A flexible-reliable operation optimization model of the networked energy hubs with distributed generations, energy storage systems and demand response

This paper presents a novel optimization model for the flexible-reliable operation (FRO) of energy hubs (EHs) in electricity, natural gas, and district heating networks. To achieve flexible EH in the presence of renewable energy sources (RESs) and combined heat and power (CHP) system, energy storage systems (ESS) and incentive-based demand response program (IDRP) are used. The proposed problem minimizes the total expected costs of operation, reliability, and flexibility of the energy networks including EHs. The optimization scheme is constrained to the optimal power flow (OPF) equations and the reliability requirements of these networks and the EH model in the presence of sources and active loads, namely ESS and IDRP. Scenario-based stochastic programming (SBSP) is utilized to model uncertainties of load, energy cost, power generation of RES, and network equipment availability. The problem has a mixed integer nonlinear programming (MINLP) nature. Consequently, a hybrid teaching-learning-based optimization (TLBO) and crow search algorithm (CSA) is used to obtain a reliable optimal solution with a low standard deviation. Finally, by simulating the proposed scheme on a sample test system, the capabilities of this scheme in improving the reliability, operation, and flexibility of energy networks in accordance with the optimal scheduling for EHs are confirmed. (c) 2021 Published by Elsevier Ltd.

Automated summary

This study presents a novel optimization model for the flexible-reliable operation of energy hubs in electricity, natural gas, and district heating networks, utilizing energy storage systems and incentive-based demand response program. The optimization scheme minimizes the total expected costs of operation, reliability, and flexibility of the energy networks, while considering uncertainties of load, energy cost, power generation of RES, and network equipment availability through scenario-based stochastic programming. A hybrid teaching-learning-based optimization and crow search algorithm is used to obtain a reliable optimal solution with low standard deviation.