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DRAFT City Light Report

Validation of 2012 Advanced Metering Infrastructure Business Case

NOVEMBER 2014

Prepared for: Seattle City LightPrepared by: Leidos Engineering, Inc.

TABLE OF CONTENTS

TABLE OF CONTENTS.................................................................................................................2

Section 1: Executive Summary.....................................................................................................3

Section 2: Business Case Update...............................................................................................9

2.1 Business Case Methodology..........................................................................9

2.2 Assumptions..................................................................................................9

2.3 Overview of AMI Benefits............................................................................12

2.4 City Light’s AMI Benefits............................................................................12

2.3 Overview of AMI Costs................................................................................23

Section 3 Advance Metering Technologies.................................................................................31

Advanced Metering Infrastructure....................................................................31

Meter Data Management...................................................................................33

Volt/Var Management........................................................................................33

Distribution........................................................................................................34

Electric Vehicles................................................................................................36

Communications Infrastructure........................................................................37

Workforce Efficiency.........................................................................................38

Section 4 AMI Use Cases............................................................................................................39

Appendix A: List of Acronyms.....................................................................................................43

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Section 1: Executive SummaryBeginning in late August 2014, Seattle City Light (City Light) engaged Leidos Engineering Services to perform work to validate its 2012 Business Case. The analysis from a previous 2012 AMI study was reviewed to understand the circumstances of City Light at that point in time, as well as understand how the AMI technology has further matured since the original business case in 2009.

In 2012 there was first an intensive data gathering effort that was performed for two months focusing on how City Light performs business today in the areas where typical implementations of Advanced Metering Infrastructure (AMI) technology show improvements. Leidos leveraged the same proprietary AMI business case model used in 2012 in order to capture the calculations of AMI benefits, costs, City Light system parameters, and most importantly financial results of multiple AMI implementation scenarios. This business case model produced the results and graphics found within this report.

During the initial meeting Leidos was directed to perform analysis involving the following five scenarios:

1. A “Build, Own and operate” utilizing a point-to-point (Star) AMI technology solution.2. A “Build, Own and Operate” utilizing mesh AMI solution.3. A 3rd-party, hosted solution utilizing a mesh AMI solution.4. A “Build Own and Operate” utilizing a point-to-point Cellular AMI solution.5. A 3rd party, hosted solution utilizing a point to point Cellular AMI solution.

The work reviewed the advantages and disadvantages of today’s primary AMI network infrastructures involving Radio Frequency (RF) Point-to-Point (Star) and Mesh technologies, as examined in 2012. The team directed the review to also include Point-to-Point Cellular AMI identifying the benefits and cost associated with the cellular technology.

Beyond the AMI network, 3rd-party hosted solutions have become a major change in how businesses conduct portions of their operations. Hosted solutions is where the management of data centers and software applications takes place off-site which allows for economies of scale to be achieved. This is advantageous for utilities like City Light because they provide the operation and maintenance of computing hardware and software along with disaster recovery and cyber security. These capabilities, quite frankly, can be unachievable within the cost structures of many utility organizations. Leidos was directed to include “Managed Hosted Service” as part of the update of the business case analysis.

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Business Case Results

The business case review produced the following financial results:

The “Build, Own, and Operate” scenarios 1, 2 &4 were modelled at three years of project deployment. The “Managed Service” models scenarios 3 & 5 were modelled at two years. A “Managed Service” inherently has the systems already built, a large portion of integration already completed, and an operating data center with people managing a network in operation. Hence, the deployment can be done in less time with the ability of delivering the business benefit stream in one fewer year than in an own and operate scenario.

Overall capital requirements range from $94.7 million to $130.3 million with the cost of the meter replacement being the largest component of that capital requirement ranging from $88.0 million to $123.0 million respectively.

The meter replacement cost was higher than the 2012 study of $66.5 million for two reasons. First, the additional cost of purchasing a remote disconnect switch for 294,000 form 2S meter and 104,000 form 12 S meter adds $21.9 million. Secondly, the meter population has an additional 16,000 meters than the 2012 study.

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Annual O&M cash flow ranged from $3.6 million annually to $6.2 million. The “Managed Service” scenario 3 &5, has a higher operating cost because the service is modelled as a monthly $0.75 / meter / per month fee. The operating cost of the Cellular PTP, scenarios 4 & 5, are also higher because they carry a $ 0.25 / meter / month operating fee to the cellular provider as a data feed for the data backhaul.

The next to last column of the financial table, “SCL Post Deployment Support” shows the level of ongoing support of the technology one the program is fully deployed. The managed service options are less support, two people. The service supplies all but network communication support of the collector system.

Whereas the “Build, Own, and Operate” scenarios, scenario 1 &2, require ten people to support the ongoing program. The “Build, Own, and Operate” PTP scenario, scenario 4 requires slightly smaller support of nine people. This is because the cellular network will have less communication backhaul work activity.

Benefits

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The most significant benefits are shown in the above table. Differences from the previous study worth mentioning are in the - Meter Accuracy, Connects / Disconnects / & Account Transfers. Meter Accuracy in the previous study used a very conservative 0.5% benefit for the population when moving to solid state metering. Every utility is different based on quality of their electro-mechanical metering and vintage. A 1% accuracy improvement was taken as being more realistic.

The 2S and 12S meter population is planned to be specified with a remote disconnect with an indicative retail price of an additional $55 per meter. The reason for bearing this cost is in the area of business benefit in regards to operating this switch from the office (without rolling a truck) in those situations involving work orders to connect, disconnect, or transfer accounts. The remote switch saves office time, but the majority of the benefit for City Light lies in the 9,754 annual field trips involving 14,631 hours. Overall this function drives $1.5 million of benefit annually.

The other benefits previously reviewed simply changed because of updated labor rates assuring a “fully” loaded rate was used. Or the benefit was driven by revenue or sales which have increased in the two years since the previous study.

Leidos was specifically asked to include two new areas not taken given consideration in the previous business case, the leveraging of AMI data for Volt Var Optimization and Demand Management.

Volt Var Optimization (VVO)

Volt Var Optimization has evolved significantly since City Light’s 2009 study period. Vendors are now leveraging the voltage sensing capability of the AMI meter to tune the distribution infrastructure. This has resulted in the industry can take former traditional approaches of 1% voltage reduction to possibly increase to over 3.2%.

In regard to further analysis, Leidos brought in a leading vendor in VVO technology, DVI. The purpose was to demonstrate why greater voltage reduction can be achieved and thus greater benefit justified in the business case analysis.

The technology improvements were demonstrated in several areas on September 8, 2014 to City Light’s AMI project team and distribution staff with engineering and construction expertise:

Foremost, the ability to initially monitor every meter point. Utilities with AMI no longer need to fly blind with sampling of voltage recording to understand fluctuations of their system across variables of different seasons and demand loading of their systems.

The use of AMI voltage sensing meter data at the secondary voltage level, rather than past practices typically limited to primary voltage sensing of the distribution feeder.

The statistical use of algorithms to identify bellwether data points driving the low voltage points of a feeder.

The ability to adjust to different load patterns driven by seasonality used in the algo-rithms.

Two of the main obstacles to successfully implementing CVR are developing a practical method of controlling voltage that is adaptive to the dynamic changes that typical distribution circuits undergo and measuring the energy saved when the circuit is operating in the more precise lower voltage band. Techniques that control voltage using state estimation rely on primary level

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sensors and can only approximate the voltage drop on secondary conductors and service transformers affecting the customer meter.

After understanding this current technique of leveraging AMI meter sensing data with VVO, considerable discussion occurred at the committee level in regards to what reasonable level of benefit should be taken in the business case.

VVO was considered in two ways, for demand management purposes and overall daily operational efficiencies of the system. The consensus was reached that although a demand management purpose was obtainable, a conservative approach would to limit the benefit to overall system efficiencies (running the system with tighter voltage tolerances daily). Furthermore, because of the nature of City Light’s 26kv feeder system, it was felt taking benefit on 33% of the system would a better starting position versus a larger portion of the system.

This debate and modelling resulted in $2,596,641 of benefit under the rationale of operational efficiencies only using a 2% voltage reduction against 33% of the system.

Recommendation: Volt Var Optimization having a high benefit potential needs early validation considering previous analysis performed by Leidos. The benefit may even exceed expectations beyond operational benefit in the area of demand management. Leidos recommends testing this capability early in deployment with a pilot concept against City Light distribution infrastructure.

Demand ManagementLeidos was asked to review the ability of leveraging AMI with Demand Management a thorough vetting. With the understanding demand management may not have immediate value in the short term, or the next few years under City Light’s circumstances, however over the life of AMI system (20 years) it was certain demand management will become a necessary tool in City Light’s operations.

The downside of a broad Demand Management program can be the communication cost of integrating new devices beyond the meter, additional cost of load control devices, a heavy burden of administration costs, and the risk the designed program not being coincident with the peak intervals. As a starting point, an approach where there is a low burden of administration and installation of additional equipment was taken.

