A control strategy of ES system based on short term wind-PV power prediction. (7th December 2019)

• Record Type: Journal Article
• Authors: Ji, Huizheng; Niu, Dongxiao; Wu, Han; Wu, Meiqiong; Li, Bingjie
• Is Part Of: International journal of technology, policy and management. Volume 19:Number 4(2019)
• Page Start: 329
• Page End: 351
• Publication Date: 2019-12-07
• Subjects: chance-constrained programming -- optimal scheduling of energy storage -- MCS -- Monte Carlo simulation -- AGA -- adaptive genetic algorithm; Technological innovations -- Management -- Periodicals; Technology and state -- Periodicals; 658.51405
• Languages: English
• ISSNs: 1468-4322

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.