Optimal capacity configuration for multi-station integration considering multiple uncertainties

doi:10.3969/j.issn.2097-0706.2022.01.005

Abstract

Abstract:

The uncertainties in renewable energies,such as solar energy and wind power,and load demands make multi-station integration changeable.In order to effectively boost the economy of multi-station integration,on the premise of reliable power supply and new energy consumption of the power system, an optimal capacity configuration method for multi-station integration which takes levelized energy cost as the optimization objective is proposed.To solve the optimal capacity configuration considering multiple uncertainties,Lévy flight is introduced into it on the basis of quantum-inspired gravitational search algorithm to improve the search ability,and the random parameters are generated by Monte Carlo simulation.Taking a multi-station integration project as an example,the simulation analysis is compared with the traditional particle swarm algorithm and unimproved quantum-inspired gravitational search algorithm.The simulation results show that the proposed method improves the accuracy and stability of the solution for capacity allocation problem,and can effectively reduce the power consumption cost of multi-station integration.

Key words: multi-station integration, quantum-inspired gravitational search algorithm, Lévy flight, Monte Carlo simulation, renewable energy

CLC Number:

TK01⁺9:TM73

LU Xiaomin, ZHANG Ming, DENG Xing, WANG Liwei, TAO Yibin, HU Anping. Optimal capacity configuration for multi-station integration considering multiple uncertainties[J]. Integrated Intelligent Energy, 2022, 44(1): 31-38.

Figures/Tables 8

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项目	单位	风力发电机	光伏电池	蓄电池
额定功率	kW	100	1	—
逆变器效率	%	90	97	96
额定电压	V	220	36	2
使用年限	年	20	20	10
单价	元/台	800 000	10 000	1 000

算法指标	LCOE/[元·（kW·h）^-1]	时间/s
LQGSA-MCS均值	0.79	9.53
LQGSA-MCS标准差	0.04	2.38
QGSA-MCS均值	0.81	10.07
QGSA-MCS标准差	0.05	1.56
PSO-MCS均值	0.90	10.89
PSO-MCS标准差	0.07	2.48