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The Study of Distribution Asset Deferral Using Distributed Energy Resources

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Alectra Utilities (Background) Alectra Utilities was formed on

January 31st 2017 following the merger of Enersource, Horizon Utilities and PowerStream. Hydro One Brampton was acquired on February 28th 2017

Alectra Utilities serves approximately one million homes and businesses across an 1,800 square kilometer service territory comprising 15 communities including Alliston, Aurora, Barrie, Beeton, Brampton, Bradford, Hamilton, Markham, Mississauga, Penetanguishene, Richmond Hill, St. Catharine’s, Thornton, Tottenham and Vaughan

Alectra Utilities is divided into Alectra West, Alectra Central, and Alectra East service areas

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Enabling Customer choices.

Flexibility in operation, size and expandability.

Reliability and Power Quality

Challenges in locating Transmission and Distribution facilities in dense urban areas.

Why Distributed Resources?

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Learning Objectives

To identify distribution network constraints

To model and analyze distributed energy resources

To evaluate mass penetration of distributed assets on the feeder

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Planning Case Studies

Planning case studies of non-traditional options for deferring capital investments.

Case Study #1: Municipal Substation (10 MVA)

Grid scale battery storage

Case Study #2: Transformer Station (170 MVA DESN)

Aggregation of mass distributed resources creating a Virtual Power Plant (Combination of Solar and Storage)

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Study Process

1) Specify supply and contingency capacity requirements.

2) Model alternative options for each respective solution.

3) Evaluate asset deferral options based on criteria:

Performance

Distribution system benefits

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Study Questions

The technical feasibility assessment attempted to answer the following questions:

1. Can the alternative solutions identified by accommodated on Alectra’s distribution system?

2. Are there additional upgrades required?

3. What are the limiting factors from a technical system perspective?

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Case Study #1: Deferring Municipal Substation Capital Investment

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Location - New Tecumseth The Town of New Tecumseth in Simcoe County encompasses Alliston, Beeton, and Tottenham

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Location - Tottenham Alectra has two existing municipal

substations in Tottenham There are 2,600 existing homes in

Tottenham

New Developments:Three new residential developments totaling 1,300 homes: Development #1 (450 homes) Development #2 (500 homes) Development #3 (335 homes)

Total - 3.2 MVA of new residential load by 2020

Existing and New Growth in Tottenham

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System Constraints

MS834: 1985 vintage 10 MVA transformer

MS835: 1975 vintage 6 MVA transformer

MS834 has a maximum contingency rating of 15.2 MVA based on a 4-hour contingency loading of 152% assuming a 0.5% loss of transformer life

MS835 has a maximum contingency rating of only 9.1 MVA based on a 4-hour contingency loading of 152% assuming a 0.5% loss of transformer life.

Upgrade MS835 transformer.

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Tottenham Constraints

Insufficient capacity between MS834 and MS835 during contingency conditions following residential developments

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Tottenham Constraints

Upon completion of the three residential development there will be 1.3 MVA of load at risk following a loss of MS834

A sustained outage at MS834 during the summer peak would resulting in customer load shedding

Approximately 600 customers would be affected by rolling outages; 15% of Tottenham residential customers

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Tottenham Options

Proposed options to meet Tottenham growth demand and satisfy contingency backup conditions:

1) Construct a new municipal substation

2) Self-contained mobile substation for contingency conditions

3) Installation of grid scale battery storage system

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Study Purpose

Study the impact of installing a battery storage system on the 8.32 kV network

Determine the battery system rated power to ensure:

1) MS835 is able to operate within ONAN rating during normal conditions

2) MS835 is able to operate within maximum contingency rating during N-1 condition with loss of MS834

Determine spare capacity of battery storage system to perform secondary services

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Study Approach

Smarter Grid Solutions (SGS) was engaged to perform a detailed battery system analysis.

Alectra compiled historical loading information and load forecast data for Tottenham including residential developments.

