Generation companies decision-making modeling by linear control theory

Gutiérrez-Alcaraz, G.; Sheblé, G.B.

doi:10.1016/j.epsr.2009.12.007

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…For the comparative studies, let us assume that GenCo2 represents a company that computes its bids using the supplyfunction-based method (developed in this paper), whereas GenCo1 represents a company that uses the Cournot-based method presented in [14] to develop its bids. Furthermore, assume that GenCos 3-6 are boundedly rational firms who bid according to (12).…”

Section: A Assumptionsmentioning

confidence: 99%

“…In the past decade, numerous methods have been proposed in the literature to develop bidding strategies for electricity market participants, including generating and load-serving entities and large consumers. Mathematical programming with equilibrium constraints [4], [5], nonlinear complementarity approach [6], [7], Lagrangian relaxation [8], ordinal optimization [9], stepwise nonlinear mixedinteger optimization [10], control theory [11], [12], optimal control theory [13], [14], stochastic optimization [15]- [17], binary expansion [18], [19], residual demand curve (RDC) with optimal power flow [20], RDC with stochastic dual dynamic programming [21], and RDC with stochastic mixed-integer programming [22] are a few of the methods.…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Risk-Constrained Bidding Strategy for Generation Companies Based on Linear Supply Function Model

Ansari

Rahimi-Kian

2015

IEEE Systems Journal

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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.Index Terms-Bidding strategy, dynamic programming (DP), pool-based electricity market, risk management, supply function model. 1932-8184

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Section: A Assumptionsmentioning

confidence: 99%