A SVM-based implicit stochastic joint scheduling method for ‘wind-photovoltaic-cascaded hydropower stations’ systems

With the gradual expansion of the development scale of wind power and photovoltaic (PV) power plants, the multi-energy complementary power generation system, typically represented by hydro-PV/hydro-wind/hydro-wind-PV, has become an important part of modern power systems. Aiming at the joint operation of the cascaded hydropower stations after wind-PV grid connection, a medium- and long-term implicit stochastic joint dispatching function model for wind-PV-cascaded hydropower stations based on the SVM(support vector machine) method is developed in this paper, which selects the final water levels of the reservoirs as the dependent variables, and the initial water levels of the reservoirs, the reservoir inflow, the interval inflow as well as the wind and PV output are independent variables. First, the optimization of main parameters C (Penalty coefficient), g (Kernel function parameter) and p (Insensitive loss coefficient) of the model are achieved by particle swarm algorithm. The Gaussian radial basis function is then used to fit the scheduling function proposed in this paper. Finally, the rolling simulation calculation and correction of the obtained scheduling function are realized by C# programming language of VS2017 platform. The results show that the proposed scheduling function is an effective method for scheduling decision-making, and the revised water level process, output process as well as annual electricity production of the scheduling model are not significantly different from the optimal scheduling results. Moreover, the simulation results conform to the existing scheduling rules, which has shown it can be used to inform the operation of cascaded hydropower stations under the multi-energy complementary system.