Modern power systems require increased intelligence and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. This issue is becoming more significant today due to the increasing number of microgrids (MGs). The MGs mostly use renewable energies in electrical power production that are varying naturally. These changes and usual uncertainties in power systems cause the classic controllers to be unable to provide a proper performance over a wide range of operating conditions. In response to this challenge, the present paper addresses a new online intelligent approach by using a combination of the fuzzy logic and the particle swarm optimization (PSO) techniques for optimal tuning of the most popular existing proportional-integral (PI) based frequency controllers in the ac MG systems. The control design methodology is examined on an ac MG case study. The performance of the proposed intelligent control synthesis is compared with the pure fuzzy PI and the Ziegler-Nichols PI control design methods.

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The increasing need for electrical energy, limited fossil fuel reserves, and the increasing concerns with environmental issues call for fast development in the area of distributed generations (DGs) and renewable energy sources (RESs). A Microgrid (MG) as one of the newest concepts in the power systems consists of several DGs and RESs that provides electrical and heat power for local loads. Increasing in number of MGs and nonlinearity/complexity due to entry of MGs to the power systems, classical and nonflexible control structures may not represent desirable performance over a wide range of operating conditions. Therefore, more flexible and intelligent optimal approaches are needed. Following the advent of optimization/intelligent methods, such as artificial neural networks (ANNs), some new potentials and powerful solutions for MG control problems such as frequency control synthesis have arisen. The present chapter addresses an ANN-based optimal approach scheduling of the droop coefficients for the purpose of frequency regulation in the MGs.

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