Wind-photovoltaic co-generation prediction and energy scheduling of low-carbon complex regional integrated energy system with hydrogen industry chain based on copula-MILP

Abstract

The application of hydrogen is promising for achieving carbon neutrality. To promote hydrogen utilization and carbon emission reduction, this paper attempts to integrate the hydrogen industry chain, carbon capture and storage (CCS) into a regional integrated energy system (forming a complex regional integrated energy system (CRIES)) and proposes an energy scheduling model for optimizing CRIES operation considering wind and photovoltaic co-generation (WT-PV) uncertainties. Firstly, to measure the uncertainties of WT-PV, the Wasserstein distance with better geometric properties is introduced to select the best Copula to model the correlation between wind and photovoltaic generation and to generate a typical scenario of WT-PV for each season. Secondly, based on MILP, the system is modularly modeled considering the pressure difference of various gases, the time interval of carbon capture and natural gas synthesis, and the energy coupling relationship. Finally, based on the above WT-PV prediction and modeling, the energy dispatch status in each season is investigated, and the economics of the proposed system is verified in the minimum hydrogen demand variation of hydrogen metallurgy plant, hydrogen blending ratio, and uncertainties of electric vehicles and hydrogen vehicles loads. Results show that 1) hydrogen-rich natural gas improves system’s energy use efficiency, CCS weakens system’s dependence on the upstream natural gas network, and the multi-energy coupling improves system’s arbitrage ability under different prices; 2) hydrogen is richest in the summer, causing system’s revenue to be 8.54%–32.17% and 29.29%–244.21% higher than in the transition season and winter. 3) zero-carbon energy interaction with the outside world and fully accommodating WT-PV are achieved by integrating the hydrogen industry chain; 4) the proposed CRIES operates reliably and economically under multiple uncertainties.