Bayesian Inference for Estimating Generation Costs in Electricity Markets
This work addresses the need for accurate cost estimation in electricity markets for applications like market simulation and strategic bidding, but it is incremental as it builds on existing Bayesian and neural methods.
The paper tackles the problem of estimating generation costs from observed electricity market data by modeling the relationship as Bayesian inference and using balanced neural posterior estimation. Validation on the IEEE RTS-96 test system shows that marginal costs are recovered with narrow credible intervals, while start-up costs remain largely unidentifiable from schedules alone.
Estimating generation costs from observed electricity market data is essential for market simulation, strategic bidding, and system planning. To that end, we model the relationship between generation costs and production schedules with a latent variable model. Estimating generation costs from observed schedules is then formulated as Bayesian inference. A prior distribution encodes an initial belief on parameters, and the inference consists of updating the belief with the posterior distribution given observations. We use balanced neural posterior estimation (BNPE) to learn this posterior. Validation on the IEEE RTS-96 test system shows that marginal costs are recovered with narrow credible intervals, while start-up costs remain largely unidentifiable from schedules alone. The method is benchmarked against an inverse-optimization algorithm that exhibits larger parameter errors without uncertainty quantification.