Trading Off Environmental and Economic Scheduling of a Renewable Energy Based Microgrid Under Uncertainties

Smart power grids are transitioning towards effective employment of distributed energy resources including renewable energy sources to address the growing environmental concerns related to the pollutant emissions of fossil fuels. In this context, this paper proposes the directed search domain (DSD) method to compute the combined environmental and economic dispatch in a microgrid with battery energy storage systems, photovoltaic plants, wind turbines, fuel cells, and microturbines. The DSD algorithm is implemented for a multiobjective problem to obtain evenly-distributed Pareto optimal points by shrinking the original search domain into hypercone. This paper computes the optimal unit commitment and the related power dispatch while simultaneously minimizing the total pollutant emissions and operating costs. The best trade-off solution among the entire set of Pareto optimal points is computed by using the Fuzzy satisfying technique. The uncertainties associated with the forecasting of prices, load demand, wind, and photovoltaic power outputs are accounted for by employing the stochastic programming. The empirical results indicate the potential of the presented DSD algorithm in terms of the objective values, solution times, and quasi-even distribution of the Pareto set.