Open Access Open Access  Restricted Access Subscription or Fee Access

Recurrent Neural Network-Based Intelligent Bidding Strategy for Profitability Maximization and Control of Electricity Market


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/ireaco.v16i6.24275

Abstract


Electricity forecasting is very important for the electricity generation and distribution markets from the consumer’s point of view. Over the last fifteen years, there has been a significant change in the patterns toward deregulation and restructuring of the power industry. Competition in the wholesale and retail markets together gives open access to the transmission network and attracts customers with many benefits. In this paper, ERCOT electricity market modeling, price analysis, and forecasting the demand based on the market participation using machine and deep learning techniques is considered to advance the profit among the Generating Companies (GenCos) for real smart single-ended bidding in the wholesale electricity market. For efficient market involvement to accommodate the congestion cost, the Locational Marginal Pricing (LMP) method is considered. The impact of the microgrid as a non-dispatchable load with the GenCos and Load Serving Entity (LSE) on an Eight-bus ERCOT system is developed and proposed intelligent bidding approach for effective GenCos participation in meeting the social welfare accommodating the optimal location of Microgrid using AMES python tool to enhance the market operation effectively.
Copyright © 2023 Praise Worthy Prize - All rights reserved.

Keywords


Electricity Market; Optimal Power Flow; ERCOT; Locational Marginal Pricing; Bidding Strategy; Forecasting; Machine Learning Technique

Full Text:

PDF


References


S. Ciraci, J. Daily, J. C. Fuller, A.R. Fisher, L.D. Marinovici, FNCS: A framework for power system and communication networks co-simulation, Proceedings of the Symposium on Theory of Modeling & Simulation. SanDiego, CA, April, 2014.

H.T. Nguyen, S. Battula, R.R. Takkala, Z. Wang, L. Tesfatsion, An integrated transmission and distribution test system for evaluation of transactive energy designs, Applied Energy, Vol. 240:666-679, April 2019.
https://doi.org/10.1016/j.apenergy.2019.01.178

F. Ziel, R. Weron, Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks, Energy Economics, Vol. 70:396-420, February 2018.
https://doi.org/10.1016/j.eneco.2017.12.016

J. Nowotarski, R. Weron, On the importance of the long-term seasonal component in day-ahead electricity price forecasting, Energy Economics, Vo. 57:228-235, June 2016.
https://doi.org/10.1016/j.eneco.2016.05.009

A.R. Gollou, N. Ghadimi, A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, vol. 32 (Issue 6):4031-4045, 2017.
https://doi.org/10.3233/JIFS-152073

Q. Huang, T. McDermott, Y. Tang, A. Makhmalbaf, D. Hammerstrom, A. Fisher, L. Marinovici, T. Hardy, Simulation-based valuation of transactive energy systems, IEEE Transactions on Power Systems, Vol. 34(Issue 5): 4138-4147, September 2019.
https://doi.org/10.1109/TPWRS.2018.2838111

S. Battula, L. Tesfatsion, T.E. McDermott, ERCOT Test System: Code and Data Repository, 2019.

Power System Test Case Archive, University of Washington. [Online]. Available: http://www.ee.washington.edu/research/pstca/

B. Neupane, W. Woon, Z. Aung, Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting, Energies, Vol. 10(Issue 1), 2017.
https://doi.org/10.3390/en10010077

S. Lahmiri, Comparing variational and empirical mode decomposition in forecasting day-ahead energy prices, IEEE Systems Journal, Vol. 11(Issue 3):1907-1910, September 2017.
https://doi.org/10.1109/JSYST.2015.2487339

J.P. González, A.M. San Roque, E.A. Pérez, Forecasting functional time series with a new Hilbertian ARMAX model: Application to electricity price forecasting, IEEE Transactions on Power System, Vol. 33 (Issue 1): 545-556, January 2018.
https://doi.org/10.1109/TPWRS.2017.2700287

M. Rafiei, T. Niknam, M.H. Khooban, Probabilistic Forecasting of Hourly Electricity Price by Generalization of ELM for Usage in Improved Wavelet Neural Network, IEEE Transactions on Industrial Informatics, Vol. 13(Issue 1):71-79, February 2017.
https://doi.org/10.1109/TII.2016.2585378

F.E. Benth, J.S. Benth, S. Koekebakker, Stochastic Modeling of Electricity and Related Markets, 1st ed.; World Scientific: Trenton, NJ, USA, 2010.

