Topic:"Grid Modernization" in M13014

DESCRIPTION: Distributed Intelligence (DI) refers to the use of software applications (apps) deployed on field devices to collect highresolution operational data and perform localized analysis using data collected at the grid-edge, where t...

AI summary Distributed Intelligence (DI) involves deploying software apps on field devices to collect and analyze grid-edge data, improving grid reliability and situational awareness. The project will pilot DI apps on a subset of meters, assessing their efficacy before potential broader deployment. The initiative is supported by federal funding and complies with measurement regulations.

REDACTED CI C0068719 Page 2 of 7 REDACTED (CONFIDENTIAL INFORMATION REMOVED) Depreciation Class: 7800 Computer Software Estimated Life of the Asset: 10 years JUSTIFICATION: Justification Criteria: Innovation Sub Criteria: Reliability and g...

AI summary The document discusses the depreciation class 7800 for computer software with an estimated asset life of 10 years, justified under the innovation criteria focusing on reliability and grid stability.

Why do this project? Itron DI apps implemented in other utility jurisdictions have demonstrated benefits such as improved grid visibility, enhanced detection of abnormal conditions, and improved operational decision-making across areas suc...

AI summary This project aims to implement Itron DI applications to improve grid visibility, operational decision-making, and reliability. It will test the effectiveness of DI apps in improving grid model accuracy, safety monitoring, and operational efficiencies through real-world application and data collection.

Why do this project now? Proactive and timely improvement of grid model accuracy is foundational to support continued reliable system operation and effective system planning as the distribution system becomes increasingly complex. Testing...

AI summary This project aims to improve grid model accuracy and test DI technologies to enhance system reliability and customer experience. NS Power is partnering with NRCan and has secured federal funding, contingent on regulatory approval by March 31, 2027, with project completion required by March 31, 2028.

Why do this project this way? The project focuses on deploying and evaluating selected distributed intelligence applications at the meter level, while the foundational DI Iron Apps Platform will be fully implemented to enable sustained ope...

AI summary The project aims to deploy and evaluate selected distributed intelligence (DI) applications at the meter level, with the DI Iron Apps Platform fully implemented to support ongoing evaluation. Technologies will be installed following NS Power's cybersecurity controls, and apps will be selected based on their potential to improve grid accuracy, safety, and situational awareness. Testing will occur in Q3 2026, with final app selection and evaluation criteria confirmed during this phase.

Document No: XXX-XX-XXXX-XXXX An Emera Company Project Cost Estimate Input Checklist and N Naturity M atrix Equipment Replacement Required fields: Esti mate Classifica ition Project Name: IT - Distributed Intelligence Apps Started or Preli...

AI summary The document outlines the status of a project related to the implementation of Distributed Intelligence Applications (DI) by an Emera Company. It includes a checklist for project cost estimates and project definition deliverables, with various sections indicating the maturity levels and completion status of different project components.

Discrete Apps - Location Awareness - Meter Bypass Detection - Advanced Temperature Monitoring - EV Awareness

AI summary The document outlines several discrete applications related to energy management, including location awareness, meter bypass detection, advanced temperature monitoring, and EV awareness.

Location Awareness Autonomously determines which phase, feeder and transformer meters are on using peer-to-peer communication and GIS data and highlights errors in the GIS model

AI summary The system autonomously identifies phase, feeder, and transformer meters using peer-to-peer communication and GIS data, and it highlights errors in the GIS model.

Challenges • Requires GIS integration to test and operationalize Future State: An accurate GIS model with phase data lays the ground for advanced grid functions such as one-phase operation, load flow and DERMS.

AI summary The challenge involves the need for GIS integration to test and operationalize future grid functions, including one-phase operation, load flow, and DERMS, which depend on an accurate GIS model with phase data.

Meter Bypass Detection Detects attempts to bypass the meter for power theft using high-resolution sampling of voltage and current data

AI summary The Meter Bypass Detection system identifies power theft by analyzing high-resolution voltage and current data to detect attempts to bypass the meter.

EV Awareness Provides near-real-time disaggregation information for when an electric vehicle begins or stops charging.

AI summary The EV Awareness feature provides near-real-time disaggregation information for when an electric vehicle begins or stops charging, enhancing monitoring and management capabilities.

