Day-Ahead Optimal Scheduling of CSP, PV, Wind, and Thermal Power Plants Using a Fuzzy Chance-Constrained Model

In the day-ahead optimization of large-scale new energy power plants connected to a power grid, the scheduling decision are challenging due to the uncertainty of power outputs caused by weather. To address this uncertainty, this paper introduces a fuzzy chance constraint theory based approach, which employs fuzzy parameters to model the load and power output of new energy power plants, including concentrated solar power (CSP) plants. By transforming the deterministic scheduling model into a fuzzy chance constraint model, the system constraints are adjusted to fuzzy parameters. The proposed model considers the output fluctuations ofwind, photovoltaic (PV), and CSP plants, as well as the uncertainty in load demands. The effectiveness of the proposed model is verified through a case study. The numerical results demonstrate that the proposed fuzzy chance constraint model can reduce the reserve capacity of the system while optimizing the economics benefits and reliability of the system.