# Multiagent coevolutionary genetic fuzzy system to develop bidding strategies in electricity markets: computational economics to assess mechanism design

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## Abstract

This paper suggests a genetic fuzzy system approach to develop bidding strategies for agents in online auction environments. Assessing efficient bidding strategies is a key to evaluate auction models and verify if the underlying mechanism design achieves its intended goals. Due to its relevance in current energy markets worldwide, we use day-ahead electricity auctions as an experimental and application instance of the approach developed in this paper. Successful fuzzy bidding strategies have been developed by genetic fuzzy systems using coevolutionary algorithms. In this paper we address a coevolutionary fuzzy system algorithm and present recent results concerning bidding strategies behavior. Coevolutionary approaches developed by coevolutionary agents interact through their fuzzy bidding strategies in a multiagent environment and allow realistic and transparent representations of agents behavior in auction-based markets. They also improve market representation and evaluation mechanisms. In particular, we study how the coevolutionary fuzzy bidding strategies perform against each other during hourly electric energy auctions. Experimental results show that coevolutionary agents may enhance their profits at the cost of increasing system hourly price paid by demand.

## Keywords

Genetic fuzzy systems Multiagent systems Auctions Electricity markets Computational economics## Notes

### Acknowledgments

The last author acknowledges CNPq, the Brazilian National Research Council, for grant #304 857/2006-8. The authors are also grateful to the anonymous referees whose comments helped to improve the paper.

### Disclaimer

The results, interpretations and conclusions expressed in this work are of exclusive responsibility of its authors and should not be, in any hypothesis, attributed to ANEEL, the Brazilian Electricity Regulatory Agency, neither to its Board of Directors, nor to any Commission the author is affiliated to. The experiments presented in this paper have been accomplished based on general public available data. ANEEL is neither responsible for this work nor any consequence from its use.

