Energy-Efficient Power Scheduling Policy for Cloud-Assisted Distributed PV System: A TD3 Approach

Abstract

To cope with climate change and other environmental problems, countries and regions around the world have begun to pay attention to the development of renewable energy under the drive of achieving the global carbon emission peak and carbon neutrality goal. The distributed photovoltaic (PV) power grid is an effective solution that can utilize solar energy resources to provide clean a energy supply. However, with the continuous grid connection of distributed energy, it poses great challenges to the power supply stability and security of the grid. Therefore, it is particularly important to promote the local consumption of distributed energy and the construction of the energy internet. This paper aims to study the cooperative operation and energy optimization scheduling problem among distributed PV power grids, and proposes a new scheme to reduce the electricity cost under the constraint of power supply and demand balance. The optimization problem is modeled as a Markov decision process (MDP), and a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is used to solve the MDP problem. Simulation results show that the proposed algorithm outperforms other benchmark algorithms in terms of reducing electricity cost, convergence and stability, and verifies its effectiveness.