TY - JOUR
T1 - Probabilistic Hosting Capacity for Active Distribution Networks
AU - Al-Saadi, Hassan
AU - Zivanovic, Rastko
AU - Al-Sarawi, Said
N1 - Funding Information:
The authors express their gratitude to Pieter van Langen for material on distributed design research and Ashok Goel for initiating collaboration with Lilia Moshkina. The research reported in this paper has been financially supported by the NLnet foundation.
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2017/10
Y1 - 2017/10
N2 - The increased connection of distributed generation (DG), such as photovoltaic (PV) and wind turbine (WT), has shifted the current distribution networks from being passive (consuming energy) into active (consuming/producing energy). However, there is still no consensus about how to determine the maximum amount of DGs that are allowed to be connected, i.e., how to quantify a so-called 'hosting capacity' (HC). Therefore, this paper proposes a novel risk assessment tool for estimating network HC by considering uncertainties associated with PV, WT, and loads. This evaluation is performed using the likelihood approximation approach. The paper, also, proposes a utilization of clearness index for localized solar irradiance prediction of PV. In addition, we propose the use of sparse grid technique as an effective means for uncertainty computation while the use of Monte Carlo technique is taken for a comparison purpose. Two actual distribution networks (11-buses and South Australian large feeder) are considered as case studies to demonstrate the usefulness of the proposed tool.
AB - The increased connection of distributed generation (DG), such as photovoltaic (PV) and wind turbine (WT), has shifted the current distribution networks from being passive (consuming energy) into active (consuming/producing energy). However, there is still no consensus about how to determine the maximum amount of DGs that are allowed to be connected, i.e., how to quantify a so-called 'hosting capacity' (HC). Therefore, this paper proposes a novel risk assessment tool for estimating network HC by considering uncertainties associated with PV, WT, and loads. This evaluation is performed using the likelihood approximation approach. The paper, also, proposes a utilization of clearness index for localized solar irradiance prediction of PV. In addition, we propose the use of sparse grid technique as an effective means for uncertainty computation while the use of Monte Carlo technique is taken for a comparison purpose. Two actual distribution networks (11-buses and South Australian large feeder) are considered as case studies to demonstrate the usefulness of the proposed tool.
KW - Active distribution networks (ADNs)
KW - likelihood approximation approach
KW - probabilistic hosting capacity (PHC)
KW - risk assessment
KW - sparse grid
KW - uncertainty computation
UR - http://www.scopus.com/inward/record.url?scp=85031743556&partnerID=8YFLogxK
U2 - 10.1109/TII.2017.2698505
DO - 10.1109/TII.2017.2698505
M3 - Article
VL - 13
SP - 2519
EP - 2532
JO - IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
JF - IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
IS - 5
M1 - 7913608
ER -