Reliability centered planning for distributed generation considering wind power volatility

This paper investigates a stochastic planning model to minimize the lifecycle cost of distributed generation (DG) systems under the energy reliability criterion, namely the loss-of-load probability. In particular, our study focuses on the DG system penetrated by renewable wind technology. The optimization is formulated to determine the wind turbine capacity and their placement in the DG system with the intent to minimize the capital, operational and environmental costs. Statistical moments including mean and variance are utilized to characterize the wind power volatility and the load uncertainty. Genetic algorithm combined with heuristic search is used to find the best sitting and sizing of the distributed energy recourses. Our study is among the first attempts in the literature to model and optimize DG system based on continuous probabilistic theory. The moment methods are shown to be effective in characterizing the stochastic behavior of wind power and load dynamics. Case studies are provided to demonstrate the application and performance of the planning method.

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► We propose a probabilistic model to plan distributed generation systems with variable wind power.
► Moment methods are shown to be effective to characterize power volatility and load uncertainty.
► Genetic algorithm and heuristics methods solve the non-linear integer planning issues efficiently.
► Research shows the loss-of-load probability can be controlled even with 30% wind power penetration.