Multi-objective optimal design of integrated electrical and thermal campus energy systems: Simultaneous capacity planning and operation scheduling

In the decarbonization of campus energy systems, the optimal design of integrated electrical and thermal systems has become a critical challenge. Previous research has focused primarily on operational optimization with predetermined equipment capacities, leaving a significant gap in integrated system design. This study proposes a novel multi-objective optimization framework that simultaneously optimizes equipment capacity and operational scheduling, addressing this limitation through an integrated approach. The framework employs mixed-integer linear programming (MILP) with the Chebyshev scalarization method, which guarantees global optimality for each scalarized subproblem, in contrast to heuristic-based approaches that cannot certify optimality. Unlike previous methods limited to operational optimization, this comprehensive approach encompasses capacity design for solar photovoltaic (PV), battery storage, solar thermal collectors, heat pumps, and hot water thermal storage systems. The framework considers three conflicting objectives—economic cost, environmental impact, and spatial constraints (installation area minimization)—and systematically generates Pareto-optimal solutions across 66 weight patterns. Through validation using a full year (8760 h) of annual data from the Sanyo-Onoda City University campus, this study demonstrated that the proposed integrated system achieved approximately 12% cost reduction compared to baseline scenarios, while environmental-focused optimization reduced annual CO 2 emissions by 86% compared to systems without equipment integration. Pareto front analysis revealed clear trade-off relationships among the three objective functions and showed that optimal equipment configurations systematically change according to weight patterns. The multi-objective optimization framework provides diverse design alternatives that cannot be obtained through single-objective approaches, enabling flexible system design according to decision-makers’ preferences. This method is applicable to other educational institution energy systems and provides practical design guidelines for achieving carbon-neutral campuses. • Novel framework simultaneously optimizes capacity and operation of campus energy systems. • Chebyshev-based multi-objective optimization considers economics, environment, and space. • Economic optimization achieves 12% cost reduction; environmental focus cuts CO 2 by 86%. • Sensitivity analysis confirms framework robustness under varying economic conditions. • Validated using actual university data providing practical carbon-neutral design guidance.