Multi-objective energy scheduling in multiple energy system considering economic and environmental objective functions

The growing integration of renewable energy sources along with multi-energy technologies has introduced new challenges for the coordinated management of intricate energy systems amid carbon and operational uncertainties. This study presents a multi-objective risk-aware optimization framework with optimizing the operation of multiple energy system. First, a refined carbon-flow modeling technique is developed to depict carbon propagation in the multi-energy networks. An energy-quality-coefficient-based method for carbon-flow allocation is introduced for devices that convert multiple inputs and outputs to monitor the temporal movement of carbon emissions through energy storage systems. Additionally, a theoretical framework for the propagation and amplification of carbon uncertainty is established, revealing that the uncertainty in carbon flow can surpass the corresponding uncertainty in energy flow. The proposed modeling combines economic costs, carbon emissions, and operational risks through a multi-objective optimization model that includes a risk-preference coefficient and Conditional Value-at-Risk (CVaR). Numerical results show that the suggested method lowers carbon-emission deviation to 12.34%, in contrast to 45.67% for the deterministic model. Additionally, seasonal scheduling outcomes further highlight its resilience in extreme weather conditions. In scenarios of high-temperature summers and cold-wave winters, the proposed approach cuts average daily operating costs by 13.0% and 15.6%, respectively, while also reducing carbon-emission intensity by 16.6% and 17.2%, and enhancing renewable-energy usage. These findings indicate that the proposed framework effectively balances economic efficiency, carbon reduction, renewable-energy integration, and operational risk, offering a strong strategy for the sustainable operation of complex multi-energy systems amid uncertainty.