Chance Constrained Day Ahead Stochastic Unit Commitment with Multiple Uncertainties

Abstract

The large scale integration of renewable energy sources and energy storage technologies is driven by energy transition. The integrated technologies pose multiple uncertainties and challenges to System operator such as inconsistency, instability, and economic infeasibility in the Unit Commitment (UC) problem. It requires addressing multiple uncertainties in UC problem while ensuring reliable and cost-effective grid operation. In this paper, a net load demand model is proposed for incorporating multiple uncertainties. Uncertainties pertaining to photovoltaic (PV) generation, load forecasts and energy storage (ES) are modeled with a joint chance constraint approach for solving stochastic day ahead UC. The chance constraint is employed to limit the probability of joint uncertainty within the predefined bounds. The next day UC schedule and costs for IEEE 39-bus system are solved by Mixed Integer NonLinear Programming (MINLP). Three case studies are performed to validate effectiveness of proposed model. Case 1 is base-system analysis of UC costs without uncertainties. Case 2 describes impacts of load forecast uncertainty on UC. In Case 3 impact of joint chance constrained multiple uncertainties on UC cost and schedule are studied with coordinated PV-ES operation. Results prove the efficacy of proposed net load demand model for optimizing system UC with multiple uncertainties.