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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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

Received: 12 September 2023

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	Cheng Caoyang
	Yang Zhifang
	Yu Juan
	Wang Xingang
	Zhou Zhuan

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Cheng Caoyang,Yang Zhifang,Yu Juan等. An Efficient Method for Energy Storage Planning Considering Full-Scenario Security[J]. Transactions of China Electrotechnical Society, 2024, 0(0): 4-.

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