, the HEMS architecture has been programmed based on the Raspberry Pi minicomputer with Node-RED and NodeMCU modules. HEMS uses the MOAOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the peak to average ratio (PAR), and increase user comfort (UC). Real-time pricing (RTP) and critical peak pricing (CPP) signals are presumed as energy tariffs. Simulations are performed in two different scenarios: (I) appliance scheduling scheme and (II) appliance scheduling scheme with the integration of renewable energy sources (RES). The results of MOAOA are compared with Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Gray Wolf Optimizer (MOGWO), and Multi-Objective Antlion optimization (MOALO) algorithms. The results demonstrate that the use of the presented scheme remarkably reduces the cost of electricity consumption as well as PAR, in addition to the integration of MOAOA with RES, which greatly increases user comfort.
... In recent literature, numerous studies [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] have been documented, in which various forms of home energy management (HEM) system proposed for solving the home energy scheduling problem with attention to minimize consumer's electricity bill by utilizing DSM system features. In study [12], authors proposed a HEM system based on graph search algorithm -Dijkstra, in which to reduce a consumer's electricity bill during peak hours and computational efforts are considered as objective functions. ...
... In [18], authors proposed an optimization approach using bat algorithm and flower pollination algorithm for modeling the HEM system with attention to reduce consumers' electricity bills and enhance user's comfort. Besides, authors also proposed fuzzy controllers to optimize the illumination system and electricity usage in heating/cooling. ...
Article
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In literature, proposed approaches mostly focused on household appliances scheduling for reducing consumers' electricity bills, peak-to-average ratio, electricity usage in peak load hours, and enhancing user comfort level. The scheduling of smart home deployed energy resources recently became a critical issue on demand side due to a higher share of renewable energy sources. In this paper, a new hybrid genetic-based harmony search (HGHS) approach has been proposed for modeling the home energy management system, which contributes to minimizing consumers' electricity bills and electricity usage during peak load hours by scheduling both household appliances and smart home deployed energy resources. We have comparatively evaluated the optimization results obtained from the proposed HGHS and other approaches. The experimental results confirmed the superiority of HGHS over genetic algorithm (GA) and harmony search algorithm (HSA). The proposed HGHS scheduling approach outperformed more efficiently than HSA and GA. The electricity usage cost for completing one-day operation of household appliances was limited to 1305.7 cents, 953.65 cents, and 569.44 cents in the proposed scheduling approach for case I, case II, and case III, respectively and was observed as lower than other approaches. The electricity consumption cost was reduced upto 23.125%, 43.87% and 66.44% in case I, case II, and case III, respectively using proposed scheduling approach as compared to an unscheduled load scenario. Moreover, the electrical peak load was limited to 3.07 kW, 2.9478 kW, and 1.9 kW during the proposed HGHS scheduling approach and was reported as lower than other approaches. INDEX TERMS Demand side management, demand response program, home energy scheduling, smart grid, metaheuristic algorithm.
... The second category [45][46][47][48] uses temperature, humidity, and an infrared camera. The third category uses temperature, humidity and a carbon dioxide sensor data [32,[49][50][51][52][53][54][55][56][57][58]. It is observed that over the years, smart HVAC systems' performance has gradually improved through advanced control strategies whereby ambient conditions and occupants' energy profiles become an integral part of the system. ...
... The demand-side response is mostly applied in smart grid applications to balance demand and supply by encouraging customers to modify their load profile, for example, by scheduling the load to a period of low energy demand [32] to benefit from low tariff structures or adjusting the desired comfort level by minimizing the setpoint energy consumption. For example, the existing alternative to solve power imbalance and high cost by shifting the load compromises the comfort by deviating from desirable setpoint [42,57,58,[71][72][73][74][75][76][77][78]. In the literature [57,79,80], fuzzy logic has been used to control HVAC systems' energy consumption using a programmable microcontroller to adjust temperature set point changes. ...
Article
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Smart building technology incorporates efficient and automated controls and applications that use smart energy products, networked sensors, and data analytics software to monitor environmental data and occupants’ energy consumption habits to improve buildings’ operation and energy performance. Smart technologies and controls are becoming increasingly important not only in research and development (R&D) but also in industrial and commercial domains, leading to a steady growth in their application in the building sector. This study examines the literature on SBEMS published between 2010 and 2020 with a systematic approach. It examines the trend with the annual number of the published studies before exploring the classification of publications in terms of factors such as domain of SBEMS, control approaches, smart technologies, and quality attributes. Recent developments around the smart building energy management systems (SBEMS) have focused on features that provide occupants with an interface to monitor, schedule, and modify building energy consumption profiles and allow a utility to participate in a communication grid through demand response programs and automatic self-report outage functionality. The study also explores future research avenues, especially in terms of improvements in privacy and security, and interoperability. It is also suggested that the smart building technologies’ smartness can be improved with the help of solutions such as real-time data monitoring and machine learning
... Recommendation systems rely on different algorithms and architectures. Prior work has explored various designs of recommendations systems for energy efficiency in buildings: content-based [Luo et al., 2017], goal-based context-aware [Sardianos et al., 2020[Sardianos et al., , 2021, collaborative filtering-based [Luo et al., 2021], fuzzy logic-based [Khalid et al., 2019], user behavior-based [Kar et al., 2019, Machorro-Cano et al., 2020, genetic-based [Yuce et al., 2016], micro-momentsoriented [Varlamis et al., 2022, Himeur et al., 2022, Alsalemi et al., 2019, and multi-agent systems [Jiménez-Bravo et al., 2019, Pinto et al., 2019, Li et al., 2015. To our best knowledge, there has been limited work on device usage recommendation systems for energy efficiency via load shifting in residential buildings. ...
... Thus, the authors claim it contributes to sustainable demand and potentially create opportunities to save energy. An energy management controller based on the fuzzy logic by Khalid et al. [2019] targets the reduction of energy consumption, costs, and peak-to-average ratio. This system uses occupancy data in the household, temperature, and electricity price to control lights, heating ventilation and air conditioning and schedule the appliances' usage. ...
Preprint
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A significant part of CO2 emissions is due to high electricity consumption in residential buildings. Using load shifting can help to improve the households' energy efficiency. To nudge changes in energy consumption behavior, simple but powerful architectures are vital. This paper presents a novel algorithm of a recommendation system generating device usage recommendations and suggests a framework for evaluating its performance by analyzing potential energy cost savings. As a utility-based recommender system, it models user preferences depending on habitual device usage patterns, user availability, and device usage costs. As a context-aware system, it requires an external hourly electricity price signal and appliance-level energy consumption data. Due to a multi-agent architecture, it provides flexibility and allows for adjustments and further enhancements. Empirical results show that the system can provide energy cost savings of 18% and more for most studied households.
... The simulation results showed that the thermal comfort factor is achieved. The study in [187] classified the appliances according to consumption patterns and designed the fuzzy controller to control illumination and HVAC systems. ...
... In the manuscript, we have listed demand-side management strategies for appliance scheduling, which include classical techniques, LP based techniques [6,[71][72][73][74][75][76][77][78][79], NLP based techniques [80][81][82][83][88][89][90][91], convex programming and dynamic programming [81,[92][93][94][95][96], genetic algorithm [104][105][106][107][108]128], particle swarm optimization [128][129][130][131][132], ant colony optimization [128,[133][134][135][136], population based meta-heuristic algorithms [102,[137][138][139][140][141][142][143][144][145][146], hybrid-heuristic algorithms [103,[154][155][156][157][158][159][160][161][162][163][164][165][166][167], artificial neural network based soft computing techniques [168][169][170][171][172][173][174][175][176][177], fuzzy logic based techniques [178][179][180][181][182][183][184][185][186][187], artificial intelligence based techniques [171,[188][189][190][191][192][193][194], storage system-based techniques [195][196][197], sharing and coordinating neighborhood techniques [198][199][200][201][202][203][204][205][206][207][208][209][210], consumer comfort maximization techniques [200,[202][203][204][205]207,210], and incentive-based DR [69,[211][212][213][214][215][216][217][218][219]. • We provided a brief overview of important components of the smart grid to manage household consumption daily. ...
Article
Full-text available
The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak load, maximize local energy generation and improve consumer comfort. To increase the effectiveness of demand response programs for smart homes, various demand-side management strategies are used to enable consumers to optimally manage their loads. This study lists out DSM techniques used in the literature for appliance scheduling. Most of these techniques aim at energy management in residential sectors to encourage users to schedule their power consumption in an effective manner. However, the performance of these techniques is rarely analyzed. Additionally, various factors, such as consumer comfort and dynamic pricing constraints, need to be incorporated. This work surveys most recent literature on residential household energy management, especially holistic solutions, and proposes new viewpoints on residential appliance scheduling in smart homes. The paper concludes with key observations and future research directions.
... Moreover, FLC is applied in illumination management [108], thus leading to a decrease in energy consumption. Furthermore, the FLC employed in HVAC systems results in a significant drop in monetary cost, and peak to average ratio (PAR) [109]. Nevertheless, FLC has shortcomings in terms of selecting the appropriate parameter values [110]. ...
... These techniques also have demerits in dealing with multi-objective problems and their implementation in real-time due to their deterministic nature [271]. In [109], the bat algorithm (BAT) is integrated with the flower pollination (FP) algorithm to develop a new optimization algorithm named the BAT pollination algorithm. The developed algorithm is applied for scheduling home appliances to attain a considerable amount of reduced energy consumption, energy cost, and PAR. ...
Article
Full-text available
Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.
... As shown in Fig. 3, the coordinated operation for the multiple grouped hybrid storage systems includes three layers: allocated layer, dispatching layer, and reference tracking layer. The allocated layer is represented as the above problem (P-a), where P i allocated is the allocated power to ith hybrid storage system; P total is the total expected power to be allocated to n hybrid storage systems; H S con i is the condition of the ith storage system; S OC i , L O H i are the state of charge (SOC) of the battery storage system and level of hydrogen (LOH) of the hydrogen storage system; MC D M represents the multi-criteria decision making algorithm, such as the fuzzy logic [9], the fuzzy membership function or TOPSIS [10]. ...
... The charging and discharging of the battery cannot happen at the same time, i.e., U B ch + U B disch ≤ 1. The state of charge constraint is shown in (9). The electricity power balance is presented in Eq. (10). ...
