Title: A control strategy of ES system based on short term wind-PV power prediction

Authors: Huizheng Ji; Dongxiao Niu; Han Wu; Meiqiong Wu; Bingjie Li

Addresses: School of Economics and Management, North China Electric Power University, Beijing 102206, China ' School of Economics and Management, North China Electric Power University, Beijing 102206, China ' School of Economics and Management, North China Electric Power University, Beijing 102206, China ' School of Economics and Management, North China Electric Power University, Beijing 102206, China ' State Grid Jiangsu Economic Research Institute, Jiangsu 210000, China

Abstract: Based on short term wind-PV power forecasting, a charge and discharge power control method of ES system containing two control coefficients is proposed, which may make the output of the hybrid wind-PV-ES system furthest matched with the scheduled output. This method takes the output of wind-PV-ES in scheduled range and the cost of ES as the objectives, considers the constraints of power output of energy storage equipment and electric quantity, and wields adaptive genetic algorithm (AGA) based on Monte Carlo simulation (MCS) to obtain each time frame's charge and discharge power day-ahead. Finally, taking national wind-PV-ES and transmission power station for simulation, this paper compares the effect of tracking scheduled output in fixed coefficients situation and variable coefficients situation. The results verify the feasibility and flexibility of the proposed strategy. Furthermore, the results between multi-objective and single-objective optimisation in fixed coefficient case indicate that multi-objective optimisation is more comprehensive and economy.

Keywords: chance-constrained programming; optimal scheduling of energy storage; MCS; Monte Carlo simulation; AGA; adaptive genetic algorithm.

DOI: 10.1504/IJTPM.2019.104059

International Journal of Technology, Policy and Management, 2019 Vol.19 No.4, pp.329 - 351

Received: 28 Sep 2017
Accepted: 22 Apr 2018
Published online: 10 Dec 2019 *

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