Experimental Assessment of a Centralised Controller for High-RES Active Distribution Networks

Abstract

This paper assesses the behaviour of active distribution networks with high penetration of renewable energy sources when the control is performed in a centralised manner. The control assets are the on-load tap changers of transformers at the primary substation, the reactive power injections of the renewable energy sources, and the active and reactive power exchanged between adjacent feeders when they are interconnected through a DC link. A scaled-down distribution network is used as the testbed to emulate the behaviour of an active distribution system with massive penetration of renewable energy resources. The laboratory testbed involves hardware devices, real-time control, and communication infrastructure. Several key performance indices are adopted to assess the effects of the different control actions on the system’s operation. The experimental results demonstrate that the combination of control actions enables the optimal integration of a massive penetration of renewable energy.

Full text

energies
Article
Experimental Assessment of a Centralised Controller
for High-RES Active Distribution Networks
Francisco de Paula García-López , Manuel Barragán-Villarejo * ,
Alejandro Marano-Marcolini , José María Maza-Ortega and José Luis Martínez-Ramos
Department of Electrical Engineering, Universidad de Sevilla, Camino de los Descubrimientos s/n,
41092 Seville, Spain; fdpgarcia@us.es (F.d.P.G.-L.); alejandromm@us.es (A.M.-M.); jmmaza@us.es (J.M.M.-O.);
camel@us.es (J.L.M.-R.)
*Correspondence: manuelbarragan@us.es; Tel.: +34-954-481-281
Received: 31 October 2018; Accepted: 28 November 2018; Published: 1 December 2018


Abstract:
This paper assesses the behaviour of active distribution networks with high penetration of
renewable energy sources when the control is performed in a centralised manner. The control assets
are the on-load tap changers of transformers at the primary substation, the reactive power injections
of the renewable energy sources, and the active and reactive power exchanged between adjacent
feeders when they are interconnected through a DC link. A scaled-down distribution network is used
as the testbed to emulate the behaviour of an active distribution system with massive penetration of
renewable energy resources. The laboratory testbed involves hardware devices, real-time control,
and communication infrastructure. Several key performance indices are adopted to assess the effects
of the different control actions on the system’s operation. The experimental results demonstrate
that the combination of control actions enables the optimal integration of a massive penetration of
renewable energy.
Keywords:
active distribution network; laboratory testbed; renewable energy sources; DC link;
centralised control
1. Introduction
Massive penetration of renewable energy sources (RES) is unstoppable nowadays because of
the need to reduce the dependency of fossil fuels. This new technology of generation assets is being
deployed in small units within medium voltage (MV) and low voltage (LV) distribution systems,
the so-called distributed generation, in contrast to the conventional connections of large-scale power
plants to high voltage (HV) systems. The drivers behind this change in the generation paradigm are
threefold: technical because of the maturity of the technology [
1
], economical due to the related
cost reduction [
2
], and social because of the citizen involvement in decarbonising the electrical
consumption [3].
The traditional operation of radial distribution systems cannot be maintained in cases where
there is very high RES penetration, because the design of these systems has been done to cope with
power flows from primary and secondary substations to the final users [
4
]. The problems that RES
may create have been profusely described in specialised literature [
5
], for example, higher simultaneity
coefficients, reverse power flows, out of control nodal voltages, power quality deterioration, increase
of short-circuit power, etc. These technical problems can be released using conventional network
reinforcement strategies ranging from increasing the cross-section of existing lines to installing new
lines and/or power transformers. However, it has to be questioned as to whether this is the best
solution considering the increases in cost and connection time [
6
] as well as the spare capacity of the
Energies 2018,11, 3364; doi:10.3390/en11123364 www.mdpi.com/journal/energies

Energies 2018,11, 3364 2 of 16
new assets over a large number of hours per year [
7
]. Therefore, new alternatives must be explored to
overcome the shortcomings related to this Fit & Forget approach.
Several active network operation approaches have been proposed recently. In general, these can
be classified according to the following characteristics: the control assets used to optimise the network
operation, the applied control algorithms, and the testing procedure used to validate their performance.
Control assets: Regarding the first issue, HV/MV transformers equipped with on-load tap changers
(OLTCs) and step voltage regulators were proposed in [
8
]. In addition, RES may also contribute to
voltage regulation and congestion management by resorting to curtailment [
9
] or even by using
adequate reactive power injections [
10
–
12
]. Most of the major RES inverter manufacturers include
the possibility of controlling the reactive power in order to fulfil the grid codes imposed by the
system operators (either distribution system operators (DSOs) or transmission system operators
(TSOs)). These grid codes are becoming more and more restrictive and they include minimal technical
requirements for RES connections to occur [
13
,
14
], including, among others, voltage regulation issues
by means of reactive power injection.It is important to mention that most of the active operation
approaches consider several control assets that are managed in a coordinated manner, for example,
HV/MV, OLTC, and RES [
15
–
17
]; HV/MV, OLTC, and energy storage systems [
18
,
19
]; and HV/MV,
OLTC, RES reactive power injection, and direct current (DC) links [20].
Control methodology: The active management solutions can be broadly classified into centralised,
distributed, and local methodologies. The centralised approaches rely on a control centre in charge
of computing the optimal setpoints for all the control assets that considers the available network
measurements [
11
]. The main drawback of this approach is the need for an extensive communication
system. Therefore, this solution is suitable for MV distribution systems because of two reasons.
On the one hand, the cost of the communication infrastructure is marginal with respect to the cost
of the large RES units (in the range of several MVA). On the other hand, nowadays, utilities are
equipped with centralised Advanced Demand Management Systems (ADMS) which incorporate
monitoring and automation functionalities. However, it should be considered that a failure of part of
the communication infrastructure may deteriorate the performance of the controller. For this reason,
advanced control strategies providing enhanced system resilience can be found in the specialised
literature [
21
]. Local approaches are just the opposite because the actions taken by the control assets
are calculated based on local measurements [
10
,
12
,
16
,
22
], and therefore, they are suitable for LV
distribution systems. Distributed methods can be considered a compromise between the previous
alternatives as they have several advantages related to robustness and scalability [23,24].
Testing methodology: The methodologies are usually validated by applying steady-state simulations
that consider the daily load and generation profiles. However, other proposals use real-time digital
simulators [23] and power hardware-in-the-loop platforms [19].
The results obtained by some of the previous control approaches can be summarised as follows.
In [
17
], a 32% reduction of power losses was reached by using an adequate RES reactive power injection.
After including the OLTC as an additional control asset, [
16
] reported an extra 7% reduction in total
daily energy losses. Finally, a similar approach that considered the actual capability curves of the RES
units achieved a 14% reduction in power losses [12].
This paper tests the use of centralised control of active assets to manage MV distribution networks
with a massive RES penetration. An Optimal Power Flow (OPF) is used in the centralised control
to compute the optimal setpoints for three kinds of control assets: (1) HV/MV-transformer OLTCs,
(2) RES reactive power injections, and (3) active and reactive power through DC link meshing radial
feeders. A high-RES but realistic load/generation scenario is analysed that considers some test cases
involving different sets of control assets with the aim of evaluating their performance. These test
cases are implemented in a laboratory scaled-down active distribution network including hardware
devices, controllers, communication infrastructure, and a real-time monitoring system, as presented
in [
25
]. This testbed can be used to evaluate practical implementation issues of any centralised
control algorithm related to the applied control strategy, the required data field, the communication