Multiple on-site and teleconference meetings were held with City Light Power Marketing covering potential areas from future periods of generation constraints, transmission constraints, to even periods where there are not expected to be system constraints however it may make sense to earn a greater margin at a wholesale level by using demand management at the retail level.

The Power Marketing group made the assumption demand management can provide AMI benefit at price point above the 95th percentile over a 13-year forecast. Understanding the MWH available during those periods also assumed AMI could provide 5% savings. To allow a low risk, low infrastructure approach, the 5% was reduced to 2% which produced a leveled $501,419 annual benefit for the program.

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Cellular Point-to-Point (PTP) Technology

Cellular Point to Point Technology (scenario 4 and 5) is an additional technology considered in the analysis which was not covered in the 2012 study. The cellular technology refers to the back haul of data from the meter to the data center. Cellular uses a meter with an integrated communications board capable of using a cell phone carrier for communications.

The analysis keeps all other parts of the system equal in comparing systems, which centers the comparison of on the value of AMI proprietary system against the common carrier system.

In regard to benefit, the cellular PTP is optimum where sparse customer density exists whereas RF PTP or RF Mesh systems are not viable. In the case of RF PTP the 3500 customers to 1 tower ratio used becomes too low and cost prohibitive. Or similarly in the case of RF Mesh the 800 customer to 1 collector ratio becomes too low and cost prohibited.

The other area where cellular PTP has benefit compared to proprietary network is the case where a RF mesh network has a utility where water meters need data backhaul with no electric meters to network the data back.

Neither of these circumstances exist at City Light, and therefore, there is no apparent benefit for choosing cellular.

On the cost side of considering Cellular PTP, a premium occurs with the cost of the metering deployment. Considerable discussion has occurred on what the cost of the cellular PTP meter can be obtained. As example, the price of a 2S meter for a RF mesh meter of $80 goes to $180 for cellular PTP meter. This price premium adds $30.2 million the overall capital cost of the project if all cellular PTP meters where used system wide.

O&M cost also are higher for Cellular PTP. A $0.25 per meter per month charge by the cellular carrier is expected. ($1.3 million annually)

Recommendation: Considering the customer density of the SCL territory, cellular com-munications backhaul should only be considered where significant communications is-sues exist for the proprietary network. Therefore, a small handful of locations could be expected.

These benefit values were used consistently across all five scenarios of the modeling. Although the likelihood of achieving this overall operational efficiency is stronger in the “Managed Service” scenarios because many components are prebuilt, the implementation period can be achieved in shorter period, and the service is tested. Hence there is considerable less implementation risk in scenarios 3 &5, “Managed,” versus scenario 1, 2, 4 “Build, Own, & Operate”.

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Section 2: Business Case Update Leidos (formerly SAIC) Engineering Services, Robert Maurer and Brandon Garcia, have been engaged to update the 2012 Business Case work performed by SAIC.

This section discusses the benefits and costs associated with various Smart Grid investments and provide an economic evaluation of Smart Grid applications for City Light. The Leidos business case model is an Excel based spreadsheet and should be used in conjunction with this section to fully understand the benefits and associated cost of the technologies.

The benefits have been derived from the specific circumstances currently existing at City Light. The costs outlined herein have been developed through research of publicly available data (mainly EPRI, NETL, City Light, the Energy Commission, the Department of Energy, and the Battelle Group) as well as information used in detailed business case models we have developed for other utility clients.

New areas of consideration compared to the last evaluation were also reviewed. Additional expertise in the areas of Volt Var and Demand Management were brought in for analysis of City Light ability to leverage AMI technologies and shall be discussed in this section.

2.1 Business Case Methodology Leidos and City Light planned a multiple-day business case workshop to be held at City Light to review business case modeling and ascertain benefits from subject matter experts (SMEs). In August 2014, the Leidos team held an initial meeting with Smart Grid committee to demonstrate the methodology and agree upon scope. Specific City Light data was received involving areas such as labor rates, number of billing complaints, existing labor to read electro-mechanical meters and turn on and shut offs where gathered in advance.

Three other workshops occurred between September and October, 2014.

Meeting 1 – Leveraging AMI to maximize Volt Var benefit assisted with help from DVI.

Meeting 2 – Using AMI to maximize future Demand Management Capability, assisted by Tangent Solutions

Meeting 3 – Data review with Smart Grid Committee members for validation of SCL data.

This workshop with the City Light data preloaded in the Leidos AMI business case modeling tool. The workshop accomplished the objectives of determining consensus on overarching assumptions, providing indicative industry cost data for AMI implementations, and the discussion and tuning of benefits applicable to the specific circumstances at City Light.

On November 6, 2014, Leidos presented to the AMI Advisory Committee the initial results of the modeling. And lastly, prior to issuing the draft report results where communicated to the Steering Committee on November 24, 2014.

2.2 AssumptionsThe following overarching assumptions were identified during the course of the workshop:

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1. Address each of the three competing RF Architecture Technologies, of interest to SCL (Mesh, Star, and 3G/4G/LTe) in the Communication Architecture section

2. Address the benefits of an accelerated implementation timetable (estimate is two years plus 6 months stabilization) compared to the current published schedule.

Taking these assumptions into account, the Leidos business case model focuses on 5 implementation scenarios.

6. A “Build, Own and operate” utilizing a point-to-point (Star) AMI technology solution.7. A “Build, Own and Operate” utilizing mesh AMI solution.8. A 3rd-party, hosted solution utilizing a mesh AMI solution.9. A “Build Own and Operate” utilizing a point-to-point Cellular AMI solution.10. A 3rd party, hosted solution utilizing a point to point Cellular AMI solution.

A “Build, Own, and Operate” refers to the situation where a utility installs and implements AMI technology and after implementation the utility is fully responsible for day-to-day operations. As Figure 1 below depicts, managing the daily operations of all major components, the metering, the two way communications from the meter to the collector to the Meter Data Management System, the hardware and software involved with the AMI headend, MDMS, Portals, and interfaces such as the CIS system.

Figure 1. “Build, Own, and Operate” architecture diagram

The Leidos business case model further analyzes this implementation option by considering two leading AMI communication options; a “Point to Point” and “Mesh” type of AMI communication technologies. (Further defined in Section 6.2)

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Utility Owned

Due to the recent maturity of hosted applications, the option of an AMI Smart Grid implementation that utilizes a 3rd-party hosted solution is now entering the market. Figure 2 illustrates the architecture diagram of 3rd-party hosted AMI implementation. .

Figure 2: 3rd-party hosted architecture diagram

The main components of a 3rd-party hosted solution are the same as the “Build, Own and Operate” model. The project implementation and installation of the field components (meters, collectors and data backhaul) is the same for both implementation models.

What is different is in the center of Figure 2, where the application servers are hosted in a data center and connected to the utility through a secure connection. The Meter Data Management System (MDMS), web portals, the AMI Head-end, and Outage Management System (OMS) are operated by a 3rd-party.

On the far left of the diagram above, the utility continues to own their Customer Information System and Geographic Information System which are integrated with the hosted AMI solution.

Thus the “Hosted Solution” has two major benefits for the midsize utility such as City Light. The first benefit is that it brings the power of economies of scale to the implementing a full AMI system. Much of the work of integrating new AMI systems doesn’t scale regardless if you’re a large IOU or a small POU. The pre-designed and pre-built systems of a “Hosted Solution” allow the utility implementation to focus on using the data instead of managing data. The “Hosted Solution” also allows the installation of the field equipment to be done by a vendor who has done it multiple times and has an understanding of the complexity and nuances of standing up a reliable system.

The second major benefit of a “Hosted Solution” is avoiding the need to acquire the skillsets and resources to operate a new set of applications and equipment. Some hosted solutions provide an ongoing service where the operation of the new MDMS, Portals, AMI head-end, collectors, and AMI meters is the responsibility of the vendor. This allows the utility to focus on their core utility business rather than operating new systems.

For this study, the “Hosted Solution” scenario considered a “Mesh” AMI technology. To date, the market only offers mesh hosted solutions.

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2.3 Overview of AMI BenefitsBusiness case calculations considered if benefits would apply involving:

Meter Accuracy

Meter Reading Efficiency

Billing Exceptions

Billing Complaints

Unbilled/Uncollectible Accounts

Group Accounts

Cash Flow

Connects, Disconnects, and Account Transfers

Outage Restoration

Phase Balance

Transformer Overloads and Sizing

Vegetation Management

Avoided CO2 Emissions

Pre-Pay

Demand Response

Voltage Optimization

Distribution Automation

Distributed Energy Resources

2.4 City Light’s AMI Benefits The following benefits were derived for each of the five scenarios:

1. Build Own Operate Point to Point AMI Technology2. Build Own Operate Mesh AMI Technology3. Hosted AMI Mesh Technology4. Build Own Operate Point to Point Cellular Technology5. Hosted Cellular Technology

In all five scenarios, the benefits side of the business case is the same. Regardless of the use of Point to Point AMI technology or Mesh AMI Technology, the business benefits analyzed are essentially identical. The difference between the technologies is captured in the cost side of the analysis.

