Non-anticipativity Probabilistic Supply-demand Balance Method Considering Primary Energy Price Fluctuations

To address the supply-demand balance risks in high-penetration renewable energy power systems under uncertainties of renewable energy output and price fluctuations of primary energy sources such as coal and natural gas, a non-anticipative probabilistic supply-demand balance analysis method that incorporates primary energy price volatility is proposed. The model accounts for the non-anticipative nature of renewable energy output(wind and solar) using affine functions and constructs uncertainty constraints for thermal power generation adjustments. Constraints containing uncertain stochastic variables are transformed into distributionally robust chance constraints under fuzzy sets. Based on the conditional value-at-risk(CVaR) theory, the power balance risk is analytically expressed, enabling stochastic variables to participate in sequential simulations. Case studies on the IEEE 30-bus system demonstrate that rising coal prices increase load shedding while reducing renewable curtailment, whereas natural gas price hikes lead to simultaneous increases in both load shedding and curtailment. Analysis reveals that coal-fired power plants primarily provide energy amount and peaking support in high-renewable systems, while gas-fired units, with their operational flexibility, serve as fast-response regulators, thus leading to differentiated impacts of coal-fired and gas-fired power under primary energy price fluctuations.