Feeder Visibility / Advanced Outage Awareness Back-office analytics that use data from multiple DI apps to provide dashboards for power factor and load flow management as well as outage scope determination

AI summary The document discusses back-office analytics that utilize data from multiple DI apps to provide dashboards for power factor and load flow management as well as outage scope determination under the topic of Feeder Visibility / Advanced Outage Awareness.

Benefits - Low-effort solution to evaluate ATVM/ATLM data short-term without a robust ADMS integration. - Uses machine-learning algorithms to forecast future states

AI summary The benefits section highlights a low-effort solution for evaluating ATVM/ATLM data in the short term without requiring a robust ADMS integration, and the use of machine-learning algorithms to forecast future states.

Challenges - Significant data validation required before it can be trusted in operations - Requires human intervention to make system and scheduling changes - Requires 2 additional apps to function - Still under development by Itron Future...

AI summary The system currently requires significant data validation, human intervention, and additional applications to function, and is still under development by Itron. It is intended to be a temporary solution until data can flow directly into ADMS for operational decision-making.

Advanced Transformer Voltage & Load Monitoring Near real-time monitoring of distribution transformer load statistics and premise voltage statistics

AI summary The document introduces Advanced Transformer Voltage Monitoring (ATVM) and Advanced Transformer Load Monitoring (ATLM), which provide near real-time monitoring of distribution transformer load statistics and premise voltage statistics.

Benefits - Better load planning, voltage regulation and system operation in areas without SCADA - Real-time visibility into system voltage/load at a granular level

AI summary The benefits discussed include improved load planning, voltage regulation, and system operation in areas without SCADA, along with real-time visibility into system voltage and load at a granular level.

Voltage Insights Provides premise-level CBEMA/ITIC curve voltage tracking and IEEE 519 harmonics data.

AI summary The Voltage Insights system provides premise-level tracking of CBEMA/ITIC curve voltages and IEEE 519 harmonics data, enhancing monitoring and analysis capabilities for electrical systems.

Scoring Matrix Арр Added Value 🕌 Current State Implementation Complexity Location Awareness Very High Poor Medium Meter Bypass Detection High None Low Advanced Temperature Monitoring Medium Good Low EV (Electric Vehicle) Awareness Medium N...

AI summary The document presents a scoring matrix evaluating various technologies based on their added value, current state, and implementation complexity. It includes metrics like location awareness, meter bypass detection, and advanced transformer monitoring, among others, with varying levels of implementation difficulty.

Location Awareness Distributed Intelligence Effectively using your smart meters to support monitoring and control of your secondary distribution requires knowing exactly where they are in your distribution network. One of the greatest chal...

AI summary Location Awareness is a new capability in Itron's distributed intelligence platforms that allows smart meters to continuously know their electrical connectivity in the distribution grid, improving grid operations and enabling new smart grid applications without relying on GIS or communication network topology.

The Value of Location Awareness - » Sub-second data resolution provides higher accuracy than previously possible - » Requires significantly less back office infrastructure and data science expertise - » Device self-awareness of electrical...

AI summary The text highlights the benefits of sub-second data resolution and device self-awareness in improving outage detection, network connectivity, and overall business value for utilities with over a million customers. It also mentions the reduced need for back office infrastructure and data science expertise.

CI C0068719 NSEB IR-2 Attachment 2 Page 2 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) Based on Itron research with utility customers, the accuracy of connectivity data can vary greatly, with many lacking accurate and updated connectiv...

AI summary The document discusses the challenges of inaccurate connectivity data in utility networks and highlights Itron's Location Awareness solution, which uses advanced algorithms and GIS mapping to improve connectivity accuracy, enhance grid operations, and support new applications like detecting unsafe grid conditions.

Distributed Intelligence … distributing analytics to the edge - » analysis - » decision making - » action Moving the solution closer to the problem

AI summary The text discusses the concept of Distributed Intelligence, emphasizing the movement of analytics, decision making, and action closer to the problem. It highlights the benefits of bringing solutions to the edge of the network.

Grid Edge Intelligence Base Requirements A Grid Edge platform should provide: Every meter is a grid condition-sensing waveform analyzer Distributed intelligence extends the value of every meter High resolution processing locally pushed to...

AI summary The document outlines the base requirements for a Grid Edge Intelligence platform, emphasizing the need for every meter to function as a grid condition-sensing waveform analyzer, distributed intelligence, high-resolution local processing, peer-to-peer communications, and support for multiple communication networks.

Grid Edge Visibility – the Scale Challenge DI is Visibility , monitoring and control in the low voltage network at scale Typical SCADA (~1000 assets per 1M customers) Typical ADMS GridMod Scale 1 Order of Magnitude beyond SCADA (~10,000 as...

AI summary The text discusses the challenge of achieving visibility and control in low voltage networks at scale, highlighting the significant increase in the number of assets monitored by ADMS GridMod compared to typical SCADA systems and the even greater scale required for visibility and control at the point of service.

Monitoring and Managing the Low Voltage Grid DI Integration with Grid Management CI C0068719 NSEB IR-2 Attachment 3 Page 6 of 42

AI summary This document discusses the integration of Demand Intelligence (DI) with grid management in the context of monitoring and managing the low voltage grid.

High Impedance Detection – Field Examples ©2024 ITRON CONFIDENTIAL PROPRIETARY HOME CI C0068719 NSEB IR-2 Attachment 3 Page 11 of 42

AI summary The document discusses field examples related to high impedance detection, with a focus on technologies and systems used in the energy sector. It includes references to advanced monitoring systems and is associated with Nova Scotia Power Inc. and the Nova Scotia Energy Board.

Revenue Assurance Team You don't have meter readers in the field anymore, with AMI. However, you want insight on theft, capturing value from backbilling and avoided future losses. CI C0068719 NSEB IR-2 Attachment 3 Page 14 of 42

AI summary The Revenue Assurance Team discusses the absence of meter readers in the field due to the implementation of AMI, and the need for insights on theft, backbilling, and avoiding future losses.

Meter Bypass Production » Function: Detects meter bypass and theft conditions

AI summary The document discusses a production function aimed at detecting meter bypass and theft conditions, indicating a focus on ensuring accurate metering and preventing energy theft.

Location Awareness Production - » Function: Determines connectivity of meter to transformers, phases, and feeders utilizing PLC and RF peer-to-peer - » Benefits: Maintain up-to-date grid topology awareness for DI and back-office utility sy...

AI summary Location Awareness functionality determines the connectivity of meters to transformers, phases, and feeders using PLC and RF peer-to-peer communication. Benefits include maintaining grid topology awareness, advanced transformer management, storm simulation, DER analytics, and accurate asset loading and planning.

» Outputs: - Discrepancy and recommended corrects list in .csv or through API - API for other system integration (e.g. other apps) CI C0068719 NSEB IR-2 Attachment 3 Page 18 of 42

AI summary The text references a discrepancy and recommended corrections list in .csv or through an API, as well as an API for other system integration. It also includes a document reference: CI C0068719 NSEB IR-2 Attachment 3 Page 18 of 42.

Location Awareness Production - Meter to Transformer (M2T) - 329,000 meters - Identified 8,519 Discrepancies (01/03/2023) - Field Validated ~150 Meter Discrepancies - 99% Accuracy - Meter to Phase (M2P) - 40,000 meters - Field Validated ~1...

AI summary The document outlines the progress and accuracy rates of meter validation processes, including Meter to Transformer (M2T), Meter to Phase (M2P), and Transformer/Meter to Feeder/Bank (M2F), highlighting discrepancies identified and field validation efforts.

- Subsequent algorithm refinements and testing are underway Location Awareness (LA) - M2T 3-Jan Location Awareness (LA) - W21 Count Percentage M2T Accuracy (Precision) 99.3% Total Meters with M2T Data (DS Database) 329,844 Total M2T Discre...

AI summary The document discusses ongoing algorithm refinements and testing related to Location Awareness (LA) for M2T and M2P systems. It provides data on accuracy, discrepancies, and validation rates for these systems, indicating high accuracy levels but also noting some discrepancies.

» Benefits: - Preserve transformer lifetime - Reduce unplanned outages - Reduced network traffic through use of spokesmeters - More effective programs (e.g. VVO/CVR) or better resolution control (e.g. load flow)

AI summary The benefits outlined include preserving transformer lifetime, reducing unplanned outages, decreasing network traffic with spokesmeters, and enabling more effective programs like VVO/CVR and better resolution control such as load flow.

