| 引用本文: | 刘 浩,王 丹,刘佳委,等.计及分布式水风光发电时空相关性的多微网协同优化策略[J].电力系统保护与控制,2025,53(13):23-35.[点击复制] |
| LIU Hao,WANG Dan,LIU Jiawei,et al.Multi-microgrid collaborative optimization strategy considering spatiotemporal correlation of distributed hydro-wind-solar generation[J].Power System Protection and Control,2025,53(13):23-35[点击复制] |
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| 摘要: |
| 分布式水电站启停速度快、运行灵活，能够有效平抑风光出力波动，提高新能源就地消纳率。然而水风光资源与本地负荷分布并不均衡，不同台区微电网间协同优化是进一步提升新能源利用水平的重要方式。为此，提出了计及分布式水风光发电时空相关性的多台区微电网日前-日内协同优化互补消纳策略。首先，基于小时输出波动定量评估了水电和风光发电的时空相关性，提出了互补率指标来衡量水电和风光出力的互补性。其次，构建了考虑水风光互补率约束的日前多微电网协同优化模型，基于分散协调优化方法确定日前微电网间功率交换计划。再次，构建了日内互补消纳滚动修正模型，降低了日前预测误差对模型结果的影响。最后，通过算例分析验证了所提模型的有效性，为促进新能源高效利用提供了重要技术支撑。 |
| 关键词: 水风光互补 可再生能源 分散协调优化 多时间尺度 多微网 |
| DOI：10.19783/j.cnki.pspc.241217 |
| 投稿时间：2024-09-08修订日期：2024-12-24 |
| 基金项目:国家电网公司总部科技项目资助(5400-202323225A-1-1-ZN) |
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| Multi-microgrid collaborative optimization strategy considering spatiotemporal correlation of distributed hydro-wind-solar generation |
| LIU Hao1,WANG Dan1,LIU Jiawei1,XIE Xueyuan2,YU Qian2,HE Guixiong3 |
| (1. Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China;
2. State Grid Hunan Comprehensive Energy Service Company Limited, Changsha 410000, China;
3. China Electric Power Research Institute, Beijing 100192, China) |
| Abstract: |
| Distributed hydropower plants, characterized by fast start-stop capability and flexible operation, can effectively smooth the output fluctuations of wind and solar power, thereby enhancing the local consumption of renewable energy. However, the spatial mismatch between water-wind-solar resources and local loads necessitates coordinated optimization among multiple microgrids in different regions to further improve renewable energy utilization. This paper proposes a day-ahead and intra-day coordinative optimization strategy for multi-microgrids, considering the spatiotemporal correlation of distributed water-wind-solar generation. First, the spatiotemporal correlation of hydro, wind, and solar outputs is quantitatively evaluated based on hourly fluctuations, and a complementarity index is introduced to measure the degree of output complementarity. Second, a day-ahead coordinated optimization model is established, incorporating constraints on hydro-wind-solar complementarity, and the day-ahead power exchange plan among microgrids is determined using a decentralized coordinated optimization method. Third, an intra-day rolling correction model is formulated to complementarily accommodate renewables in real time and mitigate the impact of day-ahead forecasting errors. Finally, case studies validate the effectiveness of the proposed model, providing important technical support for the efficient utilization of renewable energy. |
| Key words: hydro-wind-solar complementarity renewable energy decentralized coordination and optimization multi- timescale multi-microgrid |
