11 reno sandia_dgi_reno_sand2016-4701 pe

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.

Advanced Inverter Functions and Control of DER

Matthew Reno Sandia National Laboratories

May 10, 2016 SAND2016-4701 PE

Outline Advanced Inverter Functions Advanced Inverters to Increase Hosting Capacity Specific Optimal Advanced Inverter Settings found Using QSTS

Centralized Control of Advanced PV Inverters Optimality vs. Fairness

Control of DER Energy Storage Control for Ramp Rate Smoothing Control of DER for Voltage Regulation Impact of Communication Delays Vermont Regional Partnership for Rebound Mitigation

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Hosting Capacity Analysis for 216 feeders

3 • Analyses captures a wide range of feeder types, voltages, topologies and controls.

Detailed analysis of a large number of potential PV scenarios to determine impact to the distribution system

Results in finely detailed feeder maps of locational hosting capacity highlighting the regions for best placement of additional PV or other DG systems

Advanced Inverters PV Control Control of DER

PV Advanced Inverters Advanced inverter functions (“smart inverters”) present new

opportunities to increase feeder hosting capacity


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Determining Inverter Settings The objective is to determine advanced inverter settings to

accommodate more PV without system upgrades Not all advanced inverter function settings are beneficial to

the system Settings are unique to each feeder and location specific


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Derive Custom Settings Using QSTS A parametric study is performed using quasi-static time series

(QSTS) analysis for each control type to determine how well certain measurable network metrics improve as a function of the control parameters

Impacts considered for each control: Time over-voltage Time under-voltage Regulator tap changes Capacitor switches Network losses PV power curtailed PV vars generated

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Derive Settings Using QSTS Analysis investigates settings for 6 advanced inverter controls

1. Ramp-Rate Control 2. Fixed Power Factor Control 3. Volt/Watt Control 4. Watt-Triggered Power Factor Control 5. Watt-Priority Volt/Var Control 6. Var-Priority Volt/Var Control

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Control of Distributed PV Enhanced grid operation and optimized PV penetration

utilizing highly distributed sensor data Local, centralized, or distributed control of storage and PV

with advanced inverter capabilities

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Control Effectiveness vs. Fairness Developed different control methods that achieved curtailment of

less than 3% of kWh produced by PV on the feeder while maintaining feeder voltages within ANSI standards 98%+ of the time

Investigated reactive power control in PV inverters: results show that real-power curtailment could be avoided over 97% of the time while maintaining feeder voltages within ANSI standards at all times.

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Control Type ZCI Volt/ Watt Volt/Var Central

Fair (1m) Sensitivity-based (1m)

Violations Mitigated (%) 100.0 100.0 98.7 99.0 100.0

Power Curtailed (%) 21.6 5.39 0 5.89 3.99

Curtailment Deviation (%) 0.75 6.2 0 0.36 8.21

Comparison of the performance of PV inverter control types


Distributed Storage For Smoothing PV Ramps A control algorithm smooths the variability

of PV power output by using distributed batteries (300 +systems).

Both local and central control algorithms demonstrate that large numbers of highly distributed current, voltage, and irradiance sensors can be utilized to control distributed storage in a more optimal manner.

Centralized energy storage control for PV ramp rate smoothing does require very fast communication of less than 15 second update rate.

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Control Name Regulator Tap Changes (weekly) Total Required Storage (kWh)

Basecase with PV and No Batteries 193 - Local 1 154 662 Local 2 137 2710

Central 1 152 629 Central 2 135 2648


Communication Requirements: Smoothing

Recently funded 3-year project investigating the requirements for the communication infrastructure for DER control

Analysis of communication delays, cyber security, and bandwidth limitations on distributed control

Using the previous example of dispatching distributed storage based on PV output for smoothing

1 2 3 4 5 10 15 20 30 45 60 90 120 180 240 300150

200

250

Communication Resolution (s)

Num

ber o

f Tap

Cha

nges

Start at t=0Start at 1/2Start at 1/4Start at 3/4AverageNo Storage

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Control of DER for Voltage Regulation

Simulation using voltage measurements at the voltage regulator to dispatch PV reactive power to keep the voltage in band in real-time

Works for central or distributed (unbalanced) PV using derived feeder voltage sensitivities

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OpenDSS Solve w/Seq. control

Time to communicate

?

Log data and go to next time step

Communication interval No

Receive All PPV, QPV, and VREG

Yes

Any phase VREG out of

band?No

Calculate phase ΔVs to deadband

with gain

Controller deadband and gain Yes

Calculate phase ΔQsSQ 3x3 matrix

Night or day?

Night control enabled

?

Night

Night control flag

Dispatch phase ΔQsDay

OpenDSS Re-Solve

Set PFs to unity

Dispatch phase ΔQs

Yes

No

Historical Datalog

𝑆𝑆𝑖𝑖 =Δ𝑉𝑖Δ𝑆𝑖

∆𝑆 = 𝑆𝑆 −1 ∗ [∆𝑉𝑟𝑟𝑟]

Communication Requirements: Voltage

Impact based on the speed of the feedback control from voltage measurement at the voltage regulator

Fast communication mitigates all voltage regulator tap operations

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Control of DER: Vermont Vermont Regional Partnership Enabling the use of DER Development of innovative control strategies (utilizing

aggregates of storage systems along with improved wind and solar forecasts and controllable loads) that will reduce peak transmission demand costs while also mitigating solar-induced feeder voltage variability problems

Involves control of PV, energy storage, and load management to provide essential grid services

There is also a power systems planning component to develop methods to determine the optimal amount and placement of DER (PV and storage) on distribution feeders

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Summary Advanced inverters can provide significant benefits to the

grid, but determining appropriate settings is not always straightforward

Fairness is important in DER control design Centralized control requires significant and reliable

communication infrastructure, but the necessary update rate depends on the application.

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QUESTIONS? Sandia National Laboratories

Robert J. Broderick

[email protected]

Matthew J. Reno [email protected]

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mailto:[email protected]

mailto:[email protected]

11 reno sandia_dgi_reno_sand2016-4701 pe

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