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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.

In this work, a new hybrid cuckoo search and genetic algorithm optimization method using a novel adaptive penalty function was proposed to solve the economic dispatch (ED) problem in smart grid. Please check and confirm the edit made in article title. This method was also paralyzed in order to solve the problem within specific time suitable to solve Energy management Problems. Three improvements are achieved through this combination. First, parallelism allows further reduction of the execution time. Second, the hybridization of both cuckoo search and genetic algorithm methods allows better diversification and exploration of search space which increases the solution quality. Third, the new adaptive penalty function was developed to discard infeasible solutions and to choose near-optimal ones within a short time. The efficiency of the developed algorithm is proven theoretically and experimentally. Three scenarios are considered to prove experimentally the out-performance of the developed method: (1) the proposed method is compared with Cuckoo Search and Genetic Algorithm methods using a set of benchmark functions. (2) A comparative study is carried out by applying the method to the ED continuous problem optimization case study. (3) The method is compared with Cuckoo search to solve discrete demand side management problem, considering each consumer as an independent parameter. The performance evaluation was conducted using Matlab data parallelism library.

This work presents methodology and results used to estimate both wind and solar energies availability and their cost. The objective is to analyze the potential of these two major sources of alternative energy in Tangier region and to show their ability to replace fossil fuel energy sources especially for remote areas and agriculture applications. Thus, energy availability and its cost for these technologies are determined based on meteorological data, and their engineering and technical characteristics. The obtained results provide the hourly average energy production (in kWh) and its cost (in USD/kWh) for a maximum designed power output of 5 kW for each technology and for each month in the year. Thus, these results show that energy production cost is ranging between 0.01 and 0.3 USD/kWh for solar energy using photovoltaic panels and it is ranging between 0.05 and 0.35 USD/kWh for wind energy using wind turbines with rated power equals to 1 kW. Results of this work could be used as data estimation reference for engineering and research works related to renewable energy applications in the studied region.

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.