Citation: Sameer Thakare, Neeraj Dhanraj Bokde, Andrés E. Feijóo-Lorenzo. Forecasting different dimensions of liquidity in the intraday electricity markets: A review[J]. AIMS Energy, 2023, 11(5): 918-959. doi: 10.3934/energy.2023044
Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models.
| [1] |
Ciarreta A, Muniain P, Zarraga A (2017) Modeling and forecasting realized volatility in German–Austrian continuous intraday electricity prices. J Forecasting 36: 680–690. https://doi.org/10.1002/for.2463 doi: 10.1002/for.2463
|
| [2] | Linnet U (2005) Tools supporting wind energy trade in deregulated markets. Master's thesis. Technical University of Denmark, Department of Informatics and Mathematical Modelling. Available from: http://www2.compute.dtu.dk/pubdb/pubs/3969-full.html. |
| [3] |
Green R (2006) Electricity liberalisation in Europe—How competitive will it be? Energy Policy 34: 2532–2541. https://doi.org/10.1016/j.enpol.2004.08.016 doi: 10.1016/j.enpol.2004.08.016
|
| [4] |
Karabiber OA, Xydis G (2019) Electricity price forecasting in the Danish day-ahead market using the TBATS, ANN and ARIMA methods. Energies 12: 928. https://doi.org/10.3390/en12050928 doi: 10.3390/en12050928
|
| [5] |
Pape C, Hagemann S, Weber C (2016) Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market. Energy Econ 54: 376–387. https://doi.org/10.1016/j.eneco.2015.12.013 doi: 10.1016/j.eneco.2015.12.013
|
| [6] |
Bokde N, Tranberg B, Andresen GB (2020) A graphical approach to carbon-efficient spot market scheduling for Power-to-X applications. Energy Convers Manage 224: 113461. https://doi.org/10.1016/j.enconman.2020.113461 doi: 10.1016/j.enconman.2020.113461
|
| [7] | Klein N, Smith MS, Nott DJ (2020) Deep distributional time series models and the probabilistic forecasting of intraday electricity prices. J Appl Econom. https://doi.org/10.1002/jae.2959 |
| [8] |
Gürtler M, Paulsen T (2018) The effect of wind and solar power forecasts on day-ahead and intraday electricity prices in Germany. Energy Econ 75: 150–162. https://doi.org/10.1016/j.eneco.2018.07.006 doi: 10.1016/j.eneco.2018.07.006
|
| [9] |
Bokde ND, Yaseen ZM, Andersen GB (2020) ForecastTB—An R package as a Test-Bench for time series forecasting—Application of wind speed and solar radiation modeling. Energies 13: 2578. https://doi.org/10.3390/en13102578 doi: 10.3390/en13102578
|
| [10] | Bokde N, Troncoso A, Asencio-Corté G, et al. (2017) Pattern sequence similarity based techniques for wind speed forecasting. In: Proceedings of the International Work-Conference on Time Series, Granada, Spain, 18–20. |
| [11] | Usaola J, Moreno MA (2009) Optimal bidding of wind energy in intraday markets. In: 6th International Conference on the European Energy Market, IEEE, 1–7. |
| [12] |
Bokde ND, Tranberg B, Andresen GB (2021) Short-term CO$_2$ emissions forecasting based on decomposition approaches and its impact on electricity market scheduling. Appl Energy 281: 6061. https://doi.org/10.1016/j.apenergy.2020.116061 doi: 10.1016/j.apenergy.2020.116061
|
| [13] | Jong C, Kovaleva S (2021) PPA Insights: Short-term forecasting and imbalance costs. In: KYOS, kyos.com. |
| [14] |
Chaves-Ávila JP, Hakvoort RA, Ramos A (2014) The impact of European balancing rules on wind power economics and on short-term bidding strategies. Energy Policy 68: 383–393. https://doi.org/10.1016/j.enpol.2014.01.010 doi: 10.1016/j.enpol.2014.01.010
|
| [15] |
Van Der Veen RA, Abbasy A, Hakvoort RA (2012) Agent-based analysis of the impact of the imbalance pricing mechanism on market behavior in electricity balancing markets. Energy Econ 34: 874–881. https://doi.org/10.1016/j.eneco.2012.04.001 doi: 10.1016/j.eneco.2012.04.001
|
| [16] |
Kath C, Nitka W, Serafin T (2020) Balancing generation from renewable energy sources: Profitability of an energy trader. Energies 13: 205. https://doi.org/10.3390/en13010205 doi: 10.3390/en13010205
