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投稿时间:2023-05-31 修订日期:2023-09-04
投稿时间:2023-05-31 修订日期:2023-09-04
中文摘要: 随着“双碳”政策的推进,发展以电网为核心,电、热、气多能互补、协同供能的综合能源系统是落实“双碳”的重要手段。电-热-气联合运行的综合能源系统迅速发展,该系统运行存在的经济性问题和稳定性问题有待解决,本文致力于采用机器学习算法,在兼顾运行稳定性的情况下解决电-热-气联合运行系统的经济性问题。本文首先对包含储能和电转气装置的综合能源系统进行建模,结合优化运行问题优化目标-约束条件的一般框架,在约束条件建模中考虑功率平衡、各机组出力限制、爬坡率限制和容量限制;然后,本文设计了基于DRL的电-热-气联合系统优化运行问题求解策略,DRL算法结合了强化学习策略选择的优势和深度学习环境模拟的优势,在DRL算法设计中详细考虑动作空间、回报函数、状态空间、DRL 算法、DRL网络五大模块;最后,本文设计了4个算例,结合电-热-气联合系统典型日运行条件,验证了采用电-热-气联合运行供能模式可以有效实现多能互补降低用能成本,并且本文设计的DRL方法可以有效求解电-热-气联合系统的优化运行问题。
Abstract:With the promotion of the "double carbon" policy, the construction of an integrated energy system with power grid as the core and complementary and coordinated supply of electricity, heat and gas is an important means to realize the "double carbon". Integrated energy system where electricity, heat, and gas operate coordinately is rapidly developing. The economic and stability issues that exist in the operation of the system need to be addressed. This paper is committed to using machine learning algorithms to solve the economic issues in operation of integrated energy system while taking stability into account. Firstly, this paper proposes operation model of integrated energy system that includes energy storage devices and P2G. In the proposed model, considering the general framework of optimal operation, optimization objective and constraints are included. Power balance, output limitations, ramp rate limitations, and capacity limitations of each device are considered in the constraints. Then, this paper designs a solution strategy for the optimal operation of integrated energy system based on DRL . The DRL algorithm combines advantages of reinforcement learning on strategy selection and advantages of deep learning on environment simulation. In the DRL algorithm design, five modules are considered in detail: action space, return function, state space, DRL algorithm, and DRL network. Finally, four cases are designed in this paper under the typical daily operation conditions of integrated energy system including electricity, heat, and gas devices. The simulation verifies that the combination of the electric, heat and gas devices in an energy network can effectively achieve multi-energy complementarity and reduce energy costs. The DRL method designed in this paper can effectively solve the optimization operation problem of integrated energy system including electricity, heat, and gas devices.
keywords: integrated energy system optimal operation reinforcement learning deep learning DRL algorithm
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作者 | 单位 | |
周楠* | 常州供电公司 | 852429533@qq.com |
梁馨予 | 常州供电公司 | |
于向华 | 常州供电公司 | |
秦彦玮 | 常州供电公司 | |
孙斌 | 常州供电公司 | |
陈俊 | 常州供电公司 | |
徐烨 | 常州供电公司 |
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