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A Decision Support System for Detection of Trading Behavior in Energy Markets

Year 2024, Volume: 12 Issue: 2, 625 - 643, 29.06.2024
https://doi.org/10.29109/gujsc.1470266

Abstract

The increasing complexity and regulatory needs in energy markets necessitate the development of innovative tools for monitoring and evaluating trading activities. This study introduces a decision support system (DSS) that accurately detects commercial behaviors of market participants using artificial intelligence techniques and big data analytics. The mentioned DSS allows for trading behavior scoring with LGBM based on the plant and/or plant type that market participants want to track, enabling them to make data-driven bids by detecting the main players’ trading behaviors in the market.

Supporting Institution

TUBİTAK

Project Number

3220630

Thanks

This study was supported with the project number 3220630 under the program of "TÜBİTAK TEYDEB 1501 - Industry R&D Projects".

References

  • [1] Borenstein, S., Bushnell, J. B., & Wolak, F. A. (2002). Measuring market inefficiencies in California’s restructured wholesale electricity market. American Economic Review, 92(5), 1376–1405.
  • [2] Li, B., Wang, X., Shahidehpour, M., Jiang, C., & Li, Z. (2019). DER aggregator’s data-driven bidding strategy using the information gap decision theory in a non-cooperative electricity market. IEEE Transactions on Smart Grid, 10(6), 6756–6767.
  • [3] Xu, X., Yan, Z., Shahidehpour, M., Li, Z., Yan, M., & Kong, X. (2020). Data-driven risk-averse two-stage optimal stochastic scheduling of energy and reserve with correlated wind power. IEEE Transactions on Sustainable Energy, 11(1), 436–447.
  • [4] Dehghanpour, K., Nehrir, M. H., Sheppard, J. W., & Kelly, N. C. (2016). Agent-based modeling in electrical energy markets using dynamic Bayesian networks. IEEE Transactions on Power Systems, 31(6), 4744–4754. https://doi.org/10.1109/TPWRS.2016.2524678
  • [5] Guo, H., Chen, Q., Xia, Q., & Kang, C. (2021). Deep inverse reinforcement learning for objective function identification in bidding models. IEEE Transactions on Power Systems, 36(6), 5684–5696.
  • [6] Guo, H., Chen, Q., Fang, X., Liu, K., Xia, Q., & Kang, C. (2019). Market power mitigation clearing mechanism based on constrained bidding capacities. IEEE Transactions on Power Systems, 34(6), 4817–4827. https://doi.org/10.1109/TPWRS.2019.2913334
  • [7] Zou, P., Chen, Q., Yu, Y., Xia, Q., & Kang, C. (2017). Electricity markets evolution with the changing generation mix: An empirical analysis based on China 2050 high renewable energy penetration roadmap. Applied Energy, 185, 56–67.https://doi.org/10.1016/j.apenergy.2016.10.061
  • [8] Ruiz, C., Conejo, A. J., & Smeers, Y. (2012). Equilibria in an oligopolistic electricity pool with stepwise offer curves. IEEE Transactions on Power Systems, 27(2), 752–761. https://doi.org/10.1109/TPWRS.2011.2170439
  • [9] Li, T., & Shahidehpour, M. (2005). Strategic bidding of transmission constrained GENCOs with incomplete information. IEEE Transactions on Power Systems, 20(1), 437–447.
  • [10] Baringo, L., & Conejo, A. J. (2016). Offering strategy of wind power producer: A multi-stage risk-constrained approach. IEEE Transactions on Power Systems, 31(2), 1420–1429. https://doi.org/10.1109/TPWRS.2015.2411332
  • [11] Yazdani-Damavandi, M., Neyestani, N., Shafie-khah, M., Contreras, J., & Catalao, J. P. S. (2018). Strategic behavior of multi-energy players in electricity markets as aggregators of demand side resources using a bilevel approach. IEEE Transactions on Power Systems, 33(1), 397–411. https://doi.org/10.1109/TPWRS.2017.2688344.
  • [12] Guo, H., Chen, Q., Xia, Q., & Kang, C. (2019). Efficiency loss for variable renewable energy incurred by competition in electricity markets. IEEE Transactions on Sustainable Energy. https://doi.org/10.1109/TSTE.2019.2946930
  • [13] Doraszelski, U., Lewis, G., & Pakes, A. (2018). Just starting out: Learning and equilibrium in a new market. American Economic Review, 108(3), 565–615.
  • [14] Hortaçsu A. and Puller, S. L. (2008). Understanding strategic bidding in multi- unit auctions: A case study of the Texas electricity spot market,” RAND J. Econ., vol. 39, no. 1, pp. 86–114.
  • [15] Kohansal, M., Sadeghi-Mobarakeh, A., & Mohsenian-Rad, H. (2017). A data-driven analysis of supply bids in California ISO market: Price elasticity and impact of renewables. In Proc. IEEE International Conference on Smart Grid Communications (SmartGridComm) (pp. 58–63).
  • [16] Kohansal, M., & Mohsenian-Rad, H. (2016). A closer look at demand bids in California ISO energy market. IEEE Transactions on Power Systems, 31(4), 3330–3331. https://doi.org/10.1109/TPWRS.2015.2484062
