DocumentCode
3602371
Title
A Dynamic Risk-Constrained Bidding Strategy for Generation Companies Based on Linear Supply Function Model
Author
Ansari, Bananeh ; Rahimi-Kian, Ashkan
Author_Institution
Dept. of Electr. Eng. & Comput. Sci., Colorado Sch. of Mines, Golden, CO, USA
Volume
9
Issue
4
fYear
2015
Firstpage
1463
Lastpage
1474
Abstract
A risk-constrained bidding model for generation companies (GenCos) competing in a pool-based electricity market is presented in this paper. In the proposed model, it is assumed that GenCos submit linear supply functions to the market operator. The intercept of the supply function is the decision variable to be optimized during the strategic bidding process by the GenCo. The model takes into account the uncertainties in system demand and uses mean-variance portfolio theory to assess and manage the players´ risk. Moreover, electricity market dynamics are modeled assuming boundedly rational GenCos. The strategic bidding process is then formulated as an optimal control problem and solved using a dynamic programming algorithm. A real-time adaptive feedback is also added to the bidding model to manage the congestion situations. Finally, the proposed bidding scheme is applied to two different case studies, and the simulated results are analyzed and compared with those in some similar research studies.
Keywords
adaptive control; dynamic programming; feedback; investment; optimal control; power generation economics; power markets; risk analysis; tendering; GenCos; dynamic programming algorithm; dynamic risk-constrained bidding strategy; generation company; linear supply function model; mean-variance portfolio theory; optimal control problem; player risk management; pool-based electricity market; real-time adaptive feedback; Aggregates; Companies; Cost function; Electricity supply industry; Optimal control; Reactive power; Bidding strategy; dynamic programming (DP); pool-based electricity market; risk management; supply function model;
fLanguage
English
Journal_Title
Systems Journal, IEEE
Publisher
ieee
ISSN
1932-8184
Type
jour
DOI
10.1109/JSYST.2015.2427374
Filename
7110520
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