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Related Concept Videos

Power System Distribution01:25

Power System Distribution

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Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
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Electrical Energy01:10

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Using electric appliances for a longer period of time consumes more electrical energy and results in a higher electric bill. The energy produced by the transfer of electrons from one point to another is known as electrical energy. If power is delivered at a constant rate, the electrical energy can be defined as the product of power used by the device for a period of time. The energy unit on electric bills is the kilowatt-hour, where one kilowatt-hour is equivalent to 3.6 × 106 joules.
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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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Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
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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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Load-frequency control01:28

Load-frequency control

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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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Dynamic Energy Management System for a Smart Microgrid.

Ganesh Kumar Venayagamoorthy, Ratnesh K Sharma, Prajwal K Gautam

    IEEE Transactions on Neural Networks and Learning Systems
    |January 23, 2016
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    Summary
    This summary is machine-generated.

    This study introduces an intelligent dynamic energy management system (I-DEMS) for smart microgrids, utilizing evolutionary reinforcement learning. The I-DEMS optimizes renewable energy use and ensures reliable power supply, even with intermittent sources.

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    Area of Science:

    • Smart Grid Technology
    • Renewable Energy Systems
    • Artificial Intelligence in Energy

    Background:

    • Smart microgrids rely on renewable energy sources (RESs) like wind and solar, which are inherently uncertain and nondispatchable.
    • Ensuring a consistent power supply for critical loads requires robust energy management, especially during grid-connected and islanded operations.
    • Existing energy management systems may not optimally handle the dynamic and unpredictable nature of RESs.

    Purpose of the Study:

    • To develop an intelligent dynamic energy management system (I-DEMS) for smart microgrids.
    • To implement an evolutionary adaptive dynamic programming and reinforcement learning framework for online I-DEMS evolution.
    • To demonstrate the I-DEMS's capability for optimal or near-optimal energy dispatch in diverse microgrid operational states.

    Main Methods:

    • Developed an intelligent dynamic energy management system (I-DEMS) using an evolutionary adaptive dynamic programming and reinforcement learning framework.
    • Integrated backup battery energy storage and thermal generation to complement intermittent renewable energy sources.
    • Utilized a forward-looking network to evaluate energy dispatch control signals over time based on microgrid system states.

    Main Results:

    • The I-DEMS effectively schedules energy dispatches, maximizing the utilization of RESs and energy storage devices to consistently supply critical loads.
    • Performance comparisons with a decision tree approach-based DEMS (D-DEMS) demonstrated the superior robustness of the I-DEMS.
    • Evaluations under varying generation/load profiles and different battery energy storage conditions confirmed the I-DEMS's reliable operation.

    Conclusions:

    • The developed I-DEMS provides an optimal or near-optimal solution for dynamic energy management in smart microgrids.
    • The evolutionary reinforcement learning framework enables online adaptation and robust performance of the I-DEMS.
    • The I-DEMS ensures reliable power delivery from renewable energy sources, enhancing microgrid stability and efficiency.