电网技术
電網技術
전망기술
Power System Technology
2015年
4期
1019-1025
,共7页
杨玉青%牛利勇%田立亭%黄梅%鲍谚%时玮
楊玉青%牛利勇%田立亭%黃梅%鮑諺%時瑋
양옥청%우리용%전립정%황매%포언%시위
分布式电源%储能配置%削峰填谷%平滑负荷%两阶段粒子群算法
分佈式電源%儲能配置%削峰填穀%平滑負荷%兩階段粒子群算法
분포식전원%저능배치%삭봉전곡%평활부하%량계단입자군산법
distributed generations (DG)%energy storage system (BESS)%load shaving%load smoothing%two-stage particle swarm optimization (PSO)
针对分布式电源接入区域配电网对负荷特性的负面影响，综合考虑储能充放电功率约束、运行约束以及配电网潮流平衡约束，建立了储能系统优化配置模型。以“削峰填谷”和“平滑负荷”分别作为负荷控制目标。针对“削峰填谷”提出控制负荷峰谷差、控制负荷方差以及控制负荷率3种优化策略，针对“平滑负荷”提出控制负荷变化量、控制负荷变化平方量以及控制负荷变化率3种优化策略，并结合储能系统的成本优化，利用两阶段粒子群算法进行模型求解。最后得出，控制负荷方差对于“削峰填谷”最有效，控制负荷变化平方量对于“平滑负荷”最有效。此外，得出不同储能充放电功率约束下负荷特性的优化趋势和储能容量最优配置变化趋势，为储能系统配置提供了有效参考。
針對分佈式電源接入區域配電網對負荷特性的負麵影響，綜閤攷慮儲能充放電功率約束、運行約束以及配電網潮流平衡約束，建立瞭儲能繫統優化配置模型。以“削峰填穀”和“平滑負荷”分彆作為負荷控製目標。針對“削峰填穀”提齣控製負荷峰穀差、控製負荷方差以及控製負荷率3種優化策略，針對“平滑負荷”提齣控製負荷變化量、控製負荷變化平方量以及控製負荷變化率3種優化策略，併結閤儲能繫統的成本優化，利用兩階段粒子群算法進行模型求解。最後得齣，控製負荷方差對于“削峰填穀”最有效，控製負荷變化平方量對于“平滑負荷”最有效。此外，得齣不同儲能充放電功率約束下負荷特性的優化趨勢和儲能容量最優配置變化趨勢，為儲能繫統配置提供瞭有效參攷。
침대분포식전원접입구역배전망대부하특성적부면영향，종합고필저능충방전공솔약속、운행약속이급배전망조류평형약속，건립료저능계통우화배치모형。이“삭봉전곡”화“평활부하”분별작위부하공제목표。침대“삭봉전곡”제출공제부하봉곡차、공제부하방차이급공제부하솔3충우화책략，침대“평활부하”제출공제부하변화량、공제부하변화평방량이급공제부하변화솔3충우화책략，병결합저능계통적성본우화，이용량계단입자군산법진행모형구해。최후득출，공제부하방차대우“삭봉전곡”최유효，공제부하변화평방량대우“평활부하”최유효。차외，득출불동저능충방전공솔약속하부하특성적우화추세화저능용량최우배치변화추세，위저능계통배치제공료유효삼고。
ABSTRACT:In allusion to the negative influences of distributed generations (DG) connected to regional distribution network on load characteristics, synthetically considering charging/discharging power constraints and operation constraint of battery energy storage system (BESS) as well as power flow balance constraint of distribution network, an optimal configuration model of energy storage system based on load optimization control is proposed. Taking peak load shaving and load smoothing as load control objectives respectively, three optimization strategies for peak load shaving such as controlling peak-valley difference of load, controlling load variance and controlling load rate, as well as three optimization strategies for load smoothing such as controlling load variation, controlling the square of load variation and controlling load variation rate are put forward, and combining with the cost optimization of energy storage devices, the proposed model is solved by two-stage particle swarm optimization (PSO) algorithm. Results of example simulation show that the controlling load variance is the most effective measure for peak load shaving and the controlling the square of load variation is the most effective one for load smoothing. Besides, the optimization tendency of load characteristic and the variation tendency of optimal configuration of energy storage capacity under different charging/discharging power constraints of energy storage system, which is available for reference to the configuration of energy storage system, are obtained.
