基于电池储能和风能的智能交流和微直流电网DSM

N. Babu, L. Saikia, D. Saha
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引用次数: 1

摘要

发电量不足会导致负荷的意外减少。需求侧管理(DSM)通过客户积极参与响应时段电价,将可控负荷从高峰时段转移到非高峰时段,从而找到了解决方案。本文通过考虑智能交流电网系统、智能交流电网和带电池储能系统的太阳能直流微电网,对现有住宅、商业和工业建筑的需求侧管理进行了评估。通过进一步整合风能发电,改进了现有的DSM结构。采用遗传算法(GA)、粒子群优化(PSO)和混合粒子群优化(HPSO)等进化算法,对现有和改进的DSM架构进行最优负荷转移,目标是最大限度地减少峰值负荷需求,从而降低峰值平均比(PAR)、能源费用和重塑负荷分布。改进后的DSM架构在降低峰值负荷需求方面优于现有架构。通过对遗传算法、粒子群算法和HPSO的性能比较,可以看出HPSO在最小化峰值负荷、PAR、能源账单和重塑负荷剖面等方面具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart AC and Micro DC Grid Based DSM Using Battery Storage and Wind Energy
Inadequate energy generation leads to unscheduled shedding of loads. Demand side management (DSM) finds a solution for it by shifting the controllable loads from peak hours to off-peak hours by active participation of customers in response to time of day tariff. This article evaluates the existing DSM for residential, commercial and industrial architecture by considering smart AC grid system and, smart AC grid and solar operated DC micro grid with battery storage system. The existing DSM structure is modified by further integrating generation from wind energy. The optimal load shifting for both existing and modified DSM architecture is done by using evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and hybrid particle swarm optimization (HPSO) with an objective to minimize the peak load demand which in turn reduces peak-to-average-ratio (PAR), energy bills and reshapes the load profile. The modified DSM architecture outperforms the existing one in terms of peak load demand reduction. Performance comparison among GA, PSO and HPSO witness the superiority of HPSO in terms of minimizing the peak load, PAR, energy bills and, reshaping the load profile.
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