Day-Ahead Joint Scheduling of Economic Costs and Carbon Emissions for Power Systems Using Chaotic Evolution Optimization

With the increasing integration of renewable energy, power systems must balance economic dispatch with low-carbon operation. To address these dual requirements, this paper proposes a day-ahead joint scheduling method based on Chaotic Evolution Optimization (CEO) for bi-objective optimization of operating costs and carbon emissions. Firstly, a mathematical model for jointly minimizing power generation cost and carbon emissions is established, considering power balance constraints of the power system, output constraints of various types of generating units, and ramp rate constraints. Then, a hybrid power generation system composed of coal-fired units, gas turbine units, wind turbines, and photovoltaic units is constructed based on the IEEE 14-node system as the test system. Finally, case studies are conducted on the improved IEEE 14-node system to comprehensively compare the performance of CEO, Enzyme Activity Optimizer (EAO), Gradient-Based Optimizer (GBO), Starfish Optimization Algorithm (SFOA), and Particle Swarm Optimization (PSO) in the joint day-ahead dispatch problem of power system economy and carbon emissions. The tests demonstrate that CEO has advantages in both convergence stability and convergence accuracy. Particularly, under the premise of ensuring the feasibility of the dispatch scheme, CEO’s optimal dispatch scheme achieves a 22.07% reduction in total power generation cost compared with PSO; compared with SFOA, it achieves a 9.13% reduction in total power generation cost and a 4.27% reduction in carbon emissions. Therefore, CEO can provide both economical and low-carbon solutions for day-ahead dispatch in power systems.