Energies 2018,11, 3364 3 of 16
systems, etc., as a step prior to field deployment. Therefore, the main contribution of this paper is the
experimental validation of the centralised controller proposed in [
20
] within an updated version of the
testbed described in [
25
] in which an OLTC transformer, a DC link, and a new control scheme and
communication system are incorporated.
The paper is organised as follows. In Section 2, a description of the centralised control to manage
high-RES active distribution networks is presented. In Section 3, the benchmark distribution network
is described in detail, including its main components and how they are represented in the laboratory
scaled-down testing platform. Section 4depicts and analyses the system’s performance in different test
cases, comparing them in a quantitative manner by means of key performance indices (KPIs). Finally,
Section 5closes with the main conclusions.
2. Proposed Centralised Control
Smart grids are characterized by extensive measurement, automation, and communication
infrastructures which allows a safe and optimized network operation that takes advantage of
centralised ADMSs. The main role of any ADMS in this environment is to concentrate the field
data to extract the required information about the network status and, in cases where control assets
are in operation, compute and send the required control actions to optimize the network operation
according to a given criterion.
Figure 1depicts this centralised control approach. First, the smart meters are in charge of
measuring the load demanded by industrial (
Pil
and
Qil
) and residential (
Phl
and
Qhl
) clients.
In addition, the RES active power injections, such as the wind turbine (WT) and photovoltaic (PV)
plants, Pwt and Ppv, respectively, are measured.
...
LC
Advanced Distribution
Management System
HV
RTU
MV
DC link
VSC1
VSC2
OLTC
... ...
...... ...
Figure 1. Architecture of the centralised control of an active distribution system.
All field data are sent to the ADMS by means of Remote Terminal Units (RTUs) at regular time
intervals (typically 5 to 15 min). Considering all this information, it is possible to compute setpoints for
the installed control assets using an OPF to optimize any technical or economic objective. This paper
considers the following control assets:
•RES, which can regulate the reactive power injections Qopt
wt and Qopt
pv .
•Transformer OLTCs, which can adjust the tap position topt.
•
A DC link, which is composed of two Voltage Source Converters (VSCs) in a back-to-back topology
connecting two radial feeders. This device can regulate the active power flow between the feeders,

Energies 2018,11, 3364 4 of 16
Popt
link
, and two independent reactive power injections,
Qopt
vscj
. It is important to point out that the
DC link is an interesting control asset with proven capability to reduce the network active power
losses, maximize the penetration of RES, improve the network voltage profiles, and avoid branch
saturations [20,26].
On the other hand, the selected OPF objective is to minimize the active power losses of the system
to take advantage of the already available control assets to optimise the operation of the distribution
grid, which leads to the following formulation:
min
xPloss(x,y), (1)
where
x
is the set of control variables (
Popt
link
,
Qopt
vscj
,
Qopt
wt,pv
,
topt
) and
y
is the set of load and generation
power injections for a given time interval (Pil,Qil,Phl ,Qhl ,Pwt,Ppv ).
The optimization problem is completed by including the relevant constraints. First, the network
operational limits have to be considered. The voltages and currents of the sets of buses,
N
, and
branches,
B
, have to be within the regulatory boundaries,
[Vmin
i
,
Vmax
i]
, and below the cable ampacities,
Imax
b, respectively, as stated in (2) and (3):
Vmin
i≤Vi≤Vmax
i∀i∈ N , (2)
0≤Ib≤Imax
b∀b∈ B. (3)
Second, the OLTC tap has to be within the limits and the apparent power levels of the RES and
DC-link VSCs have to be below their rated capability according to (4)–(6):
tmin ≤to pt ≤tma x, (4)
Spv,wt ≤Srat
pv,wt , (5)
SDClink ≤Srat
DClink . (6)
Finally, other constraints which are included in the OPF are the active and reactive bus power
balances and the power constraints that model the DC link behaviour, which can be found in [26].
3. Laboratory Testing Platform
The objective of building the laboratory testing platform was to faithfully represent the real
behaviour of an active distribution system including all of its components to asses the performance
of the centralised control strategy outlined in Section 2. In this way, the testing platform was built
based on the MV benchmark distribution network proposed by the International Council on Large
Electric Systems (CIGRE in french) Task Force C06.04.02 devoted to study the RES integration in MV
networks [27]. The main reasons that motivated the selection of this system are detailed below:
•
First, this network is based on an actual MV German distribution system, fulfilling the proposed
objective of the laboratory testing platform described above.
•Second, an important RES penetration is integrated into the network.
•
Third, all the network data, including topology, parameters of lines and cables, loads, RES, and
their corresponding daily load/generation curves are available and are well documented.
•
Fourth, the benchmark network includes a DC link, a key component of the future active
distribution system with high RES penetration.
The next subsections present the MV benchmark distribution system and its scaled-down version
built in the laboratory for testing purposes, including the implemented control scheme and the
communication infrastructure designed to operate the system as a flexible platform to evaluate the
benefits of active distribution networks.

