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In this paper, we have evaluated the performance of heuristic algorithms: Genetic Algorithm (GA) and Artificial Fish Swarm Algorithm (AFSA) for Demand Side Management. Our prime focus in this paper, is to optimally schedule appliances in a smart home in such a way that the Peak to Average Ratio (PAR) and the electricity cost can be reduced. The pricing scheme used in this paper is real time pricing. Our Simulation results validate that the two nature inspired schemes successfully reduce PAR and electricity cost by transferring load of on peak hours to off peak hours. Our results also depict a trade off between electricity cost and comfort of a user.

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... The utility, smart grid and customers often have distinct and conflicting objectives. This has motivated extensive research on multi-objective optimal resource management, notably MPC [27], [28], linear programming (LP) and non-linear programming (NLP) [4], [29], [30] as well as evolutionary algorithms (EAs) [31]- [34]. ...

... Washing Machine 27 [30], [33], [34], [39], [41], [42], [43], [52], [53], [54], [55], [56], [57], [13], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [46], [69] Dishwasher 25 [30], [34], [38], [41], [42], [43], [52], [53], [54], [55], [57], [47], [70], [13], [59], [60], [61], [63], [64], [65], [66], [67], [68], [46], [69] Electric Vehicle 23 [30], [33], [34], [38], [40], [52], [56], [13], [61], [62], [63], [47], [70], [71], [72], [73], [74], [75], [76], [77], [78], [45], [46] Dryer Machine 21 [30], [38], [40], [41], [42], [43], [52], [57], [13], [58], [59], [60], [61], [63], [64], [65], [66], [67], [70], [47], [71] Air Conditioning 21 [30], [32], [33], [38], [40], [41], [42], [58], [60], [61], [62], [64], [47], [71], [73], [79], [80], [45], [68], [46], [81] Water Heater 17 [30], [33], [34], [38], [40], [41], [42], [52], [53], [54], [55], [60], [64], [47], [72], [78], [69] Light Spots 15 [33], [40], [41], [42], [43], [55], [62], [63], [79], [80], [82], [68], [45], [46], [69] Heating System 14 [27], [28], [31], [39], [41], [42], [57], [58], [72], [78], [83], [84], [85], [69] Refrigerator 11 [30], [38], [39], [41], [42], [60], [61], [79], [68], [46], [69] Oven 9 [41], [42], [43], [55], [60], [63], [47], [70], [46] Television 7 [30], [41], [42], [55], [79], [68], [46] Water Pump (Well, Pool) 6 [38], [41], [42], [59], [72], [78] Vacuum Cleaner 6 [30], [38], [55], [58], [63], [68] Computer 5 [30], [41], [42], [55], [63] Microwave 5 [30], [43], [55], [63], [68] Iron 5 [30], [39], [55], [68], [46] Battery/Energy Storage 4 [62], [64], [83], [46] Cooker Hob 3 [43], [60], [63] Fan 3 [30], [41], [42] Toaster 3 [30], [33], [ possible. A generic formulation for a robust optimisation problem is as follows [50]: ...

... Washing Machine 27 [30], [33], [34], [39], [41], [42], [43], [52], [53], [54], [55], [56], [57], [13], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [46], [69] Dishwasher 25 [30], [34], [38], [41], [42], [43], [52], [53], [54], [55], [57], [47], [70], [13], [59], [60], [61], [63], [64], [65], [66], [67], [68], [46], [69] Electric Vehicle 23 [30], [33], [34], [38], [40], [52], [56], [13], [61], [62], [63], [47], [70], [71], [72], [73], [74], [75], [76], [77], [78], [45], [46] Dryer Machine 21 [30], [38], [40], [41], [42], [43], [52], [57], [13], [58], [59], [60], [61], [63], [64], [65], [66], [67], [70], [47], [71] Air Conditioning 21 [30], [32], [33], [38], [40], [41], [42], [58], [60], [61], [62], [64], [47], [71], [73], [79], [80], [45], [68], [46], [81] Water Heater 17 [30], [33], [34], [38], [40], [41], [42], [52], [53], [54], [55], [60], [64], [47], [72], [78], [69] Light Spots 15 [33], [40], [41], [42], [43], [55], [62], [63], [79], [80], [82], [68], [45], [46], [69] Heating System 14 [27], [28], [31], [39], [41], [42], [57], [58], [72], [78], [83], [84], [85], [69] Refrigerator 11 [30], [38], [39], [41], [42], [60], [61], [79], [68], [46], [69] Oven 9 [41], [42], [43], [55], [60], [63], [47], [70], [46] Television 7 [30], [41], [42], [55], [79], [68], [46] Water Pump (Well, Pool) 6 [38], [41], [42], [59], [72], [78] Vacuum Cleaner 6 [30], [38], [55], [58], [63], [68] Computer 5 [30], [41], [42], [55], [63] Microwave 5 [30], [43], [55], [63], [68] Iron 5 [30], [39], [55], [68], [46] Battery/Energy Storage 4 [62], [64], [83], [46] Cooker Hob 3 [43], [60], [63] Fan 3 [30], [41], [42] Toaster 3 [30], [33], [ possible. A generic formulation for a robust optimisation problem is as follows [50]: ...

