The ACEWEM framework: An integrated agent-based and statistical modelling laboratory for repeated power auctions

Expert Systems with Applications

Volumn 42, Issue 5, 2015, Pages 2731-2748

  Kiose, Daniil ^a   Voudouris, Vlasios ^a  

^a ESCP EUROPE BUSINESS SCHOOL   (United Kingdom)

Author keywords

ACEWEM; Agent based modelling; Stochastic learning; Wholesale power markets

Indexed keywords

AUTONOMOUS AGENTS; COMPUTATIONAL METHODS; LEARNING ALGORITHMS; POWER MARKETS; STATISTICAL METHODS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

ACEWEM; AGENT-BASED COMPUTATIONAL ECONOMICS; AGENT-BASED MODELLING; COMPUTATIONAL LEARNING; GENERALIZED ADDITIVE MODEL; METHODOLOGICAL CONTRIBUTIONS; STOCHASTIC LEARNING; WHOLESALE ELECTRICITY MARKETS;

COMMERCE;

EID: 84918841046     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2014.11.024     Document Type: Article

References (40)

1. S. Borenstein, J. Bushnell, and C.R. Knittel Market power in electricity markets: Beyond concentration measures The Energy Journal 1999 65 88
2. J. Bower, and D.W. Bunn A model-based comparison of pool and bilateral market mechanisms for electricity trading The Energy Journal 21 3 2000 1 29
3. J. Bower, and D. Bunn Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market Journal of Economic Dynamics and Control 25 3-4 2001 561 592
4. D.W. Bunn, and C.J. Day Computational modelling of price formation in the electricity pool of England and Wales Journal of Economic Dynamics and Control 33 2 2009 363 376
5. D.W. Bunn, and F.S. Oliveira Agent-based simulation an application to the new electricity trading arrangements of England Transactions on Evolutionary Computation 5 5 2001 493 503
6. Conzelmann, G., Boyd, G., Koritarov, V., & Veselka, T. (2005). Multi-agent power market simulation using emcas. Technical report, Power Engineering Society General Meeting.
7. De Vries, L. (2001). Capacity allocation in a restructured electricity market: Technical and economic evaluation of congestion management methods on interconnectors. In Power tech proceedings, 2001 IEEE porto (vol. 1, pp. 1-6).
8. I. Erev, and A.E. Roth Predicting how people play games: Reinforcement learning American Economic Review 88 1998 848 881
9. A. Faruqui, and S.S. George The value of dynamic pricing in mass markets The Electricity Journal 15 6 2002 45 55
10. Gao, C., Bompard, E., Napoli, R., & Zhou, J. (2008). Design of the electricity market monitoring system. In Third international conference on electric utility deregulation and restructuring and power technologies, DRPT 2008 (pp. 99-106).
11. E. Guerci, M. Rastegar, and S. Cincotti Agent-based modeling and simulation of competitive wholesale electricity markets S. Rebennack, P.M. Pardalos, M.V.F. Pereira, N.A. Iliadis, Handbook of power systems II Energy systems 2010 Springer Berlin, Heidelberg 241 286
12. W.W. Hogan Contract networks for electric power transmission Journal of Regulatory Economics 4 3 1992 211 242
13. N.R. Jennings On agent-based software engineering Artificial Intelligence 117 2 2000 277 296
14. Kannan, A., & Zavala, V. M. (2011). A game-theoretical model predictive control framework for electricity markets. In 2011 49th Annual Allerton conference on communication, control, and computing (Allerton), Monticello, IL.
15. P. Klemperer Auctions: Theory and practice Princeton paperbacks 2004 Princeton University Press
16. V. Koritarov Real-world market representation with agents Power and Energy Magazine, IEEE 2 4 2004 39 46
17. A. Kowalska-Pyzalska, K. Maciejowska, K. Suszczynski, K. Sznajd-Weron, and R. Weron Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs Energy Policy 72 C 2014 164 174
18. Lai, L., Motshegwa, T., Suasinghe, H., Rajkumar, N., & Blach, R. (2000). Feasibility study with agents on energy trading. In 2000 International conference on advances in power system control, operation and management, APSCOM-00 (Vol. 2, pp. 505-510).
