Multiobjective invasive weed optimization: Application to analysis of Pareto improvement models in electricity markets

  • Authors:

  • Amir Hossein Nikoofard;Hossein Hajimirsadeghi;Ashkan Rahimi-Kian;Caro Lucas

  • Affiliations:

  • Control and Intelligent Processing Center of Excellence, ECE Department, College of Engineering, University of Tehran, PO Box 14395-515, Tehran, Iran;Control and Intelligent Processing Center of Excellence, ECE Department, College of Engineering, University of Tehran, PO Box 14395-515, Tehran, Iran;Control and Intelligent Processing Center of Excellence, ECE Department, College of Engineering, University of Tehran, PO Box 14395-515, Tehran, Iran;Control and Intelligent Processing Center of Excellence, ECE Department, College of Engineering, University of Tehran, PO Box 14395-515, Tehran, Iran

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2012

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Abstract

This paper presents a proposal for multiobjective Invasive Weed Optimization (IWO) based on nondominated sorting of the solutions. IWO is an ecologically inspired stochastic optimization algorithm which has shown successful results for global optimization. In the present work, performance of the proposed nondominated sorting IWO (NSIWO) algorithm is evaluated through a number of well-known benchmarks for multiobjective optimization. The simulation results of the test problems show that this algorithm is comparable with other multiobjective evolutionary algorithms and is also capable of finding better spread of solutions in some cases. Next, the proposed algorithm is employed to study the Pareto improvement model in two complex electricity markets. First, the Pareto improvement solution set is obtained for a three-player oligopolistic electricity market with a nonlinear demand function. Then, the IEEE 30-bus power system with transmission constraints is considered, and the Pareto improvement solutions are found for the model with deterministic cost functions. In addition, NSIWO algorithm is used to analyze this system with stochastic cost data in a risk management problem which maximizes the expected total profit but minimizes the profit risk in the market.