Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Evolutionary Intelligence
Erschienen in: Evolutionary Intelligence 1-2/2009

01.11.2009 | Special Issue

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

verfasst von: Igor Walter, Fernando Gomide

Erschienen in: Evolutionary Intelligence | Ausgabe 1-2/2009

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.
Vorheriger Artikel Rule base and adaptive fuzzy operators cooperative learning of Mamdani fuzzy systems with multi-objective genetic algorithms
Nächster Artikel Extending a simple genetic cooperative-competitive learning fuzzy classifier to low quality datasets

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
1
The application of game theory in electricity markets is a broad area and its review is out of the scope of this paper.
 
2
A more detailed description of this work can be found in [34].
 
3
This and all previous papers are a result of the PhD thesis of the first author. They have no links with the Regulatory Agency.
 
4
An “active” rule is a rule that is processed during fuzzy inference while an “inactive” rule remains in the rule base genotype but it is not processed during fuzzy inference.
 
5
ONS: Operador Nacional do Sistema.
 
6
The choice of the evolutionary parameters is not subject to any optimization process.
 
7
The training process can take about 2 h of processing time for two species, corresponding to 500 generations, and above 6 h for four species and 1,000 generations. Experiments were done using a Pentium 4,2 GHz 256 Mb RAM PC running GNU/Linux Fedora.
 
8
In [5] two thermal plants coevolved. They are the same used as evolutionary agents in [3]: Argentina I and Argentina II, respectively.
 
9
This behavior of the coevolutionary agents may change if one assumes zero cost for no output, that is, C(0) = 0.
 
10
Notice that for the conservative strategy, Argentina II is the marginal generator during 84 h (25%) over the 2 weeks test period.
 
11
Using the conservative strategy, Argentina I becomes the marginal generator only for 53 h (16%) over the test period.
 
12
Knowledge bases are shown in Appendix C.
 
13
Using the conservative strategy, TermoRio is the marginal generator for 75 h (22%) over the 336 h period only.
 
14
For the conservative strategy Ibirité is the marginal generator for 55 h (16%) of the 336 h test period.
 
15
The result is similar for N < p ≤ N + M − 1.
 
16
The letters A and I indicate respectively an Active or Inactive rule. The 1’s (0’s) represent a linguistic term that is used (is not used) in a given rule, as detailed in Appendix A.
 
