考虑状态相依老化特性的电池储能参与调峰投资效益评估方法

doi:10.19595/j.cnki.1000-6753.tces.241171

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摘要

参与电力系统调峰是当前电池储能的重要应用手段之一。电力市场环境下,电池储能在电价信号的引导下进行充放电获取收益,但同时将造成老化损失,产生相应老化成本。现有固定参数的电池储能老化模型无法在长时间模拟中考虑储能老化参数随寿命的变化。该文基于阿伦尼乌斯经验模型,建立了考虑电池储能老化特性受寿命变化影响的状态相依老化模型,实现电池储能全寿命周期老化特性建模;构建该模型的离散形式以适应电力系统的应用需求;将模型嵌入调峰优化模型中并对其进行线性化以便于模型求解。进一步地,将该方法应用于长时间尺度调峰模拟中,通过动态计算并滚动更新电池储能老化参数,精准地刻画了电池储能全寿命周期老化过程,实现了考虑电池储能状态相依老化特性的调峰收益测算,提升了电池储能参与调峰投资效益评估的有效性。

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关键词 ：电力系统调峰, 电池储能, 阿伦尼乌斯经验模型, 状态相依老化模型, 投资效益评估

Abstract：

Battery energy storage (BES) is widely used in various applications in the power system as a flexible regulation resource. One of the key applications is peak shaving. BES can charge and discharge to capture the price difference between peak and valley periods in the electricity market. However, due to the inherent operational characteristics of BES, peak shaving behavior results in irreversible aging loss, leading to a reduction in lifespan and available capacity. Establishing an accurate aging model for BES is fundamental for simulating its peak shaving behavior and evaluating investment benefits. The aging characteristics of BES are state-dependent at different stages of its lifespan. However, aging models with fixed parameters fail to consider the variation in aging parameters over time during long-term peak shaving simulations.
A state-dependent aging model for BES is proposed, based on the Arrhenius empirical model, which accounts for the impact of lifespan on aging characteristics. This model can represent the full aging behavior of BES throughout its lifespan. A discrete form of the model is developed to meet the application requirements of power system optimization. The discrete variables are adapted to those of the power system, enabling their integration. Furthermore, the discrete aging model is embedded in a peak shaving optimization model. To address the challenge of solving the highly nonlinear discrete aging model, linearization techniques are employed to facilitate model computation. A dynamic parameter updating mechanism for BES is also designed and incorporated into the discrete aging model, enabling dynamic updates of the aging loss rate and available capacity during long-term peak shaving simulations, thus accurately capturing the full aging process throughout the lifespan.
To validate the effectiveness of the proposed method in investment benefits evaluation, historical locational marginal prices from the Southern Power Pool (SPP) electricity market are used as inputs to simulate the daily peak shaving operations of BES. The results of the daily simulations are used to update BES parameters via the dynamic updating mechanism, and the peak shaving benefits are then calculated. By updating boundary conditions, long-term peak shaving simulations of BES throughout its lifespan are performed, and the cumulative net present value (NPV) of investing in BES for peak shaving is computed. The results show that the proposed method allows for considering aging loss variation over time, reflecting the impact of state-dependent aging characteristics on the peak shaving performance of BES. This method enables accurate evaluation of BES investment benefits.
From the analysis, the following conclusions can be drawn: (1) The state-dependent aging model of BES, based on the Arrhenius empirical model and after discretization and linearization, can accurately capture the aging characteristics throughout the entire lifespan of BES. It also facilitates effective application in power system peak shaving simulations. (2) The state-dependent aging characteristics influence the charge and discharge behavior decisions of BES under electricity price signals. Using the state-dependent aging model allows for precise quantification of the aging costs arising from BES's peak shaving behavior, enabling accurate evaluation of the investment benefits of BES in peak shaving applications.

Key words： power system peak shaving battery energy storage Arrhenius empirical model state-dependent aging model investment benefits evaluating

收稿日期: 2024-07-04

PACS:

TM732

基金资助:

国家电网有限公司科技项目资助（SGCQDK00DWJS2400058）

通讯作者: 雍培男,1995年生,副教授,研究方向为电力系统运行优化与分析、分布式资源协同、可再生能源。E-mail：peiyong@cqu.edu.cn

作者简介: 郭飞男,1996年生,博士研究生,研究方向为电化学储能建模、优化运行与配置。E-mail：guofei@stu.cqu.edu.cn

引用本文:

郭飞, 雍培, 刘欣宇, 余娟, 杨知方. 考虑状态相依老化特性的电池储能参与调峰投资效益评估方法[J]. 电工技术学报, 0, (): 1171-. Guo Fei, Yong Pei, Liu Xinyu, Yu Juan, Yang Zhifang. Evaluating the Investment Benefits of Battery Energy Storage Participating in Peak Shaving Considering the State-Dependent Aging Characteristics. Transactions of China Electrotechnical Society, 0, (): 1171-.

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