## References

- 1.Tesfatsion L, Judd KL (eds) (2006) Handbook of computational economics: agent-based computational economics volume 2 of Handbooks in Economics. North HollandGoogle Scholar
- 2.Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol Intell 1:27–46CrossRefGoogle Scholar
- 3.Walter I, Gomide F (2006) Design of coordination strategies in multiagent systems via genetic fuzzy systems. Soft Comput 10(10):903–915. Special Issue: New Trends in the Design of Fuzzy SystemsGoogle Scholar
- 4.Walter I, Gomide F (2007) Genetic fuzzy systems to evolve coordination strategies in multiagent systems. Int J Intell Syst 22(9):971–991. Special Issue on Genetic Fuzzy SystemsGoogle Scholar
- 5.Walter I, Gomide F (2008) Coevolutionary fuzzy multiagent bidding strategies in competitive electricity markets. In: 3rd International workshop on genetic and evolving fuzzy systems (GEFS 08). Witten-Bommerholz, Germany, IEEE, pp 53–58Google Scholar
- 6.Walter I, Gomide F (2009) Coevolutionary genetic fuzzy system to assess multiagent bidding strategies in electricity markets. In: Proceedings of the joint 2009 international fuzzy systems association world congress and 2009 european society of fuzzy logic and technology conference. Lisbon, Portugal, pp 1114–1119Google Scholar
- 7.Silva C, Wollenberg BF, Zheng CZ (2001) Application of mechanism design to electric power markets. IEEE Trans Power Syst 16(4):862–869CrossRefGoogle Scholar
- 8.Green R (2000) Competition in generation: the economic foundations. Proceedings of the IEEE 88(2):128–139Google Scholar
- 9.David AK, Wen FS (2000) Strategic bidding in competitive electricity markets: a literature survey. In: IEEE PES 2000 summer power meeting, vol 4. IEEE Power Engineering Society, IEEE, Seattle, pp 2168–2173Google Scholar
- 10.Visudhipan P, Ilic M (1999) Dynamic games-based modeling of electricity markets. In: IEEE power engineering society winter meeting, vol 1. IEEE, New York, pp 274–281Google Scholar
- 11.Monclar F-R, Quatrain R (2001) Simulation of electricity markets: a multi-agent approach. In: International conference on intelligent system application to power systems. IEEE Power Engineering Society, Budapest, Hungary, pp 207–212Google Scholar
- 12.Richter CW, Sheblé GB (1997) Building fuzzy bidding strategies for the competitive generator. In: North american power symposium. Laramie, Wyoming, USAGoogle Scholar
- 13.Widjaja M, Sugianto LF, Morrison RE (2001) Fuzzy model of generator bidding system in competitive electricity markets. In: 10th IEEE international conference on fuzzy systems, vol 3. IEEE, Melbourne, Australia, pp 1396–1399Google Scholar
- 14.Richter CW Jr, Sheblé GB (1998) Genetic algorithm evolution of utility bidding strategies for the competitive marketplace. IEEE Trans Power Syst 13(1):256–261CrossRefGoogle Scholar
- 15.Richter CW Jr, Sheblé GB, Ashlock D (1999) Comprehensive bidding strategies with genetic programming/finite state automata. IEEE Trans Power Syst 14(4):1207–1212CrossRefGoogle Scholar
- 16.Xiong G, Hashiyama T, Okuma S (2002) An evolutionary computation for supplier bidding strategy in electricity auction market. In: IEEE power engineering society transmission and distribution conference vol 1. IEEE, Yokohama, Japan, pp 83–88Google Scholar
- 17.Bagnall AJ (2000) A multi-adaptive agent model of generator bidding in the UK market in electricity. In: Genetic and evolutionary computation conference GECCO 2000. Morgan Kaufmann, Los Altos, pp 605–612Google Scholar
- 18.Tesauro G (2000) Pricing in agent economies using neural networks and multi-agent Q-learning. In: Sun R, Giles CL, (eds) Sequence learning volume 1828 of Lecture Notes in Artificial Intelligence. Springer, Berlin, pp 288–307Google Scholar
- 19.Hu J, Wellman MP (2003) Nash Q-learning for general-sum stochastic games. J Mach Learn Res 4:1039–1069CrossRefMathSciNetGoogle Scholar
- 20.Singh H (1999) Introduction to game theory and its application in electric markets. IEEE Comput Appl Power 12(4):18–22CrossRefGoogle Scholar
- 21.Axelrod R (1984) The evolution of cooperation. Basic Books, New YorkGoogle Scholar
- 22.Sandholm TW, Crites RH (1996) Multiagent reinforcement learning in the iterated prisoner’s dilemma. Biosystems 37(1–2):147–166CrossRefGoogle Scholar
- 23.Hingston P, Kendall G (2004) Learning versus evolutionin iterated prisoner’s dilemma. In: IEEE congress on evolutionary computation (CEC2004) vol 1. pp 364–372Google Scholar
- 24.Borges PSS, Pacheco RCS, Barcia RM, Khator SK (1997) A fuzzy approach to the prisoner’s dilemma. Biosystems 41(2):127–137CrossRefGoogle Scholar
- 25.Amaral W, Gomide F (2008) Granular computing: at the junction of rough sets and fuzzy sets. In: Studies in fuzziness and soft computing. volume 224, chapter A coevolutionary approach to solve fuzzy games. Springer, Berlin, pp 121–130Google Scholar
- 26.Amaral W, Gomide F (2007) Theoretical advances and applications of fuzzy logic and soft computing. In: Advances in soft computing, volume 42, chapter an algorithm to solve two-person non-zero sum fuzzy games. Springer, Berlin, pp 296–302Google Scholar
- 27.Chen H, Wong K, Nguyen D, Chung C (2006) Analyzing oligopolistic electricity market using coevolutionary computation. IEEE Trans Power Syst 21(1):143–152zbMATHCrossRefGoogle Scholar
- 28.Chen H, Wong K, Wang X, Chung C (2005) A coevolutionary approach to modeling oligopolistic electricity markets. In: IEEE 2005 power engineering society general meeting, vol 1. pp 230–236Google Scholar
- 29.Zhang SX, Chung CY, Wong KP, Chen H (2009) Analyzing two-settlement electricity market equilibrium by coevolutionary computation approach. IEEE Trans Power Syst 23(3):1155–1164CrossRefGoogle Scholar
