摘 要
随着能源结构的转型与可再生能源的快速发展，多能源系统的协同运行成为提升能源利用效率、降低碳排放的关键技术路径。本研究针对多能源系统中电、热、气等多种能源形式耦合复杂、优化难度大的问题，提出了一种基于混合整数线性规划的多能源系统协同优化算法。该算法通过构建包含源-网-荷-储的多维耦合模型，综合考虑了系统运行的经济性、可靠性与环保性等多重目标。研究创新性地引入了动态权重调整机制，实现了不同运行场景下优化目标的灵活切换；同时，开发了基于深度强化学习的预测校正策略，有效提升了系统对可再生能源出力波动的适应能力。
关键词：多能源系统 协同优化 深度强化学习
Abstract
With the transformation of energy structure and the rapid development of renewable energy, the coordinated operation of multi-energy systems has become a key technical path to improve energy utilization efficiency and reduce carbon emissions. This paper presents a collaborative optimization algorithm based on mixed integer linear planning, considering the problem of complex coupling of various energy forms such as electricity, heat and gas in multiple energy systems. By constructing a multi-dimensional coupling model including source-network-load-storage, the algorithm comprehensively considers the multiple ob jectives of the economy, reliability and environmental protection of the system operation. Through the dynamic weight adjustment mechanism to realize the flexible switching of optimization target in different operation scenarios, the prediction correction strategy based on deep reinforcement learning is developed, which effectively improves the adaptability of the system to the output fluctuation of renewable energy.
Keyword: Multi-energy system Collaborative optimization deep reinforcement learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2研究现状 1
1.3本文研究方法与技术路线 1
2多能源系统协同运行建模方法 2
2.1多能源系统耦合特性分析 2
2.2能量流建模与转换机制 2
2.3系统约束条件与目标函数构建 3
3多能源系统协同优化算法设计 4
3.1基于混合整数规划的优化算法 4
3.2分布式协同优化策略 4
3.3动态调度与实时优化方法 5
4多能源系统协同运行策略研究 6
4.1源-网-荷-储协调控制策略 6
4.2经济性与可靠性平衡策略 6
4.3典型场景下的应用案例分析 7
5结论 7
参考文献 9
致谢 10
随着能源结构的转型与可再生能源的快速发展，多能源系统的协同运行成为提升能源利用效率、降低碳排放的关键技术路径。本研究针对多能源系统中电、热、气等多种能源形式耦合复杂、优化难度大的问题，提出了一种基于混合整数线性规划的多能源系统协同优化算法。该算法通过构建包含源-网-荷-储的多维耦合模型，综合考虑了系统运行的经济性、可靠性与环保性等多重目标。研究创新性地引入了动态权重调整机制，实现了不同运行场景下优化目标的灵活切换；同时，开发了基于深度强化学习的预测校正策略，有效提升了系统对可再生能源出力波动的适应能力。
关键词：多能源系统 协同优化 深度强化学习
Abstract
With the transformation of energy structure and the rapid development of renewable energy, the coordinated operation of multi-energy systems has become a key technical path to improve energy utilization efficiency and reduce carbon emissions. This paper presents a collaborative optimization algorithm based on mixed integer linear planning, considering the problem of complex coupling of various energy forms such as electricity, heat and gas in multiple energy systems. By constructing a multi-dimensional coupling model including source-network-load-storage, the algorithm comprehensively considers the multiple ob jectives of the economy, reliability and environmental protection of the system operation. Through the dynamic weight adjustment mechanism to realize the flexible switching of optimization target in different operation scenarios, the prediction correction strategy based on deep reinforcement learning is developed, which effectively improves the adaptability of the system to the output fluctuation of renewable energy.
Keyword: Multi-energy system Collaborative optimization deep reinforcement learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2研究现状 1
1.3本文研究方法与技术路线 1
2多能源系统协同运行建模方法 2
2.1多能源系统耦合特性分析 2
2.2能量流建模与转换机制 2
2.3系统约束条件与目标函数构建 3
3多能源系统协同优化算法设计 4
3.1基于混合整数规划的优化算法 4
3.2分布式协同优化策略 4
3.3动态调度与实时优化方法 5
4多能源系统协同运行策略研究 6
4.1源-网-荷-储协调控制策略 6
4.2经济性与可靠性平衡策略 6
4.3典型场景下的应用案例分析 7
5结论 7
参考文献 9
致谢 10
