Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)

Increased behind-the-meter (BTM) solar generation causes additional errors in short-term load forecasting. To ensure power grid reliability, it is necessary to consider the influence of the behind-the-meter distributed resources. This study proposes a method to estimate the size of behind-the-meter...

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Auteurs principaux:	Ji-Won Cha, Sung-Kwan Joo
Format:	article
Langue:	EN
Publié:	MDPI AG 2021
Sujets:	load forecasting load disaggregation behind-the-meter (BTM) hidden capacity capacity estimation Technology T
Accès en ligne:	https://doaj.org/article/975b913abb814f3fbf9ff6aa02f9efd3
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spelling	oai:doaj.org-article:975b913abb814f3fbf9ff6aa02f9efd32021-11-11T15:52:23ZProbabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)10.3390/en142170671996-1073https://doaj.org/article/975b913abb814f3fbf9ff6aa02f9efd32021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7067https://doaj.org/toc/1996-1073Increased behind-the-meter (BTM) solar generation causes additional errors in short-term load forecasting. To ensure power grid reliability, it is necessary to consider the influence of the behind-the-meter distributed resources. This study proposes a method to estimate the size of behind-the-meter assets by region to enhance load forecasting accuracy. This paper proposes a semi-supervised approach to BTM capacity estimation, including PV and battery energy storage systems (BESSs), to improve net load forecast using a probabilistic approach. A co-optimization is proposed to simultaneously optimize the hidden BTM capacity estimation and the expected improvement to the net load forecast. Finally, this paper presents a net load forecasting method that incorporates the results of BTM capacity estimation. To describe the efficiency of the proposed method, a study was conducted using actual utility data. The numerical results show that the proposed method improves the load forecasting accuracy by revealing the gross load pattern and reducing the influence of the BTM patterns.Ji-Won ChaSung-Kwan JooMDPI AGarticleload forecastingload disaggregationbehind-the-meter (BTM)hidden capacitycapacity estimationTechnologyTENEnergies, Vol 14, Iss 7067, p 7067 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	load forecasting load disaggregation behind-the-meter (BTM) hidden capacity capacity estimation Technology T
spellingShingle	load forecasting load disaggregation behind-the-meter (BTM) hidden capacity capacity estimation Technology T Ji-Won Cha Sung-Kwan Joo Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)
description	Increased behind-the-meter (BTM) solar generation causes additional errors in short-term load forecasting. To ensure power grid reliability, it is necessary to consider the influence of the behind-the-meter distributed resources. This study proposes a method to estimate the size of behind-the-meter assets by region to enhance load forecasting accuracy. This paper proposes a semi-supervised approach to BTM capacity estimation, including PV and battery energy storage systems (BESSs), to improve net load forecast using a probabilistic approach. A co-optimization is proposed to simultaneously optimize the hidden BTM capacity estimation and the expected improvement to the net load forecast. Finally, this paper presents a net load forecasting method that incorporates the results of BTM capacity estimation. To describe the efficiency of the proposed method, a study was conducted using actual utility data. The numerical results show that the proposed method improves the load forecasting accuracy by revealing the gross load pattern and reducing the influence of the BTM patterns.
format	article
author	Ji-Won Cha Sung-Kwan Joo
author_facet	Ji-Won Cha Sung-Kwan Joo
author_sort	Ji-Won Cha
title	Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)
title_short	Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)
title_full	Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)
title_fullStr	Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)
title_full_unstemmed	Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)
title_sort	probabilistic short-term load forecasting incorporating behind-the-meter (btm) photovoltaic (pv) generation and battery energy storage systems (besss)
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/975b913abb814f3fbf9ff6aa02f9efd3
work_keys_str_mv	AT jiwoncha probabilisticshorttermloadforecastingincorporatingbehindthemeterbtmphotovoltaicpvgenerationandbatteryenergystoragesystemsbesss AT sungkwanjoo probabilisticshorttermloadforecastingincorporatingbehindthemeterbtmphotovoltaicpvgenerationandbatteryenergystoragesystemsbesss
_version_	1718433124749148160

Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)

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