Hydro-photovoltaic complementary dispatch based on active regulation of cascade hydropower considering multi-transmission channel constraints

Hydropower and photovoltaic (PV) power, the two dominant renewable energy sources in China, are crucial for decarbonization and climate change mitigation, leveraging their exceptional complementary value in the power system. However, the large-scale integration of PV power exacerbates several key issues concerning increased load variation, enhanced flexibility demand, and challenging congestion management. This study addresses these issues by developing a medium-short-term dispatch approach for a hydro-PV complementary system (HPCS), which leverages the flexibility of cascade hydropower and the potential value of hydro-PV complementarity. Firstly, a mathematical model is developed to optimize peak load shaving, while incorporating hydraulic-electrical coupling constraints and active regulation constraints of cascade hydropower in response to PV uncertainty. The model endeavors to optimize the hybrid operation of HPCS, exploiting hydro-PV complementarity to mitigate output variability and ensure a stable electricity supply. Subsequently, multidimensional decision variables in the model are subjected to an equivalent reformulation, leveraging the extremal principle and duality theory, and the nonlinear constraints are linearized by the special ordered sets of type 2 constraints. These methods enhance the model’s solvability and computational efficiency. Finally, the proposed model is applied to the Beipan HPCS in China, determining the hydro-PV complementary dispatch strategies. The research findings can be summarized as follows: (1) the developed hydropower active regulation method ensures stable and adjustable hydro-PV output, reducing load variation by 137.96 MW and fully exploiting hydropower flexibility and hydro-PV complementarity; (2) the model demonstrates high reliability by significantly enhancing peak shaving during the flood season, even in the face of increased congestion management challenges; (3) the model balances congestion management difficulties across multiple channels, outperforming the conventional model by reducing the risk of renewable energy curtailment by up to 1885.95 MWh; (4) the model adapts to the dynamic combined growth of VRE and transmission capacity, making it a promising technology for alleviating transmission congestion caused by large-scale hydropower and VRE.