Development and validation of a physically-based computer model for predicting winter electric heating loads

Dolan, P.S.; Gerez, V.; Jameson, W.J.

doi:10.1109/59.373949

Cited by 28 publications

(10 citation statements)

References 9 publications

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“…C is calculated according to (5), where A mean and B mean are the mean values of A and B, respectively, calculated according to (6) and σ A and σ B the corresponding standard deviations calculated by (7)…”

Section: Discussionmentioning

confidence: 99%

“…Multiple models have been proposed to account for the effects of CLPU. This includes the delayed exponential model [4] and various physical models such as [5][6][7][8][9]. The physical models typically rely on the information on the amount of electrical heating and cooling, temperature, etc.…”

Section: Motivationmentioning

confidence: 99%

“…For this investigation, a CLPU curve, specifically the exponential decay model, is selected to describe the CLPU phenomenon. The advantages of this modelling approach are: (i) a reasonably good fit to the measurements; (ii) it is also theoretically justified since it describes behaviour that is expected from some of the physical effects [3,28,29]; and (iii) only two parameters are needed. Therefore, the estimation of parameters from limited data for the CLPU curve model is more feasible than for other models, such as complex physical models.…”

Section: Clpu Curvementioning

confidence: 99%

See 2 more Smart Citations

Cold load pickup model parameters based on measurements in distribution systems

Hachmann

Lammert

Hamann³

et al. 2019

IET Generation, Transmission & Distribution

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Section: Discussionmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Clpu Curvementioning

confidence: 99%

See 1 more Smart Citation

Cold load pickup model parameters based on measurements in distribution systems

Hachmann

Lammert

Hamann³

et al. 2019

IET Generation, Transmission & Distribution

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“…In [9][10][11], complex physical models to represent thermostatically-controlled loads are described. In particular, in [9] the information obtained regarding the thermal mass and thermal insulation of buildings together with meteorological data, are exploited to develop a thermic model to estimate the winter power demand of heating loads.…”

Section: A Literature Reviewmentioning

confidence: 99%

A Novel Dynamic Load Modeling for Power Systems Restoration: An Experimental Validation on Active Distribution Networks

et al. 2022

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In this paper, a novel approach to assess the power demand of distribution networks during a restoration process following an outage is presented. The possibility of correctly estimating such power demand represents a very important support in the choice and management of reliable restoration paths which significantly contributes to increase the resilience of the power system. In fact, due to the ever-growing penetration of renewable energy sources in the worldwide networks, electrical systems are often pushed to operate closer to their design limits and so, in particular conditions, black outs can be more frequent compared to the past. In this context, the aim of this work is to develop a general expression which can consider and represent all the key elements which affect the active and reactive power demand of distribution grids during the restoration process. The proposed method considers also the relationship between distributed generation and frequency behaviour in the power demand estimation. This aspect is generally neglected in literature although it has a significative impact. The effectiveness of the present approach is experimentally demonstrated on field. More specifically, the developed tool is applied to estimate the power demand of two real distribution networks and the tool results are compared with on-field recordings. This comparison demonstrates that the proposed approach represents a promising tool which, together with the restoration tests, could be an important ally in the design and management of the restoration plan of electrical networks.INDEX TERMS Load modelling, Cold load pick up, Black start, power system restoration, modelling of distributed generation.

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“…• Reflecting the dynamical load behaviour under DLC actions • Allowing us the knowledge of comfort parameters when control periods have been exerted • Model parameters have to be identifiable [7] Physically based load modelling methodologies appear to be one of the most promising approaches for the load modelling problem applied to DSM purposes [8][9][10]. A model of ETS devices is also proposed in this section to simulate its behaviour when external input actions are considered, such as duty cycle modifications or voltage variations.…”

Section: Physically Based Load Modellingmentioning

confidence: 99%

A Physically based Load Model of Residential Electric Thermal Storage: Application to LM Programs

Molina¹,

Gabaldón²,

Álvarez³

et al. 2004

International Journal of Power and Energy Systems

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This paper describes and assesses a physically based load model of residential Electric Thermal Storage (ETS) devices, for both static and dynamic loads. This load model is based on an energy balance between the indoor environment, the dwelling constructive parameters, the ETS device, and the internal mass through a discrete state-space equation system. Therefore, detailed information about several physical magnitudes of the whole system are given along the time: ceramic brick temperature, electrical demand, heat fluxes, and indoor temperature. The main application of this load model has been oriented towards the simulation of the ETS device performances, in order to assess load management (LM) programs.The proposed model has been implemented and validated using data collected for the last two years in residential areas, in order to evaluate its accuracy and flexibility. Finally, a simulation case study is presented to show the possibilities of limiting and reducing the actual winter-peak by means of an LM program, proposed by the authors, that takes into account customer minimum comfort levels and the experimental data of residential load curve profiles.

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Development and validation of a physically-based computer model for predicting winter electric heating loads

Cited by 28 publications

References 9 publications

Cold load pickup model parameters based on measurements in distribution systems

Cold load pickup model parameters based on measurements in distribution systems

A Novel Dynamic Load Modeling for Power Systems Restoration: An Experimental Validation on Active Distribution Networks

A Physically based Load Model of Residential Electric Thermal Storage: Application to LM Programs

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