technical advisory panel - hawaiian electric …...2018/09/26 · technical advisory panel...

Integrated Grid Planning Technical Advisory Panel

September 25-26, 2018

Agenda – September 26, 2016 8:30-9:45a Transmission Planning Review 9:45-10:00a Break 10:00-11:30a Forecasts, Assumptions & Sensitivities 11:30-12:30p Lunch 12:30-2:30 p IGP Workplan Discussion 2:30-2:45p Break 2:45-4:00p TAP Member Topics 4:00-4:30p Summary of Key Takeaways & Action Items

Transmission Planning Review

Date Modified: 9/26/2018

Integrated Grid Planning Transmission Planning Process

Technical Advisory Panel Kick-off Meeting

September 26, 2018


Transmission Planning

Determine system requirements to support resource plans. Determine infrastructure to enable load growth. Develop non-transmission alternatives. Evaluate system performance to major contingency events. Justify large capital projects.

2


Transmission Planning Analysis Steady-State – Violation of voltage and thermal overloads – N-1-1 for HECO, N-1* for HELCO and MECO

Stability/Contingency Analysis – Loss of generation

− Frequency response – Faults (normal and delayed clearing)

− Transient voltage − Rotor angle stability

Transmission Planning Criteria and HI-TPL-001 determines contingencies and performance requirements

3


Public Utilities Commission Directives Must-Run Generation Reduction Plan Evaluate Prodsim data from the low-cost resource plan Address the probability of occurrences Define system requirements in technology-neutral terms (e.g. kinetic energy, FFR, PFR, short-circuit MVA)

4


Screening Analysis

Use production simulation hourly output Single-bus condensed system PSSE model Trip largest generator in every hour of the year Determine frequency nadir for every hour of the year

5


Screening Analysis Results 6


Typical and Boundary Hours 7

Pmax PminInertia

HUnit MVA

Unit K.E. Pgen

up reg (spin)

down reg Pgen

up reg (spin)

down reg

HPOWER-1 46.0 25.0 2.78 75.0 209 46.0 0.0 21.0 35.0 11.0 10.0HPOWER-2 22.5 10.0 3.41 42.1 144 17.7 4.8 7.7AES 189.0 63.0 2.57 239.0 615 189.0 0.0 126.0 189.0 0.0 126.0Kalaeloa CT-1 84.0 29.0 4.96 119.2 591 55.8 28.2 26.8 84.0 0.0 55.0Kalaeloa ST 40.0 10.0 4.70 61.1 287 13.3 6.7 3.3 40.0 0.0 30.0Kalaeloa CT-2 84.0 29.0 4.96 119.2 591 84.0 0.0 55.0Kahe 1 82.2 23.8 25.0 5.0 4.44 96.0 426 25.0 57.2 1.2 25.0 57.2 1.2Kahe 2 82.2 23.8 25.0 5.0 4.44 96.0 426 25.0 57.2 1.2 41.6 40.6 17.8Kahe 3 86.2 23.7 25.0 5.0 3.54 101.0 357 31.8 54.4 8.1 38.0 48.2 14.3Kahe 4 85.3 23.6 25.0 5.0 3.54 101.0 357 25.0 60.3 1.4Kahe 5 134.6 21.0 4.36 158.8 692Kahe 6 133.8 40.0 4.36 158.8 692Waiau 3 47.0 23.7 4.51 57.5 259Waiau 4 46.5 23.5 4.51 57.5 259Waiau 5 54.5 23.5 4.07 64.0 261 25.0 29.5 1.5Waiau 6 53.7 23.8 4.00 64.0 256Waiau 7 83.3 23.8 25.0 5.0 4.44 96.0 426 25.6 57.7 1.8Waiau 8 86.2 24.1 25.0 5.0 4.44 96.0 426Waiau 9 52.9 5.9 7.84 57.0 447Waiau 10 49.9 5.9 7.84 57.0 447CIP1 112.2 41.2 4.72 162.0 765Schofield 1 8.0 2.0 0.99 10.9 11Schofield 2 8.0 2.0 0.99 10.9 11Schofield 3 8.0 2.0 0.99 10.9 11Schofield 4 8.0 2.0 0.99 10.9 11Schofield 5 8.0 2.0 0.99 10.9 11Schofield 6 8.0 2.0 0.99 10.9 11Total Wind 133 0 34 49 -Kahuku 30 0 6 23 -Kawailoa 69 0 14 16 -Na Pua Makani 24 0 12 7 -CBRE Wind 10 0 2 2DG-PV 655 0 437 0Station PV 183 0 160 0

