Optimization-Based Bidding in Day-Ahead Electricity Auction Markets: A Review of Models for Power Producers

Kwon, Roy H.; Frances, Daniel M.

doi:10.1007/978-3-642-23193-3_2

Cited by 49 publications

(21 citation statements)

References 28 publications

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“…Because UC is solved ahead of actual operations, possibly precisely with the aim of computing the bids that will contribute to the setting of these prices (cf. e.g., Borghetti et al 2003a;Bompard and Ma 2012;Kwon and Frances 2012;Rocha and Das 2012), this requires nontrivial forecast models in order to obtain reasonable estimates of the prices (e.g., Oudjane et al 2006;Li et al 2010;Zareipour 2012). Depending on the time horizon and specific application, different price models can be considered.…”

Section: The Objective Functionmentioning

confidence: 98%

Large-scale Unit Commitment under uncertainty

Ackooij²,

et al. 2015

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The Unit Commitment problem in energy management aims at finding the optimal productions schedule of a set of generation units while meeting various system-wide constraints. It has always been a large-scale, non-convex difficult problem, especially in view of the fact that operational requirements imply that it has to be solved in an unreasonably small time for its size. Recently, the ever increasing capacity for renewable generation has strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex, uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focusing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, also providing entry points to the relevant literature on optimization under uncertainty

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Section: The Objective Functionmentioning

confidence: 98%

Large-scale Unit Commitment under uncertainty

Ackooij²,

et al. 2015

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“…The optimal bidding strategy problem for generators and/or consumers is how best to construct their offer bid prices given their own costs and constraints and their anticipation of rival and market behavior [17]. Latest detailed surveys of the optimal bidding strategies for practical and contemporary day-ahead electricity markets can be found in [18], [19]. In this background, generators submit their cost functions to the utility based on their marginal production costs in our framework.…”

Section: Introductionmentioning

confidence: 99%

Optimal bidding and worst case pricing under dynamic integration mechanism for LQG power networks

Murao

Hirata

Uchida

2015

2015 European Control Conference (ECC)

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We investigate an optimal bidding and an optimal and worst case pricing under a dynamic integration mechanism for linear quadratic Gaussian (LQG) power networks. The participant of the dynamic integration mechanism is divided into two kind of players, that are generators and/or consumers, called agents, and one public commission, called utility. In the mechanism, each agent decides private control to minimize his/her own cost functional, and the utility decides prices and incentives to minimize a public cost functional. The dynamic integration mechanism satisfies both public optimality by private optimal controls of each agent and incentive compatibility. In this setting, we present an optimal bidding strategy under the condition such that each agent selects his/her preference from a set of his/her preferences and reports his/her state truthfully. Based on the proposed optimal bidding strategy, each agent can select his/her optimal preference which minimizes his/her minimal private cost. In addition, we discuss optimal pricing and worst case pricing for the whole power networks.

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“…Unlike most commodities, electricity markets are built on a transmission network with thousands of nodes [10], have temporal output constraints on generation equipment [16], and are typically structured as a series of sequential markets [17]. To address these issues, a parallel body of literature has sprung up in which engineering models are used to reflect the technical decisions faced by individual producers [18]. These models are often nonlinear, nonconvex, and computationally intractable for modeling the decisions of more than a single producer with a small fleet of generators.…”

Section: A Relevant Literaturementioning

confidence: 99%

Robust Cournot-Bertrand equilibria on power networks

Mather

Munsing

2017

2017 American Control Conference (ACC)

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Abstract-We present a convex model describing risk-averse strategies for electricity producers in congested electricity networks. Extending prior work on Cournot-Bertrand equilibria in Poolco-style spot markets with locational marginal pricing, we propose a formulation which integrates uncertainty through robust convex optimization. We find that producers uniformly benefit from robust strategies under small uncertainty intervals, and explore the impacts of congestion and network effects on strategic behavior. We demonstrate our results on a simple example and explore the impacts of robust strategies on social welfare, finding that these risk-averse strategies reduce welfare by restricting output and increasing prices. By formulating the equilibrium conditions as a convex optimization problem, we are able to scale our results to large networks and accommodate many sources of uncertainty.

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Optimization-Based Bidding in Day-Ahead Electricity Auction Markets: A Review of Models for Power Producers

Cited by 49 publications

References 28 publications

Large-scale Unit Commitment under uncertainty

Large-scale Unit Commitment under uncertainty

Optimal bidding and worst case pricing under dynamic integration mechanism for LQG power networks

Robust Cournot-Bertrand equilibria on power networks

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