Low-carbon economic dispatch of power systems based on wind energy and flexible carbon capture devices

The power generation process in thermal power plants releases significant amounts of carbon dioxide, contributing to environmental pollution. To address the environmental challenges in power generation, the power system must evolve toward higher efficiency and reduced emissions. Carbon capture power plants (CCPPs) and wind power are expected to play increasingly important roles because of their cleaner energy characteristics. However, the high energy consumption of CCPPs and the complexity of scheduling models limit the development of low-carbon economic scheduling. To address these issues, a low-carbon economic dispatch method is proposed to coordinate the operation of wind power and CCPPs. First, a wind‒fire low-carbon economic dispatch model with CCPPs is constructed. Second, an archive self-learning-based multi-objective particle swarm optimization (AS-MOPSO) algorithm is proposed, with static scheduling results used as initial guidance particles for solving a carbon capture plant’s wind‒fire low-carbon economic dispatch model. Finally, a constraint-handling method is proposed. It repairs infeasible solutions outside the solution region to bring them within the feasible region, offering more intermediate solutions for the AS-MOPSO algorithm. The simulation results of the IEEE 39-node system show that the proposed method reduces the dispatch system’s carbon emissions and generation cost. Compared with the comparative method, the carbon emissions of AS-MOPSO decreased by about 2000 tons. A scheduling model that balances power generation costs and carbon emissions. This study proposes a wind fire joint scheduling model that considers power generation costs and carbon emissions. In order to effectively utilize wind power generation and improve the coordinated operation capabilities of carbon capture power plants, wind power plants, and thermal power plants, a flexible energy coupling relationship model between these power sources has been established. This model balances the contradiction between power generation costs and carbon emissions targets through multi-objective optimization. Prioritize the use of discarded wind power for carbon capture. Utilize the discarded amount of wind power during the scheduling process to supply energy to carbon capture equipment, in order to improve the overall wind power utilization rate of the system. Afterwards, through the carbon trading mechanism, profits were obtained by selling carbon quotas, reducing the power generation costs of carbon capture power plants. By utilizing the carbon trading mechanism, guide power plants to develop towards low-cost and low-carbon emissions. Power dispatch solutions with lower costs and less carbon emissions. This study proposed archive self-learning-based multi-objective particle swarm optimization (AS-MOPSO) Algorithm. The proposed AS-MOPSO algorithm uses the static scheduling results of each time period as initial guiding particles to reduce the blindness of the search in the early stage of the optimization algorithm, accelerate the convergence speed of the algorithm, and increase the number of high-quality solutions. The AS-MOPSO algorithm can provide scheduling solutions with lower power generation costs and less carbon emissions.