Estimation of Residential Heat Pump Consumption for Flexibility Market Applications

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20 Citationer (Scopus)

Resumé

Recent technological advancements have facilitated the evolution of traditional distribution grids to smart grids. In a smart grid scenario, flexible devices are expected to aid the system in balancing the electric power in a technically and economically efficient way. To achieve this, the flexible devices’ consumption data are theoretically recorded, elaborated and their upcoming flexibility is bid to flexibility markets. However, there are many cases where explicit flexible device consumption data are absent. This paper presents a way to circumvent this problem and extract the potentially flexible load of a flexible device, namely a Heat Pump (HP), out of the aggregated energy consumption of a house. The main idea for accomplishing this, is a comparison of the flexible consumer with electrically similar non-flexible consumers. The methodology is based on machine learning techniques, probability theory and statistics. After presenting this methodology, the general trend of the HP consumption is estimated and an hour-ahead forecast is conducted by employing Seasonal Autoregressive Integrated Moving Average modeling. In this manner, the flexible consumption is predicted, establishing the basis for bidding flexibility in intra-day markets even in the absence of explicit device measurements.
OriginalsprogEngelsk
TidsskriftI E E E Transactions on Smart Grid
Vol/bind6
Udgave nummer4
Sider (fra-til)1852 - 1864
Antal sider13
ISSN1949-3053
DOI
StatusUdgivet - jul. 2015

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Pumps
Learning systems
Energy utilization
Statistics
Hot Temperature

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title = "Estimation of Residential Heat Pump Consumption for Flexibility Market Applications",
abstract = "Recent technological advancements have facilitated the evolution of traditional distribution grids to smart grids. In a smart grid scenario, flexible devices are expected to aid the system in balancing the electric power in a technically and economically efficient way. To achieve this, the flexible devices’ consumption data are theoretically recorded, elaborated and their upcoming flexibility is bid to flexibility markets. However, there are many cases where explicit flexible device consumption data are absent. This paper presents a way to circumvent this problem and extract the potentially flexible load of a flexible device, namely a Heat Pump (HP), out of the aggregated energy consumption of a house. The main idea for accomplishing this, is a comparison of the flexible consumer with electrically similar non-flexible consumers. The methodology is based on machine learning techniques, probability theory and statistics. After presenting this methodology, the general trend of the HP consumption is estimated and an hour-ahead forecast is conducted by employing Seasonal Autoregressive Integrated Moving Average modeling. In this manner, the flexible consumption is predicted, establishing the basis for bidding flexibility in intra-day markets even in the absence of explicit device measurements.",
keywords = "Heat Pump, Estimation, Prediction, Flexibility, Non-intrusive Load Identification",
author = "Konstantinos Kouzelis and Zheng-Hua Tan and Birgitte Bak-Jensen and Pillai, {Jayakrishnan Radhakrishna} and Ewen Ritchie",
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Estimation of Residential Heat Pump Consumption for Flexibility Market Applications. / Kouzelis, Konstantinos; Tan, Zheng-Hua; Bak-Jensen, Birgitte; Pillai, Jayakrishnan Radhakrishna; Ritchie, Ewen.

I: I E E E Transactions on Smart Grid, Bind 6, Nr. 4, 07.2015, s. 1852 - 1864 .

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Estimation of Residential Heat Pump Consumption for Flexibility Market Applications

AU - Kouzelis, Konstantinos

AU - Tan, Zheng-Hua

AU - Bak-Jensen, Birgitte

AU - Pillai, Jayakrishnan Radhakrishna

AU - Ritchie, Ewen

PY - 2015/7

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AB - Recent technological advancements have facilitated the evolution of traditional distribution grids to smart grids. In a smart grid scenario, flexible devices are expected to aid the system in balancing the electric power in a technically and economically efficient way. To achieve this, the flexible devices’ consumption data are theoretically recorded, elaborated and their upcoming flexibility is bid to flexibility markets. However, there are many cases where explicit flexible device consumption data are absent. This paper presents a way to circumvent this problem and extract the potentially flexible load of a flexible device, namely a Heat Pump (HP), out of the aggregated energy consumption of a house. The main idea for accomplishing this, is a comparison of the flexible consumer with electrically similar non-flexible consumers. The methodology is based on machine learning techniques, probability theory and statistics. After presenting this methodology, the general trend of the HP consumption is estimated and an hour-ahead forecast is conducted by employing Seasonal Autoregressive Integrated Moving Average modeling. In this manner, the flexible consumption is predicted, establishing the basis for bidding flexibility in intra-day markets even in the absence of explicit device measurements.

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