|
| [17] | Bokde ND, Pedersen T, Andresen GB (2021) Optimal scheduling of flexible power-to-x technologies in the day-ahead electricity market. arXiv preprint. https://doi.org/10.48550/arXiv.2110.09800 |
| [18] | Borggrefe F, Neuhoff K (2011) Balancing and intraday market design: Options for wind integration. |
| [19] | Hagemann S, Weber C (2013) An empirical analysis of liquidity and its determinants in the German intraday market for electricity. EWL Working Paper No. 17/2013. http://dx.doi.org/10.2139/ssrn.2349565 |
| [20] |
Goodarzi S, Perera HN, Bunn D (2019) The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices. Energy Policy 134: 110827. https://doi.org/10.1016/j.enpol.2019.06.035 doi: 10.1016/j.enpol.2019.06.035
|
| [21] |
Kiesel R, Paraschiv F (2017) Econometric analysis of 15-minute intraday electricity prices. Energy Econ 64: 77–90. https://doi.org/10.1016/j.eneco.2017.03.002 doi: 10.1016/j.eneco.2017.03.002
|
| [22] |
Browell J, Gilbert C (2022) Predicting electricity imbalance prices and volumes: Capabilities and opportunities. Energies 15: 3645. https://doi.org/10.3390/en15103645 doi: 10.3390/en15103645
|
| [23] |
Narajewski M (2022) Probabilistic forecasting of German electricity imbalance prices. Energies 15: 4976. https://doi.org/10.3390/en15144976 doi: 10.3390/en15144976
|
| [24] |
Bokde N, Feijóo A, N Al-Ansari, et al. (2020) The hybridization of ensemble empirical mode decomposition with forecasting models: Application of short-term wind speed and power modeling. Energies 13: 1666. https://doi.org/10.3390/en13071666 doi: 10.3390/en13071666
|
| [25] | Bourry F, Kariniotakis G (2009) Strategies for wind power trading in sequential short-term electricity markets. In: European wind energy conference (EWEC). |
| [26] | Hagemann S (2015) Price determinants in the German intraday market for electricity: An empirical analysis. J Energy Mark, EWL Working Paper No. 18/2013. https://dx.doi.org/10.2139/ssrn.2352854 |
| [27] | Michel N (2018) Analysis and forecast of intraday prices based on econometric models and machine-learning algorithms. Ph.D. thesis, University of Duisburg-Essen. |
| [28] |
Weron R (2014) Electricity price forecasting: A review of the state-of-the-art with a look into the future. Int J Forecasting 30: 1030–1081. https://doi.org/10.1016/j.ijforecast.2014.08.008 doi: 10.1016/j.ijforecast.2014.08.008
|
| [29] | Berger J, Yalcinoz T, Rudion K (2020) Investigating the intraday continuous electricity market using auto regression integrated moving average model with exogenous inputs. In: 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I & CPS Europe), 1–6. |
| [30] | Kulakov S, Ziel F (2021) The impact of renewable energy forecasts on intraday electricity prices. Econ Energy Environ Policy 10: 1–2. Available from: https://ideas.repec.org/a/aen/eeepjl/eeep10-1-kulakov.html. |
| [31] | Maciejowska K, Nitka W, Weron T (2019) Day-ahead vs. intraday—Forecasting the price spread to maximize economic benefits. Energies 12: 631. https://doi.org/10.3390/en12040631 |
| [32] | Kremer M, Kiesel R, Paraschiv F (2021) An econometric model for intraday electricity trading. Philos Trans Royal Soc A 379: 20190624. https://dx.doi.org/10.2139/ssrn.3489214 |
| [33] | Glas S, Kiesel R, Kolkmann S, et al. (2019) Intraday renewable electricity trading: Advanced modeling and optimal control. In: Progress in industrial mathematics at ECMI 2018, Springer, 469–475. |
| [34] |
Hu X, Jarait${\rm{\dot e}}$ J, Kažukauskas A (2021) The effects of wind power on electricity markets: A case study of the Swedish intraday market. Energy Econ 96: 105159. https://doi.org/10.1016/j.eneco.2021.105159 doi: 10.1016/j.eneco.2021.105159
|
| [35] |
Wozabal D, Rameseder G (2020) Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity. Eur J Oper Res 280: 639–655. https://doi.org/10.1016/j.ejor.2019.07.022 doi: 10.1016/j.ejor.2019.07.022
