Bayesian Inference for Estimating Generation Costs in Electricity Markets

TL;DR

This paper introduces a Bayesian inference approach using neural networks to estimate generation costs in electricity markets from observed data, providing credible intervals and outperforming inverse-optimization methods.

Contribution

It applies balanced neural posterior estimation to accurately recover generation cost parameters with uncertainty quantification from market data.

Findings

Marginal costs are recovered with narrow credible intervals.

Start-up costs remain largely unidentifiable from schedules alone.

The method outperforms inverse-optimization algorithms in parameter accuracy.

Abstract

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.