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Hybrid-integer algorithm for a multi-objective optimal home energy management system

Clean energy 2023年第2期7卷 375-388页

作者： Saad Gheouany Hamid Ouadi Saida El Bakali ERERA National School of Arts and CraftsMohammed V UniversityRabatMorocco

Most of the energy produced in the world is consumed by commercial and residential *** the growth in the global economy and world demographics,this energy demand has become increasingly *** has led to higher unit electricity prices,frequent stresses on the main electricity grid and carbon emissions due to inefficient energy *** paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity *** simultaneously reduces the electricity bill and the peaks,while maintaining user comfort in terms of the operating waiting time of *** proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming,which involves constraints and consumer *** optimal scheduling,the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound *** techniques are proposed to manage the trade-off between the conflicting *** first technique is the Pareto-optimal solutions classification using supervised learning *** second technique is called the lexicographic *** simulations were performed based on residential building energy consumption,time-of-use pricing(TOU)and critical peak pricing(CPP).The algorithms were implemented in *** results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio(PAR)by 28% and 49.32%,respectively,for the TOU tariff rate,and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer’s comfort level.

关键词： home energy management system smart building coordination of home appliances metaheuristic algorithm day-ahead scheduling multi-objective binary non-linear constraint problem

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Optimal home energy management system with Mixed Types of Loads

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CSEE Journal of Power and energy systems 2015年第4期1卷 29-37页

作者： Chen Zhao Shufeng Dong Furong Li Yonghua Song Department of Electronic and Electrical Engineering University of BathBathBA27AYU.K. Department of Electrical Engineering Zhejiang UniversityHangzhou 310027China

This paper presents a novel home area energy management system(HEMS)for smart homes with different load profiles installed with photovoltaic generation,energy storage,and DC *** developed HEMS is shown to optimize the utilization of local renewables while minimizing energy waste between AC and DC conversions and between storage charging and *** studies on HEMS have not considered the impact of load *** this study,the performance of the proposed HEMS is demonstrated on different smart homes with and without electric heating.A comparative study is carried out to investigate battery behavior,characteristics of AC and DC conversion,and benefits that customers receive.A sensitivity analysis is also conducted to discuss the effects from varied energy storage capacities,AC/DC conversion efficiencies,and PV *** results show that cost reduction in energy bills can be greatly impacted by load profiles,and customers with electric heating load coupled with sufficiently large energy storage would receive the most reduction in their energy bills.

关键词： Consumer behavior customer benefit home energy management system hybrid AC/DC system low carbon technologies

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Integrated Optimization of Smart home Appliances with Cost-effective energy management system

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CSEE Journal of Power and energy systems 2019年第2期5卷 249-258页

作者： Tesfahun Molla Baseem Khan Bezabih Moges Hassan Haes Alhelou Reza Zamani Pierluigi Siano B.Moges are with Hawassa University HawassaSouthern Nations 1530Ethiopia Tishreen University 2230 LattakiaSyria Tarbiat Modares University TehranIran Siano is with The Department of management&Innovation systems University of SalernoFiscianoCampania 84084Italy

Smart grid enables consumers to control and sched-ule the consumption pattern of their appliances,minimize energy cost,peak-to-average ratio(PAR)and peak load *** this paper,a general architecture of home energy management system(HEMS)is developed in smart grid scenario with novel restricted and multi-restricted scheduling method for the residen-tial *** optimization problem is developed under the time of use pricing(TOUP)*** optimize the formulated problem,a powerful meta-heuristic algorithm called grey wolf optimizer(GWO)is utilized,which is compared with particle swarm optimization(PSO)algorithm to show its effectiveness.A rooftop photovoltaic(PV)system is integrated with the system to show the cost effectiveness of the *** analysis,eight different cases are considered under various time scheduling algorithms.

