Probabilistic Multi‐Objective Energy Management System Model for an Energy Hub With PtG Technology for Cost Reduction and System Flexibility Improvement

ABSTRACT In this paper, a probabilistic bi‐objective energy management system (EMS) model is proposed for an energy hub (EH) equipped with renewable energy sources such as photovoltaic and wind turbine connected to the main power grid, boiler, combined heat and power unit, along with thermal and electrical storages, in addition to power to gas (PtG) technology. The proposed EMS model aims to minimize operating costs and maximize the system flexibility index (SFI) while considering demand response programs (DRPs) and uncertainties in both load and generation. To solve the EMS model, a dynamic parameter multi‐objective cuckoo search (DP‐MOCS) algorithm is proposed. The high accuracy and superior performance of the proposed DP‐MOCS algorithm have been verified by solving standard ZDT benchmark functions and comparing the results with those of other multi‐objective optimization algorithms. The performance of the proposed EMS model for the EH is evaluated through simulations conducted in three sections, considering uncertainties and DRPs, as well as neglecting them. Simulation results demonstrate a reduction in costs and an increase in the EH's flexibility following the implementation of a DRP in the EMS. Incorporating DRP into the EMS has resulted in a 6.24% decrease in operating costs and a 3.41% increase in flexibility at the EH. Additionally, considering uncertainties in the EH led to a 2.25% rise in operating costs and a 2.72% decrease in SFI. Moreover, the proposed DP‐MOCS algorithm outperformed other algorithms in addressing the energy management problem.