Reconfigurable interdependent infrastructure systems: Advances in distributed sensing, modeling, and control

Giacomoni, Anthony M.; Amin, Saurabh; Wollenberg, B.F.

doi:10.1109/acc.2011.5991197

Cited by 11 publications

(9 citation statements)

References 11 publications

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“…When the feeder supplies the maximum amount of electricity, the marginal electricity cost is dC/dx| x=3500 = 4.9¢/kWh = 49$/MWh. The real-time market clearing prices (MCPs) taken from the Midwest Independent Transmission System Operator (MISO) range between 1.79 $/MWh and 78.85 $/MWh [26]. Since our cost parameter fits this range, it is a reasonable choice.…”

Section: B Large-scale Scenario With Constant Parametersmentioning

confidence: 99%

Stackelberg-Game-Based Demand Response for At-Home Electric Vehicle Charging

Yoon

Choi

Park

et al. 2016

IEEE Trans. Veh. Technol.

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Consumer electricity consumption can be controlled through electricity prices, which is called demand response. Under demand response, retailers determine their electricity prices and customers respond accordingly with their electricity consumption levels. In particular, the demands of customers who own electric vehicles (EVs) are elastic with respect to price. The interaction between retailers and customers can be seen as a game because both attempt to maximize their own payoffs. This study models an at-home EV charging scenario as a Stackelberg game, and proves that this game reaches an equilibrium point at which the EV charging requirements are satisfied and retailer profits are maximized when customers use our proposed utility function. The equilibrium of our game can vary according to the weighting factor for the utility function of each customer, resulting in various strategic choices. Our numerical results confirm that the equilibrium of the proposed game lies somewhere between the minimum generation cost solution and the result of the equalcharging scheme.Index Terms-demand response, electric vehicle, real-time pricing, Stackelberg game. 0018-9545 (c)

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Section: B Large-scale Scenario With Constant Parametersmentioning

confidence: 99%

Stackelberg-Game-Based Demand Response for At-Home Electric Vehicle Charging

Yoon

Choi

Park

et al. 2016

IEEE Trans. Veh. Technol.

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“…DR is facilitated by deployment of the advanced metering infrastructure (AMI), which comprises a two-way communication network between utility companies and end-users (see Fig. 8) [24], [29]. Smart meters installed at end-users' premises are the AMI terminals at the end-users' side.…”

Section: B Demand Responsementioning

confidence: 99%

“…Demand response (DR) or load response is the adaptation of end-user power consumption to time-varying (or time-based) energy pricing, which is judiciously controlled by the utility companies to elicit desirable energy usage [24], [29]. The smart grid vision entails engaging residential end-users in DR programs.…”

Section: B Demand Responsementioning

confidence: 99%

“…The smart grid vision aspires to address such challenges by capitalizing on state-of-the-art information technologies in sensing, control, communication, and machine learning [2], [24]. The resultant grid is envisioned to have an unprecedented level of situational awareness and controllability over its services and infrastructure to provide fast and accurate diagnosis/prognosis, operation resiliency upon contingencies and malicious attacks, as well as seamless integration of distributed energy resources.…”

Section: Introductionmentioning

confidence: 99%

“…Enhancing grid efficiency without compromising stability and quality in the face of deregulation is imperative. Soliciting consumer participation and exploring new business opportunities facilitated by the intelligent grid infrastructure hold a great economic potential.The smart grid vision aspires to address such challenges by capitalizing on state-of-the-art information technologies in sensing, control, communication, and machine learning [2], [24]. The resultant grid is envisioned to have an unprecedented level of situational awareness and controllability over its services and infrastructure to provide fast and accurate diagnosis/prognosis, operation resiliency upon contingencies and malicious attacks, as well as seamless integration of distributed energy resources.…”

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confidence: 99%

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Monitoring and Optimization for Power Grids: A Signal Processing Perspective

Giannakis

Kekatos

Gatsis

et al. 2013

IEEE Signal Process. Mag.

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251

176

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I. INTRODUCTIONAlbeit the North American power grid has been recognized as the most important engineering achievement of the 20th century, the modern power grid faces major challenges [87]. Increasingly complex interconnections even at the continent size render prevention of the rare yet catastrophic cascade failures a strenuous concern. Environmental incentives require carefully revisiting how electrical power is generated, transmitted, and consumed, with particular emphasis on the integration of renewable energy resources. Pervasive use of digital technology in grid operation demands resiliency against physical and cyber attacks on the power infrastructure. Enhancing grid efficiency without compromising stability and quality in the face of deregulation is imperative. Soliciting consumer participation and exploring new business opportunities facilitated by the intelligent grid infrastructure hold a great economic potential.The smart grid vision aspires to address such challenges by capitalizing on state-of-the-art information technologies in sensing, control, communication, and machine learning [2], [24]. The resultant grid is envisioned to have an unprecedented level of situational awareness and controllability over its services and infrastructure to provide fast and accurate diagnosis/prognosis, operation resiliency upon contingencies and malicious attacks, as well as seamless integration of distributed energy resources. A. Basic Elements of the Smart GridA cornerstone of the smart grid is the advanced monitorability on its assets and operations. Increasingly pervasive installation of the phasor measurement units (PMUs) allows the so-termed synchrophasor measurements to be taken roughly 100 times faster than the legacy supervisory control and data acquisition (SCADA) measurements, time-stamped using the global positioning system (GPS) signals to capture the grid dynamics. In addition, the availability of low-latency two-way communication networks will pave the way to high-precision real-time grid state estimation and detection, remedial actions

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Autonomous appliance scheduling based on time of use probabilities and load clustering

Adika

Wang

2012

2012 10th International Power &Amp; Energy Conference (IPEC)

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Reconfigurable interdependent infrastructure systems: Advances in distributed sensing, modeling, and control

Cited by 11 publications

References 11 publications

Stackelberg-Game-Based Demand Response for At-Home Electric Vehicle Charging

Stackelberg-Game-Based Demand Response for At-Home Electric Vehicle Charging

Monitoring and Optimization for Power Grids: A Signal Processing Perspective

Autonomous appliance scheduling based on time of use probabilities and load clustering

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