Energies 2018,11, 3364 5 of 16
3.1. MV Benchmark Distribution Network
A one-line diagram of the benchmark distribution system is shown in Figure 2which is composed
of two radial subsystems departing from a primary substation where a 40 MVA 110/20 kV transformer
equipped with an OLTC is installed. The total network comprises 14 buses grouped in two radial
feeders: 11 buses for subsystem 1 and 3 buses for subsystem 2. The total line length of subsystem 1 is
about 15 km, while subsystem 2 is just 8 km. In addition, different types of load, involving industrial
and domestic customers as well as a large amount of RES, are connected into the different buses.
Although [
27
] considered different types of RES, this work exclusively included PV and WT plants
because its current maturity foresees that they will be massive deployed in upcoming years. In addition,
the benchmark network includes a DC link to connect both radial subsystems between nodes N8
and N14.
PV plant
WT plant
Subsystem 1
N14
N13
N1
N2
N3
N4
N5 N10
N9
N6
N7
N8
N11
Disconnected in
normal operation
110/20 kV
40 MVA
Load
VSC1 VSC2
N12
Subsystem 2
DC link
Figure 2.
The medium voltage (MV) benchmark distribution network proposed by the CIGRE Task
Force C06.04.02.
The 24-h profiles of the total loads and RES of subsystems 1 and 2 are depicted in Figure 3. It is
interesting to point out that subsystem 1 was more loaded than subsystem 2. Moreover, most RES were
located within subsystem 1 which partially compensate for its higher load with this local generation.
It is also worth noting that, in order to analyse a case with a massive RES penetration, the generation
was multiplied by 4 and 400 in the case of the WT and PV plants, respectively, with respect to the
scenario described in [
27
]. In this way, the peak generation of the RES units and the peak demands of
the loads during the day were established at 0.446 pu and 0.381 pu, respectively (the base power of the
MV system was 100 MVA). The ratio between the peak generation and the peak demand was equal to
1.1724—a scenario of high-RES penetration.

Energies 2018,11, 3364 6 of 16
0.0
0.1
0.2
0.3
0.4
Act./React. Power (pu)
PSub1
Load
PSub2
Load
QSub2
Load
QSub2
Load
0 4 8 12 16 20 24
Time (h)
0.0
0.1
0.2
0.3
0.4
Active Power (pu)
WTSub1
PVSub1
PVSub2
Figure 3. Top
: Daily profile of the total loads in subsystems 1 and 2;
Bottom
: Daily profile of the total
WT and PV generation in subsystems 1 and 2.
3.2. Laboratory Scaled-Down Distribution Network
This subsection provides a brief outline of the components and functionalities of the scaled-down
testbed used to validate the benefits of the centralised controller. Basically, this hardware test rig,
depicted in Figure 4, is a three-phase scaled-down 400 V (base/rated voltage) and 100 kVA (base/rated
power) representation of the MV benchmark network described in Section 3.1 which is composed of
the following components:
•
Distribution network branches: The electrical lines of both scaled-down subsystems are
represented by a lumped parameter model comprising the series resistor and reactor. The per unit
values of these impedances are identical to those of the actual MV system. Therefore, the original
line R/X ratios and equivalent lengths are maintained, leading to similar per unit voltage drops
and power losses. Table 1collects the exact values of the resistors and reactors used in the
scaled-down network.
•
Omnimode Load Emulators (OLEs): These are the building blocks that are responsible for
representing any load, generator, or a combination of the two connected to any network node.
Basically, each OLE is a VSC with a local controller (LC) whose AC and DC sides are connected to
a scaled-down network node and a common DC bus, respectively, as shown in Figure 4. The VSC
is a three-phase, three-wire, two-level insulated gate bipolar transistor (IGBT) VSC, rated at 400 V,
20 kVA with a switching frequency of 10 kHz. LCL coupling filters are used to connect the AC-side
of the VSC to the scaled-down network. The inductors and the capacitor have the following
ratings: L1 = L2 = 2.5 mH and C = 1
µ
F. Note that all of the OLEs share a common DC bus which
is regulated by an extra balancing VSC rated to 100 kVA. This is directly connected to the LV
laboratory network by its AC side, providing the net active power required by OLEs:
∑Pi
. In this
way, each OLE may absorb/inject (load/generator) any active power into the AC scaled-down
distribution system within the technical constraints imposed by the VSCs. The OLEs are connected
to the following nodes: N3, N5, N6, N7, N8, N9, and N10 (subsytem 1), and N14 (subsystem 2).
The active and reactive power references to the OLEs are set by a Signal Management System
(SMS) which is detailed in the next subsection.

