Optimal Dispatch of a Zero-Carbon Park-Level Integrated Energy System Considering Uncertainties in Wind and Solar Power: A Information Gap Decision Theory Method

The park-level integrated energy system (PIES), serving as the foundational core of zero-carbon park construction, faces challenges in optimal operation due to multiple uncertainties within the system. Information gap decision theory (IGDT), a widely adopted methodology for addressing uncertainties, enables the formulation of risk management strategies to either mitigate risks or pursue riskbenefit trade-offs. To effectively reduce the impact of renewable energy output uncertainties (e.g., wind and solar power) on PIES scheduling, this study proposes an IGDT-based optimization framework for PIES dispatch. Specifically, two scheduling models are established: a risk-aversion strategy (RAS) model and a risk-seeking strategy (RSS) model. The CPLEX optimization solver is employed to determine the optimal power outputs of system components and total operational costs within the scheduling horizon, along with comparative analyses of total costs under varying deviation factors in both risk-aversion and risk-seeking scenarios. Case studies conducted on a representative PIES validate that the proposed models provide effective solutions for economic dispatch under uncertainty. Results demonstrate the capability of the framework to balance operational costs and risk tolerance while maintaining system reliability.