Power allocation and spectrum sharing in wireless networks: an implementation theory approach

Kakhbod, Ali; Nayyar, Ashutosh; Sharma, Shabnam; Teneketzis, Demosthenis

doi:10.1017/cbo9781139524421.007

Cited by 4 publications

(4 citation statements)

References 48 publications

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“…We adopt Nash Equilibrium (NE) as a solution/equilibrium concept. The interpretation of NE is the same as in [15]- [16].…”

Section: B Contributions Of the Papermentioning

confidence: 99%

“…Such a provision of incentives must be based, in general, on all the information available at the current stage and must, in general, take the whole future into account. Algorithms with the above features are currently unavailable [15]. Future problems along this line of research include the consideration of inelasticity for demand, transmission constraints, and dynamic games for a number of markets over a time horizon.…”

Section: Conclusion and Reflectionsmentioning

confidence: 99%

See 1 more Smart Citation

Electricity Pooling Markets with Strategic Producers Possessing Asymmetric Information I: Elastic Demand

Rasouli,

Teneketzis

2014

Preprint

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In the restructured electricity industry, electricity pooling markets are an oligopoly with strategic producers possessing private information (private production cost function). We focus on pooling markets where aggregate demand is represented by a non-strategic agent. We consider demand to be elastic.We propose a market mechanism that has the following features. (F1) It is individually rational. (F2) It is budget balanced. (F3) It is price efficient, that is, at equilibrium the price of electricity is equal to the marginal cost of production. (F4) The energy production profile corresponding to every non-zero Nash equilibrium of the game induced by the mechanism is a solution of the corresponding centralized problem where the objective is the maximization of the sum of the producers' and consumers' utilities.We identify some open problems associated with our approach to electricity pooling markets.

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“…We adopt Nash Equilibrium (NE) as a solution/equilibrium concept. The interpretation of NE is the same as in [15]- [16].…”

Section: B Contributions Of the Papermentioning

confidence: 99%

Section: Conclusion and Reflectionsmentioning

confidence: 99%

Electricity Pooling Markets with Strategic Producers Possessing Asymmetric Information I: Elastic Demand

Rasouli,

Teneketzis

2014

Preprint

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show abstract

“…In this paper we focus on electricity pooling markets with strategic producers possessing private information, nonstrategic (price-taking) inelastic demand, and no transmission constraints. We adopt Nash Equilibrium (NE) as a solution/equilibrium concept; the interpretation of NE is the same as in [15]- [16].…”

Section: B Contributions Of the Papermentioning

confidence: 99%

Electricity Pooling Markets with Strategic Producers Possessing Asymmetric Information II: Inelastic Demand

Rasouli¹,

Teneketzis²

2014

Preprint

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In the restructured electricity industry, electricity pooling markets are an oligopoly with strategic producers possessing private information (private production cost function). We focus on pooling markets where aggregate demand is represented by a non-strategic agent.Inelasticity of demand is a main difficulty in electricity markets which can potentially result in market failure and high prices. We consider demand to be inelastic.We propose a market mechanism that has the following features. (F1) It is individually rational. (F2) It is budget balanced. (F3) It is price efficient, that is, at equilibrium the price of electricity is equal to the marginal cost of production. (F4) The energy production profile corresponding to every non-zero Nash equilibrium of the game induced by the mechanism is a solution of the corresponding centralized problem where the objective is the maximization of the sum of the producers' and consumers' utilities.We identify some open problems associated with our approach to electricity pooling markets.

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“…It is also shown that the proposed clearing strategy can realize the team optimal solution. Different from many existing works [2], [3], the proposed mechanism can coordinate a group of TCLs for demand response with a systematic consideration of various practical factors, such as heterogeneous load dynamics, private information, communication resources, etc.…”

Section: Introductionmentioning

confidence: 99%

Market-Based Coordination of Thermostatically Controlled Loads—Part II: Unknown Parameters and Case Studies

Zhang

Lian

et al. 2016

IEEE Trans. Power Syst.

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This two-part paper considers the coordination of a population of thermostatically controlled loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the bidding and market clearing strategy to motivate self-interested users to realize efficient energy allocation subject to a peak energy constraint. The companion paper (Part I) formulates the problem and proposes a load coordination framework using the mechanism design approach. To address the unknown parameters, Part II of this paper presents a joint state and parameter estimation framework based on the expectation maximization algorithm. The overall framework is then validated using realworld weather data and price data, and is compared with other approaches in terms of aggregated load response. Simulation results indicate that our coordination framework can effectively improve the operational efficiency of the distribution system and reduce power congestion at key times.

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Power allocation and spectrum sharing in wireless networks: an implementation theory approach

Cited by 4 publications

References 48 publications

Electricity Pooling Markets with Strategic Producers Possessing Asymmetric Information I: Elastic Demand

Electricity Pooling Markets with Strategic Producers Possessing Asymmetric Information I: Elastic Demand

Electricity Pooling Markets with Strategic Producers Possessing Asymmetric Information II: Inelastic Demand

Market-Based Coordination of Thermostatically Controlled Loads—Part II: Unknown Parameters and Case Studies

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