Towards complementary operations of offshore wind farm and photovoltaic array: A centralized reinforcement learning enabled control approach

Abstract

Integrated offshore wind and photovoltaic (PV) power generation has high potential in significantly improving renewable power utilization, but the complementary operation of the integrated power system is fundamentally challenging, especially under high uncertainties of meteorological conditions. In this paper, the joint regulation of the offshore wind farm and the PV array is considered where the generator torque of each wind turbine and the tilt angles of the PV array are regarded as the control actions to simultaneously maximize the overall power generation and improve power quality. The model of the offshore wind farm including wake models and the turbine drive-train dynamic model is established while model of the PV array using the tilt angle as the regulation variable is also constructed. By considering the control of the integrated power system of the wind farm and the PV array as a typical partially-observable Markov decision process (MDP), a twin-delayed deep deterministic policy gradient (TD3) algorithm is developed to jointly regulate the wind farm and PV array. Design experiments have been conducted to evaluate the potential and effectiveness of the proposed TD3 control method for the integrated offshore wind and PV power system. The test results demonstrate that the TD3 method has good convergence performances in the overall power output and power variation rate regulations. The control objectives of improving the overall power output and smoothing the power variations can be simultaneously achieved by using appropriate parameter settings in the TD3 algorithm.