Alectra established base case scenario details for construction of a new 10 MVA municipal substation.

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Study Methodology

Alectra developed CYME models for base case scenario:

1) Simulation of normal conditions with two municipal substations

2) Simulation of N-1 contingency condition with two municipal substations

3) Simulation of normal and contingency conditions with three municipal substations

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Study Methodology

Alectra provided SGS with historical loading information and load forecast data for Tottenham including residential developments

SGS developed a Python tool to interface with CYME and perform a time series load flow analysis

Battery control technology was modelled with the assumption of Active Network Management (ANM) technology enabling battery system control in real-time to facilitate both normal operation and contingency conditions

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Base Case – New Municipal Substation

A new 10 MVA municipal substation east of Development #1 and Development #2

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Battery Storage System Analysis

Battery rated output power (MW) and energy capacity (MWh) was calculated on 8.32 kV network

CYME load flow simulation was performed for two scenario’s:

1) Peak loading at MS835 (normal network operation)

2) MS834 outage (contingency network operation)

Combination of MS835 overload support and MS834 outage support was calculated to determine peak discharge capacity

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Battery Storage Normal Network Operation

1) Normal network operation

CYME simulation was used to determine amount of storage capacity required to remove MS835 overloads above 6 MVA

Overloads were removed by discharging battery to supply the load Battery was charged during MS835 loading below 6 MVA

Consecutive time series analysis over each hour of the year was performed to develop a utilization profile describing the energy supplied and received by the battery storage

The maximum amount of discharge capacity within the utilization profile determines the required battery capacity to support MS835 overloads

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Battery Storage Contingency Operation

2) Contingency network operation

CYME simulation was used to determine amount of storage capacity required to support load with loss of MS834

MS834 demand supported by MS835 and battery storage system; storage discharged to keep MS835 within maximum contingency rating of 9.1 MVA

Outage lengths for analysis were 8 hours, 24 hours, and 48 hours

Storage permitted to charge during outage when below MS835 maximum contingency rating

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Battery Storage Design Capacity

Storage capacity was calculated based on the energy profile required to remove MS835 overloads while maintaining capacity to support an outage at MS834

Energy profiles were simulated for each respective 8 hour, 24 hour and 48 hour outage duration at MS834

It was assumed that the battery was brought back to sufficient charge before being put back into service following an outage

Annual profile was multiplied by a factor to ensure resultant peak load aligned with historical data and load forecast

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Battery Storage Analysis Results

Battery system rating requirements tightly correlated with the peak demand periods

MS834 outage scenario dominated battery capacity requirements

The capacity required to supply all intact network peak shaving needs while maintaining ability to supply 100% of any individual 24 hour outage at MS834 was determined to be 72.1 MWh by 2023

72.1 MWh battery storage would provide 8 year deferment

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Battery Storage vs. Deferment Years

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Battery Storage Analysis Results

Significant excess capacity exists outside of the peak design hour

Battery capacity is significantly underutilized when applied only for removal of MS835 over load and contingency capacity for MS834; should consider secondary services for batteries such as peak shaving, arbitrage services, balancing, etc.

With the Modular nature of battery storage it is possible to install capacity in phases

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Case Study #2: Deferring Transformer Station Capital Investment

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Power House Size

The Power.House system consists of three hardware components:• Solar Integrated System (SIS) comprised of an li-on storage system and

all associated power electronics provided by Sunverge.• Rooftop solar panels• Bi-directional meter

For analytical simplicity, two system sizes were selected for the analysis. The sizes were selected to represent the average system size for each type of home.

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The general approach adopted was conducted by undertaking the following three steps:

• Select three representative feeders in the York Region; • Evaluate the three feeders against the constraints of thermal,

short circuit, reverse power flow and voltage; and • Determine the maximum distributed generation (DG) that can

be connected assuming each constraint.• The analysis assumed that the assets were uniformly spread

across the feeder.