Energies 2018,11, 3364 7 of 16
A comprehensive description of this scaled-down system can be found in [
25
]. In addition,
two new elements were incorporated with respect to the system described in [
25
] with the aim of
integrating additional active control resources:
•
Transformer with OLTC: The underlying idea of this feature is to represent the HV/MV
transformers within the primary substations which are equipped with OLTCs to regulate the MV
voltage. The transformer used for this purpose is a 400 V
±
5%/400 V, 100 kVA equipped with a
thyristor-based tap changer, as shown in Figure 4.
•
DC link: This DC link, originally included in the benchmark distribution system [
28
],
is incorporated between N8 and N14 as a suitable device to maximise the RES penetration,
as stated previously. Although several topologies can be used to create a flexible loop between
radially operated feeders [
29
], the DC link is based on conventional back-to-back VSCs rated at
400 V and 10 kVA. Note that the DC bus of the DC link is totally independent of the one shared by
the OLEs and the balancing VSC.
The optimal setpoints for these two control assets are also managed by the SMS in a similar
manner to that of the OLE power references.
Branches SMS
OLEs DC link
Scaled-down
Subsystem 1
Scaled-down
Subsystem 2
Balancing
VSC LV laboratory network
Scaled-down
Subsystem 1
Scaled-down
Subsystem 2
OLEN1 OLEN14
SMS
SMS
From
Balancing
VSC
P and Q Injected Powers
Signal References
DC link
Local Controller
VSC1VSC2
... ... ... ...
OLTC
N8 N14
N1
(RTCS)
(Host PC)
SMS
(Host PC/RTCS)
Figure 4. Left
: Layout of the laboratory testbed.
Right
: One-line diagram of the updated testbed
including the DC link and the transformer with the on-load tap changer (OLTC).
Table 1. Values of the resistors and reactors of each branch of the scaled-down network.
Initial Node End Node Resistance (mΩ) Reactance (mΩ)
N1 N2 60.00 39.25
N2 N3 25.00 15.75
N3 N4 5.00 3.25
N4 N5 10.00 3.25
N5 N6 25.00 7.75
N6 N7 5.00 1.50
N7 N8 25.00 7.75
N8 N9 5.00 1.50
N9 N10 10.00 3.25
N10 N11 5.00 1.50
N11 N12 10.00 3.25
N3 N8 10.00 6.25
N12 N13 60.00 62.50
N13 N14 25.00 15.75

Energies 2018,11, 3364 8 of 16
3.3. Control Scheme and Communication System
The control system is a two-level hierarchical structure, as shown in Figure 5. The first control
level comprises the SMS, which is in charge of sending the references to the hardware components,
whereas the second control level is composed of several LCs attached to the hardware devices (OLEs,
DC link and OLTC) that are responsible for tracking these references.
The SMS performs two tasks in a sequential manner which can be summarised as follows:
•
Offline tasks: They are carried out by a host PC and mainly consist of the configuration of the
setpoint profiles. The OLE active and reactive daily power curves (
P?
i
,
Q?
i
) are defined through
two tools developed in the host PC [
25
]. Once these profiles have been determined, the daily
setpoints of the DC link,
Popt
link
and
Qopt
vscj
, the reactive power injected by the RES,
Qopt
wt,pv
, and the
optimal OLTC tap position,
topt
, are automatically computed by the OPF described in Section 2.
These setpoints and their computations are new features that are incorporated into the host PC
with respect to [
25
]. Finally, all these data are compiled and uploaded to the Real-Time Control
System (RTCS) for real-time operation.
•
Online tasks: These are executed by the RTCS which is responsible for two undertakings. On the
one hand, the RTCS is in charge of sending the setpoints to the second control level composed of
the LCs attached to each hardware controllable component during the online operation according
to the profiles previously determined in the offline tasks. On the other hand, the RTCS receives
measurements from each each LC attached to the OLEs (
Vi
,
Pi
and
Qi
), DC-link VSCs (
Vvscj
,
Plink
and
Qvscj
) and the tap position of the transformer OLTC (
topt
). After processing this information,
it provides real-time monitoring of the system which is displayed in the host PC.
The second level of the control system is composed of the LCs of each OLE, the DC-link VSCs,
and the transformer OLTC which are implemented in Digital Signal Processors. These are in charge of
tracking the setpoints sent by the RTCS during the online operation.
The communication infrastructure required to connect the centralised RTCS with the LCs is
based on a 100 MBs Ethernet local-area network as a physical layer that implements a communication
protocol based on UDP/IP. Finally, an asynchronous communication protocol, TCP/IP, is implemented
between the host PC and the RTCS.
UDP (1 s)
Control Signals
Signal Management System
POWER
SIK 5/04 CPU
RUN
FLT
BATT
FORCE
ENET
RS232
RUN REM PROG
OUTPUT
POWER
INPUT
1 2 3 4
5 6 7 8
9 10 11 12
13 14 15 16
DARKLY
COMM FAULT
OUTPUT
1 2 3 4
5 6 7 8
9 10 11 12
13 14 15 16
COMMUNICATION
RX TX FLT
LCiOLEi
OLTC
LCjj
DC link
LCT
i=N3, N5, N6, N7,
N8, N9, N10, N14 j=1,2
Real-Time Control System
TC/IP (Async.)
Host PC
i
First Level Second Level
Figure 5. General control scheme of the testing environment.

