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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:
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The Power Flow Problem and Solution01:26

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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 power flow program computes...
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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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Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand, use...
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Related Experiment Video

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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Optimal on-off scheduling for intermittent pumping wells under grid-connected pv systems.

Meixia Qiao1, Fushen Ren1, Yanchun Li1

  • 1College of Mechanical Science and Engineering, Northeast Petroleum University, Daqing, 163318, China.

Scientific Reports
|November 7, 2025
PubMed
Summary

This study optimizes oil well scheduling to integrate intermittent wind and solar power, boosting green electricity use and reducing costs. The new approach significantly enhances efficiency and accuracy in energy management for oilfields.

Keywords:
Improved NSGA-IIIntermittent pumping wellsOn-off scheduling optimizatioNRenewable energy utilizatioN

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Area of Science:

  • Energy Engineering
  • Artificial Intelligence
  • Petroleum Engineering

Background:

  • The
  • dual carbon
  • goal necessitates integrating renewable energy in oilfields.
  • Intermittent wind-solar power generation conflicts with stable oil well energy demands, causing low green electricity use and high curtailment rates.
  • Existing methods struggle with efficient green energy utilization in oilfield operations.

Purpose of the Study:

  • To develop an optimization approach for oil well operation scheduling that couples photovoltaic power fluctuations with intermittent pumping technology.
  • To minimize grid electricity consumption per unit of liquid production while maximizing green electricity share.
  • To improve the efficiency and accuracy of optimization algorithms for renewable energy integration in oilfields.

Main Methods:

  • A multiobjective optimization model was formulated to balance grid electricity consumption and green energy utilization.
  • Run-length encoding was used to map on-off schedules into constrained binary sequences, reducing the solution space.
  • The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was enhanced with dual-mode initialization, key-gene-preserving crossover, and peak-valley-guided mutation strategies.

Main Results:

  • The proposed method doubled green electricity consumption under stable production conditions.
  • Grid electricity consumption per unit of liquid produced was reduced by 41.67%.
  • Computational efficiency improved by two to three orders of magnitude, with a 26.69% enhancement in solution accuracy.

Conclusions:

  • The developed optimization approach effectively addresses the mismatch between intermittent renewable energy sources and oilfield energy demands.
  • The enhanced NSGA-II algorithm provides a practical and efficient solution for maximizing green energy utilization and economic benefits in oilfield operations.
  • The method demonstrates strong applicability for achieving sustainable energy management in the petroleum industry under the
  • dual carbon
  • targets.