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.
Highlights
Under the goal of carbon neutrality, China's economic transformation and structural adjustment have entered a critical period [1]
Under the optimal quota parameters calculated by HIRO-MADDPG algorithm, the carbon emission of thermal power units will reach the peak of 4.043 billion tons in 2026, which realizes the carbon peak of the power industry in advance
IMPLICATIONS Taking into full consideration the influence of the flexibility game results on the policy parameters under the tripartite bounded rationality of green power generators, thermal power generators and power utilities, this paper solves the problem of setting the optimal policy parameters to ensure the fair competitiveness of green power generators in the electricity market after the subsidy policy is cancelled
Summary
Under the goal of carbon neutrality, China's economic transformation and structural adjustment have entered a critical period [1]. The dynamic simulation model and scenario design method to existing researches mainly focus on the influence of improve sustainable development of Chinese power established policy parameters on the comprehensive factors industry considering the integration impact of the green of all parties. In order to solve the above problems, the algorithm will produce tremendous changes under different policy should be able to achieve the joint optimization of policy parameters which could have significant impacts on the making and market behavior. While the computational complexity of the algorithm is greatly reduced, the accuracy of the lower layer game results is improved, which helps to find out the policy parameter solutions that are most in line with the policy objectives of quota system
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