Stochastic Simulator for Smart Microgrid Planning

Abstract

Developing world microgrids often balance insufficient supply with growing, unpredictable demand. Deterministic and probabilistic simulators exist to model these microgrids, and each focuses on different technical aspects. With the addition of smart meters into microgrids, monitoring and control is now available at high granularity, which enriches microgrid planning and operation. This research is designing a new simulator that incorporates models of smart meters that allow real-time power clipping for demand side management, effectively smoothing the system load curve as needed to represent inherent uncertainty. Individual supplies and demands are modeled as discrete probability mass functions (PMFs). To compare clipping schemes for grid operation and generation mixes for planning, we aggregate the probabilistic inputs by convolution and then compute expected energy sold and probability of avoiding power cuts. We compare these values for load profiles of different days of the week and test microgrid reliability with different numbers of customers clipped.