Statistical synthesis of power system functional load models

Chong, Chee-Yee; Debs, Atif S.

doi:10.1109/cdc.1979.270177

Cited by 104 publications

(82 citation statements)

References 6 publications

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“…Thus, the single-device model described by (1) and (2) has been extensively used as a starting point for numerical studies of the behaviour of populations of TCLs [12,10,14,16,17,4].…”

Section: A Preliminary Model For the Power Dynamics Of An Aggregationmentioning

confidence: 99%

“…The hybrid model (1)-(2) has been used (with u(t) = 0) in early mathematical studies of the dynamics of aggregate power of populations of ACs [12], [10], and more recently in [4], which incorporated the temperature set point offset u(t) as an external control signal to achieve load tracking to compensate the variability of wind energy generators. Note that controlling a group of ACs using a set point offset avoids forcing a global absolute reference temperature for all the devices, allowing the occupants to choose the temperature set point they are most comfortable with.…”

Section: A Preliminary Model For the Power Dynamics Of An Aggregationmentioning

confidence: 99%

“…Some authors concentrate on models based on first principles [12,10,13,14,15,16,17], while others focus on identifying the model parameters from a real population of devices [18,19], including the use of black-box model identification techniques [4,20]. However, the majority of these models are too complex to be effectively used by well-understood feedback control design methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Load management: Model-based control of aggregate power for populations of thermostatically controlled loads

Perfumo

Kofman

Braslavsky

et al. 2012

Energy Conversion and Management

234

187

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“…Thus, the single-device model described by (1) and (2) has been extensively used as a starting point for numerical studies of the behaviour of populations of TCLs [12,10,14,16,17,4].…”

Section: A Preliminary Model For the Power Dynamics Of An Aggregationmentioning

confidence: 99%

Section: A Preliminary Model For the Power Dynamics Of An Aggregationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Load management: Model-based control of aggregate power for populations of thermostatically controlled loads

Perfumo

Kofman

Braslavsky

et al. 2012

Energy Conversion and Management

234

187

View full text Add to dashboard Cite

“…An electric heating-cooling thermal dynamic based upon the energy balance analysis is used, as discussed in [4] and [16]. The first-order stochastic differential equation of the continuous temperature state x t is stated as following:…”

Section: B Thermal Dynamic Modelmentioning

confidence: 99%

Modeling and stochastic control for Home Energy Management

McLaughlin

Jia

et al. 2012

2012 IEEE Power and Energy Society General Meeting

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Abstract-The problem of modeling and control for Home Energy Management (HEM) is considered. A first order thermal dynamic model is considered and its parameters are extracted using real measurements over a period of three summer months. The identified model is validated using separate data sets. The extracted model shows certain nonstationarity and non-Gaussianity. However, local approximations using a stationary model are shown to have relatively small modeling and prediction errors. The extracted model is then used for developing a multi-scale multi-stage stochastic optimization framework for the control of the Heating, Ventilation, and Air Conditioning (HVAC) unit, the charging of Plug-in Hybrid Electric Vehicle (PHEV), and the scheduling of deferrable load such as washer/dryer operations. A two time scale Model Predictive Control (MPC) strategy is proposed that minimizes the discomfort level subject to power and budget constraints: at the slow time scale, a power budget is allocated across different appliances at the hourly level; at the fast time scale, sensor measurements are used for the scheduling and control of different loads. Using parameters extracted from the real data, the proposed approach is compared with the simple rule based control strategy typically used in HVAC controllers.

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“…Previous works on thermal inertial load management [4]- [11] have focused on obtaining an aggregate model for a population of loads, and then used the resulting model, or an approximation of it, to track a given reference power trajectory. The purpose of this paper is to present a comprehensive architecture and algorithms (Section III) so that the LSE can manage a population of thermal inertial loads while addressing the three questions mentioned in the preceding paragraph.…”

Section: Introductionmentioning

confidence: 99%

Architecture and Algorithms for Privacy Preserving Thermal Inertial Load Management by a Load Serving Entity

Halder

Geng

Kumar

et al. 2017

IEEE Trans. Power Syst.

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Abstract-Motivated by the growing importance of demand response in modern power system's operations, we propose an architecture and supporting algorithms for privacy preserving thermal inertial load management as a service provided by the load serving entity (LSE). We focus on an LSE managing a population of its customers' air conditioners, and propose a contractual model where the LSE guarantees quality of service to each customer in terms of keeping their indoor temperature trajectories within respective bands around the desired individual comfort temperatures. We show how the LSE can price the contracts differentiated by the flexibility embodied by the width of the specified bands. We address architectural questions of (i) how the LSE can strategize its energy procurement based on price and ambient temperature forecasts, (ii) how an LSE can close the real time control loop at the aggregate level while providing individual comfort guarantees to loads, without ever measuring the states of an air conditioner for privacy reasons. Control algorithms to enable our proposed architecture are given, and their efficacy is demonstrated on real data.

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Statistical synthesis of power system functional load models

Cited by 104 publications

References 6 publications

Load management: Model-based control of aggregate power for populations of thermostatically controlled loads

Load management: Model-based control of aggregate power for populations of thermostatically controlled loads

Modeling and stochastic control for Home Energy Management

Architecture and Algorithms for Privacy Preserving Thermal Inertial Load Management by a Load Serving Entity

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