Day-Ahead Prediction and Shaping of Dynamic Response of Demand at Bulk Supply Points

Xu, Yuzhi; Milanović, Jovica V.

doi:10.1109/tpwrs.2015.2477559

Cited by 31 publications

(24 citation statements)

References 15 publications

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“…Since the ANN is trained in terms of per unit quantities the absolute measurements (rms) need to be converted using the base value or the rated demand (in kW/MW) at the BSP which varies with time depending on the amount of loads connected. As the rated demand is not known at any BSP, a Monte Carlo based probabilistic method could be used to estimate the rated demand [23]. A large set of per unit aggregate active power is calculated using (4) corresponding to each measured voltage value at the BSP.…”

Section: E Load Disaggregationmentioning

confidence: 99%

Estimation of Aggregate Reserve With Point-of-Load Voltage Control

Chakravorty

Chaudhuri

Hui

2018

IEEE Trans. Smart Grid

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Voltage dependent loads can collectively provide a certain amount of power reserve (by virtue of the ability to change their power consumption within the stipulated voltage tolerance) which could be exploited for grid frequency regulation through voltage control at the substation/feeder or at the point of load. The amount of such power reserve would vary with time of the day depending on the incidence of different types of voltage dependent loads and also the voltage profile across the feeders. It is important for the grid operators to know the aggregate power reserve from the voltage dependent loads during different times of the day in order to schedule other forms of reserves accordingly. This paper presents a methodology to estimate such power reserve from the measured power and voltage at the bulk supply points without knowing the actual distribution network topology and/or load profile of individual customers. The proposed method is applied to estimate the time variation of the aggregate reserve offered by the voltage dependent loads within the domestic sector in Great Britain (GB). Studies on a standard IEEE distribution network are presented to validate the estimated reserve margins under typical voltage profiles across the distribution feeders.

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Section: E Load Disaggregationmentioning

confidence: 99%

Estimation of Aggregate Reserve With Point-of-Load Voltage Control

Chakravorty

Chaudhuri

Hui

2018

IEEE Trans. Smart Grid

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“…Following methodology discussed in [5], load categories are defined as groups of appliances with similar voltage-dependent steady-state and dynamic load characteristics. Furthermore, load categories are divided into controllable and uncontrollable based on their potential to be shifted in time.…”

Section: Demand Profiling Of Residential Loadmentioning

confidence: 99%

Application of data analytics for advanced demand profiling of residential load using smart meter data

Ponoćko

Milanović

2017

2017 IEEE Manchester PowerTech

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Abstract-This paper proposes a methodology for demand profiling, namely load decomposition, of aggregated residential load based on smart meter (SM) data. The methodology is applicable to both active and reactive load, following an assumption that SMs can monitor real-time active power consumption of individual appliances. Only a number of households in the aggregation are equipped with SMs in this study. The non-monitored users' load is decomposed using artificial neural network (ANN) trained with the available SM data. Information about load composition, in terms of load categories or load controllability, can be highly beneficial for various demand response (DR) applications. Different levels of SM coverage are considered in the study to illustrate the effect of the level of SM coverage on the accuracy of total aggregated load decomposition. The results show that the consumption of some load categories can be estimated with high confidence, even at lower levels of SM coverage.

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“… Since the majority of presently installed smart meters don't have the functionality of monitoring individual appliances (these measurements have only been done in limited number of test sites), estimation of the amount of controllable loads can at this point be made only by using some data mining and machine learning methods. Voltage measurements at end-user level can be used, besides facilitating detection of faults at measurement point, to complement types of data (real and reactive power) needed for load disaggregation process at aggregation point [17]. In this way, the state estimation could be performed with still limited, but improved accuracy compared to present practice, using smart meter data instead of measurements at bus points.…”

Section: Discrepancies Between Information Needs and Actual Data Avaimentioning

confidence: 99%

“…As the issue of disaggregated load attracts a lot of intention in the area of future DSM programs, a probabilistic approach was developed in [17] by application of data analytics and pattern recognition tools over total consumption data in order to retrieve information about consumption of individual categories of appliances. This gave as an output a probabilistic estimation of the amount of controllable and uncontrollable load within total load.…”

Section: Discrepancies Between Information Needs and Actual Data Avaimentioning

confidence: 99%

Comparative analysis of data availability and data requirements for efficient management and control of future distribution networks

Ponoćko¹,

Milanović²

2016

Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2016)

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This paper starts by discussing the data requirements for efficient control and management of future power networks and justifies the need for different types of data. It then compares the data requirements with presently available or routinely collected data by distribution system operators (DSOs) and identifies the mismatch between the two. Finally, considering growing deployment and increasing reliance on smart meters, it presents an overview of the technical specifications of typical commercially available smart meters and discusses the possibilities of information retrieval from the data provided by smart meters. The results of this analysis illustrate to what extent the existing smart meters can support demand side management (DSM) and identify what additional functionalities smart meters should have in order to facilitate envisaged services.

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Day-Ahead Prediction and Shaping of Dynamic Response of Demand at Bulk Supply Points

Cited by 31 publications

References 15 publications

Estimation of Aggregate Reserve With Point-of-Load Voltage Control

Estimation of Aggregate Reserve With Point-of-Load Voltage Control

Application of data analytics for advanced demand profiling of residential load using smart meter data

Comparative analysis of data availability and data requirements for efficient management and control of future distribution networks

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