Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development
The main objective of this study was to conduct multi-stage and multi-variant prognostic research to assess the impact of e-mobility development on the Polish power system for the period 2022–2027. The research steps were as follows: forecast the number of electric vehicles (using seven methods), fo...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/15/5578 |
| _version_ | 1797432887266508800 |
|---|---|
| author | Paweł Piotrowski Dariusz Baczyński Marcin Kopyt |
| author_facet | Paweł Piotrowski Dariusz Baczyński Marcin Kopyt |
| author_sort | Paweł Piotrowski |
| collection | DOAJ |
| description | The main objective of this study was to conduct multi-stage and multi-variant prognostic research to assess the impact of e-mobility development on the Polish power system for the period 2022–2027. The research steps were as follows: forecast the number of electric vehicles (using seven methods), forecast annual power demand arising solely out of the operation of the forecast number of electric vehicles, forecast annual power demand with and without the impact of e-mobility growth (using six methods), forecast daily profiles of typical days with and without the impact of e-mobility growth (using three methods). For the purpose of this research, we developed a unique Growth Dynamics Model to forecast the number of electric vehicles in Poland. The application of Multi-Layer Perceptron (MLP) to the extrapolation of non-linear functions (to the forecast number of electric vehicles and forecast annual power demand without the impact of e-mobility growth) is our original, unique proposal to use the Artificial Neural Network (ANN). Another unique, innovative proposal is to include Artificial Neural Networks (Multi-Layer Perceptron and Long short-term memory (LSTM)) in an Ensemble Model for simultaneous extrapolation of 24 non-linear functions to forecast daily profiles of typical days without taking e-mobility into account. This research determined the impact of e-mobility development on the Polish power system, both in terms of annual growth of demand for power and within particular days (hourly distribution) for two typical days (summer and winter). Under the (most likely) balanced growth variant of annual demand for power, due to e-mobility, such demand would grow by more than 4%, and almost 7% under the optimistic variant. Percentage growth of power demand in terms of variation according to time of day was determined. For instance, for the balanced variant, the largest percentage share of e-mobility was in the evening “peak” time (about 6%), and the smallest percentage was in the night “valley” (about 2%). |
| first_indexed | 2024-03-09T10:08:13Z |
| format | Article |
| id | doaj.art-13dbbac6d0c540d38b8f0de6f8ec1eb7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T10:08:13Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-13dbbac6d0c540d38b8f0de6f8ec1eb72023-12-01T22:55:14ZengMDPI AGEnergies1996-10732022-08-011515557810.3390/en15155578Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility DevelopmentPaweł Piotrowski0Dariusz Baczyński1Marcin Kopyt2Electrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, PolandElectrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, PolandElectrical Power Engineering Institute, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, PolandThe main objective of this study was to conduct multi-stage and multi-variant prognostic research to assess the impact of e-mobility development on the Polish power system for the period 2022–2027. The research steps were as follows: forecast the number of electric vehicles (using seven methods), forecast annual power demand arising solely out of the operation of the forecast number of electric vehicles, forecast annual power demand with and without the impact of e-mobility growth (using six methods), forecast daily profiles of typical days with and without the impact of e-mobility growth (using three methods). For the purpose of this research, we developed a unique Growth Dynamics Model to forecast the number of electric vehicles in Poland. The application of Multi-Layer Perceptron (MLP) to the extrapolation of non-linear functions (to the forecast number of electric vehicles and forecast annual power demand without the impact of e-mobility growth) is our original, unique proposal to use the Artificial Neural Network (ANN). Another unique, innovative proposal is to include Artificial Neural Networks (Multi-Layer Perceptron and Long short-term memory (LSTM)) in an Ensemble Model for simultaneous extrapolation of 24 non-linear functions to forecast daily profiles of typical days without taking e-mobility into account. This research determined the impact of e-mobility development on the Polish power system, both in terms of annual growth of demand for power and within particular days (hourly distribution) for two typical days (summer and winter). Under the (most likely) balanced growth variant of annual demand for power, due to e-mobility, such demand would grow by more than 4%, and almost 7% under the optimistic variant. Percentage growth of power demand in terms of variation according to time of day was determined. For instance, for the balanced variant, the largest percentage share of e-mobility was in the evening “peak” time (about 6%), and the smallest percentage was in the night “valley” (about 2%).https://www.mdpi.com/1996-1073/15/15/5578mid-term forecaste-mobilityelectric vehicles (EVs)power system demandload profile forecastmachine learning (ML) |
| spellingShingle | Paweł Piotrowski Dariusz Baczyński Marcin Kopyt Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development Energies mid-term forecast e-mobility electric vehicles (EVs) power system demand load profile forecast machine learning (ML) |
| title | Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development |
| title_full | Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development |
| title_fullStr | Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development |
| title_full_unstemmed | Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development |
| title_short | Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development |
| title_sort | medium term forecasts of load profiles in polish power system including e mobility development |
| topic | mid-term forecast e-mobility electric vehicles (EVs) power system demand load profile forecast machine learning (ML) |
| url | https://www.mdpi.com/1996-1073/15/15/5578 |
| work_keys_str_mv | AT pawełpiotrowski mediumtermforecastsofloadprofilesinpolishpowersystemincludingemobilitydevelopment AT dariuszbaczynski mediumtermforecastsofloadprofilesinpolishpowersystemincludingemobilitydevelopment AT marcinkopyt mediumtermforecastsofloadprofilesinpolishpowersystemincludingemobilitydevelopment |