Article
Full-text available
Multiple hybrid storage systems are commonly grouped together forming as a larger energy and power density storage system, which can better satisfy different demands and situations. However, efficiently and healthily cooperating these multiple hybrid storage systems is still a tactical problem, especially considering various storage numbers, complex electrochemical reactions, multiplex physical and healthy operation conditions. In this paper, both the hydrogen and battery storage are formed as a single hybrid storage, where the hydrogen storage is composed of the fuel cell, hydrogen tanks, and the electrolyzer. Temperature effects are considered to build a two-dimension model of hybrid storage. A three-layer algorithm is then proposed to cooperate the grouped hybrid storage systems: first, an Entropy-fuzzy membership method is adopted to allocate the energy to each hybrid storage system; second, a model predictive control associated with the Kalman filter prediction method is used to dispatch the allocated power to hydrogen storage and battery; third, a proportional–integral–derivative (PID) controller is used to achieve the reference signals tracking. The simulation results indicate that the proposed three-layer algorithm can efficiently and orderly dispatch power to each storage, and can extend the lifetime of the storage system. Featuring the hydrogen storage, the invisible spare power can be stored in tanks, and can be further utilized at any time in the future.
... Their main objective was the minimization of energy cost, ensuring the thermal comfort of the user. A Fuzzy logic and a heuristic optimization were employed to control the throttleable and interruptible appliances, as well as the schedule of shiftable appliances in the smart grid examined in [28]. The simulation results provided in [28] show a reduction of energy costs and peak of demand. ...
... A Fuzzy logic and a heuristic optimization were employed to control the throttleable and interruptible appliances, as well as the schedule of shiftable appliances in the smart grid examined in [28]. The simulation results provided in [28] show a reduction of energy costs and peak of demand. ...
Article
A new methodology, that combine three different Artificial Intelligence techniques, is proposed in this paper to solve the energy demand planning in Smart Homes. Conceived as a multi-objective scheduling problem, the new method is developed to reach the compromise between energy cost and the user comfort. Using an Elitist Non-dominated Sorting Genetic Algorithm II, the concept of demand-side management is applied taking into account electricity price fluctuations over time, priority in the use of equipment, operating cycles and a battery bank. The demand-side management also considers a forecast of a distributed generation for a day ahead, employing the Support Vector Regression technique. Validated by numerical simulations with real data obtained from a smart home, the user comfort levels were determined by the K-means clustering technique. The efficiency of the proposed Artificial Intelligence combination was proved according to a 51.4% cost reduction, when Smart Homes with and without distributed generation and battery bank are compared.
... DSM uses different strategies, as shown in Figure 1, especially peak demand reduction and load shifting [20]. Fuzzy Logic (FL) based EMS have been developed to reduce cost, energy consumption, and Peak to Average Ratio (PAR) without sacrificing comfort and to improve further decision making regarding intelligent fault detection [22][23][24]. For example, an FLC has obtained savings of 15% and 18.5% in electricity costs, respectively, during peak periods by shifting loads from peak periods with high electricity prices to hours with low electricity prices, resulting in [22]. ...
... For example, an FLC has obtained savings of 15% and 18.5% in electricity costs, respectively, during peak periods by shifting loads from peak periods with high electricity prices to hours with low electricity prices, resulting in [22]. Intuitive optimization techniques with FL are used for planning controllable devices to reduce PAR in addition to cost and energy consumption [23]. Also, there are studies for the same aim [21,25]. ...
... However, the peaks in demand may emerge in high price hours, threatening the utility grid station. Furthermore, several models have proposed to solve the energy management problem of residential buildings using fuzzy logic based models [189], and game-theoretic based models [190]. ...
Thesis
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Accurate, fast, and stable electrical energy consumption forecasting plays a vital role in decision making, energy management, effective planning, reliable and secure power system operation. Inaccurate forecasting can lead to the electricity shortage, wastage of energy resources, power outage, and in the worst case, power grid collapse. Contrarily, accurate forecasting enables policymakers and public agencies to make real-time decisions imperative for the energy management and power system’s secure and reliable operation. However, accurate, fast, and stable forecasting is challenging due to consumers’ uncertain and intermittent electrical energy consumption behavior. In this context, a rigid forecasting model with assertive stochastic and non-linear behavior capturing abilities is needed. Thus, several forecasting strategies have been emerged in state-of-the-art work, starting from conventional time series to modern data analytic methods to solve the non-linear electrical consumption prediction problems. The individual techniques partially resolved the forecasting problem by improving forecast accuracy. However, the improvement in accuracy is not up to the mark. Besides, individual techniques (conventional or modern) suffer from their inherent limitations. Due to inherent limitations, forecasting results of individual methods (conventional or modern) are no longer as accurate as required. To solve such problems, hybrid models developed, which fully utilize individual methods’ advantages and have comparatively improved performance. Only some models are commendable that improve accuracy, while others perform better in convergence rate. However, considering only one aspect (accuracy or convergence rate) is insufficient. Thus, accuracy and convergence rate both are of prime importance and can be improved simultaneously. Therefore, scholars and industries have the primary goal of developing a forecasting model, which provides robust, stable, and accurate electrical energy consumption forecasting for efficient energy management. With this motivation, a novel two-stage hybrid model is developed by integrating the electrical energy consumption forecasting stage with the energy management stage. The first stage is for electrical energy consumption forecasting, and the second stage is for efficient energy management. The first stage composed of four modules: (i) a novel cascaded framework based on factored conditional deep belief network (FCDBN), (ii) deep learning based forecaster cascaded with a heuristic algorithm based optimizer framework, (iii) support vector machine (SVM) based forecaster integrated with modified enhanced differential evolution (mEDE) algorithm framework, and (iv) accurate and fast converging deep learning based forecaster framework. The first module framework consists of data preprocessing phase, FCDBN training phase, and FCDBN based forecasting phase. The first module framework is developed in a cascaded manner, where each former phase’s output is fed into the later phase as input, namely cascaded framework. The second module is a hybrid framework composed of data preprocessing and feature selection, training and forecasting, and optimization phases. The third module is an integrated framework of data preprocessing and features engineering, SVM based forecaster, and mEDE based optimizer, namely FA-HELF. The fourth module is a cascaded framework of feature selector, FCRBM based forecaster, and GWDO based optimizer, namely FS-FCRBM-GWDO. The purpose of the modules in the first stage is to provide fast, accurate, and stable electrical energy consumption forecasting. To accomplish this goal first, the data is preprocessed to convert it into a usable format. Secondly, clean and prepared data is passed through feature selection and extraction phases to select the most relevant and desired features from the data. Thirdly, the feature engineering phase’s output is fed to the training phase to empower the forecaster through training and learning processes to accurately forecast electrical energy consumption. Finally, the forecasted electrical energy consumption is given as an input to the optimization phase to further minimize the error in predicted results by optimizing the model’s hyperparameters. The first stage results are fed to the second stage of the proposed model for efficient energy management. The second stage is based on optimization strategies that utilize the forecasted electrical energy consumption pattern for efficient energy management. The second stage comprises three modules: (i) day ahead genetic modified enhanced differential evolution algorithm based module, (ii) genetic modified enhanced differential evolution algorithm based scheduling module, and (iii) genetic wind driven optimization algorithm based energy management controlling module. The purpose of the second stage is to reduce the bill of electricity, mitigate peaks in demand, and acquire the desired tradeoff between electricity bill and user discomfort by utilizing forecasted electrical energy consumption. The proposed model is favorable for both consumers and power companies because it fulfills the need both parties. For consumers, the proposed model minimizes electricity bill and discomfort in terms of waiting time simultaneously. In contrast, the proposed model rewards power companies by alleviating peaks in demand to increase power system stability. Simulation results confirmed the effectiveness and productiveness of the proposed model by comparing it with benchmark models.
... In [13], ML algorithms and statistical models are used to develop smart energy theft system (SETS) for securing Internet of things (IoT) based smart homes [132,133,134]. Whereas, in [17], an artificial neural network (ANN) based scheme is presented to detect illegal electricity consumption patterns using probabilistic ANN and Levenberg-Marquardt algorithm. ...
... A whole community setup that works in both island and connected mode to get price, energy balancing and personal comfort optimally. LITERATURE REVIEW According to [9], multi-dimensional array uses different algorithms such as heuristic optimization to reduce cost, energy consumption, and peak-to-average reduction, bat flower pollination, and hybrid bat, inspiring pollination for scheduling in demand-side management (DSM). Small home setup is optimized by the operation based on Bayesian optimal algorithm (BOA) data-driven online energy management system having distributed generations (DGs) powered by photovoltaic (PV) array and wind working in islanded and connected mode with the grid is calculated in [10]. ...
Thesis
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Smart community setups nowadays are subjected to complicated issues such as instability, intermittent integration of the load at the demand side and lack of intelligent two-way communication process. These issues need to be addressed in terms of a balanced power demand dispatch (DD) in the real-time or day-ahead duplex signal regime under multi-microgrids. This paper offers an intelligent multi-agent-based approach that works between different levels of communication and their respective layers for a community-based system to optimize the power in community-based multi-microgrids model. This will further enhance user personal comfort. Constraints relative to cost minimization also have a relation with this model. A three-level structure with various layers of autonomous agents take intelligent decisions based on prioritized particle swarm optimization (P-PSO), prioritized plug and play (PPnP), and knapsack; considering DD as the main driver of the system to handle price and power consumption uncertainties. Distinct smart home models, depending upon their living habits, are keenly observed providing their power infrastructure and personal comfort. Load appliances considered as load agents are individually contemplated for maximum proficiency. Furthermore, two-way communication between utility and consumers lower downs the risk of inefficiency of the system where anyone seems unsatisfied with the other.
... In [4], ML algorithms and statistical models are used to develop smart energy theft system (SETS) for securing Internet of things (IoT) based smart homes [39], [40]. Whereas, in [6], an artificial neural network (ANN) based scheme is presented to detect illegal electricity consumption patterns using probabilistic ANN and Levenberg-Marquardt algorithm. ...
Article
Full-text available
In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset’s dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models’ performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD.
... Khalid [4] also followed the same EMC model to construct an MKP optimisation, but incorporated the BA, the FPA and the Hybrid Flower Pollination Bat Algorithm (HFBA), which is a hybrid of the BA and the FPA. The HFBA follows the same structure as the BA but replaces the random generation step with the flower swarm step from the FPA. ...
... The outcome of this study showed that the controller enhances the consumer's comfort and decrease power consumption. Furthermore, a FLC based energy management controller for illumination system management is proposed in [18], efficiently achieving a reduction of power consumption and electricity cost. However, the proposed controller is not considered users comfort level and electricity demand of HVAC systems. ...
Article
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There has been a rising concern in decreasing the power consumption in residentialhouse. The greatest user of power in a domestic house is heating ventilation and aircondition (HVAC) system. In numerous countries, limited energy from suppliers andthe enhancement in power demands leads to a new chance in utilization of homeenergy management system (HEMS) for an accurate power utilization. In this study,the backtracking search algorithm (BSA) is utilized for HVAC system to decreaseannual power consumption, reduce electricity cost and maximize thermal comfort.The developed models analyzed the (HVAC heating and cooling system) energyconsumption and cost sceneries during peak, off-peak and both peak and off-peakhours. To validate the BSA results, particle swarm optimization (PSO) is also usedin this study. The results showed BSA enhances the power saving for the HVAC(cooling system) 36.17% as well as for the HVAC (heating system) 32.57%. Theresults indicate the BSA shows good performance than PSO schedule controller todecrease the power consumption and electricity cost toward efficient HEMS.
... The results demonstrated that the proposed method can considerably reduce the energy bill for the household. In another study, the FLC is used to determine the output power of the battery and minimize the cost and energy consumption [19]. In this study, the FLC also is developed and employed to decrease power consumption and cost of both illumination and HVAC systems, resulting in a significant reduction in the energy consumption, monetary cost, and peak to average ratio (PAR). ...
Article
Full-text available
Recently, homes consume around 40% of world power and produce 21% of the total greenhouse gas emissions. Thus, the proper management of energy in the domestic sector is a vital element for creating a sustainable environment and cost reduction. In this study, an intelligent home energy management system (HEMS) is developed to control domestic appliances load. The motivation of this work is reduced the electricity cost and power consumption from all the appliances by maintaining the customer’s high comfort level using an efficient optimized controller. The domestic household appliances such as heating ventilation and air conditioning (HVAC), electric water heater (EWH) and lighting were modelled and analysed using Simulink/Matlab. The developed models analysed the appliances’ energy consumption and cost sceneries during peak, off-peak and both peak and off-peak hours. Fuzzy logic controller (FLC) was developed for the HEMS to perform energy utilization estimation and cost analysis during these periods taking the Malaysian tariff for domestic use into consideration. To improve the FLC outcomes and the membership function constraint, particle swarm optimization (PSO) is developed to ensure an optimal cost and power consumption. The results showed that the developed FLC controller minimized the cost and energy consumption for peak period by 19.72% and 20.34%, 26.71% and 26.67%, 37.5% and 33.33% for HVAC, EWH, and dimmable lamps, respectively. To validate the optimal performance, the obtained results shows that the FLC-PSO can control the home appliances more significantly compared to FLC only. In this regard, the FLC-PSO based optimum scheduled controller for the HEMS minimized power and cost by 36.17%-36.54%, 54.54%-55.76%, and 62.5%-58% per day for HVAC, EWH, and light, respectively. In sum, the PSO shows good performance to reduce the cost and power consumption toward efficient HEMS. Thus, the developed fuzzy-based heuristic optimized controller of HEMS is beneficial towards sustainable energy utilization.
... A considerable amount of payment records are generated during the energy transfer between EVs and smart grids. The payment records of energy usage can be shared for load and price forecasting, valuable services of energy and scheduling of energy consumption [123,124,125]. The sharing of payment records comes with several privacy concerns. ...
Thesis
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The radically increasing amount and enormous types of data generated by vehicles have brought in the innovated application of data trading in vehicular networks. Whereas the immense usage of Electric Vehicles (EVs) as mobile energy carriers have supported distributed energy trading due to their bidirectional charging and discharging capabilities. The trustless environment of Internet of Electric Vehicles (IoEV), including fuel vehicles and EVs, faces conflicting interests and disputes among trading parties. We exploit consortium blockchain for secure data trading to achieve information transparency and build trust in IoEV. All trading actions are performed by using smart contracts to tackle disputes and illegal actions. Moreover, bloom filters are used for fast data lookup and data duplication verification through previously stored hash-list at roadside units (RSUs). Removing data duplication at an earlier stage helps in reducing storage cost. The reliability and integrity of traded data are ensured by using the digital signature scheme based on elliptic curve bilinear pairing. An external distributed storage, InterPlanetary File System (IPFS), is used for long term availability of traded data, which provides reliable and high capacity storage resources. On the other hand, the energy trading transactions among EVs face some security and privacy protection challenges. An adversary can infer the energy trading records of EVs, and launch the data linkage attacks. To address this issue, an account generation technique is used that hides the energy trading trends. The new account generation for an EV depends upon its traded volume of energy. The experimental results verify that the proposed solution is efficient for data and energy trading in IoEV with the reliable and long term availability of data storage.
... A considerable amount of payment records are generated during the energy transfer between EVs and smart grids. The payment records of energy usage can be shared for load and price forecasting, valuable services of energy and scheduling of energy consumption [120,121,122]. The sharing of payment records comes with several privacy concerns. ...
Research Proposal
Full-text available
The radically increasing amount and enormous types of data generated by vehicles have brought in the innovated application of data trading in vehicular networks. Whereas the immense usage of Electric Vehicles (EVs) as mobile energy carriers have supported distributed energy trading due to their bidirectional charging and discharging capabilities. The trustless environment of Internet of Electric Vehicles (IoEV), including fuel vehicles and EVs, faces conflicting interests and disputes among trading parties. We exploit consortium blockchain for secure data trading to achieve information transparency and build trust in IoEV. All trading actions are performed by using smart contracts to tackle disputes and illegal actions. Moreover, bloom filters are used for fast data lookup and data duplication verification through previously stored hash-list at roadside units (RSUs). Removing data duplication at an earlier stage helps in reducing storage cost. The reliability and integrity of traded data are ensured by using the digital signature scheme based on elliptic curve bilinear pairing. An external distributed storage, Inter-Planetary File System (IPFS), is used for long term availability of traded data, which provides reliable and high capacity storage resources. On the other hand, the energy trading transactions among EVs face some security and privacy protection challenges. An adversary can infer the energy trading records of EVs, and launch the data linkage attacks. To address this issue, an account generation technique is used to hide the energy trading trends that depends upon EVs' traded volume of energy.
... Simulation results have shown a significant decrease in the peak to average ratio (PAR), monetary cost, and power consumption [26]. Bissey et al. [27] presented the fuzzy logic systems to effectively optimize energy consumption in individual housing. The purpose of the study was to analyse the power consumption of the home appliances and decrease the energy consumption during the peak hours to prevent the need to extend the grid and thus save considerable costs. ...
Article
Full-text available
While home energy prices keep rising, homeowners nowadays are searching for the right options to reduce their electricity bills. Besides, the increase in power consumption can contribute to environmental pollution. Therefore, the proper management of energy in the domestic sector is a vital element for creating a sustainable environment and cost reduction. In this study, the most domestic household appliances consumption of energy are modelled and analysed using the fuzzy logic controller (FLC) in order to permit the home energy management system (HEMS) to perform energy utilization estimation and cost analysis. These appliances are the heating ventilation and air conditioning (HVAC), electric water heater (EWH), and lighting, respectively. The developed system can help to analyse the appliances’ energy consumption and cost sceneries during peak and off-peak hours. The modelling of a fuzzy-based domestic appliances controller for HEMS takes the peak and non-peak tariff of Malaysian grid into consideration. The simulation results demonstrate that the developed models are able to manage energy consumption and cost reduction efficiently. By using the proposed FLC, the cost of energy is reduced by 21.75 %, 30.77 %, and 41.96 % for the HVAC, EWH, and dimmable lamps, respectively. In sum, the FLC shows good performance to reduce the cost and power consumption toward efficient HEMS.
... As the number of distributed energy generators increases, energy trading becomes essential within a SC. Several research works are proposed to solve the problems of energy trading systems in the SC [204,205,206,207,208,209]. However, many issues are partially left unresolved or neglected. ...
Thesis
Full-text available
The rapid deployment of Electric Vehicles (EVs) and usage of renewable energy in day-to-day activities of energy consumers have contributed toward the development of a greener smart community. However, load balancing problems, security threats, privacy leakages, and lack of incentive mechanisms remain unresolved in energy systems. Many approaches have been used in the literature to solve the aforementioned challenges. However, these approaches are not sufficient to obtain satisfactory results because of the resource and time-intensiveness of the primitive cryptographic executions on the network devices. In most cases, energy trading systems manage transactions using a centralized approach. This approach increases the risk of a single point of failure and overall system cost. In this study, a blockchain based Local Energy Market (LEM) model considering Home Energy Management (HEM) system and demurrage mechanism is proposed to tackle the issue of a single point of failure in the energy trading system. It allows both the prosumers and consumers to optimize their energy consumption and minimize electricity costs. This model also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. On the other hand, users’ privacy leakages are still not solved in blockchain and can limit its usage in many applications. This research also proposes a blockchain based distributed matching and privacy-preservation model that uses a reputation system for both residential homes and EVs to preserve users’ privacy and efficiently allocate energy. A starvation free energy allocation policy is presented in the model. In addition, a charging forecasting scheme for EVs is introduced that allows users to plan and manage their intermittent EVs’ charging. Partial homomorphic encryption based on a reputation system is used to hide the EVs users’ whereabouts. Identity Based Encryption (ID Based encryption) technique is incorporated in the model to preserve the users’ information privacy in the blockchain. Another bottleneck in the energy trading systems is to perform efficient and privacy-preserving transactions. Therefore, an efficient and secure energy trading model leveraging contract theory, consortium blockchain, and a reputation system is proposed. Firstly, a secure energy trading mechanism based on consortium blockchain is developed. Then, an efficient contract theory based incentive mechanism considering the information asymmetry scenario is introduced. Afterwards, a reputation system is integrated to improve x transaction confirmation latency and block creation. Next, a shortest route and distance algorithm is implemented in order to reduce the traveling distance and energy consumption by the EVs during energy trading. Cheating attacks launched by both buyers and sellers are also issues, which are still not resolved. Thus, a mutual-verifiable fairness mechanism during energy trading based on timed commitment is presented. Proof-of-Energy Reputation Generation (PoERG) and Proof-of Energy Reputation Consumption (PoERC) consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by Proof-of-Work (PoW) and Proof-of-Stake (PoS) existing mechanisms. The mechanisms are developed based on reputation where energy trading transactions are audited, validated, and added into blocks of a blockchain. In order to protect the proposed model from impersonation attacks and minimize malicious validators, a two-stage peer-to-peer secure energy trading model based on blockchain is proposed. The proposed model has two layers: a mutual authentication process layer, and a secure and privacy-preserving energy trading layer. Afterwards, an incentive-punishment algorithm is introduced to motivate energy prosumers to contribute more energy in the proposed model. Next, a dynamic contract theory based supply-demand ratio pricing scheme is proposed. The purpose of the proposed pricing scheme is to solve the issues associated with the existing pricing scheme. Also, to preserve the privacy of the actual energy consumption behavior of the trading participants. Furthermore, storage overhead and delay in communication are challenges that need urgent attention, especially in resource constrained devices for sustainable and efficient transactions. Therefore, a consortium blockchain based vehicular system is proposed in this work for secure communication and optimized data storage in Internet of Vehicles (IoV) network. To secure the proposed system from active and passive attacks, an encryption technique and an authentication mechanism are proposed based on public key encryption scheme and hashing algorithm, i.e., Advanced Encryption Standard-256 and Rivest Shamir Adleman (AES-256+RSA), and Keccak-256. It also protects the model from double spending attack. Moreover, a cache memory technique is introduced to reduce service delay and high resource consumption. In the cache memory, the information of frequently used services is stored, which results in the reduction of service delivery delay. Simulation results show that all of the proposed models perform significantly better as compared to the existing schemes.
... As the number of distributed energy generators increases, energy trading becomes essential within a SC. Several research works are proposed to solve the problems of energy trading systems in the SC [169][170][171][172][173]211]. However, many issues are partially left unresolved or neglected. ...
Research Proposal
Full-text available
The rapid deployment of Electric Vehicles (EVs) and usage of renewable energy in day-to-day activities of energy consumers have contributed toward the development of a greener smart community. However, load balancing problems, security threats, privacy leakages, and lack of incentive mechanisms remain unresolved in energy systems. Many approaches have been used in the literature to solve the aforementioned challenges. However, these approaches are not sufficient to obtain satisfactory results because of the resource and time-intensiveness of the primitive cryptographic executions on the network devices. In most cases, energy trading systems manage transactions using a centralized approach. This approach increases the risk of a single point of failure and overall system cost. In this study, a blockchain based Local Energy Market (LEM) model considering Home Energy Management (HEM) system and demurrage mechanism is proposed to tackle the issue of a single point of failure in the energy trading system. It allows both the prosumers and consumers to optimize their energy consumption and minimize electricity costs. This model also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. On the other hand, users’ privacy leakages are still not solved in blockchain and can limit its usage in many applications. This research also proposes a blockchain based distributed matching and privacy-preservation model that uses a reputation system for both residential homes and EVs to preserve users’ privacy and efficiently allocate energy. A starvation free energy allocation policy is presented in the model. In addition, a charging forecasting scheme for EVs is introduced that allows users to plan and manage their intermittent EVs’ charging. Partial homomorphic encryption based on a reputation system is used to hide the EVs users’ whereabouts. Identity Based Encryption (ID Based encryption) technique is incorporated in the model to preserve the users’ information privacy in the blockchain. Another bottleneck in the energy trading systems is to perform efficient and privacy-preserving transactions. Therefore, an efficient and secure energy trading model leveraging contract theory, consortium blockchain, and a reputation system is proposed. Firstly, a secure energy trading mechanism based on consortium blockchain technology is developed. 1 Then, an efficient contract theory based incentive mechanism considering the information asymmetry scenario is introduced. Afterwards, a reputation system is integrated to improve transaction confirmation latency and block creation. Next, a shortest route and distance algorithm is implemented in order to reduce the traveling distance and energy consumption by the EVs during energy trading. Cheating attacks launched by both buyers and sellers are also issues, which are still not resolved. Thus, a mutual-verifiable fairness mechanism during energy trading based on timed commitment is presented. Proof-of-Energy Reputation Generation (PoERG) and Proof-of Energy Reputation Consumption (PoERC) consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by Proof-of- Work (PoW) and Proof-of-Stake (PoS) existing mechanisms. The mechanisms are developed based on reputation where energy trading transactions are audited, validated, and added into blocks of a blockchain. In order to protect the proposed model from impersonation attacks and minimize malicious validators, a two-stage peer-to-peer secure energy trading model based on blockchain is proposed. The proposed model has two layers: a mutual authentication process layer, and a secure and privacy-preserving energy trading layer. Afterwards, an incentivepunishment algorithm is introduced to motivate energy prosumers to contribute more energy in the proposed model. Next, a dynamic contract theory based supply-demand ratio pricing scheme is proposed. The purpose of the proposed pricing scheme is to solve the issues associated with the existing pricing scheme. Also, to preserve the privacy of the actual energy consumption behavior of the trading participants.
... From a brief literature review, it is observed that the integration of information and communication technologies enables the electricity users and utility companies to interact and exchange information [76,77,78]. Both market participants want to get maximum benefits by actively participating in energy management strategies [79,80]. ...
Thesis
Full-text available
With the advent of the smart grid (SG), the concept of energy management flourished rapidly and it gained the attention of researchers. Forecasting plays an important role in energy management. In this work, a recurrent neural network, long short term memory (LSTM), is used for electricity price and demand forecasting using big data. This model uses multiple variables as input and forecasts the future values of electricity demand and price. Its hyperparameters are tuned using the Jaya optimization algorithm to improve the forecasting ability. It is named as Jaya LSTM (JLSTM). Moreover, the concept of local energy generation using renewable energy sources is also getting popular. In this work, to implement a hybrid peer to peer energy trading market, a blockchain based system is proposed. It is fully decentralized and allows the market members to interact with each other and trade energy without involving a third party. In addition, in vehicle to grid and vehicle to vehicle energy trading environments, local aggregators perform the role of energy brokers and are responsible for validating the energy trading requests. A solution to find accurate distance with required expenses and time to reach the charging destination is also proposed, which effectively guides electric vehicles (EVs) to reach the relevant charging station and encourages energy trading. Moreover, a fair payment mechanism using a smart contract to avoid financial irregularities is proposed. Apart from this, a blockchain based trust management method for agents in a multi-agent system is proposed. In this system, three objectives are achieved: trust, cooperation and privacy. The trust of agents depends on the credibility of trust evaluators, which is verified using the proposed methods of trust distortion, consistency and reliability. To enhance the cooperation between agents, a tit-3-for-tat repeated game strategy is developed. The strategy is more forgiving than the existing tit-for-tat strategy. It encourages cheating agents to re-establish their trust by cooperating for three consecutive rounds of play. Also, a proof-of-cooperation consensus protocol is proposed to improve agents’ cooperation while creating and validating blocks. The privacy of agents is preserved in this work using the publicly verifiable secret sharing mechanism. Additionally, a blockchain based edge and cloud system is proposed to resolve the resource management problem of EVs in a vehicular energy network. Firstly, a min-max optimization problem is formulated to construct the proposed entropy based fairness metric for resource allocation. This metric is used to determine whether users have received a fair share of the system’s resources or not. Secondly, a new deep reinforcement learning based content caching and computation offloading approach is designed for resource management of EVs. Lastly, a proof-of-bargaining consensus mechanism is designed for block’s validation and selection of miners using the concept of iterative negotiation. Besides, a survey of electricity load and price forecasting models is presented. The focus of this survey is on the optimization methods, which are used to tune the hyperparameters of the forecasting models. Moreover, this work provides a systematic literature review of scalability issues of the blockchain by scrutinizing across multiple domains and discusses their solutions. Finally, future research directions for both topics are discussed in detail. To prove the effectiveness of the proposed energy management solutions, simulation are performed. The simulation results show that the energy is efficiently managed while ensuring secure trading between energy prosumers and fair resource allocation.
... In (Aquino-Lugo, 2011), agent-based technologies were used to manage data processing in smart grids. In Khalid et al. (2019), fuzzy logic and heuristics were used for energy management and control of home appliances with three criteria: cost, user comfort, and peak-to-average ratio. ...
Article
Full-text available
The application of big data in the energy sector is considered as one of the main elements of Energy Internet. Crucial and promising challenges exist especially with the integration of renewable energy sources and smart grids. The ability to collect data and to properly use it for better decision-making is a key feature; in this work, the benefits and challenges of implementing big data analytics for renewable energy power stations are addressed. A framework was developed for the potential implementation of big data analytics for smart grids and renewable energy power utilities. A five-step approach is proposed for predicting the smart grid stability by using five different machine learning methods. Data from a decentralized smart grid data system consisting of 60,000 instances and 12 attributes was used to predict the stability of the system through three different machine learning methods. The results of fitting the penalized linear regression model show an accuracy of 96% for the model implemented using 70% of the data as a training set. Using the random forest tree model has shown 84% accuracy, and the decision tree model has shown 78% accuracy. Both the convolutional neural network model and the gradient boosted decision tree model yielded 87% for the classification model. The main limitation of this work is that the amount of data available in the data set is considered relatively small for big data analytics; however the cloud computing and real-time event analysis provided was suitable for big data analytics framework. Future research should include bigger datasets with variety of renewable energy sources and demand across more countries.
... Furthermore, a Home Energy Management System (HEMS) reduces energy demand and cost with the assistance of renewable energy sources and an energy storage system [26]. In addition to this, [27] presents a Hybrid Flower pollination BAT Algorithm (HFBA) for energy, cost, and Peak-to-Average Ratio (PAR) reduction that initially categorizes appliances based on their energy consumption patterns. It includes a fuzzy logic controller for controlling appliances and a heuristic optimization technique for scheduling appliances for energy, cost, and PAR reduction. ...
Article
Full-text available
The exponential increase in energy demands continuously causes high price energy tariffs for domestic and commercial consumers. To overcome this problem, researchers strive to discover effective ways to reduce peak-hour energy demand through off-peak scheduling yielding low price energy tariffs. Efficient off-peak scheduling requires precise appliance profiling to identify a scheduling recommendation for peak load management. We propose a novel off-peak scheduling technique that provides instant energy scheduling recommendations by monitoring appliances in real-time following user-devised criteria. Once an appliance operates during a peak hour and fulfills the user criteria, a real-time scheduling recommendation is presented for users’ approval. The proposed technique utilizes appliance energy consumption data, user-devised criteria, and energy price signals to identify the recommendation points. The energy cost-saving performance of the proposed technique is evaluated using two publicly available real-world energy consumption datasets with four price signals. Simulation results show a significant cost-saving performance of up to 84% for the experimented datasets. Moreover, we formulate a novel evaluation metric to compare the performance of various off-peak scheduling techniques on similar criteria. Comparative analysis indicates that the proposed technique outperforms the existing methods.
... According to different prediction errors, when describing them with fuzzy sets, the choice of MF may be different [28,29], which will affect the optimal scheduling results of the IES. The MF in the type−I fuzzy set that is commonly used now is single-valued [30,31], which cannot describe the uncertainty in the selection of MF. In contrast, the interval type−II fuzzy set expresses the affiliation degree of the type−I fuzzy set as a fuzzy set, and its type−II MF has a multivalued nature. ...
Article
Full-text available
Renewable energy sources (RES) generation has huge environmental and social benefits, as a clean energy source with great potential. However, the difference in the uncertainty characteristics of RES and electric–thermal loads poses a significant challenge to the optimal schedule of an integrated energy system (IES). Therefore, for the different characteristics of the multiple uncertainties of IES, this paper proposes a type−II fuzzy interval chance-constrained programming (T2FICCP)-based optimization model to solve the above problem. In this model, type−II fuzzy sets are used to describe the uncertainty of RES in an IES, and interval numbers are used to describe the load uncertainty, thus constructing a T2FICCP-based IES day-ahead economic scheduling model. The model was resolved with a hybrid algorithm based on interval linear programming and T2FICCP. The simulations are conducted for a total of 20 randomly selected days to obtain the advance operation plan of each unit and the operation cost of the system. The research results show that the T2FICCP optimization model has less dependence on RES output power and load forecasting error, so can effectively improve the economy of IES, while ensuring the safe and stable operation of the system.
... Management of energy on side of consumers of smart grid is complicated task; it needs reliable dispatching of equipment with a reduced delay to decrease energy consumption cost and peak-to-average ratio. With heuristic optimization techniques and fuzzy logic, a hybrid optimization algorithm for schedule of appliances in home is suggested [15]. In the sensor, a probability ratio-dependent scheduling is built to smartly pick informative transmission sensors measurement along a moderate rate of transmission limit for networks that are secure [16]. ...
Article
Full-text available
The computing in real-time is rapidly focusing much developments in technologies so that the real-time jobs are to be scheduled and executed on computing systems in particular time frame. The scheduling and load balancing techniques in distributed systems face numerous challenges because of lack of centralized strategy to dispatch the jobs in multiprocessors systems. In this work, we propose an algorithm fuzzy scheduling (AFS) for real-time jobs that includes of arrival time, deadline and computation time as the scheduling parameters of input. The approach AFS is analyzed and compared with existing fuzzy algorithm (EFA) model for evaluation of performances from the outcome of the simulation. The jobs are scheduled on multiprocessor at higher system load by making use of fuzzy mechanisms in the algorithms. The experimental results prove that the proposed AFS achieves a better performance comparatively to EFA at various system load factors with respect to mean turnaroundtime, mean response time and count of missed deadlines. This is the initial phase of the algorithm, that will be enhanced to consider a greater number of parameters to be associated with jobs for better decision making and to investigate the scope for algorithm level parallelism.
... A PVbased HEMS structure enhancement and design of the electrical system is reported in (Iimura, Yamazaki, and Maeno 2014). To bring up customer's comfort, a fuzzy logic controller is established for smart home in terms of load priority (Chekired et al. 2017) and peak-to-average case (Khalid et al. 2019). In terms of electricity bill and customer comfort, ...
Article
The concept of smart homes is considered either to enhance life quality of people or to ensure energy management of buildings, where intelligent technologies are used to achieve the comfort and energy management aims in smart homes. This technology is still under fast development, and it is noticeable that a detailed research study is needed to point out state-of-the-art and future perspectives regarding smart home applications. Thus, considering the developments in smart homes, this paper investigates smart home applications in literature and market, and conducts a systematic overview by considering energy management systems and numerical researches. A comprehensive survey of smart homes is carried out to evaluate their system configurations, functional capabilities, objectives, and hardware applications in this context. Management devices, field devices, tracking systems, small appliances, and communication devices are listed as the five major hardware in the current study. Furthermore, fundamental functions of smart homes are introduced as monitoring, data logging, control, alarm/caution, and management in the current study. Subsequently, state-of-the-art of smart home technology is given to investigate the numeric values of scientific research studies, the percentage values of studies in different discipline areas, the number of scientific studies according to the nations, and the numeric values of smart home appliances. According to the numerical results, it is clear that studies on smart homes/HEMS have increased exponentially after 2000 years. The percentage values of studies in different discipline areas and the number of studies conducted by the leading countries interested in smart homes/HEMS are conducted in this work. The current study also analyzes how the future perspective of smart home technology has been shaped over the years. According to the future perspectives, the numeric values show that the number of smart home applications and their market value are expected to grow in the near future, where smart appliances and market budget are expected to be 75.4 billion units 262.8 billion dollars by 2025, respectively.
... Topic 58 also includes research on energy optimization and energy management of smart homes [53][54][55][56][57]. As the spread of ICT in the energy field has recently expanded, efforts for smart home research are being emphasized more. ...
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With the emergence of new technologies and policies to transition to clean energy, the household energy consumption sector is also changing. In response to policy, environmental, and technical changes, researchers need to find out what significant issues are related to household energy consumption, and comprehensively analyze which issues are likely to attract attention in the future to contribute to research in the household sector. Based on the abstracts of academic papers published between 2011 and 2020, this study uses probabilistic topic modeling to increase understanding of academic issues in the household energy consumption sector and statistically reviews changes in issues over time. As a result of the analysis, topics related to digitalization and renewable energy, such as microgrid system, smart home, residential solar power generation systems, and non-intrusive load monitoring (NILM), belonging to Strong signals, are being actively studied. Weak Signals, which can attract attention in the future, are included in discussions on coal energy consumption, air pollutant emissions, energy poverty, and energy performance evaluation. The analysis results show that carbon neutrality, such as decarbonization and fossil energy consumption reduction, is expanding to research in the household energy consumption sector.
... Fuzzy Petri nets allow us to simulate the ambiguity that exists in both the real world and emotional states [4]. The authors [5] also addressed fuzzy logic in IoT. They used fuzzy logic and heuristic techniques to reduce costs, energy consumption and to control gas and interruptible appliances. ...
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Emotion recognition relies heavily on physiological reactions and facial expressions. There may be a few research that deal with the collection of physiological data from users using various Internet of Things (IoT) devices, but even fewer studies exist that deal with the classification and prediction of emotional states based on collected physiological data. In the majority of studies, research use invasive devices during monitoring methods such as electroencephalography (EEG) and electrocardiography (ECG). This paper investigates the collection of physiological data by creating a complex sensory network. The network uses some non-invasive IoT devices such as smart bands and different modules that are connected to the Raspberry Pi microcomputer and Arduino microcontroller. The goal is to make the sensory network as less invasive as possible. The Petri net model simulating how certain emotional states affect physiological data has already been created by us, however it is necessary to customize that model to include the physiological data, that the created sensory network can collect. The aim is to be able to collect enough physiological data from users so a dataset can be created, which will later serve as an input to a classification and prediction model. The physiological data will be sent to a server to process and store the data in a database. For the heart rate data, a mobile application will be created to partially automatize the collection and storage of data.
... The ACSs have a special place in BEMS. The methods for implementation of BEMS are as follows: optimization algorithms (Pal et al. 2019;Yang and Wang 2013), fuzzy logic (Khalid et al. 2019), integer linear programming (Farrokhifar et al. 2018), decision support model (Doukas et al. 2007), distributed model predictive control (Scherer et al. 2014), prediction model (Lazos, Sproul, and Kay 2015), simulation and control model (Fanti, Mangini, and Roccotelli 2018), data envelopment analysis (Lee and Lee 2009), reinforcement learning algorithms (Mason and Grijalva 2019), multidimensional model (Cavalheiro and Carreira 2016), graph mining-based methodology (Fan et al. 2019), artificial intelligence methods (Hosseinzadeh-Bandbafha, Nabavi-Pelesaraei, and Shamshirband 2017), etc. The present study investigates the first application of the qualitative comparative analysis (QCA) for testing a specific hypothesis related to an STS. ...
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The present study provides potentially interesting for researchers in the novel integrated method of the fuzzy set qualitative comparative analysis (fsQCA) and digital design methodology to apply in the field of energy and sustainability of socio-technical systems (STSs) used in the building energy management (BEM). The research sample is the air-conditioning system (ACS). The social and technical aspects of ACS are satisfaction and energy consumption. Ten sustainability criteria are applied to assess and monitor the fields of energy consumption and satisfaction with the aim of achieving sustainable consumption in the use of ACSs for BEM. This action can be carried out by nesting a circuit in a microprocessor and microcontroller for use in a building energy management system (BEMS). According to, the circuit obtained from the assessment of the study can be practically implemented and assembled in a BEMS as online. This work can define a sustainability-based BEMS.
... Some proposed models use game theory [20] and fuzzy logic-based models [21] to solve energy management problems of residential buildings. But, these models are based on a very small data set of a day, a limited number of houses, and appliances that do not depict practical scenarios. ...
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Power scheduling of domestic appliances is a vital preference for bridging the gap between demand and generation of electricity in a microgrid. For a stable microgrid, an acceptable mechanism must reduce the peak to average ratio (PAR) of power demand with supplementary benefits for consumers as reduced electricity charges. Recent studies have focused on PAR and cost reduction for a small consumer population. Furthermore, researchers have mainly considered homogeneous consumer loads. This study focuses on residential power scheduling for electricity cost reduction for consumers and load profile PAR curtailment for a relatively large consumer population with non-homogeneous loads. A sample population of 1000 consumers from various classes of society is considered. The proposed dynamic clustered community home energy management system (DCCHEMS) allows the clustering of appliances based on time overlap criteria. Comparatively flatter power demand is attained by utilizing the clustered appliances in conjunction with particle swarm optimization under the influence of user-defined constraints. Modified inclined block rates with real-time electricity pricing strategies are deployed to minimize the electricity costs. DCCHEMS achieved higher efficiency rates in contrast to the traditional non-clustering and static clustering optimization schemes. An improvement of 21% in peak to average ratio, 4% in cost reduction, and 19% in variance to mean ratio is obtained.
... The aim of this research was to investigate the realworld economic advantages of altering load behavior using a novel fuzzy model and economic impact analysis. In [98], DSM was controlled by a fuzzy controller and a smart home scheduler. Costs and energy usage were minimized via fuzzy logic. ...
