Efficient operation of combined residential and commercial energy hubs incorporating load management and two-point approximation for uncertainty modeling

Abstract

In the era of advancing technology, energy networks have evolved into sophisticated smart networks, allowing seamless communication and exchange of information among network components. This transformation opens doors for innovative concepts such as the Multi-Carrier Energy System, or Energy Hub. While integrating energy hubs into traditional networks has posed challenges, the expansion of smart networks has facilitated their adoption within the power grid. Unlike conventional systems that optimize a single energy carrier, energy hubs focus on the optimal operation of a system encompassing multiple energy carriers like natural gas, electric grid, and thermal energy. Serving as intermediaries between energy supplier companies and diverse end-users, these hubs play a pivotal role in the intricate landscape of modern energy systems. This paper introduces a novel strategy for optimizing the utilization of residential and commercial energy hubs, specifically integrating Time-of-Use (ToU) tariffs and Load Response Programs (LRPs). To address inherent uncertainties, the proposed system employs the two-point estimation method within its model. For achieving optimal scheduling of energy resources aligned with ToU tariffs and load management plan signals, the well-established Cuckoo Optimization Algorithm (COA) is applied. The results presented in this study highlight the effectiveness of the proposed approach in significantly reducing energy costs for the energy hub. Notably, these cost reductions are achieved while adhering to the constraints of the ToU Load Management Program (LMP). Furthermore, the paper explores various scenarios to investigate the impact of LRPs on both residential and commercial energy hubs, providing valuable insights into the dynamics of these systems. The numerical outcomes of the study affirm the efficacy of the proposed energy hub system. Implementation of load management plans markedly reduces unsupplied electrical and thermal energy, with scenario 3 showcasing a significant drop from 0.52 to 0.2 in unsupplied electrical energy for the home hub.