研究目的
To design an optimal rooftop wind-PV hybrid system to meet the energy demand for a typical residential home over a 20-year lifetime projection, focusing on minimizing the difference between generation and demand through discrete optimization.
研究成果
The study demonstrates that a hybrid wind-PV system is the most suitable for meeting the energy requirements of residential buildings in Düzce, Turkey, over a 20-year lifetime. The system's economic viability is confirmed, with a minimal cost increase when transitioning from a standalone WT system to a hybrid WT-PV system. The use of combinatorial optimization and load shifting strategies significantly improves system efficiency and reduces operational costs.
研究不足
The study is limited to a specific location (Düzce, Turkey) and may not be directly applicable to other regions with different wind and solar potentials. The economic analysis is based on current technology costs and may vary with future advancements.
1:Experimental Design and Method Selection:
The study employs a mathematical model for discrete optimization to balance power generation and consumption. Genetic Algorithm (GA) is used for combinatorial optimization to optimize the working hours of household electrical applications.
2:Sample Selection and Data Sources:
The study uses average wind speed and insolation rate data from 2014, 2015, and 2016 obtained from a local weather station in Düzce, Turkey.
3:List of Experimental Equipment and Materials:
Includes 1 kW wind turbines, 0.25 kW PV panels, 12 V 200 Ah gel batteries, and a 3 kW pure sine inverter.
4:25 kW PV panels, 12 V 200 Ah gel batteries, and a 3 kW pure sine inverter.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: The system operates by generating electricity through PV arrays and wind turbines, storing excess energy in batteries, and converting DC to AC for household use. Load shifting strategy is applied to minimize power imbalance.
5:Data Analysis Methods:
The study calculates the total system annual cost for each combination of system components and selects the combination with the lowest cost.
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