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Electric power reliability is one of the most important factors in the social and economic evolution of a smart city, whereas the key factors to make a city smart are smart energy sources and intelligent electricity networks. The development of cost-effective microgrids with the added functionality of energy storage and backup generation plans has resulted from the combined impact of high energy demands from consumers and environmental concerns, which push for minimizing the energy imbalance, reducing energy losses and CO2 emissions, and improving the overall security and reliability of a power system. It is now possible to tackle the problem of growing consumer load by utilizing the recent developments in modern types of renewable energy resources (RES) and current technology. These energy alternatives do not emit greenhouse gases (GHG) like fossil fuels do, and so help to mitigate climate change. They also have in socioeconomic advantages due to long-term sustainability. Variability and intermittency are the main drawbacks of renewable energy resources (RES), which affect the consistency of electric supply. Thus, utilizing multiple optimization approaches, the energy management system determines the optimum solution for renewable energy resources (RES) and transfers it to the microgrid. Microgrids maintain the continuity of power delivery, according to the energy management system settings. In a microgrid, an energy management system (EMS) is used to decrease the system’s expenses and adverse consequences. As a result, a variety of strategies and approaches are employed in the development of an efficient energy management system. This article is intended to provide a comprehensive overview of a range of technologies and techniques, and their solutions, for managing the drawbacks of renewable energy supplies, such as variability and load fluctuations, while still matching energy demands for their integration in the microgrids of smart cities.
... The aim of this research was to investigate the realworld economic advantages of altering load behavior using a novel fuzzy model and economic impact analysis. In [98], DSM was controlled by a fuzzy controller and a smart home scheduler. Costs and energy usage were minimized via fuzzy logic. ...
Article
Electric power reliability is one of the most important factors in the social and economic evolution of a smart city, whereas the key factors to make a city smart are smart energy sources and intelligent electricity networks. The development of cost-effective microgrids with the added functionality of energy storage and backup generation plans has resulted from the combined impact of high energy demands from consumers and environmental concerns, which push for minimizing the energy imbalance, reducing energy losses and CO2 emissions, and improving the overall security and reliability of a power system. It is now possible to tackle the problem of growing consumer load by utilizing the recent developments in modern types of renewable energy resources (RES) and current technology. These energy alternatives do not emit greenhouse gases (GHG) like fossil fuels do, and so help to mitigate climate change. They also have in socioeconomic advantages due to long-term sustainability. Variability and intermittency are the main drawbacks of renewable energy resources (RES), which affect the consistency of electric supply. Thus, utilizing multiple optimization approaches, the energy management system determines the optimum solution for renewable energy resources (RES) and transfers it to the microgrid. Microgrids maintain the continuity of power delivery, according to the energy management system settings. In a microgrid, an energy management system (EMS) is used to decrease the system’s expenses and adverse consequences. As a result, a variety of strategies and approaches are employed in the development of an efficient energy management system. This article is intended to provide a comprehensive overview of a range of technologies and techniques, and their solutions, for managing the drawbacks of renewable energy supplies, such as variability and load fluctuations, while still matching energy demands for their integration in the microgrids of smart cities.
... An energy management controller has been presented in [74] for optimizing energy consumption and DSM. For scheduling the shift able appliances, heuristic optimization algorithms, "BAT inspired and flower pollination," are used, and for scheduling the home appliances, a hybrid optimization algorithm, named "hybrid BAT pollination optimization algorithm (HFBA)," is proposed. ...
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The concept of smart grid was introduced a decade ago. Demand side management (DSM) is one of the crucial aspects of smart grid that provides users with the opportunity to optimize their load usage pattern to fill the gap between energy supply and demand and reduce the peak to average ratio (PAR), thus resulting in energy and economic efficiency ultimately. The application of DSM programs is lucrative for both utility and consumers. Utilities can implement DSM programs to improve the system power quality, power reliability, system efficiency, and energy efficiency, while consumers can experience energy savings, reduction in peak demand, and improvement of system load profile, and they can also maximize usage of renewable energy resources (RERs). In this paper, some of the strategies of DSM including peak shaving and load scheduling are highlighted. Furthermore, the implementation of numerous optimization techniques on DSM is reviewed.
... In (Aquino-Lugo, 2011), agent-based technologies were used to manage data processing in smart grids. In Khalid et al. (2019), fuzzy logic and heuristics were used for energy management and control of home appliances with three criteria: cost, user comfort, and peak-to-average ratio. ...
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The challenge of Big data is fundamentally concerned with performing data analytics for large amount of heterogeneous data. This data can be collected from different and/or uncorrelated sources. Due to the complexity of such technology; there are still various possible applications and integrations under study particularly in the fields of smart systems with using trending technologies such as Internet of Things and Cloud computing and utilizing relevant tools and equipment such as advanced sensors and smart meters. The application of big data in the energy sector is considered as one of the main elements of Energy Internet. Crucial and promising challenges exist especially with the integration of renewable energy sources and smart grids. The ability to collect data and to properly use it for better decision-making is a key feature of smart grids. For this purpose, the convenient storing, processing, provision and analysis of information on the renewable energy system behavior is addressed. In this work, the benefits and challenges of implementing big data analytics (BDA) for renewable energy power stations are addressed. The framework and recommendations for this implementation are proposed. Data from a decentralized smart grid data system consisting of 60,000 instances and 12 attributes was used to predict the stability of the system through three different machine learning methods. The results of fitting the penalized linear regression model show the accuracy of 96% for the regression model implemented using 70% of the data as a training set. Using the random forest tree model showed as well 84% accuracy and 78% accuracy for the decision tree model and 87% for the conventional neural network.
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A microgrid is a power distribution system that mixes distributed energy resources with controlled loads, and it has the capability to operate both grid-connected mode and islanding mode. However, increasing electricity demand and electricity cost remains a major problem worldwide. To mitigate these cost issues, several organizations have developed innovative techniques for power control, monitoring and security. The Energy Management System (EMS) focuses more on managing power between load and source sides. To overcome the aforementioned issues, an intelligent EMS controller is proposed in this paper. The proposed intelligent controlling system manages power flows as well as reduce electricity cost very effectively. The proposed method contains four steps of the operation such as system design, data gathering, design of intelligent controller and EMS. Dataset is created based on the behaviour of a single person and corresponding load activation for that period, which is used for implementation and performance validation of the proposed method. The proposed method is validated for two modes of operations, namely, grid-connected mode and islanding modes. In both modes, the proposed method offers cost-effective control of energy flow. MATLAB/Simulink software has been used to design the proposed method and test its performance. The proposed method provides better accuracy of 95%. Furthermore, the outcome of the proposed method is compared with other existing methods such as k-nearest neighbours (KNN) and Naive Bayes (NB). The result demonstrates that the ANN-based EMS can interface with various power sources and offer well performance for the task of energy management.
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The rapid diffusion of smart home technologies imposes new energy management and consumption challenges to residential prosumers. Considering the additional energy demands of these technologies and restrictions imposed on occupants to manage their energy consumption regularly, the need for autonomous energy management systems (EMSs) arises. In particular, an effective decision-making system fed with real-time data can improve the overall building consumption performance. In this paper, a new multimodal EMS is presented for implementing smart residential prosumers. The proposed EMS is equipped with multiple modes to satisfy different objectives throughout a year, thus allowing the utilization of prosumers with various lifestyles driven by current needs. The residential building is modeled using the simulation software GridLAB-D, utilizing weather and pricing data from three geographical regions of the United States for 3 years period. The performance of the proposed multimodal system is compared to a single-mode EMS whose sole objective is the reduction of the electricity bill. Due to the multiple objectives of the proposed EMS, we utilized three different metrics including electricity bill and average state of charge (SOC). Analysis of the results exhibits a reduction in the yearly electricity bill of about 1.8 dollars ($) in two out of three regions and higher average SOC by at least 10% while satisfying the desired lifestyle.
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The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints.
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Hydrogen-based hybrid storage system has a high energy density, which can operate as the long-term storage system, and play an important role in future smart cities. In the hydrogen storage system, fuel cell, hydrogen tanks, and electrolyzer are often combined together and operating with complex electrochemical reactions. How to efficiently operate the hydrogen storage system and considering the convoluted electrochemical reactions is a problem. In addition, multiple hydrogen storage systems are often grouped together to supply the demands. Thus, cooperating the dispatching of these storage systems is another complicated problem. In this paper, we first present a two-dimension model considering temperature influences for hydrogen-based microgrid, where a regression method is adopted. Moreover, a combined allocating-and-dispatching methodology involving two layers is proposed to cooperate the multiple storage systems. Specifically, both TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and fuzzy logic are adopted as the first-layer allocating algorithm. Then, the model predictive control (MPC) is utilized as the second-layer dispatching algorithm. Based on the combined method, power is firstly allocated to hybrid storage system considering each hybrid storage system health conditions, and secondly scheduled to battery storage and hydrogen storage based on MPC method. The simulation results showed that with the combined Dematel-TOPSIS and MPC algorithm, the degradation index and operation cost were the smallest among three algorithms, and can further extend the lifetime of hybrid hydrogen storage systems in microgrids.
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Hybrid storage is often integrated to tackle the uncertainty of renewable energy sources. To face a variety of ancillary services, hybrid storage systems are often grouped together forming a larger energy and power densities storage system. However, to healthily coordinated schedule the grouped hybrid storages is still a problem. In this paper, a three-stage combined algorithm is proposed to cooperate the grouped hybrid storage systems: first, multi-criteria decision making is adopted to allocate expected power to each hybrid storage system; second, model predictive control (MPC) associated with support vector machine and Kalman filter is used to dispatch the allocated power to hydrogen storage and battery; third, a PID controller is deployed for reference tracking. Simulation results show that prediction errors in MPC cause the increases of operation cost and degradation index. For the operation cost, the combined fuzzy membership and MPC-Kalman filter algorithm has a better performance. The PID controller has a good ability to track reference signals.
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In this paper we investigate a public Fast Charge (FC) station nanogrid equipped with a Photovoltaic (PV) system and an Energy Storage System (ESS) using second-life Electric Vehicle (EV) batteries. Since the nanogrid is intended for installation in urban areas, it is designed with a very limited connection with the grid to assure peak shaving and encourage PV autoconsumption. To demostrate the effectiveness of this approach to FC stations, an Energy Management System (EMS) is developed to manage the energy demand uncertainty of EVs and the power gap between the grid connection and the FC service. In particular, we propose a machine learning procedure for the automatic synthesis of a suitable (fuzzy) rule-based EMS. Indeed, we posit that a prediction based EMS would result not effective because of the stochastic and intermittent behavior of the FC load, and that a crisp rule-based system, defined by expert knowledge, would be too limited to capture uncertain behavior. The concept is demonstrated in a simulated environment inspired by the “Smart Columbus” project, implementing a mixed deterministic-stochastic process to simulate EV energy demand. In particular, different EV fleets and PV sizes are considered for EMS training, offering insights into the optimal size of PV system and nanogrid system effectiveness. The proposed approach is evaluated by comparing the EMS performance with related optimal benchmark solutions, evaluated by considering known a priori the overall PV and FC demand. The results show that the EMS performance is approximately within 10% of the benchmark optimal value.
