Economic Cost and Carbon Emission Reduction of Microgrid via Bi-Objective Optimization

In the evolving landscape of energy systems, microgrids play a pivotal role in enhancing electricity supply efficiency and integrating renewable energy. This study addresses the critical challenge of balancing operational costs with carbon emissions reduction in microgrid energy management. We formulate this as a bi-objective optimization problem and propose an innovative hybrid methodology that merges the augmented $\epsilon$-constraint method with the TOPSIS decision-making technique. Our approach efficiently resolves subproblems defined by specific $\epsilon$ values using a MILP solver, constructing a Pareto front to illustrate the trade-offs between economic and environmental objectives. Utilizing TOPSIS, we successfully identify a balanced compromise solution, significantly reducing the secondary objective by $55.1 \%$ in economic cost and $16.7 \%$ in carbon emissions for a preference weight vector of $[0.7,0.3]$. This study not only addresses the immediate challenge of dual-objective optimization in microgrids but also sets the foundation for future research in more intricate demand response programs and comparative analyses with other multi-objective optimization methods. The code is accessible at https://gitee.com/shuhuagao/mgbo.