4098 35021111 585

UnitUnit Ratings

DR - AES TripTypical

Thu 3/21/19 Hour 14

DR - AES TripBoundary

Tue 3/19/19 Hour 3

Total Kinetic EnergyTotal Load


Loss of Generation 8


Fault Analysis 9


New Transmission Planning Landscape The Companies do not control the location of generation One-for-one displacement of synchronous generation with inverter generation (loss of fundamental properties to operate an AC system) Weak-grid stability issues

13


Improve Analytical Capabilities Consolidated system model (e.g., Power Factory) – Fault induced voltage recovery – Inertialess system

Improve PSSE models – Composite load model – Governor/exciter models – Protection

PSCAD analysis – Short circuit ratio – Harmonics

14

Forecasts, Assumptions & Sensitivities

Development of the Sales And Peak Forecast

Forecasting Department

September 26, 2018

2

The forecast is developed in layers

Underlying Forecast

Energy Efficiency

Distributed Energy Resources

Electrification of Transportation

Sales Forecast at Customer

Level Forecast will be further modified by DR and controllable DG

3

Key Assumptions Economic Drivers Weather Electricity Price Energy Efficiency – Near term sales reductions from the Public Benefit Fund

Administrator and a percentage from the EEPS potential study’s codes and standards

– Assume achieve EEPS target of 30% reduction of sales in 2030 and targeting 40-45% by 2045

4

Key Assumptions Distributed Energy Resources – Near term: planned projects and build-out of existing

programs – Longer term based on economic uptake model – Lower storage costs yields higher uptake of PV + storage

Electrification of Transportation – Light duty electric vehicles – Electric buses – Mass transit

Adjustment to commercial sales assumption from large projects

*LDV = Light Duty Vehicle

5

Sales Forecast With Layers

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048

YOY

% C

hang

e

GW

h Sa

les

Recd YOY Chg Fcst YOY Chg Recd SalesFcst No Layers Fcst With DER Fcst With DER & EEFcst With DER, EE & EV Fcst With DER, EE, EV & e-Bus

6

Peak Forecast With Layers

-6%

-3%

0%

3%

6%

9%

12%

15%

18%

21%

0

200

400

600

800

1000

1200

1400

1600

1800

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048

YOY

% C

hang

e

Net

MW

Recd YOY Chg Fcst YOY Chg Net MW PeaksFcst No Layers Fcst w/ battery Fcst w/ battery & DERFcst w/ battery, DER & EE Fcst w/ battery, DER, EE & EV Fcst w/ battery, DER, EE, EV & e-Bus

7

Future Forecast Work Development of hourly load forecasts Assisting Distribution Planning to develop locational load forecasts for specific circuits Updated energy efficiency potential study is forthcoming Shaping of energy efficiency

7

Stochastic Analyses in PLEXOS

Corporate Energy Planning Department

September 26, 2018

24

Production Simulation in PLEXOS PLEXOS determines the least-cost dispatch of electric generation to serve load based on a set of inputs and constraints

– System security, must run constraints – Regulating reserve requirements – Curtailment order for variable renewables – Generator characteristics (e.g. heat rate, fuel cost,

minimum rating, maximum rating, ramp rate)

36 Modeling Assumptions Generator

Min/Max Power Output Heat Rate Rating Factor Ramp Rate Min Up/Down time Start time/cost Scheduled and Random Outages (Maint/Forced) Operational Cost (Variable/Fixed/Running) Regulation capability Fuel consumption Operating Constraints

– Operating hours – Must-run – Curtailment Order – Energy/Capacity Factor Limit

Expansion Inputs – Build Cost – WACC – Technical Life – Economic Life

Fuel Fuel types Fuel prices Fuel constraints

– Min/Max supply Battery energy storage, pumped storage hydro

Charge/Discharge Power Storage Capacity Round-trip efficiency O&M Cost Operating Constraints

– Tied to renewable resource – Cycles/year – Operating hours – Degradation – Max/Min State-of-Charge

Regulating reserve and/or contingency reserve

25

Modeling Assumptions

PLEXOS

Storage Characteristics

Fuel Forecast

Regulating Reserve

Requirement

Renewable Profiles

Generator Characteristics

Operational Constraints

Load Forecast

Candidates for stochastic analysis

26

Types of Modeling Analyses

Deterministic Input A → Solution A

Monte Carlo Input A → Solution A

Input B → Solution B

Input C → Solution C

Stochastic Optimization Input A Input B Solution ABC Input C

27

Stochastic Input Development

• Use the built in ARIMA or GARCH models within PLEXOS to create additional forecasts