Energies 2018,11, 3364 9 of 16
4. Experimental Assessment of the Proposed Centralised Control
This section describes the analysis of the performance of the centralised control on the scaled-down
system under different test cases. These are evaluated through KPIs to quantify the influence of the
considered control assets in high-RES active distribution networks.
4.1. Definitions of Test Cases
Table 2shows the definitions of the designed test cases. The first case, C1, is the base case
where no control assets are included in the distribution system and the OLTC is set in the central tap
position. The subsequent test cases add the control assets in the centralised control in an incremental
manner. In this way, it should be possible to quantify the impact that each control asset has on the
system’s performance.
Table 2. Definitions of test cases.
Control Assets C1 C2 C3 C4
OLTC • • •
RES reactive power • •
DC link •
4.2. Definitions of KPIs
The following KPIs were selected to analyse the performance of the centralised control and its
related control assets:
•
Daily energy loss (
Eloss
/
∆Eloss
): This KPI measures the daily active energy loss in kWh/day,
Eloss
,
and the percentage of loss reduction with respect to the base case, C1, ∆Eloss .
•
Voltage violation (
Tvv
): This KPI evaluates the percentage of time during the day that which the
nodal voltages are outside the technical limits [0.95–1.05 pu].
•
Variation of nodal voltages (
∆V
): This index provides a global measurement of the daily voltage
variations at the nodes of the network. It is computed as the average value of the difference
between the maximum and minimum nodal voltages, measured in pu,
∆V=∑N(Vmax
i−Vmin
i)
Ni , (7)
where Ni is the total number of network nodes.
•
OLTC operation (
NOLTC
): This KPI shows the number of OLTC operations that occur during the
24-h testing period.
•
RES reactive power injection (
QRES
): This index provides a global measurement of the RES
collaboration to the network reactive power support. It is computed by dividing the average
value of the reactive power injected by the RES during the 24-h period by the total number of RES,
QRES =∑i,tQRESi,t
Nt×NRES
, (8)
where
QRESi,t
is the reactive power injected by
RESi
in period
t
,
NRES
is the number of RES in the
network, and Ntis the number of time periods considered during the 24-h period.
•
DC link load (
SLlink
): This evaluates the daily average load of the DC link during the day, and it
is computed as
SLlink =∑j,tSvscj,t
Nt×SDClink
, (9)
where Svscj is the apparent power of each VSC and SDClink is the rated power of the DC link.

Energies 2018,11, 3364 10 of 16
•
Transformer load (
TL
): This represents the daily average load of the transformer as a percentage
of its rated power, which can be computed as
TL=∑tST
t
Nt×SN
, (10)
where
ST
is the apparent power through the transformer and
SN
is the rated power of
the transformer.
4.3. Experimental Results
The objective function proposed for the operation of high-RES active distribution networks is
based on an operation with minimal technical losses. This section describes the evaluation of the
previously described test cases, which involved the analysis of the following electrical magnitudes:
power losses, nodal voltages, and current circulating at the primary substation transformer. In addition,
the previously defined KPIs allowed the key magnitudes to be quantified in a comprehensive manner
to assess the performance of the proposed control.
Table 3shows the
Eloss
for the studied test cases and the loss reduction with respect to the base
case, C1,
∆Eloss
, when the load and generation daily profiles presented in Section 3were implemented
into the testing platform. In the laboratory testbed, the 24-h profiles were scaled to the last 48 min and
the duration of the tests was reduced.
Table 3. Key performance indices (KPIs) used for the evaluation of the test cases.
C1 C2 C3 C4
Eloss/∆El oss (kWh/%) 58.37/−55.69/4.58 50.17/16.33 46.47/25.59
QRES (pu) − − 0.117 0.095
Tvv (%) 38.69 0 0 0
NOLTC 0 2 4 2
∆V(pu) 0.087 0.061 0.058 0.042
TL(%) 24.95 24.43 20.62 20.20
C1 presented the greatest daily power losses as no control assets were operating to act on the
voltages and power flows to reduce the system losses. The introduction of the OLTC operation in C2
reduced energy losses by almost 5%. The OLTC setpoint was computed in the OPF whose objective
function was to reduce the total power losses in the network. Therefore, the tap was established in the
−5% position to increase the nodal voltages and to achieve the intended objective.
In test case C3, the RES reactive power capability was also included in the control. This caused
the daily energy losses to be reduced by more than 15% with respect to C1. This occurred because
the RES were able to provide reactive power to the system. Figure 6shows the RES reactive power
injected at nodes N3 and N8 with respect to their rated power levels for test cases C3 and C4. This is
represented using violin plots which allow the distribution of any magnitude as well as its range of
variation and frequency of occurrence to be visualized. Note that most of the time, which corresponds
to the wider part of the violin plot, the RES were injecting reactive power corresponding to 20% of their
rated power levels. This high RES reactive power injection was used to provide part of the reactive
power demanded by the loads, thus avoiding the need to supply it from the primary substation, as
shown in Figure 7. Note that the reactive power supplied from the primary substation in C3 was lower
than 0.05 pu during the 24-h period, helping to reduce the energy losses.
The DC link integration in C4 further reduced the energy losses by up to 25% with respect
to C1, as shown in Table 3. This device injected reactive power at the interconnected nodes N8
and N14 by means of VSC1 and VSC2 respectively during the 24-h period, as depicted in Figure 8.
This power, added to the RES reactive power, led to almost zero reactive power being supplied from
the primary substation, as shown in Figure 7. In this way, the energy losses reduced with respect to C3.
An additional effect on the RES reactive power injections was observed. In C4, the RES did not to have

