Load and PV Generation Forecast Based Cost Optimization for Nanogrids with PV and Battery
| Autor: | Mustafa Erdem Sezgin, M. Ugur Gudelek, Murat Gol, Cem Recai Çirak, Efe Arin, A. Murat Ozbayoglu |
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| Přispěvatelé: | TOBB ETU, Faculty of Engineering, Department of Computer Engineering, TOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Özbayoğlu, Ahmet Murat, H-2328-2011 |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Battery (electricity)
business.industry Computer science Deep learning 020208 electrical & electronic engineering Photovoltaic system 02 engineering and technology Grid Reliability engineering Electric power system Smart grid Solar energy Robustness (computer science) Daily global 0202 electrical engineering electronic engineering information engineering Grid connection Solar radiation Artificial intelligence business |
| Popis: | 53rd International Universities Power Engineering Conference (2018 : United Kingdom) Power system resiliency and robustness became major concerns of the system operators and researchers after the introduction of the smart grid concept. The improvements in the battery storage systems (BSS) and the photovoltaic (PV) systems encourage power systems operators to enable the use of those systems in resiliency and robustness studies. Utilization of those systems not only contributes to the robustness of the power systems but also decrease the operational costs. There are several methods in literature to operate the grid systems with partitions of PV and BSS in the most economical way. Although these methods are straightforward and work fine, they can not guarantee the most economical result on a daily basis. In this paper, deep learning based PV generation and load forecasts are used to improve the results of optimization in terms of economic aspects in nano-grid applications. In the considered system, there are loads, PV generation units, BSS and grid connection. Bi-directional power flow is permitted between the main grid and the nano-grid system. The forecasting methodologies and used optimization algorithms will be explained in this paper. © 2018 IEEE. EDF,IEEE,RTDS Technologies,Scottish and Southern Electricity Networks,TJ - H2b |
| Databáze: | OpenAIRE |
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