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储能系统技术 储能系统 ★ 5.0

增强风力发电机可调度性的储能系统容量迭代启发式优化方法

An Iterative Heuristic Optimization Method for the Optimum Sizing of Battery Energy Storage System

作者 Shubham Kashyap · Tirthadip Ghose
期刊 IEEE Access
出版日期 2025年1月
技术分类 储能系统技术
技术标签 储能系统
相关度评分 ★★★★★ 5.0 / 5.0
关键词 电池储能系统 风能系统 系统规模优化 荷电状态 成本分析
语言:

中文摘要

本研究旨在设计方法优化支持风能系统WES的储能系统BESS容量,以增强能源市场中的功率承诺灵活性。方法涉及三个关键步骤:(i)估算额定kW,(ii)初始化BESS额定kWh,(iii)基于启发式规则迭代调整BESS容量以防止负荷周期后SOC限制违规。为BESS生成三个真实负荷周期,其中一个基于最大误差值生成,其他负荷周期基于印度泰米尔纳德邦Agasthianpalli风电场预测误差正态分布曲线的均值和1σ生成。提出两个简单有效的启发式规则优化BESS容量,确保每天开始时最大SOC并全天维持SOC在限制内。这导致两种场景:单组BESS服务负荷周期和两组BESS交替运行,次日通过电网充电达到最大SOC。成本分析表明场景1在成本和BESS容量方面更有利,分别超过场景2 8.89%和9.95%,导致场景1投资回收期更短。使用遗传算法GA验证,比较BESS成本,强调所提技术的适用性。

English Abstract

This research aims to devise a methodology for optimizing the size of a Battery Energy Storage System (BESS) supporting Wind Energy Systems (WES) to enhance power commitment flexibility in the energy market. The methodology involves three essential steps: (i) estimating rated kW, (ii) initializing rated kWh of the BESSs, and (iii) iteratively adjusting the BESS size based on heuristic rules to prevent State of Charge (SoC) limit violations following the load cycle. Three realistic load cycles for the BESSs out of which one load cycle is generated based on maximum error values and the other load cycles are generated based on the mean and 1 of the Normal Distribution Curve (NDC) of forecast errors of WES located at Agasthianpalli, Tamil Nadu, India. Two simple yet effective heuristic rules have been proposed to optimize the BESS size, ensuring maximum SoC at the start of each day and maintaining SoC within limits throughout the day. This leads to two scenarios considering a single set of the BESS to serve the load cycle and two sets of the BESS operating alternatively, reaching maximum SoC on the subsequent day by charging from the grid. Cost analysis indicates that scenario 1 is more favorable in terms of both cost and BESS size, surpassing scenario 2 by 8.89% and 9.95%, respectively. This analysis results in shorter payback period for scenario 1. Validation using Genetic Algorithm (GA) is done by comparing the costs of BESSs, emphasizing the suitability of the proposed technique.
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SunView 深度解读

该储能容量优化技术直接对应阳光电源风储一体化解决方案。阳光在风电配储项目中需要精确计算储能容量以实现风电平滑输出和可调度性。该研究的启发式优化方法考虑SOC管理和负荷周期,可集成到阳光EMS系统的容量规划模块。在风电场储能配置中,该方法可优化阳光ST储能系统容量,平衡投资成本和调度收益。研究的双场景对比分析可指导阳光为客户提供最优储能配置方案,缩短投资回收期。结合阳光iSolarCloud平台的风功率预测和历史数据分析,该技术可实现储能系统精准配置,提升风储项目经济性和电网友好性,支持新能源高比例接入。