» Outputs: - Configurable output of transformer load statistics through API or through Itron UI - Integration with control systems through API at configurable intervals (e.g. 5-minute) and the ability to report by exception. CI C0068719 NS...

AI summary The document discusses the configurable output of transformer load statistics through API or Itron UI, as well as integration with control systems through API at configurable intervals and the ability to report by exception.

Prototype » Function: Near real-time monitoring of voltage statistics for service points connected to a distribution transformer

AI summary The prototype function involves near real-time monitoring of voltage statistics for service points connected to a distribution transformer, likely aimed at improving grid efficiency and reliability.

» Benefits: - Accurate voltages at endpoints for worst-case planning models - Reduced network traffic through use of spokesmeters - More effective programs (e.g. VVO/CVR) or better resolution control (e.g. load flow)

AI summary The benefits discussed include accurate voltage measurements for planning models, reduced network traffic via spokesmeters, and enhanced program effectiveness such as VVO/CVR and improved load flow resolution.

» Outputs: - Configurable output of transformer voltage statistics through API or through Itron UI - Integration with control systems through API at configurable intervals (e.g. 5-minute) CI C0068719 NSEB IR-2 Attachment 3 Page 24 of 42

AI summary The document discusses the configuration of transformer voltage statistics output through an API or Itron UI, as well as integration with control systems at configurable intervals, such as every 5 minutes.

Greenfield ATLM/ATVM Data Flow DI capability enables real-time distribution transformer monitoring capabilities CI C0068719 NSEB IR-2 Attachment 3 Page 25 of 42

AI summary The document discusses the DI capability that enables real-time monitoring of distribution transformers through the Greenfield ATLM/ATVM data flow.

Brownfield ATLM/ATVM Data Flow DI capability enables real-time distribution transformer monitoring capabilities CI C0068719 NSEB IR-2 Attachment 3 Page 26 of 42

AI summary The document discusses the DI capability that enables real-time monitoring of distribution transformers through the Brownfield ATLM/ATVM data flow.

» Outputs: Alarms through API containing peak kW charging and kWh for sessions CI C0068719 NSEB IR-2 Attachment 3 Page 28 of 42

AI summary The document discusses alarms transmitted through an API that provide data on peak kW charging and kWh for sessions, as part of a regulatory proceeding in Nova Scotia.

» Benefits: - Detect wide variety of anomalies using highfidelity waveform data from a greater density of meters across the grid - Example grid anomalies include vegetation contacts, wildfires, protection device failures, grid equipment is...

AI summary The benefits section highlights the ability to detect various grid anomalies using high-fidelity waveform data from a greater density of meters. Examples include vegetation contacts, wildfires, protection device failures, grid equipment issues, and power quality issues.

» Outputs: Alarms and events presented through API or through Itron UI CI C0068719 NSEB IR-2 Attachment 3 Page 32 of 42

AI summary The document mentions alarms and events being presented through API or through Itron UI, as part of a technical attachment in a regulatory proceeding.

Example 1: Hurricane Ian Voltage Anomaly Event Detecting grid events using high-fidelity data CI C0068719 NSEB IR-2 Attachment 3 Page 33 of 42

AI summary This section discusses the detection of grid events using high-fidelity data, specifically referencing an example involving Hurricane Ian and a voltage anomaly event.

Example 2: Capacitor Bank Switching Triggered Harmonics Detecting grid events and equipment health using high-fidelity data CI C0068719 NSEB IR-2 Attachment 3 Page 34 of 42

AI summary This example discusses the detection of grid events and equipment health using high-fidelity data, specifically focusing on capacitor bank switching triggered harmonics.

Advanced Outage Awareness » Function: Provides power outage indication and scope, as well as associated accuracy estimate (confidence factor)

AI summary The function of Advanced Outage Awareness is to provide information on power outages, including their scope and an estimate of accuracy, known as the confidence factor.

HW4.1+ mixed w/ HW4.0 HW4.1+ HW4.1+ GenX HW4.2+ Existing Apps 2025 2026+ Deployed in-field (scaled or pilot) Under Development / Committed Strategic Initiatives / Directional • Advanced Temperature Monitoring • High Impedance Detection • M...

