The basic model forecasting renewable power generation is based primarily on weather forecasts at locations with the highest concentration of wind and photovoltaic farms. On the demand side, on the other hand, we have projected power demand, which also depends to some extent on the weather. Information on holidays is also important because then it […]
The basic model forecasting renewable power generation is based primarily on weather forecasts at locations with the highest concentration of wind and photovoltaic farms.
On the demand side, on the other hand, we have projected power demand, which also depends to some extent on the weather. Information on holidays is also important because then it „knows” why the demand for power will fall (e.g., due to a reduction in power consumption by companies). For some locations in Poland as well as abroad, it is also useful to add some weather data separately.
On the basis of the above-mentioned variables, a basic model was created to forecast the spot price of energy in Poland (TGEBase index forecast).
Among other things, the basic model has the advantage of being quite easy to interpret. The table below lists the basic model’s explanatory variables and their impact on the energy price forecast. The model’s response [%] tells how much the energy price will change if an explanatory variable in the list changes by 1[%] and other variables remain unchanged.
| Model variables : ID[59614] Model: RES vs TGEBase | Model response[%] |
| PSE PROGNOSIS of power generation from wind and photovoltaic sources Poland [MW] smoothed | -0.492850779 |
| Gdańsk – temperature min | -0.125198939 |
| Gdańsk – temperature max | -0.042660698 |
| Amsterdam – temperature min | -0.042007839 |
| TIME_Days_free_including weekends_Poland | -0.00012186 |
| PSE PRONOZA Forecasted grid demand | 0.076150017 |
| TGEBase smoothed rate | 0.35766362 |
The course of the price and its forecasts in the basic model.
Extending the basic model to include several additional explanatory variables improves the accuracy of the model.
Additional variables take into account more information on, for example, the fossil fuel segment, or the costs associated with the need for power producers to purchase CO2 emission rights. Planned power unit shutdowns (total or partial) can also be important. Unplanned mergers unfortunately cannot be taken into account sufficiently in advance. Therefore, we have the forecast of the basic model at our disposal, and to it we attach as additional explanatory variables the aforementioned data series.
Explanatory variables in the extended model:
| Model variables : ID[51036] Model: RES extended vs TGEBase | Model response[%] |
| Model forecast: [59614] Model: RES vs TGEBase | 0.544234913 |
| TTFGASBilling Price/Recent Price[EURO/MWh] | 0.178982705 |
| PL Gaz ziemny Final Consumers [m3/d] | 0.093672148 |
| CO2_CFI2Z7_D | 0.043361342 |
| PL Natural Gas Final Consumers[m3/d] | 0.039870326 |
| Outages P U, PSE SA BZ, ALL, PL | 0.027798104 |
The course of the actual energy price and its forecast in the extended model:
On average, between 2018 and 2022, a 1[%] increase in the share of RES generation in Poland’s energy mix resulted in a 0.229[%] reduction in the price of energy.
Increasing the share of RES in the mix supports energy price declines. The higher the share of RES in the energy mix, the greater its impact on the price of energy. Intuitively, increasing the share of RES in the mix supports declines in electricity prices. This is due to a reduction in the cost of energy production.
