Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm

详细信息	查看全文 | 推荐本文 |

• 作者：Taher Niknam^a ; ^{taher_nik@yahoo.com} ; Faranak Golestaneh^a ; Ahmadreza Malekpour^b ; ^c
• 关键词：Energy and operation management ; microgrid (MG) ; Point estimate method ; Self-adaptive gravitational search algorithm (SGSA) ; Uncertainty
• 刊名：Energy
• 出版年：2012
• 期刊代码：11_03605442
• 类别：pw
• 出版时间：July, 2012
• 卷：43
• 期：1
• 页码：427-437
• 文件大小：369 K

摘要

Recently, due to technology improvements, governmental incentives for the use of green energies and rising concerns about high cost of energy from fossil fuels, renewable energy sources (RESs) appears to be a promising approach for producing local, clean, and inexhaustible energy. This motivates the implementation of microgrids (MGs) introduced as a cluster of electrical and/or thermal loads and different RESs. Due to different uncertainties linked to electricity supply in renewable microgrids, probabilistic energy management techniques are going to be necessary to analyze the system. This paper proposes a probabilistic approach for the energy and operation management (EOM) of renewable MGs under uncertain environment. The proposed framework consists of 2m point estimate method for covering the existing uncertainties in the MGs and a self-adaptive optimization algorithm based on the gravitational search algorithm (GSA) to determine the optimal energy management of MGs. This paper considers uncertainties in load demand, market prices and the available electrical power of wind farms and photovoltaic systems. In this study, a self-adaptive mutation technique is offered to enhance the convergence characteristics of the original GSA and avoid being entrapped into local optima. The Weibull and normal distributions are employed to model the input random variables. Moreover, the Gram-Charlier expansion is used to find an accurate distribution of the total energy and operational cost of MGs for the next day-ahead. The effectiveness of the proposed method is validated on a typical grid-connected MG including energy storage and different power generating units.