Optimal sizing and control of hybrid energy storage system for wind power using hybrid Parallel PSO-GA algorithm | Zendy

Ming Pang | Zendy; Yikai Shi | Zendy; Wendong Wang | Zendy; Shun Pang | Zendy

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Optimal sizing and control of hybrid energy storage system for wind power using hybrid Parallel PSO-GA algorithm

Author(s) -

Ming Pang,

Yikai Shi,

Wendong Wang,

Shun Pang

Publication year - 2018

Publication title -

energy exploration and exploitation

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.435

H-Index - 30

eISSN - 2048-4054

pISSN - 0144-5987

DOI - 10.1177/0144598718784036

Subject(s) - sizing , particle swarm optimization , wind power , energy storage , energy management , hybrid power , genetic algorithm , computer science , power management , power (physics) , hybrid system , piecewise , energy (signal processing) , energy management system , computer data storage , mathematical optimization , control theory (sociology) , algorithm , engineering , control (management) , electrical engineering , mathematics , art , artificial intelligence , mathematical analysis , visual arts , operating system , quantum mechanics , machine learning , statistics , physics

This paper proposes a frequency-based method for sizing the hybrid energy storage system in order to smoothen wind power fluctuations. The main goal of the proposed method is to find the power and energy capacities of the hybrid energy storage system that minimizes the total cost per day of all the systems. The energy management strategy used in this paper is designed as a two-level energy distribution scheme: the first level is responsible for setting the output power of hybrid energy storage system, the second level manages the power flow between the battery and supercapacitor. The hybrid parallel particle swarm optimization-genetic algorithm (PSO-GA) optimization algorithm is proposed to solve the control parameters of energy management strategy. In addition, the proposed method uses the piecewise fitting function to describe the lifetime of battery. Obtained results show that the hybrid energy storage system with the proposed energy management strategy is able to offer the best performances for the wind power system in terms of cost and lifetime.

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