  • [17] Wang, L., Zhang, Z., & Chen, J. (2017). Short-term electricity price forecasting with stacked denoising autoencoders. IEEE Transactions on Power Systems, 32(4), 2673–2681. https://doi.org/10.1109/TPWRS.2016.2628873
  • [18] Wang, Y., Gan, D., Sun, M., Zhang, N., Lu, Z., & Kang, C. (2019). Probabilistic individual load forecasting using pinball loss guided LSTM. Applied Energy, 235, 10–20.
  • [19] Teeraratkul, T., O’Neill, D., & Lall, S. (2018). Shape-based approach to household electric load curve clustering and prediction. IEEE Transactions on Smart Grid, 9(5), 5196–5206.
  • [20] Velloso, A., Street, A., Pozo, D., Arroyo, J. M., & Cobos, N. G. (2019). Two stage robust unit commitment for co-optimized electricity markets: An adaptive data-driven approach for scenario-based uncertainty sets. IEEE Transactions on Sustainable Energy. https://doi.org/10.1109/TSTE.2019.2915049
  • [21] Kwac, J., Flora, J., & Rajagopal, R. (2014). Household energy consumption segmentation using hourly data. IEEE Transactions on Smart Grid, 5(1), 420–430.
  • [22] Liang, H., Ma, J., Sun, R., & Du, Y. (2019). A data-driven approach for targeting residential customers for energy efficiency programs. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2019.2933704
  • [23] Zheng, K., Chen, Q., Wang, Y., Kang, C., & Xia, Q. (2019). A novel combined data-driven approach for electricity theft detection. IEEE Transactions on Industrial Informatics, 15(3), 1809–1819. https://doi.org/10.1109/TII.2018.2873814
  • [24] Sun, W., Zamani, M., Hesamzadeh, M. R., & Zhang, H.-T. (2019). Data-driven probabilistic optimal power flow with nonparametric Bayesian modeling and inference. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2019.2931160
  • [25] Wang, Y., Wan, C., Zhou, Z., Zhang, K., & Botterud, A. (2018). Improving deployment availability of energy storage with data-driven AGC signal models. IEEE Transactions on Power Systems, 33(4), 4207–4217. https://doi.org/10.1109/TPWRS.2017.2780223
  • [26] Bagheri, Wang, J., & Zhao, C. (2017). Data-driven stochastic transmission expansion planning. IEEE Transactions on Power Systems, 32(5),3461–3470. https://doi.org/10.1109/TPWRS.2016.2635098
  • [27] Ruiz, A. J., Conejo, A. J., & Bertsimas, D. J. (2013). Revealing rival marginal offer prices via inverse optimization. IEEE Transactions on Power Systems, 28(3), 3056–3064. https://doi.org/10.1109/TPWRS.2012.2234144
  • [28] Saez-Gallego, J., Morales, J. M., Zugno, M., & Madsen, H. (2016). A data-driven bidding model for a cluster of price-responsive consumers of electricity. IEEE Transactions on Power Systems, 31(6), 5001–5011.
  • [29] Lu, T., Wang, Z., Wang, J., Ai, Q., & Wang, C. (2019). A data-driven Stackelberg market strategy for demand response-enabled distribution systems. IEEE Transactions on Smart Grid, 10(3), 2345–2357. https://doi.org/10.1109/TSG.2018.2795007
  • [30] Chen, R., Paschalidis, I. C., Caramanis, M. C., & Andrianesis, P. (2019). Learning from past bids to participate strategically in day-ahead electricity markets. IEEE Transactions on Smart Grid, 10(5), 5794–5806.
  • [31] Mitridati, L., & Pinson, P. (2018). A Bayesian inference approach to unveil supply curves in electricity markets. IEEE Transactions on Power Systems, 33(3), 2610–2620. https://doi.org/10.1109/TPWRS.2017.2757980
  • [32] Sun, M., Wang, Y., Teng, F., Ye, Y., Strbac, G., & Kang, C. (2019). Clustering based residential baseline estimation: A probabilistic perspective. IEEE Transactions on Smart Grid, 10(6), 6014–6028. https://doi.org/10.1109/TSG.2019.2895333
  • [33] Hu, J., & Li, H. (2019). A new clustering approach for scenario reduction in multi-stochastic variable programming. IEEE Transactions on Power Systems, 34(5), 3813–3825. https://doi.org/10.1109/TPWRS.2019.2901545
  • [34] Shahmohammadi, Sioshansi, R., Conejo, A. J., & Afsharnia, S. (2018). The role of energy storage in mitigating ramping inefficiencies caused by variable renewable generation. Energy Conversion and Management, 162, 307–320. https://doi.org/10.1016/j.enconman.2017.12.054
  • [35] Xu, Y., Shi, Y., Kirschen, D. S., & Zhang, B. (2018). Optimal battery participation in frequency regulation markets. IEEE Transactions on Power Systems, 33(6), 6715–6725. https://doi.org/10.1109/TPWRS.2018.2846774
  • [36] He, G., Chen, Q., Moutis, P., Kar, S., & Whitacre, J. F. (2018). An intertemporal decision framework for electrochemical energy storage management. Nature Energy, 3(5), 404–412. https://doi.org/10.1038/s41560-018-0129-9
  • [37] Khajeh, H., & Foroud, A. A. (2017). Behavioural market power indices in a transmission-constrained electricity market. IET Generation, Transmission & Distribution, 11(18), 4608–4616. https://doi.org/10.1049/iet-gtd.2017.0911

