Multiobjective Optimization of Renewable Energy Penetration Rate in Power Systems

Nowadays, multi-source systems based on renewable energy technologies become the key to a sustainable energy supply infrastructure against the rising cost and the pollutant nature of fossil primary energy used in conventional power plant. However, the cost of renewable energy technologies and the reliability of a multi-sources generation system are generally conflicting with each other. This paper presents a multiobjective formulation to allow optimizing simultaneously both the annualized renewable energy cost the system reliability defined as the renewable energy - load disparity (RELD). This later takes into account the lack of energy as well as the exceed weighted by a penalty factor. The optimization is reach by acting on the penetration rate of each type of renewable generation technologies in order to satisfy a certain load curve. In order to solve this problem, this work suggests to use the fast and elitist multiobjective genetic algorithm: NSGA-II. A case study shows that the use of diversified resources allows to handle the RELD and to decrease the exceed renewable energy (RERE) and load energy notsupplied (LENS).