Energy is a vital resource for human activities and lifestyle, powering important everyday infrastructures and services. Currently, pollutant and non-renewable sources, such as fossil fuels, remain the main source of worldwide consumed energy. The environmental impact of their exploitation has boosted research and investments in alternative, clean and renewable sources, including photovoltaic and wind-based systems. As a whole, buildings are one of the major energy consumption sectors. Hence, improving energy efficiency in buildings will result in economical and environmental gains. In the case of households, home energy management systems are mainly used for monitoring real-time consumption and to schedule appliance operations so that the energy bill could be minimised, or according to another specific criterion. This work aims to survey the most recent literature on home energy management systems, providing an aggregated and unified perspective in the context of residential buildings. In addition, an updated literature list regarding commonly managed household appliances and scheduling objectives are included. Physical and operational constraints, and how they are addressed by home energy management systems along with security issues are also discussed.

... Conventionally, The research community categorized the MH algorithms in accordance with the number of initial solutions into local search-based and populationbased where the later is classified into evolutionary-based algorithms and swarm-based algorithms. The MH algorithms used for PSP problems include Genetic algorithm [18], [19], [20], Particle swarm optimization [21], [22], differential evolution [23], [24], grey wolf optimizer [25], and Artificial immune algorithms. [26]. ...

The power schedule problem (PSP) is the problem of managing, controlling, and scheduling power consumption of electrical appliances/devices to operate at the best periods according to several constraints and objectives. The PSP is a complex and high-constraint scheduling problem, making its search space extensive and rugged. The PSP components can be controlled and managed by utilizing a communication approach that interconnects the appliances and enhances exchanging data. Several communication approaches were used for the PSP, where the Internet of Things (IoT) is the best for data exchange. The PSP has been extensively handled using various optimization approaches, particularly metaheuristics, due to their capabilities to optimize different search space scales. Nevertheless, in some cases, these optimization algorithms suffer from low execution abilities, especially with huge search spaces like the PSP. In this study, a recent metaheuristic, called white shark optimizer (WSO), is adapted and enhanced to address the PSP efficiently. The proposed enhanced method is introduced to improve the WSO optimization performance and find better schedules for the PSP by hybridizing the WSO components with a well-known optimization algorithm called equilibrium optimizer. The proposed method is called the white shark equilibrium optimizer (WSEO). The proposed method is operated through a residential IoT system to manage home appliances efficiently. Moreover. the PSP is mathematically formulated as multi-objective PSP considering three main objectives, including electricity bills, power consumption balance, and users’ comfortabilities. In the evaluation stage, a new case study in the United Arab Emirates (UAE) is proposed that contains most of the available appliances in the UAE. The evaluation is presented in three main phases, including original, original with a hybrid approach, and hybrid approach evaluations. The proposed WSEO outperformed all compared methods in optimizing the PSP.

... It has been the target of study within power systems due to its effectiveness in solving problems with a high number of variables and constraints. As such several artificial intelligence (AI) algorithms have been applied to the ERM problem such as Particle Swarm Optimization (PSO) and its variants [3], Differential Evolution (DE) [4], Genetic Algorithm (GA) [5], Estimation of Distribution Algorithm (EDA) [6], and many others. The literature presents multiple works on day-ahead DER scheduling [7,8], with few that go further and do this scheduling for intraday time horizon. ...

Demand response (DR) programs and local markets (LM) are two suitable technologies to mitigate the high penetration of distributed energy resources (DER) that is vastly increasing even during the current pandemic in the world. It is intended to improve operation by incorporating such mechanisms in the energy resource management problem while mitigating the present issues with Smart Grid (SG) technologies and optimization techniques. This paper presents an efficient intraday energy resource management starting from the day-ahead time horizon, which considers load uncertainty and implements both DR programs and LM trading to reduce the operating costs of three load aggregator in an SG. A random perturbation was used to generate the intraday scenarios from the day-ahead time horizon. A recent evolutionary algorithm HyDE-DF, is used to achieve optimization. Results show that the aggregators can manage consumption and generation resources, including DR and power balance compensation, through an implemented LM.

... The Chinese Ministry of Housing and Urban-Rural Development required all new residential communities in northern China to implement household heat metering since 2010. To fulfill the requirement, residential heating needs to be commercialized, and controlled and measured on a household basis [16][17][18][19][20][21][22]. In other words, each user should be enabled to adjust the heat, and the heating fee should be charged as per the amount of heat consumed. ...

