A dynamic and proactive multi-microgrid network reconfiguration model for load factor improvement

Independent operation of a single microgrid (MG) is facing significant operational challenges such as high operational expenses, limited self-consumption of local renewable energy, and fluctuating peak and valley load because of the increased uncertainty and variability of net load generation caused by high penetrations of renewable energy. In this paper, connected multiple microgrids reconfiguration strategy is proposed while considering renewable generation uncertainty. Reconfiguration strategy is proposed to aggregate different load patterns belonging to various microgrids and match them with a certain microgrid. Then, the new aggregation of loads is aimed to improve the load factor of each MG within the network. The contribution of this study is to solve the peak load problem within the power network without affecting the costumers' life routine by using a mixed integer linear programming model for the dynamic proactive network reconfiguration of connected microgrids. The formulated model aims to maximize the load factor of each microgrid in the network and flatten the peak load curve without disturbance for the households' consumption patterns. The proposed model determines the near optimal topology for the network, The proposed mixed integer linear programming model is used to solve the reconfiguration problem with respect to distribution losses, microgrids output and households' consumption. Moreover, a linearization approach of the load factor equation is proposed to reduce the model complexity and computational time. An artificial dataset contains 3 microgrids and 15 households is used to validate the model performance. The obtained results show that the network reconfiguration could affect the average demand of each individual MG by reducing the fluctuations within the curve. Therefore, the load factor for the three microgrids increases with a percentage ranges from 8% to 53% in comparison with the unconnected system. & COPY; 2023 Elsevier Ltd. All rights reserved.