This paper presents a modular decision-making platform designed to coordinate the scheduling of generation and energy storage while balancing economic and environmental goals. The platform combines a mixed-integer linear optimization engine with an interactive interface, creating a link between advanced modeling methods and practical analysis tools. Using an epsilon-constraint approach, the model traces the trade-off between cost and emissions, generating a discrete Pareto frontier for decision support. The platform also streamlines workflow by standardizing data input, supporting reproducible batch solving and offering clear visualization and export of dispatch patterns, cost structures, reserve indicators, and trade-off frontiers. A case study highlights the interaction between system cost and carbon cost, showing that under the tested conditions, the lowest total cost occurs at a storage capacity of 200 MW. These results demonstrate how the platform can be used to compare scenarios and identify transparent compromises, providing researchers and system operators with a practical tool for planning and operating grids with increasing renewable penetration.
Rong et al. (Sun,) studied this question.