Distribution Network Reconfiguration Program Based on Load Balancing

Distribution network reconfiguration is a technical approach to optimize power system operations by adjusting network topology, with core objectives including load balancing, loss reduction, and reliability improvement. The load-balancing-focused distribution network reconfiguration program typically involves these key phases:

Problem Modeling The distribution network is abstracted as a graph structure where nodes represent loads or power sources, and edges represent lines. The objective function generally includes metrics like line loss minimization and load balancing degree, while constraints must satisfy radial topology, voltage limits, current capacity, and other grid operational rules. In MATLAB implementation, this can be structured using object-oriented programming with classes defining nodes, branches, and network constraints.

Load Balancing Strategy By dynamically adjusting switch states (e.g., sectionalizing switches, tie switches), loads from overloaded lines are transferred to lightly loaded lines. Common methods include heuristic rules (like optimal power flow algorithms) or intelligent optimization algorithms (such as genetic algorithms or particle swarm optimization). For code implementation, the switch operation logic can be encoded as binary decision variables, with algorithm iterations evaluating fitness based on load distribution indices.

MATLAB Implementation Key Points Use adjacency matrices or node-branch models to describe network topology Validate reconfiguration feasibility through power flow calculations solved by methods like Newton-Raphson Implement reconfiguration optimization using MATLAB's Optimization Toolbox (e.g., `fmincon` solver) or custom iterative logic with convergence criteria monitoring

Debugging Critical Checks Post-reconfiguration validation must ensure: Connectivity checks (avoiding islanded nodes) Loop detection (ensuring radial operation) Real-time load rate comparison (verifying balance improvement through metrics like load variance reduction)

This program is particularly crucial in distributed energy integration scenarios. Future extensibility includes dynamic load prediction integration, multi-objective optimization, and SCADA system interoperability through MATLAB's instrument control and data acquisition toolboxes.