Household Energy Management System Research Articles

A study of home energy management considering carbon quota

The household energy management system (HEMS) has become an important system for energy conservation and emission reduction. In this study, home energy management considering carbon quota has been established. Firstly, the household photovoltaic output model, load model of various electrical appliances, battery load model, and charging and discharging of electric vehicles (EVs) model are established. Secondly, the carbon emission and carbon quota of household appliances and EVs are considered in these models. Thirdly, the energy optimization model of minimum the household user’s total comprehensive operation cost with the minimum total electricity consumption, carbon trading cost, battery degradation cost, and carbon quota income are proposed, taking into account constraints such as the comfort of users’ energy use time. Subsequently, the improved particle swarm optimization (IPSO) algorithm is used to tackle the problem. Compared to the standard particle swarm optimization (PSO), the IPSO has significantly improved the optimization effect. By comparing the optimization results in different scenarios, the effectiveness of the strategy is verified, and the influence of different carbon trading prices on optimal energy scheduling has been analyzed. The result shows that the comprehensive consideration of carbon trading cost and total electricity cost can reduce the household carbon emissions and the total electricity cost of the household user. By increasing the carbon trading price, the user’s carbon trading income and the EV carbon quota income increase, and the overall operating cost decreases; the guidance and regulation of carbon trading price can make a valuable contribution to HEMS optimization. Compared to the original situation, the household carbon emissions are reduced by 14.58 kg, a decrease of over 21.47%, while the total comprehensive operation cost are reduced by 14.12%. Carbon quota trading can guide household users to use electricity reasonably, reducing household carbon emissions and the total cost of household electricity.

Auction-based peer-to-peer energy trading considering echelon utilization of retired electric vehicle second-life batteries

Lately, the growing emergence of electric vehicles (EVs) brings problems of retired batteries proliferation. Seeking effective application scenarios targeting echelon utilization of retired EV second-life batteries (EVSLBs) becomes important. This paper proposes an auction-based P2P energy trading approach in a residential community market that incorporates retired EVSLBs utilization. A unified P2P energy trading model is proposed to coordinate the economic layer and the physical layer. In the economic layer, the household energy management system (HEMS) of each agent is in charge of operating and market bidding through optimization models to maximize their net profit. Comprehensive operation models of fresh batteries and retired EVSLBs have been proposed and embedded into HEMS models. The battery life degradation issue is also considered by calculating the cycling cost. The P2P energy trading is conducted by a double-sided auction method with an average pricing market (APM) mechanism, in which the re-bidding process is allowed. In the physical layer, voltage issues and line congestion management are considered. There is a community manager in the P2P market to ensure the economic layer decisions do not violate the constraints in the physical layer. The proposed models are tested on the modified IEEE 33-bus distribution system and the comparison between new batteries and retired EVSLBs in the community market is discussed based on the simulation results. The echelon utilization effectiveness of EVSLBs in residential P2P trading has been verified as well.

Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System

With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting.

A heuristic optimization approach for the scheduling home appliances

In order to develop and execute a demand response (DR) system for a household energy management system, an effective and adaptable energy management architecture is provided in this study. Several issues related to the current home energy management system (HEMS) are among those that do not give their consumers a choice to assure user comfort (UC) or a long-term answer to lowered carbon emissions. Our research suggests a programmable heuristic-based energy management controller (HPEMC) to manage a residential building in order to minimize power costs, reduce carbon emissions, increase UC, and lower the peak-to-average ratio (PAR). In this study, the demand-responsive appliance scheduling problem is solved using an energy management system to reduce the cost and a PAR. Numerous case studies have been used to demonstrate the viability of the suggested method. The simulation results confirmed the effectiveness of the proposed method and that it is capable of running a hybrid microgrid in various modes. The findings indicate that the proposed schedule controller saved 25.98% of energy.

