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Secondary distribution systems provide electrical energy at the utilization voltage levels from distribution transformers to customer meters. Typical secondary voltages in the United States include 120/240 V for residential use, 208Y/120 V for residential and commercial use, and 480Y/277 V for industrial and high-rise commercial use.
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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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Power System Distribution01:25

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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.
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Electric power is the product of current and voltage, represented in units of joules per second, or watts. For example, cars often have one or more auxiliary power outlets with which you can charge a cell phone or other electronic devices. These outlets may be rated at 20 amps and 12 volts, so that the circuit can deliver a maximum power of 240 watts. Consider a 25 Watt bulb and a 60 Watt bulb. The conversion of electrical energy produces heat and light, while the kinetic energy lost by the...
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A Microservices-Based Solution with Hybrid Communication for Energy Management in Smart Grid Environments.

Artur F S Veloso1, José V Reis1, Ricardo A L Rabelo1

  • 1Department of Computing, Federal University of Piauí (UFPI), Teresina 64049-550, PI, Brazil.

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Summary

This study introduces a microservice-based energy management system using hybrid Low Power Wide Area Networks (LPWAN) for smart grids. It achieves significant peak demand reduction and cost savings through an AI-driven demand response algorithm.

Keywords:
Internet of Things (IoT)LoRaMESHLoRaWANdemand responseenergy managementhybrid communicationload shiftingmicroservices architecturepredictive artificial intelligenceresiliencesmart gridsustainability

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Area of Science:

  • Smart Grid technology and energy management systems.
  • Wireless communication networks for the Internet of Things (IoT).
  • Artificial Intelligence (AI) applications in energy systems.

Background:

  • Increasing residential demand variability and distributed energy resources challenge Smart Grid (SG) stability.
  • Centralized management models are insufficient for current SG operational demands.
  • Scalable, data-driven architectures are essential for modern SGs.

Purpose of the Study:

  • To propose a scalable, resilient energy management solution for SGs.
  • To enhance data acquisition reliability for IoT and Demand Response (DR) applications.
  • To develop and evaluate an AI-driven demand response algorithm for peak load reduction.

Main Methods:

  • Development of a microservice-based energy management system utilizing hybrid Low Power Wide Area Networks (LPWAN), integrating Long Range Wide Area Network (LoRaWAN) and LoRaMESH.
  • Prototype evaluation of Smart Meter (SM), Data Aggregation Point (DAP), and Concentrator (CON) for communication robustness.
  • Application of aggregation and clustering techniques to generate Load Profiles (LPs) from household data and evaluation of fourteen load shifting algorithms.

Main Results:

  • Prototype achieved high Packet Delivery Rates (>97%), validating hybrid LPWAN communication robustness.
  • Critical peak demand identified between 18:00-21:00, reaching 42% above the daily average.
  • The proposed Hybrid Adaptive Algorithm based on Intention and Resilience (HAAIR) demonstrated superior performance: 1.83% peak reduction, US$65.40 cost savings, 60 kg CO2 reduction, 0.04 Comfort Loss Index, 9.5 resilience, and 0.98 reliability.

Conclusions:

  • The integration of hybrid LPWAN communication and microservices enhances SG connectivity and data reliability.
  • The AI-driven HAAIR algorithm effectively reduces peak demand and associated costs and emissions.
  • This integrated approach offers a scalable, resilient, and energy-efficient pathway for future Smart Grids.