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Aggregated Supply Curves Forecasting

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Data Analytics in Power Markets

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Abstract

One of the key steps for optimal bidding in power markets is to estimate the rivals’ bidding behaviors. However, for most participants, it would be difficult to directly forecast the rivals’ individual bids due to the information privacy and volatile characteristics of individual bidding behaviors. From another point of view, the aggregation of individual bids, denoted as aggregated supply curve (ASC), might be helpful to offset the uncertainties of individual bidding behaviors and can be used as reference for optimal bidding. In fact, the real ASC data contains bidding information from thousands of participants, which would be formulated with high dimensionality and unstructured formats, not applicable for general forecasting methods. Thus, a novel data-driven ASC forecasting framework based on a long-short term memory (LSTM) model and corresponding data processing techniques is proposed in this chapter. In detail, A paradigmatic data integration method is proposed to fix the unstructured data formats. A feature extraction method is developed to simplify the high dimensionality of ASC. Then, an LSTM model is customized to forecast ASCs. At last, real data from the Midcontinent Independent System Operator in the U.S. are utilized to demonstrate the forecasting performance of the proposed framework.

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References

  1. Li, Gong, **g Shi, and Qu **uli. 2011. Modeling methods for genco bidding strategy optimization in the liberalized electricity spot market’a state-of-the-art review. Energy 36 (8): 4686–4700.

    Google Scholar 

  2. Ye, Yujian, Dawei Qiu, Mingyang Sun, Dimitrios Papadaskalopoulos, and Goran Strbac. 2020. Deep reinforcement learning for strategic bidding in electricity markets. IEEE Transactions on Smart Grid 11 (2): 1343–1355.

    Article  Google Scholar 

  3. Moiseeva, Ekaterina, and Mohammad Reza Hesamzadeh. 2018. Bayesian and robust nash equilibria in hydrodominated systems under uncertainty. IEEE Transactions on Sustainable Energy 9 (2): 818–830.

    Google Scholar 

  4. Baringo, Luis, and Antonio J. Conejo. 2011. Strategic offering for a wind power producer. IEEE Transactions on Power Systems 28 (4): 4645–4654.

    Article  Google Scholar 

  5. AlAshery, Mohamed Kareem, Dongliang **ao, and Wei Qiao. 2020. Second-order stochastic dominance constraints for risk management of a wind power producer’s optimal bidding strategy. IEEE Transactions on Sustainable Energy 11 (3): 1404–1413.

    Article  Google Scholar 

  6. He, Guannan, Qixin Chen, Chongqing Kang, Pierre Pinson, and Qing **a. 2016. Optimal bidding strategy of battery storage in power markets considering performance-based regulation and battery cycle life. IEEE Transactions on Smart Grid 7 (5): 2359–2367.

    Article  Google Scholar 

  7. Bolun, Xu, Yuanyuan Shi, Daniel S. Kirschen, and Baosen Zhang. 2018. Optimal battery participation in frequency regulation markets. IEEE Transactions on Power Systems 33 (6): 6715–6725.

    Google Scholar 

  8. Kardakos, Evaggelos G., Christos K. Simoglou, and Anastasios G. Bakirtzis. 2016. Optimal offering strategy of a virtual power plant: A stochastic bi-level approach. IEEE Transactions on Smart Grid 7 (2): 794–806.

    Google Scholar 

  9. Kazemi, Mostafa, Hamidreza Zareipour, Mehdi Ehsan, and William D. Rosehart. 2017. A robust linear approach for offering strategy of a hybrid electric energy company. IEEE Transactions on Power Systems, 32 (3): 1949–1959.

    Google Scholar 

  10. Chen, Ruidi, Ioannis Ch Paschalidis, Michael C. Caramanis, and Panagiotis Andrianesis. 2019. Learning from past bids to participate strategically in day-ahead electricity markets. IEEE Transactions on Smart Grid 10 (5): 5794–5806.

    Article  Google Scholar 

  11. Kaveh Dehghanpour, M., Hashem Nehrir, John W. Sheppard, and Nathan C. Kelly. 2016. Agent-based modeling in electrical energy markets using dynamic bayesian networks. IEEE Transactions on Power Systems 31 (6): 4744–4754.

    Article  Google Scholar 

  12. Mitridati, Lesia, and Pierre Pinson. 2018. A bayesian inference approach to unveil supply curves in electricity markets. IEEE Transactions on Power Systems 33 (3): 2610–2620.

    Article  Google Scholar 

  13. Kiannejad, Mohammad, Mohammad Reza Salehizadeh, Majid Oloomi-Buygi, and Miadreza Shafie-khah. 2020. Artificial neural network approach for revealing market competitors’ behaviour. IET Generation, Transmission and Distribution 14 (7): 1292–1297.

    Article  Google Scholar 

  14. Guo, Hongye, Qixin Chen, Qing **a, Chongqing Kang, and **an Zhang. 2018. A monthly electricity consumption forecasting method based on vector error correction model and self-adaptive screening method. International Journal of Electrical Power and Energy Systems 95: 427–439.

