Abstract 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.

中文翻译：

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具有可再生能源的综合电力和天然气系统的动态能量转换和管理策略：深度强化学习方法

摘要 随着先进信息技术在电力和天然气系统集成中的应用，制定优良的能源转换和管理策略已成为实现既定目标的有效方法。与以往的工作不同，本文提出了一种峰值负荷转移模型，通过协调电转气机组和发电机的运行来平滑电力和天然气一体化系统的净负荷曲线。此外，该研究旨在在考虑系统经济性的同时实现多目标优化。提出了一种动态的能量转换与管理策略，通过调整经济系数来协调经济成本目标和削峰填谷目标。为了说明复杂的能量转换过程，采用深度强化学习将动态能量转换与管理问题表述为离散马尔可夫决策过程，采用深度确定性策略梯度解决决策问题。通过使用深度强化学习方法，系统运营商可以通过在线流程自适应地确定风电、电转气和燃气轮机运行以及发电机输出的转换率，其中风力发电的灵活性、批发气价格，并考虑了能源需求的不确定性。仿真结果表明，该算法能够提高系统运营商的利润，减少弃风量，实时有效地平滑净负荷曲线。