Enerji Piyasalarında Ticari Davranış Tespiti İçin bir Karar Destek Sistemi

Year 2024, Volume: 12 Issue: 2, 625 - 643, 29.06.2024
https://doi.org/10.29109/gujsc.1470266

Abstract

Enerji piyasalarında artan karmaşıklık ve düzenleme ihtiyacı, ticari faaliyetlerin izlenmesi ve değerlendirilmesi için yenilikçi araçların geliştirilmesini zorunlu kılmaktadır. Bu çalışma, piyasa katılımcılarının davranışlarını yapay öğrenme tekniklerini ve büyük veri analitiğini kullanarak ticari davranışları yüksek bir doğrulukla tespiti yapan bir karar destek sistemini (KDS) ortaya koymaktadır. Söz konusu KDS, piyasa katılımcılarının takip etmek istedikleri santral ve/veya santral tipine göre LGBM ile ticari davranış skorlaması yaparak piyasadaki ana oyuncuların ticari davranış tespit edebilmekte ve bu sayede piyasa katılımcılarının veriye dayalı teklif verebilmelerini sağlamaktadır.

Project Number

3220630

References

  • [1] Borenstein, S., Bushnell, J. B., & Wolak, F. A. (2002). Measuring market inefficiencies in California’s restructured wholesale electricity market. American Economic Review, 92(5), 1376–1405.
  • [2] Li, B., Wang, X., Shahidehpour, M., Jiang, C., & Li, Z. (2019). DER aggregator’s data-driven bidding strategy using the information gap decision theory in a non-cooperative electricity market. IEEE Transactions on Smart Grid, 10(6), 6756–6767.
  • [3] Xu, X., Yan, Z., Shahidehpour, M., Li, Z., Yan, M., & Kong, X. (2020). Data-driven risk-averse two-stage optimal stochastic scheduling of energy and reserve with correlated wind power. IEEE Transactions on Sustainable Energy, 11(1), 436–447.
  • [4] Dehghanpour, K., Nehrir, M. H., Sheppard, J. W., & Kelly, N. C. (2016). Agent-based modeling in electrical energy markets using dynamic Bayesian networks. IEEE Transactions on Power Systems, 31(6), 4744–4754. https://doi.org/10.1109/TPWRS.2016.2524678
  • [5] Guo, H., Chen, Q., Xia, Q., & Kang, C. (2021). Deep inverse reinforcement learning for objective function identification in bidding models. IEEE Transactions on Power Systems, 36(6), 5684–5696.
  • [6] Guo, H., Chen, Q., Fang, X., Liu, K., Xia, Q., & Kang, C. (2019). Market power mitigation clearing mechanism based on constrained bidding capacities. IEEE Transactions on Power Systems, 34(6), 4817–4827. https://doi.org/10.1109/TPWRS.2019.2913334
  • [7] Zou, P., Chen, Q., Yu, Y., Xia, Q., & Kang, C. (2017). Electricity markets evolution with the changing generation mix: An empirical analysis based on China 2050 high renewable energy penetration roadmap. Applied Energy, 185, 56–67.https://doi.org/10.1016/j.apenergy.2016.10.061
  • [8] Ruiz, C., Conejo, A. J., & Smeers, Y. (2012). Equilibria in an oligopolistic electricity pool with stepwise offer curves. IEEE Transactions on Power Systems, 27(2), 752–761. https://doi.org/10.1109/TPWRS.2011.2170439
  • [9] Li, T., & Shahidehpour, M. (2005). Strategic bidding of transmission constrained GENCOs with incomplete information. IEEE Transactions on Power Systems, 20(1), 437–447.
  • [10] Baringo, L., & Conejo, A. J. (2016). Offering strategy of wind power producer: A multi-stage risk-constrained approach. IEEE Transactions on Power Systems, 31(2), 1420–1429. https://doi.org/10.1109/TPWRS.2015.2411332
