An Integrated Architecture for Demand Response Communications and Control

LeMay, Michael; Nelli, R.; Gross, George; Gunter, Carl A.

doi:10.1109/hicss.2008.60

Cited by 84 publications

(43 citation statements)

References 7 publications

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“…The Smart Grid, rolled out by the National Institute of Standards and Technology (NIST) [1], is a next generation power system which allows consumers and electric utilities to communicate interactively. Compared with legacy power systems, the Smart Grid has new energy management capabilities, such as an advanced metering infrastructure (AMI) [2] and a demand response [3]. AMI is an architecture for automated, twoway communication between a consumer with an IP address and an electric utility.…”

Section: Motivationmentioning

confidence: 99%

SPaCIS: Secure Payment Protocol for Charging Information over Smart Grid

Kishimoto

Yanai

Okamura

2017

Journal of Information Processing

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A use of an electric outlet by a consumer forces the outlet manager to pay for the consumer's power usage in current electrical power systems. Even if a consumer uses an outlet managed by another person, one bill for both indoor and outdoor charging information should be required to the consumer in their contract with the utility company. For this purpose, we define a model for the Smart Grid security and propose a Secure Payment Protocol for Charging Information over Smart grid, SPaCIS for short, as a protocol satisfying the model. Our model provides for the unlinkability of consumers as well as for the undeniability and unforgeability of billing information using digital signatures and identity federations. SPaCIS is also efficient in the sense that time complexity is constant relatively to a trivial use such as an individual verification for each signatures, unless a verification error happens. We furthermore evaluate performance of SPaCIS via cryptographic implementation, and simulate SPaCIS in a case that one thousand users generate thirty signatures. Then, we show that SPaCIS with ECDSA can be executed within 6.30 msec for signing and 21.04 msec for verification of signatures, and conclude that SPaCIS is fairly practical.

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Section: Motivationmentioning

confidence: 99%

SPaCIS: Secure Payment Protocol for Charging Information over Smart Grid

Kishimoto

Yanai

Okamura

2017

Journal of Information Processing

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“…By suitably adjusting the energy prices, electrical load can be shifted from periods of high or peak demand to other periods, thereby reducing the ratio of peak to average loads [1,2]. But, load control also faces several decision-making challenges [3]. Each decision implicitly requires the consumer to estimate what future energy prices may be and weigh this differential cost against the dis-utility of waiting.…”

Section: Introductionmentioning

confidence: 99%

Genetic Based Reinforcement Learning Load Control for Smart Grids

Zang

2013

AMR

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Abstract. As the improvement of smart grids, the customer participation has reinvigorated interest in demand-side features such as load control for domestic users. A genetic based reinforcement learning (RL) load controller is proposed. The genetic is used to adjust the parameters of the controller. The RL algorithm, which is independent of the mathematic model, shows the particular superiority in load control. By means of learning procedures, the proposed controller can learn to take the best actions to regulate the energy usage for equipments with the features of high comfortable for energy usage and low electric charge meanwhile. Simulation results show that the proposed load controller can promote the performance energy usage in smart grids.

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“…According to [3], demand response can be defined as the changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time. But, demand response also faces several decision-making challenges [4]. Each decision implicitly requires the consumer to estimate what future energy prices may be and weigh this differential cost against the dis-utility of waiting.…”

Section: Introductionmentioning

confidence: 99%

Pricing scheme based Nash Q-learning load control in smart grids

Xiaoning²,

Zang

et al. 2013

2013 25th Chinese Control and Decision Conference (CCDC)

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Calls to improve customer participation as a key element of smart grids have reinvigorated interest in demand-side features such as load control for residential users. For the load control problems, a pricing scheme based Nash Q-learning load controller is proposed. It considers a game with some non-cooperative energy equipments. The energy pricing scheme is introduced to the design of the reward value in the learning process of Q-learning. Because of the uncertainties and highly time-varying, the Nash Q-learning, which is independent of mathematic model, is adopted to deal with the incomplete information for smart grid. By means of learning procedures, the proposed controller can learn to take the best actions to regulate the energy usage for equipments with the features of high comfortable for energy usage and low electric charge meanwhile. Simulation results show that the proposed load controller can promote the performance energy usage in smart grids.

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An Integrated Architecture for Demand Response Communications and Control

Cited by 84 publications

References 7 publications

SPaCIS: Secure Payment Protocol for Charging Information over Smart Grid

SPaCIS: Secure Payment Protocol for Charging Information over Smart Grid

Genetic Based Reinforcement Learning Load Control for Smart Grids

Pricing scheme based Nash Q-learning load control in smart grids

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