19. H. Li, and L. Tesfatsion Co-learning patterns as emergent market phenomena: An electricity market illustration Journal of Economic Behavior and Organization 82 23 2012 395 419
20. North, M. J., Conzelmann, G., Koritarov, V. S., Macal, C. M., Thimmapuram, P. R., & Veselka, T. D. (2002). E-laboratories: Agent-based modeling of electricity markets. In Proceedings of the American power conference.
21. Petrov, V., & Sheble, G. B. (2000). Power auctions bid generation with adaptive agents using genetic programming. In Proceedings of the 2000 North American power symposium, Waterloo-Ontario, Canada.
22. R. Rigby, and M. Stasinopoulos Generalized additive models for location, scale and shape (with discussion) Journal of Applied Statistics 2005 507 554
23. M.H. Rothkopf Daily repetition: A neglected factor in the analysis of electricity auctions The Electricity Journal 12 3 1999 60 70
24. Sensfuß, F., Ragwitz, M., Genoese, M., & Möst, D. (2007). Agent-based simulation of electricity markets: A literature review. Working papers sustainability and innovation S5/2007, Fraunhofer Institute for Systems and Innovation Research (ISI).
25. C.K. Simoglou, P.N. Biskas, S.I. Vagropoulos, and A.G. Bakirtzis Electricity market models and {RES} integration: The greek case Energy Policy 67 0 2014 531 542
26. C. Skoulidas, C. Vournas, and G. Papavassilopoulos Adaptive game modeling of deregulated power markets Power Engineering Review, IEEE 22 9 2002 42 45
27. Sousa, T. M., Pinto, T. W., Vale, Z. A., Praça, I., & Morais, H. (2012). Adaptive learning in multiagent systems: A forecasting methodology based on error analysis. In Highlights on practical applications of agents and multi-agent systems - 10th international conference on practical applications of agents and multi-agent systems, PAAMS 2012 special sessions, Salamanca, Spain, 28-30 March, 2012 (pp. 349-357).
28. J. Sun, and L. Tesfatsion DC optimal power flow formulation and solution using QuadProgJ Economics Department, Iowa State University 6014 2006 1 36
29. J. Sun, and L. Tesfatsion Dynamic testing of wholesale power market designs: An open-source agent-based framework Computational Economics 30 3 2007 291 327
30. Sutton, R. and Barto, A. (1998). Reinforcement learning: An introduction. A Bradford book.
31. L. Tesfatsion Agent-based computational economics: A constructive approach to economic theory Handbook of Computational Economics 2 2006 831 880 (Chapter 16)
32. R.A. van der Veen, A. Abbasy, and R.A. Hakvoort Agent-based analysis of the impact of the imbalance pricing mechanism on market behavior in electricity balancing markets Energy Economics 34 4 2012 874 881
33. M. Ventosa, A. Baillo, A. Ramos, and M. Rivier Electricity market modeling trends Energy Policy 33 7 2005 897 913
34. P. Visudhiphan, and M.D. Ilic An agent-based approach to modeling electricity spot markets R. Neck, Modeling and control of economic systems 2001 2003 Elsevier Science Ltd Oxford 407 412 (Chapter 68)
35. V. Voudouris Towards a conceptual synthesis of dynamic and geospatial models: Fusing the agent-based and object field models Environment and Planning B: Planning and Design 38 1 2011 95 114
36. A. Weidlich, and D. Veit A critical survey of agent-based wholesale electricity market models Energy Economics 30 4 2008 1728 1759
37. R. Weron Electricity price forecasting: A review of the state-of-the-art with a look into the future International Journal of Forecasting 30 4 2014 1030 1081
38. A. Wood, and B. Wollenberg Power generation, operation, and control 1996 A Wiley-Interscience publication. Wiley
39. D. Young, S. Poletti, and O. Browne Can agent-based models forecast spot prices in electricity markets? evidence from the new zealand electricity market Energy Economics 45 0 2014 419 434
40. Yusta, J. & Dominguez, A. (2002). Measuring and modeling of industrial demand response to alternative prices of the electricity. In 14th power systems computation conference, session 15, paper 3, Seville, Spain.