Stochastic optimization in multi-energy hub system operation considering solar energy resource and demand response

• Published in: International journal of electrical power & energy systems 2022-10, Vol.141, p.108132, Article 108132
• Main Authors: Thang, V.V., Ha, Thanhtung, Li, Qinhao, Zhang, Yongjun
• Format: Article
• Language: English
• Subjects: Demand response programs; Multi-energy hub systems; Renewable energy sources; Stochastic optimization
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2022.108132

•Proposing a stochastic scheduling framework to simultaneously optimize both energy hubs and distribution networks consisting of electricity distribution network and gas distribution network in large-scale multi-energy hub systems which integrate the renewable energy sources. The objective function is to minimize the cost terms of energy and operation as well as cost terms related to emissions of the system. The energy forms can be converted, conditioned, and stored to optimize the operation of both energy hubs and distribution networks.•Considering the uncertainty of output power of renewable energy sources, energy demand of consumers, and electrical price along with demand response programs in the optimal model.•Calculating and integrating the energy loss of the device together with electricity distribution network and gas distribution network into the operation framework by nonlinear constraints in which electricity distribution network employs the AC power flow model.•Evaluating the feasibility of the optimal operating model and the efficiency of multi-energy hub systems with different structures of energy hubs and the impact of demand response programs. The optimal operation of multi-energy systems based on energy hubs (EHs) is a significant challenge for operators due to the mutual impact of different energies and the uncertainty of renewable energy sources, electricity prices, the energy demand of consumers. To address this challenge, a stochastic scheduling framework for multi-energy hub systems is proposed in this paper to simultaneously optimize both EHs and distribution networks. The proposed framework considers RESs, uncertain parameters, DRPs, and emission to ensure system operation in fact. Furthermore, the uncertainty of electrical, heating, and cooling demands, electrical prices, and output power of RESs is modeled by probability density functions. They are divided into states by clustering technique to form the scenario matrix. A scenario reduction technique is then employed to reduce the number of scenarios to 10 to reduce the computational burden. The objective of the proposed framework is to minimize the total operation cost of the system, which is modeled as a mixed-integer nonlinear programming (MINLP) problem. The numerical results from different case studies show that the operation costs of the multi-energy systems using the proposed method are reduced by 2.0–14.5% among different analyzed cases. Additionally, CO2 emission is reduce