Recent developments in energy systems, driven by the increasing penetration of renewable energy sources,

the widespread adoption of electric vehicles, and the expansion of controllable demand-side resources, have made conventional operation strategies insufficient for addressing the requirements of complex modern energy systems. In multi-carrier energy networks, the interaction among different energy carriers, such as electrical and thermal energy, provides significant opportunities for enhancing system flexibility and improving economic performance. However, the stochastic nature of renewable generation, variations in energy demand, and operational limitations of energy conversion devices introduce considerable challenges into the decision-making process. Therefore, the development of intelligent energy management approaches capable of simultaneously addressing uncertainties, technical constraints, and multiple operational objectives has become increasingly important .



This paper proposes a model predictive control (MPC)-based energy management strategy for the optimal operation

of a multi-carrier energy hub. Unlike conventional static scheduling approaches, where operational decisions are

obtained based on predetermined conditions, the proposed framework employs a receding horizon strategy to

continuously evaluate the current system state and update control decisions based on future forecasts. In the developed model, renewable energy resources, including wind and photovoltaic generation, are modeled considering their inherent uncertainties. The probabilistic behavior of wind speed is characterized using the Weibull probability distribution, while the variability of solar radiation is represented through the Beta probability distribution .



Within the proposed framework, demand-side components are considered as active elements of the energy system.

Electric vehicles with charging and discharging capabilities, along with responsive loads capable of load shifting and curtailment, are incorporated into the optimization model as sources of operational flexibility. The energy management problem is formulated as a multi-objective economic-environmental optimization framework, in which the minimization of operational costs and the reduction of carbon dioxide (CO₂) emissions are simultaneously considered in the decision-making process. The proposed model is implemented in the MATLAB environment, while the optimization problem is formulated using YALMIP and solved by the Gurobi optimization solver.



To evaluate the effectiveness of the proposed approach, three different operational scenarios are investigated. The

simulation results for the base scenario indicate that, without utilizing flexible energy resources, the daily operating cost and CO₂ emissions are 28.64 USD/day and 215 kg/day, respectively. In the second scenario, by enabling electric vehicle management and demand response programs, energy consumption is shifted toward more favorable operating conditions, resulting in a 10.4% reduction in operating cost and a 24.7% reduction in CO₂ emissions compared with the base case. In the third scenario, by incorporating the environmental objective into the optimization framework, the proposed model increases the utilization of cleaner energy resources and reduces CO₂ emissions to 120 kg/day. This result corresponds to a 44.2% reduction in emissions compared with the base scenario. Although achieving this level of emission reduction leads to a limited increase in operating cost, it demonstrates the capability of the proposed approach to establish an effective trade-off between economic and environmental objectives.



Furthermore, the performance of the proposed controller is compared with conventional PID and LQR control strategies.

The results demonstrate that MPC provides superior performance due to its predictive capability, direct handling of operational constraints, and ability to repeatedly solve the optimization problem under changing sy stem conditions. Based on the obtained results, the proposed framework can serve as an effective solution for managing next-generation energy systems, including multi-carrier energy hubs, microgrids, and intelligent energy infrastructures equipped with renewable energy sources, electric vehicles, and flexible demand resources