A bi-level optimization strategy of electricity-hydrogen-carbon integrated energy system considering photovoltaic and wind power uncertainty and demand response.

To address the power supply-demand imbalance caused by the uncertainty in wind turbine and photovoltaic power generation in the regional integrated energy system, this study proposes a bi-level optimization strategy that considers the uncertainties in photovoltaic and wind turbine power generation as well as demand response. The upper-level model analyzes these uncertainties by modeling short-term and long-term output errors using robust optimization theory, applies an improved stepwise carbon trading model to control carbon emissions, and finally constructs an electricity-hydrogen-carbon cooperative scheduling optimization model to reduce wind and carbon emissions. The lower-level model incentivizes users to participate in integrated demand response through dynamic energy pricing, thereby reducing the annual consumption cost of load aggregator. The Karush-Kuhn-Tucker conditions and the Big-M method are used to solve the bi-level optimization model. Simulation results indicate that the improved carbon trading model reduces carbon emissions by approximately 40.12 tons per year, a decrease of 1.1%; the prediction accuracy of the short-term error model improves by 6.77%, and the prediction accuracy of the long-term error model improves by 15.16%; the electricity-hydrogen-carbon synergistic dispatch optimization model enhances the total profit of integrated energy system operator by 14.07% and reduces the total cost of load aggregator by 10.06%.