The benefits derived for the “Hosted” scenarios (scenarios 3&5), for consistency, was also the same in the analysis expect in two significant areas.

First, the time to fully deploy the system in a hosted scenario is much shorter. In the hosted scenario back office IT systems exist and are running, they only need to be configured to the client’s requirements. Conservatively, a utility can achieve steady-state deployment six months to one year sooner than in “Build, Own, and Operate” projects.

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Second, the drivers of business benefit are typically stronger and achieved sooner. Typically, leveraging the new way of doing business or utilizing the data to improve decision making is realized earlier in the project cycle. Not re-inventing the wheel allows energies to be used in improving the core business.

The multiple workshops produced annual benefit valued at $17.9 million after deployment in the following areas:

(Note: Comparison information is provided to the 2012 study)

(Note: Tables below do not explain all value drivers just the primary value driver. The spreadsheets used at the time of this report provide additional areas of benefit such as vehicle cost avoided, elimination minor systems cost, and CO2 emissions benefits)

(Note: Updated 2014 labor rates are fully loaded.)

Bill Complaints - Annual Benefits

Definition: AMI will reduce the duration of calls related to bill complaints and questions because customers are provided accurate data on their energy usage.

Current Study (2014)

Previous Study (2012) Primary Value Diver Notes

$29,907 $22,574 576 Office Labor Hours avoided

Increase benefit due to change in fully loaded labor rate.

Bill Exceptions - Annual Benefit (Office / Field / Meter Testing)

Definition: AMI will reduce the duration of calls related to bill complaints and questions because customers are provided accurate data on their energy usage.

Office



$829,129 $619,436 15,957 Office hours avoided

- Office rate of $36.54 increased to $51.96

- Hours decreased from 16,592

Field Portion of Annual Benefit



$251,884 $123,497 - 2,688 field hours avoided

-Field rate of $46.97 increased to $88.38

-Hours increased from 2,597

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Meter Testing Portion of Annual Benefit


Previous Study (2012) Value Diver Notes

$38,417 $35,820 - 447 Meter Testing hours avoided

Only change was labor rate increase from $80 to $85.80

Improved Cash Flow on Standard Billing Practice - Annual Benefit

Definition: AMI will improve cash flow by providing accurate meter read data to the billing system sooner for processing and mailing to customers.



$1,135,266 $1,107,338

- 18 days of improve-ment Residential and small C&I cash flow

- 6 days of improve-ment for Large C&I cash flow

-Increase driven by change in 2012 revenue versus 2014.

Improved Business Process in handling of Connects, Disconnects, and Account Transfer - Annual Benefit

Definition: An AMI system with remote service switches will reduce transaction costs associated with connects, disconnects, and/or account transfers.

Office Portion of Annual Benefit



$139,504 $321,631 -Avoided 2,684 office hours (5 minutes per order)

- 105,626 orders used in 2012 versus 32,218 in 2014.

- Office labor rate increase from $36.54 to $51.96

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Field Portion of Annual Benefit



$1,269,770 $273,943 -Avoided 14,631 field hours

- 9,754 field orders in 2013 versus 3,356 (estimated) in 2012.

- Field labor rate increase from $46.97 to $88.38

Ability to Easily Group Accounts - Annual Benefit

Definition: AMI will reduce back office labor and cost required to administer group accounts and monthly billing of the group accounts.



$157,179 $55,267 - 3,025 hours of office labor avoided

- Office rate of $36.54 increased to $51.96

- Annual hours in-creased from 1,513 to 3,025

Improved Metering Accuracy – Annual Benefit

Definition: AMI will produce additional margins because more accurate solid-state meters will replace electromechanical meters during AMI deployment.



$3,335,007 $2,129,943

- 1% accuracy im-provement on resi-dential

- 0.4% accuracy im-provement on C&I

-Based on Sales Revenue

- Residential accuracy increased from 0.5% to 1%

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Eliminate the need for ERT Metering – Annual Benefit

Definition: AMI will reduce costs to install ERT meters for inaccessible or "hard to read" meters, & maintain a load research program.



$138,960 $94,260- Elimination of 600

special ERT meters and the handling in-volved.

- Primary change was meter tech rate from $48.55 to $85.80

Elimination of Manual Meter Reading – Annual Benefit

Definition: AMI will eliminate the costs associated with manual meter reading and related activities



$4,515,611 $3,903,920 - 89,784 hours of me-ter reading avoided

- Labor rate increase from $32.68 to $46.47

- Number of Meter Readers now 43 ver-sus 36 in 2012 study.

Eliminate the need replace Electro-Mechanical Meters – Annual Benefit

Definition: AMI will eliminate the costs associated with annual routine replacements of electro-mechanical meters and provide one-time revenue as replaced meters are sold for scrap.



$545,610 $478,530 -6,000 Meter Tech hours avoided

- Only change from previous study was labor rate from $80 to $85.80

Improved Trouble Management & Outage Management – Annual Benefit

Definition: The outage detection and 2-way communication of AMI will reduce outage restoration related costs and improve reliability. Improved trouble analysis will eliminate truck rolls and field labor. Ability to ping metering to determine line side voltage at the meter will save field trips.



$278,595 $71,731 -2499 Field Operation Hours Avoided

-2,840 Dispatch Hours

- Field Hours increase from 1,249 to 2,499. Difficulty in obtaining SCL basis for this benefit. Leidos believes this

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Avoided

- 220 Call Center Hours Avoided

number even though increased from 2012 is understated given industry experience.

- Labor rates increase

Field Operations $57.26 to 93.89

Dispatch $69.40 to $110.73

Call Center $39.22 to $55.77

Ability to Phase Balance Circuits (with better data) – Annual Benefit

Definition: AMI-SG will reduce losses and the cost to field collect and process phase loading data needed to investigate, assess, correct, and maintain proper phase balance. The AMI meter is essentially a remote sensor at each house.



$21,257 $14,930- 221 hours of field op-

erations and engi-neering hours avoided

- Leidos believes this is conservative or un-derstated. Difficult to ascertain future ben-efit until data at the line end if fully under-stood.

- Field Labor rate in-creased from $50 to $93.89.

- $9,402 of 2012 C02

benefit eliminated because we see this as soft benefit.

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Improved Vegetation Management – Annual Benefit

Definition: AMI can reduce vegetation maintenance cost of utility right-of-way (ROW) by using momentary outage or "blink" reports and outage reports to prioritize and schedule activities in lieu of a maintaining a traditional cyclically based program. (Better efficiency of vegetation “Hot Spot” activity)



$372,600 $279,460

- Improved efficiency of targeting vegeta-tion management hot spot program re-duces overall pro-gram costs

- Net change versus 2012 driven off of in-crease in $4.5M 2012 budget to $6.0M 2014 budget.

Avoid Transformer Overloading to Reduce Emergency Replacement – Annual Benefit

Definition: Utility staff can use AMI meter-level interval data to monitor transformer loads and schedule upgrades to avoid overload failures and the cost of purchasing



$373,450 $343,783- 92 transformer fail-

ures could be avoided

- Little difference in 2012 study, same transformer capital cost data however current of blend of recent transformer failures slightly differ-ent.

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Unbilled Accounts – Annual Benefit

Definition: AMI will reduce unbilled revenue due to diversion by customers or where the responsible party cannot be identified for billing.



$219,568 $219,568 - See Calculation be-low

- No change from pre-vious study

1 Energy Sales Volume

2 Annual MWh sales to residential customers 3,188,241

3 Est % of annual line loss. 6.00%4 Residential MWh line loss. (2 × 3) 191,294 56 Energy Sales Volume on Unbilled Accounts or Theft7 Est % of line loss attributed to unbilled accts or theft. 3.00% 8 Residential MWh line loss attributed to unbilled accts or theft. (4 × 7) 5,739

9 Est % of Residential MWh sales attributed to unbilled accts or theft. (8 ÷ 2) 0.18%

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11 Avoided Revenue Loss on Unbilled Accounts or Theft

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Wt avg residential retail rate/kWh (net lighting, customer charges, fuel base) $0.07652

13 Est revenue loss on energy attributed to unbilled accts or theft. (8 × 12) $439,136

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Est % of revenue loss attributed to unbilled accts or theft that is col-lectible. 50%

15 Avoided revenue loss on unbilled accts or theft. (13 × 14) $219,568

Reduction in Year End Bad Debts – Annual Benefit

Definition: An AMI system with remotely controlled service switches will reduce annual uncollectible account balances expensed as year-end bad debts.



