MissionTo accelerate the developmeof good engineering and opesupporting the appropriate inand solar power into the elec

NREL Impact: Strategic Stakeholder EngagementNational Renewable Energy LaboratoryDebra Lew, Michael Milligan, Marissa Hummon, Lori Bird

Large-scale deployment of solar energy will require a favorable environment for interconnecting and operating solar power plants.

But realizing high volumes and economies of scale will be difficult if solar penetrations are limited because of reliability, interconnection,and operational rules and requirements. NREL stakeholder engagement activities provide data, analysis, and tools that inform utilities, 

regulators, and policymakers. Policymakers need to know whether high penetrations of solar are technically feasible. Regulators need to understand the reliability and balancing impacts of solar. Utilities need tools and data to study high solar penetrations on their power systems.

Conducting Analysis: Energy Imbalance Market Engaging Stak

ObjectiveProvide inputs to operation modeling and develop insights into potential operational changes (e.g., ramping needs and flexibility reserves) of an energy imbalance market (EIM) to inform Western regulators about options for the cost-effective integration of renewable energy.PartnersXcel Energy, Western Interstate Energy Board, Western Electricity Coordinating Council (WECC), Energy ExemplarDetails•Develop flexibility reserves analysis for multiple EIM footprints and scheduling periods•Run electricity production simulations of the EIM with PLEXOS to evaluate the impact of sub-hourly economic dispatch over a large electrical footprint•Provide input to the Western Electricity Coordinating Council and its consultants and analysis assistance to individual balancing areas•Offer educational webinars to stakeholders•Support to WECC Technical Advisory Subcommittee Modeling Work Group to incorporate flexibility reserves into 

Transmission Expansion Planning Policy Committee cases•Author multiple conference papers and reports 

Conclusions•Full-footprint EIM cuts flexibility reserve deployment approximately 50% compared to business as usual•Reserve impact depends on who participates in the EIM, how many separate EIMs exist, the penetration of wind and solar, and the dispatch interval•Reserve analysis does not account for transmission constraints, increased efficiency of dispatch, potential coordination among participating balancing areas that find themselves overcommitted (EIM does not coordinate unit commitment), or long-term implications for resource requirements•PLEXOS modeling will address transmission constraints and efficient dispatch

-3000-2000-1000

0

100020003000

Reduction in Ramp Demand (MW/hr) 1 25 49 73 97 121 145 169

Hours

1-Hour Ramp Reduction - Footprint EIM Savings over BAU

Typical Week

NetLoad

Existing balancing areas of the Western Interconnection

ObjectiveEncourage stakeholder engagcommittees and working grou

forums; and developing techn

Partners• Subcontractors: Utility VariabCollege Dublin/Electricty Rese• Partners and collaborators: 

Utility VariabIntegration G

UVIG is the leading forum for cand solar utility industry on intetransmission topics. Its offering• Annual spring and fall technic• 2012 Solar Integration Works• 2012 Distributed Generation • Annual Forecasting Worksho• 2010, 2011 Advanced Method• 2010, 2012 Short CourseIn addition, UVIG co-sponsors Power Administration Interconn

Providing Data: Sub-Hour Solar Power Data

MotivationRapid changes in solar power, caused by passing clouds, are observable only in sub-hour time series. ObjectiveDevelop and share sub-hour solar power data sets that have appropriate variability at various timescales; intra-plant 

and plant-to-plant spatial correlation; temporal correlation; capacity factor; and forecast error distributions in time, space, and magnitude for use in integration studies and production cost modeling. 

Details• The algorithm generates simulated global horizontal irradiance (GHI) values at an interval of 1 minute for a specific 

location using SUNYmet hourly irradiance values for that grid cell and grid cells within 40 km.

• During each hour, the observed GHI value for the grid cell of interest and the surrounding grid cells is related, via 

probability distributions, to one of five temporal cloud coverage classifications (Class I, II, III, IV, V). 

• Each class of cloud coverage variability has a separate algorithm to select 1-minute time-step GHI values based on 

the classification of the grid cell of interest in a particular hour.

• Sub-hour GHI values are integrated with other meteorological data to generate an input file for solar power 

conversion via the System Advisor Model (SAM).

Conclusions• Using spatial variability as an input to the sub-hour solar power algorithm generates a data set with appropriate 

spatial-temporal ramping correlation statistics.

• Individual site simulated time series show a remarkable degree of similarity to sites with measured time series.• Large-scale data set (~1200 locations) is in use by the Western Wind and Solar Integration Study Phase 2, Demand 

Response and Storage Integration Study, and the Variable Generation Subcommittee of the Western Electric Coordinating Council

ImpactSub-hour, spatially correlated data sets are necessary to answer questions about thermal unit ramping, intra-hour reserve requirements, and aggregate/systemwide response to high-penetration solar power. Measured, 1-minute resolution global horizontal irradiance at two sites (Boulder and Golden, Colorado) and hourly average satellite image-derived 

irradiance values. Sub-hour data sets must account for spatial and temporal correlations across all locations at all times. This is called coherence.

0

100020003000400050006000700080009000

10000

Footprint Regional BAU

Average Total Regulation (MW)

Average Total Regulation for 6 Dispatch/Lead

Schedules by Aggegation (Dispatch interval ‐

Forecast lead time)

10‐1030‐1030‐3060‐1030‐4060‐40

 Faster  Faster  Faster

Large Medium Small

Average Total Regulation for 6 Dispatch/Lead Schedules 

by Aggegation (Dispatch Interval–Forecast Lead Time)

Utilities

UVIG, NRECA,

APPA, EEI

Informing utility integratiooperational practices, tooInforming industry practicdata and tools for analysi

Industry

SEIA, SEPA, IEEE 

Example Activities

Objective

Presented at the SunShot Grand Challenge Sum

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewab