Profit Based Unit Commitment in Deregulated Electricity Markets Using A Hybrid Lagrangian Relaxation - Particle Swarm Optimization Approach

  • Adline K Bikeri Jomo Kenyatta University of Agriculture and Technology
  • Christopher M Maina Technical University of Kenya
  • Peter K Kihato, Jomo Kenyatta University of Agriculture and Technology

Abstract

In deregulated electricity markets, individual generation companies (GENCOs) carry out independent unit commitment based on predicted energy and revenue prices. The GENCOs unit com-mitment strategies are developed with the aim of maximizing profit based on the cost characteristics of their generators and revenues from predicted prices of energy and reserve subject to all prevailing constraints in what is known as Profit Based Unit Commitment (PBUC). A tool for carrying out PBUC is an important need for the GENCOs. This paper demonstrates the development of a solution methodology for the PBUC optimization problem in deregulated electricity markets. A hybrid of the Lagrangian Relaxation (LR) and Particle Swarm Optimization (PSO) algorithms is used to determinean optimal UC schedule in a day-ahead market using the expected energy and reserve prices taking advantage of the strengths of both algorithms. The PSO algorithm is used to update the Lagrange multipliers giving a better quality solution. An analysis of the PSO algorithm parameters is carried out to determine the parameters that give the best solution. The algorithm is implemented in MATLAB software and tested for a GENCO with 54 thermal units adapted from theĀ  standard IEEE 118-bus test system.
Published
Sep 15, 2017
How to Cite
BIKERI, Adline K; MAINA, Christopher M; KIHATO,, Peter K. Profit Based Unit Commitment in Deregulated Electricity Markets Using A Hybrid Lagrangian Relaxation - Particle Swarm Optimization Approach. Proceedings of Sustainable Research and Innovation Conference, [S.l.], p. 1-6, sep. 2017. ISSN 2079-6226. Available at: <http://sri.jkuat.ac.ke/ojs/index.php/proceedings/article/view/568>. Date accessed: 18 aug. 2018.