Household Energy Management Research Articles

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

Analysis of Microgrid’s Operation Integrated to Renewable Energy and Electric Vehicles in View of Multiple Demand Response Programs

A suitable energy management scheme and integrating renewable energy resources (RERs) can significantly increase energy efficiency and the stability of future grids operation. This work modeled a household energy management comprising a microgrid (MG) system and demand response programs (DRPs). Residential loads with price-based tariffs are introduced to reduce peak load demands and energy costs. For incorporating the uncertainties in RERs, their stochastic nature is modeled with a probabilistic method. This paper proposes a joint optimization approach for the optimal planning and operation of grid-connected residential, rural MG integrated into renewable energy and electric vehicles (EVs) in view of DRPs. The investigation focuses on energy saving of residential homes under different DRPs and RERs integration. The EVs are integrated into MG by including photovoltaic (PV), wind turbine (WT), fuel cell (FC), and diesel engines (DEs). A multi-objective optimization problem has been formulated to minimize the operating cost, pollutant treatment cost, and carbon emissions cost defined as C1, C2, and C3, respectively. The load demand has been rescheduled because of three DRPs, i.e., critical peak pricing (CPP), real-time electricity pricing (RTEP), and time of use (TOU). Further, the EV load has also been analyzed in autonomous and coordinated charging strategies. Using a judgement matrix, the proposed multi-objective problem is transformed into a single-objective problem. The results of an artificial bee colony (ABC) algorithm are compared with the particle swarm optimization (PSO) algorithm. The simulation analysis was accomplished by employing ABC and PSO in MATLAB. The mathematical model of MG was implemented, and the effects of DRPs based MG were investigated under different numbers of EVs and load data to reduce different costs. To analyze the impact of DRPs, the residential, rural MG is implemented for 50 homes with a peak load of 5 kW each and EV load with 80 EVs and 700 EVs, respectively. The simulation results with different test cases are formulated while analyzing the tradeoff between ABC and PSO algorithms. The simulation analysis shows that multiple DRPs, EVs, and RERs offered a substantial trade-off.

A High‐Proportion Household Photovoltaic Optimal Configuration Method Based on Integrated–Distributed Energy Storage System

As energy shortages and environmental pollution continue to worsen, household photovoltaics has gradually become an important part of household energy management. However, with the penetration rate of household photovoltaics increasing, the access of the high‐proportion household photovoltaics (HPHP) will seriously endanger the operation of the distribution network. Therefore, it is urgent to stabilize the voltage fluctuations after the HPHP connected to the grid. This paper proposes a high‐proportion household photovoltaic optimal configuration method based on integrated–distributed energy storage system. After analyzing the adverse effects of HPHP connected to the grid, this paper uses modified K‐means clustering algorithm to classify energy storage in an integrated and distributed manner. Then based on the modified particle swarm optimization (MPSO), the integrated and distributed energy storage system is configured to realize the voltage stability of HPHP. The analysis of the example shows that the method proposed in this paper can effectively improve the power quality. At the same time, it can achieve the goal of economic optimization, which can effectively solve the adverse impact of HPHP grid connection. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Integrated demand response based on household and photovoltaic load and oscillations effects

Due to the attractiveness of household gas-electric tools, in this paper, an optimization technique is suggested based on the integrated demand response (IDR) and degree of tolerance for household energy management. The proposed method is mostly used to express the dynamic change in the forms of energy and undetermined variables in the systems, resulting from household and photovoltaic (PV) load. Thermostatically controlled demands include gas-electricity and air conditioning, and cut-able loads include gas-electric stove and washing machine. The interval optimization is modeled for optimizing the operation and greenhouse gas emission costs in multi-purpose systems. The undetermined variables are formulated as interval statistics and the limitations are simplified by degree of tolerance. In order to solve it, the interval optimization technique is converted into certainty optimization with the interval order relationship and the delayed probability degree. Then, the developed grasshopper search algorithm is based on the chaos theory to solve the interval optimization model in order to respond to uncertainty and demands of the users, such that degree of tolerance of cost that is acceptable by users is optimized. Contrary to other optimization algorithms, the grasshopper search algorithm can be combined with other methods. In this paper, the chaos theory is adopted to find a better solution. Since the information is placed in the search space without order, using this technique considerably leads to good convergence speed, precise final solution finding, not being trapped in local minima, lower SD, and robustness. Both methods of IDR and degree of tolerance for the household gas-electric equipment manage to reduce energy consumption by about 25% compared to traditional methods.

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.

Household Energy Management

Ensuring flexibility and security in power systems requires the use of appropriate management measures on the demand side. The article presents the results of work related to energy management in households in which renewable energy sources (RES) can be installed. The main part of the article is about the developed elastic energy management algorithm (EEM), consisting of two algorithms, EEM1 and EEM2. The EEM1 algorithm is activated in time periods with a higher energy price. Its purpose is to reduce the power consumed by the appliances to the level defined by the consumer. In contrast, the EEM2 algorithm is run by the Distribution System Operator (DSO) when peak demand occurs. Its purpose is to reduce the power of appliances in a specified time period to the level defined by the DSO. The optimization tasks in both algorithms are based on the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic algorithm. The EEM1 and EEM2 algorithms also provide energy consumer comfort. For this purpose, both algorithms take into account the smart appliance parameters proposed in the article: sections of the working devices, power reduction levels, priorities and enablingof time shifting devices. The EEM algorithm in its operation also takes into account the information about the production of power, e.g., generated by the photovoltaic systems. On this basis, it makes decisions on the control of smart appliances. The EEM algorithm also enables inverter control to limit the power transferred from the photovoltaic system to the energy system. Such action is taken on the basis of the DSO request containing the information on the power limits. Such a structure of EEM enables the balancing of energy demand and supply. The possibility of peak demand phenomenon will be reduced. The simulation and experiment results presented in the paper confirmed the rationality and effectiveness of the EEM algorithm.

Geospatial mapping of biomass supply and demand for household energy management in Nepal

This paper presents a geospatial mapping model for assessing spatial distribution and demand of biomass sources for household energy use in Nepal. In the context of rural households, correlation between supply and demand of biomass is crucial for designing effective rural energy programs. Three districts were considered to represent the country's main topographical regions: lowlands, hills, and mountains, where geospatial distribution and demand of biomass are different. The supply potential of fuelwood was assessed using Geographical Information System (GIS) tool, and the potential of crop residues and dung and household energy demands were determined by field surveys and experiments. The results showed that households with secure access to biomass sources in lowlands, hills and mountains were 57%, 50% and 3% respectively. In lowlands, crop residues and dung were extensively used due to lack of forest biomass, whereas forest biomass was extensively used in hills and mountains, with negligible use of crop residues and animal dung. The results indicate that use of improved cooking stoves and biogas was negligible and thus cleaner biomass energy conversion and cooking technologies are needed to achieve universal target of clean cooking for all. The GIS model provided better estimation of biomass energy supply potential in the communities, which is crucial in the design of energy policies for sustainable clean cooking solutions. It is anticipated that this geospatial mapping model is also applicable to the cases of other developing countries, which have dominant biomass consumption for household energy use.

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.

Price-sensitive home energy management method based on Pareto optimisation

ABSTRACT With the emergence of smart home, residents can achieve demand response through home energy management system (HEMS), so as to improve energy utilisation. However, on the one hand, the existing household energy management method lacks the balance consideration of user economy and comfort, on the other hand, it lacks the response of adjustment scale to real-time electricity price. Aprice sensitive response mechanism considering the comfort of HVAC is added to the objective function of household energy management, and aprice sensitive household energy control method based on Pareto optimisation is proposed, which is solved dynamically by mixed integer linear programming (MILP). The simulation results show that PSHEMS can reduce household daily expenditure by 53%, and the PAPR value of home nano grid can be reduced by 70%.

Home energy management in smart households: Optimal appliance scheduling model with photovoltaic energy storage system

At present, with the applying of distributed energy resource (DER) in the demand side, the difficulty of demand-side management (DSM) is increasing. Improving the consumption rate of DER is currently one of the important research contents of the home energy management system (HEMS). How to better coordinate household appliances to participate in the HEMS, so as to improve the situation is the focus of this paper. Due to the development of artificial intelligence and communication technology, we establish a household appliance optimization scheduling system. According to the household electricity-using habits, most household appliances are divided into three categories. Based on this, we propose a HEMS model, which aims to minimize the peak load and electricity cost of a smart home, and achieve single-objective and multi-objective optimization. In order to better implement the research of DER, the solar radiation data of one day in summer in Shanghai is used to compare with different capacities of photovoltaic energy storage system (PESS). Because the variables in the model are mostly binary or decimal integers, it is essentially a mixed integer programming (MIP). Therefore, a simulator has been implemented in the MATLAB software environment to facilitate the solution of proposed HEMS. The results show that the configuration of PESS is beneficial to the optimal scheduling of household appliances. In the single objective optimization, the peak load and electricity cost of scheme 4 is decreased by 61.51% and 71.39% compared with scheme 1, respectively. Besides, Pareto front is the most ideal in multi-objective optimization under scheme 4, and Pareto solutions distribution are more uniform, which can provide more scheduling choices for residential users. In addition, by comparing the different photovoltaic (PV) subsidy policies in 2013 and 2018, it is reasonable for China to gradually realize PV subsidy-free.

Investigating urban household water - energy nexus towards supporting sustainable and smart city policies: The case of Hanoi City, Vietnam

Sustainable and smart management of urban water and energy sectors is one of challenges facing by Vietnamese cities today. This study examined the water-energy nexus in urban households by analysing the “cause-and-effect” relationships among four major individual attributes (age, gender, education level, and family size) and three household behaviours (showering, cooking, and washing) with water and water related energy uses for the case of Hanoi City. The amount and component of water and water related energy uses with respect to those household behaviours and individual attributes were explored. The results showed that both water use and water related energy use had relationship with gender, education level, and family size. Specifically, people with more water and energy uses were female with age ≤ 30 years old, especially who have higher education level and live alone. The better understanding on how household behaviours and individual attributes influencing the household water and water related energy uses provided by this study could help the urban planners, policy makers, and relevant stakeholders to identify the opportunities for designing better and smart solutions for household water and energy management, towards achieving the goals of sustainable and smart cities.

A study on household energy-use patterns in rural, semi-urban and urban areas of Nepal based on field survey

Energy use is required for securing a certain living standard. Availability of energy sources, electricity in particular, is the basic requirement for the quality of living. The rationality of household energy use is not only for improving individual living conditions, but also for the economic growth of a nation. To formulate the national energy strategy plan, it is important to know the current energy use and availability conditions. This study describes the current state of household energy use available from related papers and also from a field survey on household energy use including electricity use. We have collected the data in the form of electricity bills, family income, family size, the number of electric appliances, and energy use for lighting, cooking, heating and cooling from 442 households in Kalikot, Chitwan and Kathmandu districts of Nepal. The monthly rate of firewood use was found 2.08 GJ/household/month in rural areas, which is quite higher than other developing countries. The average electricity use of all areas in Nepal was 2.06 GJ/household/year, which is quite low compared to developed and even other developing countries. The electricity is used mainly for lighting purpose in the rural area, while it was also used to run other electric appliances in semi-urban and urban areas. By analyzing the data, it was found that the amount of electricity use was a function of household income level, occupation, family size and educational level of household responsible person. The educational level of people was correlated to the use of light-emitting diode lamps. The result of this series of survey should be useful for future planning of household energy management in the Nepalese society.

Optimal household energy management based on smart residential energy hub considering uncertain behaviors

Abstracts Nowadays, confronting with the emerging energy crisis and environmental pressure, multi energy integrating technologies are considered as effective patterns to augment the renewable energy consumption and improve energy efficiency in the context of energy transformation and reform. In this paper, household energy management based on smart residential energy hub (SREH) whose inputs include electricity and natural gas is designed for modern households. Relevant energy-using equipment models as well as control strategies are proposed through the physical characteristics and household users’ preferences, respectively. A multi-objective optimization problem is formulated to allocate energy supply in the SREH, and provide scheduling schemes for energy-using equipment beside the classified ordinary appliances. Six kinds of uncertain behaviors are modelled in comfort deviation as sub-objective. The overall objective of the problem is to minimize both the energy consumption expense and comfort deviation. Then, four cases studies are presented to verify the effectiveness of the proposed model, where both of the sub-objective value improves as a result. Finally, the robustness of the model are illustrated with actual behaviors of household users. The sensibility analysis of departure time distribution, weighing factors and number of uncertain scenarios are carried out to optimize the decision configuration.

