Power Factor and Energy Loss Cost Evaluation of Radial Distribution Systems

This paper has adopted the new bio-inspired Manta-Ray Foraging Optimization (MRFO) algorithm for optimal allocation of multiple Distributed Generation (DG) units attached to Radial Distribution Systems (RDSs) in order to reduce the total energy loss of the studied system. The DG units are optimized to work with a unity power factor (UPF) and optimal power factor (OPF) during a 24-h time-varying demand. The MRFO algorithm optimized single, two, and three DG units. The total energy loss and energy-saving during the time-varying demand are calculated and compared with the original case. The MRFO algorithm behavior is compared to the Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO) algorithms regarding energy loss and energy-saving values. The standard 69-bus RDS is used as a test system. Considerable improvements in energy saving, loss reduction, and voltage profile are achieved after installing DG units, mainly when operating with optimal power factors. The MRFO algorithm achieves energy losses of 817.91, 751.08, and 730.25 kWh with 1, 2, and 3 DG units with UPF allocations, respectively. On the other hand, when the DG units are optimized to work with OPF, the MRFO achieves energy losses of 233.24, 142.08, and 106.79 kWh with the same number of DG units, respectively. Furthermore, the MRFO algorithm has efficient behavior compared with the PSO, ASO, and other algorithms for different operations and conditions.

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Planning models for optimal routing of radial distribution systems

International Journal of Applied Power Engineering (IJAPE) ◽

10.11591/ijape.v10.i2.pp108-117 ◽

2021 ◽

Vol 10 (2) ◽

pp. 108

Author(s):

Mahmoud Ali Farrag ◽

Maged Gamal Zahra ◽

Shaimaa Omran

Keyword(s):

Energy Loss ◽

Radial Distribution ◽

Distribution Systems ◽

Voltage Drop ◽

Optimal Routing ◽

Linear Segment ◽

Thermal Limit ◽

Planning Models ◽

Voltage Limit ◽

Bus Voltage

<span>This paper presents three planning models for optimal routing of radial distribution systems. In the first two models, the cost function includes capital cost of lines, energy loss cost, and bays cost. The constraints equations include power balance equations, voltage drop equations, radiality equations, logic equations, thermal limit equations, and bus voltage limit equations. The first model considers the energy loss equation in its quadratic form while the second model approximates the energy loss equation of each cable size by a simple linear segment considering the economic loading of each cable size. In the third model, two sub-models are used where the first one gets the optimal radial network configuration regardless of the cable sizes and voltage constraints. In the second sub-model the best cable size on each selected line of the first model is determined to minimize the system costs while considering the bus voltage limit constraint and thermal limit constraint. Verification of the proposed planning models has been made using a real 11 kV 34-bus distribution network with 68 initial lines.</span>

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Energy Loss Reduction of Distribution Systems Equipped with Multiple Distributed Generations Considering Uncertainty using Manta-Ray Foraging Optimization

International Journal of Renewable Energy Development ◽

10.14710/ijred.0.37482 ◽

2021 ◽

Vol 10 (4) ◽

pp. 779-787

Author(s):

Ahmad Eid ◽

Almoataz Y. Abdelaziz ◽

Mostafa Dardeer

Keyword(s):

Energy Loss ◽

Energy Saving ◽

Power Factor ◽

Distribution Systems ◽

Energy Losses ◽

Time Varying ◽

Loss Reduction ◽

Optimal Power ◽

Varying Demand ◽

Manta Ray

This paper has adopted the new bio-inspired Manta-Ray Foraging Optimization (MRFO) algorithm for optimal allocation of multiple Distributed Generation (DG) units attached to Radial Distribution Systems (RDSs) in order to reduce the total energy loss of the studied system. The DG units are optimized to work with a unity power factor (UPF) and optimal power factor (OPF) during a 24-h time-varying demand. The MRFO algorithm optimized single, two, and three DG units. The total energy loss and energy-saving during the time-varying demand are calculated and compared with the original case. The MRFO algorithm behavior is compared to the Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO) algorithms regarding energy loss and energy-saving values. The standard 69-bus RDS is used as a test system. Considerable improvements in energy saving, loss reduction, and voltage profile are achieved after installing DG units, mainly when operating with optimal power factors. The MRFO algorithm achieves energy losses of 817.91, 751.08, and 730.25 kWh with 1, 2, and 3 DG units with UPF allocations, respectively. On the other hand, when the DG units are optimized to work with OPF, the MRFO achieves energy losses of 233.24, 142.08, and 106.79 kWh with the same number of DG units, respectively. Furthermore, the MRFO algorithm has efficient behavior compared with the PSO, ASO, and other algorithms for different operations and conditions.

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