Data-Driven Microgrid Operation Toward Optimized Battery Energy Storage Lifetime Degradation面向电池储能寿命退化优化的微电网数据驱动运行
英文 Abstract
This paper proposes a new data-driven approach for two-stage operation of a microgrid (MG) towards optimized battery energy storage (BES) lifetime degradation. At the first stage (day-ahead), the BES degradation range over the scheduled day is optimally allocated under day-ahead forecast of random variables. To accurately quantify BES degradation under temperature impact, a convex BES electrothermal-aging model is constructed. Then, a dynamic model constraint coefficient determination method is proposed for optimizing BES lifetime degradation using the historical operation and updated current information. At the second stage (real-time), an approximate dynamic programming method is developed to real-time optimize the MG operation after multiple uncertainties are realized. Considering the temperature dependent constraints of BES, a sequential decomposed value function slope update method is designed for enhanced optimization feasibility, BES aging quantification accuracy, and training efficacy, which can deliver high-quality solutions under uncertainties. Case studies on a MG system demonstrate the effectiveness of the proposed approach.
中文翻译
本文提出一种新的数据驱动方法，用于微电网两阶段运行优化，以减缓电池储能系统的寿命退化。在第一阶段（日前阶段），依据随机变量的日前预测，对计划日内电池储能的退化范围进行最优分配。为准确量化温度影响下的电池退化，研究构建了凸形式的电热—老化模型，并利用历史运行数据和实时更新信息，提出动态确定模型约束系数的方法，以优化电池寿命损耗。在第二阶段（实时阶段），当多种不确定性逐步实现后，采用近似动态规划实时优化微电网运行。考虑电池储能系统与温度相关的约束，研究设计了顺序分解的价值函数斜率更新方法，以提高优化可行性、老化量化精度和训练效率，并在不确定条件下获得高质量解。微电网算例验证了该方法的有效性。
中文概述
本文提出面向微电网的两阶段数据驱动运行方法，以降低电池储能寿命损耗。日前阶段分配退化范围并建立电热老化模型，实时阶段利用近似动态规划应对不确定性，在兼顾温度约束的同时提升优化可行性和训练效率。