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Illustration of the hourly energy consumption of different appliances (per household) from survey data. The image corresponds to average energy consumption of all participating household for working days.
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This paper proposes a new demand response scheduling framework for an array of households, which are grouped into different categories based on socio-economic factors, such as the number of occupants, family decomposition and employment status. Each of the households is equipped with a variety of appliances. The model takes the preferences of parti...
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Context 1
... each household and individual appliance, energy consumption has been monitored every 10 min. An illustration of aggregated data can bee seen in Figure 2. ...
Context 2
... energy consumption of the other appliances is used as a base load. It is important to point out that water heating appliances are also excluded from scheduling because the vast majority of surveyed households used natural gas instead of electricity (see Figure 2). Although the monitored data show the energy consumption, they also give information on the time periods when households have used such appliances. ...
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Demand Response Management System (DRMS) is used in a smart grid to reduce the gap between power generation and its demand. The knowledge of the demand of the customers is very important because failing to fulfill this demand can lead to serious issues, like system failures and blackouts. Home Energy Management System (HEMS) is a DRMS that is desig...
The growth of renewable energy has accelerated globally toward a low-carbon economy since the Paris Agreement entered into force in 2016. As a result of the increase of variable renewable energy (VRE), namely solar PV and wind, power systems require more flexibility from conventional power plants with less power generation to regulate increased var...
The increasing load demand in residential area and irregular electricity load profile encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. We propose a multi-objective optimization based solution that shifts the electricity load from On-peak to Off-peak hours according to the defined...
Citations
... MG energy management has been studied under different perspectives, e.g., regarding to the improvement in dynamic performance when considering economic aspects (Mohan et al., 2016;Li and Fang, 2016), when optimizing both operational cost and economic performance (Wang et al., 2016;Yunjia et al., 2017;Nguyen and Crow, 2016;De Leone et al., 2017;Zeng et al., 2019), and reliability studies (Nikmehr and Ravadanegh, 2016). Although many other studies have analyzed DR problems under different scenarios and approaches (Pop et al., 2018;Karaboghossian and Zito, 2018;Zunnurain and Maruf, 2017;Li et al., 2017;Jovanovic et al., 2016;Tabar et al., 2017;Liasi and Golkar, 2017;Tushar et al., 2018;Yang and Fang, 2017;Dong and Chen, 2019;Elsayed et al., 2018;Herath et al., 2019), a thorough search of the relevant literature did not yield a single work that investigates and proposes a preferencedriven DR optimization model (Thiele et al., 2009) considering home appliance operational characteristics and the utilization of DER within a mechanism that allows for adjustments in scheduling. ...
Due to the widespread deployment of distributed energy resources, renewable energies and battery energy storage, the peer to peer (P2P) energy trading schematic has gained the staple attention for improving the energy efficiency and energy flexibility of power grids. This is while, smart demand response programming (DRP) is considered as the bridge between these two indicators of smart grid. Moreover, the subtle point of proliferating P2P schematics is the regulation towards the maximization of social welfare leading to economic profitability of customers and owner’s of microgrid and, eventually, reduction of pollutant emission of fossil fuels. Also, uncertainty, aroused by electrical consumption and renewable energy resources, is the core of every considerations, which has to be dealt with intelligent algorithms for strengthening the stability of transactions. On the other hand, compatibility with upper grid’s regulations, i.e. power loss and voltage deviation, along with determining fair price of energy trading are the subjects of P2P-based tactics. Therefore, this paper proposes a P2P-based transactive energy sharing architecture, as two stage mixed integer non-linear programming, using smart DRP integrated with machine learning approach, i.e. radial basis neural network.Firstly, the uncertainty of electrical demand and renewable energies are relaxed through short term forecasting. Doing so, the day-ahead transactions of peers are obtained based on their energy management objective, targeting the energy reliability of customers, which energy not supplied criterion has to be equal to zero. Then, participation of customers in DRP, cost of customers, revenue of microgrid’s owner and transactions of real time programming are optimally acquired based on Pareto front technique. Also, the simulations are conducted on IEEE 85 bus test system to realize the considerations. The results convey that the profitability of customers and owners is tied with the implementation of smart DRP and accurate forecasting of uncertain variables. In addition, the maximum improvements towards maximizing the revenue of owners and minimizing the cost of customers take place at hours which the electrical consumption is shifted from peaks to off-peaks and mid-peaks, certifying the performance of proposed methodology.
... In [19], mixed linear and integer linear programming were used for domestic appliance scheduling within home area network simulation on ripple control service cost and peak load minimization did not consider user comfort level. In [20], multi-objective mixed integer programming was applied for multi-level preference of appliance modelling, overall production cost, and individual electricity bill minimization. In this proposed work, the production costs were reduced and took into account user preference or satisfaction. ...
