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

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 reduced by 1.9% and 22.3% considering DRPs and RESs, respectively.

Automated summary

This paper proposes a stochastic scheduling framework for multi-energy hub systems to optimize the operation cost and reduce CO2 emissions. By modeling uncertainties and employing clustering and scenario reduction techniques, the computational burden is reduced and the system operation is optimized.