Bidding Papers – Dr. Vittal’s EE 553 Course (Lecture 33)
Provides a general framework for competitive electricity markets under Pool-co concept with perfect competition for supply-side bidders. An optimal bidding strategy is proved to be a unit’s true cost under the assumption that the bid of each unit does not affect the market-clearing price. For future work, the authors attempts to investigate the relaxation of perfect competition that also considers demand-side bidders, the study of market power, the impacts of transmission, and the incorporation of financial contracts into the strategies of bidders.
This paper presents
a model and a method for the bidding and self-scheduling problem from the
viewpoint of a utility. The model considers ISO bid selections and uncertain
bidding information of other market participants. Uses Lagrangian Relaxation to
solve the utility’s bidding and self-scheduling model.
This paper solves an Optimal Power Flow based on the maximization of social welfare to determine the generation and load dispatch and system spot prices. Using two-level optimization model, this paper attempts to maximize market participants’ own profit subject to their own dispatch constraint and the OPF determines the price. When all participants are trying to maximize their profit, it leads to Nash equilibrium.
Presents a simple suboptimal bidding strategy
that arises from the unknown information about other bidders. Mainly models the
market in the view of buyers perspective and assumes triangular density
functions on other buyers.
Presents a model on maximizing strategic bids of a generation supplier in a multi-period auction (Pool-co concept) in a perfectly competitive market. Employs non-linear programming method with Lagrangian relaxation method to solve the model.
Uses two-level optimization method. Top level solves a Centralized Economic Dispatch that implements the priority list method and bottom level solves the Decentralized Bidding (DB) sub problems. The DB is based on parametric dynamic programming approach to produce hourly bids. Both levels of optimization are based on revenue maximization. The algorithm results in a reliable economic dispatch and market clearing price discovery.
Model a supplier single side auction where the bidding decision is optimized from a single supplier’s viewpoint. Competing suppliers are modeled with probabilistic model described by probability distributions and solved by stochastic optimization. Shows that in a “multiple-commodity second price auction”, the suppliers have an incentive to bid at marginal costs.