Optimizing performance and economic viability of combined energy systems: A novel energy hub framework

This paper introduces a novel framework for optimizing combined energy systems through energy hubs, integrating electricity, heating, and cooling with renewable energy sources (RES) and demand response programs (DRPs). The framework addresses key uncertainties such as demand fluctuations for electricity, heating, and cooling, as well as variations in wind speed, solar radiation, and energy carrier prices. The proposed model is based on a mixed-integer nonlinear programming (MINLP) formulation, aiming to maximize energy hub profits by effectively utilizing DRPs and RES. To solve this complex optimization problem, the spider monkey optimization (SMO) algorithm is modified and applied. Simulation results demonstrate the model’s effectiveness, showing significant improvements in energy hub profits, a reduction in grid power purchase costs, and a decrease in operational expenses. These findings highlight the novel contribution of the paper in enhancing the efficiency of energy hubs, improving flexibility, and supporting the integration of renewable resources for sustainable energy systems. The proposed optimization framework significantly enhances the economic efficiency of energy hubs by integrating renewable energy sources (RES) and demand response programs (DRPs). The results presented a clear trend of decreasing costs and electrical costs across the scenarios, with Scenario 1 having the highest values and Scenario 4 showing the most cost-effective outcomes. Specifically, the total cost decreases by approximately 11.83 % from Scenario 1 to Scenario 2, with further reductions in Scenario 3 and Scenario 4. Electrical costs follow a similar pattern, dropping by 33.14 % from Scenario 1 to Scenario 2 and continuing to decrease in the subsequent scenarios. The modified Spider Monkey Optimization (SMO) algorithm outperforms PSO and GA, achieving a 3.2 % and 5.6 % higher objective function value, respectively, with a 25 % faster convergence rate. • Optimizing electricity, heating, cooling, and renewables to tackle operational issues. • Incorporating cooling load management to optimize electrical and cooling efficiency. • Using stochastic methods to manage price, demand, and renewable energy uncertainties. • Using the modified spider monkey algorithm to enhance speed, scalability & robustness. • Focusing on profit maximization with demand response & renewables for sustainability.