Novel economic models for advancing urban energy management and transition: Simulation of urban energy system in digital twin

Abstract

Globally, industrial sectors consume over 27 % of the overall power produced, half of it being utilized in equipment to process, produce, and assemble different products for the human society. Therefore, one can estimate that the planet would face a serious power shortfall which would affect the social welfare effectively. As a promising solution, utilizing current energy sources optimally will be one of the challenges to overcome the power shortages. The optimal scheduling of loads requires a useful method for achieving three technical and social goals: minimizing power consumption, optimizing peak-to-average power rate, and minimizing user wait time. In order to accomplish the above goals, this paper proposes a useful energy management system (EMS) to schedule the loads optimally. The proposed EMS is constructed based on automatic operation machines and day-ahead price methods, and it is integrated into a digital twin of the urban energy system. Moreover, a novel technical-social model based on gray wolf optimization algorithm is proposed to optimize power consumption for the industrial sector within the digital twin framework. The proposed model is applied and examined on woolen mill's various load units based on their regular operations in order to evaluate its performance and assess the potential benefits of this digital twin-based approach to urban energy management and transition. IGWOA's prowess in reducing daily power costs by 26.2 %, albeit with slightly longer wait times (1.7 h), and achieving a PAR of 7.4. Results demonstrate IGWOA's efficient computational performance with a running time of 8.702 s, enhancing its practical feasibility in real-world scenarios.