This paper presents a new application of the sine cosine algorithm (SCA) to obtain optimal home energy management systems (HEMS) that use the load shifting strategy of demand side management (DSM) to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to decrease electricity bills and peak to average ratio (PAR) while maintaining user comfort through coordination among home devices. In order to meet the load demand of electricity consumers, this study schedules the load on a day-ahead and real-time basis. The main objective of the paper is to help balance the load through ON-peak hours and OFF-peak hours. Three price signals, namely: real-time pricing (RTP), time of use (TOU), and critical peak pricing (CPP), are used to evaluate the proposed algorithm. The results show that the electricity bill and PAR are minimized by up to 40% and 50%, respectively. The scheduling using coordination between devices in real-time does not significantly impact the cost of electricity and the peak to average ratio.
... Energy management in residential buildings is a critical research issue, and practitioners have used multiple approaches to manage the energy more efficiently in smart homes [14,15] through metaheuristics approaches [16,17], balancing the load between on-peak and off-peak hours [18], smart meters [19], and smart grids for scheduling optimal energy consumption [20]. In [21,22], the authors used multiobjective optimization with Renewable Energy Resources (RES) in a smart grid to improve the cost and achieve emission-based optimization using the DE metaheuristics approach and designed an AC-DC smart microgrid with RES for energy optimization, respectively. ...
... The feasible region, also known as the search space, is a region defined by a subset of all optimization problems. The overall cost region is shown by points P1 (0.600, 15 ...
... Energies 2022,15, 1752 ...
The smart grid has given users the ability to regulate their home energy more efficiently and effectively. Home Energy Management (HEM) is a difficult undertaking in this regard, as it necessitates the optimal scheduling of smart appliances to reduce energy usage. In this research, we introduced a meta-heuristic-based HEM system in this research, which incorporates Earth Worm Algorithm (EWA) and Harmony Search Algorithms (HSA). In addition, a hybridization based on EWA and HSA operators is used to optimize energy consumption in terms of electricity cost and Peak-to-Average Ratio (PAR) reduction. Hybridization has been demonstrated to be beneficial in achieving many objectives at the same time. Extensive simulations in MATLAB are used to test the performance of the proposed hybrid technique. The simulations are run for multiple homes with multiple appliances, which are categorized according to the usage and nature of the appliance, taking advantage of appliance scheduling in terms of the time-varying retail pricing enabled by the smart grid two-way communication infrastructure algorithms EWA and HSA, along with a Real-Time Price scheme. These techniques help us to find the best usage pattern for energy consumption to reduce electricity costs. These metaheuristic techniques have efficiently reduced and shifted the load during peak hours to off-peak hours and reduced electricity costs. In comparison to HSA, the simulation results suggest that EWA performs better in terms of cost reduction. In comparison to EWA and HSA, HSA is more efficient in terms of PAR. However, the proposed hybrid approach EHSA gives the maximum reduction in cost which is 2.668%, 2.247%, and 2.535% in the case of single, 10, 30, and 50 homes, respectively as compared to EWA and HSA.
... ii) Communicated by the user. In [12], authors formulate the problem as Mixed Integer Programming. The 250 users express the level of preference for each period where each ap- [13] Trade-off between cost and satisfaction 1 15 min [14] Trade-off between cost and satisfaction 1 15 min [15] Trade-off between cost and satisfaction 1 (x3HHs) 1 hour [16] Trade-off between cost and satisfaction (Learnt) iii) Trade-off between cost and satisfaction. ...
Residential demand side management (DSM) strategies increase the efficiency of the smart grid. However, the efficacy of these strategies relies on the participation of customers in DSM programs, an issue usually neglected in the analysis. To encompass all aspects, we tried to identify what are the drivers for the user engagement, focusing on the social and psychological behavior of the user in order to simulate and analyze a residential DSM program with a centralized approach. In particular, the DSM program minimizes costs taking into account different energy sources and performing load shifting considering and learning users’ acceptance of requests. The results show the advantage of a preferences-aware approach, highlighting the importance of user satisfaction on participation.
... A mixed integer programming formulation where preferences are communicated by the customer once and updated only if limits change is proposed in [8]. The authors considered 250 users that manifest the level of preference in different periods where the appliance may be turned on. ...
... Specific charge and discharge rates are associated with the battery since these bounds cannot be exceeded (Equations 4-5, respectively). The maximum capacity and a minimum charge characterise the battery (Equations 6-7), while the energy stored at the beginning of a new day is equal to the energy stored at the end of the previous day (Equation 8). Furthermore, the battery follows the simplified model (Equation 9). ...
