Using Load Forecasting to Control Domestic Battery Energy Storage Systems

The profitability of domestic battery energy storage systems has been poor and this is the main barrier to their general use. It is possible to increase profitability by using multiple control targets. Market price-based electricity contracts and power-based distribution tariffs alongside storage of...

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Main Authors: Juha Koskela, Antti Mutanen, Pertti Järventausta
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/13/15/3946
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spelling doaj-4a8cbe04a13e4f51b8f408b23f1cb1842020-11-25T03:35:25ZengMDPI AGEnergies1996-10732020-08-01133946394610.3390/en13153946Using Load Forecasting to Control Domestic Battery Energy Storage SystemsJuha Koskela0Antti Mutanen1Pertti Järventausta2Unit of Electrical Engineering, Tampere University, FI-33014 Tampere, FinlandUnit of Electrical Engineering, Tampere University, FI-33014 Tampere, FinlandUnit of Electrical Engineering, Tampere University, FI-33014 Tampere, FinlandThe profitability of domestic battery energy storage systems has been poor and this is the main barrier to their general use. It is possible to increase profitability by using multiple control targets. Market price-based electricity contracts and power-based distribution tariffs alongside storage of surplus photovoltaic energy make it possible to have multiple control targets in domestic use. The battery control system needs accurate load forecasting so that its capacity can be utilized in an optimally economical way. This study shows how the accuracy of short-term load forecasting affects cost savings by using batteries. The study was conducted by simulating actual customers’ load profiles with batteries utilized for different control targets. The results of the study show that knowledge of customers’ load profiles (i.e., when high and low peaks happen) is more important that actual forecast accuracy, as measured by error criteria. In many cases, the load forecast based on customers’ historical load data and the outdoor temperature is sufficient to be used in the control system, but in some cases a more accurate forecast can give better cost savings.https://www.mdpi.com/1996-1073/13/15/3946battery energy storage systemload forecastcontrol system
collection DOAJ
language English
format Article
sources DOAJ
author Juha Koskela
Antti Mutanen
Pertti Järventausta
spellingShingle Juha Koskela
Antti Mutanen
Pertti Järventausta
Using Load Forecasting to Control Domestic Battery Energy Storage Systems
Energies
battery energy storage system
load forecast
control system
author_facet Juha Koskela
Antti Mutanen
Pertti Järventausta
author_sort Juha Koskela
title Using Load Forecasting to Control Domestic Battery Energy Storage Systems
title_short Using Load Forecasting to Control Domestic Battery Energy Storage Systems
title_full Using Load Forecasting to Control Domestic Battery Energy Storage Systems
title_fullStr Using Load Forecasting to Control Domestic Battery Energy Storage Systems
title_full_unstemmed Using Load Forecasting to Control Domestic Battery Energy Storage Systems
title_sort using load forecasting to control domestic battery energy storage systems
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2020-08-01
description The profitability of domestic battery energy storage systems has been poor and this is the main barrier to their general use. It is possible to increase profitability by using multiple control targets. Market price-based electricity contracts and power-based distribution tariffs alongside storage of surplus photovoltaic energy make it possible to have multiple control targets in domestic use. The battery control system needs accurate load forecasting so that its capacity can be utilized in an optimally economical way. This study shows how the accuracy of short-term load forecasting affects cost savings by using batteries. The study was conducted by simulating actual customers’ load profiles with batteries utilized for different control targets. The results of the study show that knowledge of customers’ load profiles (i.e., when high and low peaks happen) is more important that actual forecast accuracy, as measured by error criteria. In many cases, the load forecast based on customers’ historical load data and the outdoor temperature is sufficient to be used in the control system, but in some cases a more accurate forecast can give better cost savings.
topic battery energy storage system
load forecast
control system
url https://www.mdpi.com/1996-1073/13/15/3946
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