03 broderick qsts_sand2016-4697 c

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Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. Accelerating Solar Deployment on the Distribution Grid: Rapid QSTS Simulations for comprehensive assessment of distributed PV Robert Broderick Sandia National Laboratories May 10th, 2016 SAND2016-4697 C

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Page 1: 03 broderick qsts_sand2016-4697 c

Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin

Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

Accelerating Solar Deployment on the

Distribution Grid: Rapid QSTS

Simulations for comprehensive

assessment of distributed PV

Robert Broderick

Sandia National Laboratories

May 10th, 2016

SAND2016-4697 C

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Outline

Overview of Sandia’s DOE funded project to radically speed up Quasi-Static Time-Series (QSTS) Simulations

1. Why do we need QSTS?

2. Goals of the Project

3. Technical Path Forward

4. Results to date

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Time Series Simulations What is QSTS?

Quasi-static time series (QSTS) analysis captures time-dependent aspects of power flow, including the interaction between the daily changes in load and PV output and control actions by feeder devices and advanced inverters.

QSTS is not directly a PV screening or hosting capacity calculation, but a detailed tool to directly simulate grid impacts for a variety of future scenarios.

Why do we need QSTS?

PV output is temporally variable and the potential interaction with control systems may not be adequately analyzed with traditional snapshot tools

Many potential impacts, like the duration of voltage violations and the increase in voltage regulator operations, cannot be accurately analyzed without it.

What is the problem with today’s methods?

Snapshot analyses that only investigates specific time periods can be overly pessimistic about PV impacts because it does not include the geographic and temporal diversity in PV production and load

QSTS simulation in existing software is too slow for the needed simulations (year-long) at the high time resolution needed to capture solar variability (time steps less than 15 seconds)

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Time Series Simulations

What is the next step?

Utilities and developers need high speed modeling tools and access to high resolution data to accurately determine the impact of high penetrations of PV and other DER on the distribution system

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Impact Analysis

High penetrations of PV can affect the distribution feeder equipment and the operation of the system 1. Designed for radial flow in one direction from the substation

2. Designed for aggregated loads with little short-term variability

Distribution system impacts Voltage Regulation Device Operations

Steady State Voltage

Voltage Flicker

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Impact Analysis

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Time Series Results

QSTS simulates the number of tap changes on voltage regulators

Results depend the input data and seasonal variations

QSTS simulations present a significant computational burden

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Goals of QSTS Project

Existing QSTS algorithms can take anywhere from 10 to 120 hours to perform a high resolution 1-year simulation at 1-second resolution.

During the course of the project, algorithms will be developed to dramatically reduce the computational time to less than 5 minutes for a year-long high-resolution time-series simulation, allowing QSTS to be incorporated into the utility interconnection process and decision support tools.

We are going to transform how the industry performs impact analyses by replacing old fashion static tools with new fast and accurate tools that are focused on solving the time dependent problems of PV in the modern distribution system.

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Project Team

Robert Broderick (Sandia Lead)

Barry Mather (NREL co-lead),

Matthew Reno (Sandia)

Matthew Lave (Sandia)

Santiago Grijalva (Georgia Tech)

Tom McDermott (UPITT)

Roger Dugan ( EPRI)

Jean-Sebastien Lacroix (CYME)

DOE Funds: $4,000,000 for 36 months

Cost-Share Funding (CYME & EPRI): $810,000

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Proposed Solution – Technical Approach

Task 1 – Fast Time-Series Approximations

Task 2 – Improved Power Flow Solution Algorithms

Task 3 – Circuit Reduction

Task 4 – Parallelization of QSTS Analysis

Task 5 – Implementation of Accelerated QSTS

Task 6 – High-Resolution Input Data.

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Year-long high-resolution (1 Sec) time-series solutions that can be run in less than 5 minutes for utility decision support systems.

The ratio of relative speed improvements between algorithm improvements and parallelism is part of the R&D question, but hypothetically we could do it using:

Target Performance Level

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Simulation Duration

1 Day 1 Month 1 Year

Existing Methods 1.6 – 20

minutes 0.8 - 10 hours 10 - 120 hours

Proposed Algorithm

Target 3 minutes 4 minutes 5 minutes

Computation times for 1-second resolution QSTS

𝟏𝟐𝟎 𝐡𝐨𝐮𝐫𝐬

𝟏𝟎 × 𝟐 × 𝟏𝟎 × 𝟕 = 𝟓 𝐦𝐢𝐧𝐮𝐭𝐞𝐬

Fast

Time-Series

Approximation

Improved

Power Flow

Solution

Circuit

Reduction Parallelization

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Project began December 1st 2015. Project status meeting completed 5/4/16 at IEEE T&D.

Preliminary Results

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Preliminary Results

Task 1 Fast Time-Series

Approximations

~6 different approaches are being investigated

Task 2 Improved Power Flow Solution

Algorithms

~60% reduction in power flow solve time using methods

that reuse solution from previous time step

Task 3 Circuit Reduction

Circuit Reduction methods showing great promise to

achieve at least a 90% reduction in complexity

Task 4 Parallelization of QSTS

Diakoptics Methodology under development

Task 5 Implementation

Begins in Year 2

Task 6 High-Resolution Input Data

Solar irradiance algorithm is being evaluated

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Circuit Reduction on Range of Feeders Feeder UT11

96% Reduction

<0.01% Error

Feeder Ckt 7

98% Reduction

<0.02% Error

Feeder UQ11

97% Reduction

<0.02% Error

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QUESTIONS?

Sandia National Laboratories

Robert J. Broderick

[email protected]

Matthew J. Reno

[email protected]

Matthew Lave

[email protected]

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