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Centralized and distributed active and reactivepower control of a utility connected microgrid using
IEC61850Alba Colet-Subirachs, Albert Ruiz-Alvarez, Oriol Gomis-Bellmunt, Member, IEEE,Felipe Alvarez-Cuevas-Figuerola, Antoni Sudria-Andreu, Senior Member, IEEE
Abstract—This paper describes the control algorithm of a util-ity connected microgrid, based on independent control of activeand reactive power and operating in centralized and distributedoperation mode. The microgrid is composed of several (three inthe experimental tests conducted) configurable units includinggeneration, storage and load units. These units are interfacedwith the microgrid through a Voltage Source Converter (VSC)and are controlled by the nodes of the communication system bymeans of IEC 61850. The proposed control strategy is validatedby simulation in Matlab/Simulinkr and with experimental tests.
Index Terms—Microgrid, control algorithm, IEC 61850.
NOMENCLATURE
AcronymsCHP Combined Heat and PowerRTC Real Time ClockSoC State of ChargeVSC Voltage Source Converter
Subscript Superscriptsi Number of iSocket ∗ Set pointM Maximumm Minimump Active powerq Reactive power
I. INTRODUCTION
THE need for more reliable and flexible power systemsalong with the great potential of modern control and
communication systems and power electronics, has led todevelopment of the smart grid concept [1]–[3]. Modern gridswill be required to be active and to adapt to a number offault events ensuring the system optimum performance duringand after faults occur. Furthermore, modern grids will haveto integrate the increasing penetration of renewable energy of
A. Colet-Subirachs, A. Ruiz-Alvarez, O. Gomis-Bellmunt and A. Sudria-Andreu are with Catalonia Institute for Energy Research (IREC), Electri-cal Engineering Area, C Josep Pla, 2, edifici B2, Planta Baixa - 08019Barcelona, Spain (e-mail: acolet@irec.cat, aruiz@irec.cat, ogomis@irec.cat,asudria@irec.cat)
O. Gomis-Bellmunt and A. Sudria-Andreu are with Centre d’InnovacioTecnologica en Convertidors Estatics i Accionaments (CITCEA-UPC), De-partament d’Enginyeria Electrica, Universitat Politecnica de Catalunya, ETSd’Enginyeria Industrial de Barcelona, and EU d’Enginyeria Tecnica Industrialde Barcelona, Barcelona - 08028, Spain (e-mail: gomis@citcea.upc.edu,antoni.sudria@citcea.upc.edu)
F. Alvarez-Cuevas-Figuerola is with Endesa Servicios.sl, Av Paral·lel, 51 -08004 Barcelona, Spain (e-mail: felipe.alvarezcuevas@endesa.es)
intermittent nature. This can be achieved using more flexiblepower systems including power electronics, energy storagesystems, demand side management and microgrids.
A microgrid is defined as an aggregator of several microgen-eration units, storage devices and controllable loads operatingas a single system that provides electricity and thermal energy[4], [5]. Microgrid is a concept that incorporates distributedenergy resources (DER), including distributed generation (DG)and distributed storage (DS). To manage it, a network ofcommunication devices must provide the microgrid with thenecessary intelligence to allow customers and utility compa-nies to collaboratively manage the power generated, deliveredand consumed through real-time, bidirectional communica-tions. Thus, communications are essential in order to ensurethe proper operation of all the microgrid components [6]. Pro-tection devices, control commands and power flow regulation,together with real-time measurements must be integrated ina network that provides the necessary levels of quality andreliability. The architecture of this network can be centralized,distributed or hierarchical. In a hierarchical architecture thereare different types of controllers that, depending on the controllevel, can be classified into: Distribution Management System(DMS), Microgrid Central Controller (MGCC) and LocalControllers (LC) [7].
A control strategy must be devised to ensure the long-termstable operation of the microgrid under various load conditionsand different configurations [8], [9]. Depending on the timeframe this control algorithms can be separated in:• Primary, there is a momentary adjust,• Secondary, the operation range is several minutes [10]
and• Tertiary, the operation range is 15-20 minutes.
