A new stochastic multi-objective model for optimal management of a PV/Wind integrated energy system with demand response, P2G, and energy storage devices

Hossein Faramarzi ^1* Navid Ghaffarzadeh ¹ Farhad Shahnia ²

• ¹ Imam Khomeini International University, Qazvin, Iran
• ² Murdoch University, Perth, Western Australia, Australia

Optimal energy hub scheduling (EHS) has emerged as a promising strategy to improve the efficiency and flexibility of power systems. Energy hubs (EHs) offer several advantages over conventional power grids, including enhanced flexibility, reduced emissions, and improved efficiency. However, EHS poses several challenges, including uncertainty, complexity, and computational burden. To tackle these challenges, this paper proposes an innovative optimal scheme for the operation of an integrated PV/wind energy system. The scheme incorporates a comprehensive set of components, including combined heat and power (CHP), power-to-gas (P2G), energy storage systems (ESS), heat storage systems (HSS), gas storage (GS), and electric and gas boilers (EB and GB). A demand response (DR) program is implemented for both electric and thermal loads to address the inherent uncertainty of renewable energy sources (RES) and electrical load fluctuations. The proposed optimal management model is a multi-objective optimization problem aiming to minimize total losses, cost, and emissions while meeting energy demands. This novel approach offers significant advantages for utilities in terms of reducing losses, cost, and air pollution, contributing to a more sustainable energy system. The optimal management scheme is designed based on the optimized objective functions and implemented through steady-state energy analysis. Non-dominated sorting algorithm-III (NSGA-III) is employed to efficiently search for the optimal solutions. Scenario analysis is adopted to address the stochastic nature of RES and load demand, and the Sim&Corrloss clustering strategy is used to reduce the computational burden. To demonstrate the effectiveness of the proposed approach, the results obtained from applying the proposed algorithm are compared with the results from analyzing the problem using GAMS software, as well as the multi-objective seagull optimization algorithm (MOSOA). The proposed method enhances flexibility and ultimately increases system stability while maintaining diversity in energy sources. Additionally, the utilization of equipment such as various storage devices and P2G enhances system resilience, reducing load fluctuations and improving resource utilization. The results demonstrate that the proposed method significantly improves system performance and can effectively contribute to energy management in multi-energy systems. The superior performance of the proposed algorithm is demonstrated under various operating scenarios.