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.
- Bilateral negotiations Day-ahead markets Peer-to-peer bilateral transactions Machine learning Heuristic methods Adaptive agents Agent-based models
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering