Low-Carbon Economic Scheduling of a Park Microgrid with Wind, Solar, and Coal-Fired Power Units Based on Carbon Trading Mechanism

The paper proposes a low-carbon economic optimization scheduling model based on Particle Swarm Optimization (PSO) algorithm, aiming to optimize the economic dispatch of a wind-solar-hybrid coal-fired power plant. First, a comprehensive system model is constructed by integrating the coupling characteristics of renewable energy units and traditional coal-fired units, along with carbon trading and time-of-use electricity purchasing mechanisms. Next, based on the probability distribution models of wind speed and solar irradiance, representative daily scenarios are randomly generated, from which the wind and photovoltaic output for a typical day are forecasted. Then, an objective function that considers the microgrid's electricity purchasing mechanism and the carbon trading mechanism is established, and the PSO algorithm is applied to optimize this objective function for low-carbon economics. Simulation results demonstrate that the proposed method effectively reduces the overall economic cost, including carbon emission costs, validating its effectiveness in improving both economic and environmental benefits.