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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:
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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.
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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...
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A leader supply-demand-based optimization for large scale optimal power flow problem considering renewable energy

Fatima Daqaq1, Mohamed H Hassan2, Salah Kamel3

  • 1Laboratory of Study and Research for Applied Mathematics, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, 10090, Morocco.

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A new Leader Supply-Demand Optimization (LSDO) method enhances power flow optimization for hybrid wind and solar systems. This approach improves exploration, reducing local optima for more efficient power grid management.

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Area of Science:

  • Engineering
  • Computer Science
  • Optimization Algorithms

Background:

  • Modern power systems face challenges with non-linearity and complexity in optimal power flow (OPF).
  • Existing stochastic optimization methods like Supply-Demand-based Optimization (SDO) require enhancements for integrating hybrid renewable energy sources.
  • The need for improved OPF solutions that efficiently incorporate wind and solar power is critical.

Purpose of the Study:

  • To propose an enhanced stochastic optimization algorithm, Leader Supply-Demand-based Optimization (LSDO), for improved OPF.
  • To address the limitations of existing SDO methods in handling complex power system constraints and renewable integration.
  • To validate the effectiveness of LSDO in solving real-world OPF problems with hybrid wind and solar power.

Main Methods:

  • Development of the Leader Supply-Demand-based Optimization (LSDO) algorithm, incorporating simultaneous crossover and mutation mechanisms.
  • Testing LSDO on 23 benchmark functions to evaluate its performance against state-of-the-art algorithms.
  • Application of LSDO to IEEE 30, 57, and 118-bus test systems with hybrid wind and solar power, utilizing the Superiority of Feasible Solutions (SF) constraint handling technique.

Main Results:

  • LSDO demonstrated improved exploration capabilities, reducing the likelihood of getting trapped in local optima.
  • Comparative analysis on benchmark functions showed LSDO outperforming or matching well-regarded competitors.
  • Successful application to IEEE test systems confirmed LSDO's effectiveness in solving constrained OPF problems with renewable energy integration.

Conclusions:

  • The proposed LSDO algorithm offers a promising and competitive approach for solving complex optimal power flow problems.
  • LSDO effectively handles the integration of hybrid wind and solar power sources within power system optimization.
  • The LSDO variant provides enhanced performance compared to its predecessor and other state-of-the-art optimization techniques.