... The cost of integration of renewable technologies can be decreased by planning of shiftable loads and storage, i.e. load and energy management. These management approaches optimally schedule a device pool, whereby technical restrictions have to be considered [15][16][17][18]. To develop future energy management systems, it is necessary to define the expected equipment and its requirements. ...

The decarbonization of the energy system will bring substantial changes, from supranational regions to residential sites. This review investigates sustainable energy supply, applying a multi-sectoral approach from a residential site perspective, especially with focus on identifying crucial, plausible factors and their influence on the operation of the system. The traditionally separated mobility, heat, and electricity sectors are examined in more detail with regard to their decarbonization approaches. For every sector, available technologies, demand, and future perspectives are described. Furthermore, the benefits of cross-sectoral integration and technology coupling are examined, besides challenges to the electricity grid due to upcoming technologies, such as electric vehicles and heat pumps. Measures such as transport mode shift and improving building insulation can reduce the demand in their respective sector, although their impact remains uncertain. Moreover, flexibility measures such as Power to X or vehicle to grid couple the electricity sector to other sectors such as the mobility and heat sectors. Based on these findings, a morphological analysis is conducted. A morphological box is presented to summarize the major characteristics of the future residential energy system and investigate mutually incompatible pairs of factors. Lastly, the scenario space is further analyzed in terms of annual energy demand for a district.

... In the online way, the achieved dataset was utilized to work the ANN. Talha et al. (2017) have assessed for demand side management the performance of heuristic algorithms included, which are the genetic algorithm (GA) and the artificial fish swarm algorithm (AFSA). The prime centre was to plan machines in a smart home optimally so that the Peak to Average Ratio (PAR) gives decreased electricity cost. ...

This paper proposes an efficient hybrid approach–based energy management strategy
(EMS) for grid‐connected microgrid (MG) system. The primary objective of the proposed
technique is to reduce the operational electricity cost and enhanced power flow
between the source side and load side subject to power flow constraints. The proposed
control scheme is a consolidated execution of both the random forest (RF) and quasioppositional‐
chaotic symbiotic organisms search algorithm (QOCSOS), and it is named
as QOCSOS‐RF. Here, the QOCSOS can have the capacity to enhance the underlying
irregular arrangements and joining to a superior point in the pursuit space. Likewise,
the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert
and extrapolate the arbitrary information with high exactness. Here, the required load
demand of the grid‐connectedMGsystem is continuously tracked by the RF technique.
The QOCSOS optimized the perfect combination of the MG with the consideration of
the predicted load demand. Furthermore, in order to reduce the influence of renewable
energy forecasting errors, a two‐strategy for energy management of the MG is
employed. At that point, proposed model is executed in MATLAB/Simulink working
platform, and the execution is assessed with the existing techniques

... The knowledge base classified and analyzed the behavior of each user, and then recommended more reasonable behaviors to users, but had no quantitative calculation. Reference [9] evaluated the application effects of the GA and the artificial fish swarm algorithm in the HEDMS. Under the premise of real-time price and without considering the user comfort, the two algorithms reduced the total EC by 21% and 30% respectively, but ignored the fact that equipment power is changing with time. ...

Effective and adaptable household energy management system needs to be established to promote and implement demand response projects in smart grids. The current household energy demand management strategy cannot provide users with a choice to ensure user comfort, its time sampling accuracy is not high enough, and the operation using the rated power results in a large deviation from the actual cost. In order to solve these problems, this paper proposes an optimization control strategy to achieve the minimum electricity cost based on the user response, equipment operating power and dynamic pricing. The genetic algorithm is used for calculating the optimal operating parameters of each equipment by using the operating power. The correctness and the high accuracy of the algorithm are verified by comparing with the loop search optimization algorithm. The results show that the daily electricity cost is reduced by 29.0%, and the peak-to-average ratio is reduced by 36.2% after adopting the proposed strategy.

... Therefore, in most cases black-box attack can be modeled as an optimization problem. Genetic algorithm is widely applied to various applications as a typical optimization tool, such as energy optimization [31], distribution network optimization [32], ontology alignments optimization [33] and web crawler [34], and all of them achieve good optimization performance. In this paper, a novel adversarial perturbation optimization attack based on genetic algorithm is proposed to implement black-box attack. ...

Most deep learning models are easily vulnerable to adversarial attacks. Various adversarial attacks are designed to evaluate the robustness of models and develop defense model. Currently, adversarial attacks are brought up to attack their own target model with their own evaluation metrics. And most of the black-box adversarial attack algorithms cannot achieve the expected success rate compared with white-box attacks. In this paper, comprehensive evaluation metrics are brought up for different adversarial attack methods. A novel perturbation optimized black-box adversarial attack based on genetic algorithm (POBA-GA) is proposed for achieving white-box comparable attack performances. Approximate optimal adversarial examples are evolved through evolutionary operations including initialization, selection, crossover and mutation. Fitness function is specifically designed to evaluate the example individual in both aspects of attack ability and perturbation control. Population diversity strategy is brought up in evolutionary process to promise the approximate optimal perturbations obtained. Comprehensive experiments are carried out to testify POBA-GA's performances. Both simulation and application results prove that our method is better than current state-of-art black-box attack methods in aspects of attack capability and perturbation control.

