|
ИСТИНА |
Войти в систему Регистрация |
ИСТИНА ПсковГУ |
||
Scalable Assessment of Rooftop Solar Potential in Central Asia using Open Data and Geometric Orientation Analysisстатья
Информация о цитировании статьи получена из
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 23 января 2026 г.
- Авторы: Madvaliev U., Mahmudov B.N., Rustamzoda A.R., Kudusov M.A., Bakhromzod R.
- Журнал: Applied Solar Energy (English translation of Geliotekhnika)
- Том: 61
- Номер: 2
- Год издания: 2025
- Издательство: Allerton Press Inc.
- Местоположение издательства: United States
- Первая страница: 206
- Последняя страница: 215
- DOI: 10.3103/s0003701x25601280
- Аннотация: This study presents a scalable methodology for assessing rooftop solar photovoltaic (PV) potential in Central Asia, utilizing open-source geospatial data from OpenStreetMap (OSM) and PVGIS, and integrating the Minimum Rotated Rectangle (MRR) method for rooftop orientation analysis. Applied to Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, the approach estimates installable capacity, annual energy yield, and economic viability through metrics such as Levelized Cost of Electricity (LCOE), Net Present Value (NPV), and Payback period (PP). Results indicate significant technical potential, with Kazakhstan leading at 61.43 GW and 49.00 TWh/year, followed by Uzbekistan at 35.07 GW and 32.28 TWh/year. Smaller nations like Kyrgyzstan (12.32 GW, 10.67 TWh) and Tajikistan (9.92 GW, 8.68 TWh) show substantial per-capita potential, while Turkmenistan benefits from the highest specific yield (1584.89 kWh/kW). LCOE ranges from 0.0273 USD/kWh (Turkmenistan) to 0.0378 USD/kWh (Kazakhstan), but economic feasibility varies due to tariff structures, with Kazakhstan and Uzbekistan approaching unsubsidized viability, while Kyrgyzstan and Turkmenistan require significant subsidies (453.17 USD/kW and 1622.22 USD/kW, respectively). Validated against operational PV systems in Dushanbe (±10% deviation from PVGIS benchmarks), the methodology demonstrates reliability for data-scarce regions. By providing geospatial outputs, this study supports energy planning and policy formulation, contributing to SDG 7 (Affordable and Clean Energy) and offering a transferable framework for developing regions with limited geospatial data.
- Добавил в систему: Бахромзод Ризои