AI summary The document outlines existing and planned advanced applications for grid management, including technologies like Advanced Temperature Monitoring, Voltage Bellwether, and EV Awareness, with deployment timelines extending into 2026 and beyond. It highlights initiatives such as Solar Awareness, Virtual RTU, and Advanced Outage Awareness as part of strategic directions for future implementation.

Proven Benefits Asset Management with Advanced Metering Infrastructure CI C0068719 NSEB IR-2 Attachment 4 Page 3 of 9

AI summary This section discusses the integration of advanced metering infrastructure with asset management practices, highlighting benefits related to improved operational efficiency and resource optimization.

Description - » More than 50% of PG&E's service territory is now classified as high fire-risk areas. - » PG&E has started a DI pilot deployment of Itron Riva meters. which are fully backwards compatible with the existing Itron GenX network...

AI summary PG&E has implemented a DI pilot using Itron Riva meters, which are compatible with existing infrastructure, and tested three DI applications. The pilot observed 1.6 million meter events, aiding in early detection of grid failures, improved mapping, and reduced wildfire risk.

Description - » Tampa Electric serves more then 830k customers. TECO is making a transformational investment in new customer experience and operational programs - » Itron's DI Location Awareness application was validated at TECO to automat...

AI summary Tampa Electric is investing in new customer experience and operational programs, and Itron's DI Location Awareness application has been validated at TECO to improve grid topology accuracy and response times.

Description - » LUMA Energy operates Puerto Rico's transmission & distribution system (since 2021) within an inherited grid marked by aging equipment, vegetation risks, storm exposure and funding constraints. - » Instead of waiting for the...

AI summary LUMA Energy is managing Puerto Rico's transmission and distribution system, facing challenges from aging infrastructure and environmental risks. They have begun deploying the High Impedance Detection (HID) DI App in San Juan, identifying issues such as high impedance levels on a transformer.

Initial Results 1 "Installing smart meters is a fundamental step in our commitment to modernize Puerto Rico's energy infrastructure. This technology optimizes consumption, improves efficiency, and empowers our citizens to make informed ene...

AI summary LUMA's SVP of Capital Programs, Juan Rodríguez, emphasizes the importance of installing smart meters as a step toward modernizing Puerto Rico's energy infrastructure, improving efficiency, and empowering citizens with informed energy decisions.

CI C0068719 - IT - Distributed Intelligence Apps (NSEB M13014) NSPI Responses to NSEB Information Requests 1 Request IR-13: Meter Bypass Detection Safety-Related Signal Detection Detects attempts to bypass the meter using high-resolution s...

AI summary The document outlines responses from Nova Scotia Power Inc. (NSPI) to information requests regarding the deployment of Distributed Intelligence (DI) Apps for Meter Bypass Detection and Operations Optimizer - Revenue Assurance. These applications aim to improve revenue assurance by detecting meter bypasses and prioritizing investigations using data from Advanced Metering Infrastructure (AMI) meters.

CI C0068719 - IT - Distributed Intelligence Apps - $3,165,790 This project was originally included in the 2026 Annual Capital Expenditure (ACE) Plan at a cost of $2,637,356. The estimate subsequently increased by $528,434 following additio...

AI summary NS Power is requesting $3,165,790 for a pilot project to evaluate Distributed Intelligence (DI) Apps, which aim to improve operational efficiency and safety. The project is compatible with existing Advanced Metering Infrastructure (AMI) and supported by funding from Natural Resources Canada. The Board approves the request, citing its potential to enhance grid reliability and support provincial energy goals.

Request IR-1: - a) Please explain the need for the proposed Distributed Intelligence (DI) Apps Project, and what type of current system limitation the project is intended to address. - b) Please explain how the project is expected to impro...

AI summary Request IR-1 seeks explanations regarding the proposed Distributed Intelligence (DI) Apps Project, including its purpose, intended system improvements, and expected benefits for Nova Scotia Power's operations, reliability, safety, planning, and customer service.

Request IR-3: - Please describe why existing Advanced Metering Infrastructure (AMI) and related distribution automation systems cannot meet the same needs. - a) Please explain whether the AMI metering system is required to implement the DI...