$253,788 $253,788- 6 less days of usage

@ $14.67 per bad debt account

- No change from pre-vious study

New Areas of Benefit Analyzed (Not taken in previous study)

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Benefits captured thus far where typical for utility AMI implementation. After initial review of benefits area, Leidos recommended deeper analysis in the area of Volt Var Optimization and Demand Management. Benefits in these areas are heavily driven by circumstances existing at City Light.

Volt Var Optimization (VVO)

Volt Var Optimization has evolved significantly since SCL’s 2009 study period. Vendors are now leveraging the voltage sensing capability of the AMI meter to tune the distribution infrastructure. This has resulted in the industry can take former traditional approaches of 1% voltage reduction to possibly increase to over 3.2%.

In regards to further analysis, Leidos brought in a leading vendor in VVO technology, DVI. The purpose was to demonstrate why greater voltage reduction can be achieved and thus greater benefit justified in the business case analysis.

The technology improvements were demonstrated in several areas on September 8, 2014 to City Light’s AMI project team and distribution staff with engineering and construction expertise:

Foremost, the ability to initially monitor every meter point. Utilities with AMI no longer need to fly blind with sampling of voltage recording to understand fluctuations of their system across variables of different seasons and demand loading of their systems.

The use of AMI voltage sensing meter data at the secondary voltage level, rather than past practices typically limited to primary voltage sensing of the distribution feeder.

The statistical use of algorithms to identify bellwether data points driving the low voltage points of a feeder.

The ability to adjust to different load patterns driven by seasonality used in the algo-rithms.

The EDGE solution developed by DVI and integrated with AMI tehcnologies is a new approach to conserving energy by optimizing voltage. Conservation Voltage Reduction has been around the industry for over 30 years and is based on operating electric customer voltages in the lower half of the 10 percent voltage band required by ANSI equipment standards. CVR-based energy savings have been tested in numerous field tests over this time period. , , Results of these studies support approximate average savings of 0.8 percent energy reduction for every 1 percent reduction in circuit voltage.

Two of the main obstacles to successfully implementing CVR are developing a practical method of controlling voltage that is adaptive to the dynamic changes that typical distribution circuits undergo and measuring the energy saved when the circuit is operating in the more precise lower voltage band. Techniques that control voltage using state estimation rely on primary level sensors and can only approximate the voltage drop on secondary conductors and service transformers affecting the customer meter.

Simulation modeling techniques fail to keep up with the dynamic nature of the distribution system and typically have difficulty modeling the proliferation of technologies such as electric vehicles, distributed generation assets, and home area networks. Modeling approaches also have difficulties continuously representing circuit dynamics such as penetration of demand-side management programs, new customer additions, seasonality of loads, and other changes that occur unpredictably on a distribution circuit. By using the most recently developed AMI technology, along with (supervisory control and data acquisition) SCADA measurement of

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operational data, DVI’s adaptive voltage control is able to monitor the dynamic nature of the distribution system in real time, make voltage decisions based on that real-time information, and measure the energy savings that result from those decisions.

After understanding this current technique of leveraging AMI meter sensing data with VVO, considerable discussion occurred at the committee level in regards to what reasonable level of benefit should be taken in the business case.

VVO was considered in two ways, for demand management purposes and overall daily operational efficiencies of the system. The consensus was reached that although a demand management purpose was obtainable, a conservative approach would to limit the benefit to overall system efficiencies (running the system with tighter voltage tolerances daily). Furthermore, because of the nature of City Light’s 26kv feeder system, it was felt taking benefit on 33% of the system would a better starting position versus a larger portion of the system.

This debate and modelling resulted in:



$2,596,641 $0- 2% Voltage Reduc-

tion across 33% of System

- Basis driven on oper-ational efficiencies, Demand Manage-ment benefit not taken at this time.

The core AMI system typical does not account for the additional costs for achieving VVO benefit. Hence additional cost are modelled in the business case. A substation infrastructure cost of $17,500 per substation was used. A $3 million IT infrastructure cost added for VVO software and SCADA integration. And finally a 10% or $300,000 O&M cost for such a system.

Demand Management

In the previous study, the topic on Demand Management was tabled stating:

“Demand management does not have high value for City Light at this time for several reasons. Almost 90 percent of City Light’s supply is hydropower. Most of the distribution system is operating well within rated capacity. City light’s available energy supply is ample to meet native load, and City Light sells its ex-

cess supply in the regional wholesale market.

Demand management has high value for many utilities, and may become valuable for City Light in the future as native load growth raises challenges in two domains: supply resources, and transmission and distribution (T&D) capacity. The value of managing demand is that it raises the load factor for the utility in both of these domains. This increases asset utilization. That is, with demand management, City Light will deliver more kWh with the existing infrastructure,

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continuing to meet customer needs without incurring costs to expand T&D capacity or acquire new supply.

Most utilities include demand management value in the AMI business case. Demand manage-ment benefits can be quantitatively estimated based on projected load growth and existing re-sources. At the time of this report, City Light judged that such challenges are far enough in the future that their value is uncertain. Demand management benefits are therefore omitted from this business case update.”

Leidos was asked to give this area a thorough vetting, with the understanding demand manage-ment may not have immediate value in the short term, or the next few years, however over the life of AMI system (20 years) it was certain demand management will become a necessary tool in City Light’s operations.

In the area of Demand Management, Leidos choose to use another Vendors expertise, Tangent Energy, for two reasons to assist in leveraging AMI for demand management. The first reason was Tangent’s level of expertise in the field for providing a framework of demand management opportunity at City Light. The second reason was their low burden of administration and installation of additional equipment, in short, simplicity.

The downside of a broad demand management program can be the communication cost of integrating new devices beyond the meter, additional cost of load control devices, a heavy burden of administration costs, and the risk the designed program not being coincident with the peak intervals.

Tangent Energy’s analysis leverages forecasting techniques against prime commercial and industrial customers (cherry pick) to often use existing energy efficiency, existing building controls, photovoltaic, or even existing distributed generation during critical transmission or generation supply periods. Hence a means to achieve Demand Management benefit without a high cost / high risk of infrastructure administered throughout the service territory.

Multiple on site and teleconference meetings were held with City Light Resource Planning covering potential areas from future periods of generation constraints, transmission constraints, to even periods where there are not expected to be system constraints however it may make sense to earn a greater margin at a wholesale level by using demand management at the retail level.

A 13 year view of the potential provided the following:

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The business case now models annually:



$501,419 $0

- Demand Manage-ment capable of real-izing 2% peak shav-ing when reaching 95th percentile

- 36MW per hour of demand reduction for 439 hours per year

- Choose conservative approach of 2% ver-sus 5% allow target-ing to limit infrastruc-ture costs.

The basic cost of deploying an AMI technology does not include the infrastructure cost associated with demand management programs. Using the advice of the subject matter experts to begin small versus a wide scale program involving load control devices and large administration burden, the Resource Planning 5% AMI savings was reduced to 2%. Whereas large to medium C&I customers could be targeted and hence limit the administrative burden of initiating a program. An initial $75,000 capital cost with an ongoing 20% of savings or O&M cost of $90,000 was therefore used in the business case modelling.

2.3 Overview of AMI CostsThe Leidos business case tool categorized cost in four areas:

Meter Infrastructure Costs - Includes 426,144 electric AMI solid-state meters. A population of 294,087 2S residential meters is currently installed and 103,955 12S meters. It was assumed every 2S and 12S electric meter included the cost of an integrated remote disconnect switch. The population of electric meters for the AMI project for all five scenarios in the modelling is as-sumed as follows:

Meter Description QuantityForm 1S - 1φ, Self-Contained 1,530 Form 2S - 1φ, Self-Contained 294,087 Form 3S - 1φ, Instrument Rated 236 Form 5S - 3φ or 1φ Network, Instr Rated 377 Form 6S - 3φ, Instr Rated 48 Form 9S - 3φ, Instr Rated 7,688 Form 12S - 3φ or 1φ Self-Contained Network 103,955 Form 16S - 3φ or 1φ Self-Contained Network 13,114 Form 4S - 3φ or 1φ Self-Contained Network 5,109

Total 426,144

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The differences in metering cost build up are shown in the table below:

ScenarioMetering Cost Current Study

(2014)

Metering Cost Previous Study

(2012)

2S Meter Cost

(Installation, meter cost,

remote disconnect cost)

Notes

1 ) PTP $92.4 M $60.7 M $149

- Meter Population was 410,474 in pre-vious study

- No Disconnects were used in previ-ous study

2) Mesh$92.4 M $66.5 M $149

- Meter Population was 410,474 in pre-vious studyNo Disconnects were used in previ-ous study

3) Mesh Hosted $88.0 M Not Studied $122

- Hosted Solution has discount for national sales volume

4) PTP Cellular $123.9 M Not Avail. $249

5) PTP Cellular Hosted

$101.3 Not Avail. $197

- Hosted Solution has discount for national sales volume

Discussions in meetings with AMI project team in regard to metering cost focused on the cost of cellular metering. Cost per meters for PTP cellular meter ranged from $110 – $200 per PTP cellular meter. Leidos check two sources of metering vendors to determine their “retail” pricing to be $180 - $200 PTP cellular meter.

