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

Ji-Won Cha and Sung-Kwan Joo
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Ji-Won Cha: The School of Electrical Engineering, Korea University, Seoul 02841, Korea
Sung-Kwan Joo: The School of Electrical Engineering, Korea University, Seoul 02841, Korea

Energies, 2021, vol. 14, issue 21, 1-19

Abstract: 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.

Keywords: load forecasting; load disaggregation; behind-the-meter (BTM); hidden capacity; capacity estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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