|
| [36] |
Féron O, Tankov P, Tinsi L (2020) Price formation and optimal trading in intraday electricity markets with a major player. Risks 8: 133. https://doi.org/10.3390/risks8040133 doi: 10.3390/risks8040133
|
| [37] |
Cramer E, Witthaut D, Mitsos A, et al. (2022) Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows. Appl Energy 346: 121370. https://doi.org/10.1016/j.apenergy.2023.121370 doi: 10.1016/j.apenergy.2023.121370
|
| [38] | Kath C, Ziel F (2020) Optimal order execution in intraday markets: Minimizing costs in trade trajectories. arXiv preprint. https://doi.org/10.48550/arXiv.2009.07892 |
| [39] |
Narajewski M, Ziel F (2019) Estimation and simulation of the transaction arrival process in intraday electricity markets. Energies 12: 4518. https://doi.org/10.3390/en12234518 doi: 10.3390/en12234518
|
| [40] | Coskun S, Korn R (2021) Modeling the intraday electricity demand in Germany. In: Mathematical Modeling, Simulation and Optimization for Power Engineering and Management, Springer. 34: 3–23. https://doi.org/10.1007/978-3-030-62732-4_1 |
| [41] | Martin H, Otterson S (2018) German intraday electricity market analysis and modeling based on the limit order book. In: 2018 15th International Conference on the European Energy Market (EEM), 1–6. https://doi.org/10.1109/EEM.2018.8469829 |
| [42] |
Kramer A, Kiesel R (2021) Exogenous factors for order arrivals on the intraday electricity market. Energy Econ 97: 105186. https://doi.org/10.1016/j.eneco.2021.105186 doi: 10.1016/j.eneco.2021.105186
|
| [43] | Martin H (2017) A limit order book model for the German intraday electricity market. Thesis for Master of Science, Electrical and Computer Engineering, Technical University Munich, Germany. |
| [44] | Favetto B (2019) The European intraday electricity market: A modeling based on the hawkes process. hal-02089289. Available from: https://hal.science/hal-02089289/file/Hawkes_process_and_electricity_market.pdf. |
| [45] |
Fatih Karanfil YL (2017) The role of continuous intraday electricity markets: The integration of large-share wind power generation in Denmark. Energy J 38: 107–130. http://dx.doi.org/10.5547/01956574.38.2.fkar doi: 10.5547/01956574.38.2.fkar
|
| [46] | Kolberg JK, Waage K (2018) Artificial intelligence and nord pool's intraday electricity market elbas: A demonstration and pragmatic evaluation of employing deep learning for price prediction: using extensive market data and spatio-temporal weather forecasts. Master's thesis. |
| [47] |
Oksuz T, Ugurlu U (2019) Neural network based model comparison for intraday electricity price forecasting. Energies 12: 4557. https://doi.org/10.3390/en12234557 doi: 10.3390/en12234557
|
| [48] |
Andrade JR, Filipe J, Reis M, et al. (2017) Probabilistic price forecasting for day-ahead and intraday markets: Beyond the statistical model. Sustainability-Basel 9: 1990. https://doi.org/10.3390/su9111990 doi: 10.3390/su9111990
|
| [49] |
Uniejewski B, Marcjasz G, Weron R (2019) Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO. J Forecasting 35: 1533–1547. https://doi.org/10.1016/j.ijforecast.2019.02.001 doi: 10.1016/j.ijforecast.2019.02.001
|
| [50] |
Maciejowska K, Uniejewski B, Serafin T (2020) PCA forecast averaging—Predicting day-ahead and intraday electricity prices. Energies 13: 3530. https://doi.org/10.3390/en13143530 doi: 10.3390/en13143530
|
| [51] |
Yorulmus H, Ugurlu U, Oktay T (2018) A long short term memory application on the Turkish intraday electricity price forecasting. PressAcademia Proc 7: 126–130. https://doi.org/10.17261/Pressacademia.2018.867 doi: 10.17261/Pressacademia.2018.867
|
| [52] |
Kath C (2019) Modeling intraday markets under the new advances of the cross-border intraday project (XBID): Evidence from the German intraday market. Energies 12: 4339. https://doi.org/10.3390/en12224339 doi: 10.3390/en12224339
|