Communication Infrastructure Cost (Backhaul) - Communication infrastructure costs for all AMI related point-to-point, RF mesh, power line carrier, towers, or hybrid communication system anticipated returning meter data to the utility. The analysis assumed for simplicity a cellular backhaul cost using a unit cost per every 800 metering end points for the mesh technologies. A unit cost per every 3500 metering points was used for the Point-to-Point tower technology. This was modelled as a capital cost for scenarios 1, 2 and 3.

Point to Point Cellular Backhaul would be by a telecommunications carrier. Meetings with the AMI project team offered a data point for the region to expect a $.025 per meter point per month cost. This cost was used for both Point to Point Cellular scenarios 4 and 5. This was modelled as an O&M cost throughout the 20 year life of the evaluation.

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ScenarioBackhaul Cost Current Study

(2014)Notes

1 ) PTP $1.4 M (One Time Capital

- 3500 metering points per tower

2) Mesh $2.9 M (One Time Capital)

- 800 metering points to mesh collector

3) Mesh Hosted

$2.4 M (One Time Capital)

- 800 metering points to mesh collector

4) PTP Cellular

$1.3 M (Annual O&M)

- $.025 per meter / month

5) PTP Cellular Hosted

$1.3 M (Annual O&M)

- $.025 per meter / month

Systems Infrastructure Costs - Systems infrastructure costs for all AMI/MDMS/Portal/OMS hardware, software, implementation, integration, testing, project management, & training.

The AMI project requires an operating center involving the main applications, hardware, mainte-nance, initial implementation, daily operating, and periodic updates of systems software. It’s ex-tensive and can be complex. For purposes of this report we’ve simply listed the areas where cost has been detailed for the five scenarios.

(The actual cost information can be found in the five pdf files on the IT Infrastructure cost sheets.)

AMI IT Infrastructure - Initial Hardware CostsProduction database server (AMI).Production integration server (includes FTP and SOA servers.)Production AMI application server (head-end). (Per cust)Development database server.Development integration server. Test/training database server.Test/training integration server.Test/training AMI application server (head-end). (Per technology, not cus-tomer)Disaster recovery

Storage expansion for backup (online - DASD, offline - tape).Portal ServerAnalytics

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AMI IT Infrastructure - Initial Software CostsAMI application softwareDB softwareETL software.OS software and server clients Disaster recovery

Backup softwarePortalCVO softwareMonitoring/BatchAnalyticsTicketing Software for Customer Requests

AMI IT Infrastructure - Initial Implementation & Integration Costs

Project managerDeputy Project managerIntegration leadTesterDeveloper.Business Process analystSystem ArchitectQuality AssuranceTraining ManagerSecurity administrator

AMI IT Infrastructure - Periodic Hardware CostsFrequency (Years) of periodic upgrades or additions to AMI IT systems, etc.

Production database server (AMI)Production integration server (includes FTP and SOA servers)Production AMI application server (head-end)Development database serverDevelopment integration serverTest/training database serverTest/training integration serverTest/training AMI application server (head-end)PortalAnalytics

AMI IT Infrastructure - Periodic Software Costs

AMI application softwareDBMS software (Oracle SE or SQL EE)ETL softwareOS software and server clients (AV, IPS, etc.) for IBM serversDisaster recovery (Backup software Comvault clients)

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PortalSecurityMonitoring/BatchAnalyticsTicketing Software for Customer Requests

AMI IT Infrastructure - Periodic Implementation & Integration CostsFrequency (Years) of periodic upgrades or additions to AMI IT systems, etc.Duration (months) of proposed implementation & integration.

Project managerDeputy Project managerIntegration leadTesterDeveloperBusiness Process analystSystem ArchitectQuality AssuranceTraining ManagerSecurity administratorInstallation manager

AMI IT Infrastructure - Annual O&M CostsAnnual AMI IT systems O&M, e.g. license and maintenance fees, etc.

MDM IT Infrastructure - Initial Hardware CostsMDM Production ServerMDM Test ServerMDM Dev ServerMDM Production DRMDM Development DRIntegration Production ServerIntegration Test ServerIntegration Development ServerDatabase ServerDR

MDM IT Infrastructure - Initial Software CostsMDM application software w/ MDM PortalDatabase server OS softwareUC4 batch scheduling softwareETL softwareOS software and server clients (AV, IPS, etc.) for IBM servers

Integration SoftwareDisaster recovery (Backup software Comvault clients)Monitoring S/W

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MDM IT Infrastructure - Initial Implementation & Integration CostsDuration (months) of proposed implementation & integration.

Meter Data Management leadDeveloper

MDM IT Infrastructure - Periodic Hardware CostsFrequency (Years) of periodic upgrades or additions to MDM IT systems, etc.

MDM Production ServerMDM Test ServerMDM Dev ServerMDM Production DRMDM Test DBMDM Prod DBIntegration Production ServerIntegration Test ServerIntegration Development ServerAMP Database AMP BladesMDM Disk Space (For under 100K meter, 1 Tb/year)

MDM IT Infrastructure - Periodic Software CostsFrequency (Years) of periodic upgrades or additions to MDM IT systems, etc.

MDM application softwareDatabase server OS softwareUC4 batch scheduling softwareETL softwareOS software and server clients (AV, IPS, etc.) for IBM servers.Sort softwareIntegration SoftwareDisaster recovery (Backup software Comvault clients)

MDM IT Infrastructure - Periodic Implementation & Integration CostsFrequency (Years) of periodic upgrades or additions to MDM IT systems, etc.Duration (months) of proposed implementation & integration.

Project managerWindows OS supportNetwork supportArchitectDeveloperBusiness analyst / user experienceDatabase administrationReport writersMDM application developers

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Cyber securitySecurity administrator

MDM IT Infrastructure - Annual O&M CostsAnnual MDM IT systems O&M, e.g. license and maintenance fees, etc.Hosting SpaceData Lines MonthMonitoring Software3rd Party Support Staffing3rd Part Commination w/ LDC

MDM IT Infrastructure - Initial O&M CostsContract mobilization fees, etc.

This laundry list of IT data center hardware, software and operations should help the reader consider does the City Light organization build, own, and operated its own AMI data center or consider other alterna-tives.

The analysis modelled in the five scenarios found the cost of IT infra-structure involving the areas listed above. The “Build, Own, and Oper-ate” models to be a one-time implementation cost of $6.4 million. Whereas the managed hosted service is a monthly service or monthly service fee per meter end point. The monthly service fee is modelled as an O&M cost of $.80 per meter per month.

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AMI Program Management Costs – Labor to run the program involving a Program Manager, AMI operational staff, MDMS operational staff, IT infrastructure Support, and Network / Telecomm Analyst when comparing a build, own and operate scenario.

Scenario 1) PTP 2) Mesh 3) Mesh Hosted

4) PTP Cell

5) Cell Hosted

Program Manager 2 2 1 2 1

AMI Operational Staff (meter tech)

1 1 0 1 0

MDM Operations Staff

2 2 0 2 0

IT Infrastructure Support

3 3 0 3 0

Network Telecom Analyst

2 2 1 1 1

Total 10 10 2 9 2

The above chart shows the level of FTE support for the program once it is fully deployed. The 2012 study estimated 7 FTE’s which is aggressive for a 426,000 meter utility particularly at the early formation of the program. Leidos believes the support will range from 8 to 10 people and used the 10 as a basis for the business case model. The salaries for these roles are modelled as an O&M expense for 20 years.

The major distinctions that are noteworthy are as follows.

First, the Network Telecom Analyst role is reduced to one person in Scenarios 3,4, and 5. In Scenario 3, Mesh Hosted, the activity for the City Light responsibilities will be a portion of the field collector trouble diagnostics and any collector replacement activities. The managed hosted service will be responsible for the majority of the backhaul operations activity. (Daily operations) In Scenario 4 and 5, a point to point cellular will depend on the cellular carrier for backhaul, however their remains to be a diagnostic responsibility of handling the pinpointing of communication trouble events (whose side is it on) and the follow through on corrective action. Hence the role is less (1 person) versus the complete elimination of the need for the role.

The second distinct area is between the “Build, Own, and Operate” (scenarios 1,2,&4) and the “Managed Hosted” Solution, (scenarios 3 &5). The operation of the IT infrastructure and the majority of communication backhaul are what is being provided day in and day out Hence there is no City Light staffing for this activity. This O&M cost is a part of the $.80 per meter per month service fee. The risk and the management shifts to a third party.

