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Load-frequency control

Load-frequency control

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...

Generator Voltage Control

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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,...

Turbine-Governor Control

Turbine-Governor Control

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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:

Multimachine Stability

Multimachine Stability

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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The Internet of Things for the Intelligent Management of the Heating of a Swimming Pool by Means of Smart Sensors.

Sensors (Basel, Switzerland)·2023

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Energy Management System for Polygeneration Microgrids, Including Battery Degradation and Curtailment Costs.

Yassine Ennassiri¹, Miguel de-Simón-Martín², Stefano Bracco³

¹Department of Computer Science, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Via Opera Pia 13, 16145 Genoa, Italy.

Sensors (Basel, Switzerland)

|November 27, 2024

Summary

This study introduces an advanced energy management system (EMS) for smart microgrids, optimizing electric vehicles and storage. The novel model enhances efficiency by considering user comfort and battery lifespan, reducing renewable energy waste.

Keywords:

electrical vehicle energy management system energy polygeneration energy storage systems solar photovoltaics wind energy

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Area of Science:

Energy Systems Engineering
Smart Grid Technology
Sustainable Building Management

Background:

Advancements in sensor technology enable precise monitoring and control of energy parameters for smart microgrids.
Effective energy management systems (EMS) are crucial for optimizing resources in grid-connected polygeneration microgrids.
Existing EMS often lack comprehensive integration of user comfort and energy storage degradation.

Purpose of the Study:

To present a novel EMS model for grid-connected polygeneration microgrids.
To optimize the management of electrical storage systems, electric vehicles (EVs), and deferrable loads.
To incorporate climate comfort variables and energy storage degradation into the control strategy, penalizing surplus energy dumping.

Main Methods:

Development of a novel energy management system (EMS) model.
Integration of climate comfort variables and battery degradation considerations into the control strategy.
Application and case study analysis within a smart, sustainable building environment.

Main Results:

The proposed EMS model demonstrates high adaptability to diverse weather conditions.
Minimized renewable energy losses were achieved while satisfying energy demand and ensuring user comfort.
Electric vehicles' dynamic charging schedules were identified as critical for energy balance.
Battery degradation was minimized through optimized cycling, averaging one cycle per day.
Seasonal weather patterns significantly impact microgrid management, highlighting the role of EVs and storage.

Conclusions:

The novel EMS model effectively balances energy supply and demand in smart microgrids.
Integrating user comfort and battery health optimizes microgrid performance and sustainability.
Strategic utilization of electric vehicles and storage systems is vital for efficient and resilient microgrid operation.