Demand bidding optimization for an aggregator with a genetic algorithm

Leehter Yao*, Wei Hong Lim, Sew Sun Tiang, Teng Hwang Tan, Chin Hong Wong, Jia Yew Pang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
40 Downloads (Pure)


Demand response (DR) is an effective solution used to maintain the reliability of power systems. Although numerous demand bidding models were designed to balance the demand and supply of electricity, these works focused on optimizing the DR supply curve of aggregator and the associated clearing prices. Limited researches were done to investigate the interaction between each aggregator and its customers to ensure the delivery of promised load curtailments. In this paper, a closed demand bidding model is envisioned to bridge the aforementioned gap by facilitating the internal DR trading between the aggregator and its large contract customers. The customers can submit their own bid as a pairs of bidding price and quantity of load curtailment in hourly basis when demand bidding is needed. A purchase optimization scheme is then designed to minimize the total bidding purchase cost. Given the presence of various load curtailment constraints, the demand bidding model considered is highly nonlinear. A modified genetic algorithm incorporated with efficient encoding scheme and adaptive bid declination strategy is therefore proposed to solve this problem effectively. Extensive simulation shows that the proposed purchase optimization scheme can minimize the total cost of demand bidding and it is computationally feasible for real applications.

Original languageEnglish
Article number2498
Issue number10
Publication statusPublished - 20 Sept 2018


  • Demand bidding
  • Demand response
  • Genetic algorithm
  • Load curtailment
  • Optimization

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering


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