研究目的
Determining the penetration limit of solar distributed generation (DG) in the distribution network considering multiple bus integration to ensure system reliability and power quality.
研究成果
The penetration limit of solar DG was successfully determined using genetic algorithm, showing significant improvements in power losses and voltage profile. Case 2, with DG distributed to 18 buses, achieved the highest penetration limit and best performance in terms of loss reduction and voltage stability. Further increases in bus integration beyond 50% provided marginal benefits, indicating an optimal distribution level for maximizing system advantages.
研究不足
The study is limited to the IEEE 37-bus test system and specific solar DG model; results may not generalize to other networks or DG types. The stochastic approach assumes beta distribution for irradiance, which might not capture all real-world variability. Only unity power factor operation is considered for solar DG, ignoring reactive power capabilities.
1：Experimental Design and Method Selection:
The study uses genetic algorithm (GA) as an optimization tool to determine the optimal siting and sizing of solar DG units, considering stochastic nature through beta distribution for solar irradiance. The objective is to minimize system losses subject to voltage and power flow constraints.
2：Sample Selection and Data Sources:
The IEEE 37-bus test system is used as the distribution network model. Solar irradiance data is modeled using beta distribution with parameters derived from mean and standard deviation.
3：List of Experimental Equipment and Materials:
Software tools include MATLAB for GA implementation and OpenDSS for power flow simulations. The solar DG model is based on Astronergy 290W STAR CHSM6610M PV modules.
4：Experimental Procedures and Operational Workflow:
The process involves generating initial population in GA, running load flow analysis in OpenDSS via COM interface, evaluating fitness functions, performing crossover and mutation, and iterating until specified generations. Stochastic deration is applied with 20 samples per hour over 24 hours to account for time-varying generation.
5：Data Analysis Methods:
System losses and voltage deviations are calculated and averaged. Percent reductions in real and reactive power losses and total voltage deviation are computed for comparison across different cases of DG distribution.
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