Implementation and Evaluation of a Distribution Load Flow Algorithm for Networks with Distributed Generation

Power flow calculation serves as a fundamental tool in power systems for analyzing network voltage distribution and power flow patterns. With the widespread integration of distributed generation (DG), traditional load flow algorithms face new challenges. This paper explores the implementation and evaluation approach for a distribution load flow algorithm suitable for networks with distributed generation resources. Distribution networks differ from transmission networks due to their high resistance-to-reactance (R/X) ratio characteristics, which often cause convergence issues in traditional Newton-Raphson methods. To address this characteristic, the improved forward-backward sweep method has become a common choice. This algorithm iteratively calculates the relationship between node injection power and voltage through sequential voltage updates and power flow corrections, making it more suitable for radial network structures. The implementation typically involves nested loops for backward sweep (power summation) and forward sweep (voltage update) operations. The introduction of distributed generation transforms the unidirectional power supply pattern of distribution networks. The algorithm must account for different operational modes of DG units such as photovoltaic systems and wind turbines, including PQ (constant power), PV (constant voltage), or droop control modes. PQ-type DG units can be directly treated as negative loads in the power injection calculations, while PV-type units require additional reactive power compensation iterations to maintain voltage regulation. The code implementation needs conditional branching to handle different DG models with appropriate power injection equations. Evaluation metrics should cover three aspects: 1) Convergence performance, particularly in high DG penetration scenarios where algorithm stability becomes critical; 2) Computational efficiency, comparing iteration counts and processing time through benchmark tests; 3) Accuracy verification, achieved by comparing results with commercial software outputs or field measurement data. Practical testing requires constructing typical use cases, such as varying DG connection locations and penetration level gradients. The evaluation framework should include automated testing scripts that systematically vary parameters and record performance metrics. The engineering value of this algorithm lies in providing rapid analysis tools for DG-integrated distribution network planning and real-time dispatch. Key implementation considerations include network topology processing using node-branch incidence matrices, DG modeling adaptation through object-oriented programming approaches, and convergence condition optimization with adaptive tolerance settings. Future extension directions include integration with smart meter data streams for real-time analysis, or combining with deep learning techniques to predict power flow trend patterns through neural network-based forecasting modules.