Constraints and Assessment Methodology

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• 27.6kV feeder egress cables are rated for 413A to 600A, or 20 to 30 MVA. The majority of feeder trunk conductors are 556 MCM AL rated for 777A. Some of the trunk conductors are 336 MCM AL rated for 564A, much higher than the egress cable ratings.

• Each feeder has numerous 1/0 AL underground loops that supply residential and commercial customers. The 1/0 AL cables are rated for 180A or 9MVA when installed in ducts. Since there are many 1/0 AL loops, the combined ratings are much higher than the trunk feeder rating of 20 MVA.

• Each feeder has many distribution transformers connected, and the transformer capacity is much higher than feeder capacity of 20MVA.

• There is no concern on thermal limit.

Thermal Constraints

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• The fault level at the first customer on each feeder was calculated.

• Maximum DG capacity was calculated to ensure fault levels not exceeding equipment ratings for each feeder.

• When DGs are connected to multiple feeders from a transformer station, they will cause short circuit levels increase at the station buses and on all feeder breakers, but should not exceed equipment ratings in the station.

Short Circuit Constraints

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• Reverse power flow on a distribution system upstream of a DG system may occur during times of light load and high DG generation. Reverse flow can cause problems for the protection system and for voltage regulators.

• To avoid power going to the transformer station (reverse load flow), the maximum DG that can be installed on a feeder is the minimum load on a feeder. The minimum load on a feeder is assumed to be 30% of the peak load. The minimum peak usually occurs in mid night to early morning on weekends in the spring or fall.

Reverse Power Flow Constraints

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• Voltage was studied for four scenarios:- Maximum load, maximum DG (Case 1-1)- Maximum load, minimum DG (Case 1-2)- Minimum load, maximum DG (Case 1-3)- Minimum load, minimum DG (Case 1-4)

Voltage Related Constraints

Case Overload Count

Over-Voltage Count

Under-Voltage Count

Worst Loading (%)

Worst Loading Location

Highest Voltage (%)

Highest Voltage Location

Lowest Voltage (%)

Lowest Voltage Location

kW Losses

Case 1 -1 0 0 0 6.8 27M1 103.3 27M1 96.7 28T136_T133 71-XFO 316.6

Case 1 -2 0 0 0 6.8 27M1 103.3 27M1 96.7 28T136_T133 71-XFO

316.6

Case 1 -3 0 0 0 100 PRIOH_19120 8-2 103.6 21T110_T137

66-XFO 102.6 28T136_T133 71-XFO 103.4

Case 1 -4 0 0 0 2.5 27M1 103.3 27M1 101.5 28T136_T133 71-XFO

125.5

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System Constrains Results

The results indicate that the maximum DG connected to each feeder should not exceed 3.5MW. This constrain is due to the reverse load flow restrictions at minimum load.

It was determined that the maximum DG that could be connected to each station is between 20 to 43MW depending on feeder loading and number of feeders on a station.

The maximum DG that can be connected in Alectra South York Region is 251MW.

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Inventory of Homes in the York Region

• System size: 5 kW (single), 3 kW (semi/row)• Number of homes (South York Region)

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Study Results• The feasibility study found that just over 30,000 homes can potentially

adopt Power.House under the current program structure by 2031. This translates to 140 MW of installed solar and 330 MW of one-hour storage. From the peak capacity planning perspective it would yield approximately 140 MW of local dependable capacity.

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Value Analysis

Alectra

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Conclusion

Power. House can feasibly reach a meaningful uptake in the York Region within the study period (30,000 units and 140MW)

Due to the high growth in the South York Region and the project adoption rates the Power.house could defer 2 years of local transmission/distribution investment in the late 2020 timeframe.

There are several other value streams that the Power.House could participate in addition to the deferral of traditional wires solution.

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For more information http://www.ieso.ca/en/corporate-ieso/media/news-releases/2017/04/alectra-study-

identifies-residential-solar-storage-potential https://www.powerstream.ca/attachments/POWER_HOUSE_Feasibility_Study.pdf