  • [11] Yazdani-Damavandi, M., Neyestani, N., Shafie-khah, M., Contreras, J., & Catalao, J. P. S. (2018). Strategic behavior of multi-energy players in electricity markets as aggregators of demand side resources using a bilevel approach. IEEE Transactions on Power Systems, 33(1), 397–411. https://doi.org/10.1109/TPWRS.2017.2688344.
  • [12] Guo, H., Chen, Q., Xia, Q., & Kang, C. (2019). Efficiency loss for variable renewable energy incurred by competition in electricity markets. IEEE Transactions on Sustainable Energy. https://doi.org/10.1109/TSTE.2019.2946930
  • [13] Doraszelski, U., Lewis, G., & Pakes, A. (2018). Just starting out: Learning and equilibrium in a new market. American Economic Review, 108(3), 565–615.
  • [14] Hortaçsu A. and Puller, S. L. (2008). Understanding strategic bidding in multi- unit auctions: A case study of the Texas electricity spot market,” RAND J. Econ., vol. 39, no. 1, pp. 86–114.
  • [15] Kohansal, M., Sadeghi-Mobarakeh, A., & Mohsenian-Rad, H. (2017). A data-driven analysis of supply bids in California ISO market: Price elasticity and impact of renewables. In Proc. IEEE International Conference on Smart Grid Communications (SmartGridComm) (pp. 58–63).
  • [16] Kohansal, M., & Mohsenian-Rad, H. (2016). A closer look at demand bids in California ISO energy market. IEEE Transactions on Power Systems, 31(4), 3330–3331. https://doi.org/10.1109/TPWRS.2015.2484062
  • [17] Wang, L., Zhang, Z., & Chen, J. (2017). Short-term electricity price forecasting with stacked denoising autoencoders. IEEE Transactions on Power Systems, 32(4), 2673–2681. https://doi.org/10.1109/TPWRS.2016.2628873
  • [18] Wang, Y., Gan, D., Sun, M., Zhang, N., Lu, Z., & Kang, C. (2019). Probabilistic individual load forecasting using pinball loss guided LSTM. Applied Energy, 235, 10–20.
  • [19] Teeraratkul, T., O’Neill, D., & Lall, S. (2018). Shape-based approach to household electric load curve clustering and prediction. IEEE Transactions on Smart Grid, 9(5), 5196–5206.
  • [20] Velloso, A., Street, A., Pozo, D., Arroyo, J. M., & Cobos, N. G. (2019). Two stage robust unit commitment for co-optimized electricity markets: An adaptive data-driven approach for scenario-based uncertainty sets. IEEE Transactions on Sustainable Energy. https://doi.org/10.1109/TSTE.2019.2915049
  • [21] Kwac, J., Flora, J., & Rajagopal, R. (2014). Household energy consumption segmentation using hourly data. IEEE Transactions on Smart Grid, 5(1), 420–430.
  • [22] Liang, H., Ma, J., Sun, R., & Du, Y. (2019). A data-driven approach for targeting residential customers for energy efficiency programs. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2019.2933704
  • [23] Zheng, K., Chen, Q., Wang, Y., Kang, C., & Xia, Q. (2019). A novel combined data-driven approach for electricity theft detection. IEEE Transactions on Industrial Informatics, 15(3), 1809–1819. https://doi.org/10.1109/TII.2018.2873814
  • [24] Sun, W., Zamani, M., Hesamzadeh, M. R., & Zhang, H.-T. (2019). Data-driven probabilistic optimal power flow with nonparametric Bayesian modeling and inference. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2019.2931160
  • [25] Wang, Y., Wan, C., Zhou, Z., Zhang, K., & Botterud, A. (2018). Improving deployment availability of energy storage with data-driven AGC signal models. IEEE Transactions on Power Systems, 33(4), 4207–4217. https://doi.org/10.1109/TPWRS.2017.2780223
