The Demand Response Spinning Reserve Demonstration--Measuring the Speed and Magnitude of Aggregated Demand Response

Eto, Joseph H.; Nelson-Hoffman, Janine; Parker, Eric J.; Bernier, Clark; Young, Paul Thomas; Sheehan, Dave; Kueck, J.D.; Kirby, Brendan

doi:10.1109/hicss.2012.39

Cited by 26 publications

(16 citation statements)

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“…Using this definition, we can evaluate if DR was successful in performing its function as a spinning reserve in the presence of a cyber-physical attack (i.e., whether DR was resilient to cyber-physical attacks). Studies have shown that DR signals can be sent from the utility to customers' loads within about 70 s [3]. If DR was not successful in its function as a spinning reserve, then this means that certain requirements were violated, system stability was not maintained and additional actions should be taken to stabilize the system (like increasing generation).…”

Section: Dr As Spinning Reservementioning

confidence: 99%

“…There are existing implementations where DR was implemented on ACs [3,25,26]. ACs are suitable for DR activities because turning them off for 10-15 min to compensate for a contingency is not intrusive for customers.…”

Section: Resilience Evaluation Of Drasrmentioning

confidence: 99%

“…Typically, Demand Response (DR) can be used for many purposes, including energy efficiency, price response, peak shaving, reliability (contingency) response, and regulation response [3]. Many factors determine the type of response a load can provide, such as time of use, duration and speed of response, frequency and magnitude of load required.…”

Section: Introductionmentioning

confidence: 99%

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Resilience Evaluation of Demand Response as Spinning Reserve under Cyber-Physical Threats

2016

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Abstract:In the future, automated demand response mechanisms will be used as spinning reserve. Demand response in the smart grid must be resilient to cyber-physical threats. In this paper, we evaluate the resilience of demand response when used as spinning reserve in the presence of cyber-physical threats. We quantify this evaluation by correlating the stability of the system in the presence of attacks measured by system frequency (Hz) and attack level measured by the amount of load (MW) that responds to the demand response event. The results demonstrate the importance of anticipating the dependability of demand response before it can be relied upon as spinning reserve.

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Section: Dr As Spinning Reservementioning

confidence: 99%

Section: Resilience Evaluation Of Drasrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resilience Evaluation of Demand Response as Spinning Reserve under Cyber-Physical Threats

2016

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“…Additionally, limited field tests of DR resources providing various forms of AS (Kirby and Kueck 2003;Todd et al 2008;Kiliccote et al 2009;Eto et al 2012) have verified its technical capability.…”

Section: Introductionmentioning

confidence: 99%

Market and Policy Barriers for Demand Response Providing Ancillary Services in U.S. Markets

Cappers¹

2013

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“…A deployments report [3] shows the feasibility of delaying the starting of air conditioners by using signals from the distributor.…”

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confidence: 99%

Stability of a Stochastic Model for Demand-Response

Boudec

Tomozei

2013

Stochastic Systems

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We study the stability of a Markovian model of electricity production and consumption that incorporates production volatility due to renewables and uncertainty about actual demand versus planned production. We assume that the energy producer targets a fixed energy reserve, subject to ramp-up and ramp-down constraints, and that appliances are subject to demand-response signals and adjust their consumption to the available production by delaying their demand. When a constant fraction of the delayed demand vanishes over time, we show that the general state Markov chain characterizing the system is positive Harris and ergodic (i.e., delayed demand is bounded with high probability). However, when delayed demand increases by a constant fraction over time, we show that the Markov chain is nonpositive (i.e., there exists a non-zero probability that delayed demand becomes unbounded). We exhibit Lyapunov functions to prove our claims. In addition, we provide examples of heating appliances that, when delayed, have energy requirements corresponding to the two considered cases.

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The Demand Response Spinning Reserve Demonstration--Measuring the Speed and Magnitude of Aggregated Demand Response

Cited by 26 publications

References 1 publication

Resilience Evaluation of Demand Response as Spinning Reserve under Cyber-Physical Threats

Resilience Evaluation of Demand Response as Spinning Reserve under Cyber-Physical Threats

Market and Policy Barriers for Demand Response Providing Ancillary Services in U.S. Markets

Stability of a Stochastic Model for Demand-Response

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