Centralized and decentralized control for demand response

Lu, Shuai; Samaan, Nader; Diao, Ruisheng; Elizondo, Marcelo; Jin, Chunlian; Mayhorn, Ebony T.; Zhang, Yu; Kirkham, Harold

doi:10.1109/isgt.2011.5759191

Cited by 93 publications

(52 citation statements)

References 3 publications

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“…Several modeling studies have previously evaluated the potential of ERWH to provide peak curtailment, load following and ancillary services and found significant potential and benefit for ERWH to perform these grid functions (Mathieu et al 2012;Sepulveda et al 2010;Konodoh et al 2011;Diao et al 2012;Saker et al 2011;Lu et al 2011). However, no extensive field evaluation has verified these model results.…”

Section: 2mentioning

confidence: 82%

“…However, with the increased communication and control capabilities inherent in the smart grid, it is now possible to dynamically modulate loads to match supply more conveniently and cost-effectively than the previous generation-side control. Such a strategy, of controlling demand rather than supply, is referred to as demand-side management or DR (Lu et al 2011).…”

Section: Background On Demand Response With Water Heatersmentioning

confidence: 99%

“…DR benefits include increased system reliability, defrayed cost of new infrastructure investment, improved system efficiency, and decreased carbon emissions through increased penetration of intermittent renewable resources (Lu et al 2011). …”

Section: Background On Demand Response With Water Heatersmentioning

confidence: 99%

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Demand Response Performance of GE Hybrid Heat Pump Water Heater

Widder¹,

Parker²,

Petersen³

et al. 2013

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SummaryIncreasing penetration of heat pump water heaters (HPWHs) in the residential sector will offer an important opportunity for energy savings, with a theoretical energy savings of up to 63% per water heater 1 and up to 11% of residential energy use (EIA 2009). However, significant barriers must be overcome before this technology will reach widespread adoption in the Pacific Northwest region and nationwide. One barrier is that the demand response (DR) performance and characteristics of HPWHs is unknown. Previous research has demonstrated the potential of electric resistance water heaters (ERWHs) to provide significant grid stability and control benefits through demand-side management, or DR, strategies (Diao et al. 2012). However, if ERWHs are to be replaced with HPWHs to improve residential energy efficiency, it is important to understand the DR characteristics of HPWHs and how these characteristics will impact DR programs and overall grid stability now and in the future.This project evaluates and documents the DR performance of an HPWH as compared to an ERWH for two primary types of DR events: peak curtailments and balancing reserves. The experiments were conducted with General Electric (GE) second-generation "Brillion™"-enabled GeoSpring™ hybrid water heaters in the Pacific Northwest National Laboratory (PNNL) Lab Homes 2 , with one GE GeoSpring water heater operating in "Standard" electric resistance mode to represent the baseline and one GE GeoSpring water heater operating in "Heat Pump" mode to provide the comparison to heat pump-only DR. Signals were sent simultaneously to the two water heaters in the side-by-side PNNL Lab Homes under highly controlled, simulated occupancy conditions. It is expected that "Hybrid" DR performance, which would engage both the heat pump and electric elements, could be interpolated from these two experimental extremes.Based on the data collected in these DR experiments, both ERWHs and HPWHs are capable of performing peak curtailment and regulation services. However, their characteristics differ, as can be seen in Table 5.1, which shows the average impact on power use during the DR event, energy use during the DR event, and daily energy use for ERWH and HPWH for peak curtailment, 1-2 hour balancing events when generation and load are mismatched either due to higher load than generated power (INC events) or greater power generation than available load (DEC events). In general, the HPWH has much lower power use than the ERWH (587 Watts [W] versus 4,650 W) and provides approximately 38% of the potential to reduce load for peak curtailment or INC balancing events of the ERWH. The ERWH provides more dynamic response with a high magnitude of power increase or decrease per water heater. However, the HPWH has longer and more frequent operating times, which means the HPWH has a higher likelihood of being able to respond when an INC event or peak curtailment is called for. In addition, the inherent efficiency savings of HPWHs (61.7 ± 1.7%, as measured in the PNNL Lab Homes) will resul...

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Section: 2mentioning

confidence: 82%

Section: Background On Demand Response With Water Heatersmentioning

confidence: 99%

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Demand Response Performance of GE Hybrid Heat Pump Water Heater

Widder¹,

Parker²,

Petersen³

et al. 2013

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“…This is the essence of Demand Response. Electrical demand is modulated by autonomous control to match supply (or other concerns) [2]. The amount of flexibility that a particular device can exhibit depends on its functional requirements.…”

Section: Introductionmentioning

confidence: 99%

Self-organising algorithms for residential demand response

Taylor¹,

Dusparić²,

Harris³

et al. 2014

2014 IEEE Conference on Technologies for Sustainability (SusTech)

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Abstract-Residential Demand Response has shown promising results in smart grid applications. It can be achieved manually or autonomously. The variety of algorithms applied to achieve autonomous Demand Response have lacked a common baseline. The selection of testing environment is typically skewed by the desire to find one in which a particular algorithm performs well.This work evaluates several algorithms in a common evaluation environment. Which has been designed to encompass the range of conditions in a typical Residential Demand Response application. The environments described exhibit the following characteristics: non-stationary, dynamic, multi-actor, multi-objective. These characteristics will then be used to provide heuristics for algorithm selection.The algorithms used were selected to cover the spectrum of possible approaches to Demand Response. Some are centralised, others distributed. There are collaborative approaches and noncollaborative ones. Some are learning based, others require no training. This work provides criteria for which particular algorithms should be applied to a given application.

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“…Integrating (34) over [0, 1) in (the switched on TCLs), setting the resulting expression equal to the right hand side of (38), and substituting our expression in (36a) for ON (0) establish the following implicit equation for :…”

Section: Uniform Distribution Of Tclsmentioning

confidence: 99%

A Stability Analysis of Thermostatically Controlled Loads for Power System Frequency Control

Webborn

MacKay

2017

Complexity

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Thermostatically controlled loads (TCLs) are a flexible demand resource with the potential to play a significant role in supporting electricity grid operation. We model a large number of identical TCLs acting autonomously according to a deterministic control scheme to provide frequency response as a population of coupled oscillators. We perform stability analysis to explore the danger of the TCL temperature cycles synchronising: an emergent phenomenon often found in populations of coupled oscillators and predicted in this type of demand response scheme. We take identical TCLs as it can be assumed to be the worst case. We find that the uniform equilibrium is stable and the fully synchronised periodic cycle is unstable, suggesting that synchronisation might not be as serious a danger as feared. Then detailed simulations are performed to study the effects of a population of frequency-sensitive TCLs acting under real system conditions using historic system data. The potential reduction in frequency response services required from other providers is determined, for both homogeneous and heterogeneous populations. For homogeneous populations, we find significant synchronisation, but very minimal diversity removes the synchronisation effects. In summary, we combine dynamical systems stability analysis with large-scale simulations to offer new insights into TCL switching behaviour.

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Centralized and decentralized control for demand response

Cited by 93 publications

References 3 publications

Demand Response Performance of GE Hybrid Heat Pump Water Heater

Demand Response Performance of GE Hybrid Heat Pump Water Heater

Self-organising algorithms for residential demand response

A Stability Analysis of Thermostatically Controlled Loads for Power System Frequency Control

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