- GREDOR Gestion des Réseaux Electriques de Distribution
Ouverts aux Renouvelables
Real-time control: the last safety net
Journée de présentation GREDOR
Thierry Van Cutsem, ULg
Moulin de Beez, 29/04/2015
Context (1/3)

Distribution networks are
expected to host larger
amounts of dispersed
renewable generation

voltage and congestion
(thermal overload) problems
are expected to occur more
often

but, hopefully, over limited
periods of time
Test system with:
•
75 MV buses
•
22 DG units (doubly fed
induction & small
synchronous generators)
2
Context (2/3)



Reinforcing the network (“fit-and-forget”) to deal with such temporary
problems would be too expensive
there is a good opportunity to use Distributed Generation (DG) units as
“control means” to remove the security limit violations
this is a service for which DG unit operators/owners could be financially
compensated


Loads with new consumption profiles


see Task 1 of GREDOR
e.g. electric vehicles, heat pumps, etc.
Flexible loads are expected to also provide control means


through remote control, complementing smart meters
this presentation, however, focuses on DG units only.
3
Context (3/3)

Automatic control schemes are needed to assist the Distribution
System Operator in:



correcting voltage and/or congestion emergencies
keeping the MV grids within desired operating limits
coordinating their actions with transmission system operator
4
Desired features of automatic corrective control

Centralized control with system-wide monitoring and model preferred




exploit less expensive controls first


requires a communication infrastructure…
…but offers more advanced control capabilities
…and communication cost will be much lower than network reinforcement
e.g. reactive power modulation preferred to active power curtailment
act in a non discriminatory and transparent manner

optimize a system-wide objective with efforts shared by all relevant DG units

drive the system from the current (unacceptable) to the desired (secure)
operating point

do not rely on models which may not be available / accurate



especially for loads (sensitivity to voltage not well known !)
rely on a simplified model (e.g. infrequently updated)
be robust with respect to inaccuracies of this simplified model
5
Centralized controller: inputs and outputs
(volt. set-point of load tap changer)
V
controller
P, Q , V
measurements
P, Q
(refreshed
every ~ 10 s)
set-points
P, Q , V
(updated
every ~ 10 s)
P, Q
P, Q , V
P, Q
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Model Predictive Control
At time 𝑘, the controller :
 uses the last measurements and a model of the system to predict its response at
𝑁𝑝 future times
 computes an optimal sequence of 𝑁𝑐 future controls (𝑁𝑐 ≤ 𝑁𝑝 )
 applies the first component only.
At time 𝑘 + 1 , the whole procedure is repeated.
predicted
output
measurement
𝑘
𝑘 + 𝑁𝑝
𝑘+1
discrete time
computed
set-point
(sent to DG unit)
discrete time
𝑘
𝑘+1
𝑘 + 𝑁𝑐
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Mode 1
static data
measurements
Network
data
set points
DSO :
Distribution
System
Operator
DSO
State
estimation
Controller
∆𝑷
∆𝑸
Real-time
measurements
Local
controller
𝑃 𝑚𝑒𝑎𝑠
𝑄 𝑚𝑒𝑎𝑠
𝑉 𝑚𝑒𝑎𝑠
𝑷,𝑸
Non
Dispatchable MPPT
DG units
MPPT :
MaximumPower
Point
Tracking
8
Mode 2
static data
measurements
Decision by
non-DSO
actor
DSO
State
estimation
Real-time
measurements
𝑃 𝑚𝑒𝑎𝑠
𝑄 𝑚𝑒𝑎𝑠
𝑉 𝑚𝑒𝑎𝑠
Corrective reports
Network
data
set points
Controller
DSO :
Distribution
System
Operator
∆𝑷
∆𝑸
𝑷 ,𝑸
Dispatchable
DG units
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Test system

22 DG units controlled

controls adjusted every 10 s

Nc = 3

Np = 3 (larger if LTC actions
anticipated)
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Example 1
Mode 1. Wind increase (t = 20 → 70 s, all 22 wind generators)
Congestion corrected by controller
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Mode 3.a
static data
Decision by
non-DSO
actor
measurements
set points
Network
data
State
estimation
Real-time
measurements
𝑃 𝑚𝑒𝑎𝑠
𝑄 𝑚𝑒𝑎𝑠
𝑉 𝑚𝑒𝑎𝑠
𝑷 ,𝑸
DSO
Corrective reports
information
Controller
0
near-future
schedule
∆𝑷
∆𝑸
𝑷 ,𝑸
DG
units
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Mode 3.b
Operational
planning
Network
data
State
estimation
Real-time
measurements
𝑷 ,𝑸
set points
information
Corrective reports
DSO
static data
measurements
0
near-future
schedule
Controller
∆𝑷
∆𝑸
𝑷 ,𝑸
𝑃 𝑚𝑒𝑎𝑠
𝑄 𝑚𝑒𝑎𝑠
𝑉 𝑚𝑒𝑎𝑠
DG
units
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Example 2
Mode 1 : 9 generators
- wind increase
(t= 20 → 70 s)
Mode 3 : 13 generators
- power schedule (t= 150 → 180 s)
Overvoltages corrected by controller
generation
schedule
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Overview of various modes
Mode 1
Mode 2
Modes
3a & 3b
DG units
Control strategy
nondispatchable
Normal operating conditions:
take no corrective action
dispatched by
non-DSO actor
Emergency conditions:
deviate as few as possible from the
last normal operating conditions
both
dispatchable
and nondispatchable
Normal operating conditions:
control system to follow the schedule
Emergency conditions:
deviate as few as possible from the
schedule
Capability of
anticipating limit
violation ?
No.
Correction is
applied after
violation is
observed
Yes.
Controls are
applied to avoid
exceeding the
limits
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Summary

Centralized controller collecting measurements and adjusting set-points
of DG units to satisfy operating constraints:




currents below limits
voltages inside bounds
power factor at connection point with transmission system
relies on concept of Model Predictive Control



moving the operating point progressively from current to desired state
compensating for modelling inaccuracies (as a closed-loop control)
anticipating the effect of known changes (Modes 3.a & 3.b)

uses a simple, infrequently updated sensitivity model

takes into account the load tap changer operation



as a separate controller
or by controlling its voltage set-point
constrained optimization problem compatible with real-time operation
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Ongoing work in GREDOR

Extensions of formulation

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
treat discrete controls as such in optimization
reset DG units at maximum / scheduled power after emergency situation
has been corrected and operating conditions improve
treat flexible loads and storage devices as additional control variables
mitigate high voltage problems in LV grid due to photo-voltaic installations
etc.
Implementation aspects and further tests


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Assess practical telecommunication needs
Provide more meaningful results with the networks of GREDOR DSO partners
Further integration with Task 3 (operational planning) and Task 1
(interactions)
etc.
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