Electricity markets price projection: an innovative approach for risk assessment based on a convolution algorithm. ERCOT case study

In electricity markets, the determination of future energy prices is relevant for risk assessment associated with investment projects in new generation capacity and in the evaluation of risks associated with the operation of the market whose mitigation requires increasing the operating reserves in the electrical system. Traditional methods for estimating energy prices in electricity markets are based on using simulation models that represent the expected future operation of the electricity market. These models fail to estimate market prices when the greatest uncertainty that affects the system reserve is due to the unavailability of the generation fleet and the volatility of the production of renewable wind and solar generation. This document presents a new methodology that allows determining energy prices in electricity markets that is considered superior to traditional methods. It is an innovative approach for risk assessment in power markets with high participation of renewable generation (wind, solar) and thermal generation. The model determines market prices considering the randomness in the typical production of renewable generation and the randomness in the availability of thermal power plants due to forced failures. Market prices are determined through a convolution algorithm applied to the probability functions that characterize energy demand, the randomness in the production of renewable generation, and the availability of thermal generation units. The calculation methodology is considered superior to Monte Carlo-type methodologies used by other simulation programs. A case study is included where the electricity market of Texas USA (ERCOT) is simulated and comparing the market prices resulting from the new simulation model with the real market prices recorded in the year 2022. The market prices determined by the proposed new methodology provide relevant information for consumers to evaluate energy purchase alternatives correctly and for investors in new generation capacity to determine the profitability of their projects. In particular, it allows the correct determination of energy prices in periods of scarcity allowing storage media (BESS) to be correctly sized so that they can provide a quickly managed reserve and thus improve the reliability of the electrical system.