Volume 8, Issue 2 (June 2012)                   IJEEE 2012, 8(2): 164-176 | Back to browse issues page

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Gorgizadeh S, Akbari Foroud A, Amirahmadi M. Strategic Bidding in a Pool-Based Electricity Market under Load Forecast Uncertainty. IJEEE 2012; 8 (2) :164-176
URL: http://ijeee.iust.ac.ir/article-1-415-en.html
Strategic Bidding in a Pool-Based Electricity Market under Load Forecast Uncertainty
Sh. Gorgizadeh , A. Akbari Foroud , M. Amirahmadi
Abstract:   (8590 Views)
This paper proposes a method for determining the price bidding strategies of market participants consisting of Generation Companies (GENCOs) and Distribution Companies (DISCOs) in a day-ahead electricity market, while taking into consideration the load forecast uncertainty and demand response programs. The proposed algorithm tries to find a Pareto optimal point for a risk neutral participant in the market. Because of the complexity of the problem a stochastic method is used. In the proposed method, two approaches are used simultaneously. First approach is Fuzzy Genetic Algorithm for finding the best bidding strategies of market players, and another one is Mont-Carlo Method that models the uncertainty of load in price determining algorithm. It is demonstrated that with considering transmission flow constraints in the problem, load uncertainty can considerably influences the profits of companies and so using the second part of the proposed algorithm will be useful in such situation. It is also illustrated when there are no transmission flow constraints, the effect of load uncertainty can be modeled without using a stochastic model. The algorithm is finally tested on an 8 bus system.
Keywords: Electricity Market , Bidding Strategy , Game Theory , Genetic Algorithm , Fuzzy Sets , Mont-Carlo Simulation
Full-Text [PDF 301 kb]   (3936 Downloads)    
Type of Study: Research Paper | Subject: Market Deregulation
Received: 2011/07/14 | Revised: 2013/05/25 | Accepted: 2013/05/25
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.