Application of GridEye
for Optimal Control of Grid
This document provides a use case for the application
of GridEye for the optimal control of low voltage grids.
GridEye modules primarily measure the electrical
quantities and process the measurements using the
distributed intelligence on every module. Thus, only
useful data for the system operators and/or endcustomers are communicated and stored. In this way,
the high communication costs and big data issues are
avoided. The transmitted data is used for the optimal
control purposes. Based on the decentralized
intelligence, GridEye modules are able to perform the
optimal control actions on the controllable devices by
considering objectives and/or constraints of system
operators and end-customers. The constraints are
mainly the grid voltage and current limits and
controllable devices operation limits. The important
particularity of GridEye’s optimal control approach is
the consideration of the grid aspect. In other words,
the impact of every control action on the rest of the grid
is appropriately taken into consideration through the
sensitivity coefficients without knowledge of the grid
parameters and topology. GridEye optimal control
approach is an efficient and scalable approach for
ensuring the secure and economic operation of low
voltage grids. This functionality of GridEye provides
the opportunity to satisfy both of system operators and
end-customers. It allows system operators ensuring
the security of supply, minimizing grid losses and
operation costs, and improving the satisfaction of endcustomers. It also allows end-customers to have
access to reliable and economic power supply,
decrease their electricity bills, and guarantee secure
operation of its installations.
Author: Omid Alizadeh-Mousavi
Date: March 8th, 2017
© DEPsys SA
Application of GridEye for Optimal Control of Grid
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1. INTRODUCTION OF THE USE
CASE AND GRIDEYE INSTALLATION
The grid topology of the use case and the installation
of GridEye modules are shown in Figure 1. The
electricity consumptions in the grid are mainly
residential and agricultural. Moreover, there are PV
installations at three locations shown by G1, G2 and
G3. In this use case, the GridEye modules are used
for the control of electric quantities.
In this use case, the distribution system operator is
interested in monitoring and optimal control of the
shown grid as well as the monitoring and optimization
of the energy consumptions and productions of a
private end-customer. For this private customer in
node 104, shown within the dashed line, an additional
GridEye module is installed.
The installation of GridEye module at MV/LV
transformer as well as on distribution cabinets are
demonstrated in Figure 2. GridEye module installation
for the monitoring and control of the PV inverter (72
kW) and electric boilers (2 x 7.6 kW) at node 104 is
shown in Figure 3 and Figure 4, respectively. The
communication of the module with the inverter is
through Modbus RS485. Regarding the electric
boilers, it should be noted that the system of boilers
include two boilers which are connected in series in
which the cold water enters in boiler 2 and the hot
water consumption is from the output of boiler 1.
Figure 1. The use case and the installations of GridEye modules.
a) MV/LV transformer
b) Rogowski coils for the current measurements at
transformer LV side
Application of GridEye for Optimal Control of Grid
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c) Distribution cabinet
d) PV power plant
Figure 2. Installations of GridEye modules
a) PV inverters.
b) Modbus communication.
Figure 3. Installation for monitoring and control of PV inverters.
a) Installation for monitoring and control of boilers.
b) Boilers configuration.
Figure 4. Installation for monitoring and control of electric boilers.
Application of GridEye for Optimal Control of Grid
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2. OPTIMAL GRID CONTROL
APPROACH
2.1. Sensitivity coefficients
The optimal grid control approach is based on the
voltage and current sensitivity coefficients. The
voltage sensitivity coefficients are defined as the
variation of the voltages at each measuring node with
respect to the variations of active and reactive powers
at all the measuring nodes. Similarly, the current
sensitivity coefficients are defined as the variation of
the currents of branches (i.e. transformers, cables,
lines) with respect to the variations of active and
reactive powers at all the measuring nodes. In other
words, the sensitivity coefficients describe the impact
of the change of active and reactive power set-points
at every node on the voltages and currents of the grid.
The automatic calculation of the sensitivity coefficients
only by using the GridEye measurements enables the
plug and play functionality and the modeless approach
without knowledge of the grid parameters and
topology for the optimal control of distribution grids.
2.2. Objectives, constraints, and outputs
The optimal control approach should satisfy the
distribution system operators and the end-customers
which they may have different objectives and
constraints. The end-customers are interested in
having access to reliable and economic power supply
while the secure operation of their installations (e.g.
PV, inverter, boiler, heat pump, etc.) is guaranteed.
The distribution system operators are responsible for
the technically secure operation of the grid,
minimization of losses and operation costs, and
satisfaction of customers.
In this context, the objective of optimal control
approach is to determine the globally optimal solution
for the secure and economic aspects of grid and
customers. The system operators can primarily
choose the objective for ensuring the security of nodal
voltages and branch currents as well as for minimizing
the grid operation costs which results in the
satisfaction of customers. Moreover, the endcustomer's objective can be the maximization of active
power production of PV installations and/or
maximization of the customers self-consumption. The
latter directly decreases the customer's electricity bill
and implicitly reduces the challenges of system
operators by decreasing power flow of PV productions
in the grid. The objective can be adjusted according to
requirements of the system operators and/or the endcustomers.
