DWG Meeting August 9, 2016 TEPPC 2026 Common Case – SIND and WIND Geographic Profiles Jamie Austin, PacifiCorp DWG Chair

Overview TEPPC 2026 CC V1.0 I.Pan-Canadian Wind Integration Study (PCWIS) – Use the PCWIS wind data in TEPPC 2026CC II.The use of SIND 1 & WIND Geographic Data Profiles from the TEPPC 2024CC III.SIND & WIND Higher Geographic Granularity Data Profiles?? 1: Solar Integration National Dataset Toolkit (SIND) by NREL 2: Items II & III are covered in separate presentations 2

Pan-Canadian Wind Integration Study (PCWIS) 3

The Pan-Canadian Wind Integration Study (PCWIS) was co- funded by Natural Resources Canada (NRCan) through the ecoEnergy Innovation Initiative (ecoEII) and the Canadian Wind Energy Association (CanWEA), with in kind support from each organization. – Prepared for: Canadian Wind Energy Association (CanWEA) – Prepared by: GE Energy Consulting Group – Report Date: July 6, 2016 – Using PCWIS wind data for BC and Alberta will result in better matched coincident wind energy to that developed by NREL for the western states. 4

Project Team The project team, led by GE Energy Consulting Group, consisted of five companies providing a broad range of technical analysis required for this study: – GE Energy Consulting Group - Overall project leadership, production cost simulation and reliability analysis – Vaisala - Wind profile and forecast data – EnerNex - Wind plant data assembly and management, statistical analysis, regulation/reserve requirements – Electranix - Transmission reinforcement design – Knight Piésold - Canadian hydropower resource data and modeling 5

Data Sources PCWIS used a combination of publicly available and confidential data to model the interconnected power grid, covering the majority of Canada and the USA (Eastern Interconnection, Western Interconnection, and Quebec). The hourly production simulation analysis was performed using GE’s Concorda Suite Multi-Area Production Simulation (GE MAPS) model. In order to protect the proprietary interests of Canadian grid operators, the production simulation analysis was primarily based on: – Publically available data; however, – reviewed and in some cases modified by the grid operators to assure consistency with the operating characteristics of the provincial power grid and the power plants under their control. 6

Data Development Methodology ( consistent with the NREL approach) Mesoscale simulations for this project were carried out using the limited-area configuration of the Global Environmental Multiscale (GEM) atmospheric model (GEM-LAM hereafter). – In general, the GEM model works by first solving a set of dynamical equations directly on the model grid. Physical processes includes: – atmospheric radiation – fluxes from different land-surface components – boundary-layer turbulent mixing as well as clouds – precipitation are not directly resolved at the grid scale 7

Data Development Methodology Wind speed and meteorological data were provided by Environment and Climate Change Canada and were the basis for developing wind power profiles for both existing and future wind plants in Canada. This wind data set included 54,846 individual 2km square grid cells at 10- minute time intervals for 3 calendar years (2008 to 2010). Vaisala processed the grid-cell wind speed data into power output profiles by applying composite wind turbine power curves and also accounting for : – the effects of dynamic turbulence/wake losses, icing effects due to temperature and humidity, and turbine low-temperature cut-off. – EnerNex aggregated the grid-cells into a large number of wind plants across all Canadian provinces, ranging from 16 megawatts (MW) to 448 MW in capacity. – GE then selected appropriate combinations of these wind plants to create the required simulation models for each of the study scenarios. Wind plants in the USA were modeled using publically available wind profile data from the National Renewable Energy Laboratory (NREL). 8

PCWIS Scenarios 5% BAU The 5% Business As Usual (BAU) reference case includes existing wind plants as well as new plants under construction as of 4/25/2015. This case serves as a benchmark for how grid operations will change as wind penetration increases. 20% Dispersed (DISP) wind resources, refers to 20% wind penetration in the study footprint and in each Canadian province. 20% Concentrated (CONC) wind resources, is also a 20% wind penetration for the entire study footprint. 35% TRGT scenario refers to wind resources concentrated in selected, or targeted, provinces (TRGT), with 35% wind penetration across Canada 9

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Study Results (High Level) In the 20% & 35% scenarios, wind energy displaced more expensive gas and coal-fired generation in both Canada and the USA. – This study did not include any carbon tax. – If a carbon tax were implemented, then more of the energy displacement would shift from natural gas to coal generation. Canada has high quality wind resources in all provinces. Capacity factors of potential wind plants range from 34% in British Columbia to 40% in Nova Scotia. – The study results indicated that there is no significant advantage to concentrate wind resources in provinces with slightly higher wind capacity factors. Instead, it is more beneficial to add the wind generation in regions where the energy can be partially used within the province and partially shared with neighboring USA states. Hydro generation, particularly hydro with pondage, provides a valuable complement to wind generation. – The combination of wind and hydro provides a firm energy resource for use within Canada or as an opportunity to increase exports to USA neighbors. 11

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