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NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
Realizing virtual power plants
Changhong Zhao
Control of Complex Systems Workshop – May 23, 2017
Acknowledgments
Andrea Simonetto Sairaj Dhople Emiliano Dall’Anese Christine Chen Swaroop Guggilam
Network Optimized Distributed Energy
Systems (NODES)
Funding agency:
Objective
1
Objective
1
Objective
1
Enable DER coordination to pursue objectives of customers and utility/aggregator
Enable feeder to emulate a virtual power plant providing services to the main grid
Feeder as a virtual power plant
2
- Primary frequency response
- Secondary frequency response
- Follow dispatch signals
Feeder as a virtual power plant
2
Respect electrical limits (e.g., voltage regulation)
Maximize customers’ and
utility/aggregator objectives
- Inertial response
- Primary frequency response
- Secondary frequency response
- Follow dispatch signals
Outline
3
Modeling
Tracking of setpoints at the feeder head
Primary frequency response
Leveraging linear models
Nonlinear AC power flows
4
Leveraging linear models
Nonlinear AC power flows
(Approximate) linear relationships
How to obtain (and update) the model parameters?
Regression-based; e.g., online recursive least squares [Angelosante-Giannakis’09]
Model-based [Baran-Wu’89, Dhople at al’15, Bolognani-Dorfler’15]
4
Leveraging linear models
Example
Bus0 5 10 15 20 25 30 35 40
p.u
.
1.01
1.015
1.02
1.025
1.03
1.035
1.04
1.045
1.05
1.055Voltage Approximation Results for Peak and Off-Peak Solar
Voltage Approximation at 12:00 AM
Actual Voltage at 12:00 AM
Voltage Approximation at 12:30 PM
Voltage Approximation at 12:30 PM
5
Leveraging linear models
Example
For future developments …
Bus0 5 10 15 20 25 30 35 40
p.u
.
1.01
1.015
1.02
1.025
1.03
1.035
1.04
1.045
1.05
1.055Voltage Approximation Results for Peak and Off-Peak Solar
Voltage Approximation at 12:00 AM
Actual Voltage at 12:00 AM
Voltage Approximation at 12:30 PM
Voltage Approximation at 12:30 PM
5
Tracking AGC, ramping, and dispatch signals
6
Controller design principles
1
“Sample and solve”: series of optimization problems, one every
Leverage the time-varying optimization formalism [Simonetto-Leus’14]
7
Controller design principles
1
Leverage the time-varying optimization formalism [Simonetto-Leus’14]
“Sample and solve”: series of optimization problems, one every
Not practical: computation/communication limits; convergence
7
Controller design principles
1
Leverage the time-varying optimization formalism [Simonetto-Leus’14]
“Sample and solve”: series of optimization problems, one every
Not practical: computation/communication limits; convergence
Not accurate: model mismatches (linear)
7
Controller design principles
8
1
Low-complexity online algorithms to find and track optimal solutions
.
Controller design principles
1
Low-complexity online algorithms to find and track optimal solutions
Feedback from the system to cope with model mismatches and promote adaptability
[Jokic at al’09, Dall’Anese at al’15, Bernstein at al’16, Dall’Anese-Simonetto’16, Gan-Low’16]
8
Controller design principles
1
Low-complexity online algorithms to find and track optimal solutions
Feedback from the system to cope with model mismatches and promote adaptability
[Jokic at al’09, Dall’Anese at al’15, Bernstein at al’16, Dall’Anese-Simonetto’16, Gan-Low’16]
Establish analytical results for tracking capabilities
8
Formalizing operational target
9
Targeted setpoint at feeder head:
Formalizing operational target
Targeted setpoint at feeder head:
Targeted problem:
Tracking setpoints
Voltage regulation
Hardware constraints
Cost/objective
9
Formalizing operational target
Targeted setpoint at feeder head:
Targeted problem:
Tracking setpoints
Voltage regulation
Hardware constraints
Cost/objective
(Ass. 1) convex and continuously differentiable
(Ass. 3) Problem is feasible
(Ass. 2) The map is Lipschitz continuous
9
Controller design
10
Primal-dual gradient method for regularized Lagrangian [Koshal et al’11]
+ online + feedback
Controller design
Primal-dual gradient method for regularized Lagrangian [Koshal et al’11]
+ online + feedback
10
Controller design
Primal-dual gradient method for regularized Lagrangian [Koshal et al’11]
+ online + feedback
: unique primal-dual (time-varying) solutions
10
Real-time control strategy
11
[S1] Measure voltages and power at the substation
Real-time control strategy
[S2a] Update dual variables using measurements
11
Real-time control strategy
[S2a] Update dual variables using measurements
11
Real-time control strategy
[S2b] Broadcast dual variables
11
Real-time control strategy
[S3] Compute and command setpoints
11
Convergence
12
Convergence
(Ass. 4) There exist and such that:
12
Convergence
13
Assumption captures measurement errors and slow DER dynamics
(Ass. 5) There exists such that:
Convergence
14
Theorem [Dall’Anese et al’16]. Under current modeling assumptions, if the stepsize is
chosen such that:
then the following holds for the closed-loop system above:
Where and .
Representative results
15
IEEE 37-node test feeder
Real load and solar irradiance data from Anatolia, CA
PQ updated every 300ms
PV inverter mimics first-order system
Representative results
16
Voltage regulation Dispatchable VPP
Representative results
17
The value of fast communication
Representative results
19
9:00 10:00 11:00 12:00 13:00 14:00 15:001.00
1.01
1.02
1.03
1.04
1.05
1.06
Node 2
Node 28
Node 35
Volt
age
mag
nit
ude
[pu]
Outline
3
Modeling
Tracking of setpoints at the feeder head
Primary frequency response
Primary frequency response provided by DERs
inertial response
inertial response
Primary frequency response provided by DERs
Integral
Frequency response provided by DERs
Integral
How to design dk to achieve prescribed droop characteristic at the feeder head?
Outline
3
Modeling
Tracking of setpoints at the feeder head
Primary frequency response
Guggilam, Zhao, Dall’Anese, Chen, and Dhople,
“Primary frequency response with aggregated DERs,”
Thursday 16:20—16:40, Willow A
Validation
28
Power hardware-in-the-loop at NREL:
Algorithms embedded in microcontrollers
100 physical DERs controlled in real time
Real feeder with 2000 customers
Concluding remarks
29
Feeder as virtual power plants to provide services to the main grid
Primary frequency response and setpoint tracking to enhance reliability
Address voltage regulation and pursue optimization objectives
Value of fast communications to enable coordination
Future efforts towards
Convergence to solution of nonconvex problems
Optimization layer to dispatch HVAC systems, EVs, and other DERs
Validation and implementation
Thank you!
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