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Energy consumption in homes with Electric Water Heaters accounts currently means up to 30% of the total energy consumption. Their low cost and easy installation are priority factors for users, despite the high cost of electricity consumption in these equipments. The aim of this paper is to develop a modular and low cost Energy Management System which minimizes the electricity bill without compromising the level of comfort. An algorithm is developed that estimates the appropriate hours of operation of the Electric Water Heater that guarantees comfort based on the consumption habits, the monitorization of temperatures and dynamic electricity prices. Another of the functionalities implemented in the Energy Management System is to manage the instantaneous power demanded at homes by disconnecting certain non-priority loads when the maximum contracted power is going to be exceded and to reduce the fixed cost of the bill. The Energy Management System has been installed in a home and results of cost optimization are provided. The device uses free distribution software with Raspberry/NodeMCU platforms and allows monitoring and control of the system through a web page.
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Forecasting of electricity load through combining different deep learning and machine learning techniques is most active and hot topic in electrical engineering. The modern grid is known as a Smart Grid (SG) provide affective, reliable and economic energy consumption to users. Accurate electricity load prediction helps in efficient power load management. In the SG, the key issue is to predict accurate electricity load. There still need some better structure for Long-term electricity load forecasting. In our proposed model, we focus on Long-term electricity load forecasting to maximize the accuracy. For this purpose, we will use hybrid feature selector that is consists on XG-boost, Relief-f and Random Forest (RF) in order to choose features. For feature extraction we will use Principal Component Analysis (PCA). For the prediction of electricity load we will use Convolution Neural Network and Long Short-term Memory Network (CNN-LSTM) and Extreme Learning Machine (ELM) as a classifier. CNN-LSTM optimize with Harmony Search Algorithm (HSA) called CNN-LSTM-HSA and the hyper parameters of ELM tuned with Social Learning Optimization (SLO) algorithm called ELM-SLO. Our proposed techniques CNN-LSTM-HSA and ELM-SLO perform 97% and 92% accuracy in predicting the electricity load. The error rate of MAPE, MSE, MAE, RMSE are very low.
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The flower pollination algorithm (FPA) is a recently developed meta-heuristic algorithm inspired by the pollination process of flowers. Similar to other meta-heuristic algorithms, it encounters two probable problems, i.e., entrapment in local optima and slow convergence speed, in solving challenging complex real world problems. Similar to the chaos in actual flower pollination process, this paper proposes new FPAs that employ chaotic maps for adjustment of parameters with the aim to improve the convergence rate and prevent the FPAs to get trapped on local optima. This is achieved by employing chaotic number generators every time, a random number is needed by the classical FPA. Two new chaotic FPAs have been proposed and various test problems are used for their performance evaluation. To check the effectiveness of the proposed algorithms, they are tested on various benchmark functions and engineering design problems with different characteristics having real world applications. The simulation results demonstrate that the chaotic maps are able to significantly boost the performance of FPAs.
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This paper investigates the energy cost minimization problem for smart grids with distributed renewable energy resources. Unlike earlier research studies that either have assumed all the appliance jobs are interruptible or power-shiftable and that the electricity prices as well as the availability of renewable resources are known, this paper focuses on more challenging scenarios in which appliance jobs are non-interruptible and non-power-shiftable, the electricity prices vary with the overall load of the entire grid in real-time, and the renewable power generation is uncertain. Because home solar systems are widely available, this paper assumes that each consumer in the grid can have a photovoltaic system and a side battery. Collected solar energy can be used to meet a consumer's individual power demand, stored in the battery for future use, or sold back to the grid during peak hours to lower electricity bills and the overall load on the entire grid. To solve this problem, a two-stage robust optimization model is proposed, and the C&CG method is utilized to solve it. However, to solve the problem more efficiently when the number of consumers and appliance jobs is large, a second approach called SRDSM is proposed. The SRDSM algorithm consists of two parts: The first part is a job scheduling algorithm that minimizes electricity costs for all consumers. The second part is a power management algorithm based on dynamic programming that reduces the energy cost further by utilizing renewable energy. The numerical results show that, although the C&CG method produces optimal solutions, the SRDSM algorithm is much more scalable and efficient when the problem size is large.
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The present study investigates the use of fuzzy logic in Heating Ventilation and Air Conditioning (HVAC) control as means of enhancing thermal comfort provision for the building occupants while maintaining or improving the resulting energy consumption of the building’s HVAC system. In order to assess the application of fuzzy logic on HVAC control, a single zone building model along with an HVAC model are created using EnergyPlus software and Transient System Simulation Tool, respectively. In addition a HVAC fuzzy logic controller along with a conventional on-off controller, both thermal comfort based, were developed using Simulink. The latter simulation models, implemented via different software, were coupled using the Building Control Virtual Test Bed platform as a middleware. The simulation outcomes of the on-off controller were used as a benchmark for the evaluation of the fuzzy HVAC controller. In terms of the provided thermal comfort to the building occupants the fuzzy HVAC controller appeared superior, as it managed to reduce the annual mean percentage of dissatisfied occupants by 33% as well as the non-comfort hours by more than 50%. In terms of energy consumption the simulation results suggested that the two controllers perform almost on par.
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The energy management in residential buildings according to occupant’s requirement and comfort is of vital importance. There are many proposals in the literature addressing the issue of user’s comfort and energy consumption (management) with keeping different parameters in consideration. In this paper, we have utilized artificial bee colony (ABC) optimization algorithm for maximizing user comfort and minimizing energy consumption simultaneously. We propose a complete user friendly and energy efficient model with different components. The user set parameters and the environmental parameters are inputs of the ABC, and the optimized parameters are the output of the ABC. The error differences between the environmental parameters and the ABC optimized parameters are inputs of fuzzy controllers, which give the required energy as the outputs. The purpose of the optimization algorithm is to maximize the comfort index and minimize the error difference between the user set parameters and the environmental parameters, which ultimately decreases the power consumption. The experimental results show that the proposed model is efficient in achieving high comfort index along with minimized energy consumption.
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Lighting generally consumed 25%-50% of total electricity consumption in a building. Nowadays, the building lighting source is dominated by the use of fluorescent lamps. The previous technical papers by other researchers had focused on power density control of incandescent lamps, which is now rarely used, unconsidered national standard as control reference value, and required a high-cost in investment. By these reasons, this paper proposes a building lighting system based on fuzzy logic scheme to automate fluorescent lamps in order to achieve illumination according to Indonesian National Standard (SNI). The input variables were indoor lighting, inference from outdoor lighting, and occupancy. The output variable was the required illumination to achieve the standard. The required illumination determined the number of lamps that had to be turned on. In the experiment result, a classroom illumination of lighting without controller in workdays was about 350 lux, while with the proposed controller it varied between 250–300 lux close to the SNI, i.e. 250 lux. Meanwhile, with the proposed controller the electricity consumption for a classroom was 75% lower than the lighting without controller.
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Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.
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Smart and energy-efficient buildings have recently become a trend for future building industry. The major challenge in the control system design for such a building is to minimize the power consumption without compromising the customers comfort. For this purpose, a hierarchical multiagent control system with an intelligent optimizer is proposed in this study. Four types of agents, which are switch agent, central coordinator-agent, local controller-agent, and load agent, cooperate with each other to achieve the overall control goals. Particle swarm optimization (PSO) is utilized to optimize the overall system and enhance the intelligence of the integrated building and microgrid system. A Graphical User Interface (GUI) based platform is designed for customers to input their preferences and monitor the results. Two sets of case studies are carried out and corresponding simulation results are presented in this paper.
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Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed. Comment: 10 pages, 2 figures
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Integration of different energy resources at the customers’ premises in recent years, advances in Information and Communication Technologies (ICTs), and Advanced Metering Infrastructure (AMI) systems are becoming attractive tools for developing new real time demand response at the supplier side and the management of energy resources at the customers’ side. The management programs can be classified as; smart grid management from the supplier side and intelligent Energy, ‘‘i-Energy” from the consumer energy management side. There are two types of programs; (i) time based program like real time pricing and (ii) incentive based demand response. A combination of these two programs is proposed in this paper with the merged program to be real time with incentive demand response. The time based demand response programs can be improved by using smart metering infrastructure and different resources. The incentive demand comes from the feasibility of providing new concept known as ‘i-energy’ at the customers’ sides. To achieve this, Smart Meters (SMs) and different resources at the customers’ premises using this concept are applied. By integrating different resources at the customers’ premises, using the i-energy concept, can change the limitation given in the time based program. The first developed program at the supplier side depends on purchasing MW from the customers who participate in the program. The second contribution is the ‘‘i-Energy” management technique at the customers’ side that is based on congestion and potential games through strategy of load control using different resources. Revenue for different participants in the program from the commercial and industrial sectors, at different levels of reduction and different usage of different resources, is discussed.
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A crucial issue in the smart grid is how to manage the controllable load resources of end-users, in order to reduce the economic costs of system operation and facilitate to utilize renewable energies. This paper investigates a fast randomized first-order optimization method to explore the solution of dynamic energy management (DEM) for the smart grid integrated large-scale distributed energy resources. A complicated time-coupling and multi-variable optimal problem is presented to determine the load scheduling for the electricity customers. The main challenge of the proposed problem is to enable the efficient processing of the large data volumes and optimization of aggregated data involved in DEM. The first-order method as one of big data optimization algorithms is able to exhibit significant performance for computing globally optimal solutions based on randomization techniques. Using such solution approach, we can reformulate the original problem into an unconstrained augmented Lagrangian function. The optimal results can be obtained from computing the gradient based on the information of the first-order derivative. To speed up the calculations of obtaining the feasible solutions, the optimization variable matrix used to update the Lagrangian multiplier can be replaced with the corresponding low-rank representation in the iteration process. Both theoretical analysis and simulation results suggest that the proposed approach may effectively solve the optimal scheduling problem of DEM considering users’ participation.
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Heating, Ventilation and Air Conditioning (HVAC) systems represent a significant portion of total residential energy consumption in North America. Programmable thermostats are being used broadly for automatic control of residential HVAC systems while users initialize their everyday schedules and preferences. The main aim of smart grid initiatives such as time-varying prices is to encourage consumers to reduce their consumption during high electricity demand. However, it is usually a hassle to residential customers to manually re-programme their thermostats in response to dynamic electricity prices or environmental conditions that vary over time. In addition, the lack of energy management systems such as thermostats capable of learning autonomously and adapting to users’ schedule and preference changes are major obstacles of existing thermostats in order to save energy and optimally benefit from smart grid initiatives. To address these problems, in this paper an adaptable autonomous energy management solution for residential HVAC systems is presented. Firstly, an autonomous thermostat utilizing a synergy of Supervised Fuzzy Logic Learning (SFLL), wireless sensors capabilities, and dynamic electricity pricing is developed. In the cases that the user may override the decision made by autonomous system, an Adaptive Fuzzy Logic Model (AFLM) is developed in order to detect, learn, and adapt to new user’s preferences. Moreover, to emulate a flexible residential building, a ‘house energy simulator’ equipped with HVAC system, thermostat and smart meter is developed in Matlab-GUI. The results show that the developed autonomous thermostat can adjust the set point temperatures of the day without any interaction from its user while saving energy and cost without jeopardizing user’s thermal comfort. In addition, the results demonstrate that if any change(s) occurs to user’s schedules and preferences, the developed AFLM learns and adapts to new modifications while not ignoring energy conservation aspects.
