Research on Reactive Power Optimization Calculation in Distribution Networks with Distributed Generation (DG)

Reactive power optimization calculation plays a crucial role in power systems, primarily aiming to improve grid voltage quality and reduce network losses through rational regulation of reactive power distribution. With the large-scale integration of distributed generation (DG), traditional reactive power optimization methods face new challenges and opportunities. Implementing this typically involves power flow calculation algorithms like Newton-Raphson or Gauss-Seidel methods to model network behavior.

Distributed generation typically includes various types such as wind power generation, photovoltaic cells, micro gas turbines, and fuel cells. The integration of these power sources alters the power flow distribution in distribution networks while providing new regulation means for reactive power optimization. Wind power and photovoltaic outputs exhibit intermittency and volatility, making reactive power optimization problems more complex - often requiring probabilistic power flow analysis or Monte Carlo simulation in code implementations. Although micro gas turbines and fuel cells have relatively stable outputs, their grid connection methods also affect the system's reactive power balance, which can be modeled using inverter control algorithms in simulation software.

In reactive power optimization of distribution networks containing DG, several key factors need consideration: first is the reactive power output capability of DG, where different types of DG have varying reactive power regulation characteristics, implemented through capability curve modeling in optimization algorithms; second is voltage stability issues, where DG integration may cause voltage limit violations or fluctuations, requiring constraint handling in optimization code; finally, the diversity of optimization objectives necessitates balancing between reducing network losses, improving voltage quality, and minimizing regulation costs, typically formulated as multi-objective optimization problems using weighted sum or Pareto-based approaches.

To address these issues, researchers have proposed various optimization algorithms including traditional mathematical programming methods (like linear programming, quadratic programming) and modern intelligent algorithms (such as genetic algorithms, particle swarm optimization, and artificial neural networks). These methods aim to achieve reactive power optimization operation in distribution networks under conditions of random DG output, often implemented through iterative optimization frameworks with constraint validation loops. As more types of DG are integrated in the future, distribution network reactive power optimization calculations will continue to develop toward more efficient and intelligent directions, potentially incorporating machine learning and real-time optimization techniques.