研究目的
To develop and analyze a cost-optimal autonomous hybrid renewable energy system (HRES) for remote islands, considering the saturation level of each renewable energy source to mitigate disharmony between generation and supply, and to evaluate its effects on system reliability, cost, and performance.
研究成果
The study concludes that a hybrid PV-wind-battery system with higher wind energy saturation (around 90%) and smaller wind turbines (2 kW) is the most cost-effective and reliable option for the remote island, with a COE of $0.0943/kWh and low LPSP. Sensitivity analysis shows wind energy, battery cost, and load demand are major cost drivers. The methodology helps design optimal HRES by considering resource saturation at the design stage.
研究不足
The study assumes no wake effects for wind turbines, neglects certain losses (e.g., array losses for wind), and uses simplified models. It is specific to the remote island case study and may not generalize to other locations. The battery model is based on lead-acid technology, which has limitations in cycle life and efficiency.
1:Experimental Design and Method Selection:
A mathematical model is developed to analyze the effect of varying saturation factors (from 0 to 1 in steps of 0.02) on HRES components. The model includes energy balance equations, reliability constraints (LPSP), and economic indicators (NPC, COE, SPB). Three systems with different wind turbine sizes (2 kW, 5 kW, 10 kW) are simulated for 150 configurations.
2:02) on HRES components. The model includes energy balance equations, reliability constraints (LPSP), and economic indicators (NPC, COE, SPB). Three systems with different wind turbine sizes (2 kW, 5 kW, 10 kW) are simulated for 150 configurations.
Sample Selection and Data Sources:
2. Sample Selection and Data Sources: Meteorological data (solar radiation, wind speed) from a station at Shanghai Jiao Tong University for Jiuduansha Island (31°4′N, 121°45′E). Load data synthesized for ten houses with 255 kWh/day average, including 5% randomness and 20% losses.
3:List of Experimental Equipment and Materials:
PV modules (polycrystalline, 265 W), wind turbines (2 kW, 5 kW, 10 kW), lead-acid batteries (2 V/1000 Ah), converters (26 kW, 85% efficiency), charge control station, dump load.
4:Experimental Procedures and Operational Workflow:
Hourly simulations over a year using MATLAB 2017a. Energy production calculated based on equations for PV and wind output. Battery charging/discharging managed based on energy balance, with SOC constraints (min 20%, max 100%). Excess energy dumped if battery full.
5:7a. Energy production calculated based on equations for PV and wind output. Battery charging/discharging managed based on energy balance, with SOC constraints (min 20%, max 100%). Excess energy dumped if battery full.
Data Analysis Methods:
5. Data Analysis Methods: Statistical analysis of key indicators (LPSP, COE, NPC, SPB, excess energy). Sensitivity analysis performed with ±25% changes in input parameters. Energy balance and simulation results visualized and compared.
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