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Related Concept Videos

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.
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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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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.
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Multimachine Stability01:25

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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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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Related Experiment Video

Updated: Nov 20, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Multi-energy conversion based on game theory in the industrial interconnection.

Jianjia He1,2, Xiumeng Wu1, Junxiang Li1,2

  • 1Business School, University of Shanghai for Science and Technology, Shanghai, China.

Plos One
|January 19, 2021
PubMed
Summary

This study introduces a multi-energy conversion system (MCS) using a cloud platform and game theory to optimize energy resource utilization. The new MCS enhances efficiency, reduces costs for consumers, and lowers peak demand for suppliers.

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Area of Science:

  • Energy Systems Engineering
  • Information Technology
  • Game Theory

Background:

  • Multi-energy conversion systems (MCS) are crucial for energy resource utilization and the energy transition.
  • New information technologies enable real-time interaction, collaboration, and demand response in MCS.
  • Traditional optimization methods struggle with complex interactions and data challenges in modern MCS.

Purpose of the Study:

  • To propose a cloud-coupled MCS addressing information explosion and data security.
  • To model participant interactions using non-cooperative game theory.
  • To maximize energy supplier profit and minimize customer costs within the MCS.

Main Methods:

  • Developed a multi-energy conversion system (MCS) integrated with a cloud platform.
  • Applied non-cooperative game theory to model participant interactions.
  • Utilized a gradient projection algorithm for simulation and analysis.

Main Results:

  • The proposed MCS improves energy utilization efficiency via energy conversion.
  • Consumer satisfaction is maintained while reducing energy consumption costs.
  • Peak load demand is reduced, benefiting both customers and suppliers.

Conclusions:

  • The developed MCS effectively balances energy conversion, cost reduction, and demand management.
  • The game model ensures a single Nash equilibrium, validating the interaction dynamics.
  • The system enhances supply quality and diversifies energy consumption patterns.