Accelerated hierarchical optimization method for emergency energy management of microgrids with energy storage systems. Issue 3 (28th January 2022)

• Record Type: Journal Article
• Authors: Zhou, Weihao; Li, Qiang; Wu, Kunming; Zhang, Leiqi; Hassan, Muhammad Arshad Shehzad; Chen, Minyou
• Is Part Of: Energy science & engineering. Volume 10:Issue 3(2022)
• Page Start: 962
• Page End: 972
• Publication Date: 2022-01-28
• Subjects: energy management -- hierarchical optimization method -- microgrids; Energy industries -- Periodicals; Energy development -- Periodicals; Power resources -- Periodicals; 621.042
• DOI: 10.1002/ese3.1077
• Languages: English
• ISSNs: 2050-0505

Abstract

Abstract: When failures occur in microgrids (MGs), the energy management for emergencies is required. To respond to emergencies in MGs rapidly, an accelerated hierarchical optimization method has been proposed, where the outputs of energy storage systems (ESSs) are controlled to provide urgent supports, before the MG reconfiguration starts. However, it is time‐consuming to find the optimal schemes for MG reconfiguration. To reduce the time of reconfiguration, an optimization method based on deep neural networks (DNNs) for MG reconfiguration is presented, which consists of three levels. First, the combinational optimization for load shedding amounts is solved to determine the loads that have to be cut off. Second, a DNN is used to find the parameters of reconfiguration instead of the time‐consuming calculation of power flow, which is a good way to reduce time of reconfiguration. Third, according to these parameters, the optimal scheme for reconfiguration is selected by a comprehensive evaluation method, where the Delphi method (DM) is employed to adjust the weights of preferences in a comprehensive evaluation function, so it offers the diversity of decisions for the MG reconfiguration. Finally, to test our method, a modified IEEE 33‐bus system is built in MATLAB for simulations. Compared to traditional methods, our method can obtain the same reconfiguration scheme under different on/off states of load switches, but the time of reconfiguration is only one‐sixty‐seventh of that of other methods. Furthermore, in terms of our comprehensive evaluation method, reconfiguration schemes can be selected under different preferences. Abstract : In this paper, an accelerated hierarchical optimization method has been proposed to reduce reconfiguration time, where deep neural networks (DNNs) for MG reconfiguration are involved. Finally, a modified IEEE 33‐bus system is established in MATLAB for simulations. Compared to traditional methods, the accelerated hierarchical optimization method can obtain the same reconfiguration scheme, but the reconfiguration time is only one‐seventieth of that of the traditional methods.