Dynamic energy hub-based planning and operation for optimal integration of variable energy conversion efficiency in community-level energy systems

This study introduces a framework that couples dynamic energy hubs with the planning and operation of integrated community energy systems (ICESs). As opposed to the traditional approach based on static integration factors, in this work, we utilize a dynamic energy hub model (EHM) that accommodates real-time fluctuations of energy conversion efficiencies. We have developed a dual-level optimization framework with a planning level for optimizing capacities and an operational level based on an efficiency correction model (ECM). The proposed framework enhances accuracy by taking into account the time-dependent variations of the efficiencies of energy conversion devices (ECDs). Validation has been performed with a prototype ICES using a mixed-integer nonlinear programming formulation solved by the Tunicate Swarm Optimization (TSO) Algorithm. The proposed framework is validated through case studies, showing a 16.7 % reduction in annual investment costs while optimizing energy utilization. The model adapts to natural gas price fluctuations, shifting from a combined heat and power (CHP)-based system (600 kW CHP, 600 kWh battery) at $0.05/kWh to a boiler-dominated setup with a 130 % capacity increase at $0.15/kWh. Heat pump (HP) capacity also rises from 60 kW to 250 kW. The Chaos-Enhanced Tunicate Swarm Optimization (CETSO) algorithm outperforms metaheuristics, reducing optimization error by 78 % for unimodal and improving accuracy by 65 % for multimodal functions, with only a 0.23-s execution time increase.