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This paper presents an energy management framework for building climate comfort systems that are interconnected in a grid via aquifer thermal energy storage (ATES) systems in the presence of two types of uncertainty, namely private and common uncertainty sources. The ATES system is considered as a seasonal storage system that can be a heat source or sink, or a storage for thermal energy. While the private uncertainty source refers to uncertain thermal energy demand of individual buildings, the common uncertainty source describes the uncertain common resource pool (ATES) between neighbors. To this end, we develop a large-scale stochastic hybrid dynamical model to predict the thermal energy imbalance in a network of interconnected building climate comfort systems together with mutual interactions between the local ATES systems. We formulate a finite-horizon mixed-integer quadratic optimization problem with multiple chance constraints at each sampling time, which is in general a non-convex problem and hard to solve. We then provide a computationally tractable framework based on an extension to the so-called robust randomized approach which offers a less conservative solution for a problem with multiple chance constraints. A simulation study is provided to compare three different configurations, namely: completely decoupled, centralized and move-blocking centralized solutions. In addition, we present a numerical study using a geohydrological simulation environment (MODFLOW) to illustrate the advantages of our proposed framework.
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Residential buildings are currently equipped with energy production facilities, e.g., solar rooftops and batteries, which in conjunction with smart meters, can function as smart energy hubs coordinating the loads and the resources in an optimal manner. This paper presents a mathematical model for the optimal energy management of a residential building and proposes a centralized energy management system (CEMS) framework for off-grid operation. The model of each component of the hub is integrated within the CEMS. The optimal decisions are determined in real-time by considering these models with realistic parameter settings and customer preferences. Model predictive control (MPC) is used to adapt the optimal decisions on a receding horizon to account for the deviations in the system inputs. Simulation results are presented to demonstrate the feasibility and effectiveness of the proposed CEMS framework. Results show that the proposed CEMS can reduce the energy cost and energy consumption of the customers by approximately 17% and 8%, respectively, over a day. Using the proposed CEMS, the total charging cycles of the ESS were reduced by more than 50% in a day.
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In this paper, we comparatively evaluate the performance of home energy management controller which is designed on the basis of heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO). In this regard, we introduce a generic architecture for demand side management (DSM) which integrates residential area domain with smart area domain via wide area network. In addition, problem formulation is carried via multiple knapsack problem. For energy pricing, combined model of time of use tariff and inclined block rates is used. Simulation results show that all designed models for energy management act significantly to achieve our objections and proven as a cost-effective solution to increase sustainability of smart grid. GA based energy management controller performs more efficiently than BPSO based energy management controller and ACO based energy management controller in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.
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Of the total electricity-generating capacity in the United States, 20% is dedicated to meet peak loads. Strategies to mitigate volatility in energy consumption have the potential to reduce the need for this surplus capacity. Here we investigate the potential for residential consumers to lower community-level peak demand through home energy management systems. We focus on the combination of air-conditioning use with the operation of time-shiftable appliances in the southern U.S. A centralized model predictive control (MPC) scheme minimizes peak air-conditioning (A/C) energy use by altering the thermostat set-points in individual homes. We simultaneously schedule the operation of time-shiftable appliances to further reduce the community peak load. The scheduling problem is formulated as a mixed-integer linear program (MILP) aimed at minimizing peak load under constraints that reflect the start times and allowed delays of individual appliances (e.g. dishwashers, washing machines, dryers) in each house. Using sample data collected from residential homes and consumer survey data located in Austin, TX, USA we show that the proposed integrated control and scheduling approach can minimize the peak load for the neighborhood by leveraging the physical differences and individual preferences between houses. On average, our framework is able to reduce the daily peak load for the group of houses by 25.5% (18.2 kW) when compared with the load for individually controlled thermostat settings and appliance start times.
Article
Power companies are unable to withstand the consumer power requirement due to growing population, industries and buildings. The use of automated electrical appliances have increased exponentially in day to day activity. To maintain a possible balance between the supply and demand the power companies are introducing the demand side management approach. As a result, consumers are adopted for load shifting or scheduling their loads into off-peak hours to reduce the electricity bill. When all the consumers are trying to run the scheduled electrical appliances at the same time then the usage of energy in the off peak hour curve is marginally high. However, service providers are in need of a load balancing mechanism to avoid over or under utilization of the power grid. In the existing works, threshold limit is applied for a home to maintain the balanced load and if the consumer exceeds it then the additional charges are applied in the bill. To overcome the above mentioned drawbacks there is a need to increase the power usage with minimum cost and reducing the waiting time. For this purpose, in this paper we implement multi-objective evolutionary algorithm, which results in the cost reduction for energy usage and minimize the waiting time for appliance execution. The result reveals that if the consumer exceeds the threshold limit, the scheduled running electrical appliances temporarily stops to maintain the energy usage under threshold level for cost benefit and resumes the stopped appliances later. Further, the proposed technique minimizes the overall electricity bill and waiting time for the execution of electrical appliances.
Article
The incentives such as demand response (DR) programs, time-of-use (TOU) and real-time pricing (RTP) are applied by utilities to encourage customers to reduce their load during peak load hours. However, it is usually a hassle for residential customers to manually respond to prices that vary over time. In this paper, a fuzzy logic approach (FLA) utilizing wireless sensors and smart grid incentives for load reduction in residential HVAC systems is presented. Programmable communicating thermostats (PCTs) are used to control residential HVAC systems in order to manage and reduce energy use, while consumers accommodate their everyday schedules. Hence, the FLA is embedded into existing PCTs to augment more intelligence to them for load reduction, while maintaining thermal comfort. To emulate an actual thermostat, a PCT capable of handling both TOU and RTP is simulated in Matlab/GUI. It is utilized as a ‘simulator engine’ to evaluate the performance of FLA via applying several different scenarios. The results show that the FLA decreases/increases the initialized set points without jeopardizing thermal comfort by applying specific fuzzy rules through evaluating the information received from wireless sensors and smart grid incentives. Our approach results in better energy and cost saving in residential buildings versus existing PCT.
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Formerly, energy had been inexpensive and management of energy was efficient and was limited to elementary considerations. In the current scenario, due to a rapid increase in demand, complexity of the electrical network, probability of contingency and electricity cost have equally increased. In the recent past, Smart Grids are proven to be the best way to minimize these problems in an easier and smart way. Smart grid is defined as an electric network which has information technology fused to it. This paper proposes a way to reduce the total electricity cost in a smart grid using Genetic Algorithm. The system considered has renewable energy and battery banks apart from the grid to meet the demand. Short term time averaged electricity cost is formulated as an objective for optimization by GA with discharge of battery, energy from the grid to charge battery and meet load etc. as decision variables. The optimization problem is run for a 24 hours data of renewable input, real-time electricity price and load using MATLAB software; and the obtained results are furnished.
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Global warming is one of the most serious issues faced by today's world. The increase in world population and adoption of modern lifestyle have dramatically increased the demand for energy. Over the last decade Higher Educational Institution (HEI) buildings have seen massive increase in energy consumption due to increased use of IT equipments, longer occupancy and increased use of Heating Ventilation and Air Conditioning (HVAC) systems. Current Building Management Systems (BMS) fail to optimize energy consumption of HVAC systems in commercial and educational buildings. In this paper we present an intelligent agent based system to optimize energy consumption of HVAC system in HEI buildings. The system employs artificial intelligence techniques to predict the demand of the system and optimize energy consumption of the HVAC system. The experimental results have shown that the deployment of the system has resulted in 3% reduction in energy consumption of HVAC.
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A flow-shop scheduling problem with blocking has important applications in a variety of industrial systems but is underrepresented in the research literature. In this study, a novel discrete artificial bee colony (ABC) algorithm is presented to solve the above scheduling problem with a makespan criterion by incorporating the ABC with differential evolution (DE). The proposed algorithm (DE-ABC) contains three key operators. One is related to the employed bee operator (i.e. adopting mutation and crossover operators of discrete DE to generate solutions with good quality); the second is concerned with the onlooker bee operator, which modifies the selected solutions using insert or swap operators based on the self-adaptive strategy; and the last is for the local search, that is, the insert-neighbourhood-based local search with a small probability is adopted to improve the algorithm's capability in exploitation. The performance of the proposed DE-ABC algorithm is empirically evaluated by applying it to well-known benchmark problems. The experimental results show that the proposed algorithm is superior to the compared algorithms in minimizing the makespan criterion.
Article
This paper proposes a fully distributed Demand-Side Management system for Smart Grid infrastructures, especially tailored to reduce the peak demand of residential users. In particular, we use a dynamic pricing strategy, where energy tariffs are function of the overall power demand of customers. We consider two practical cases: (1) a fully distributed approach, where each appliance decides autonomously its own scheduling, and (2) a hybrid approach, where each user must schedule all his appliances. We analyze numerically these two approaches, showing that they are characterized practically by the same performance level in all the considered grid scenarios. We model the proposed system using a non-cooperative game theoretical approach, and demonstrate that our game is a generalized ordinal potential one under general conditions. Furthermore, we propose a simple yet effective best response strategy that is proved to converge in a few steps to a pure Nash Equilibrium, thus demonstrating the robustness of the power scheduling plan obtained without any central coordination of the operator or the customers. Numerical results, obtained using real load profiles and appliance models, show that the system-wide peak absorption achieved in a completely distributed fashion can be reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to meet the growing energy demand.
Article
With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.
Article
Fig. 1 shows a new genetic operator by integrating the mutation and the crossover operators, called the M+C operator, where a genetic probability pe is used to control the recombination speed among particles. In the 8-th line of Fig.1, whenG(0, 1)takes a small value, like the crossover operator in an evolutionary algorithm, crossing with random Pbestlk can help a particle to construct good schemas rapidly; whenG(0, 1)takes a big value, like the mutation operator, the value of G(0, 1) × rang can help the particle to escape from local optima.