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Energy management becomes essential when distributed energy sources such as solar, wind, and fuel cell are connected in a micro-grid. In this paper, using a combination of two powerful neural network tools and fuzzy logic, intelligentization, and adaptation of droop control along with voltage and current control as one of the most common methods of decentralized control is done. One of the essential features of this method is its fast performance and the need for telecommunication infrastructure. In this paper, we provide a comprehensive control system that enables proper operation in both upstream and island network modes for both AC and DC microgrids. The proposed method is simulated to evaluate its effectiveness. The proposed structure, by adapting the control system by ANFIS structure, can properly distribute power between distributed products in a brilliant way and without the need for operators and costly telecommunications infrastructure in the face of severe disturbances such as change. The state between the connection and the island or fault occurrence ensures the stability of both the AC and DC parts of the microgrid. The results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the optimal scheduling of energy grids and networked energy hubs based on their participation in the day-ahead energy wholesale and retail markets is presented. The problem is formulated as a bilevel model. Its upper level minimizes the expected energy cost of electricity, gas, and heating grids, especially in the form of private distribution companies in the mentioned markets, in the first objective function, and it minimizes the expected energy loss of these networks in the second objective function. This problem is constrained by linearized optimal power flow equations. The lower-level formulation minimizes the expected energy cost of hubs (equal to the difference between sell and purchase of energy) as an objective function in the retail market. Constraints of this model are the operation formulation of sources and active loads and the flexibility limit of hubs. The unscented transformation approach models the uncertainties of load, renewable power, energy price, and energy demand of mobile storage. Then, the Karush–Kuhn–Tucker approach and Pareto optimization technique based on ε-constraint are adopted to extract the single-level single-objective formulation. Finally, obtained results verify the capability of the present method in improving the economic status of hubs and the economic and operation situation of the mentioned networks simultaneously so that the proposed scheme by managing the power of energy hubs compared with power flow studies has been able to reduce operating costs by 8%, reduce energy losses by 10%, and improve voltage profile and temperature by 36% and 30%. [ABSTRACT FROM AUTHOR]