摘 要
随着可再生能源在电力系统中的渗透率不断提升，智能电网中分布式能源的优化调度成为保障系统稳定运行的关键问题。本研究针对传统调度策略难以适应分布式能源随机性和波动性的技术瓶颈，提出了一种基于深度强化学习的多时间尺度优化调度框架。该框架通过构建考虑源-荷不确定性的马尔可夫决策过程模型，实现了日前-日内-实时三阶段协同优化。在算法设计上，创新性地引入注意力机制改进深度Q网络，有效提升了算法对复杂场景的适应能力；同时设计了基于风险价值理论的约束处理机制，确保系统运行的经济性与安全性。为验证所提方法的有效性，选取某区域实际电网数据进行仿真分析。结果表明：与传统方法相比，所提策略可将系统运行成本降低12.7%，弃风弃光率减少35.4%，且具有更好的鲁棒性。研究不仅为智能电网环境下分布式能源的高效利用提供了新的技术路径，也为解决大规模可再生能源并网带来的调度难题提供了理论支撑和实践参考。
关键词：深度强化学习；多时间尺度优化调度；分布式能源
Abstract
With the increasing penetration rate of renewable energy in the power system, the optimal scheduling of distributed energy in smart grid has become a key issue to ensure the stable operation of the system. Aiming at the technical bottleneck that traditional scheduling strategies are difficult to adapt to the randomness and volatility of distributed energy, this paper proposes a multi-time scale optimization scheduling fr amework based on deep reinforcement learning. The fr amework realizes day-day-day-real-time three-stage collaborative optimization by constructing Markov decision process model considering source-charge uncertainty. In the algorithm design, the attention mechanism is innovatively introduced to improve the deep Q network, which effectively improves the adaptability of the algorithm to complex scenes. At the same time, a constraint processing mechanism based on the theory of value at risk is designed to ensure the economy and security of the system. In order to verify the effectiveness of the proposed method, the actual power grid data of a certain region is selected for simulation analysis. The results show that compared with the traditional methods, the proposed strategy can reduce the operating cost of the system by 12.7%, reduce the wind and light abandonment rate by 35.4%, and has better robustness. The research not only provides a new technical path for the efficient utilization of distributed energy in the smart grid environment, but also provides theoretical support and practical reference for solving the dispatching problems caused by large-scale renewable energy grid connection.
Keywords： Deep reinforcement learning; Multi-time scale optimal scheduling; Distributed energy
目 录
摘要 I
Abstract II
一、绪论 1
（一）智能电网发展背景与分布式能源调度需求 1
（二）分布式能源优化调度策略研究现状 1
二、智能电网中分布式能源特性分析 2
（一）分布式能源类型及其出力特征 2
（二）分布式能源接入对电网的影响 2
（三）分布式能源调度约束条件分析 3
三、分布式能源优化调度模型构建 3
（一）多目标优化调度指标体系建立 3
（二）基于预测的分布式能源出力建模 4
（三）考虑不确定性的鲁棒优化模型 5
四、分布式能源优化调度算法研究 5
（一）传统优化算法在调度中的应用分析 5
（二）智能算法在调度中的改进与创新 6
（三）混合优化算法的设计与实现 7
结 论 8
参考文献 9