Energies 2018,11, 3364 11 of 16
to inject as much reactive power as in C3, as can be observed in Figure 6, even becoming zero in some
nodes, like N8. This effect was quantified in a global manner with
QRES
collected in Table 3, where
lower values for this KPI in C4 with respect to those in C3 can be appreciated. Table 4summarises
the rated power and the reactive power injections of the RES units in C3 and C4. The second and
third columns indicate the rated power of the RES used in the scaled-down system and the MV
system respectively. The two last columns depict the maximum reactive power injected by the VSCs
interfacing the RES units during the day in cases C3 and C4. These values refer to the rated power
of each device. The RES connected to N5 injected the maximum amount of reactive power, reaching
31.45% of its rated power. With the current technology, these reactive power values are easily reachable
due to the combined effect of two actions: (i) the VSC coupling reactance is becoming smaller by using
LCL filters, and (ii) the VSC DC voltage is continuously increasing. This extends the VSC reactive
power range.
Table 4.
Rated power and maximum reactive power injection of the renewable energy source (RES)
units in C3 and C4.
RES Connected to Bus Srat ed (kVA) Scaled down System Srat ed (MVA) MV System QC3
max (pu) QC4
max (pu)
N3 12 12 0.3116 0.2584
N5 12 12 0.3145 0.3144
N6 12 12 0.2372 0.2372
N7 7 7 0.0309 0.0309
N8 12 12 0.2552 0.0100
N9 12 12 0.1147 0.1147
N10 16 16 0.1588 0.1588
C3 C4
0.0
0.1
0.2
0.3
0.4
Reactive Power (pu)
N3
C3 C4
0.0
0.1
0.2
0.3
0.4
Reactive Power (pu)
N8
Figure 6. Violin plots of RES reactive power injections for test cases C3 and C4 at nodes N3 and N8.
Notice that the DC link also controlled the active power transferred from subsystem 1 to
subsystem 2, as shown in Figure 8. Outside the period of high injection of RES active power (0–10 h
and 13–0 h), the DC link absorbed active power from N14 and injected it into N8. This meant that
part of the load from subsystem 1 was powered by subsystem 2 which is less loaded and has shorter
branches, helping to reduce the total power losses of the system. Conversely, within the hours of high
RES active power injection, the active power flow was inverted in the DC link: VSC1 absorbed active
power from subsystem 1 and it was injected by the VSC2 into subsystem 2. In this way, part of the
power generated by RES in subsystem 1 was transferred to feed the loads in subsystem 2. Therefore,
this active power was not supplied by the primary substation, thus reducing the current in this system
and the energy losses.
Finally, note that the DC-link loading,
SLlink
, during the day was 49.4%. This means that the DC
link was used at half load and there is therefore still a wide margin to take advantage of its flexibility
of operation. For example, the RES penetration in subsystem 1 could increase and still be managed by
the current DC link.

Energies 2018,11, 3364 12 of 16
0 4 8 12 16 20 24
Time (h)
0.00
0.05
0.10
0.15
0.20
0.25
Reactive Power (pu)
C1
C2
C3
C4
Figure 7. Reactive power flow through the primary substation for test cases C1–C4.
−1.2
−0.8
−0.4
0.0
0.4
0.8
1.2
Act./React. Power (pu)
PVSC1
QVSC1
0 4 8 12 16 20 24
Time (h)
−1.2
−0.8
−0.4
0.0
0.4
0.8
1.2
Act./React. Power (pu)
PVSC2
QVSC2
Figure 8. DC link active and reactive power daily profiles.
Figure 9shows the 24-h nodal voltages at nodes N3, N6, N8, and N14 for the different test cases.
These buses were selected to represent the behaviour of nodes nearby (N3) and far from (N6) the
primary substation. In addition, nodes N8 and N14 were also included because they are the connection
points of the DC link. The analysis of Figure 9reveals that undervoltage situations—voltages below
0.95 pu—exclusively occurred in the base case, C1, due to the lack of control assets operating in the
network. This situation led to a very high
Tvv
value in C1, as shown in Table 3. These voltage violations
were more severe at nodes N6 and N8 corresponding to subsystem 1 because of two reasons. First,
subsystem 1 was more loaded than subsystem 2, as depicted in Figure 3, especially during the hours
without RES generation. This caused greater current flows and, consequently, greater voltage drops
along the lines. This effect was especially significant around 8 and 20 h when the RES generation was
almost zero and the demand was peaking.

Energies 2018,11, 3364 13 of 16
0.90
0.95
1.00
1.05
1.10
Voltage (pu)
N3
0.90
0.95
1.00
1.05
1.10
Voltage (pu)
N6
C1 C2 C3 C4
0.90
0.95
1.00
1.05
1.10
Voltage (pu)
N8
C1 C2 C3 C4
0.90
0.95
1.00
1.05
1.10
Voltage (pu)
N14
Figure 9. Violin plots of nodal voltages for test cases C1 to C4 at nodes N3, N6, N8, and N14.
The introduction of the OLTC in C2 pushed the voltages within the
±
0.05 pu regulatory band
around the rated voltage and, consequently, voltage violations were eliminated, as illustrated by its
Tvv
. In C2, the tap was established in the
−
5% position for most of the day. However, according to the
information provided in Table 3, two OLTC operations
NOLTC
(from
−
5% to 0% position) over the 24-h
period were required to maintain the voltages within the limits. These changes occurred at around 11 h
and 13 h when RES generation was maximum, as shown in Figure 3, and the network voltages were
excessively high. The range of variation of nodal voltages
∆V
was significantly reduced with respect to
C1, as shown in Table 3. This effect can also be observed in Figure 9where the violin plots are shortened,
concentrating the nodal voltages within a narrower band. This trend was maintained in C3 due to the
contribution of RES to the regulation of voltage with reactive power injections. In addition, it can be
seen that the average voltage of nodes N3, N6, and N8 from subsystem 1 increased due to the local
effect of the reactive power injections. As a consequence, additional OLTC changes
NOLTC
(from
−
5%
to 0% position) were required to maintain the voltages within the technical limits. This longer time of
the tap within the 0% position caused lower voltages within subsystem 2, as can be observed for the
node N14 in Figure 9.
C4 incorporated the operation of the DC link between nodes N8 and N14 allowing the injection
of additional reactive power into these nodes and active power transfer between both subsystems.
This led to a minimum range of variation in the nodal voltages
∆V
and maximum values of these
in all the test cases. In fact, in C4, the voltages oscillated in a range between 1 and 1.05 pu over the
24-h period.
Figure 10 shows the daily evolution of the current circulating through the primary substation
transformer for the studied test cases. This current reduced as the number of control assets increased.
The analysis of C4 revealed that during some periods, the current was almost zero. This means that the
generation of RES with adequate management by the control assets is enough to operate the system
without the need of supplementary power from the primary substation. Finally, it is worth noting that
the state of load of the transformer
TL
also progressively reduced in the subsequent test cases, as shown
in Table 3. As a consequence, the benefits for the distribution system are clear in this respect: reduction
of transformer losses, increment of useful life, and increase of the system loadability, which allows
new investment in power assets to be deferred.