... Optimization problem is solved using different algorithms including GA. An optimal HEM system is suggested in [28] for appliances scheduling to deduce PAR and energy cost. Simulation results proved the efficacy of the suggested algorithm. ...

Smart Gird is a technology that has brought many advantages with its evolution. Smart Grid is indispensable as it will lead us towards environmentally sustainable economic growth. Home energy management in Smart Grid is a hot research topic now a days. It aims at reducing the energy cost of users, gaining energy self-reliance and decreasing Greenhouse gas emissions. Renewable energy technologies nowadays are best suitable for off grid services without having to build extensive and complicated infrastructure. With the advent of Smart Grid (SG), the occupants have the opportunity to integrate with renewable energy sources (RESs) and to actively take part in demand side Management (DSM). This review paper is comprehensive study of various optimization techniques and their implementation with respect to electricity cost diminution, load balancing, power consumption and user's comfort maximization etc. for Home Energy Management in Smart Grid. This paper summarizes recent trends of energy usage from hybrid renewable energy integrated sources. It discusses several methodologies and techniques for hybrid renewable energy system optimization.

... Authors have used a pricing scheme so that the consumers get an idea of cost and use electricity accordingly and avoid usage of energy during peak hours to avoid the extra cost of electricity. An artificial fish swarm algorithm and genetic algorithm have been used to reduce the peak to average ratio (PAR) and cost of electricity by [78]. An electricity optimization rescheduling scheme using mixed integer linear programming method (MELP) and a daily maximum energy scheduling device for South Africa has been proposed by [79]. ...

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

... In the online way, the achieved dataset was utilized to work the ANN. Talha et al. (2017) have assessed for demand side management the performance of heuristic algorithms included, which are the genetic algorithm (GA) and the artificial fish swarm algorithm (AFSA). The prime centre was to plan machines in a smart home optimally so that the Peak to Average Ratio (PAR) gives decreased electricity cost. ...

This paper proposed an optimal control technique for power flow control of hybrid renewable energy systems (HRESs) like a combined photovoltaic and wind turbine system with energy storage. The proposed optimal control technique is the joined execution of both the whale optimization algorithm (WOA) and the artificial neural network (ANN). Here, the ANN learning process has been enhanced by utilizing the WOA optimization process with respect to the minimum error objective function and named as WOANN. The proposed WOANN predicts the required control gain parameters of the HRES to maintain the power flow, based on the active and reactive power variation in the load side. To predict the control gain parameters, the proposed technique considers power balance constraints like renewable energy source accessibility, storage element state of charge, and load side power demand. By using the proposed technique, power flow variations between the source side and the load side and the operational cost of HRES in light of weekly and daily prediction grid electricity prices have been minimized. The proposed technique is implemented in the MATLAB/Simulink working stage, and the effectiveness is analyzed via the comparison analysis using the existing techniques.

... In [8] an EMS is purposed, which utilizes micro economic principles to coordinate components of a microgrid. Heuristic algorithms, like a genetic algorithm, are capable of load scheduling for smart home appliances, reducing peak to average ratio and electricity cost [9]. Anvari-Moghaddam et al. [10] purposed a multi-agent approach for optimizing cost and user comfort at a microgrid energy system, containing distributed generation. ...

An energy management system (EMS) for a household energy system is proposed in this paper, which is composed of a photovoltaic (PV) generator , a home energy storage (HES), an electric vehicle (EV), an electrical household load and a grid connection, with 24 h operation horizon. The EMS objective is to reduce the electricity cost of the household by using a linear optimization algorithm. Two different EV schedules are utilized for simulations. One mainly describes rides to work and the other describes rides in a domestic context, such as rides to a supermarket. A forecast algorithm for the electrical load of the household, based on k-means clustering and an artificial neural network, is evaluated and integrated into the EMS to realistically represent the household’s load profile. It is shown that the developed forecast algorithm performs better than two of the benchmarks. Another finding is that the more storage is available at PV-production intervals, the higher the effect of forecast uncertainties and the lower the electricity cost of the household, disregarding the investment cost.

... Another performance of non traditional optimization algorithms like GA and AFSA. These algorithms are implemented to optimally schedule appliances in a smart home in such a way that reduction in the PAR and electricity cost can be attained [4]. EHO and EDE algorithms are also used for solving scheduling problem. ...

In this paper, we presented JAYA algorithm which is a recently developed scheme that do not need any specific parameter to be adjusted except the known control parameters. To achieve the set of objectives like: electricity bill minimization, peak to average ratio (PAR) reduction, minimum user dissatisfaction , a proposed JAYA energy management controller (JAYA-EMC) for optimization is implemented. In increasing sustainability of smart grid, the simulation results of our proposed scheme is proven to be cost effective solution. In addition, the feasible region (FR) of appliances are calculated which show relationship between energy load, cost and delay.

