An Efficient and Incentive Compatible Mechanism for Wholesale Electricity Markets

Xu, Yunjian; Low, Steven H.

doi:10.1109/tsg.2015.2483523

Cited by 81 publications

(59 citation statements)

References 32 publications

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“…Proof A similar result was proven in [18], using convex analysis in the context of DC-OPF markets. We provide a simple and more general proof applicable to any setting where the LMP mechanism ensures the existence of a competitive equilibrium.…”

Section: Comparison Of the Lmp And The Vcg Paymentssupporting

confidence: 63%

“…We further assume that 0 ∈ X l and c l (0) = 0. This assumption holds for many electricity markets, for instance, control reserve markets and day-ahead markets that include generators' startup costs [18,29]. Each bidder l then submits a bid function to the central operator, denoted by b l :X l → R + , where 0 ∈X l ⊆ R t + and b l (0) = 0.…”

Section: Mechanism Frameworkmentioning

confidence: 99%

“…Alternatively, they may also correspond to procurement of the required amounts of power supplies, for instance, in the Swiss control reserve markets accepted reserves must have a deficit probability of less than 0.2%. Thus, problem (1) defines a general class of electricity market problems, including energy-reserve co-optimized markets [18], stochastic markets [29,30], and AC-OPF problems [10]. As a remark, if the problem (1) is infeasible, the objective value is unbounded,…”

Section: Mechanism Frameworkmentioning

confidence: 99%

“…Our results can be generalized to exchanges, see Section 3.2.2 3. There are markets that include shut-down costs or minimum output levels[18]. To address these markets, we draw attention to the properties and the results for which the assumptions, c l (0) = 0 and b l (0) = 0, are pivotal 4.…”

mentioning

confidence: 99%

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Core-Selecting Mechanisms in Electricity Markets

Karaca

Kamgarpour

2020

IEEE Trans. Smart Grid

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Due to its theoretical virtues, several recent works propose the use of the incentive-compatible Vickrey-Clarke-Groves (VCG) mechanism for electricity markets. Coalitions of participants, however, can influence the VCG outcome to obtain higher collective profit. To address this issue, we propose core-selecting mechanisms for their coalition-proofness. We show that coreselecting mechanisms generalize the economic rationale of the locational marginal pricing (LMP) mechanism. Namely, these mechanisms are the exact class of mechanisms that ensure the existence of a competitive equilibrium in linear/nonlinear prices. This implies that the LMP mechanism is also core-selecting, and hence coalition-proof. In contrast to the LMP mechanism, core-selecting mechanisms exist for a broad class of electricity markets, such as ones involving nonconvex costs and nonconvex constraint sets. In addition, they can approximate truthfulness without the price-taking assumption of the LMP mechanism. Finally, we show that they are also budget-balanced. Our results are verified with case studies based on optimal power flow test systems and the Swiss reserve market.

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Section: Comparison Of the Lmp And The Vcg Paymentssupporting

confidence: 63%

Section: Mechanism Frameworkmentioning

confidence: 99%

Section: Mechanism Frameworkmentioning

confidence: 99%

mentioning

confidence: 99%

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Core-Selecting Mechanisms in Electricity Markets

Karaca

Kamgarpour

2020

IEEE Trans. Smart Grid

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“…Our approach di↵ers from Silva et al [87] and the more recent work of Xu and Low [110] and Jofré et al [47] in that we consider iterative mechanisms. In an iterative mechanism, market participants are asked to report the cost or willingness to pay for small adjustments at each iteration.…”

mentioning

confidence: 99%

Multi-agent Control and Optimization under Uncertainty

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Many natural and engineering systems can be characterized as a collection of interacting agents each having access to local information, making local decisions, having local interactions with neighbors, and seeking to optimize local objectives. The analysis and design of such systems falls under the broader framework of "multi-agent control and optimization".Here depending on the context, "agents" could be animals, autonomous vehicles, power plants, etc. Multi-agent systems have the potential for solving problems that are di cult or impossible for an individual agent or monolithic system to solve. However, the complexity associated with a potentially large number of interacting agents brings about challenging control and optimization problems for system designers and coordinators. Such di culty is often enhanced by the presence of noise/uncertainty that is pervasive in both biological and engineering systems.In this dissertation, we focus on the analysis and design of several multi-agent control/optimization algorithms for various problems under uncertainty. Swarming, flocking, schooling and other aggregations of organisms in groups have been studied extensively in biology (see [64,65,74]). Organisms in swarms can exploit several advantages of staying close to each other for more e↵ective foraging. For example, in [38] Grünbaum explains how social foragers more successfully perform chemotaxis over noisy gradients than individuals.Such biological advantage is also demonstrated in Passino [76] by modeling the behavior of E. coli and M. xanthus bacteria. In our first line of work, we develop a mathematical model for analyzing the benefits of social foraging in a noisy environment. We identify conditions i on the nutrient profile ensuring that local agent actions will lead to cohesive foraging. For convex, smooth nutrient profiles we formalize the way in which swarming for social foraging is better at handling the e↵ects of noise when compared to the average of individual foraging strategies. Under a swarming discipline, observational noise realizations that induce trajectories di↵ering too much from the group average are likely to be discarded because of each individual's need to maintain cohesion. As a result, the group trajectories are less a↵ected by noise. Simulation experiments indicate that our theoretical results are also robust to inter-agent communication constraints and non-convex nutrient profiles.The above results suggest that swarming-like approaches for the control and/or optimization of networked agents may provide an additional level of robustness. This is precisely the gist of our second line of work in which we consider a distributed computing algorithmic scheme for stochastic optimization which relies on modest communication requirements amongst processors and most importantly, does not require synchronization. Specifically, we analyze a scheme with N > 1 independent threads each implementing a stochastic gradient algorithm. The threads are coupled via a perturbation of the gradient (with...

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