Smart Grid Implementation Lessons Learned

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1 Smart Grid Implementation Lessons Learned EMMOS 2016 September 13, 2016 Andrew Hanson Distribution Operations & Automation Lead North America

2 Agenda Smart Meters & AMI DSCADA / ADMS Data Readiness Field Devices Communications Analytics 2

3 Smart Meters and AMI Deployments often focus on meter-to-cash to the exclusion of other potential uses of smart meter data $ Utility leverage exists at time of initial meter/ami vendor selection, so consideration beyond meter to cash is needed prior to selection including: Remote connect/disconnect Smart meter interval voltage reads Smart meter interval kvar/kvarh reads Adding functionality after deployment is usually time consuming, expensive and for some meters/functionalities requires a site visit Non-billing related data may be routed differently (i.e. may not need validate, estimate edit process) and be stored outside meter data management 3

4 Agenda Smart Meters & AMI DSCADA / ADMS Data Readiness Field Devices Communications Analytics 4

5 ADMS/DSCADA Lessons Learned Program Execution Define detailed requirements early in project/program. Define all terms to speak the same language as the vendor. Include specific examples. Data cleanup can take years (GIS as built model data, phasing data, load profiles, substation one-lines, etc.) Plan accordingly. Use a Technical Go Live wherever possible, including all interfaces, functionality, etc. that can be run in parallel with existing production systems. This allows identification and resolution of critical issues without the pressure of operations being impacted. Use experienced system integrators familiar with industry practices and vendor landscape/products. Full time utility resources to gain the most efficiencies in building, testing, and resolving issues with the new system. Change Management Detailed change management plans are critical for successful adoption Leverage multiple change tactics/communication techniques to reach target audience including offering multiple types of training to cover the best learning environment for target stakeholders (e.g. online, self-directed courses, classroom training, one-on-one training, specific stakeholder group training, WebEx/Skype demonstrations, etc.)

6 ADMS/DSCADA Lessons Learned (2) Environment Setup Define expectations about test environment needs and support required before the environment is allocated or built. (e.g. full production scale or subset of production data, run all processes like production environment or selected processes, how often refreshes are done, how should the environment be supported, etc. ) Define end to end testing requirements and ensure testing environment ability to support. Clearly define versions of any third party software required by ADMS vendor, including documenting plan for current versions and future upgrades. Ensure vendor commitment to work with planed upgrades. Define standard naming conventions (e.g. when there are IP naming conventions that are desired they should be used up front). Inconsistent naming can cause project delays.

7 ADMS/DSCADA Lessons Learned (3) Stakeholder Commitment Confirm program alignment with all executives before starting to affirm direction and need for the program. Example, some executives may desire an off-the-shelf solution, others may want to avoid any loss any of the legacy functionality. Prioritize programs across the organization, so if there is a conflict the higher priority program gets what is required first. Ensure stakeholders who minimally use the systems of the program are still involved/informed, to avoid challenges so they don't blame issues on your program due to lack of information

8 Agenda Smart Meters & AMI DSCADA / ADMS Data Readiness Field Devices Communications Analytics 8

9 Data Readiness Transmission data is well defined and of generally high quality distribution system data is subject to far greater variation including: Incorrect or missing phasing data A, B, C? Leading to incorrect unbalanced modelling or restricting modelling to balanced conditions Incorrect meter to transformer mapping Leading to inaccurate load allocations and incorrect voltage control constraints 9

10 Data Readiness (2) Transmission data is well defined and of generally high quality distribution system data is subject to far greater variation including: Unknown / questionable de-energized tap positions and LTC controller settings Leading to significant differences in modelled and actual voltages, hindering voltage control Topological errors and bad segment impedances Leading to inaccurate power flow results, constraining operations 10

11 Agenda Smart Meters & AMI DSCADA / ADMS Data Readiness Field Devices Communications Analytics 11

12 Field Device Selection Field device functionality is evolving rapidly: Proliferation of available settings and control schemes Move toward unganged control (increasing complexity and expense) Sometimes frequent firmware updates, including custom firmware based on individual customer request Selection of automated version of legacy controllers should be taken with great care to avoid constraining future options

13 Field Devices - Available Point Count Increases Example counts for capacitor controllers have increased at a startling rate (indicative counts below). Early 2000 s Binary/Status: 27 Analog Input: 33 Binary Control: 7 Analog Output: 7 Early 2010 s Binary/Status: 173 Analog Input: 107 Binary Control: 6 Analog Output: 147 Mid 2010 s Binary/Status: 178 Analog Input: 356 Binary Control: 18 Analog Output: 262 The increased point availability requires care in: Selection of points Sizing of SCADA / DSCADA systems Design of communication system

14 Settings Complexity Modern device complexity leads to a volume of settings vastly exceeding those required for traditional non-automated devices. Historically, the handful of settings with a capacitor or recloser might be stored in a small number of fields in GIS or an asset management application Modern devices are relay-like in complexity, necessitating similar requirements for storing settings Storage of settings files for modern field devices should include: Tracking of installed firmware version Device settings file Radio configuration? IP address, etc.

15 Agenda Smart Meters & AMI DSCADA / ADMS Data Readiness Field Devices Communications Analytics 15

16 Defining Requirements Requirement Polling / Report by Exception Minimum Data Rate Maximum Latency Minimum Reliability Licensed/Unlicensed Typical Requirements and Comment 2 sec 5 min polling, as needed to support advanced applications 100 kbps and up for polling; Potential for lower rates for report by exception 1-15 seconds, as needed to support application needs and operator expectations 95-99% as needed to effectively support applications Requirements vary Protocols and Encryption Typically IP based and supports encryption Network Management Deployment / Support Provides configuration management, firmware upgrades, diagnostics, alarming, etc. Internal / third party staffing for deployment and/or support

17 Defining Requirements Data Rates Raw Data rates referenced in device specifications can be misinterpreted Raw Data rates do not typically include protocol and encryption overheads Estimates only. Efficiency decreases with small payload sizes Cumulative "Efficiency" "Efficiency" "Raw Data Payload 100.0% 100.0% TCP/IP Headers 97.3% 97.3% VLAN Tagging 97.3% 94.7% Security Protocol 92.6% 87.7% Also, assumes no packet loss and no retries, which ties to latency and reliability

18 Defining Requirements Latency Latency requirements are most often influenced by two key items: Advanced application requirements (e.g. VVO or FLISR applications) Operator expectation (often based on experience with transmission SCADA systems) Requirements to support OTA firmware and settings updates Latency approach should be carefully considered (e.g. round trip ping times versus data polls)

19 Agenda Smart Meters & AMI DSCADA / ADMS Data Readiness Field Devices Communications Analytics 19

20 Leveraging Smart Grid Data Smart Meter and SCADA data can be leveraged to improve operation and asset management. Traditional Transformer Load Management Smart Meter Data Enabled Transformer Load Management 20

21 Leveraging Smart Grid Data (2) Smart Meter and SCADA data can be leveraged to improve operation and asset management. In some cases, the trend line may exhibit a positive slope, indicating unexpected control operation and requiring investigation of LTC line drop compensation settings and/or capacitor controller settings. 21

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