Scenario-based robust capacity planning of regional integrated energy systems considering carbon emissions

With the development of multi-energy systems and the urgent demand for low-carbon energy provision, the regional integrated energy system (IES) is considered an efficient paradigm to improve energy efficiency and carbon emission reduction. The optimal capacity planning of different IES components is considered a non-trivial task due to the uncertainties of renewable power generation and various demands (e.g., electricity, heating and cooling) as well as the carbon emission constraints. This paper addresses this challenge and presents a scenario-based robust optimal planning solution for the regional low-carbon IES fully considering the economic cost, carbon emission and energy supply reliability. The operational uncertainties of different forms of energy sources and demands are characterized by a controllable generative adversarial network (GAN). The proposed method is extensively assessed based on an IES case study in China through a comparative analysis, the numerical results show that compared with the traditional planning method, the proposed capacity planning solution can reduce the total cost by 4.24% and the carbon emissions by 42.61%, the effectiveness and benefits of the planning solution have been effectively confirmed.

Automated summary

This paper proposes a scenario-based robust optimal planning solution for regional low-carbon integrated energy systems (IES), considering economic cost, carbon emissions, and energy supply reliability. The uncertainties of renewable power generation and various demands are addressed through a controllable generative adversarial network (GAN). A case study in China shows that the proposed capacity planning solution can reduce total cost by 4.24% and carbon emissions by 42.61%, confirming its effectiveness and benefits.