Regret-based multi-objective optimization of carbon capture facility in CHP-based microgrid with carbon dioxide cycling

Nowadays it is estimated that the Earth's temperature is above the standard level compared to pre-industrial time. The main reason for this issue is the burning of conventional energy resources namely oil, gas, and coal which speeds up global warming. In this concern, a CHP-based multi-energy microgrid is studied in this paper which involves heating, cooling, and electricity carriers to supply the energy demands. The proposed system is a multi-objective problem with operation cost management and emission cost reduction. In order to develop the environmental and economic aspects, a power-to-gas (P2G) technology as an environmentally friendly system is taken into account to curb CO2 emissions and achieve a sustainable function. The presence of the P2G system can minimize the emission level and improve the ability to take advantage of Notwithstanding this, the uncertain parameters originate from the thermal, electrical, and cooling loads, electricity price, and the renewable generations impose inflexibility and financial threats to the system function. In this regard, a scenario-based risk assessment approach, p-robust optimization, is applied to handle these uncertainties. The uncertainty analysis is carried out in With-p-robust, No-p-robust, With-P2G, and No-P2G models to appraise the components' performance. Results showed 11% of the operation cost increment to achieve a zero-level risk. Also, 55% of the emission cost is minimized in the presence of the P2G facility, which enhances the efficiency of the system.

Automated summary

This paper investigates a CHP-based multi-energy microgrid to address the issue of increased Earth's temperature due to burning of conventional energy resources. The proposed system aims to meet energy demand through heating, cooling, and electricity carriers while reducing operation and emission costs. The inclusion of a power-to-gas (P2G) technology helps minimize emissions and improve system efficiency. Uncertainties arising from various parameters are addressed using a scenario-based risk assessment approach (p-robust optimization).