研究目的
To propose an adaptive decentralized control strategy for residential-scale battery energy storage systems to reduce voltage and thermal issues in distribution networks while still benefiting customers by reducing grid imports.
研究成果
The proposed adaptive decentralized control strategy effectively mitigates voltage and thermal issues in PV-rich MV-LV networks while maintaining high benefits for customers, achieving performance close to an ideal optimization-based approach without the need for extensive infrastructure or forecasts.
研究不足
The study assumes perfect clear-sky irradiance estimation, which may have errors due to factors like temperature and dust. It focuses on a specific Australian network and may not generalize to other regions. The control strategy requires local measurements and may not account for all real-world variabilities.
1:Experimental Design and Method Selection:
The study involves designing and testing an adaptive decentralized control strategy for BES systems, comparing it with off-the-shelf and optimization-based controls. It uses algorithms for charging and discharging based on clear-sky irradiance, PV generation, demand, and state of charge.
2:Sample Selection and Data Sources:
A real Australian MV feeder with 79 LV networks and anonymized smart meter data from 342 residential customers in 2014 is used. PV generation profiles and clear-sky irradiance data are generated for Melbourne, Victoria.
3:List of Experimental Equipment and Materials:
Residential-scale BES systems (5kW/
4:5kWh), PV systems (5, 5, 5, 8kWp), distribution transformers, MV and LV lines, and software tools like OpenDSS and Python for simulations. Experimental Procedures and Operational Workflow:
Time-series power flow analyses are conducted using OpenDSS and Python. Control strategies (OTS, AD, OPT) are implemented and evaluated over yearly data with 30-minute resolution. Performance metrics include voltage profiles, utilization levels, and grid dependency index.
5:Data Analysis Methods:
Statistical analysis of voltage compliance, asset utilization, and GDI using boxplots and topological visualizations. Optimization problems are solved with AIMMS and CPLEX solver.
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获取完整内容-
Battery Energy Storage System
5kW/13.5kWh
Tesla
Storing excess PV generation and discharging to reduce grid imports and manage network issues.
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Photovoltaic System
Various sizes (2.5kWp, 3.5kWp, 5.5kWp, 8kWp)
Generating electricity from solar energy.
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Distribution Transformer
Stepping down voltage from MV to LV for residential supply.
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OpenDSS
Distribution system analysis software for power flow simulations.
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Python
Programming language used for control implementations and data analysis.
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AIMMS
Software for solving optimization problems.
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CPLEX
12.8
IBM
Solver for mixed-integer quadratic programs.
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Smart Meter
Measuring and recording electricity demand data.
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