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Nowadays microgrids have been widely considered due to their economic and technical features. The most important feature of microgrid is its flexibility which means microgrids are be able to operate in both grid-connected and islanded operation modes. This paper presents a bi-level scheduling model for optimal operation of an integrated microgrid between grid-connected and islanded modes by using Benders decomposition method. Also a neighbor microgrid is considered in the model and it is assumed that the main microgrid enables to exchange energy with its neighbor in the islanding mode. The proposed model consists of a master problem for the grid-connected operation mode and a sub-problem for the islanding operation mode. The objective function of the master problem minimizes microgrid operation cost including cost of fuel, start up, spinning reserve as well as purchasing power from main grid or neighbors. However, the objective function of the sub-problem investigates whether the power generation of the microgrid is enough or the loads can be supplied continuously in the islanding mode of microgrid. Uncertainties of distributed energy sources, energy price and load are modelled by two-point estimate method.

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In this paper, a hybrid algorithm has been presented for energy management in multi-microgrid systems considering security constraints. The energy management system is responsible for accurately dispatching the amount of required energy among multiple microgrids and units in a muti-microgerid system. The energy management procedure is done hierarchically, in a way that each microgrid performs a local energy management, which determines surplus and shortage amounts of energy at each time interval. Accordingly, Independent System Operator (ISO), schedules the units. Each microgrid, contains a wind turbine (WT) and Photovoltaic (PV) panels as renewable and nondispatchable resources and a diesel generator as a dispatchable energy resource. Also an energy storage system (ESS) is responsible for balancing the produced and consumed energy. A demand response program (DRP) is performed through energy management system for the objective of MG load management and flattening the load curve and reducing the operation cost. Finally, the proposed approach is tested on IEEE 33-bus distribution test system, in presence of microgrids, using GAMS and MATLAB softwares. The simulation results would be presented in the final section to show the effectiveness of the proposed algorithm.

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This paper presents a bi-level mathematical model for optimizing the unit commitment of energy generation units in the presence of worst-case scenarios of renewable resource failures. The main innovation of this research is the integration of optimal load management, intelligent scheduling of electric vehicle charging and discharging, and precise modeling of renewable resource failures within a comprehensive framework. The high-level model is designed to reduce operational costs, manage flexible loads, and minimize pollution, while the low-level model examines the effects of resource failures and ensures system stability under critical conditions. To solve the problem, the Karush-Kuhn-Tucker (KKT) optimality conditions are employed as an efficient tool to reduce the complexity of the bi-level problem, and the model is transformed into a Mixed-Integer Linear Programming (MILP) problem. The proposed model is evaluated on the standard IEEE network, and simulation results demonstrate that this approach significantly outperforms existing models. Specifically, operational costs are reduced by up to 18.5%, system reliability in critical conditions is improved by up to 35%, and energy losses are reduced by 22%. These achievements highlight the model's capability to provide resilient and efficient solutions for optimal energy resource management under uncertainty scenarios. Overall, this research offers a novel framework for enhanced productivity in renewable energy systems, which can serve as an effective tool in the development of future sustainable and resilient systems.