An integrated energy system (IES) is combined with electricity, gas and heating subnetworks to satisfy various loads, which results in a complicated energy coupling. Considering the power interaction between IES and power grid, the real-time dynamic optimal dispatch policies for the on-grid IES are proposed and compared by learning methods to improve the operation economic and peak-shaving performances in this study. Owing to the uncertainties of the load demands and real-time peak-shaving demand and renewable energy, together with the sequential operation of the controllable sources, the energy dispatch optimization problem of the IES is described as a stochastic dynamic optimization process. A source-load collaborative operation mode is proposed as the coupling of thermal energy and electrical power in the IES and compared with different energy dispatching mode. And reinforcement learning methods are adopted to achieve the dynamic optimal policy. Since the multiple systems with energy coupling would result in modeling difficulty and dimension curse during the optimization, a deep reinforcement learning method (DDQN) is used to solve this problem. A numerical analysis is performed by comprehensively comparing the different learning algorithms and operation modes, and the simulation results show that the derived dispatch policy achieved by DDQN method with source-load collaboration can effectively improve the performance of the IES in the stochastic environment. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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基于学习方法的考虑调峰需求的并网综合能源系统动态优化

集成能源系统 (IES) 与电力、燃气和供热子网相结合，以满足各种负载，这导致了复杂的能量耦合。考虑到IES与电网之间的电力相互作用，提出并网IES的实时动态优化调度策略，并通过学习方法进行比较，以提高运行经济性和调峰性能。由于负荷需求、实时调峰需求和可再生能源的不确定性，以及可控源的顺序运行，IES的能源调度优化问题被描述为一个随机动态优化过程。提出了一种源-荷协同运行模式作为IES中热能和电能的耦合，并与不同的能源调度模式进行了比较。并采用强化学习方法实现动态最优策略。由于具有能量耦合的多个系统在优化过程中会导致建模困难和维度诅咒，因此使用深度强化学习方法（DDQN）来解决这个问题。通过对不同学习算法和运行模式的综合比较进行数值分析，仿真结果表明，源荷协同的DDQN方法得到的派生调度策略可以有效提高随机环境下IES的性能。© 2022 日本电气工程师学会。