| Title |
Photovoltaic Integration in Smart City Power Distribution A Probabilistic Photovoltaic Hosting Capacity Assessment based on Smart Metering Data |
| ID_Doc |
39512 |
| Authors |
Klonari, V; Toubeau, JF; Lobry, J; Vallée, F |
| Title |
Photovoltaic Integration in Smart City Power Distribution A Probabilistic Photovoltaic Hosting Capacity Assessment based on Smart Metering Data |
| Year |
2016 |
| Published |
|
| DOI |
|
| Abstract |
Maximizing the share of renewable resources in the electric energy supply is a major challenge in the design of smart cities. Concerning the smart city power distribution, the main focus is on the Low Voltage (LV) level in which distributed Photovoltaic (PV) units are the mostly met renewable energy systems. This paper demonstrates the usefulness of smart metering (SM) data in determining the maximum photovoltaic (PV) hosting capacity of an LV distribution feeder. Basically, the paper introduces a probabilistic tool that estimates PV hosting capacity by using user-specific energy flow data, recorded by SM devices. The probabilistic evaluation and the use of historical SM data yield a reliable estimation that considers the volatile character of distributed generation and loads as well as technical constraints of the network (voltage magnitude, phase unbalance, congestion risk, line losses). As a case study, an existing LV feeder in Belgium is analysed. The feeder is located in an area with high PV penetration and large deployment of SM devices. The estimated PV hosting capacity is proved to be much higher than the one obtained with a deterministic worst case approach, considering voltage margin (magnitude and unbalance). |
| Author Keywords |
Smart Cities Power Distribution; Low Voltage; Hosting Capacity; Smart Meters; Photovoltaic; Probabilistic Analysis |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000615693100023 |
| WoS Category |
Computer Science, Artificial Intelligence; Green & Sustainable Science & Technology; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Science & Technology - Other Topics; Engineering |
| PDF |
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