The microgrid can operate in grid-connected or stand-alonemode. Concerning the methods used for managing the powerof a microgrid two different classes can be distinguished: thecentralized operation that concentrates information in a nodeand the decentralized operation which uses local informationand provides more autonomy to the DER units [11], [12].
The present paper focuses on a simple method for con-trolling the active and reactive power of a utility connectedMicrogrid operating in grid-connected mode. It is based onpeer-to-peer and plug-and-play concepts [13] and operateseither in centralized or in distributed mode. Around the worldthere are a number of active microgrid projects involved intesting control strategies and operational modes [14]. This
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paper presents the design, simulation and experimental resultsof a control algorithm implemented in a utility connectedmicrogrid. Communications between devices is based on thestandard IEC61850.
The paper has been structured as follows. Section II de-scribes the microgrid under investigation and the projectframework. The control algorithm to manage the microgridis presented in section III. The overall system is simulatedin Matlab/Simulinkr (section IV). Section V describes themicrogrid test platform where the control algorithms arecurrently being tested. Finally, the results are analysed insection VI.
II. MICROGRID CONCEPT
The Catalonia Institute for Energy Research (IREC) iscurrently developing a microgrid based on real and emulatedenergy resources in order to evaluate different scenarios [15].This paper describes a part of such microgrid that is alsoinvolved in the Smartcity project located in Malaga (southof Spain) and managed by Endesa, the local utility.
The Malaga Smartcity project is a demonstration projectin which it is intended to deploy and integrate the followingitems in the current grid:• A highly reliable and efficient Broadband Power Line
communications framework.• Micro generation and micro storage within the low-
voltage grid.• Mini generation and mini storage within the medium-
voltage grid.• A small fleet of bi-directional electrical vehicles.• A new and efficient street lighting system.• A few thousands of smart meters.• Improved grid self healing automation.The communication architecture of such city is composed
of a hierarchical layer system. The bottom layers are embodiedby these two elements:
iNode (Intelligent Node): Develops the global managementof microgrid tasks and connects supervising and control sys-tems (through a Gateway) to the terminal equipment (iSocket).Its functions are managing the data received from iSockets andsetting overall operation of the microgrid, developing its ownalgorithms. Its main tasks include:• Regulation: Control of energy generation and consuming
entities.• Billing: Energy measurement and real-time pricing.• Management: Asset management and condition based-
maintenance.• Metering: full system monitoring.• Security of the microgrid electrical system
The operational requests of this controller are aggregation andcoordination of iSockets and electrical safety guarantee.
iSocket (Intelligent Socket): It is an element located inthe lowest hierarchy layer of the communication system. Ithandles the device connected to it (generation, storage orloading), based on the instructions received from the iNode.The operational requests of this controller are local regulationand electrical safety guarantee.
III. CONTROL ALGORITHM
This paper proposes implementing a control algorithm withthe capability to achieve the system goals using the availableunits, interfaced to the microgrid through a voltage-sourceconverter (VSC). The algorithm uses decision laws that havecertain parameters to manage the microgrid and establishesthe equilibrium points of the system in a indirect manner.It is based on independent control of active and reactivepower in grid connected mode (PQ control) and consists onsecondary and tertiary microgrid’s control. One advantage ofthis algorithm is its simplicity in terms of the reduction ofinformation flow and data exchange between all nodes in thenetwork and the fast adaptability to configuration changes inthe microgrid, such as unit losses or reconnections.
Two operational control modes are considered:A The centralized control mode suggests that a central node,
iNode, collects the microgrid measurements (sent fromiSockets) and decides next actions according to the utilitygoals.
B The distributed control mode suggests that advancedcontrollers are installed in each node forming a dis-tributed control system. These controllers are completelyindependent from the superior nodes and only use theelectric market price for the regulation.