Energies 2018,11, 3364 14 of 16
C1 C2 C3 C4
0.0
0.1
0.2
0.3
0.4
0.5
Current (pu)
Figure 10. Violin plots of MV current at the primary substation transformer for test cases C1 to C4.
5. Conclusions
This paper assessed the benefits of a centralised controller for active distribution networks
with high-RES penetration in an experimental manner. The paper proposed the optimization of the
operation of the system through the minimization of active power losses through an OPF with the
following control assets: (i) transformers equipped with OLTC, (ii) RES reactive power injections,
and (iii) DC links. The assessment of the proposed centralised controlled was carried out on a
laboratory scaled-down version of the MV network that was proposed by the CIGRE Task Force
C06.04.02. This testing platform was described, including its main components and functionalities
as well as the new control assets (transformer OLTC and DC link) which were incorporated into a
previous version to improve its testing capabilities. The paper defined a comprehensive design of
the testing procedure including some test cases involving different control assets and a set of KPIs to
allow a quantintative comparison of performance. The obtained results revealed that a centralised
control of high-RES active distribution networks may improve their operation. As a matter of fact,
the obtained results, ranging from 15% to 25% of active power loss reduction, are consistent with
those of similar works commented on in Section 1. Moreover, this improvement is significant for
control assets which are commonly present in distribution networks, i.e., transformers with OLTC
and RES reactive power injections. This enhancement could be even larger if uncommon but matured
technologies, like DC links, were progressively introduced into the distribution business. This would
increase the RES network hosting capacity, contributing to the decarbonization of our society.
Author Contributions:
F.P.d.G.-L reviewed the state-of-the-art, designed the test cases and the control algorithms,
was responsible for laboratory testing and discussion of the obtained results, and wrote part of the paper; M.B.-V.
supported the laboratory testing, contributed to the analysis of the results and wrote part of the paper; A.M.-M.
was responsible for the OPF definition, contributed to the analysis of results, and wrote part of the paper; J.M.M.-O.
defined the KPIs, contributed to the discussion of results, and wrote part of the paper; J.L.M.-R. supported the
OPF definition and practical implementation issues, and discussed the results of the paper.
Funding:
The authors would like to acknowledge the financial support of the Spanish Ministry of Economy and
Competitiveness under Grants ENE2015-69597-R, PCIN-2015-043 and ENE2017-84813-R.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
ADMS Advanced Distribution Management System
CIGRE International Council on Large Electric Systems
DC Direct Current
DSO Distribution System Operator

Energies 2018,11, 3364 15 of 16
HV High Voltage
IGBT Insulated Gate Bipolar Transistor
KPI Keys Performance Index
LC Local Controller
LV Low Voltage
MV Medium Voltage
OLE Omnimode Load Emulator
OPF Optimal Power Flow
OLTC On-Load Tap Changer
PV Photovoltaic
RES Renewable Energy Sources
RTCS: Real-Time Control System
RTU Remote Terminal Unit
SMS Signal Management System
TSO Transmission System Operator
VSC Voltage Source Converter
WT Wind Turbine
References
1.
Guerrero, J.M.; Blaabjerg, F.; Zhelev, T.; Hemmes, K.; Monmasson, E.; Jemei, S.; Comech, M.P.; Granadino, R.;
Frau, J.I. Distributed Generation: Toward a New Energy Paradigm. IEEE Ind. Electron. Mag.
2010
,4, 52–64.
[CrossRef]
2.
Distributed Generation System Characteristics and Costs in the Buildings Sector; Technical Report; U.S. Energy
Information Administration: Washington, DC, USA, 2013.
3.
Pérez-Arriaga, I.; Knittel, C. Utility of the Future. An MIT Energy Initiative Response; Technical Report;
Massachusetts Institute of Technology: Cambridge, MA, USA, 2016.
4. Power Distribution Planning Reference Book, 2nd ed.; CRC Press Book: Boca Raton, FL, USA, 2004.
5.
Barker, P.P.; Mello, R.W.D. Determining the impact of distributed generation on power systems. I.
Radial distribution systems. In Proceedings of the 2000 Power Engineering Society Summer Meeting
(Cat. No.00CH37134), Seattle, WA, USA, 16–20 July 2000; Volume 3, pp. 1645–1656.
6. Walling, R.A.; Saint, R.; Dugan, R.C.; Burke, J.; Kojovic, L.A. Summary of Distributed Resources Impact on
Power Delivery Systems. IEEE Trans. Power Deliv. 2008,23, 1636–1644. [CrossRef]
7.
Assessing the Impact of Low Carbon Technologies on Great Britain’s Power Distribution Networks. 3
August 2012. Available online: https://www.ofgem.gov.uk/publications-and-updates/assessing-impact-
low-carbon-technologies-great-britains-power-distribution-networks (accessed on 5 April 2018).
8.
Elkhatib, M.E.; El-Shatshat, R.; Salama, M.M.A. Novel Coordinated Voltage Control for Smart Distribution
Networks With DG. IEEE Trans. Smart Grid 2011,2, 598–605. [CrossRef]
9.
Ueda, Y.; Kurokawa, K.; Tanabe, T.; Kitamura, K.; Sugihara, H. Analysis Results of Output Power Loss Due to
the Grid Voltage Rise in Grid-Connected Photovoltaic Power Generation Systems. IEEE Trans. Ind. Electron.
2008,55, 2744–2751. [CrossRef]
10.
Molina-García, A.; Mastromauro, R.A.; García-Sánchez, T.; Pugliese, S.; Liserre, M.; Stasi, S. Reactive Power
Flow Control for PV Inverters Voltage Support in LV Distribution Networks. IEEE Trans. Smart Grid
2017,8, 447–456. [CrossRef]
11.
Calderaro, V.; Galdi, V.; Lamberti, F.; Piccolo, A. A Smart Strategy for Voltage Control Ancillary Service in
Distribution Networks. IEEE Trans. Power Syst. 2015,30, 494–502. [CrossRef]
12.
Karagiannopoulos, S.; Aristidou, P.; Hug, G. Hybrid approach for planning and operating active distribution
grids. Transm. Distrib. IET Gener. 2017,11, 685–695. [CrossRef]
13.
Puerto Rico Electric Power Authority (PREPA). Minimum Technical Requirements (MTR) for Photovoltaic
Generation (PV) Projects; Technical Report; Puerto Rico Electric Power Authority (PREPA): San Juan,
Puerto Rico, 2012.
14.
National Energy Regulator of South Africa (NERSA). Grid Connection Code for Renewable Power Plants (RPPs)
Connected to the Electricity Transmission System or the Distribution System in South Africa; Technical Report;
National Energy Regulator of South Africa (NERSA): Pretoria, South Africa, 2012.

