Optimal DG allocation using the Dingo Optimization Algorithm: robust power loss reduction with concomitant voltage...

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Power Systems Analysis

Background:

• Power grids face challenges with active power losses and voltage instability.
• Integrating renewable distributed generators (DG) offers potential solutions but requires careful management.
• Optimal allocation and sizing of DG units are crucial for grid efficiency and stability.

Purpose of the Study:

• To develop and apply a comprehensive model for optimal allocation and sizing of DG units.
• To target significant reduction in active power losses.
• To achieve concomitant improvement in voltage stability in distribution and transmission networks.

Main Methods:

• Utilized the Dingo Optimization Algorithm (DOA) for optimal DG placement and sizing.
• Evaluated DG effectiveness in IEEE 33-node distribution network (DN) and IEEE 118-node transmission network (TN).
• Considered photovoltaic (PV) and hybrid PV-Wind DG configurations, with and without reactive compensation and dispatch factors.

Main Results:

• DOA achieved substantial active power loss reductions (up to 81.63% in DN) and improved voltage stability indices (VSI, VDI).
• In the TN, PV DG units reduced losses by up to 16.98%, while hybrid systems reduced losses by up to 23.98%.
• A dispatch factor improved light load scenarios, reducing losses by 12.61% and enhancing network stability.

Conclusions:

• The Dingo Optimization Algorithm is effective for optimizing DG placement and sizing in power grids.
• Optimal DG integration significantly reduces active power losses and enhances voltage stability.
• The methodology is applicable to both distribution and transmission networks with various renewable energy sources.