Optimizing Home Energy Usage: HEMS-IoT Integration with Big Data and Machine Learning

The goal of this project is to optimize household energy consumption by combining machine learning (ML), big data analytics, and the Internet of Things (IoT) with household Energy Management Systems (HEMS). The primary goals are to assess how well HEMS-IoT integration contributes to cost savings, environmental sustainability, and energy efficiency in residential contexts. The methodology includes a thorough analysis of current literature, real-world case studies, and experimental results to examine the advantages, restrictions, and policy implications of HEMS-IoT integration. Among the key findings are personalized energy management, cost savings, increased energy efficiency, and home behavioral changes. Policy implications emphasize how crucial it is to address issues with fairness, data privacy, accessibility, and interoperability through proactive regulatory frameworks and policy interventions. The study highlights how HEMS-IoT integration can revolutionize residential energy efficiency and move us closer to a more robust and sustainable energy ecosystem.

A novel forecasting based scheduling method for household energy management system based on deep reinforcement learning

• A novel forecasting based reinforcement learning energy scheduling method is proposed. • A GC-LSTM algorithm is adopted in out-door temperature prediction. • A model-free During-DDQN algorithm is adopted to manage household energy. • The proposed method performs well in the household energy management system. The demand response (DR) strategy enables household users to actively optimize and dispatch the household energy management system (HEMS), which may significantly reduce the cost of user energy consumptions. However, the uncertainty of home users behaviors, the diversity of electrical equipment types, as well as the complexity of the working status of various devices have brought severe challenges to the home energy management system. This article proposes a new forecasting based optimization method to deliver the real-time scheduling considering the future environment trend. A recent proposed dueling based deep reinforcement learning approach is adopted to optimally dispatch the HEMS. In addition, due to the delay of heating, ventilation, and air Conditioning (HVAC) indoor and outdoor temperature data non-Gaussian, a new generalized corr-entropy assisted long short-term memory (GC-LSTM) neural network is proposed where the generalized correntropy (GC) loss function is adopted to predict the outdoor temperature. The proposed method is verified in a featured HEMS benchmark problem and the experimental results show that the user costs are effectively reduced while the user satisfaction is maintained utilizing the proposed method.

Household Energy Management System: Mutual Vehicle-to-Home Operation with Home Distributed Photovoltaic Power Generation

Household Energy Management System: Mutual Vehicle-to-Home Operation with Home Distributed Photovoltaic Power Generation

Energy Consumption in Central and Eastern Europe (CEE) Households in the Platform Economics

The ongoing digitization of the economy has led to the creation and functioning of platform model socio-economic systems. It is also reflected in the changes in patterns of energy consumption in households. In the first cross-section, it is an industrial revolution, with environmental benefits. However, platforms are primarily a revolution in the consumption sphere, and here, the effects of digitization are not fully recognized. Our social needs are increasingly met “through accessibility” without us leaving our home. Due to the home’s multifunctionality, based on the availability of platform services, household energy consumption should be viewed differently today than before. The article aims to show the changes in Central and Eastern Europe (CEE) household energy consumption between 2008–2018 and their assessment through the prism of the economy’s platformization methods. The study presents the changes in energy consumption in households and determines the correlations between platformization (the author’s index) and changes in energy consumption in households with the use of taxonomic methods. The platformization leaders—Estonia and Lithuania—were subjected to a more detailed analysis. The presented method(s) may be useful in predicting the changes in households’ energy consumption caused by the digitization of other countries in the region (countries under transformation and outsiders-Bulgaria, Romania), in implementing household energy management systems, and in a better adjustment of regulations directed at these consumers.

Optimal management of demand response aggregators considering customers' preferences within distribution networks

In this study, a privacy-based demand response (DR) trading scheme among end-users and DR aggregators (DRAs) is proposed within the retail market framework and by distribution platform optimiser. This scheme aims to obtain the optimum DR volume to be exchanged while considering both DRAs' and customers' preferences. A bi-level programming model is formulated in a day-ahead market within retail markets. In the upper-level problem, the total operation cost of the distribution system is minimised. The production volatility of renewable energy resources is also taken into account in this level through stochastic two-stage programming and Monte-Carlo simulation method. In the lower-level problem, the electricity bill for customers is minimised for customers. The income from DR selling is maximised based on DR prices through secure communication of household energy management systems and DRA. To solve this convex and continuous bi-level problem, it is converted to an equivalent single-level problem by adding primal and dual constraints of lower level as well as its strong duality condition to the upper-level problem. The results demonstrate the effectiveness of different DR prices and different number of DRAs on hourly DR volume, hourly DR cost and power exchange between the studied network and the upstream network.

Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid

An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.

Household Energy Demand Management Strategy Based on Operating Power by Genetic Algorithm

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.

Optimal behavior of responsive residential demand considering hybrid phase change materials

Due to communication and technology developments, residential consumers are enabled to participate in Demand Response Programs (DRPs), control their consumption and decrease their cost by using Household Energy Management (HEM) systems. On the other hand, capability of energy storage systems to improve the energy efficiency causes that employing Phase Change Materials (PCM) as thermal storage systems to be widely addressed in the building applications. In this paper, an operational model of HEM system considering the incorporation of more than one type of PCM in plastering mortars (hybrid PCM) is proposed not only to minimize the customer’s cost in different DRPs but also to guaranty the habitants’ satisfaction. Moreover, the proposed model ensures the technical and economic limits of batteries and electrical appliances. Different case studies indicate that implementation of hybrid PCM in the buildings can meaningfully affect the operational pattern of HEM systems in different DRPs. The results reveal that the customer’s electricity cost can be reduced up to 48% by utilizing the proposed model.

Autonomous Household Energy Management Based on a Double Cooperative Game Approach in the Smart Grid

Taking advantage of two-way communication infrastructure and bidirectional energy trading between utility companies and customers in the future smart grid (SG), autonomous energy management programs become crucial to the demand-side management (DSM). Most of the existing autonomous energy management schemes are for the scenario with a single utility company or the scenario with one-way energy trading. In this paper, an autonomous household energy management system with multiple utility companies and multiple residential customers is studied by considering the bidirectional energy trading. To minimize the overall costs of both the utility companies and the residential customers, the energy management system is formulated as a double cooperative game. That is, the interaction among the residential users is formulated as a cooperative game, where the players are the customers and the strategies are the daily schedules of their household appliances; and the interaction among the utility companies is also formulated as a cooperative game, where the players are the suppliers and the strategies are the proportions of the daily total energy they provide for the customers. Without loss of generality, the bidirectional energy trading in the double cooperative game is formulated by allowing plug-in electric vehicles (PEVs) to discharge and sell energy back. Two distributed algorithms will be provided to realize the global optimal performance in terms of minimizing the energy costs, which can be guaranteed at the Nash equilibriums of the formulated cooperative games. Finally, simulation results illustrated that the proposed double cooperative game can benefit both the utility companies and residential users significantly.

Automatic Standby Power Management Using Usage Profiling and Prediction

Reducing the standby power used by home appliances is critical in a household energy management system. Although significant effort has been made to minimize the standby power use of appliances, manual operation is still required to eliminate standby power usage. Additionally, the current regulation strategy of standby power typically focuses on real-power consumption, and it does not consider the apparent power and power factors. We propose an automatic standby power reduction system that is based on user-context profiling. Our system profiles and analyzes the occupancy pattern, as well as the appliance usage. The system then actively manages standby power utilization by predicting the probabilities of future appliance usage. We built a prototype smart meter to monitor and control the power lines. We also developed software that implements the proposed scheme. Our experiments, conducted for three to five weeks in four households, show that power consumption in standby mode can be reduced.

Washing when the sun is shining! How users interact with a household energy management system

To make optimal use of sustainable energy, domestic electricity consumption should shift to match local supply conditions. Energy management systems (EMS) are a new sustainable technology that can help to disrupt consumers’ habits concerning electricity consumption, whilst reinforcing desired behaviours. This research examined the factors that influence the likelihood that people will shift their electricity consumption to match sustainable supply. Twenty-one interviews were conducted with households who had used the EMS ‘Smart Wash’ for several months. The findings showed that the likelihood of behaviour change is influenced by a combination of the user's motivation, specific contextual factors and the design of the EMS. Based on these results, several recommendations are given for the future design of EMSs. Practitioner Summary: Energy management systems (EMS) are a new technology that encourages people to shift electricity consumption to match local solar supply. Interviews among users of an EMS showed that the likelihood of behaviour change is influenced by the combination of the user's motivation, contextual factors and the EMS design.