Capacity Optimization for Minimizing the Carbon Footprint of Power Systems

The carbon footprint of electricity supply is strongly influenced by power system capacity configuration and operational flexibility under uncertainty. This paper proposes a carbon-footprint-oriented capacity optimization framework for integrated power systems comprising thermal power, wind power, photovoltaic generation, and energy storage. A dispatch model is developed to represent coordinated system operation under uncertain load and renewable generation, which are modeled using information gap decision theory and Monte Carlo simulation. The carbon footprint of electricity supply is evaluated on a life-cycle basis as carbon emissions per unit of delivered electricity, together with electricity cost and power fluctuation rate as performance indicators. A multi-objective capacity optimization problem is solved using the non-dominated sorting genetic algorithm II (NSGA-II), and a practical optimal solution is selected from the Pareto front using the technique for order preference by similarity to an ideal solution (TOPSIS). Case studies show that the optimized capacity configuration reduces the carbon footprint of electricity supply by approximately 28%, while electricity cost and power fluctuation rate decrease by about 7% and 27%, respectively. Meanwhile, renewable energy utilization increases from 71.4% to 86.8%, and the renewable energy share rises from 42.0% to 55.6%, demonstrating that coordinated deployment of renewable energy and energy storage is essential for achieving carbon-efficient electricity supply under uncertainty.