| [53] | Scholz C, Lehna M, Brauns K, et al. (2020) Towards the prediction of electricity prices at the intraday market using shallow and deep-learning methods. In: Workshop on Mining Data for Financial Applications, Springer, 101–118. |
| [54] |
Kath C, Ziel F (2018) The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts. Energy Econ 76: 411–423. https://doi.org/10.1016/j.eneco.2018.10.005 doi: 10.1016/j.eneco.2018.10.005
|
| [55] |
Narajewski M, Ziel F (2020) Econometric modelling and forecasting of intraday electricity prices. J Commod Mark 19: 100107. https://doi.org/10.1016/j.jcomm.2019.100107 doi: 10.1016/j.jcomm.2019.100107
|
| [56] | Mohammadi S, Hesamzadeh MR (2020) Econometric modeling of intraday electricity market price with inadequate historical data. In: IEEE Workshop on Complexity in Engineering (COMPENG), 1–9. https://doi.org/10.1109/COMPENG50184.2022.9905434 |
| [57] | Serafin T, Marcjasz G, Weron R (2020) Trading on short-term path forecasts of intraday electricity prices. Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology. |
| [58] | Lehna M, Hoppmann B, Heinrich R, et al. (2021) A reinforcement learning approach for the continuous electricity market of Germany: Trading from the perspective of a wind park operator. arXiv preprint. https://doi.org/10.48550/arXiv.2111.13609 |
| [59] | Demirtaş H (2020) Power imbalance prediction in Turkish energy market. Ph.D. thesis. |
| [60] | Pozzetti L, Cartlidge J (2020) Trading electricity markets using neural networks. In: 32nd European Modelling and Simulation Symposium, 311–318. |
| [61] | Ziel F (2017) Modeling the impact of wind and solar power forecasting errors on intraday electricity prices. In: 14th International Conference on the European Energy Market (EEM), 1–5. https://doi.org/10.1109/EEM.2017.7981900 |
| [62] |
Narajewski M, Ziel F (2020) Ensemble forecasting for intraday electricity prices: Simulating trajectories. Appl Energy 279: 115801. https://doi.org/10.1016/j.apenergy.2020.115801 doi: 10.1016/j.apenergy.2020.115801
|
| [63] |
Marcjasz G, Uniejewski B, Weron R (2020) Beating the naïve—Combining LASSO with naïve intraday electricity price forecasts. Energies 13: 1667. https://doi.org/10.3390/en13071667 doi: 10.3390/en13071667
|
| [64] | Hamilton G, Abeygunawardana A, Jovanović DP, et al. (2018) Hybrid model for very short-term electricity price forecasting. In: 2018 IEEE Power & Energy Society General Meeting (PESGM), 1–5. |
| [65] |
Manickavasagam J, Visalakshmi S, Apergis N (2020) A novel hybrid approach to forecast crude oil futures using intraday data. Technol Forecast Soc 158: 120126. https://doi.org/10.1016/j.techfore.2020.120126 doi: 10.1016/j.techfore.2020.120126
|
| [66] |
Kiyak C, de Vries A (2018) Electricity markets mechanism regarding the operational flexibility of power plants. Modern Econ 8: 567–589. https://doi.org/10.4236/me.2017.84043 doi: 10.4236/me.2017.84043
|
| [67] |
Cludius J, Hermann H, Matthes FC, et al. (2014) The merit order effect of wind and photovoltaic electricity generation in Germany 2008–2016: Estimation and distributional implications. Energy Econ 44: 302–313. https://doi.org/10.1016/j.eneco.2014.04.020 doi: 10.1016/j.eneco.2014.04.020
|
| [68] |
Janke T, Steinke F (2019) Forecasting the price distribution of continuous intraday electricity trading. Energies 12: 4262. https://doi.org/10.3390/en12224262 doi: 10.3390/en12224262
|
| [69] |
Sobri S, Koohi-Kamali S, Rahim NA (2018) Solar photovoltaic generation forecasting methods: A review. Energy Convers Manage 156: 459–497. https://doi.org/10.1016/j.enconman.2017.11.019 doi: 10.1016/j.enconman.2017.11.019
|
Figures(9) / Tables(5)
Sameer Thakare, Neeraj Dhanraj Bokde, Andrés E. Feijóo-Lorenzo. Forecasting different dimensions of liquidity in the intraday electricity markets: A review[J]. AIMS Energy, 2023, 11(5): 918-959. doi: 10.3934/energy.2023044
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