Present advancements in the power systems paved way for introducing the smart grid (SG). A smart grid is beneficial to consumers which enables the bi-directional flow of information between the utility and customer. Demand-side management (DSM) techniques are crucial as load-side management techniques to attain the better stability of the grid. Home energy management systems (HEMS) play a indispensable part in the DSM. Countless traditional optimization techniques are utilized to implement HEMS, but the limitations of traditional Math heuristic methods gave rise to a concept-based optimization techniques called the Meta heuristic methods. Recent advancements introduced smart optimization techniques powered by Artificial Intelligence (AI). This article elucidates the applications of AI-based optimization techniques and their advantages over other methods. Various Machine learning (ML) and Deep Learning (DL) algorithms and their utilization for HEMS are discussed in brief.

Internet of Things (IoT) has been developed as a heterogeneous environment that contains network devices with limited resources. The application of IoT principles in the smart city domain creates new opportunities and requires diligent implementation mechanisms for optimal resource utilization. With time, the IoT applications tend to generate and forward a huge amount of data in the smart cities and require a real-time response from the servers. Due to this, the traditional cloud computing architecture is unable to handle the latency-sensitive applications efficiently, and hence, the FoG architecture has been widely implemented with IoT devices to efficiently retrieve or forward the data. For the comprehensive utilization of the resources in the FoG systems-based smart cities, various energy-aware resource allocation schemes have been discussed in this chapter. The schemes suggest different mechanisms to access the required contents with minimal energy consumptions for the applications that are used in smart cities.

Current increases in the demand for electricity require sustainable energy management measures and have promoted the adoption of clean and renewable sources, particularly at the residential building level. Active demand management is usually carried out through load shifting based on specific techniques, such as optimisation, heuristics, model-based predictive control and machine learning methodologies. This work addresses the problem of residential load scheduling via optimisation techniques. A compressive receding horizon strategy is proposed for week-ahead load shifting, and the selection is driven by traditional receding horizon and day-ahead allocation strategy misalignment, with weekly household appliance usage patterns. The proposed approach is compared with receding horizon and day-ahead scheduling techniques over 30 different weeks for a prototypical smart home with non-controllable demand, which is representative of a four-resident family and includes micro power generation and battery storage. The simulation results confirm the validity of the proposed strategy in the context of household appliance scheduling problems and show competitive electricity costs and resident discomfort performance compared to state-of-the-art approaches. Furthermore, the proposed compressive receding horizon strategy fully exploits weather and photovoltaic generation forecasts to promote self-consumption and grid demand stress reduction while providing environmental gains and financial benefits to the utility service and consumers, particularly in the case of simultaneously scheduling a huge number of households.

This manuscript proposes a hybrid strategy for power flow management under a smart grid system connected to a hybrid renewable energy system (HRES). The HRES is the combination of photovoltaic, wind turbine, fuel cell and energy storage system or battery. The proposed technique is the execution of the Integrated Harris Hawk Optimization algorithm (IH²OA). Here, Harris Hawk Optimization is the consolidation of crossover and mutation function, hence it is called IH²OA. Moreover, IH²OA is enhancing the voltage source inverter of control signals under the variation of power replace among source and load side. The numerous characteristics are made up of necessary grid active with reactive power variations created under the view of available power source. The IH²OA method ensures the location of control signals using parallel implementation as opposed to active with reactive power variation. In the IH²OA method, the control scheme-based optimizes the power controller under the power flow variation. The proposed system performed on MATLAB/Simulink platform and the efficiency are compared with diverse existing techniques.

This paper proposes a new management algorithm and operation of a hybrid renewable energy system (HRES) connected to the power system. The whole hybrid system is managed in such a manner that it can produce as much needed by the grid system. The proposed algorithm is used to distribute proportionally the active and reactive power references to the PV source and wind generators according to their ability contribution. Based on the available active powers and the ability on reactive power of each sub‐system, the references are calculated using a proportional distribution algorithm and sent individually by the principal controller to each auxiliary controller. The analysis of the simulation results obtained under Matlab/Simulink shows the effectiveness of the proposed management algorithm and the flexibility of the hybrid renewable energy system studied. This paper proposes a new management algorithm and operation of a hybrid renewable energy system (HRES) connected to the power system. The whole hybrid system is managed in such a manner that it can produce needed by the grid system. The proposed algorithm is used to distribute proportionally the active and reactive power references to the PV source and wind generators according to their ability contribution. Based on the available active powers and the ability on reactive power of each sub‐system, the references are calculated using a proportional distribution algorithm and sent individually by the principal controller to each auxiliary controller. The analysis of the simulation results obtained under Matlab/Simulink shows the effectiveness of the proposed management algorithm and the flexibility of the hybrid renewable energy system studied.

