Regarding different challenges, such as integration of green energy and autonomy of microgrid (MG) in the multi-microgrid (MMG) system, this paper presents an optimized and coordinated strategy for energy management of MMG systems that consider multiple scenarios of MGs. The proposed strategy operates at two optimization levels: local and global. At an MG level, each energy management system satisfies its local demand by utilizing all available resources via local optimization, and only sends surplus/deficit energy data signals to MMG level, which enhances customer privacy. Thereafter, at an MMG level, a central energy management system performs global optimization and selects optimized options from the available resources, which include charging/discharging energy to/from the community battery energy storage system, selling/buying power to/from other MGs, and trading with the grid. Two types of loads are considered in this model: sensitive and non-sensitive. The algorithm tries to make the system reliable by avoiding utmost load curtailment and prefers to shed non-sensitive loads over sensitive loads in the case of load shedding. To verify the robustness of the proposed scheme, several test cases are generated by Monte Carlo Simulations and simulated on the IEEE 33-bus distribution system. The results show the effectiveness of the proposed model.
This paper proposes a new framework for the planning of both distributed generators (DGs) and electric vehicle charging stations (EVCSs). The proposed method efficiently produces a unified solution for the joint planning of DGs and EVCSs for both gridconnected and islanded scenarios. The problem is formulated as a novel two-stage planning problem. The first stage determines the locations and sizes of the DGs with locations of EVCSs in grid-connected scenario, whereas the second stage planning identifies the optimal islands under the islanded microgrid scenario. A non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the first stage planning problem; in this stage, the algorithm minimizes two objective functions: the system-losses and total cost. In the second stage, another single objective optimization problem is designed which minimizes supply voltage variations to find optimal islands for the DGs and EVCSs to ensure a secure supply of power for EVs. The proposed framework is implemented on the IEEE 33-bus system and verified with four test cases. The results demonstrate the effectiveness of the proposed method and show that the sizes and locations of DGs, and locations of EVCSs are adequate for both grid-connected and islanded microgrids.
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