A Stochastic Siting/Sizing Optimization Framework for Intermittent Renewable Energy DG Units

As the demand for clean and non-fossil fuel energy escalates, integration of renewable power sources into distribution systems must increase. Optimized integration of renewable energy distributed generation (DG) has the potential to reduce the environmental impacts of power generation. Careful and detailed planning is crucial to ensure expedient operation of distribution systems with renewable energy based DG units. This paper proposes an optimization method that takes into account the stochastic nature of renewable energy sources to size and place them within distribution networks. An optimization problem is designed based upon the desired constraints, and Monte Carlo Simulation is used to generate load and wind samples, and simulate the system. Genetic Algorithm is then utilized to solve the optimization problem and produce a solution. A case study is performed on a 27 node distribution test system to evaluate our method.