Generating Electricity Price Forecasting Scenarios to Analyze Whether Price Uncertainty Impacts Tariff Performance

Abstract

A higher share of renewables and electric vehicles increase the risk of congestion in electricity distribution systems. New distribution tariff designs have been proposed to prevent congestion. However, most modeling of tariff performance assumes deterministic price information. This paper proposes a method to assess the impact of price uncertainty for network tariffs, using price forecasting scenarios in a simulation model. Electricity price forecasting scenarios are generated by analyzing autoregressive forecasting errors and recursively generating time-series. The scenarios are used as price forecasting inputs in a model case study of tariff performance in a Dutch context. Results show a reduction in congestion frequency and charging costs using forecasts in this model setup, likely by enabling longer time horizons. Highest peaks however are larger when using forecasts for the fixed and capacity-based tariffs. Overall, this method provides insight into performance of new tariffs in electricity grids, incorporating the impact of price uncertainty.