The decentralization of energy supply and the fluctuating feed-in from renewable energies lead to an increasing need to generate electricity and heat locally and efficiently. Therefore, different energy systems are used together at building level. Consequently, energy management systems (EMSs) in the building sector and households are becoming increasingly complex and are facing new challenges. To face these challenges, EMSs are required to enable efficient and combined operation of multiple energy systems and components. The increasing research into EMS is leading to a growing field of research with different characteristics, objectives, and algorithms. However, the resulting diversification of the research topic is accompanied by a lack of transparency. Therefore, this article is the first to provide a comprehensive quant itative evaluation of the state of research. For this purpose, 98 relevant publications are quantitatively evaluated. This article focuses on methods, model characteristics, optimization objectives, price structures, observation horizon, and components and devices used of EMS. The detailed evaluation of these topics creates a high level of transparency in EMS research to provide precise insights into current research priorities and potential weaknesses, to allow researchers to classify their own work and to identify potential research topics.
Highlights:
• For the first time, a quantitative study on EMS research is presented
• The evaluation of 98 relevant publications provides detailed insights
• Insights about methods, characteristics, objectives, prices, horizons, and components
• Detailed graphical evaluations for each sub-area of the study

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models.

In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

Demand Side Management (DSM) through optimization of home energy consumption in smart grid environment is now one of the well-known research areas. Appliances scheduling has been done through many different algorithms to reduce peak load and consequently the Peak to Average Ratio (PAR). This paper presents a Comprehensive Home Energy Management Architecture (CHEMA) with integration of multiple appliances scheduling options and enhanced load categorization in smart grid environment. The CHEMA model consists of six layers and has been modeled in Simulink with embedded MATLAB code. Single Knapsack optimization technique is used for scheduling and four different cases of cost reduction are modeled at the second layer of CHEMA. Fault identification and electricity theft control have also been added in CHEMA. Furthermore, carbon footprint calculations have been incorporated in order to make the users aware of environmental concerns.} Simulation results prove the effectiveness of the proposed model.

One of the most challenging problems associated with operation of Smart Micro-Grids is the optimal energy management of residential buildings with respect to multiple and often conflicting objectives. In this paper, a multi-objective mixed integer nonlinear programming model is developed for optimal energy use in a smart home, considering a meaningful balance between energy saving and a comfortable lifestyle. Thorough incorporation of a mixed objective function, under different system constraints and user preferences, the proposed algorithm could not only reduce the domestic energy usage and utility bills, but also ensured an optimal task scheduling and a thermal comfort zone for the inhabitants. To verify the efficiency and robustness of the proposed algorithm, a number of simulations were performed under different scenarios using real data; and the obtained results were compared in terms of total energy consumption cost, users’ convenience rates and thermal comfort level.

In smart grid, residential consumers adopt different load scheduling methods to manage their power consumptions with specific objectives. The conventional load scheduling methods aim to maximize the consumption payoff or minimize the consumption cost. In this paper, we introduce a novel concept of cost efficiency-based residential load scheduling framework to improve the economical efficiency of the residential electricity consumption. The cost efficiency is defined as the ratio of consumer's total consumption benefit to its total electricity payment during a certain period. We develop a cost-efficient load scheduling algorithm for the demand-side's day-ahead bidding process and real-time pricing mechanism by using a fractional programing approach. Results show that the proposed scheduling algorithm can effectively reflect and affect user's consumption behavior and achieve the optimal cost-efficient energy consumption profile. For practical consideration, we also take into account the service fee and distributed energy resources (DERs) in our framework, and analyze their impacts on the cost efficiency. Simulation results confirm that the proposed algorithm significantly improves consumer's cost efficiency. It is shown that a higher service fee will decrease the cost efficiency, while the integration of DERs can effectively improve the cost efficiency.

Smart Grid is the most promising concept which is more reliable, flexible, controllable and environment friendly. Home energy management (HEM) system is an important part of the smart grid that provides a number of benefits to the end users such as savings in the electricity bill, reduction in peak demand and meeting the demand side requirements. Demand Response (DR) and Time-of-Use (ToU) pricing refer to programs which offer incentives to the end users who curtail their energy use during times of peak demand. This paper proposes an energy efficient optimization model based on Binary Particle Swarm Optimization (BPSO) for residential electricity consumers. The proposed model optimally schedules the electricity consumption of different household appliances in a dynamic pricing environment to benefice the user by minimizing electricity cost. Simulation results illustrate that the proposed method efficiently shifts the appliances operation time from high peak to low peak hours and leads to significant electricity bill saving.

Smart grid is advancing power grids significantly, with higher power generation efficiency, lower energy consumption cost, and better user experience. Microgrid utilizes distributed renewable energy generation to reduce the burden on utility grids. This paper proposes an energy ecosystem; a cost-effective smart microgrid based on intelligent hierarchical agents with dynamic demand response (DR) and distributed energy resource (DER) management. With a dynamic update mechanism, DR automatically adapts to users' preference and varying external information. The DER management coordinates operations of micro combined heat and power systems ($boldsymbol {mu }$ CHPs), and vanadium redox battery (VRB) according to DR decisions. A two-level shared cost-led $boldsymbol {mu }$ CHPs management strategy is proposed to reduce energy consumption cost further. VRB discharging is managed to be environment-adaptive. Simulations and numerical results show the proposed system is very effective in reducing the energy consumption cost while satisfying user's preference.

One of the most challenging problems associated with operation of smart micro-grids is the optimal energy management of residential buildings with respect to multiple and often conflicting objectives. In this paper, a multiobjective mixed integer nonlinear programming model is developed for optimal energy use in a smart home, considering a meaningful balance between energy saving and a comfortable lifestyle. Thorough incorporation of a mixed objective function under different sys-tem constraints and user preferences, the proposed algorithm
could not only reduce the domestic energy usage and utility bills, but also ensure an optimal task scheduling and a ther-mal comfort zone for the inhabitants. To verify the efficiency and robustness of the proposed algorithm, a number of simula-tions were performed under different scenarios using real data, and the obtained results were compared in terms of total energy consumption cost, users’ convenience rates, and thermal comfort
level.

In this paper, the benefits of distributed energy resources (DERs) are
considered in an energy management scheme for a smart community consisting of a
large number of residential units (RUs) and a shared facility controller (SFC).
A non-cooperative Stackelberg game between RUs and the SFC is proposed in order
to explore how both entities can benefit, in terms of achieved utility and
minimizing total cost respectively, from their energy trading with each other
and the grid. From the properties of the game, it is shown that the maximum
benefit to the SFC in terms of reduction in total cost is obtained at the
unique and strategy proof Stackelberg equilibrium (SE). It is further shown
that the SE is guaranteed to be reached by the SFC and RUs by executing the
proposed algorithm in a distributed fashion, where participating RUs comply
with their best strategies in response to the action chosen by the SFC. In
addition, a charging-discharging scheme is introduced for the SFC's storage
device (SD) that can further lower the SFC's total cost if the proposed game is
implemented. Numerical experiments confirm the effectiveness of the proposed
scheme.

This paper presents a new energy consumption scheduling scheme to enable Demand Side Management (DSM) in future Smart Grid Networks (SGNs). Electrical grid has been facing important challenges regarding quality and quantity to meet the increasing requirements of consumers. Environment friendly and economical generation along with efficient consumption through effective DSM in future SGNs will help in addressing most of these challenges because of integration of advanced information and commu- nication technologies. In this work, we propose an autonomous energy scheduling scheme for household appliances in real-time to achieve minimum consumption cost and reduction in peak load. We assume that every user is equipped with smart meter which has an Energy Consumption Controlling (ECC) unit. Every ECC unit is connected with its neighbours through local area network to share power consumption information. ECC units run a distributed algorithm to minimize the peak load by transferring the shiftable loads from peak hours to off-peak hours. This ultimately minimizes the total energy consumption cost. Simulation results confirm that our proposed algorithm significantly reduces the peak load and energy consumption cost.

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.

This paper proposed a new smart home community architecture in power system, in which community controller will acts as a virtual power distribution company. The traditional real time pricing schemes may not be effectively implemented in terms of reduction of power peak to average ratio over the large number of end consumers. To overcome this problem, a true real time pricing between community controller and community end users is developed based on real time pricing and inclining block rates. The proposed pricing scheme implemented in the community is charged at the end of a day according to the combined load of the community. To schedule the electric appliances in a combined way, we have developed a power scheduling algorithm as well. The simulation results have revealed that by applying anticipated technique of pricing scheme in group of households, the consumption cost of end consumers decreases and the overall power peak to average ratio reduces as well which will be beneficial for the utilities.

This paper studies the power scheduling problem for residential consumers in smart grid. In general, the consumers have two types of electric appliances. The first type of appliances have flexible starting time and work continuously with a fixed power. The second type of appliances work with a flexible power in a predefined working time. The consumers can adjust the starting time of the first type of appliances or reduce the power consumption of the second type of appliances to reduce the payments. However, this will also incur discomfort to the consumers. Assuming the electricity price is announced by the service provider ahead of time, we propose a power scheduling strategy for the residential consumers to achieve a desired trade-off between the payments and the discomfort. The power scheduling is formulated as an optimization problem including integer and continuous variables. An optimal scheduling strategy is obtained by solving the optimization problem. Simulation results demonstrate that the scheduling strategy can achieve a desired tradeoff between the payments and the discomfort.

