Title of article :
Optimal Bidding Strategies of GENCOs in Day-Ahead Energy and Spinning Reserve Markets Based on Hybrid GA-Heuristic Optimization Algorithm
Author/Authors :
Nazari, Mohammad Esmaeil Energy System Laboratory - Department of Electrical Engineering - Center of Excellence on Power Systems - Amirkabir University of Technology, Tehran , Mohammad Ardehali, Morteza Energy System Laboratory - Department of Electrical Engineering - Center of Excellence on Power Systems - Amirkabir University of Technology, Tehran
Abstract :
In an electricity market, every generation company (GENCO) attempts to maximize profit according to other participants bidding behaviors and power systems operating conditions. The goal of this study is to examine the optimal bidding strategy problem for GENCOs in energy and spinning reserve markets based on a hybrid GA-heuristic optimization algorithm. The heuristic optimization algorithm used in this study is successfully applied for validation and, it is determined that the heuristic optimization algorithm improves profits of a GENCO by 4.15-47.95% and 20.84-31.30% in single-sided and double-sided auctions, respectively.
Keywords :
bidding strategy , Energy market , Genetic Algorithm , Heuristic optimization , Spinning reserve market
Journal title :
Astroparticle Physics
