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day-ahead scheduling based on reinforcement learning with hybrid action space

Journal of Systems Engineering and Electronics 2022年第3期33卷 693-705页

作者： CAO Jingyu DONG Lu SUN Changyin School of Automation Southeast UniversityNanjing 210096China School of Cyber Science and Engineering Southeast UniversityNanjing 211189China

Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal scheduling,the total cost of the ADN can be reduced.However,the optimal dayahead scheduling problem is challenging since the future electricity price is unknown.Moreover,in ADN,some schedulable variables are continuous while some schedulable variables are discrete,which increases the difficulty of determining the optimal scheduling scheme.In this paper,the day-ahead scheduling problem of the ADN is formulated as a Markov decision process(MDP)with continuous-discrete hybrid action space.Then,an algorithm based on multi-agent hybrid reinforcement learning(HRL)is proposed to obtain the optimal scheduling scheme.The proposed algorithm adopts the structure of centralized training and decentralized execution,and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables.The simulation experiment results demonstrate the effectiveness of the algorithm.

关键词： day-ahead scheduling active distribution network(ADN) reinforcement learning hybrid action space

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Optimal day-ahead scheduling of Islanded Microgrid Considering Risk-based Reserve Decision

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Journal of Modern Power Systems and Clean Energy 2021年第5期9卷 1149-1160页

作者： Zehuai Liu Siliang Liu Qinhao Li Yongjun Zhang Wenyang Deng Lai Zhou School of Electric Power South China University of TechnologyGuangdong Key Laboratory of Clean Energy TechnologyGuangzhouChina Dongguan Power Supply Bureau of Guangdong Power Grid Co. Ltd.DongguanChina Guangzhou Power Electrical Technology Co. Ltd.GuangzhouChina

Due to the lack of support from the main grid,the intermittency of renewable energy sources(RESs)and the fluctuation of load will derive uncertainties to the operation of islanded microgrids(IMGs).It is crucial to allocate appropriate reserve capacity for the economic and reliable operation of IMGs.With the high penetration of RESs,it faces both economic and environmental challenges if we only use spinning reserve for reserve support.To solve these problems,a multi-type reserve scheme for IMGs is proposed according to different operation characteristics of generation,load,and storage.The operation risk due to reserve shortage is modeled by the conditional value-at-risk(CVaR)method.The correlation of input variables is considered for the forecasting error modeling of RES and load,and Latin hypercube sampling(LHS)is adopted to generate the random scenarios of the forecasting error,so as to avoid the dimension disaster caused by conventional large-scale scenario sampling approaches.Furthermore,an optimal day-ahead scheduling model of joint energy and reserve considering riskbased reserve decision is established to coordinate the security and economy of the operation of IMGs.Finally,the comparison of numerical results of different schemes demonstrate the rationality and effectiveness of the proposed scheme and model.

关键词： day-ahead scheduling risk-based reserve decision conditional value-at-risk(CVaR) renewable energy source(RES) islanded microgrids

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An economic and low-carbon day-ahead Pareto-optimal scheduling for wind farm integrated power systems with demand response

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Journal of Modern Power Systems and Clean Energy 2015年第3期3卷 393-401页

作者： Rui MA Kai LI Xuan LI Zeyu QIN Hunan Key Laboratory of Smart Grids Operation and Control Changsha University of Science&TechnologyChangsha 410114China

Demand response(DR)and wind power are beneficial to low-carbon electricity to deal with energy and environmental problems.However,the uncertain wind power generation(WG)which has anti-peaking characteristic would be hard to exert its ability in carbon reduction.This paper introduces DR into traditional unit commitment(UC)strategy and proposes a multi-objective day-ahead optimal scheduling model for wind farm integrated power systems,since incentive-based DR can accommodate excess wind power and can be used as a source of system spinning reserve to alleviate generation side reserve pressure during both peak and valley load periods.Firstly,net load curve is obtained by forecasting load and wind power output.Then,considering the behavior of DR,a day-ahead optimal dispatching scheme is proposed with objectives of minimum generating cost and carbon emission.Non-dominated sorting genetic algorithm-II(NSGA-II)and satisfaction-maximizing method are adopted to solve the multi-objective model with Pareto fronts and eclectic decision obtained.Finally,a case study is carried out to demonstrate that the approach can achieve economic and environmental aims and DR can help to accommodate the wind power.

关键词： Low-carbon electricity Unit commitment(UC) day-ahead scheduling Multi-objective optimization Demand response(DR) Non-dominated sorting genetic algorithm-II(NSGA-II)algorithm

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Storing Freshwater Versus Storing Electricity in Power Systems with High Freshwater Electric Demand

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 323-333页

作者： Mubarak J.Al-Mubarak Antonio J.Conejo Department of Electrical and Computer Engineering The Ohio State UniversityColumbusOHUSA Department of Electrical Engineering Kuwait UniversityP.O.Box 5969Safat 13060Kuwait Department of Integrated Systems Engineering and the Department of Electrical and Computer Engineering The Ohio State UniversityColumbusOH 43210USA IEEE

We consider a power system whose electric demand pertaining to freshwater production is high(high freshwater electric demand),as in the Middle East,and investigate the tradeoff of storing freshwater in tanks versus storing electricity in batteries at the day-ahead operation stage.Both storing freshwater and storing electricity increase the actual electric demand at valley hours and decrease it at peak hours,which is generally beneficial in term of cost and reliability.But,to what extent?We analyze this question considering three power systems with different generation-mix configurations,i.e.,a thermal-dominated mix,a renewable-dominated one,and a fully renewable one.These generation-mix configurations are inspired by how power systems may evolve in different countries in the Middle East.Renewable production uncertainty is compactly modeled using chance constraints.We draw conclusions on how both storage facilities(freshwater and electricity)complement each other to render an optimal operation of the power system.

关键词： Power system operation freshwater production and transportation coordination day-ahead scheduling

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

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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 buildings.With the growth in the global economy and world demographics,this energy demand has become increasingly important.This has led to higher unit electricity prices,frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management.This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing.This simultaneously reduces the electricity bill and the peaks,while maintaining user comfort in terms of the operating waiting time of appliances.The proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming,which involves constraints and consumer preferences.For optimal scheduling,the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound algorithm.Two techniques are proposed to manage the trade-off between the conflicting objectives.The first technique is the Pareto-optimal solutions classification using supervised learning methods.The second technique is called the lexicographic method.The simulations were performed based on residential building energy consumption,time-of-use pricing(TOU)and critical peak pricing(CPP).The algorithms were implemented in Python.The 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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