Literatur
1.
Tesfatsion L, Judd KL (eds) (2006) Handbook of computational economics: agent-based computational economics volume 2 of Handbooks in Economics. North Holland
2.
Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol Intell 1:27–46CrossRef
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 Systems
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 Systems
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 53–58
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 1114–1119
7.
Silva C, Wollenberg BF, Zheng CZ (2001) Application of mechanism design to electric power markets. IEEE Trans Power Syst 16(4):862–869CrossRef
8.
Green R (2000) Competition in generation: the economic foundations. Proceedings of the IEEE 88(2):128–139
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 2168–2173
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 274–281
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 207–212
12.
Richter CW, Sheblé GB (1997) Building fuzzy bidding strategies for the competitive generator. In: North american power symposium. Laramie, Wyoming, USA
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 1396–1399
14.
Richter CW Jr, Sheblé GB (1998) Genetic algorithm evolution of utility bidding strategies for the competitive marketplace. IEEE Trans Power Syst 13(1):256–261CrossRef
15.
Richter CW Jr, Sheblé GB, Ashlock D (1999) Comprehensive bidding strategies with genetic programming/finite state automata. IEEE Trans Power Syst 14(4):1207–1212CrossRef
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 83–88
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 605–612
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 288–307
19.
20.
Singh H (1999) Introduction to game theory and its application in electric markets. IEEE Comput Appl Power 12(4):18–22CrossRef
21.
Axelrod R (1984) The evolution of cooperation. Basic Books, New York
22.
Sandholm TW, Crites RH (1996) Multiagent reinforcement learning in the iterated prisoner’s dilemma. Biosystems 37(1–2):147–166CrossRef
23.
Hingston P, Kendall G (2004) Learning versus evolutionin iterated prisoner’s dilemma. In: IEEE congress on evolutionary computation (CEC2004) vol 1. pp 364–372
24.
Borges PSS, Pacheco RCS, Barcia RM, Khator SK (1997) A fuzzy approach to the prisoner’s dilemma. Biosystems 41(2):127–137CrossRef
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 121–130
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 296–302
27.
Chen H, Wong K, Nguyen D, Chung C (2006) Analyzing oligopolistic electricity market using coevolutionary computation. IEEE Trans Power Syst 21(1):143–152MATHCrossRef
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 230–236
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):1155–1164CrossRef
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 1534–1540
31.
Anderson EJ, Cau TDH (2009) Modeling implicit collusion using coevolution. Oper Res 57(2):439–455CrossRef
32.
Son YS, Baldick R (2004) Hybrid coevolutionary programming for nash equilibrium search in games with local optima. IEEE Trans Evol Comput 8(4):305–315CrossRef
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 York
34.
Phelps SG (2007) Evolutionary mechanism design. PhD thesis, Univeristy of Liverpool, UK
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):504–523CrossRef
36.
de la Cal Marín EA, Sánchez Ramos L (2003) Optimizing supply strategies in the spanish market. Lecture Notes on Computer Science 2687:353–360CrossRef
37.
de la Cal Marín EA, Sánchez Ramos L (2004) Supply estimation using coevolutionary genetic algorithms in the spanish market. Appl Intell 21(1):7–24CrossRef
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, CIMNE
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):2152–2160CrossRef
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):1663–1671CrossRef
41.
Yucekaya AD, Valenzuela J, Dozier G (2009) Strategic bidding in electricity markets using particle swarm optimization. Electr Power Syst Res 79(2):335–345CrossRef
42.
Harp SA, Brignone S, Wollenberg BF, Samad T (2000) SEPIA: a simulator for electric power industry agents. IEEE Control Syst Mag 20(4):53–69CrossRef
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 1931–1938
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 34–38
45.
Al-Agtash S, Yamin HY (2004) Optimal supply curve bidding using Benders decomposition in competitive electricity markets. Electr Power Syst Res 71:245–255CrossRef
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 Developments
47.
Pedrycz W, Gomide F (2007) Fuzzy systems engineering: toward human-centric computing. Wiley-IEEE, Hoboken
48.
Hillis WD (1990) Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D 42:228–234CrossRef
49.
Potter MA, De Jong KA (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1–29CrossRef
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, Singapore
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):667–674CrossRef
52.
Herrera F, Lozano M, Verdegay JL (1997) Fuzzy connectives based crossover operators to model genetic algorithms population diversity. Fuzzy Sets Syst 92(1):21–30CrossRef
53.
Vidal JM (2003) Learning in multiagent systems: an introduction from a game-theroretic perspective. Lecture Notes on Artifical Intelligence 2636:202–215
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 136:85–107MATHCrossRef
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 349–368
56.
González A, Pérez R (1998) Completeness and consistency conditions for learning fuzzy rules. Fuzzy Sets Syst 96(1):37–51CrossRef
57.
González A, Pérez R (1999) SLAVE: a genetic learning system based on an iterative approach. IEEE Trans Fuzzy Syst 7(2):176–191CrossRef
58.
Magdalena L (1997) Adapting the gain of an FLC with genetic algorithms. Int J Approx Reason 17(4):327–349MATHCrossRef
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):335–358MATHCrossRef
60.
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer, ViennaMATH
Metadaten
Titel
Multiagent coevolutionary genetic fuzzy system to develop bidding strategies in electricity markets: computational economics to assess mechanism design
verfasst von
Igor Walter
Fernando Gomide
Publikationsdatum
01.11.2009
Verlag
Springer-Verlag
Erschienen in
Evolutionary Intelligence / Ausgabe 1-2/2009
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-009-0023-2

Weitere Artikel der Ausgabe 1-2/2009

Evolutionary Intelligence 1-2/2009 Zur Ausgabe

Editorial

Special issue on genetic fuzzy systems: new advances

Special Issue

Rule base and adaptive fuzzy operators cooperative learning of Mamdani fuzzy systems with multi-objective genetic algorithms

Special Issue

Evolutionary parallel and gradually distributed lateral tuning of fuzzy rule-based systems

Special Issue

Extending a simple genetic cooperative-competitive learning fuzzy classifier to low quality datasets

Special Issue

Multi-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems