面向全场景安全的储能投资高效规划方法

doi:10.19595/j.cnki.1000-6753.tces.231501

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摘要

随着风电、光伏等新能源渗透率不断提高,系统随机性与波动性不断增强,科学合理地规划储能被认为是缓解新能源不确定性、提高电力系统安全性与灵活性最有效的手段之一。然而,现有规划方法为了保证一定的计算效率,通常仅选取少量关键场景用以制定储能规划方案,无法确保其在全场景下的安全性,倘若对于全场景进行安全校核,又会因为模型规模大导致求解时间在规划层面都难以接受。为此,本文提出一种面向全场景安全的储能投资高效规划方法。首先,针对现有规划方法存在的安全风险,提出一种面向全场景安全的闭环储能规划框架,并提出一种基于全场景集排序结果引导的场景更新策略,可以保证规划方案在全场景下的安全性,同时兼顾一定的计算效率。其次,提出了一种基于自组织映射（Self-organizing Maps, SOM）神经网络以及场景关键指标排序的初始关键场景集生成方法,该方法无需预先给定聚类数量、能够较准确反映全场景的关键信息,进一步提高计算效率。最后,基于IEEE-30节点系统以及国内某省实际341节点系统进行算例验证,结果表明所提方法可以在保障规划方案在全场景下的安全性与最优性的基础上,尽可能减少需考虑的场景数量,提高求解效率。

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关键词 ：储能规划, 多场景规划, 场景筛选, 安全校核, SOM神经网络

Abstract：

With the continuous increase in the penetration rate of renewable energy sources such as wind and photovoltaics, the randomness and volatility of the power system are constantly growing. Investing energy storage is considered one of the most effective ways to alleviate the uncertainty of renewable energy and enhance the security and flexibility of the power system. However, existing planning methods, to ensure the computational efficiency, usually select only a few key scenarios to formulate energy storage planning, which cannot guarantee its security under all scenarios. If security verification is performed for the planning across all scenarios, it will be difficult to accept due to the large model size leading to unacceptable solution time. Therefore, this paper proposes an efficient method for energy storage planning considering full-scenario security.
Firstly, addressing the security risks brought by open-loop scenario clustering and planning solutions in existing planning methods, a closed-loop energy storage planning framework oriented towards security under all scenarios is proposed. Secondly, by constructing a relaxed energy storage planning model based on fixed 0-1 variables to obtain a ranking result of the full scenario set reflecting the criticality of different scenarios, guiding the update of key scenarios based on this ranking result can ensure full-scenario security while balancing computational efficiency. Thirdly, this method requires a given set of initial key scenario sets. To address the issues of traditional clustering methods such as K-means, which require a predefined number of clusters, are prone to submerge extreme scenarios’ information, and cannot accurately reflect the critical information of all scenarios, a method for generating an initial scenario set based on Self-organizing Maps (SOM) neural network and scenario critical indicator ranking is further proposed. This method does not require a predefined number of clusters and can accurately reflect critical information under all scenarios, further improving computational efficiency.
To validate the effectiveness of the proposed energy storage planning method, case studies are conducted on the IEEE-30 bus system and an actual 341-bus system in a domestic province. Scenarios are considered with full scenario set sizes of 30 and 365, and different levels of renewable energy fluctuations and line transmission capacity limits are taken into account. The results indicate that, considering the detailed system operational characteristics, existing closed-loop energy storage planning methods, due to the lack of guidance from key scenarios’ information, take an excessively long time to solve when dealing with a large number of scenarios in practical systems, exceeding two weeks in some cases. The proposed method can ensure the security and optimality of planning under all scenarios while reducing the number of scenarios to be considered and improving solution efficiency. In practical systems, the proposed method can obtain energy storage planning solutions within 4 hours.
From the numerical analysis, the following conclusions can be drawn: (1) Considering the fine temporal granularity of the power system operational characteristics in the planning year, existing energy storage planning methods are computationally expensive, even at the planning level. (2) The proposed energy storage planning method accurately considers the fine temporal granularity of the power system operational characteristics of the planning year. While ensuring the security of planning under all scenarios, it improves solution efficiency by reducing the number of scenarios to be considered and selecting more reasonable initial key scenarios. (3) In actual power system operations, the proposed energy storage planning method can consider the benefits obtained through peak-to-valley electricity price differentials and has certain economic value.

Key words： Energy storage expansion planning multi-scenario planning scenario clustering security check SOM neural network

收稿日期: 2023-09-12

PACS:

TM715

基金资助:

国网总部科技项目（5100-202236503A-3-0-SF）资助

通讯作者: 余娟女,1980年生,教授,博士生导师,研究方向为电力与能源经济优化运行、风险评估、深度学习等。E-mail：148454745@qq.com

作者简介: 程曹阳男,1999年生,硕士研究生,研究方向为电力系统优化与规划。 E-mail：cycheng@cqu.edu.cn

引用本文:

程曹阳, 杨知方, 余娟, 王新刚, 周专. 面向全场景安全的储能投资高效规划方法[J]. 电工技术学报, 2024, 0(0): 4-. Cheng Caoyang, Yang Zhifang, Yu Juan, Wang Xingang, Zhou Zhuan. An Efficient Method for Energy Storage Planning Considering Full-Scenario Security. Transactions of China Electrotechnical Society, 2024, 0(0): 4-.

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