Energies 2018,11, 3364 16 of 16
15.
Alnaser, S.W.; Ochoa, L.F. Advanced Network Management Systems: A Risk-Based AC OPF Approach.
IEEE Trans. Power Syst. 2015,30, 409–418. [CrossRef]
16.
Kryonidis, G.C.; Demoulias, C.S.; Papagiannis, G.K. A Nearly Decentralized Voltage Regulation Algorithm
for Loss Minimization in Radial MV Networks With High DG Penetration. IEEE Trans. Sustain. Energy
2016,7, 1430–1439. [CrossRef]
17.
Kolenc, M.; Papiˇc, I.; Blažiˇc, B. Minimization of losses in smart grids using coordinated voltage control.
Energies 2012,5, 3768–3787. [CrossRef]
18.
Alnaser, S.W.; Ochoa, L.F. Hybrid controller of energy storage and renewable DG for congestion management.
In Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 22–26 July
2012; pp. 1–8.
19.
Liu, X.; Aichhorn, A.; Liu, L.; Li, H. Coordinated Control of Distributed Energy Storage System with
Tap Changer Transformers for Voltage Rise Mitigation Under High Photovoltaic Penetration. IEEE Trans.
Smart Grid 2012,3, 897–906. [CrossRef]
20.
Barragan-Villarejo, M.; Marano, A.; García-López, F.P.; Mauricio, J.M.; Maza-Ortega, J.M. Coordinated
control of distributed energy resources and flexible links in active distribution networks. In Proceedings of
the International Conference on Renewable Power Generation (RPG 2015), Beijing, China, 17–18 October
2015; pp. 1–6.
21.
Marano-Marcolini, A.; Villarejo, M.B.; Fragkioudaki, A.; Maza-Ortega, J.M.; Ramos, E.R.; de la Villa Jaén, A.;
Delgado, C.C. DC Link Operation in Smart Distribution Systems With Communication Interruptions.
IEEE Trans. Smart Grid 2016,7, 2962–2970. [CrossRef]
22.
Fazio, A.R.D.; Fusco, G.; Russo, M. Decentralized Control of Distributed Generation for Voltage Profile
Optimization in Smart Feeders. IEEE Trans. Smart Grid 2013,4, 1586–1596. [CrossRef]
23.
Kulmala, A.; Alonso, M.; Repo, S.; Amaris, H.; Moreno, A.; Mehmedalic, J.; Al-Jassim, Z. Hierarchical and
distributed control concept for distribution network congestion management. IET Gener. Transm. Distrib.
2017,11, 665–675. [CrossRef]
24.
Almasalma, H.; Claeys, S.; Mikhaylov, K.; Haapola, J.; Pouttu, A.; Deconinck, G. Experimental Validation of
Peer-to-Peer Distributed Voltage Control System. Energies 2018,11, 1304. [CrossRef]
25.
Maza-Ortega, J.M.; Barragán-Villarejo, M.; García-López, F.d.P.; Jiménez, J.; Mauricio, J.M.;
Alvarado-Barrios, L.; Gómez-Expósito, A. A Multi-Platform Lab for Teaching and Research in Active
Distribution Networks. IEEE Trans. Power Syst. 2017,32, 4861–4870. [CrossRef]
26.
Romero-Ramos, E.; Gómez-Expósito, A.; Marano-Marcolini, A.; Maza-Ortega, J.M.; Martínez-Ramos, J.L.
Assessing the loadability of active distribution networks in the presence of DC controllable links. IET Gener.
Transm. Distrib. 2011,5, 1105. [CrossRef]
27.
Rudion, K.; Orths, A.; Styczynski, Z.A.; Strunz, K. Design of benchmark of medium voltage distribution
network for investigation of DG integration. In Proceedings of the 2006 IEEE Power Engineering Society
General Meeting, Montreal, QC, Canada, 18–22 June 2006; p. 6.
28.
Benchmark Systems for Network Integration of Renewable and Distributed Energy Resources.
Available online: http://www.e-cigre.org/publication/575-benchmark-systems-for-network-integration-
of-renewable-and-distributed-energy-resources (accessed on 2 November 2016).
29.
Maza-Ortega, J.M.; Gomez-Exposito, A.; Barragan-Villarejo, M.; Romero-Ramos, E.; Marano-Marcolini, A.
Voltage source converter-based topologies to further integrate renewable energy sources in distribution
systems. IET Renew. Power Gener. 2012,6, 435–445. [CrossRef]
c
2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).