Barbara Case Studies Edge AI: Deploying AI Flexibility in a Virtualized LV/ MV Substation

	Edge AI: Deploying AI Flexibility in a Virtualized LV/ MV Substation Barbara

Edge AI: Deploying AI Flexibility in a Virtualized LV/ MV Substation

Barbara

Technology Category	Platform as a Service (PaaS) - Edge Computing Platforms
Applicable Industries	Electrical Grids Renewable Energy
Applicable Functions	Business Operation Maintenance
Use Cases	Demand Planning & Forecasting Digital Twin Edge Computing & Edge Intelligence
Services	Software Design & Engineering Services
Challenge	Cuerva a Spanish Grip Operator, was seeking to enhance grid knowledge through the implementation of the AI Energy Forecasting Model to obtain precise forecasts of user demand and energy generation. Cuerva’s grid encompasses over 16,000 diverse supply points, making cloud-based operations intricate and susceptible to issues such as connectivity loss, delays in information transmission, and reliance on centralized infrastructure, which can result in the loss of critical data. To tackle these challenges, the Edge technology has proven to be the sole alternative capable of addressing these issues effectively. It ensures real-time data access and operates in a decentralized manner, minimizing the impact of device failures on the overall functionality of the network. In this successful case, we illustrate how with Barbara DSOs can implement AI directly in substations to accurately predict the demand and production values of consumers linked to the transformation center where an Edge node run by Barbara has been deployed. Read More
About Customer	Cuerva is a Spansih Energy Operator serving the entire energy value chain, from generation, distribution, retail and customer energy services. The business has over 140 employees and operates in 3 countries: Spain, Peru and Panama. The company is undergoing a huge digitalisation process aimed at becoming more accessible to the end user to improve supply quality and offer new services of greater value. This has involved the rollout of full control systems over the network, from the Substation (operated by Cuerva Distribución) to the end user, covering lines, substations and sectioning, in order to obtain minute-by-minute measurements in real-time, providing full visibility over the network. Read More
Customer Name	CUERVA Read More
Solution	The proposed solution is based on Edge Computing nodes distributed across various Transformation Centres (TCs). More precisely the solution is composed by: • IoT Nodes designed for distributed Edge Computing, installable in TCs. • An Edge Node Management platform to manage these nodes efficiently. • And Industrialised computing algorithms for: .- Predicting active and reactive power on the consumer side to identify potential congestions and overvoltage events on the Low Voltage (LV) grid. .- Evaluating the flexibility assets within Cuerva’s grid and creating models for them. .- Determining the flexibility requirements of the system Read More Log in to view content
Data Collected	Energy Production, Energy Usage Read More Log in to view content
Contents

Technology Category

Platform as a Service (PaaS) - Edge Computing Platforms

Applicable Industries

Electrical Grids

Renewable Energy

Applicable Functions

Business Operation

Maintenance

Use Cases

Demand Planning & Forecasting

Digital Twin

Edge Computing & Edge Intelligence

Services

Software Design & Engineering Services

Challenge

Cuerva a Spanish Grip Operator, was seeking to enhance grid knowledge through the implementation of the AI Energy Forecasting Model to obtain precise forecasts of user demand and energy generation.

Cuerva’s grid encompasses over 16,000 diverse supply points, making cloud-based operations intricate and susceptible to issues such as connectivity loss, delays in information transmission, and reliance on centralized infrastructure, which can result in the loss of critical data.

To tackle these challenges, the Edge technology has proven to be the sole alternative capable of addressing these issues effectively. It ensures real-time data access and operates in a decentralized manner, minimizing the impact of device failures on the overall functionality of the network.

In this successful case, we illustrate how with Barbara DSOs can implement AI directly in substations to accurately predict the demand and production values of consumers linked to the transformation center where an Edge node run by Barbara has been deployed.

About Customer

Cuerva is a Spansih Energy Operator serving the entire energy value chain, from generation, distribution, retail and customer energy services. The business has over 140 employees and operates in 3 countries: Spain, Peru and Panama.

The company is undergoing a huge digitalisation process aimed at becoming more accessible to the end user to improve supply quality and offer new services of greater value. This has involved the rollout of full control systems over the network, from the Substation (operated by Cuerva Distribución) to the end user, covering lines, substations and sectioning, in order to obtain minute-by-minute measurements in real-time, providing full visibility over the network.

Customer Name

CUERVA

Solution

The proposed solution is based on Edge Computing nodes distributed across various Transformation Centres (TCs). More precisely the solution is composed by:

• IoT Nodes designed for distributed Edge Computing, installable in TCs.

• An Edge Node Management platform to manage these nodes efficiently.

• And Industrialised computing algorithms for:

.- Predicting active and reactive power on the consumer side to identify potential congestions and overvoltage events on the Low Voltage (LV) grid.

.- Evaluating the flexibility assets within Cuerva’s grid and creating models for them.

.- Determining the flexibility requirements of the system

Data Collected

Energy Production, Energy Usage

Impact #1	[Cost Reduction - Data Management] • Cost Reduction in Data Integration: Streamlined data integration on a single node eliminates the need for maintaining various platforms. All devices are seamlessly integrated through different data protocols.
Impact #2	[Efficiency Improvement - Asset Monitoring] • Enhanced Quality Monitoring and Alarming: Real-time alarms and monitoring of secondary substations enable operators to promptly address supply quality issues, contributing to an improvement in SAIDI (System Average Interruption Duration Index).
Impact #3	[Data Management - Data Analysis] • Centralized Data for Advanced Analytics: Centralizing data from secondary substations facilitates advanced analytics. This, in turn, provides DSOs with valuable insights and information, paving the way for data-driven decisions to optimize grid operations. • AI-Driven Forecasting: Deployment of artificial intelligence algorithms equips DSOs to forecast potential grid issues and proactively address them before compromising supply quality. This proactive approach contributes to an improvement in SAIFI (System Average Interruption Frequency Index)

Impact #1

[Cost Reduction - Data Management]

• Cost Reduction in Data Integration:

Streamlined data integration on a single node eliminates the need for

maintaining various platforms. All devices are seamlessly integrated

through different data protocols.

Impact #2

[Efficiency Improvement - Asset Monitoring]

• Enhanced Quality Monitoring and Alarming:

Real-time alarms and monitoring of secondary substations enable

operators to promptly address supply quality issues, contributing to an

improvement in SAIDI (System Average Interruption Duration Index).

Impact #3

[Data Management - Data Analysis]

• Centralized Data for Advanced Analytics:

Centralizing data from secondary substations facilitates advanced

analytics. This, in turn, provides DSOs with valuable insights and

information, paving the way for data-driven decisions to optimize grid

operations.

• AI-Driven Forecasting:

Deployment of artificial intelligence algorithms equips DSOs to forecast

potential grid issues and proactively address them before compromising

supply quality. This proactive approach contributes to an improvement in

SAIFI (System Average Interruption Frequency Index)

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Overview

Edge AI: Deploying AI Flexibility in a Virtualized LV/ MV Substation

Operational Impact