Dynamic Pricing in Electric Power Markets Affected by Distributed Energy Generation Using Agent-Based Modeling
Publication: Computing in Civil Engineering 2021
ABSTRACT
The power infrastructure in the US is facing many challenges concerning capacity, reliability, and sustainability. Some of those challenges are associated with the integration of distributed energy resources (DER) into the conventional grid system. DER may offer reliable and cost-effective distributed small-scale generation near or at end-consumers. However, it also creates new challenges for utilities and generating companies due to the uncertainties in estimating demands. Accordingly, the goal of this research is to investigate dynamic pricing in electric power markets considering the effect of the increasing penetration of DER. To achieve that goal, a complex system-of-systems model that combines agent-based modeling and machine learning is presented in this research. The model relies on reinforcement learning to enable dynamic pricing of electrical power in response to the shifts in demand resulting from the adoption of DER. Results of the emergent behavior of the complex SoS verify the interrelated causality between prices of electrical power and the penetration rate of DER, which is also called the death spiral. Ultimately, the presented framework should assist researchers and practitioners in the fields of electric power infrastructure and DER in investigating the pricing dynamics of agents in the electric power markets considering the effect of the penetration of DER.
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REFERENCES
Ahmed, M. O., El-Adaway, I. H., Coatney, K. T., and Eid, M. S. (2016). “Construction Bidding and the Winner’s Curse: Game Theory Approach.” Journal of Construction Engineering and Management, American Society of Civil Engineers, 142(2), 04015076.
Ali, G. G., and El-Adaway, I. H. (2020). “Relationship between Electric-Power Sector Development and Socioeconomic Parameters: Statistical Analysis Approach.” Journal of Energy Engineering, The American Society of Civil Engineers (ASCE), 146(5), 04020045.
ASCE. (2021). Infrastructure Report Card 2021: Energy. The American Society of Civil Engineers (ASCE), US.
Assaad, R., Ahmed, M. O., El-Adaway, I. H., Elsayegh, A., and Siddhardh Nadendla, V. S. (2021). “Comparing the Impact of Learning in Bidding Decision-Making Processes Using Algorithmic Game Theory.” Journal of Management in Engineering, American Society of Civil Engineers, 37(1), 04020099.
Barbose, G., Darghouth, N., Elmallah, S., Forrester, S., Kristina, S. H. K., Millstein, D., Rand, J., Cotton, W., Sherwood, S., and O’Shaughnessy, E. (2019). Tracking the Sun: Pricing and Design Trends for Distributed Photovoltaic Systems in the United States - 2019 Edition.
Batouli, M., and Mostafavi, A. (2014). “A hybrid simulation framework for integrated management of infrastructure networks.” Proceedings of the Winter Simulation Conference 2014, 3319–3330.
Burger, S. P., and Luke, M. (2017). “Business models for distributed energy resources: A review and empirical analysis.” Energy Policy, 109, 230–248.
Driesen, J., and Katiraei, F. (2008). “Design for distributed energy resources.” IEEE Power and Energy Magazine, 6(3), 30–40.
EIA. (2015). “EIA electricity data now include estimated small-scale solar PV capacity and generation.” <https://www.eia.gov/todayinenergy/detail.php?id=23972>(Feb. 11, 2021).
EIA. (2020). “Electricity Data.” <https://www.eia.gov/electricity/data.php>(Sep. 6, 2020).
EIA. (2021). “Use of electricity.” Electricity explained: Use of electricity, <https://www.eia.gov/energyexplained/electricity/use-of-electricity.php>(Mar. 28, 2021).
Eid, M. S., and El-Adaway, I. H. (2018). “Decision-Making Framework for Holistic Sustainable Disaster Recovery: Agent-Based Approach for Decreasing Vulnerabilities of the Associated Communities.” Journal of Infrastructure Systems, American Society of Civil Engineers, 24(3), 04018009.
El-Adaway, I. H., Sims, C., Eid, M. S., Liu, Y., and Ali, G. G. (2020). “Preliminary Attempt toward Better Understanding the Impact of Distributed Energy Generation: An Agent-Based Computational Economics Approach.” Journal of Infrastructure Systems, 26(1), 04020002.
Erev, I., and Roth, A. E. (1998). “Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria.” The American Economic Review, American Economic Association, 88(4), 848–881.
FERC. (2021). “Electric Power Markets.” Electric Power Markets: National Overview, <https://www.ferc.gov/industries-data/market-assessments/electric-power-markets>(Mar. 28, 2021).
Hunter, J. D. (2007). “Matplotlib: A 2D graphics environment.” Computing in science & engineering, 9(3), 90.
Jordan, M. I., and Mitchell, T. M. (2015). “Machine learning: Trends, perspectives, and prospects.” Science, American Association for the Advancement of Science, 349(6245), 255–260.
Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B. E., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J. B., Grout, J., and Corlay, S. (2016). Jupyter Notebooks-a publishing format for reproducible computational workflows.
Mahmud, K., Khan, B., Ravishankar, J., Ahmadi, A., and Siano, P. (2020). “An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: An overview.” Renewable and Sustainable Energy Reviews, 127, 109840.
McKinney, W. (2010). “Data structures for statistical computing in python.” Proceedings of the 9th Python in Science Conference, Austin, TX, 51–56.
Millman, K. J., and Aivazis, M. (2011). “Python for scientists and engineers.” Computing in Science & Engineering, 13(2), 9–12.
Mori, S., Makishita, Y., and Kamegai, K. (2017). “Two-Stage Approach for the Assessment of Photovoltaic and Cogeneration Systems: Integration of Regional Distributed Energy Systems and Power-Expansion Planning.” Journal of Energy Engineering, American Society of Civil Engineers, 143(3), F4016005.
Nosratabadi, S. M., Hooshmand, R.-A., and Gholipour, E. (2017). “A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems.” Renewable and Sustainable Energy Reviews, 67, 341–363.
Oliphant, T. E. (2006). A guide to NumPy. Trelgol Publishing USA.
Purushothaman, K., and Chandrakala, V. (2020). “Roth-Erev Reinforcement Learning Approach for Smart Generator Bidding towards Long Term Electricity Market Operation Using Agent Based Dynamic Modeling.” Electric Power Components and Systems, Taylor & Francis, 48(3), 256–267.
Seabold, S., and Perktold, J. (2010). “Statsmodels: Econometric and statistical modeling with python.” Proceedings of the 9th Python in Science Conference, Scipy, 61.
Sun, J., and Tesfatsion, L. (2007). “Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework.” Computational Economics, 30(3), 291–327.
Van Der Walt, S., Colbert, S. C., and Varoquaux, G. (2011). “The NumPy array: a structure for efficient numerical computation.” Computing in Science & Engineering, 13(2), 22.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., and van Mulbregt, P. (2020). “SciPy 1.0: fundamental algorithms for scientific computing in Python.” Nature Methods, Nature Publishing Group, 17(3), 261–272.
Zhang, S. (2016). “Innovative business models and financing mechanisms for distributed solar PV (DSPV) deployment in China.” Energy Policy, 95, 458–467.
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Published online: May 24, 2022
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