Aggregation of Residential Water Heaters for Peak Shifting and Frequency Response Services

Clarke, Thomas C.; Slay, Tylor; Eustis, Conrad; Bass, R.

doi:10.1109/oajpe.2019.2952804

Cited by 32 publications

(19 citation statements)

References 9 publications

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“…In previous research, EWHs were used to regulate the frequency in an electric power distribution system [30]. Another research study showed that the aggregated EWH load can be controlled to contribute to shifting the system peak load [31]. Communities with large penetration of controllable EWH have, in principle, the potential for providing ancillary services.…”

Section: Introductionmentioning

confidence: 99%

Peak Reduction and Long Term Load Forecasting for Large Residential Communities Including Smart Homes With Energy Storage

et al. 2021

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Domestic load profiles in the residential sectors are being modified with the adoption of smart home management systems and solar generation. In addition, houses with rooftop PV behave like local generators, contributing to the growth of the penetration of PV energy. Hence, the demand for power is declining day by day. However, the increasing PV penetration causes technical challenges for the power system, such as the "duck curve". This can be addressed through home energy management (HEM) techniques including peak shaving, load shifting with smart home devices. In this regard, electric water heaters (EWH), with high thermal mass and being ubiquitous, are attractive and low-cost energy storage systems. In this paper, a case study for one of the largest rural field smart energy technology demonstrators involving business, industries, and more than 5,000 residences, located in Glasgow, KY, US, is presented. Furthermore, a HEM system, which aims to minimize the total energy usage and peak demand by regulating the heating, ventilation, and airconditioning (HVAC) systems, water heaters, and batteries, thereby benefiting both the utility and the consumer is proposed. This work also demonstrates the ability of EWH to provide ancillary services while maintaining customer comfort. The minimum participation rates for EWH and batteries are calculated and compared with respect to different peak reduction targets. Long term load prediction by considering different fractions of smart homes for the utility is also provided.

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Section: Introductionmentioning

confidence: 99%

Peak Reduction and Long Term Load Forecasting for Large Residential Communities Including Smart Homes With Energy Storage

et al. 2021

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“…For the purposes of this work, all devices are assumed to operate with unity power factor, a reasonable assumption for power electronic-interfaced devices (such as EV chargers) or resistive immersion heaters. The aggregator would take responsibility for ensuring suitable availability and response from the fleet of devices [7], [18].…”

Section: B Indices For Evaluating Risk Of Violationsmentioning

confidence: 99%

“…To estimate the values of both the ECVV and the LoVV, we use a 'plug-in estimator' [33,Ch. 8], estimating the values of these indices by sampling from (4) and then calculating estimates directly from the load flow solutions from ( 5), (7). Estimated ζ % SHC values are found by linear interpolation from the estimated LoVV.…”

Section: B Indices For Evaluating Risk Of Violationsmentioning

confidence: 99%

“…Most methods follow early works such as [4], with devices responding to local frequency measurements. Devices proposed for frequency response include heat pumps [5], EVs [6], electric hot water tanks [4], [7], or general thermostatic loads [8]. On the other hand, many recent works consider stochastic hosting capacity of distribution networks (recent works such as [9] even calculate probabilistic hosting capacity for DERs which are actively controlled); these works typically focus on network impacts of DERs during normal operations [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Network Impacts of Frequency Containment Provided by Domestic-Scale Devices Using Matrix Factorization

Deakin

Greenwood

Taylor

et al. 2021

IEEE Trans. Power Syst.

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This paper studies the distribution network impacts of frequency containment services derived from domestic-scale devices. Risk metrics considering the likelihood and severity of violations are proposed, given uncertainty in the location of these devices. A novel linearization approach is proposed to enable detailed simulations of large-scale networks, capturing MV-LV coupling in European-style networks of over 100,000 nodes. The approach combines a novel factorization of the power flow Jacobian matrix and an efficient linearization update step. The first of these innovations improves the scalability and practicality of the linearization, reducing the memory and computational requirements by as much as twenty times, whilst the latter reduces the median linearization error by at least 50% in all networks studied. It is demonstrated that rapid voltage change (RVC) and overvoltage constraints could limit the uptake of these devices to just a fraction of one percent of customers if the response is not managed. Sensitivity analysis demonstrates that both the likelihood and severity of constraint violations increases rapidly with the size of the devices used for frequency response.

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“…As for the capacity, the residential loads account for a significant proportion among all the candidates [8] - [10]. Through proper control strategy, electric water heater (EWH) [11], heating, ventilation, and air conditioning (HVAC) [12] can be aggregated to provide frequency regulation services.…”

Section: Introductionmentioning

confidence: 99%

Residential HVAC Aggregation Based on Risk-averse Multi-armed Bandit Learning for Secondary Frequency Regulation

Chen

Shi

et al. 2020

Journal of Modern Power Systems and Clean Energy

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As the penetration of renewable energy continues to increase, stochastic and intermittent generation resources gradually replace the conventional generators, bringing significant challenges in stabilizing power system frequency. Thus, aggregating demand-side resources for frequency regulation attracts attentions from both academia and industry. However, in practice, conventional aggregation approaches suffer from random and uncertain behaviors of the users such as opting out control signals. The risk-averse multi-armed bandit learning approach is adopted to learn the behaviors of the users and a novel aggregation strategy is developed for residential heating, ventilation, and air conditioning (HVAC) to provide reliable secondary frequency regulation. Compared with the conventional approach, the simulation results show that the risk-averse multiarmed bandit learning approach performs better in secondary frequency regulation with fewer users being selected and opting out of the control. Besides, the proposed approach is more robust to random and changing behaviors of the users.

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Aggregation of Residential Water Heaters for Peak Shifting and Frequency Response Services

Cited by 32 publications

References 9 publications

Peak Reduction and Long Term Load Forecasting for Large Residential Communities Including Smart Homes With Energy Storage

Peak Reduction and Long Term Load Forecasting for Large Residential Communities Including Smart Homes With Energy Storage

Analysis of Network Impacts of Frequency Containment Provided by Domestic-Scale Devices Using Matrix Factorization

Residential HVAC Aggregation Based on Risk-averse Multi-armed Bandit Learning for Secondary Frequency Regulation

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