- 30.Cau TDH, Anderson E (2002) A co-evolutionary approach to modelling the behaviour of participants in competitive electricity markets. In: 2002 IEEE power engineering society summer meeting, vol 3. Chicago, pp 1534–1540Google Scholar
- 31.Anderson EJ, Cau TDH (2009) Modeling implicit collusion using coevolution. Oper Res 57(2):439–455CrossRefGoogle Scholar
- 32.Son YS, Baldick R (2004) Hybrid coevolutionary programming for nash equilibrium search in games with local optima. IEEE Trans Evol Comput 8(4):305–315CrossRefGoogle Scholar
- 33.Phelps SG, Parsons S, McBurney P, Sklar E (2002) Co-evolution of auction mechanims and trading strategies: towards a novel approach to microeconomic design. In: Proceedings of the 2nd workshop on evolutionary computation and multi-agent systems. New YorkGoogle Scholar
- 34.Phelps SG (2007) Evolutionary mechanism design. PhD thesis, Univeristy of Liverpool, UKGoogle Scholar
- 35.Nicolaisen J, Petrov V, Tesfatsion L (2001) Market power and efficiency in a computational electricity market with discriminatory double-auction pricing. IEEE Trans Evol Comput 5(5):504–523CrossRefGoogle Scholar
- 36.de la Cal Marín EA, Sánchez Ramos L (2003) Optimizing supply strategies in the spanish market. Lecture Notes on Computer Science 2687:353–360CrossRefGoogle Scholar
- 37.de la Cal Marín EA, Sánchez Ramos L (2004) Supply estimation using coevolutionary genetic algorithms in the spanish market. Appl Intell 21(1):7–24CrossRefGoogle Scholar
- 38.de la Cal Marín EA, Suárez Fernández MDR (2003) Application of an optimizing supply strategies model in the spanish electrical market. In: International congress on evolutionary methods for design, optimization and control with applications to industrial problems (EURGOEN 2003). Barcelona, Spain, CIMNEGoogle Scholar
- 39.Bajpai P, Singh SN (2007) Fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market. IEEE Trans Power Syst 22(4):2152–2160CrossRefGoogle Scholar
- 40.Ma Y, Jiang C, Hou Z, Wang C (2006) The formulation of the optimal strategies for the electricity producers based on the particle swarm optimization algorithm. IEEE Trans Power Syst 21(4):1663–1671CrossRefGoogle Scholar
- 41.Yucekaya AD, Valenzuela J, Dozier G (2009) Strategic bidding in electricity markets using particle swarm optimization. Electr Power Syst Res 79(2):335–345CrossRefGoogle Scholar
- 42.Harp SA, Brignone S, Wollenberg BF, Samad T (2000) SEPIA: a simulator for electric power industry agents. IEEE Control Syst Mag 20(4):53–69CrossRefGoogle Scholar
- 43.Praça I, Ramos C, Vale Z, Cordeiro M (2003) A new agent-based framework for the simulation of electricity markets. In: Proceedings of the IEEE/WIC international conference on intelligent agents (IAT’03). IEEE, Halifax, Canada, pp 1931–1938Google Scholar
- 44.Walter I, Gomide F (2008) Electricity market simulation: multiagent system approach. In: 23rd Annual ACM symposium on applied computing, vol 1. Fortaleza, CE, Brazil, pp 34–38Google Scholar
- 45.Al-Agtash S, Yamin HY (2004) Optimal supply curve bidding using Benders decomposition in competitive electricity markets. Electr Power Syst Res 71:245–255CrossRefGoogle Scholar
- 46.Cordon O, Gomide F, Herrera F, Hoffman F, Magdalena L (2004) Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Syst 141(1):5–31. Special Issue on Genetic Fuzzy Systems: New DevelopmentsGoogle Scholar
- 47.Pedrycz W, Gomide F (2007) Fuzzy systems engineering: toward human-centric computing. Wiley-IEEE, HobokenGoogle Scholar
- 48.Hillis WD (1990) Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D 42:228–234CrossRefGoogle Scholar
- 49.Potter MA, De Jong KA (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29CrossRefGoogle Scholar
- 50.Cordón O, Herrera F, Hoffman F, Magdalena L (2001) Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases, volume 19 of advances in fuzzy systems: applications and theory. World Scientific, SingaporeGoogle Scholar
- 51.Cordón O, Herrera F, Villar P (2001) Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base. IEEE Trans Fuzzy Syst 9(4):667–674CrossRefGoogle Scholar
- 52.Herrera F, Lozano M, Verdegay JL (1997) Fuzzy connectives based crossover operators to model genetic algorithms population diversity. Fuzzy Sets Syst 92(1):21–30CrossRefGoogle Scholar
- 53.Vidal JM (2003) Learning in multiagent systems: an introduction from a game-theroretic perspective. Lecture Notes on Artifical Intelligence 2636:202–215Google Scholar
- 54.Cordón O, Herrera F, Magdalena L, Villar P (2001) A genetic learning process for scaling the factors, granularity and contexts of the fuzzy rule-based system data base. Inf Sci 136:85–107zbMATHCrossRefGoogle Scholar
- 55.Glorennec P (1996) Constrained optimization of FIS using an evolutionary method. In: Herrera F, Verdegay JL (eds) Genetic algorithms and soft computing, vol 8 of studies in fuzziness and soft computing. Physica-Verlag, Wurzburg, pp 349–368Google Scholar
- 56.González A, Pérez R (1998) Completeness and consistency conditions for learning fuzzy rules. Fuzzy Sets Syst 96(1):37–51CrossRefGoogle Scholar
- 57.González A, Pérez R (1999) SLAVE: a genetic learning system based on an iterative approach. IEEE Trans Fuzzy Syst 7(2):176–191CrossRefGoogle Scholar
- 58.Magdalena L (1997) Adapting the gain of an FLC with genetic algorithms. Int J Approx Reason 17(4):327–349zbMATHCrossRefGoogle Scholar
- 59.Magdalena L, Monasterio-Huellin F (1997) A fuzzy logic controller with learning trough the evolution of its knowledge base. Int J Approx Reason 16(3–4):335–358zbMATHCrossRefGoogle Scholar
- 60.Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer, ViennazbMATHGoogle Scholar