In zero energy house,"renewable energy","load"and"storage"are close in space.Through real-time management and optimization,it is easier to realize the local consumption of renewable energy and reduce the energy cost of users.The zero energy house microgrid is selected as the research objective.The optimization model based on established rule is used as a benchmark.The optimization model aims at minimizing energy cost based on linear programing,Q-learning and DQN algorithms,respectively.Secondly,this work introduces the self-consumption ratio and relative expenditure cost indicators to analyze the impact of the three optimization algorithms on photovoltaic local consumption and economic benefits.The simulation model is built in the MATLAB environment based on PV generation and electricity load of two zero energy houses.The results show that the capacity of a certain capacity battery to improve PV local consumption is limited,but it can enable users to obtain more economic benefits in responding to real-time electricity price.Simulation results shown the linear programing gets the largest cost saving.The reinforcement learning model effectively avoids the huge efforts of energy system modeling.Meanwhile,the agent can ensure an effective economic dispatch scenario.
optimal operationrule-basedlinear programmingQ-learningdeep Q network(DQN)economic benefitslocal consumptionzero energy house
NETL
NSTL
维普
万方数据