In the Demand Side Management (DSM) context, residential customers have the potential for reducing costs and relieving the grid with non-thermostatic appliances. These appliances might be optimally scheduled by a central entity, taking into account user preferences. However, the user might not be able to communicate its preferences “a-priori”, leaving to the central entity the task of understanding preferences that should be learnt without causing discomfort to the user. With this premise, this study aims at exploring a DSM program that learns the acceptance of realistic simulated users to shift in time of home appliances, such as washing machines and dishwashers, analysing the benefits that arise from their inclusion. To this end, the proposed Acceptance Learning Algorithm 2.0 (ALA 2.0) minimises costs in scenarios with different energy sources and with a certain level of acceptance to shift in time, optimally scheduling the appliances according to the boundaries found by the proposed algorithm. ALA 2.0 is able to understand preferences also when modelling a behaviour of the user which is influenced by external factors not directly observable and when users make very few requests, interacting with the user in a simple way. Experimental results highlight that it is possible to understand the acceptance to the shift in time of the simulated users without any prior knowledge and without causing too much discomfort, achieving a win-win situation. As an example, more than 90% of requests were accepted in December, which is chosen as a representative month.
... The question in place regards how these plans are applied from the customer/ consumer side. From the customer point of view, when a DR request arrives, the user is expected to comply with a message demanding either the specific opera-tion of some appliances (e.g it is recommended to use the washing machine from 19:00 to 21:00, [31]) or by reducing the overall household consumption by a specific amount of power. The compliance to the DR schedule happens either in an automated manner (explicit user) via the control of the loads by a Building Management System (BMS) or in a more abstract way, where the user shuts down appliances without knowledge of their impact on their overall consumption. ...
... Living Room, Kitchen, Double Bedroom, Single Bedroom, Playroom, Guest Room, Hall, WC, Corridor) where a variety of smart loads are installed, while the power consumption of each load is measured by a smart energy meter. In general, DR schemes are implemented by either explicit directions towards the residents regarding turning on or off specific appliances at specific time intervals as suggested by Jovanic et al. [31]. However, such an approach-in case of BMS absence-may cause discomfort to a user because it demands actual actions from their part (e.g. ...
In recent years, the growing use of Intelligent Personal Agents in different human activities and in various domains led the corresponding research to focus on the design and development of agents that are not limited to interaction with humans and execution of simple tasks. The latest research efforts have introduced Intelligent Personal Agents that utilize Natural Language Understanding (NLU) modules and Machine Learning (ML) techniques in order to have complex dialogues with humans, execute complex plans of actions and effectively control smart devices. To this aim, this article introduces the second generation of the CERTH Intelligent Personal Agent (CIPA) which is based on the RASA framework and utilizes two machine learning models for NLU and dialogue flow classification. CIPA-Generation B provides a dialogue-story generator that is based on the idea of adjacency pairs and multiple intents, that are classifying complex sentences consisting of two users’ intents into two automatic operations. More importantly, the agent can form a plan of actions for implicit Demand-Response and execute it, based on the user’s request and by utilizing AI Planning methods. The introduced CIPA-Generation B has been deployed and tested in a real-world scenario at Centre’s of Research & Technology Hellas (CERTH) nZEB SmartHome in two different domains, energy and health, for multiple intent recognition and dialogue handling. Furthermore, in the energy domain, a scenario that demonstrates how the agent solves an implicit Demand-Response problem has been applied and evaluated. An experimental study with 36 participants further illustrates the usefulness and acceptance of the developed conversational agent-based system.
... The day-ahead optimal schedule is forwarded as suggestions for specific appliances' operation to the customers via a mobile application, while at the end of the day during the liquidation phase, the proposed schedule is compared with the actual disaggregated load consumption to determine the degree of success of the signal. The first key innovation of this work is comprised of the formulation of the multi-objective optimisation problem, which models the user satisfaction and the economic operation of an aggregator's portfolio at the same time, which is an improved version of [25]. The second innovation corresponds to the load forecasting, the load disag-gregation algorithms and the fact that the feature extraction process for both is expanded to include time-related information. ...
In 2020, residential sector loads reached 25% of the overall electrical consumption in Europe and it is foreseen to stabilise at 29% by 2050. However, this relatively small increase demands, among others, changes in the energy consuming behaviour of households. To achieve this, Demand Response (DR) has been identified as a promising tool for unlocking the hidden flexibility potential of residential consumption. In this work, a holistic incentive-based DR framework aiming towards load shifting is proposed for residential applications. The proposed framework is characterised by several innovative features, mainly the formulation of the optimisation problem, which models user satisfaction and the economic operation of a distributed household portfolio, the customised load forecasting algorithm, which employs an adjusted Gradient Boosting Tree methodology with enhanced feature extraction and, finally, a disaggregation tool, which considers electrical features and time of use information. The DR framework is first validated through simulation to assess the business potential and is then deployed experimentally in real houses in Northern Greece. Results demonstrate that a mean 1.48% relative profit can be achieved via only load shifting of a maximum of three residential appliances, while the experimental application proves the effectiveness of the proposed algorithms in successfully managing the load curves of real houses with several residents. Correlations between market prices and the success of incentive-based load shifting DR programs show how wholesale pricing should be adjusted to ensure the viability of such DR schemes.
