NDLI: Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

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Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

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Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

Content Provider	MDPI
Author	Zhou, Nan Liu, Nian Zhang, Jianhua Lei, Jinyong
Abstract	In order to solve the influence of uncertain photovoltaic power (PV) on the stable operation of microgrid (MG), demand response (DR) and battery energy storage system (BESS) need to be introduced simultaneously into the operation optimal scheduling of PV-based microgrid (PV-MG). Therefore, it is of great significance for commercial investment decisions of PV-MG to consider the influence of DR on BESS optimal sizing. Under the peak-valley time-of-use (TOU) price, this paper builds cross-time DR models based on price elasticity matrix. Furthermore, through the introduction of DR and BESS into PV-MG scheduling optimization, the MG investment and benefit model is proposed. Considering the constraint condition such as co-ordination of supply and demand, electricity price elasticity and energy loss of storage system, the improved non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimal allocation model of the BESS with the target of maximum PV consumptive rate and annual net profits. The optimization method was applied to a PV-MG in Guangdong. Through the regulation and control effect of demand response and BESS on load distribution, the uncertainties PV power can be suppressed so as to improve the PV system consumptive level, which is of great guiding significance for BESS optimal sizing under this situation.
File Size	515072
File Format	PDF
e-ISSN	19961073
DOI	10.3390/en9080591
Journal	Energies
Issue Number	8
Volume Number	9
Language	English
Publisher Date	2016-07-28
Access Restriction	Open
Subject Keyword	time-of-use (TOU) price multi-period demand response (DR) battery energy storage system (BESS) photovoltaic consumptive rate investment and benefit model multi-objective optimization non-dominated sorting genetic algorithm II (NSGA-II) algorithm
Content Type	Text
Resource Type	Article

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