    Article  Google Scholar 

  15. Zheng, Kedi, Yi. Wang, Kai Liu, and Qixin Chen. 2020. Locational marginal price forecasting: A componential and ensemble approach. IEEE Transactions on Smart Grid 11 (5): 4555–4564.

    Article  Google Scholar 

  16. Álvarez, Veróonica, Santiago Mazuelas, and José A. Lozano. 2021. Probabilistic load forecasting based on adaptive online learning. IEEE Transactions on Power Systems 36 (4): 3668–3680.

    Article  Google Scholar 

  17. Wang, Yi., Dahua Gan, Mingyang Sun, Ning Zhang, Lu. Zongxiang, and Chongqing Kang. 2019. Probabilistic individual load forecasting using pinball loss guided LSTM. Applied Energy 235: 10–20.

    Article  Google Scholar 

  18. Kong, Weicong, Zhao Yang Dong, and David J. Hill, Fengji Luo, and Yan Xu. 2018. Short-term residential load forecasting based on resident behaviour learning. IEEE Transactions on Power Systems 33 (1): 1087–1088.

    Google Scholar 

  19. Wang, Huaizhi, Zhenxing Lei, **an Zhang, Bin Zhou, and Jianchun Peng. 2019. A review of deep learning for renewable energy forecasting. Energy Conversion and Management 198: 111799.

    Google Scholar 

  20. Tan, Mao, Si** Yuan, Shuaihu Li, Su. Yongxin, Hui Li, and Feng He. 2019. Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning. IEEE Transactions on Power Systems 35 (4): 2937–2948.

    Article  Google Scholar 

  21. Chang, Zihan, Yang Zhang, and Wenbo Chen. 2019. Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform. Energy 187: 115804.

    Google Scholar 

  22. Yi Wang, Qixin Chen, Dahua Gan, **gwei Yang, Daniel S Kirschen, and Chongqing Kang. 2019. Deep learning-based socio-demographic information identification from smart meter data. IEEE Transactions on Smart Grid 10 (3): 2593–2602.

    Google Scholar 

  23. Pelagatti, Matteo. 2013. Complex Models and Computational Methods in Statistics, 203–213. Milano: Springer Milan.

    Google Scholar 

  24. MISO. 2019. Market reports: Day-ahead pricing. https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/.

  25. Wang, Yi., Qixin Chen, Chongqing Kang, and Qing **a. 2016. Clustering of electricity consumption behavior dynamics toward big data applications. IEEE Transactions on Smart Grid 7 (5): 2437–2447.

    Article  Google Scholar 

  26. Li, Pan, Baosen Zhang, Yang Weng, and Ram Rajagopal. 2017. A sparse linear model and significance test for individual consumption prediction. IEEE Transactions on Power Systems 32 (6): 4489–4500.

    Article  Google Scholar 

  27. Burke, Daniel J., and Mark J. O’Malley. 2011. A study of principal component analysis applied to spatially distributed wind power. IEEE Transactions on Power Systems 26 (4): 2084–2092.

    Article  Google Scholar 

  28. **e, Le., Yang Chen, and P.R. Kumar. 2014. Dimensionality reduction of synchrophasor data for early event detection: Linearized analysis. IEEE Transactions on Power Systems 29 (6): 2784–2794.

    Article  Google Scholar 

  29. Shi, **ngjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems 28: 802–810.

    Google Scholar 

  30. Weicong Kong, Zhao Yang Dong, Youwei Jia, David J Hill, Yan Xu, and Yuan Zhang. 2019. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid 10 (1): 841–851.

    Google Scholar 

  31. MISO. 2019. Market reports: Daily forecast and actual load by local resource zone. https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/.

  32. NOAA. Climate data online. https://www.ncdc.noaa.gov/cdo-web/.

  33. MISO. Market reports-generation fuel mix. https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/#nt=%2FMarketReportType%3ASummary%2FMarketReportName%3AGeneration%20Fuel%20Mix%20(xls)&t=10&p=0&s=MarketReportPublished&sd=desc.

  34. EIA. Data of petroleum & other liquids. https://www.eia.gov/petroleum/data.php#prices.

  35. Pedregosa, Fabian, Gaël. Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12: 2825–2830.

    Google Scholar 

  36. Abadi, Martín, Paul Barham, Jianmin Chen, et al. 2016. In Tensorflow: A system for large-scale machine learning, vol. 16), 265–283. Savannah, GA: USENIX Association.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Tsinghua University, Bei**g, China

    Qixin Chen

  2. Department of Electrical Engineering, Tsinghua University, Bei**g, China

    Hongye Guo

  3. Department of Electrical Engineering, Tsinghua University, Bei**g, China

    Kedi Zheng

  4. Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

    Yi Wang

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  1. Qixin Chen
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  4. Yi Wang
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Chen, Q., Guo, H., Zheng, K., Wang, Y. (2021). Aggregated Supply Curves Forecasting. In: Data Analytics in Power Markets. Springer, Singapore. https://doi.org/10.1007/978-981-16-4975-2_11

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  • DOI: https://doi.org/10.1007/978-981-16-4975-2_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-4974-5

  • Online ISBN: 978-981-16-4975-2

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