A.K. Singh, B.C. Pal, Report on the 68-bus 16-machine 5-area system, version 3.3, IEEE PES Task Force on Benchmark Systems for Stability Controls, Technical Report, December 2013.

R. Zimmerman, C. Murillo-Snchez, R. Thomas, MATPOWER: Steady-state operations, planning and analysis tools for power systems research and education, IEEE Transactions on Power Systems, Vol. 26(Issue 1):12-19, February 2011.
https://doi.org/10.1109/TPWRS.2010.2051168

J. Sun, L. Tesfatsion, Dynamic testing of wholesale power market designs: An open-source agent-based framework, Computational Economics, Vol. 30(Issue 3):291-327, 2007.
https://doi.org/10.1007/s10614-007-9095-1

AMES (2020) The AMES Wholesale Power Market Test Bed: Homepage:
http://www2.econ.iastate.edu/tesfatsi/AMESMarketHome.html

FERC, RTO unit commitment test system, Federal Energy Regulatory Commission, Technical Report, July 2012.

J. Price, J. Goodin, Reduced network modeling of WECC as a market design prototype, Proceedings, IEEE Power and Energy Society General Meeting, July 2011.
https://doi.org/10.1109/PES.2011.6039476

D. Krishnamurthy, W. Li, L. Tesfatsion, An 8-zone test system based on ISO New England data: Development and application, IEEE Transactions on Power Systems, Vol. 31(Issue 1):234-246, January 2016.
https://doi.org/10.1109/TPWRS.2015.2399171

L. Tesfatsion, S. Battula, Analytical SCUC/SCED Optimization Formulation for AMES V5.0, Working Paper, ISU Economics Department, 2019.

S. Battula, L. Tesfatsion, AMES V5.0 GitHub Repository, 2019.

A. Radovanovic, T. Nesti, B. Chen, A holistic approach to forecasting wholesale energy market prices, IEEE Transactions on Power Apparatus and Systems, Vol. 34(Issue 6):4317-4328, November 2019.
https://doi.org/10.1109/TPWRS.2019.2921611

lib.dr.iastate.edu

K.R.M. Vijaya Chandrakala, P. Kiran, Multi-agent based modeling and learning approach for intelligent day-ahead bidding strategy in wholesale electricity market, Expert Systems with Applications: An International Journal, Vol. 233 (Issue C), December 2023.
https://doi.org/10.1016/j.eswa.2023.121014

S. Balamurugan, Janani Sunil, J. Kashreya, Real power flow in a transmission line by varying the turns ratio of the injection transformer, 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), 1-5, 2019.
https://doi.org/10.1109/i-PACT44901.2019.8960069

P. Kiran, K.R.M. Vijaya Chandrakala, New interactive agent-based reinforcement learning approach towards smart generator bidding in electricity market with microgrid integration, Applied Soft Computing, Vol. 97 (Part A), 2020.
https://doi.org/10.1016/j.asoc.2020.106762

Jayanthan G. Vaidya, Balamurugan S., Lekshmi R.R., Load frequency control for transmission congestion management using price area concept, 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE), January 2020.

Chandrakala, K., Rajan, P., A Hybrid Machine Learning Approach for Price Forecasting in Electricity Market with Smart Bidding Strategies and Wind Energy Influence, (2022) International Review on Modelling and Simulations (IREMOS), 15 (6), pp. 414-424.
https://doi.org/10.15866/iremos.v15i6.22653

Katruksa, S., Jiriwibhakorn, S., Evaluation of Mid-Term Load Forecasting Case Study Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs), (2020) International Review of Electrical Engineering (IREE), 15 (4), pp. 283-293.
https://doi.org/10.15866/iree.v15i4.17766

Boulal, A., Chakir, H., Drissi, M., Ouadi, H., Energy Bill Reduction by Optimizing Both Active and Reactive Power in an Electrical Microgrid, (2020) International Review of Electrical Engineering (IREE), 15 (6), pp. 456-464.
https://doi.org/10.15866/iree.v15i6.17909

Fushuan Wen and A. K. David. Optimal bidding strategies and modeling of imperfect information among competitive generators, IEEE Transactions on Power Systems, Vol. 16(Issue 1):15-21, Feb 2001.
https://doi.org/10.1109/59.910776


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize