Abstract
The dynamically changing deregulated electricity market involves different entities and the aim of each entity is to achieve maximum profit while performing electricity price and power bidding. The agent-based modeling of electricity systems was used to model the market entities under whole sale electricity market operation. This paper discusses about the strategic learning ability of generators in an IEEE 30 bus system using Variant Roth-Erev learning algorithm. It also analyzes the variation in the generator commitments through the implemented learning algorithm during the present day schedule and helps the generator to perform smart bidding in the next electricity market operation. The results presented show that the smart generators are able to bid strategically in the electricity market and which will reflect in its net earnings in a market scheduled on a day-ahead basis.
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References
Shirmohammadi, D., Wollenberg, B., Vojdani, A., Sandrin, P., Pereira, M., Rahimi, F.: Transmission dispatch and congestion management in the emerging energy market structures. IEEE Trans. Power Syst. 13, 1466–1476 (1998). https://doi.org/10.1109/59.736292
Sun, J.: Dynamic testing of wholesale power market designs: an open-source agent-based framework. Comput. Econ. Springer 30, 291–327 (2007). https://doi.org/10.1007/s10614-007-9095-1
Fang, R.S., David, A.K.: Transmission congestion management in an electricity market. IEEE Trans. Power Syst. 14, 877–883 (1999). doi:https://doi.org/10.1109/59.780898
Conejo, Antonio J.: Electricity markets: analysis & operations. IET Gener. Transm. Distrib. 4, 123–124 (2010). https://doi.org/10.1049/iet-gtd.2010.9059
Foo, Y.S., Gooi, H.B., Chen, S.X.: Multi agent system for distributed management of microgrids. IEEE Trans. Power Syst. 30, 24–34 (2015). https://doi.org/10.1109/TPWRS.2014.2322622
Krishnamurthy, D., Li, W., Tesfatsion, Leigh: An 8-zone test system based on ISO New England data: development and application. IEEE Trans. Power Syst. 31, 234–246 (2016). https://doi.org/10.1109/TPWRS.2015.2399171
Huang, S., Wu, Q., Zhao, H., Li, C.: Distributed optimization based dynamic tariff for congestion management in distribution networks. IEEE Trans. Smart Grid 1, 1–10 (2017). https://doi.org/10.1109/TSG.2017.2735998
Ebrahimian, H., Barmayoon, S., Mohammadi, Mohsen, Ghadimi, Noradin: The price prediction for the energy market based on a new method. J. Econ. Res. 31, 313–337 (2018). https://doi.org/10.1080/1331677X.2018.1429291
Yang, J., Zhao, J., Luo, F., Wen, F., Dong, Z.Y.: Decision-making for electricity retailers: a brief survey. IEEE Trans. Smart Grid 9, 4140–4153 (2017). https://doi.org/10.1109/TSG.2017.2651499
Chen, T., Pourbabak, H., Su, W.: A game theoretic approach to analyze the dynamic interactions of multiple residential prosumers considering power flow constraints. In: Proceedings of the 2016 IEEE power and energy society general meeting, pp. 17–21 (2016). doi:https://doi.org/10.1109/pesgm.2016.7741082
Song, M., Amelin, M.: Purchase bidding strategy for a retailer with flexible demands in day-ahead electricity market. IEEE Trans. Power Syst. 32, 1839–1850 (2017). https://doi.org/10.1109/TPWRS.2016.2608762
Balamurugan, S., Lekshmi, R.R.: Control strategy development for multi-source multi area restructured system based on Genco and Transco reserve. Int. J. Electr. Power Energy Syst. 75, 320–327 (2016). https://doi.org/10.1016/j.ijepes.2015.09.015
Solanki, Z., Wani, U., Patel, J.: Demand side management program for balancing load curve for CGPIT College, Bardoli. In: 2017 international conference on energy, communication, data analytics and soft computing (ICECDS) (2017). https://doi.org/10.1109/icecds.2017.8389542
Kiran, P., Chandrakala, K.R.M.V., Nambiar, T.N.P.: Multi-agent based systems on microgrid—a review. In: 2017 international conference on intelligent computing and control (I2C2), (2017). doi:https://doi.org/10.1109/i2c2.2017.8321880
Kiran, P., Chandrakala, K.R.M.V., Nambiar, T.N.P.: Day ahead market operation with agent based modeling. In: Proceedings of the IEEE international conference on technological advancements in power and energy (Tap Energy 2017), pp. 690–693 (2017). doi:https://doi.org/10.1109/tapenergy.2017.8397302
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Kiran, P., Vijaya Chandrakala, K.R.M. (2020). Variant Roth-Erev Reinforcement Learning Algorithm-Based Smart Generator Bidding as Agents in Electricity Market. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_78
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DOI: https://doi.org/10.1007/978-981-15-0035-0_78
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