摘要
风能、太阳能等可再生能源以及负荷需求具有不确定性。这种不确定性为智能电网规划增加了不确定因素。蒙特卡洛随机模拟能够很好模拟在一定时间尺度内的系统出力情况,因此能有效处理系统不确定性因素,且在处理此类优化配置问题时,万有引力算法(GSA)能体现极强的快速性和准确性。所以为有效提高独立型微电网的经济性,在充分考虑微电网供电可靠性和可再生能源浪费的基础上,以系统平准化能源成本(LCOE)最优为目标,提出蒙特卡罗模拟嵌入万有引力搜索算法(GSA-MCS)对优化模型进行求解。GSA-MCS的主要思路是通过蒙特卡罗模拟模拟风光负荷的不确定性,然后将模拟的负荷数据代入万有引力搜索算法进行容量优化配置求解。该方法首先利用蒙特卡罗模拟处理风、光及负荷的不确定性,并采用万有引力搜索算法进行全局寻优得到风、光、储最优配置方案。配置方案确定了在不同累积概率水平下的配置容量。本文以某海岛微电网为例进行仿真分析并验证了方法的有效性。仿真结果显示不同累积概率水平下,微电网中风机、光伏板、储能电池数量相较于传统方法都有所减少,其平准化能源成本也相应下降。相较于传统的优化配置方法,本文所提模型能够在进行独立微网优化配置时兼顾准确性与经济性,且能为微电网容量配置提供一种折中且灵活的方案。
Renewable energy contains embedded uncertainties,such as wind energy,solar energy,and load demand.This uncertainty adds ambiguity to smart grid planning.The existing research only focuses on intelligent optimization algorithms or the uncertain output of the source load to solve the optimal configuration problem of the independent microgrid;it fails to combine the intelligent optimization algorithm with the uncertainty processing method.Monte Carlo simulation(MCS)can successfully simulate the system output in a certain timescale to effectively deal with the uncertainty factors associated with the system.Furthermore,the gravitational search algorithm(GSA)is fast and accurate when dealing with this kind of optimal allocation.An MCS-embedded universal gravitation search algorithm(GSA-MCS)is proposed for solving the optimization model to effectively improve the economy of independent microgrids(by fully considering the reliability of microgrid power supply and renewable energy waste)and aiming at the optimization of system levelized energy cost(LCOE).The main objective of GSA-MCS is to simulate the uncertainty of wind load through an MCS and substitute the simulated load data into the universal gravitation search algorithm to solve the capacity optimization configuration model.First,an MCS is applied to deal with the uncertainty of wind,light,and load,and the global optimization of wind,light,and storage is conducted using a gravitational search algorithm.The allocation scheme determines the allocation capacity at different cumulative probability levels.This study considers an island microgrid as an example to conduct simulation analysis and verify the effectiveness of the method used herein.The simulation results reveal that under different cumulative probability levels,the number of microgrid fans,photovoltaic panels,and energy storage cells reduce when compared with those obtained using traditional methods,and the LCOE is also reduced.When compared with the traditional optimization method,the proposed model can provide a compromise and flexible scheme for the capacity allocation of microgrid systems.
作者
姚清诚
袁晓玲
黄保乐
YAO Qingcheng;YUAN Xiaoling(College of Energy and Electrical Engineering,Hohai University,Nanjing 211100,Jiangsu,China)
出处
《储能科学与技术》
CAS
CSCD
2020年第1期188-196,共9页
Energy Storage Science and Technology
作者简介
第一作者:姚清诚(1995—),男,硕士研究生,研究方向风电功率预测技术、储能技术及其应用,E-mail:yaoqc2008@163.com;联系人:袁晓玲,教授,工学博士,研究方向综合能源服务和电力市场理论,Email:675133963@qq.com。
