Optimal energy and reserve scheduling in a renewable-dominant power system

In the shift to a sustainable, cost-effective and safe energy system, the improvement of existing energy and reserve scheduling is required to handle the volatility of the intermittent power supply. In contrast to traditional approaches that ignore correlations of intermittent energy (wind and photovoltaic) power deviation, spatiotemporal correlations are incorporated into our integrated renewable-dominant wind-photovoltaic-pumped hydro storage system to provide flexibility and stability guarantees. This paper represents a distributionally robust chance constraint (DRCC) model for optimizing day-ahead energy scheduling and real-time regulation operation in a renewable-dominant power system by minimizing the expected total cost. To capture the correlation and uncertainty associated with intermittent energy, the correlation covariance matrix and moment-based ambiguity set approaches are applied, respectively. Our model is transformed into a second-order cone programming for which commercial solvers are available. A case study is performed to illustrate how the effects of spatiotemporal correlation and the uncertainty of intermittent energy power deviation might have an effect on the power system operation. The results further demonstrate the rationality and economy of the proposed model for the energy and reserve scheduling problem.