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The major objectives of this work are: 1) to develop new efficient optimization algorithm to solve NP-hard problems, 2) to show the potential of integrating renewable energy technologies for Laayoune region- Morocco taken as case study. In this work, new parallel hybrid Genetic Algorithm-Particle Swarm Optimization algorithm (P-GA-PSO) is developed to solve both sizing and energy management problems for micro-grids. The studied micro-grid is composed of four different renewable energy technologies and energy storage system. The objectives of both optimization problems are to satisfy typical load demand, to minimize energy production cost, to maximize renewable energy integration, to avoid energy losses and overload. P-GA-PSO performances are evaluated and compared with the ordinary optimization methods based on: 1) a set of benchmark functions, 2) various scenarios used to solve the proposed micro-grid optimization problems. Obtained results demonstrated that P-GA-PSO outperforms ordinary optimization algorithms in terms of convergence time and solution quality. Moreover, the proposed micro-grid is very promising in terms of load demand satisfaction, cost and pollutant emissions reduction. Indeed, the obtained energy cost does not exceed 0.17 US$/kWh, which is close to fossil fuel energy cost, and fossil fuel replacement rate exceeds 50% during all periods.

Electricity demand increases every year and there is a need to improve the existing power grid. Smart Grid concept was developed by Institutes and research agencies to address this need. This new concept can be generally considered as an intelligent power grid system that implements distributed energy. Gathering, coordinating operation and control of multiple distributed energy resources, energy storages, loads and set of users pose a great challenge. Effective management of smart grid will have a benefit of reduced cost of energy, reduced stress on the electric energy infrastructure, and improved energy efficiency. This paper reviews the research and studies on mathematical models used to optimize energy management in smart grid. The paper compares and discusses various fitness functions used in the literature. Genetic Algorithm is explored and brief overviews of other methods are discussed.