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Section 3 Advance Metering TechnologiesThe Smart Grid technologies are organized Smart Grid in the following categories:

Advanced Metering Infrastructure (AMI)

Meter Data Management

Transmission and Substation

Distribution

Customer Enabling / Demand Response Technologies

Distributed Energy Resources/Distributed Generation Integration

Electric Vehicles

Communication Infrastructure

Workforce Efficiency

The take-away from each category is summarized below.

Advanced Metering InfrastructureTwo technologies constitute advanced metering infrastructure (AMI): metering and communication. Metering is a utility core competency, and utilities generally find that available automated meters are highly capable relative to their requirements. Most AMI communication technologies are available with meters from more than one meter manufacturer, so that metering requirements can be met with almost any communication method. Therefore, the AMI choice usually is driven by the AMI characteristics and the utility’s needs in communication.

The AMI communication technologies to revenue meters are categorized into two core options: wired and wireless. The wireless AMI technology providers have typically chosen either radio frequency (RF) mesh or hub-and-spoke (Point-to-point) architectures over either licensed and/or unlicensed frequencies, broadband wireless technologies for their solutions.

The existing (3G) cellular data networks are also being used as optional AMI infrastructure. The wired AMI technology providers use either the existing power line infrastructure or an existing broadband network as their communications medium.

The table below provides a number of advantages and disadvantages of various AMI network architectures.

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Advantages and Disadvantages of AMI Network Architectures

AMI Network Architectures

Advantages Disadvantages

RF Mesh

Typically lowest cost Data Rates from 19 kbps to 250 kbps Highly redundant architecture – every

endpoint will have multiple communi-cation paths/ options

Most regions conducive to mesh net-works

Largest number of viable vendors/competitors

Fairly mature in smart metering, early grid and DA functionalities

Flexibility architecture to changing dis-tribution system topography

Easy to deploy and implement in a phased approach

Medium level maintenance costs

Limited to wireless and fiber backhaul Medium to high functionality (AMI/DR

and some Smart Grid) Inherent uncertainty associated with

system latency and throughput Lower power devices require closer

proximity for successful communica-tion

Higher operating costs than PLC

Point-to-Point

Similar cost to RF Mesh depending on terrain

High coverage (high power) Dedicated link between the endpoint

and the hub thus has potential to de-liver relatively short and defined la-tency

Lowest cost to maintain Mature technology

Lower data rates, less than 25 kbps Potential throughput and capacity is-

sues for advanced functionality and crowded hubs

Inherent higher risk of losing connec-tion with endpoints due to having only one communication path

Medium to high functionality (AMI/DR and some Smart Grid)

Major design changes are costly

Powerline Car-rier (PLC)

Lower capital costs if substation com-munication with adequate bandwidth exists

Possible inadequate bandwidth to re-program meters and complete ad-vanced smart gird applications

Special consideration may be needed for urban areas

Higher capital cost than RF mesh

Broadband Powerline(BPL)

Potential higher communication speeds to 20 Mbps

High Functionality

Highest cost to deploy Limited wireless backhaul Limited number of large scale imple-

mentations Small number of viable players Still in early stages of grid and distri-

bution functions Limited ability to change design and

approach Highest cost to maintain Complex to implement Some terrain coverage not conducive

to BPL

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Meter Data ManagementWith the increasing implementation of AMI systems that can provide up-to-the minute interval data from service points, meter data management systems (MDMS) have also evolved within the last 10 years. Initially what started as a meter data storage and management solution that typically import data that are delivered by smart metering systems, then validate, cleanse, and process those before making it available for typically billing purposes, has evolved into more of a middleware platform that is able to integrate to existing enterprise applications, translate the vast quantities of raw meter data into systems and help to streamline utility business processes. MDMS used as a metering data aggregation platform can reduce integration complexity between multiple metering and enterprise systems, and interface with, including but not limited to, outage management system, workforce management system, asset management, and engineering systems. Furthermore, MDMS may provide reporting capabilities for load and demand forecasting, management reports, customer service metrics, and other operations and support the activities.

Today, MDMS is more seen as the backbone of AMI; that without it, some argue that fixed AMI metering systems are no more useful than mobile AMR systems. Also, MDMS makes possible most of the ancillary business benefits offered through frequent data acquisition such as outage management, network planning and operations, customer service applications, demand-side planning, etc.

Network/asset analysis requires consumption data to improve asset utilization and reliability while the increased focus on customer empowerment requires on-demand access to metering and event data for demand response and operations. And, with the increasing integration of renewable distributed generation, distributed energy resources and plug-in electric vehicles (PEVs), MDMS can also store metering data related to these assets, including load profiles and supply data characteristics, and make this information available to other external systems for improved grid operations.

Today, the MDMS market is served by a diverse group of vendors including: stand-alone energy consumption repository providers, data historian solution providers, customer information systems providers, load research and commodity management solution providers, retail and wholesale operations solution providers, and AMI solution providers

Volt/Var ManagementThe leading automation and Smart Grid technology in transmission applications is the phasor measurement unit (PMU), a device that records dynamic voltage and current simultaneously with other PMUs and provides the measurements to a processor that uses them to assess transmission conditions. Applications of these measurements include:

Validating transmission network models

Determining network stability margins

Based on the above margins, maximizing transmission line load while maintaining stability

Detecting islanding

Recording network anomalies and disturbances

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Substation technology has advanced at a steady pace for the last three decades. In the early years, SCADA became a standard method of first monitoring, and more recently controlling, resources in substations. SCADA systems included sensors on key substation points and a communication box that sent the sensor values back to a SCADA Master Station at the utility distribution operations center. Next, “Intelligent electronic devices” (IEDs) for substation management became available, and these became coordination points for gathering and handling substation sensor data before communicating them to the utility.

Application of substation automation is still rapidly evolving. The automation functions implemented by substation automation shall include but not limited to:

Volt/VAR management

Feeder voltage optimization

Transformer monitoring

Transformer load management

Switch / breaker monitoring and control

DistributionSmart Grid technologies applicable to distribution can be identified as:

Distribution Automation Devices/Technologies

o Remote Sensing Technologies and Operable Distribution Switches

o Automatic Reclosers

o Faulted Circuit Indicators

o Voltage Regulators

Distribution Management Systems/Applications

o Supervisory Control and Data Acquisition (SCADA)

o Distribution Management Systems (DMS)

o Automated Fault Location, Isolation and Service Restoration (FLISR)

o “Self-Healing” Networks

o Real-Time Load Flow (RTLF) Applications and Analysis

o Capacitor Switching

o Voltage Optimization

o Distributed Resource Optimization

Although there are many advantages of deploying distribution automation devices and technologies such as remote sensors, automatic reclosers, and voltage regulators, utilities face several challenges in their broad-scale implementation and integration as listed below:

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Standards and proprietary protocols: Proprietary protocols can limit distribution automation device/technology integration to the utility operational and back-office systems and thus reduce the overall benefits. Many such distribution automation devices and technologies today support open communication standards like DNP3, modbus, TCP/IP, RS 232, and IEC 61850. However, if an existing SCADA system uses legacy proprietary standards, there will be additional challenges and work required to interface with these devices and technologies.

Inadequate communications infrastructure: Another important challenge limiting distribution automation device and technology implementations is the need for adequate communication infrastructure to support these devices and other advanced technology requirements. This is a capital‐intensive requirement and often difficult to finance.

Cyber security: Cyber security is an essential component of Smart Grid and an important consideration for distribution automation device and technology implementations, as the integrity and availability of data are critical to the proper operation of both these devices and overall grid operations. Data collected from these devices used for operational decisions pose high security concerns as any mal-formed device data can seriously harm the system. Distribution automation devices working on IP-based systems can have inherent vulnerabilities. Proper security measures are required to minimize the probability of unauthorized access. The North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) cyber security standards are considered to be best practices. Although NERC CIP mainly applies to bulk power system security, POU’s adherence to CIP protocols can safeguard critical information infrastructure from being hacked or attacked. The National Institute of Standards and Technology (NIST) has also laid out several standards for high-level security requirements to protect the power grid from attacks, malicious code, cascading errors, and other threats.

Installation requirements: Installation requirements across vendor products can vary greatly, including the need for a system outage, cross arm replacements, or other special arrangements on poles and lines. POUs may be averse to system outages or pole/line adjustments for installation of distribution automation devices, and it might be challenging to find enough space on the electric pole to mount data concentrators for these devices. However, new products are emerging with less burdensome installation requirements. Additionally, these devices can be vulnerable to acts of nature such as lightning strikes.

Talking about the distribution management systems and applications, the first system that comes to mind is the SCADA system. SCADA systems of today typically monitor and control power system equipment down to the substations. There are very few SCADA installations beyond the substation fence. However, more and more utilities are looking closely at implementing SCADA beyond the substation as communication costs decrease and capabilities increase. High‐speed, high‐bandwidth, robust, and secure communication systems are now enabling the integration of remote sensors and other distribution automation equipment into SCADA systems.