关键词： Demand side management GWO home energy management system PSO peak-to-average ratio

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Analysis of Dynamic Appliance Flexibility Considering User Behavior via Non-intrusive Load Monitoring and Deep User Modeling

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CSEE Journal of Power and energy systems 2020年第1期6卷 41-51页

作者： Shaopeng Zhai Huan Zhou Zhihua Wang Guangyu He School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 201100China Electric Power Dispatching and Communication Center State Grid Shanghai Municipal Electric Power CompanyShanghai 201200China

The research on non-intrusive load monitoring(NILM)and the growing deployment of home energy manage-ment system(HEMS)have made it possible for households to have a detailed understanding of their power usage and to make appliances participate in demand response(DR)*** flexibility analysis helps the HEMS dispatching appli-ances to participate in DR programs without violating user’s comfort *** this paper,a dynamic appliance flexibility analysis approach using the smart meter data is *** the training phase,the smart meter data is preprocessed by NILM to obtain user’s appliances usage behaviors,which is used to train the user *** operation,the NILM is used to infer recent appliances usage behaviors,and then the user model predicts user’s appliances usage behaviors in the DR period considering long-term behaviors dependences,correlations between appliances and temporal *** flexibility of each appliance is calculated based on the appliance characteristics as well as the predicted user’s appliances usage behaviors caused by the control of the *** HEMS can choose the appliance with high flexibility to participate in the DR *** case study demonstrates the performance of the user model and illustrates how the appliance flexibility analysis is performed using a real-world case.

关键词： Appliance flexibility demandresponse home energy management system non-intrusive load monitoring user behavior

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Graph representation learning-based residential electricity behavior identification and energy management

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Protection and Control of Modern Power systems 2023年第2期8卷 218-230页

作者： Xinpei Chen Tao Yu Zhenning Pan Zihao Wang Shengchun Yang School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China China Electric Power Research Institute Nanjing 210003China

It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for *** critical issues in an efficient HEMS are identification of user behavior and energy management ***,current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different *** can lead to an insuffi-cient description of behavior and suboptimal management *** address these gaps,this paper proposes non-intrusive load monitoring(NILM)assisted graph reinforcement learning(GRL)for intelligent HEMS decision ***,a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the ***,efficient identification of user behavior and description of state transition can be ***,based on the online updating of the behavior correlation graph,a GRL model is proposed to extract information contained in the ***,reliable strategy under uncer-tainty of environment and behavior is ***,the experimental results on several datasets verify the effec-tiveness of the proposed model.

关键词： Behavior correlation graph Graph reinforcement learning home energy management system Multi-label classification Non-intrusive load monitoring

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Optimization Strategy Based on Deep Reinforcement Learning for home energy management

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CSEE Journal of Power and energy systems 2020年第3期6卷 572-582页

作者： Yuankun Liu Dongxia Zhang Hoay Beng Gooi China Electric Power Research Institute Beijing 100192China Nanyang Technological University Nanyang AvenueSingapore 639798Singapore

With the development of a smart grid and smart home,massive amounts of data can be made available,providing the basis for algorithm training in artificial intelligence *** continuous improving conditions are expected to enable the home energy management system(HEMS)to cope with the increasing complexities and uncertainties in the enduser side of the power grid *** this paper,a home energy management optimization strategy is proposed based on deep Q-learning(DQN)and double deep Q-learning(DDQN)to perform scheduling of home energy *** applied algorithms are model-free and can help the customers reduce electricity consumption by taking a series of actions in response to a dynamic *** the test,the DDQN is more appropriate for minimizing the cost in a HEMS compared to *** the process of method implementation,the generalization and reward setting of the algorithms are discussed and analyzed in *** results of this method are compared with those of Particle Swarm Optimization(PSO)to validate the performance of the proposed *** effectiveness of applied data-driven methods is validated by using a real-world database combined with the household energy storage model.

关键词： Deep reinforcement learning demand response home energy management system smart grid

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Comparison of optimizers for model predictive thermal control of buildings

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energy and AI 2024年第1期15卷 279-287页

作者： Torben Andersen Department of physics Lund UniversityProfessorsgatan 1Lund22663Sweden

Considering recent developments in the energy sector,further reduction of electricity cost and flattening of the electric power demand curve are *** have focused on an autonomous electric heater control system that can easily be implemented in existing buildings without strict comfort *** are winter heating of warehouses and vacation homes,and heat drying of buildings under *** have set up a system that typically reduces electricity cost by about 40%on the basis of automatic weather and real time pricing *** system uses the building as an energy reservoir over periods with high electricity *** a model predictive control system,we compare use of a genetic algorithm,a particle swarm optimization,and a neural network for heater control,all working in a closed loop to reduce the influence of modeling *** have simulated the performance of the systems using realistic data and found that all three optimizers give about the same performance,varying only a few percent in ***,the computational and memory requirements of the neural network are much lower than for the other optimizers,so it is preferable for use with inexpensive *** carried out a full-scale experiment at a residential house and found agreement with simulation results.

关键词： home energy management system Demand response Genetic algorithm Particle swarm optimization Neural network Model predictive control

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