Stochastic Optimization Model for the Short-term Joint Operation of Photovoltaic Power and Hydropower Plants Based on Chance-constrained Programming

Abstract Integrating photovoltaic (PV) power into large-capacity hydropower plants is considered as an efficient and promising approach for large-scale PV power accommodation. To improve the guidelines for the optimal operation of large-scale hydro-PV hybrid systems, this paper proposes a practical coordination mode of a PV plant and a large-capacity hydropower plant based on the negotiation mechanism between the power generation company and the power grid. A chance-constrained programming (CCP) based stochastic optimization model is then presented to determine the short-term joint operation of a hydro-PV system, aiming at promoting renewable energy consumption. To improve the solution efficiency, several linearization approaches are proposed to convert the original model into a scenario-based mixed integer linear programming (MILP) problem. The real-world case studies demonstrate that the joint operation of the hydro-PV hybrid system can promote the consumption of renewable energy while making the actual total power output track the schedule submitted to the power grid. Moreover, the confidence level at which the power balance constraints can be met exceeds 90% in all weather conditions, far above the confidence level of less than 50% for the deterministic model, proving the stochastic model would be a better choice for the joint operation of a hydro-PV system.