Due to the increased focus on the development of the future energy delivery infrastructure, the "intelligent / Smart Grid", the utilities are becoming increasingly interested in solutions that can integrate grid information from various platforms such as SCADA, outage management systems

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(OMS), Advanced Meter Infrastructure (AMI)/Automatic Meter Reading (AMR) systems and more into a single operations and control application. This interest is pushing utilities to consider more advanced distribution management systems (DMS) for the planning, analysis, and operation of distribution system networks. Although the idea of a DMS is relatively new, it has evolved from the monitoring and control of SCADA system technologies that now are over 30 years mature. Common DMS functionality includes:

Remote monitoring and control of distribution equipment (switches, reclosers, regulators, capacitor banks, dynamic VAR compensators, and faulted circuit indicators)

Asset health monitoring (transformers in particular, but also circuit breakers and protection and control systems)

Network modeling and analysis (typically power flow, voltage, and faulted circuit analysis)

Outage management (including automatic outage notification, fault locating, restoration, and verification)

The most advanced DMS technologies include:

Dispatch and control of distributed resources (especially solar, emergency back-up generators, onsite customer generation, and in some cases micro wind and battery storage)

Voltage optimization (optimizing service entrance voltage to improve system efficiency)

Real time/dynamic equipment rating (especially circuits, substation getaways, and transformers)

Failure prediction (especially in substation and distribution transformers)

Fault analysis (especially the identification of momentary outages and high-impedance faults)

The ‘leading edge’ DMS functionalities include:

System optimization (especially voltage optimization and DER)

Active load management (including PCTs, but more interestingly the concept of actively managing customer loads to increase load factor)

Self-healing network operations

State estimation

Electric VehiclesThe technology for large scale use of plug-in electric vehicles (PEVs) is in its adolescence. Standards efforts are still under way to define standardized interfaces for the physical charging apparatus and the information transfer between the PEV and the serving utility.

According to a recent analysis forecast by Pike Research, global investments in the applications and hardware to enable smart EV charging will grow from $168.7 million in 2011 to $454.8 million in 2015. Also according to the same research:

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“Within the next several years, EV penetration will increase within utility service territories to the point that may exacerbate or extend peak demand and in some regions possibly put grid reliability at risk. Utilities will embrace managing EVs as significant loads that can be shifted as part of DR programs.”

Advanced metering infrastructures with two-way communication networks will become vital for utility control and load monitoring for PEV applications. Use of PEVs as a dispatchable resource is still a long way to go, but some utilities are looking at using them as a demand response resource through experimentation of rate structures encouraging customers for off-peak charging and the implementation of vehicle management systems to better and monitor and control PEV loads and storage capabilities.

In addition, other central PEV issues still remain unresolved, including how long it takes to recharge them, how to pay for electric delivery infrastructure needed to charge them, and how their materials will be recycled at end-of-life.

Communications InfrastructureFrom communications infrastructure perspective, the Smart Grid can be viewed as the merger of two networks: the electrical transmission & distribution (power) network, and the modern data communications network. While this concept is not new, the integration of more deterministic resources such as renewable power generation and electric vehicles; and dynamic demand that is more responsive to price and supply elasticity through customer empowerment and participation in demand response into the grid, requires the creation of an automated, distributed, and secure control system of immense scale, with reliable, flexible, and cost-effective communications networking as the fundamental enabling technology.

Typical Smart Grid communications architecture consists of four layers; enterprise network, wide-area network (WAN), local-/neighborhood- area network (L/NAN) and home-area network (HAN). Or, from applications perspective, communication infrastructures can be categorized as enterprise, substation automation, distribution automation, advanced metering and home area networking.

The WAN provides the robust, high-capacity and low-latency communication required to fully implement a Smart Grid and is often provided by fiber or a broadband wireless technology (WiMAX, for example) or a combination of both. The L/NAN is often provided by the AMI communications infrastructure and the HANs that are being deployed today are mostly wireless, but power line communication options are also emerging. The ZigBee Alliance and the HomePlug Powerline Alliance are collaborating to provide a multiple-medium solution for HANs where no single medium can provide adequate reliability.

The Smart Grid utilizes a broad mix of public and private, wired and wireless, licensed and unlicensed, and standard and proprietary communications technologies. Thus, private fiber, point-to-point microwave, and satellite for substation and/or field applications (e.g. vehicle tracking, workforce management, etc.) connectivity, with 3G cellular and unlicensed private RF mesh nodes can all be observed in the communication network of a single utility. On the other hand, regulatory forces, coupled with government funding, is driving unparalleled standards development efforts and cooperation among many stakeholders, challenging today’s proprietary systems with internet-inspired network equipment. These efforts are expected to drive even more change in the utility applications of communication infrastructures similar to the evolution of enterprise and telecommunications networks seen over the last 20 or 30 years into a single, integrated voice/video/data network.

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Although National Institute of Standards and Technology (NIST) has created a framework for evaluating and recommending specific standards for use in the Smart Grid, there is still more to be done.

Workforce EfficiencyAmong other things, workforce efficiency can be accomplished with software solutions that give call center operators, field force managers, and dispatch teams computer resources for receiving requests, organizing the utility’s responses, tracking the work as it progresses, and analyzing work performance. Mobile workforce management adds data communications, so that field staff can retrieve current information and receive updated instructions and assignments at any time. Technological advancements and declining hardware, software, and wireless service costs are making mobile workforce solutions more reachable to every utility regardless of its size.

Some of the emerging trends that are likely to drive the implementation of mobile computing solutions in the utility environment going forward are:

Changing utility workforce: As Baby Boomers retire, a younger generation of workers is coming into organizations, bringing with them different life experiences and different expectations. Per the APPA study, the GenX generation (born between 1964 and 1981) and the Millennials (born after 1982) frequently lack the knowledge of senior workers, but are technically savvier and expect to use technology to perform their job functions. Utilities failing to provide enabling technology tools are more likely to find it more difficult to recruit and retain new generation workers and may see their operational efficiencies fall. In addition, the same technology tools, if implemented, can act as a repository to ensure that knowledge from senior workers is not lost, but instead is stored and made available to the entire field workforce.

Evolution from Client/Server-Based to Web-Based Applications: More and more utilities are providing access to corporate applications, including mobile workforce computing systems through the use of web-based services/architectures, which are often viewed as more easily supportable architectures by utility information technology departments. Mobile workforce solutions with web-based services/architectures provide an easier and more efficient platform for performing upgrades on the mobile devices that are being used by the utility's field crews. Upgrades can be done remotely using wireless technology over the network. Utilities that already have robust mobile workforce computing solutions built on client/server architecture aren't likely to migrate to web-based systems, but the web-based option may appeal to utilities implementing their first mobile workforce computing solutions or to utilities replacing an older mainframe or client/server system that has been left unsupported.

Expanded Choices in Mobile Hardware: Web-based and thin client architectures are driving introduction of even smaller, lighter-weight, less expensive, sub-notebook computing devices such as PDAs, handheld computers, tablets, and netbooks out into the field. Ruggedized and semi-ruggedized laptops will continue to be a better choice for those highly skilled mobile workers that are conducting technical work. With a larger screen and keyboard than handheld computers, laptops have a proven track record in the field. These laptops are especially useful for performing certain tasks where there is a fair amount of information to be presented to the mobile workers and a fair amount of information to be collected at the site.

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Decreasing Technology Costs: The utility IT industry has observed huge increases in hardware and software functionality at reduced costs within the last 10 years. Many technologies such as mobile workforce solutions that were initially developed for large utilities are now available for all sizes of utilities due to the continuing trend in the decreasing cost of the mobile workforce computing solutions, from mobile computing devices to the workforce management solutions and the wireless network that interconnects them. The emergence of smaller and “thinner” computing devices drove the cost of field hardware to decline as well. The increasing number of solution providers and utilities that adopted MultiSpeak and other integration architectures such as service oriented architectures (SOA) and web-services also observed large reductions in the cost of system integrations, implementations, and maintenance.

Mobile Virtual Private Network (VPN) Solutions: Due to the trends in core mobile workforce computing systems built on web-based and thin client architectures with thin, ultra-light mobile devices, deploying solutions that stand up to the most demanding communication and connectivity challenges are becoming more imperative. In the mobile environments where bandwidth is limited, connections are unstable, roaming is common, and security is essential, third-generation mobile VPN solutions offer undeniable value. Use of mobile VPN solutions is becoming more and more common in the utility environment due to the communications challenges often observed by utilities serving in expansive service territories with remote locations.

Some of the applications of mobile workforce management / computing solutions include, but are not limited to:

Automated Design and Staking Automated Field Force Tracking Automated Outage/Service Ticketing Mobile Damage Assessment & Distribution System Inspection Mobile Asset and Inventory Management Automated Meter Services Automated Right-of-Way (ROW) Maintenance

Section 4 AMI Use CasesThe seven use cases summarized below illustrate ways in which Smart Grid is widely expected to benefit utilities and energy users in California. Use cases serve as a reference against which present and future technology capabilities are compared to define the gap between now and the possible capabilities of a Smart Grid project.