The constraints are voltage limits of nodes, thermal
limits of transformers and cables, and operating limits
of controllable devices. The limitations of controllable
devices, for instances, include i) maximum capacity
and maximum power factor for PV inverter, ii)
maximum active and reactive power for energy
storage systems, iii) maximum / minimum temperature
for boilers, etc. The summary of objectives and
constraints for the system operators and the endcustomers is provided in Table 1.
The influence of every control action on all voltages
and currents of the grid is taken into consideration
through a set of constraints and using the sensitivity
coefficients. In this way, it is guaranteed that the
impact of every control action on the technically secure
and economically optimal operation of the grid is
appropriately considered.
The solution of the optimal control algorithm is the
active and reactive power set-points of controllable
devices (e.g. PV inverters, energy storage systems,
etc.) as well as the on/off switching status of heating
devices (e.g. electric boilers, etc.).
It should be noted that the developed optimal control
algorithm is based on the decentralized intelligence of
every GridEye module and minimum communication
between them. Therefore, the optimal control problem
can be solved on every module and the system is
resilient to the loss of a module or communication.
3. VALIDATION OF CONTROL
ALGORITHMS
The developed optimal control algorithm is deployed
for the control of electric quantities and thermal loads
of the grid shown in Figure 1. In this use case, the
controllable devices are PV inverters and electric
boilers. In this section, firstly, the controllability of PV
inverters and electric boilers are explained in 3.1 and
3.2, respectively. Then, the voltage and current
regulations are discussed in section 3.3 and 3.4,
respectively. The control of thermal loads and its
impact on the maximization of self-consumption is
presented in 3.5.
Application of GridEye for Optimal Control of Grid
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Table 1. objectives and constraints for system operator and end-customer.
System operator
End customer
Objectives
Constraints
• technically secure operation of grid
• minimization of grid operation costs
• satisfaction of customers
• voltage limits of nodes
• thermal limits of transformers and cables
• operational limits of controllable devices
• access to reliable and economic power
supply
• decrease its electricity bill
• increase its benefit from its installations
• guarantee secure
installations
3.1. PV inverter controllability
The active power production of PV installations
depends on the available sunlight and meteorological
situation. The available reactive power capacity of the
PV inverter depends on the available active power
production, maximum inverter capacity, and maximum
power factor, as shown in Figure 5-left. The area
colored in gray shows the operating zone of the PV
inverter considering the maximum inverter capacity
and the maximum power factor. This area varies
depending on the available active power production.
For instances, the available reactive power capacity is
equal to zero at the maximum active power production
as well as at the zero active power production.
The empirical cumulative distribution of the active
power production for 4 months (i.e. April 2016 –
August 2016) is shown in blue in Figure 5-right. During
42% of the time, there is no active power production
that corresponds to the nights when there is no PV
production. It is also observed that the active power
production has reached its maximum value for less
than 1% of the time. Therefore, for more than 57% of
operation
of
its
the time, the reactive power capacity is available for
the voltage control. The available reactive power
capacity for the voltage control at every level of active
power production is shown in red in Figure 5-right. The
available reactive power capacity is limited by the
maximum power factor for the active power production
between 0 kW and 57 kW and by the maximum power
factor for the active power production between 57 kW
and 72 kW. Therefore, the explicit set-points of the PV
inverter’s active and reactive powers can be effectively
used for the grid voltage and current control.
It should be noted that using the available reactive
power capacity does not decrease the active power
production of PV, thus the economic income of the
producers by selling active power will not be
influenced.
It is worth noting that for certain PV inverters, active
and reactive power set points can be adjusted in a
discrete way (not continuous). Although these kind of
inverters are less flexible, the change of their active
and reactive powers can still be used for the grid
control purposes.
Figure 5. PV inverter’s capability and limitation on P-Q diagram (left), cumulative distribution of PV active power
production for 4 months and available reactive power capacity at each active power level (right).
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3.2. Electric boiler controllability
The electric boilers convert the electrical energy into
heat energy in order to supply the heat demand. The
operation of electric boilers and electricity
consumption that increase the boiler’s temperature in
red, as well as the hot water consumption and heat
losses which decrease the boiler’s temperature are
shown in Figure 6. The electric boilers can be operated
within a temperature interval. This temperature
interval can provide a flexibility for switching on and off
the boilers such that the comfort of the customer will
not be influenced. This flexibility depends on boilers
volume, electricity consumption capacity, using one or
multiple boilers in series, selected temperature range,
and boiler’s heat losses. The flexibility of boilers can
be used for their economic and efficient switching
which can result in a decrease of their electricity bills
and increase their lifetime.
validates the fact that a control action at one node of
the grid has an influence on the other nodes of the grid
which can be explained by the voltage sensitivity
coefficients. Note that the presented voltage profiles at
nodes 106 and 101 have lower sampling frequency
than the one of the node with PV production.
