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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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Related Experiment Video

Updated: Apr 4, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

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Adaptive Portfolio Optimization for Multiple Electricity Markets Participation.

Tiago Pinto, Hugo Morais, Tiago M Sousa

    IEEE Transactions on Neural Networks and Learning Systems
    |September 10, 2015
    PubMed
    Summary

    This study introduces a portfolio optimization method for energy players navigating smart grids and multiple energy markets. It maximizes profits by forecasting prices with neural networks and optimizing participation using particle swarm optimization.

    Related Experiment Videos

    Last Updated: Apr 4, 2026

    Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
    06:04

    Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

    Published on: February 14, 2025

    1.2K

    Area of Science:

    • Electrical Engineering
    • Energy Systems
    • Computational Intelligence

    Background:

    • Increasing distributed energy resources (DERs), especially renewables, introduce intermittency challenges.
    • Smart grids offer a solution for integrating DERs and enabling participation in diverse energy markets.
    • Market participants face complex decisions regarding participation in multiple negotiation environments.

    Purpose of the Study:

    • To propose a portfolio optimization methodology for energy market players.
    • To determine optimal investment profiles and negotiation strategies across various energy markets.
    • To maximize participant profits by leveraging smart grid capabilities and market opportunities.

    Main Methods:

    • Price forecasting using artificial neural networks (ANNs).
    • Developing a database of expected market prices across different timeframes.
    • Employing an evolutionary particle swarm optimization (PSO) process for portfolio optimization.

    Main Results:

    • The methodology identifies the most advantageous participation portfolio for market players.
    • Simulations demonstrate the effectiveness of the proposed approach in maximizing profits.
    • Validation performed using real market data from the Iberian MIBEL market.

    Conclusions:

    • The proposed portfolio optimization methodology effectively addresses the complexities of energy markets with DERs.
    • ANNs and PSO provide a robust framework for strategic decision-making in smart grids.
    • The approach enhances energy efficiency and facilitates small player participation in power negotiations.