1. Advanced Metering – Smart Meters Enhance Utility-Customer Interaction

Customer load interaction with the grid provides opportunities for the customer to both manage their energy consumption and contribute to grid reliability and efficiency. Advanced metering systems empower customers by providing more visibility to their energy on a near real-time basis. Customers can manage loads to reduce energy and demand charges, for example, scheduling loads to run “off peak”.

Advanced metering infrastructure (AMI) networks not only provide the platform for enhanced customer service options such as remote service switching and pre-pay

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service but also enable the utility to enact new rate structures such as Time-of-Use pricing. Additionally the AMI system can play a critical role in utility’s outage management process by providing real-time outage notification data from the customer/metering locations.

2. Demand Response – Active Load Management Reduces Peak Demand

Customers actively manage their energy consumption in response to information about their energy usage, rate and market (events) information. Customer devices can either autonomously respond to rate/event information initiated by the utility or can be directly controlled by the utility. More complex dynamic rate structures can be established requiring customer devices to be equipped with automated systems that can autonomously react to utility price signals in order to fully capture the customer driven load response. Additionally, rate structures such as Critical Peak Pricing and Time-of-Use can assist in providing demand response programs.

3. Distribution Automation –Integrated Voltage and Feeder Management Improves Power Quality and Delivery Efficiency and Customer Service Reliability

The Automated Feeder Management system dynamically collects data from distribution feeders and, when a fault occurs, automatically isolates the fault and restores electric service by switching un-faulted line segments to adjacent feeders with free capacity. The unique element of this concept is the real-time identification and transfer of available capacity from adjacent feeders. This is capacity that normally is not utilized with conventional (manual) load transfer schemes.

Volt‐VAR Control (VVC) controls capacitor banks, load tap changers, and voltage regulators to regulate distribution voltage and minimize reactive power (VAR) flows through distribution lines. Voltage control and VAR control can be operated independently, but optimal benefits are achieved when they are integrated. VVC solutions generally incorporate a centralized voltage optimization algorithm referred as Conservation Voltage Regulation (CVR), whose objective is to either reduce peak load, or minimize system losses by reducing energy consumption.

Near-real‐time current and voltage data are acquired via a supervisory control and data acquisition (SCADA) system, advanced metering infrastructure (AMI) system, or other remote sensors. A state estimator and load flow analysis programs of a distribution management system (DMS) determine the voltage profile for each circuit, and switch capacitor banks and/or feeder sections to optimize the circuits. Once the voltage profile is optimized, substation regulators reduce the feeder voltage to near the practical minimum, reducing losses and saving energy.

4. Electric Vehicle Charging - Grid Monitoring and Control Enables Wide-scale Electric Vehicle Charging

When connected for charging, a plug-in electric vehicle (PEV) links with the utility via the home area network (HAN), the meter, and the AMI network. The PEV displays and the in-home display (IHD) show the customer the battery status and energy cost information, and the customer chooses a charging schedule and fee that meet the customer’s needs. The PEV battery is charged and the energy transferred during charging is measured by the utility and provided to the vehicle and the customer.

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If the customer chooses to participate in utility demand response and/or emergency load shed, the PEV may be restricted from charging until an emergency event concludes. The PEV may even discharge to give the grid power for a time. The PEV returns to the user prescribed charging scenario after a discharging event is complete or expired.

5. Asset Management - Asset Monitoring Enables Proactive System Planning & Maintenance

System assessment and planning require distribution load modeling, energy loss calculations, outage tracking and avoidance, and protective system analysis using digital fault data. Smart Grid enables more complete data acquisition and more accurate planning. Engineers can better predict load growth by applying data collected through distribution monitoring systems to a complete network model. System losses may be reduced by identifying load imbalances and redistributing load. Logged data identify feeders with excessive reactive power (VAR) flow as candidates for capacitor bank installations, reducing losses and extending equipment life. Correct operation of protective devices is verified by digital fault data acquired from microprocessor-based relays and recloser controls. Preventative maintenance may be driven by historical recloser and breaker operation trends, instead of by static timelines.

6. Substation Automation - Integrated Protection and Control Improves Service Reliability

The settings for microprocessor‐based relays, recloser controls, and protective elements down the line are normally changed only occasionally, and only after engineering analysis to determine proper protective coordination. However, in an advanced Smart Grid implementation, these settings can be dynamically programmed and controlled separately and in coordination, increasing system reliability and stability. This can best be accomplished using the state estimator and real time load flow applications of a Distribution Management System (DMS) to control distribution parameters and microprocessor-based relays, reclosers, and other protective elements to minimize outages and damage to critical distribution assets.

7. Distributed Energy Resources - Integrated Distributed Generation & Storage Support Grid

Diverse energy sources are located throughout the distribution system, including small wind and rooftop solar systems, the energy output of which is highly variable. Energy storage devices connected throughout the distribution system include flywheels, batteries, and thermal devices. In addition, the utility may have a direct load control program controlling such customer loads as air conditioning, pool pumps, and water heaters.

When the output of the distributed supplies drops in response to short term (i.e., less than five minutes) changes in wind and cloud conditions, the storage systems sustain the electric system for short periods while the utility manages bulk supply to serve system load on a longer (i.e. 15 minutes and longer) time scale. The utility’s direct load control also contributes by shedding load for periods up to a few hours, as needed, to balance load and supply.

The above use cases are examples of a far broader array of possible Smart Grid operations and benefits that is expected to emerge in the future. No one can predict with clarity all the ways

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society will leverage Smart Grid technologies. (As just one example, cellular phones that capture and send pictures are now widely used by consumers and workers alike. When cellular telephony was introduced, no one predicted that successor cell phones would function as communicating cameras that would become essential tools to many field workers.) Many other use cases are possible. Though other applications may eventually produce higher value than these examples, these use cases are particularly relevant because they embody the future value that we can recognize now, and that value is the motivation to pursue Smart Grid.

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Appendix A: List of AcronymsThe following is a list of acronyms found in the report.

AMI Advanced Metering Infrastructure

AMR Advanced Meter Reading

AMS Asset Management System

ANSI American National Standard Institute

API Application Programming Interface

Auto-DR Automatic Demand Response

B2B Business to Business

BPL Broadband over Power Line

C&I Commercial & Industrial

CA Corrective Action

CIO Chief Information Officer

CIP Critical Infrastructure Protection

CIS Customer Information Systems

CSR Customer Service Representative

CO2 Carbon Dioxide

CPP Critical Peak Pricing

CUST Customer

CVR Conservation Voltage Regulation

DER Distributed Energy Resources

DG Distributed Generation

DHS Department of Homeland Security

DLC Direct Load Control

DMS Distribution Management System

DR Demand Response

ESB Enterprise Service Bus

FMA Fault Mode Analysis

FTE Full Time Equivalent

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FTP File Transfer Protocol

GAL Gallon

GIS Geographical Information System

HAN Home Area Network

IHD In-home Display

IOU Investor Owned Utilities

IP Internet Protocol

ITIL Information Technology Infrastructure Library

IRR Internal Rate of Return

IS Information Systems

IT Information Technology

IVR Interactive Voice Response

KBD Key Business Deliverable

kV kilovolt

kW kilowatt

kWh kilowatt-hour

kVA Kilo Volt-Ampere

LAN Local Area Network

MDMS Meter Data Management System

MHz Mega Hertz

MVA Megavolt Ampere

MW Megawatt

NIST National Institute of Standards and Technology

NPV Net Present Value

O&M Operations & Maintenance

OAM Outage Analysis Module

OMS Outage Management System

OS Operating System

PCTs Programmable Communicating Thermostats

PEV Plug-in Electric Vehicle

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PID Probability Impact Diagram

PLC Power Line Carrier

POTS Plain Old Telephone Service

POU Publicly Owned Utilities or customer-owned public utilities

PTP Point-to-Point

PV Present Value

RAC Real Application Cluster

RCA Root Cause Analysis

R&D Research & Development

RF Radio Frequency

RFP Request for Proposal

RMF Risk Management Framework

ROI Return on Investment

SCADA Supervisory Control and Data Acquisition

sFTP Secure File Transfer Protocol

SLO Service Level Objectives

SOA Service-Oriented Architecture

T&D Transmission and Distribution

TCP/IP Transmission Control Protocol/Internet Protocol

TDMA Time Division Multiple Access

TECH Technology

TOU Time-of-Use

VEE Validation, Estimation, and Editing

VPN Virtual Private Network

VVO Volt Var Optimization

WAM Work and Asset Management

WAN Wide Area Network

WBS Work Breakdown Structure

WiMAX Worldwide Interoperability for Microwave Access

WLAN Wireless Local Area Network

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WMS Work Management System

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