Search

Variable-speed pump power storage is an innovative large-scale technology that is being deployed across the world. In addition to price arbitrage and provision of downward replacement reserve, its operational flexibility enables the provision of frequency restoration reserve (FRR) both in turbine and pump modes. This work proposes a bidding optimization strategy for the participation in the FRR market. The proposed framework encompasses a medium-term module to optimally allocate the yearly natural water inflows, representation of electrical and hydraulic losses in the water inflow/power curves, as well as forecasting techniques to predict market prices and natural water inflows. Moreover, it does not assume prior knowledge of the amount of activated FRR capacity band. An evaluation module is also proposed to replicate the “real” operation of the power plant and enables an accurate calculation of the revenue. A comparison between fixed and variable-speed Pump storage power (PSP) units participating in the Iberian electricity market presented an increase in revenue of almost 12%. Due to the low liquidity of the FRR market in Portugal, and the considerable capacity of the PSP unit, under some specific situations, it might be necessary to cap the size of the FRR bid to decrease the difference between the expected and realized revenue.

Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.