Endogenous Inputs

• Use historical data to create new profiles by extending each year of data into a repeating 30 year profile

• Use the historical data to train an external Python/R model to develop forecasts

Exogenous Inputs

28

Stochastic Input Development Wind • Created hourly profiles from historical data for exogenous PLEXOS sampling

PV • Used forecasting packages in Python and R to develop the parameters for an

ARIMA model • Passed the applicable ARIMA parameters to the built in ARIMA model in

PLEXOS for endogenous sampling • Used the full range of ARIMA parameters to develop forecasts for

exogenous samples externally

Fuel • Utilized reference, low and high fuel price projections from the PSIP for

exogenous sampling • Used the built in GARCH model in PLEXOS for endogenous samples

Load • Currently exploring different load profile inputs into PLEXOS as layers to the

underlying load forecast

29

Stochastic Input Development Difference in Fuel Samples using Exogenous and Endogenous Methods

Exogenous samples Endogenous samples

30

Stochastic Input Development

31

Stochastic Output Example

We can compare the deterministic result against the stochastic mean along with a range of risk using standard deviation.

$65,886

$80,511

$0

$100,000

1

Stochastic Mean Deterministic NPV + 2STD NPV- 2STD

Stochastic distribution of results

NPV, 2020$

32

Regulating Reserve Rules

EPS Formula

• 1 MW Regulating Reserve for 1 MW Available Generation up to: • 50% wind capacity • 20% of DGPV capacity • 60% of grid-scale PV

capacity • Reserves are additive for

each resource type

GE HNEI Regulation Formula

• Approximately 1 MW Regulating Reserve for 1 MW Available Generation up to: • 18% of wind and PV

capacity during the day

• 23% of wind capacity during the night

33

API-Excel Interface

System Cost Sheets

Results Summary

PLEXOS Solution Files

Stochastic Run 1

Stochastic Run 2

Stochastic Run 3

Stochastic Output Processing

34

Next Steps

Where do we want to go with stochastics? – Forecast validation – Define process for analyzing stochastic results – Investigate other ways of creating exogenous

forecasts that can be read into PLEXOS – Research stochastic methods used by other

utilities

IGP Workplan Discussion

Docket No. 2018-0165 Integrated Grid Planning Hawaiian Electric Companies shall convene a public workshop by October 1, 2018 (Order No. 35569, page 25) – Scheduled for September 25, 2018

Public comments may be filed until October 15, 2018 (Order No. 35569, pages 25-26) On or before December 14, 2018, the Hawaiian Electric Companies shall file an IGP Workplan providing additional details about the activities, timelines, and outcomes of the major components of the IGP process. (Order No. 35569, pages 27-28)

https://www.hawaiianelectric.com/Documents/about_us/investing_in_the_future/dkt_2018_0165_20180712_PUC_order_35569_opening_dkt.pdf



Docket No. 2018-0165 Integrated Grid Planning The IGP Workplan must include additional detail and description of the following: 1. the proposed Working Groups, including their specific

objectives, composition, expected deliverables, and timelines for those deliverables;

2. a specific proposal for how forecasting assumptions, system data, modeling inputs, studies, analyses, meeting summaries, and other data will be shared with the commission and stakeholders throughout the IGP process;

Docket No. 2018-0165 Integrated Grid Planning The IGP Workplan must include additional detail and description of the following: (continued)

3. the process and timeline for defining and quantifying grid needs (including generation, transmission, and distribution);

4. the process and timeline for sourcing and procuring solutions to meet identified grid needs;

5. the process and timeline for analysis for optimization of the grid solutions identified in the procurement phase;

Docket No. 2018-0165 Integrated Grid Planning The IGP Workplan must include additional detail and description of the following: (continued)

6. opportunities for midstream evaluation and potential course correction for the IGP process; and

7. when and how independent facilitation will assist the IGP process.

Request Your Thought Partnership

Several aspects of the IGP have not been done before in the industry Request your thought partnership as we develop the workplan over next 2+ months Involves reviewing draft material, 1:1 calls, webinars

Suggested Development Areas • Optimizing resource,

transmission, and distribution solutions

• Tbd • Tbd • Tbd • Tbd

TAP Member Topics

Summary of Key Takeaways & Action Items

Mahalo!