Behavioral simulation and optimization of generation companies in electricity markets by fuzzy cognitive map

Authors:
S. F. Ghaderi;A. Azadeh;B. Pourvalikhan Nokhandan;E. Fathi
Affiliations:
Department of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, University College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Ir ...;Department of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, University College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Ir ...;Department of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, University College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Ir ...;Department of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, University College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Ir ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Simulation can be used in a wide range of applications in an electricity market. There are many reasons that market players and regulators are very interested in anticipating the behavior of the market. Behavior of a generation company (GENCO) in electricity market is an important factor that affects the market behavior. Several factors affect the behavior of a GENCO directly and indirectly. In this study, a new approach based on fuzzy cognitive map (FCM) is introduced to model and simulate GENCO's behavior in the electricity market with respect to profit maximization. FCM helps the decision makers to understand the complex dynamics between a certain strategic goal and the related factors. This paper examines how effective factors affect on a GENCO's profit. To identify key factors relevant to the goal, a FCM is built and then analyzed. To analyze this problem, two cases as simple FCM and weighted FCM are considered. Simple FCM shows how the determined factors affect on goal. A hidden pattern is obtained by this case. Weighted FCM helps sensitivity analysis of the model. In addition, the weighted FCM is used usefully to clearly measure the composite effects resulting from changes of multiple factors. This application is shown by two different case studies. This is the first study that models and simulates the behavior of GENCO in electricity market with respect to profit maximization.