... Many studies have analyzed the role and potentialities of demand response in buildings, highlighting the potential benefits for the grid. Early studies analyzed the role of curtailable loads, such as smart appliances and smart lighting systems, which can be controlled by the Building Energy Management System (BEMS) [8], considering user preferences [9,10] to minimize energy consumption [11,12]. Other studies have discussed the application of DR-oriented strategies considering the management of energy systems, such as electric heaters [13] and energy storage technologies [14,15], to facilitate the participation in DR programs for residential and small commercial customers. ...
Demand Response (DR) programs represent an effective way to optimally manage building energy demand while increasing Renewable Energy Sources (RES) integration and grid reliability, helping the decarbonization of the electricity sector. To fully exploit such opportunities, buildings are required to become sources of energy flexibility, adapting their energy demand to meet specific grid requirements. However, in most cases, the energy flexibility of a single building is typically too small to be exploited in the flexibility market, highlighting the necessity to perform analysis at a multiple-building scale. This study explores the economic benefits associated with the implementation of a Reinforcement Learning (RL) control strategy for the participation in an incentive-based demand response program of a cluster of commercial buildings. To this purpose, optimized Rule-Based Control (RBC) strategies are compared with a RL controller. Moreover, a hybrid control strategy exploiting both RBC and RL is proposed. Results show that the RL algorithm outperforms the RBC in reducing the total energy cost, but it is less effective in fulfilling DR requirements. The hybrid controller achieves a reduction in energy consumption and energy costs by respectively 7% and 4% compared to a manually optimized RBC, while fulfilling DR constraints during incentive-based events.
... Implementing Case 2 of the DR program shows a reduction in the DT load beyond the 80% rating and an increase in the energy cost due to load shifting to high-price electricity cost intervals. This is shown in According to Equations (5)- (14), the HST of the transformer will be raised during overloads. As shown in Figure 10, the HST reached around 73 °C without DR, and with the DR, the average and maximum HST for transformers have been reduced in all the cases. ...
... A comparison of the total transformer load, HST, and LoL% is shown in Table 3. According to Equations (5)- (14), the HST of the transformer will be raised during overloads. As shown in Figure 10, the HST reached around 73 • C without DR, and with the DR, the average and maximum HST for transformers have been reduced in all the cases. ...
This paper proposes a Home Energy Management System (HEMS) that optimizes the load demand and distributed energy resources. The optimal demand/generation profile is presented while considering utility price signal, customer satisfaction, and distribution transformer condition. The electricity home demand considers electric vehicles (EVs), Battery Energy Storage Systems (BESSs), and all types of non-shiftable, shiftable, and controllable appliances. Furthermore, PV-based renewable energy resources, EVs, and BESSs are utilized as sources of generated power during specific time intervals. In this model, customers can only perform Demand Response (DR) actions with contracts with utility operators. A multi-objective demand/generation response is proposed to optimize the scheduling of various loads/supplies based on the pricing schemes. The customers’ behavior comfort level and a degradation cost that reflects the distribution transformer Loss-of-Life (LoL) are integrated into the multi-objective optimization problem. Simulation results demonstrate the mutual benefits that the proposed HEMS provides to customers and utility operators by minimizing electricity costs while meeting customer comfort needs and minimizing transformer LoL to enhance operators’ assets. The results show that the electricity operation cost and demand peak are reduced by 31% and 18%, respectively, along with transformer LoL % which is reduced by 28% compared with the case when no DR was applied.
... The overall goal of energy management schemes is to schedule appliance usage in accordance with factors such as real time electricity prices, user preferences and comfort levels, and appliance deadlines [Jovanovic et al., 2016a, Bayram andUstun, 2017]. Efficient management of residential loads is further critical to combat climate change and lower carbon emissions since one third of the electricity consumption takes place at the residential sector [Sahin et al., 2019]. ...
The recent advances in communication, sensing, and monitoring technologies hold a promise to revolutionize electrical power networks into smart grids that are decentralized, green, and responsive to changes in the network. On the other hand, due to safety, security, and design of power networks, most of the critical devices are located indoors, making it challenging for wireless networks to support applications that require low latency and high speed communications. Smart metasurfaces are an emerging technology that is designed to provide uninterrupted wireless connectivity by sensing ambient environments via sensors and adaptively adjusting electromagnetic waves to enhance reception quality. Integrating metasurfaces in smart buildings can improve communication capabilities,
