Low-carbon economic analysis of a virtual power plant with wind and solar power considering the integrated flexible operation mode of a carbon capture thermoelectric unit

During the period when heating is required in North China, the task of meeting the heating needs of customers is mainly undertaken by thermoelectric units under the "determining electricity by heat" operation mode. This heating mode leads to difficulties in incorporating renewable energy and high carbon dioxide emissions. To solve these problems, carbon capture technology and wind power heating technology are introduced in this study. Cogeneration units with a carbon capture integrated flexible operation mode are introduced into the system to reduce CO2 emissions, while making the electric output and the thermal output more flexible. Then, the cogeneration optimal scheduling model of a virtual power plant is constructed. This model includes wind power heating equipment and carbon capture cogeneration units under the integrated flexible operation mode. Due to the highly nonlinear characteristics of the model, we solve this problem by the Deep Q-Network (DQN) algorithm, which is an algorithm that uses the nonlinear approximation value function of a neural network to make the model more robust. Finally, we propose three technical strategies and compare the results of the three strategies in terms of low carbon levels and economic value. The results show that the model can effectively improve the economic income of the system and reduce carbon emissions.