  • [26] Bagheri, Wang, J., & Zhao, C. (2017). Data-driven stochastic transmission expansion planning. IEEE Transactions on Power Systems, 32(5),3461–3470. https://doi.org/10.1109/TPWRS.2016.2635098
  • [27] Ruiz, A. J., Conejo, A. J., & Bertsimas, D. J. (2013). Revealing rival marginal offer prices via inverse optimization. IEEE Transactions on Power Systems, 28(3), 3056–3064. https://doi.org/10.1109/TPWRS.2012.2234144
  • [28] Saez-Gallego, J., Morales, J. M., Zugno, M., & Madsen, H. (2016). A data-driven bidding model for a cluster of price-responsive consumers of electricity. IEEE Transactions on Power Systems, 31(6), 5001–5011.
  • [29] Lu, T., Wang, Z., Wang, J., Ai, Q., & Wang, C. (2019). A data-driven Stackelberg market strategy for demand response-enabled distribution systems. IEEE Transactions on Smart Grid, 10(3), 2345–2357. https://doi.org/10.1109/TSG.2018.2795007
  • [30] Chen, R., Paschalidis, I. C., Caramanis, M. C., & Andrianesis, P. (2019). Learning from past bids to participate strategically in day-ahead electricity markets. IEEE Transactions on Smart Grid, 10(5), 5794–5806.
  • [31] Mitridati, L., & Pinson, P. (2018). A Bayesian inference approach to unveil supply curves in electricity markets. IEEE Transactions on Power Systems, 33(3), 2610–2620. https://doi.org/10.1109/TPWRS.2017.2757980
  • [32] Sun, M., Wang, Y., Teng, F., Ye, Y., Strbac, G., & Kang, C. (2019). Clustering based residential baseline estimation: A probabilistic perspective. IEEE Transactions on Smart Grid, 10(6), 6014–6028. https://doi.org/10.1109/TSG.2019.2895333
  • [33] Hu, J., & Li, H. (2019). A new clustering approach for scenario reduction in multi-stochastic variable programming. IEEE Transactions on Power Systems, 34(5), 3813–3825. https://doi.org/10.1109/TPWRS.2019.2901545
  • [34] Shahmohammadi, Sioshansi, R., Conejo, A. J., & Afsharnia, S. (2018). The role of energy storage in mitigating ramping inefficiencies caused by variable renewable generation. Energy Conversion and Management, 162, 307–320. https://doi.org/10.1016/j.enconman.2017.12.054
  • [35] Xu, Y., Shi, Y., Kirschen, D. S., & Zhang, B. (2018). Optimal battery participation in frequency regulation markets. IEEE Transactions on Power Systems, 33(6), 6715–6725. https://doi.org/10.1109/TPWRS.2018.2846774
  • [36] He, G., Chen, Q., Moutis, P., Kar, S., & Whitacre, J. F. (2018). An intertemporal decision framework for electrochemical energy storage management. Nature Energy, 3(5), 404–412. https://doi.org/10.1038/s41560-018-0129-9
  • [37] Khajeh, H., & Foroud, A. A. (2017). Behavioural market power indices in a transmission-constrained electricity market. IET Generation, Transmission & Distribution, 11(18), 4608–4616. https://doi.org/10.1049/iet-gtd.2017.0911
There are 37 citations in total.

Details

Primary Language Turkish
Subjects Decision Support and Group Support Systems, Energy
Journal Section Tasarım ve Teknoloji
Authors

Ezgi Avcı 0000-0002-9826-1027

Project Number 3220630
Early Pub Date June 10, 2024
Publication Date June 29, 2024
Submission Date April 18, 2024
Acceptance Date June 2, 2024
Published in Issue Year 2024 Volume: 12 Issue: 2

Cite

APA Avcı, E. (2024). Enerji Piyasalarında Ticari Davranış Tespiti İçin bir Karar Destek Sistemi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji, 12(2), 625-643. https://doi.org/10.29109/gujsc.1470266

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