@article{imran_bilateral_2020,
	title = {Bilateral negotiations for electricity market by adaptive agent-tracking strategy},
	volume = {186},
	url = {https://www.sciencedirect.com/science/article/pii/S0378779620301966},
	abstract = {Bilateral transactions hedge both sides against uncertain price and volume risks of day-ahead auction and make up major portions of trading in electricity markets. Peer-to-peer bilateral transactions avoid broker fees but involve challenges of balancing between cooperative and competitive strategies for multi-round negotiations. To solve these challenges, this paper develops novel utility-based and adaptive agent-tracking strategies for bilateral negotiations. Relying on bilateral transaction volume and utility curves determined over a price range during unilateral pre-negotiation, utility-based strategies are developed for generation company (GenCo) agent (load serving entity (LSE) agent) to offer (bid) volumes and prices during multi-round bilateral negotiations. GenCo agent is also equipped with a new adaptive agent-tracking strategy that estimates reservation price of each LSE agent by Bayesian learning and updates the estimates in each round. The adaptive agent-tracking strategy facilitates cooperative yet competitive responses. Integration of new bilateral negotiation strategies with existing day-ahead auction in a renowned agent-based platform also enables combined simulation of the two market types. The case study demonstrates that the adaptive agent-tracking strategy empowers GenCoagents to swing bilateral negotiation results in their favor and yield 7\% more payoff than the utility-based strategy, while achieving 100\% improvement in frequency of failure of negotiation.},
	journal = {Electric Power Systems Research},
	author = {Imran, Kashif and Zhang, Jiangfeng and Pal, Anamitra and Khattak, Abraiz and Ullah, Kafait and Baig, Sherjeel Mahmood},
	month = sep,
	year = {2020},
	keywords = {Bilateral negotiations Day-ahead markets Peer-to-peer bilateral transactions Machine learning Heuristic methods Adaptive agents Agent-based models},
	pages = {1--12},
}

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Relying on bilateral transaction volume and utility curves determined over a price range during unilateral pre-negotiation, utility-based strategies are developed for generation company (GenCo) agent (load serving entity (LSE) agent) to offer (bid) volumes and prices during multi-round bilateral negotiations. GenCo agent is also equipped with a new adaptive agent-tracking strategy that estimates reservation price of each LSE agent by Bayesian learning and updates the estimates in each round. The adaptive agent-tracking strategy facilitates cooperative yet competitive responses. Integration of new bilateral negotiation strategies with existing day-ahead auction in a renowned agent-based platform also enables combined simulation of the two market types. The case study demonstrates that the adaptive agent-tracking strategy empowers GenCoagents to swing bilateral negotiation results in their favor and yield 7% more payoff than the utility-based strategy, while achieving 100% improvement in frequency of failure of negotiation.","journal":"Electric Power Systems Research","author":[{"propositions":[],"lastnames":["Imran"],"firstnames":["Kashif"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Jiangfeng"],"suffixes":[]},{"propositions":[],"lastnames":["Pal"],"firstnames":["Anamitra"],"suffixes":[]},{"propositions":[],"lastnames":["Khattak"],"firstnames":["Abraiz"],"suffixes":[]},{"propositions":[],"lastnames":["Ullah"],"firstnames":["Kafait"],"suffixes":[]},{"propositions":[],"lastnames":["Baig"],"firstnames":["Sherjeel","Mahmood"],"suffixes":[]}],"month":"September","year":"2020","keywords":"Bilateral negotiations Day-ahead markets Peer-to-peer bilateral transactions Machine learning Heuristic methods Adaptive agents Agent-based models","pages":"1–12","bibtex":"@article{imran_bilateral_2020,\r\n\ttitle = {Bilateral negotiations for electricity market by adaptive agent-tracking strategy},\r\n\tvolume = {186},\r\n\turl = {https://www.sciencedirect.com/science/article/pii/S0378779620301966},\r\n\tabstract = {Bilateral transactions hedge both sides against uncertain price and volume risks of day-ahead auction and make up major portions of trading in electricity markets. Peer-to-peer bilateral transactions avoid broker fees but involve challenges of balancing between cooperative and competitive strategies for multi-round negotiations. To solve these challenges, this paper develops novel utility-based and adaptive agent-tracking strategies for bilateral negotiations. Relying on bilateral transaction volume and utility curves determined over a price range during unilateral pre-negotiation, utility-based strategies are developed for generation company (GenCo) agent (load serving entity (LSE) agent) to offer (bid) volumes and prices during multi-round bilateral negotiations. GenCo agent is also equipped with a new adaptive agent-tracking strategy that estimates reservation price of each LSE agent by Bayesian learning and updates the estimates in each round. The adaptive agent-tracking strategy facilitates cooperative yet competitive responses. Integration of new bilateral negotiation strategies with existing day-ahead auction in a renowned agent-based platform also enables combined simulation of the two market types. The case study demonstrates that the adaptive agent-tracking strategy empowers GenCoagents to swing bilateral negotiation results in their favor and yield 7\\% more payoff than the utility-based strategy, while achieving 100\\% improvement in frequency of failure of negotiation.},\r\n\tjournal = {Electric Power Systems Research},\r\n\tauthor = {Imran, Kashif and Zhang, Jiangfeng and Pal, Anamitra and Khattak, Abraiz and Ullah, Kafait and Baig, Sherjeel Mahmood},\r\n\tmonth = sep,\r\n\tyear = {2020},\r\n\tkeywords = {Bilateral negotiations Day-ahead markets Peer-to-peer bilateral transactions Machine learning Heuristic methods Adaptive agents Agent-based models},\r\n\tpages = {1--12},\r\n}\r\n","author_short":["Imran, K.","Zhang, J.","Pal, A.","Khattak, A.","Ullah, K.","Baig, S. 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