Joint Optimization of Quota Policy Design and Electric Market Behavior Based on Renewable Portfolio Standard in China

Abstract

Under the perspective of carbon neutrality, the green electricity absorption target constrained by the quota system policy plays a crucial role in reducing the carbon emission of the power industry. However, the current green certificate policy has not achieved good results. On the premise of reducing the additional market burden as much as possible, the policy parameters should take into account the influence of market behavior to formulate better policy parameters in line with China’s carbon emission peak goal. This paper constructs a combined hierarchical reinforcement learning with off-policy correction and multi-agent deep deterministic policy gradient algorithm (HIRO-MADDPG). It realizes the benefit analysis of the existing policy parameters joint with the solution of the optimal policy parameters. The algorithm solves the problem that benefit analysis and parameter formulation cannot be jointly trained and improves the precision. The results indicate: 1) HIRO-MADDPG algorithm can reach the highest policy benefits on the premise of maintaining market fairness; 2) under the new optimal policy parameters, the income per kilowatt hour of thermal power generator(TPG) and renewable power generator(RPG) can be maintained at 10% under the condition of abolishing subsidies; 3) with the help of the new policy parameters, China’s power sector will reach the peak of carbon emissions from coal-fired power plants in 2026 ahead of schedule, and reduce carbon emissions by a further 11% by 2030.

Keywords

• Quota system policy
• hierarchical multi-agent reinforcement learning
• tradable green certificate
• carbon neutrality