In this paper, we study an electricity load scheduling problem in a residence. Compared with previous works in which only limited sets of appliances are considered, we classify various appliances into five sets considering their different energy consumption and operation characteristics, and provide mathematical models for them. With these appliance models, we propose an electricity load scheduling algorithm that controls the operation time and energy consumption level of each appliance adapting to time-of-use pricing in order to maximize the overall net utility of the residence while satisfying its budget limit. The optimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is in general, difficult to solve. In order to solve the problem, we use the generalized Benders decomposition approach with which we can solve the MINLP problem easily with low computational complexity. By solving the problem, we provide an algorithm to obtain the optimal electricity load scheduling of various appliances with different energy consumption and operation characteristics in a unified way.

In this paper, we focus on the problems of load scheduling and power trading in systems with high penetration of renewable energy resources (RERs). We adopt approximate dynamic programming to schedule the operation of different types of appliances including must-run and controllable appliances. We assume that users can sell their excess power generation to other users or to the utility company. Since it is more profitable for users to trade energy with other users locally, users with excess generation compete with each other to sell their respective extra power to their neighbors. A game theoretic approach is adopted to model the interaction between users with excess generation. In our system model, each user aims to obtain a larger share of the market and to maximize its revenue by appropriately selecting its offered price and generation. In addition to yielding a higher revenue, consuming the excess generation locally reduces the reverse power flow, which impacts the stability of the system. Simulation results show that our proposed algorithm reduces the energy expenses of the users. The proposed algorithm also facilitates the utilization of RERs by encouraging users to consume excess generation locally rather than injecting it back into the power grid.

The emergence of smart grid (SG) brings about many fundamental changes in electric power systems. In this paper, we study the problem of smooth electric power scheduling in power distribution networks. We introduce an electricity supply/demand model that takes into account the time-varying demands and their deadlines. We formulate a constrained nonlinear programming problem and incorporate the theory of majorization to develop algorithms that can compute smoothness optimal schedules for the deferrable load dominant system. An effective heuristic algorithm is also presented by extending the majorization-based algorithm for the general scenario with both priority loads and deferrable loads. After obtaining the smooth power schedule, a distributed user benefit maximization load control scheme is used to allocate the scheduled power to individual users, while maximizing their levels of satisfaction. The analysis and simulation results demonstrate the efficacy of the proposed algorithms on smooth electric power scheduling, peak power minimization, and reducing power generation cost.

In this paper, we utilize the GA method to optimize the start time units of all the OAAs to achieve our objectives. Since the start time unit is the only variable in our scheme and the constraint parameters are set in the beginning, we assume that the total fitness function is (14). In the selection process, we adopt a roulette selection method in which the individual with a better fitness value has a higher probability to be selected for further processing. In general, the time complexity of the GA process can be represented as O(generation number*(mutation complexity + crossover complexity + selection complexity)). Assume the maximal generation number, the size of the population, and the number of individuals are denoted by g, N, and na, respectively; therefore, the time complexity of our scheme is O(gNna). In this case, the time cost increases as the three parameters become larger, and, usually, the time cost of GA optimization does not satisfy people. However, in our approach, the power scheduling process is implemented at the beginning of the day; therefore, after time parameters are determined, there is enough time for power scheduling, and the algorithm running time problem is not so important. We think a time cost of a few seconds is acceptable. In this paper, the population size is 200; the probability of crossover and the probability of mutation are 90% and 2%, respectively. Finally, when the generation number reaches 1,000, the evolution process will finish.
Generally speaking, the relationship between electricity cost and DTRave is a tradeoff. In other words, as the value of DTRave increases, electricity cost decreases. However, the minimum electricity cost value would emerge at a position at which the DTRave value is about 50%, which is not definite, due to the random POE. From the result shown in Fig. 6, at the position that DTRave equals 0, it implies that the major consideration is minimizing the delay time; thus, in this case, ω1=0, ω2=1. However, when the minimum electricity cost is reached, ω1=1, ω2=0.

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.

We propose a consumption scheduling mechanism for home area load management in smart grid using integer linear programming (ILP) technique. The aim of the proposed scheduling is to minimise the peak hourly load in order to achieve an optimal (balanced) daily load schedule. The proposed mechanism is able to schedule both the optimal power and the optimal operation time for power-shiftable appliances and time-shiftable appliances respectively according to the power consumption patterns of all the individual appliances. Simulation results based on home and neighbourhood area scenarios have been presented to demonstrate the effectiveness of the proposed technique.

Energy Shortfall in Pakistan. https://en.wikipedia.org/wiki/Electricity_sector_in_Bangladesh

- Wikipedia

Cost and Load Reduction using Heuristic Algorithms in Smart Grid

- Zafar Iqbal
- Nadeem Javaid
- Imran Mobushir Riaz Khan
- Zahoor Ali Ahmed
- Umar Khan
- Qasim

Zafar Iqbal, Nadeem Javaid, Mobushir Riaz Khan, Imran Ahmed, Zahoor Ali Khan, Umar Qasim:"Cost and Load Reduction using Heuristic
Algorithms in Smart Grid."