Adaptive robust optimization for the energy management of the grid-connected energy hubs based on hybrid meta-heuristic algorithm

Highlights: • Using flexibility sources alongside renewable sources and combined heat and power systems. • Robust modeling of the operation problem of energy networks with energy hubs. • Utilizing the hybrid heuristic algorithm to achieve the optimal solution. This paper describes the energy management of energy hubs connected to electricity, gas, and heating networks in which the hub is incorporated as a coordination framework between distributed generations and energy storage systems. The deterministic model of the proposed scheme minimizes the total operating cost of these energy networks in the presence of energy hubs constrained to the optimal power flow equations of different networks and the formulation of hubs with sources and storages. The problem is subject to uncertainties of load, energy prices, renewable sources, and consumption energy of mobile storages. Additionally, the scheme inherently is a non-convex mixed-integer nonlinear programming framework. Adaptive robust optimization is used to model these uncertainties, which is based on a hybrid metaheuristic algorithm due to the nonlinear and non-convex nature of the proposed problem. Hence, a combination of Ant-lion Optimizer and Krill herd Optimization algorithm has been employed, which provides a robust optimal solution with approximate unique response conditions in the worst-case scenario. Eventually, the numerical results obtained by implementing the proposed scheme on a sample test system confirm the capability of the mentioned scheme in improving the operation condition of different energy networks in the worst-case scenario. Consequently, the total energy loss in all mentioned networks and maximum voltage and temperature drop decrease by roughly 8%, 44%, and 74% with respect to power flow analysis in this scenario.