摘 要
随着全球能源危机和环境问题的日益严峻，智能电网作为实现能源高效利用与可持续发展的重要手段，已成为学术界和工业界的关注焦点。分布式能源因其灵活性和环保性在智能电网中占据重要地位，但其随机性和间歇性对电网稳定运行带来挑战。为此，本文以提升分布式能源利用率和优化电网调度效率为目标，研究了智能电网中分布式能源的优化配置与调度策略。基于多目标优化理论和先进计算方法，提出了一种融合需求响应机制和储能系统的综合优化模型，该模型能够同时考虑经济性、可靠性和环境效益等多方面因素。通过引入深度强化学习算法，进一步提升了调度策略的适应性和鲁棒性，使其能够在复杂动态环境中实现最优决策。仿真结果表明，所提方法不仅显著降低了系统运行成本，还有效提高了可再生能源消纳能力及整体供电可靠性。此外，本文创新性地将区块链技术应用于分布式能源交易管理，构建了去中心化的点对点能源交易平台，为促进用户侧参与和增强系统透明度提供了新思路。总体而言，本研究为智能电网背景下分布式能源的高效利用提供了理论支持和技术路径，对未来相关领域的研究与发展具有重要的参考价值。
关键词：智能电网；分布式能源优化；多目标优化；深度强化学习；区块链技术
ABSTRACT
With the escalating global energy crisis and environmental issues, smart grids have become a focal point in both academia and industry as a critical means to achieve efficient energy utilization and sustainable development. Distributed energy resources (DERs), known for their flexibility and environmental friendliness, play a significant role in smart grids; however, their randomness and intermittency pose challenges to stable grid operation. To address these challenges, this study aims to enhance the utilization of DERs and optimize grid dispatch efficiency by investigating optimal configuration and dispatch strategies for DERs in smart grids. Based on multi-ob jective optimization theory and advanced computational methods, an integrated optimization model incorporating demand response mechanisms and energy storage systems is proposed, which considers multiple factors such as economic efficiency, reliability, and environmental benefits simultaneously. By introducing deep reinforcement learning algorithms, the adaptability and robustness of the dispatch strategy are further improved, enabling optimal decision-making in complex dynamic environments. Simulation results demonstrate that the proposed method not only significantly reduces system operating costs but also effectively enhances the accommodation capacity of renewable energy and overall power supply reliability. Additionally, this paper innovatively applies blockchain technology to the management of distributed energy transactions, establishing a decentralized peer-to-peer energy trading platform that promotes user-side participation and increases system transparency. Overall, this research provides theoretical support and technical pathways for the efficient utilization of DERs in the context of smart grids, offering valuable references for future studies and developments in related fields.
Keywords: Smart Grid; Distributed Energy Optimization; Multi-ob jective Optimization; Deep Reinforcement Learning; Blockchain Technology
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 智能电网与分布式能源概述 1
1.2 研究背景与意义分析 1
1.3 国内外研究现状综述 2
1.4 本文研究方法与技术路线 2
第2章 分布式能源优化配置的关键因素分析 3
2.1 分布式能源的特性与分类 3
2.2 优化配置的目标与约束条件 3
2.3 关键影响因素识别与评估 4
2.4 数学建模方法的选择与应用 4
2.5 配置优化的案例分析 5
第3章 智能电网中调度策略的设计与实现 6
3.1 调度策略的基本原理与框架 6
3.2 实时调度的需求与挑战 6
3.3 基于智能算法的调度优化方法 7
3.4 数据驱动的调度策略改进 7
3.5 调度策略的实际应用效果评估 8
第4章 分布式能源与智能电网协同优化研究 9
4.1 协同优化的意义与目标 9
4.2 分布式能源与电网交互机制分析 9
4.3 多目标优化模型的构建 10
4.4 协同优化算法的设计与验证 10
4.5 典型场景下的协同优化效果分析 11
结论 12
参考文献 13
致 谢 14
