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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
724
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
582
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

829
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the power flow program computes...
829
Maximum Power Transfer01:16

Maximum Power Transfer

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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
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Conservation of AC Power01:15

Conservation of AC Power

648
The principle of power preservation is applicable to both ac and dc circuits. This principle, when applied to AC power, asserts that the complex, real, and reactive powers produced by the source are equal to the total complex, real, and reactive powers absorbed by the loads. When two load impedances are connected in parallel to an ac source V, the complex power provided by the source can be calculated using the relation
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Control of Power Flow01:30

Control of Power Flow

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There are several methods to control power flow in power systems:
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Related Experiment Video

Updated: Jan 13, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Cuckoo optimization algorithm via Grey Wolf Optimizer for usage in engineering optimization and optimal power flow

Rabeh Abbassi1, Pavel Trojovský2, Zulkefli Mansor3

  • 1Department of Electrical Engineering, College of Engineering, University of Ha'il, 81451, Hail, Saudi Arabia. r.abbassi@uoh.edu.sa.

Scientific Reports
|October 29, 2025
PubMed
Summary

A new hybrid metaheuristic, COGWO, enhances power system optimization by integrating Grey Wolf Optimizer (GWO) and Cuckoo Optimization Algorithm (COA). This method improves resilience and efficiency in managing power distribution, especially with renewable energy sources.

Keywords:
COGWOCuckoo optimization algorithmEngineering optimizationGrey Wolf OptimizerMulti-objective OPFRenewable energy sources (RESs).

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Area of Science:

  • Electrical Engineering
  • Computational Intelligence
  • Optimization Techniques

Background:

  • Optimal Power Flow (OPF) is crucial for efficient power distribution management.
  • Existing optimization techniques face challenges with complex power systems and renewable energy integration.
  • Resilient and efficient optimization methods are needed for modern power grids.

Purpose of the Study:

  • To introduce COGWO, a hybrid metaheuristic combining GWO and COA, for enhanced OPF solutions.
  • To validate COGWO's performance against standard engineering problems and state-of-the-art methods.
  • To apply COGWO to OPF issues in large-scale power systems with fluctuating renewable energy sources.

Main Methods:

  • Developed COGWO by integrating Grey Wolf Optimizer (GWO) and Cuckoo Optimization Algorithm (COA).
  • Validated COGWO on CEC2020 benchmark problems, demonstrating superior performance.
  • Applied COGWO to IEEE 30-bus and 118-bus systems, considering renewable energy source (RES) fluctuations.

Main Results:

  • COGWO achieved superior convergence quality and solution resilience compared to GWO, COA, and other metaheuristics.
  • The method effectively minimized fuel costs, power loss, voltage variation, and emissions.
  • COGWO demonstrated an optimal balance between exploration and exploitation for non-convex and non-smooth optimization functions.

Conclusions:

  • COGWO offers a computationally efficient, flexible, and resilient solution for large-scale power system optimization.
  • The hybrid approach significantly improves solution stability and convergence speed in OPF.
  • COGWO is a promising technique for managing modern power grids with integrated renewable energy.