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Integrating Southwest Power Pool Wind to Southeast Electricity ...

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EXECUTIVE SUMMARY<br />

This DOE-funded project titled “<strong>Integrating</strong> <strong>Southwest</strong> <strong>Power</strong> <strong>Pool</strong> <strong>Wind</strong> Energy in<strong>to</strong> <strong>Southeast</strong><br />

<strong>Electricity</strong> Markets” aims <strong>to</strong> evaluate the benefits of coordination of scheduling and balancing<br />

for <strong>Southwest</strong> <strong>Power</strong> <strong>Pool</strong> (SPP) wind transfers <strong>to</strong> <strong>Southeast</strong>ern Electric Reliability Council<br />

(SERC) Balancing Authorities (BAs). The primary objective of this project is <strong>to</strong> analyze the<br />

benefits of different balancing approaches with increasing levels of inter-regional cooperation.<br />

Scenarios were defined, modeled and investigated <strong>to</strong> address production variability and<br />

uncertainty and the associated balancing of large quantities of wind power in SPP and delivery <strong>to</strong><br />

energy markets in the western regions of SERC.<br />

The study evaluates the scheduling/balancing challenges associated with delivery of sufficient<br />

wind generation from within the SPP footprint <strong>to</strong> support 20% energy from renewable resources<br />

across the SPP, Entergy (EES), Southern Company (SoCo), and the Tennessee Valley Authority<br />

(TVA) balancing areas for the year 2022. The project team worked closely with staff from each<br />

of these companies <strong>to</strong> develop a model of the possible generation fleets within each BA. Based<br />

on the 2022 generation plans identified, specific wind generation sites within the SPP footprint<br />

were identified <strong>to</strong> yield sufficient energy output <strong>to</strong> allow each of the 4 BAs <strong>to</strong> meet the net<br />

renewable energy requirement beyond the renewable energy from the base generation fleet. As<br />

the effort required for transmission planning for increased amounts of wind was outside the<br />

scope of the project, transmission constraints were ignored and a transportation model was used.<br />

The Eastern Interconnect wind generation data set developed by the National Renewable Energy<br />

Labora<strong>to</strong>ry (NREL) was utilized for identifying specific wind plants with SPP for both internal<br />

SPP consumption and for delivery <strong>to</strong> the SERC BAs.<br />

The primary analyses for the project include statistical analysis of wind and load data <strong>to</strong><br />

determine the impact on reserve requirements for each BA and unit commitment (UC) and<br />

economic dispatch (ED) simulations of the SPP-SERC regions as modeled for the year 2022.<br />

These evaluations are made for a 14 GW wind generation scenario where SPP wind generation is<br />

intended for serving only SPP load and for 4 separate high wind (48 GW) transfer balancing<br />

scenarios relative <strong>to</strong> the coordination between regions within the footprint:<br />

1. Hourly Scheduling: SPP carries all additional within-hour reserves for the wind<br />

generation for all SPP and SERC regions. Each BA schedules its own generation dayahead<br />

<strong>to</strong> meet its own forecast load and reserve requirements based on forecast wind<br />

generation from the SPP wind plants assigned <strong>to</strong> each BA without consideration of<br />

generation in neighboring BAs. Note that a variation of the Hourly Scheduling<br />

simulation (“Integration Proxy”) was conducted where all wind generation was assumed<br />

<strong>to</strong> be perfectly forecast & no additional reserve was required for within-hour variability<br />

of wind. This case was conducted as a hypothetical case <strong>to</strong> provide a measure of the<br />

balancing costs associated with the wind generation by comparison <strong>to</strong> the Hourly<br />

Scheduling base case.<br />

2. Dynamic Scheduling: Each SERC BA and SPP individually carries reserves for its wind<br />

generation output even though all wind is located in the SPP footprint. As with scenario<br />

#1, each BA schedules its own generation day-ahead <strong>to</strong> meet its own load and reserve<br />

requirements without consideration of generation from neighboring areas.<br />

iv

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