周海浪, 刘一畔, 陈雨果, 王子石, 瞿圣朋, 何凯, 包诗媛. 考虑灵活性收益的需求侧资源可行域聚合方法[J]. 中国电力, 2022, 55(9): 56-63, 155. DOI: 10.11930/j.issn.1004-9649.202110056
引用本文: 周海浪, 刘一畔, 陈雨果, 王子石, 瞿圣朋, 何凯, 包诗媛. 考虑灵活性收益的需求侧资源可行域聚合方法[J]. 中国电力, 2022, 55(9): 56-63, 155. DOI: 10.11930/j.issn.1004-9649.202110056
ZHOU Hailang, LIU Yipan, CHEN Yuguo, WANG Zishi, QU Shengpeng, HE Kai, BAO Shiyuan. Demand Side Feasible Region Aggregation Considering Flexibility Revenue[J]. Electric Power, 2022, 55(9): 56-63, 155. DOI: 10.11930/j.issn.1004-9649.202110056
Citation: ZHOU Hailang, LIU Yipan, CHEN Yuguo, WANG Zishi, QU Shengpeng, HE Kai, BAO Shiyuan. Demand Side Feasible Region Aggregation Considering Flexibility Revenue[J]. Electric Power, 2022, 55(9): 56-63, 155. DOI: 10.11930/j.issn.1004-9649.202110056

考虑灵活性收益的需求侧资源可行域聚合方法

Demand Side Feasible Region Aggregation Considering Flexibility Revenue

  • 摘要: 为实现需求侧灵活性资源的高效协同调控,一般将其以聚合体的形式参与电力市场。灵活性资源聚合问题的数学本质是求取若干资源运行可行域的闵科沃夫斯基和,该问题的计算负担随聚合对象的维度和数量增加呈指数增长趋势。考虑灵活性在不同时段的收益差异,利用奇诺多面体表征参数的特殊性质实现可行域快速聚合。首先,建立灵活性资源可行域在奇诺多面体表征形式下的近似模型,相较现有模型保留爬坡特性对可行域边界特征的影响;然后,引入随机搜索方法,提升可行域近似模型的求解效率;最后,考虑灵活性资源出力在不同时段的收益差异,提出可行域近似模型的优化目标权重修正策略。基于真实电价数据及储能模型验证了所提方法的有效性。

     

    Abstract: The flexibility of demand-side resources needs to be aggregated through a feasible region aggregation technique to participate in the spot market. This paper presents a feasible region aggregation method of demand-side resources considering flexibility revenue. Firstly, a feasible region approximation method based on Zonotope is presented, which could approximate the feasible region of power, state, and climbing constraints effectively. Secondly, the revenue of flexibility in different periods is considered, and the feasible domain with variable weight is designed to approximate the optimization objective. Finally, the efficient aggregation of the feasible domain of demand-side resources is realized, which provides the system with high-value flexible resources. The validity of the proposed method is verified based on real electricity price data and the energy storage model.

     

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