Extended Second Price Auctions With Elastic Supply for PEV Charging in the Smart Grid

Bhattacharya, Saptarshi; Kar, Koushik; Chow, Joe H.; Gupta, Aparna

doi:10.1109/tsg.2016.2546281

Cited by 37 publications

(23 citation statements)

References 20 publications

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“…General incomplete-information game-theoretic frameworks include Bayesian games and hypergames, e.g., [24]. In both VCG and PSP auctions (e.g., [25,26]), issues of truthfulness in the disclosed bids are considered, i.e., reflecting actual demand response (by marginal valuation). More prosaic approaches simply interpolate and extrapolate from presumed honest bids (by (amount, price)) to obtain a complete estimate of other players' demand response.…”

Section: Discussion: When To Penalizementioning

confidence: 99%

Generation bidding game with potentially false attestation of flexible demand

Shan

Raghuram

Kesidis

et al. 2015

EURASIP J. Adv. Signal Process.

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With the onset of large numbers of energy-flexible appliances, in particular plug-in electric and hybrid-electric vehicles, a significant portion of electricity demand will be somewhat flexible and accordingly may be responsive to changes in electricity prices. In the future, this increased degree of demand flexibility (and the onset of only short-term predictable intermittent renewable supply) will considerably exceed present level of uncertainty in day-ahead prediction of assumed inelastic demand. For such a responsive demand idealized, we consider a deregulated wholesale day-ahead electricity marketplace wherein bids by generators (or energy traders) are determined through a Nash equilibrium via a common clearing price (i.e., no location marginality). This model assumes the independent system operator (ISO) helps the generators to understand how to change their bids to improve their net revenue based on a model of demand-response. The model of demand-response (equivalently, demand-side bidding day ahead) is based on information from load-serving entities regarding their price-flexible demand. We numerically explore how collusion between generators and loads can manipulate this market. The objective is to learn how to deter such collusion, e.g., how to set penalties for significant differences between stated and actual demand, resulting in higher energy prices that benefit certain generators.

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Section: Discussion: When To Penalizementioning

confidence: 99%

Generation bidding game with potentially false attestation of flexible demand

Shan

Raghuram

Kesidis

et al. 2015

EURASIP J. Adv. Signal Process.

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“…[13]- [17]), plug-in electric vehicles system (e.g. [19]), power allocation and spectrum sharing problem (e.g. [18]), networked public good [20], networked private good [21].…”

Section: B Mechanism Design For the Num Problemsmentioning

confidence: 99%

“…conv count ← conv count + 1; Set conv flag ← 1 and broadcasts it; 17 end 18 end 19 Each agent i submits message m i to the system designer; 20 The system designer computes (t i (m), Π i (m)) according to (15)- (17) and sends them to every agent i;…”

Section: A the Iterative Denum Algorithmmentioning

confidence: 99%

“…The termination happens if the relative changes of all agents' price proposals in any continuous I sub-iterations are small enough. When the algorithm converges, agents submit their messages to the system designer to compute their imposed constraints and taxes (lines [19][20].…”

Section: A the Iterative Denum Algorithmmentioning

confidence: 99%

“…Second, existing mechanisms proposed for network resource allocation are often applicable to only specific networking scenarios (e.g. flow control problems [13]- [17], power and spectrum allocation [18], and electric vehicles systems [19]). These mechanisms often do not work for more general and sophisticated NUM Problems or the general network sharing framework.…”

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confidence: 99%

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Efficient Network Sharing With Asymmetric Constraint Information

Zhang

Huang

2019

IEEE J. Select. Areas Commun.

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Network sharing has become a key feature of various enablers of the next generation network, such as network function virtualization and fog computing architectures. Network utility maximization (NUM) is a general framework for achieving fair, efficient, and cost-effective sharing of constrained network resources. When agents have asymmetric and private information, however, a fundamental economic challenge is how to solve the NUM Problem considering the self-interests of strategic agents. Many previous related works have proposed economic mechanisms that can cope with agents' private utilities. However, the network sharing paradigm introduces the issue of information asymmetries regarding constraints. The related literature largely neglected such an issue; limited closely related studies provided solutions only applicable to specific application scenarios. To tackle these issues, we propose the Decomposable NUM (DeNUM) Mechanism and the Dynamic DeNUM (DyDe-NUM) Mechanism, the first mechanisms in the literature for solving NUM Problems considering private utility and constraint information. The key idea of both mechanisms is to decentralize the decision process to agents, who will make resource allocation decisions without the need of revealing private information to others. Under a monitorable influence assumption, the DeNUM Mechanism yields the network-utility maximizing solution at an equilibrium, and achieves other desirable economic properties (such as individual rationality and budget balance). We further establish the connection between the equilibrium structure and the primal-dual solution to a related optimization problem, based on which we prove the convergence of the DeNUM Algorithm to an equilibrium. When the agents' influences are not monitorable, we propose the DyDeNUM Mechanism that yields the networkutility maximizing solution at the cost of the balanced budget. Finally, as a case study, we apply the proposed mechanisms to solving the NUM problem for a fog-based user-provided network, and show that both mechanisms improve the network utility by 34% compared to a non-cooperation benchmark.Index Terms-Mechanism design, network sharing, network utility maximization, asymmetric constraint information.

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