新型电力系统与综合能源

考虑源荷功率不确定性的海上风力发电多微网两阶段优化调度

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  • 广东电网有限责任公司电力调度控制中心,广州 510060
陆秋瑜(1987-),女,博士,广西壮族自治区贵港市人,高级工程师,从事新能源消纳、储能技术、新能源与储能联合运行技术研究.电话(Tel.):020-85121001;E-mail: luqiuyu22@126.com.

收稿日期: 2021-10-14

  网络出版日期: 2022-11-03

基金资助

南方电网公司科技项目资助(036000KK52190025(GDKJXM20198267)

Two-Stage Optimal Schedule of Offshore Wind-Power-Integrated Multi-Microgrid Considering Uncertain Power of Sources and Loads

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  • Electric Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510060, China

Received date: 2021-10-14

  Online published: 2022-11-03

摘要

针对海上风力发电多微网源荷功率不确定性大、经济效益低的问题,提出考虑源荷不确定性的海上风力发电多微网两阶段优化调度方法,以提升海上风力发电多微网的日运行收益.所提两阶段优化调度方法包括日前和时前两个阶段.在日前阶段,所提方法基于风力发电出力和负荷需求预测数据,考虑预测误差的分布特征,建立随机优化模型,制定燃油发电机组组合计划和电池储能荷电状态值,从而最大化日运行收益的期望值.在日前优化的基础上,依托时前风力发电出力和负荷需求的预测数据,建立确定性优化模型,通过调节燃油发电机组出力、风力发电出力和电池储能充放电功率,最大化每小时的运行收益.以实际工程中的海上风力发电多微网源荷预测数据为基础,建立仿真模型,对所提方法进行验证.仿真结果表明,与传统调度方法相比,所提两阶段优化调度方法可以提高运行收益和风力发电资源整体消纳率.

本文引用格式

陆秋瑜, 于珍, 杨银国, 李力 . 考虑源荷功率不确定性的海上风力发电多微网两阶段优化调度[J]. 上海交通大学学报, 2022 , 56(10) : 1308 -1316 . DOI: 10.16183/j.cnki.jsjtu.2021.409

Abstract

Considering the high-randomness and the low-economic-benefit characteristics of the offshore wind-power-integrated multi-microgrid, a two-stage optimal scheduling method considering the uncertain power of source and load is proposed to improve the operation profits of offshore wind-power-integrated multi-microgrid. The proposed two-stage optimal scheduling method consists of a day-ahead stage and an hour-ahead stage. In the day-ahead stage, the proposed method is based on the forecast data of the wind power and the load demand, which considers the distribution characteristics of the prediction errors. A stochastic optimization model is established to determine the unit committee of the diesel generators and the state-of-charge of the battery storages, so as to maximize the expected daily operation income. A deterministic optimization model is established based on the decisions from the day-ahead optimization relying on the hour-ahead forecast data of the wind power output and load demand. By optimizing the power of the diesel generators, wind turbines and battery energy storages, the operation income of each hour is maximized. Finally, a simulation model is established to verify the proposed method based on the prediction data of sources and loads in wind-power-integrated multi-microgrid. The simulation results show that compared with the conventional schedule strategies, the proposed two-stage optimal scheduling method can achieve a higher income and a higher overall consumption rate of the wind power.

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