3.4. Current control
The optimal current control based on the current
sensitivity coefficients for this use case is shown in
Figure 8. The indicated zone in Figure 8 illustrates a
time interval in which the current control is deployed.
In this period, the maximum current limit on the
production side (i.e. negative sign) for cable 100-104
is limited by 30 A. The negative sign of current
indicates the direction of the current. The control
algorithm adjusts the active power of the PV inverter
such that the current of the cable 100-104 stays below
the selected limit.
Figure 6. Electric boiler operation and constraints.
3.3. Voltage control
The optimal voltage control based on the voltage
sensitivity coefficients for this use case is shown in
Figure 7. The indicated zone in Figure 7 by dot-dashed
line illustrates a time interval in which the voltage
control is deployed. In this period, the minimum and
maximum voltage limits for node 104 are set at 230 V
and 243 V, respectively. The control algorithm adjusts
the reactive power of the PV inverter such that the
voltage of the node 104 remains within the selected
limits. Note that in Figure 7, the positive reactive power
indicates the reactive power consumption (i.e.
inductive load). It is interesting to mention that the
active power production is not changed during the
voltage control.
The adjustment of the reactive power of the inverter
not only has an influence on the voltage of PV but also
has an impact on the voltages of all the nodes of the
grid. For instances, the voltage variations at the same
time period at nodes 106 and 101 illustrated in Figure
7 shows the same behavior. This observation
Figure 7. Temporal voltage and power profiles for the
case of voltage control.
Figure 8. Temporal current and power profiles for the
case of current control.
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3.5. Maximization of end-customer selfconsumption
The private customer with PV installations and electric
boilers, shown in Figure 1, has the potential to
increase its profits from the PV installations by
increasing its self-consumption. The idea is to avoid
selling PV productions to grid with low feed-in-tariff
and buying electricity at high tariff for heating up the
boilers when PV production is not available.
In the optimal control of the boilers, the comfort of the
client, as well as the temperature limits of the boilers,
are respected. As shown in Figure 4, the two boilers
are connected in series. The boiler 1 has narrow
temperature range since the hot water is the output of
this boiler, whereas the temperature of boiler 2 can
vary within a larger range. Note that the temperature
limits of boilers can be set by the user.
The performance of the developed control algorithm
(i.e. DEPsys control algorithm) is studied by making
reference to the existing thermostat control algorithm
of the boiler. For this purpose, the heat demand profile
of the customer is calculated using three months of
data (i.e. 21 April – 26 June), including the
measurements of boilers temperature and their on/off
status. The performances of the DEPsys control
algorithm versus the thermostat control algorithm for a
sunny and a cloudy day are shown in Figure 9.
For every control algorithm, the boilers temperature
and their on/off status as well as the net injection (i.e.
consumption minus production) at the customer
sunny day
cloudy day
Figure 9. Hot water demand profile and using thermostat control and DEPsys control algorithms for switching on and
off the boilers for a sunny day and a cloudy day.
Application of GridEye for Optimal Control of Grid
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connection point to the grid are illustrated. For both
control algorithms, it is observed that the temperature
of boiler 1 remains close to the defined value which
indicates that the comfort of the client is guaranteed.
Though, the boilers are switched on and off less
frequently in the case of DEPsys control than the case
of thermostat control which increases the lifetime of
the boilers. Regarding the net injection, the highlighted
zones for the case of thermostat control indicates the
instances that the boilers are switched on, but the
available PV production is not sufficient, and thus the
required power for heating the boilers is taken from the
grid. Hence, the customer should pay for the power
consumption at these instances. However, the
DEPsys control algorithm maximizes the selfconsumption of the customer by switching on the
boiler when the available PV production is sufficient.
Therefore, the highlighted zones for the case of the
thermostat control are not observed for the case of
DEPsys control.
Based on 3-months of data, the benefits of using
«DEPsys control algorithm» are summarized as
following:
•
•
•
•
the comfort of the client is guaranteed.
self-consumption is increased by 365.6 kWh.
the number of boilers’ switching is decreased by
48% and consequently, boilers’ lifetime is
increased.
electricity payment of boilers is reduced by 68%.
For more information please contact:
DEPsys SA
Route du Verney 20B
1070 Puidoux, Switzerland
Phone : +41 21 546 23 05
Omid Alizadeh-Mousavi
R&D Director
[email protected]
Antony Pinto
Electrical Engineer
[email protected]
Joël Jaton
Chief Technology Officer
[email protected]
www.depsys.ch
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