Stochastic Simulator for Smart Microgrid Planning

Jesse Thornburg, Bruce Krogh, Taha Selim Ustun
Developing world microgrids often balance insufficient supply with growing, unpredictable demand. Deterministic and proba-bilistic 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 to model smart microgrids with discrete probability distributions as supply and demand inputs. In our model, smart meters allow real-time power clipping for demand side management, effec-tively smoothing the system load curve as needed. To compare clipping schemes for grid operation and generation mixes for planning, we aggregate inputs by convolution then compute expected energy sold and probability of avoiding power cuts. The simulator plots these values for different combinations of power clipping threshold and number of customers clipped.