The use of the peak-clipping method for energy management in households with energy storage equipment

ABSTRACTThe purpose of this paper is to investigate how energy storage systems should be used to improve the load factor of a residential power grid, thus equalizing the hourly power consumption of these grids, which will help to mitigate power supply difficulties during load peaks. There are two methods represented in this paper. First, the schedule optimization method using continuous quadratic programming to solve each point in time for optimal objects according to forecast load. Second, the “peak-clipping” method proposed in this paper is much simpler, as it does not require load predictions and simply corrects the predicted cutoff lines based on the errors of each run. As a result, the schedule optimization method could theoretically improve this load factor up by to 65.7%, but it was only increased by 50.4% actually in real-time. The peak-clipping method could theoretically improve load factor up to 64.5%, and the simulation indicates that load factor improved by 56.7%. It is shown that the peak-clipping scheme is relatively simple and highly effective, which makes it straightforward to implement in various control devices or system platforms.

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.

Optimal Scheduling of Residential Home Appliances by Considering Energy Storage and Stochastically Modelled Photovoltaics in a Grid Exchange Environment Using Hybrid Grey Wolf Genetic Algorithm Optimizer

The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.

Smart gridlock? Challenging hegemonic framings of mitigation solutions and scalability

Urban energy transitions are key components of urgently requisite climate change mitigation. Promissory discourse accords smart grids pride of place within them. We employ a living lab to study smart grids as a solution geared towards upscaling and systematisation, investigate their limits as a climate change mitigation solution, and assess them rigorously as urban energy transitions. Our 18 month living lab simulates a household energy management platform in Bergen. Norway’s mitigation focus promotes smart meter roll-out as reducing carbon emissions, by (i) unlocking efficiency gains, and (ii) increasing awareness for demand-side management. We problematise this discourse. Raising awareness encounters intractable challenges for smart grid scalability. Scattered efficiency gains constitute modest increments rather than the substantial change requisite for rapid mitigation. Whereas promissory smart grid discourse overlooks these ground-truthed limits, our findings caution against misplaced expectations concerning mitigation. We contest discursive enthusiasm on smart grids and argue for aligning local and systemic concerns before upscaling to avoid obscuring risks. Scaling up requires understanding and addressing interdependencies and trade-offs across scales. Focus group discussions and surveys with living lab participants who used sub-meter monitors to track real-time household electricity consumption data over an extended period show that technical issues and energy behaviour, as well as political economic and policy structures and factors, pose significant limits to smart grids. Urban strategies for climate change mitigation must be informed by this recognition. Our results indicate that upscaling relies on bottom-up popular acceptance of the salient technical, organisational and standardisation measures, but that measures to improve the democratic legitimacy of and participation in energy transitions remain weak. We highlight limits to smart grids as a standalone urban mitigation solution and call for a sharper focus on accompanying thrust areas for systematisation and scalability, such as renewable energy integration and grid coordination.

Household Energy Management Model to Maximize Solar Power Utilization Using Machine Learning

Abstract Electricity from solar energy is the new trend in electricity production companies. Many plants have been set up to harness energy from the sun. Since solar energy can be harnessed from open-air, starting the same from home is the best way. Most commonly solar energy is harnessed from a rooftop-mounted solar panel but is connected only for a few selective loads. All the other loads when in need of electricity cannot be utilized from solar energy. Since this is the format for electricity usage, the maximum usage of this is not done. This framework is proposed to maximize the usage of solar power by connecting both solar and grid to the same node and employing a unique switching strategy using decision tree machine learning algorithm in python environment. The hardware setup has been done for the same by using data acquisition techniques, collecting real-time data consisting of demand and solar power which is updated into the database in the local setup server. This real-time data is exported and prediction for the switching configuration is done and sent to an Arduino board to predict the source that has to be given to the particular demand in real-time.

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.