To analyze the control algorithm it is important to take intoconsideration the sign criteria used in this paper (1):{
P < 0→ GenerationP > 0→ Load
{Q < 0→ CapacitiveQ > 0→ Inductive
(1)
A. Centralized operation mode
This operational mode centralizes the control of all devicesin the iNode, which controls the whole microgrid to followgiven active and reactive power references.
1) iNode controller: iNode uses two independent PI con-trollers (Fig. 1), which are responsible for controlling thewhole active and reactive power flux of the microgrid.
pPk
iPk
s
Active Power PI Controller
Integrator
*P
totP
pFSaturation
Reactive Power PI Controller
Integrator
*Q
totQ
Saturation
qFpQk
iQk
s
Fig. 1. iNode PI controller
According to Fig. 2, the iNode has the following inputs:• P ∗ [W], Q∗ [VAr]: active and reactive power set points
from the utility
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• Ptot [W], Qtot [VAr]: total active and reactive measure-ments which can be obtained as a direct value providedby the metering system or as a calculated value using thepower sent by each iSocket, i.e, the sum of all active andreactive power of the connected iSockets
e the current price of energy received from the utility[ce/kWh].
The iNode outputs are:• Fp active power control signal and• Fq reactive power control signal,
where −100 6 Fp 6 100 and −100 6 Fq 6 100.
1
I-NODECentralized operationP*, Q* FP, FQ
P1, Q1
VSC unit
Pi, Qi
P1, Q1
P1*, Q1*
P2, Q2
P2*, Q2*
P3, Q3
P3*, Q3*
PN, QN
2
Priority Load
Diesel generator3
VSC unit
VSC unit
Non-priority Load
Pi, Qi
Pi*, Qi*Battery
banki
VSC unit
PN*, QN*Wind
turbineN
VSC unit
P2, Q2
P3, Q3
PN, QN
Fig. 2. Centralized operation block description
2) iSocket controllers: iSockets receive the control signalsFp and Fq and apply equations (3) and (4) to calculatethe active and reactive power to set (P ∗i , Q∗i ) to the VSCconnected to them.
P ∗i =
Pi,M βpi < Fp (2a)
Pi,m+(Pi,M − Pi,m)Fp − αpi
βpi − αpiβpi≥Fp≥αpi(2b)
Pi,m αPi > Fp(2c)
with βpi > αpi
Q∗i =
Qi,M βqi < Fq(3a)
Qi,m+(Qi,M −Qi,m)Fq − αqi
βqi − αqiβqi≥Fq≥αqi(3b)
Qi,m αqi > Fq(3c)
with βqi > αqi
These equations are piecewise-defined functions dividedinto different sections depending on the power profile to beset to each microgrid unit. As an example, Fig. 3 shows theprofile corresponding to a load that has two states: connected(P > 0) and disconnected (P = 0). When Fp ≥ βpi the loadswitches from the disconnected to the connected mode and itdisconnects when αpi ≥ Fp. Finally, if βpi ≥ Fp ≥ αpi, itscurrent state depends on the previous situation.
However, this paper considers a power profile divided intothree sections (Fig. 4): the first equations (2a, 3a) corresponds
100
Pi*
FPi,m
βpi αpi-100
Generation area
Consumption area
i,M
Fig. 3. On/off load profile function set by the iSocket
to the maximum power to be set (Pi,M , Qi,M ), the secondsection (2b, 3b) is a ramp between maximum and minimumpower, and the third (2c, 3c) corresponds to the minimumvalue (Pi,m, Qi,m).
100
Pi*
Fp
i,m
βpi αpi-100
Generation area
Consumption area
100
Qi*
Fq
-100
i,m
αqi βqi
i,M
Capacitive area
Inductive area
i,M
Fig. 4. General power profile functions set by the iSocket
The parameters α and β, configured at each iSocket, areused to define the power limits and the participation prioritiesof the microgrid units. Its value is recalculated dynamically:as there is a change in the state of a microgrid unit, theiSocket interfaced with it perceives this variation, and modifiesα and β. