A Comprehensive Optimization Method for Planning and Operation of Building Integrated Photovoltaic Energy Storage System

被引:0
|
作者
Chen K. [1 ]
Xiao X. [1 ]
Tian P. [1 ]
Feng K. [2 ]
Sun P. [2 ]
机构
[1] Department of Electrical Engineering, Tsinghua University, Haidian District, Beijing
[2] Pingliang Power Supply Company, State Grid Gansu Electric Power Company, Gansu Province, Pingliang
关键词
building integrated photovoltaic (BIPV) system; capacity allocation; energy scheduling strategy; particle swarm optimization algorithm (PSO);
D O I
10.13334/j.0258-8013.pcsee.220820
中图分类号
学科分类号
摘要
Optimal capacity allocation and energy scheduling are the core problems of planning and operation stages of building an integrated photovoltaic (BIPV) system. A comprehensive design method for capacity allocation and energy scheduling is presented. Considering the coupling effects, a two-layer coupling model is established with the cost and benefit as the decisive elements under the whole life cycle. In the outer model, the shortest payback period is taken as the objective function to optimize the allocation of photovoltaic and energy storage capacity. The inner model is solved with the objective of maximizing the daily operating revenue to obtain a comprehensive optimal energy scheduling strategy. The particle swarm optimization algorithm (PSO) is improved, which effectively improves the convergence speed and enhances the global optimization ability. Compared with other scheduling strategies, “peak-valley arbitrage” of the energy storage system is utilized to shorten the investment payback period. This method can comprehensively assess the economics of BIPV systems and apply to different regions with flexible electricity market policies, and it is universal and portable to meet the diverse needs of different scenarios. ©2023 Chin.Soc.for Elec.Eng.
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页码:5001 / 5011
页数:10
相关论文
共 21 条
  • [1] PENG Jinqing, Lin LU, YANG Hongxing, Investigation on the annual thermal performance of a photovoltaic wall mounted on a multi-layer façade [J], Applied Energy, 112, pp. 646-656, (2013)
  • [2] YAO Chenni, MA Xinbo, LUO Duo, Prediction research on mid-and long-term development goals of solar photovoltaic application in buildings under carbon peak target[J], Construction Science and Technology, 2021, 11, pp. 33-35
  • [3] WANG Guanghui, TANG Xinming, ZHANG Tao, Building monitoring by remote sensing and analysis of distributed photovoltaic construction potentials [J], Strategic Study of CAE, 23, 6, pp. 92-100, (2021)
  • [4] CHEN Lili, MU Longhua, XU Xufeng, Influences of energy storage operational strategy and characteristic on microgrid reliability[J], Electric Power Automation Equipment, 37, 7, pp. 70-76, (2017)
  • [5] JIANG Quanyuan, SHI Qingjun, LI Xingpeng, Optimal configuration of standalone wind-solar-storage power supply system[J], Electric Power Automation Equipment, 33, 7, pp. 19-26, (2013)
  • [6] MA Xiyuan, WU Yaowen, FANG Hualiang, Optimal sizing of hybrid solar-wind distributed generation in an islanded microgrid using improved bacterial foraging algorithm[J], Proceedings of the CSEE, 31, 25, pp. 17-25, (2011)
  • [7] SUN Xiujuan, ZHANG Pengfei, BIAN Xiaoxue, Battery storage optimization for capacity configuration of photovoltaic-based microgrid with multi-type demand response[J], Science Technology and Engineering, 19, 9, pp. 108-114, (2019)
  • [8] JIA He, PENG Jinqing, LI Nianping, Optimization and economic analysis of distributed photovoltaic-energy storage system under dynamic electricity price[J], Acta Energiae Solaris Sinica, 42, 5, pp. 187-193, (2021)
  • [9] REN Hongbo, WU Qiong, REN Jianxing, Optimal operation of fuel cell/PV/battery based residential energy system[J], Renewable Energy Resources, 32, 4, pp. 379-384, (2014)
  • [10] NI Pingbo, ZHOU Dan, ZHU Haiping, Research on double-layer optimal configuration model of electric-thermal energy storage system in smart building [J], Electrical Measurement & Instrumentation, 58, 9, pp. 122-128, (2021)