Probabilistic Assessment of PV Hosting Capacity Under Coordinated Voltage Regulation in Unbalanced Active Distribution Networksopen access
- Authors
- Han, Changhee; Lee, Dongwon; Song, Sungyoon; Jang, Gilsoo
- Issue Date
- 2022
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Voltage control; Optimization; Inverters; Probabilistic logic; Uncertainty; Reactive power; Stochastic processes; Active network management; distribution system; hosting capacity; optimization; photovoltaic system; probabilistic analysis; smart inverter; soft open point; voltage regulation
- Citation
- IEEE ACCESS, v.10, pp.35578 - 35588
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 10
- Start Page
- 35578
- End Page
- 35588
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/140871
- DOI
- 10.1109/ACCESS.2022.3163595
- ISSN
- 2169-3536
- Abstract
- The increasing penetration of photovoltaic (PV) generators has led to a shift of the operational policy of the distribution system operator (DSO) from passive to active intervention in distribution networks (DNs), where a centralized controller governs the operation of voltage regulation devices. However, the PV output uncertainty hinders the verification of the impact of PVs on DNs. Therefore, the hosting capacity (HC) analysis framework for PV penetration should reflect both the operational benefit of the DSO and the PV output uncertainty. Thus, in this study, a two-stage optimization-based framework is proposed to analyze the probabilistic HC for PV under active network management (ANM) of DNs. In the first stage, the optimal PV base capacity (PVBC) to maximize the sum of the HC for PVs is determined based on a heuristic optimization method; in the second stage, using the predefined load and PV output profile, the maximum available power generation curve for PVBC is calculated, and the maximum HC for PV is derived by the calibration of PVBC through a comparison with the actual PV generation profile curve. The proposed method considers the time-series-based load flow results, reflecting the time-scheduling strategy by the DSO. Moreover, the uncertain characteristics of PV output are stochastically considered using a Monte Carlo simulation-based repetitive calculation approach. Case studies were implemented using the modified IEEE-123 test system, and the simulation results provided a quantitative comparison of the effect of the probabilistic HC improvement on the utilization of controllable resources and the centralized ANM by the DSO.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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