Distributionally robust chance-constrained optimal scheduling of power systems with aggregated CSP and deeply peaking thermal units

As the “dual carbon” goals continue to advance, the installed capacity and the proportion of electricity generation from renewable energy sources are continuously increasing. However, the inherent uncertainty and volatility of renewable energies represented by wind power and photovoltaic power make it difficult to guarantee the economy of the traditional operation mode, which is based on deep peaking of thermal power units. In response to these issues,this paper proposes an optimization and scheduling method for power systems with distributed robust opportunity constraints, incorporating Concentrating Solar Power（CSP） and power generation sets with deep peak regulation capabilities. Firstly, the paper analyzes the basic load-following and deep load-following capabilities of peaking power generation sets, constructing a deep load-following model that considers the cost of basic or deep load-following for peaking power generation units. Secondly, the paper analyzes the heat transfer process during the startup of solar thermal power plants, constructing a CSP model that considers startup heat constraints. Based on these analyses, a scheduling model is built with a data-driven distributed robust opportunity constraint, describing the uncertainty of renewable energy output, and minimizing the sum of the peaking power generation cost, buying and selling electricity cost, and energy storage usage cost as the optimization objective. Finally, the proposed method is validated using the improved IEEE 30-node system, demonstrating its favorable economic performance and robustness.