AI summary The document requests an explanation of the limitations of the existing Advanced Metering Infrastructure (AMI) and related distribution automation systems, and whether they can support the DI Apps. It also asks about the current functionality of AMI, the percentage of AMI meters installed, customer opt-in/opt-out rates, and the implications for DI Apps.

Request IR-4: Please explain whether the combination of AMI devices and other distribution devices with communication functions, such as existing distribution breakers, existing distribution Potential - Transformers and Current Transformer...

AI summary The request asks whether AMI devices and other distribution devices with communication functions can replace DI Apps in providing functionality. It also asks for an explanation if they cannot.

Request IR-6: In "Description," NS Power noted that: This project will install apps which are intended to support: - grid model accuracy and connectivity validation, - safety related signal detection, and - distribution grid situational aw...

AI summary NS Power is requesting information about the technical functions of proposed DI Apps, the existing distribution equipment required for their implementation, and how these apps will improve grid model accuracy, safety signal detection, and situational awareness.

Request IR-7: - Please explain how the DI Apps operate under NS Power's existing AMI environment, such as - how information is transferred between meters, head-end systems, communications networks, - and cloud platforms.

AI summary The request seeks an explanation of how DI Apps operate within NS Power's existing AMI environment, focusing on the transfer of information between meters, head-end systems, communications networks, and cloud platforms.

Request IR-8: - Please describe how the DI Apps will be deployed, including whether: - a) the apps will be deployed in existing AMI meters; - b) the deployment can be completed remotely or require local meter access; - c) any customer inte...

AI summary Request IR-8 asks for details on the deployment of DI Apps, including whether they will be deployed in existing AMI meters, whether deployment can be done remotely, if customer interruptions will occur, and if meter replacements are needed.

Request IR-9: - Please explain whether the DI Apps will replace, supplement, or modify existing meter hardware, - meter software, head-end systems, or other infrastructure.

AI summary The document requests an explanation on whether the DI Apps will replace, supplement, or modify existing meter hardware, software, head-end systems, or other infrastructure.

Request IR-10: - Please explain whether the existing AMI meters and associated communication system support - peer-to-peer meter communication. - a) If no, please explain how NS Power plans to enable such a function for the proposed DI App...

AI summary The document requests an explanation of whether existing AMI meters and communication systems support peer-to-peer meter communication and, if not, how NS Power plans to enable this function for the proposed DI Apps, including required protocols and network capabilities.

Request IR-11: - Please explain whether the DI apps could affect meter functions, such as security, integrity, performance, communications, accuracy, and firmware. Please provide the controls that prevent - the DI Apps from interfering wit...

AI summary Request IR-11 seeks clarification on the potential impact of DI apps on meter functions and the methodology behind the pilot project, including scope, deployment criteria, testing approach, and the use of pilot results for deployment decisions.

Request IR-13: In "Why do this project this way?," NS Power noted that: At this stage, the following apps have been identified for potential testing based on the criteria above: - Location Awareness: autonomously determines which phase, fe...

AI summary NS Power is seeking information on the cost structure, benefits, and necessity of various Distribution Intelligence (DI) Apps, including Location Awareness, Meter Bypass Detection, and Advanced Outage Analytics, which are being considered for testing. The request includes inquiries about whether similar functions can be achieved through existing alternatives.

CI C0068719 - IT - Distributed Intelligence Apps - $3,165,790 This project was originally included in the 2026 Annual Capital Expenditure (ACE) Plan at a cost of $2,637,356. The estimate subsequently increased by $528,434 following additio...

AI summary NS Power is requesting approval for a $3,165,790 project to pilot Distributed Intelligence (DI) Apps, which aim to improve operational efficiency and grid reliability. The project is supported by Natural Resources Canada's Energy Innovation Program and is compatible with existing Advanced Metering Infrastructure. The Board has accepted the project as necessary and justified, approving the requested funds.

NSEB APPROVAL SHEET IT - Distributed Intelligence Apps Project Title: CI Number: C0068719 Date: August 4, 2026 Expenditure Profile Type of Filing Year Budget Amount Project Estimate Capital Project Authorization Unforeseen and Unbudgeted (...

AI summary The NSEB Approval Sheet outlines the expenditure profile for the IT - Distributed Intelligence Apps Project, including budget amounts, project estimates, and authorization types for various years from 2025 to 2028. It includes comments and total figures for the project.