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Dynamic energy conversion and management strategy for an integrated electricity and natural gas system with renewable energy: Deep reinforcement learning approach
استراتژی مدیریت و تبدیل انرژی پویا برای یک سیستم برق و گاز طبیعی یکپارچه با انرژی تجدید پذیر: رویکرد یادگیری تقویتی عمیق-2020 With the application of advanced information technology for the integration of electricity and natural gas
systems, formulating an excellent energy conversion and management strategy has become an effective method
to achieve established goals. Differing from previous works, this paper proposes a peak load shifting model to
smooth the net load curve of an integrated electricity and natural gas system by coordinating the operations of
the power-to-gas unit and generators. Moreover, the study aims to achieve multi-objective optimization while
considering the economy of the system. A dynamic energy conversion and management strategy is proposed,
which coordinates both the economic cost target and the peak load shifting target by adjusting an economic
coefficient. To illustrate the complex energy conversion process, deep reinforcement learning is used to formulate
the dynamic energy conversion and management problem as a discrete Markov decision process, and a
deep deterministic policy gradient is adopted to solve the decision-making problem. By using the deep reinforcement
learning method, the system operator can adaptively determine the conversion ratio of wind power,
power-to-gas and gas turbine operations, and generator output through an online process, where the flexibility of
wind power generation, wholesale gas price, and the uncertainties of energy demand are considered. Simulation
results show that the proposed algorithm can increase the profit of the system operator, reduce wind power
curtailment, and smooth the net load curves effectively in real time. Keywords: Renewable energy accommodation | Dynamic energy conversion and management | Deep reinforcement learning |
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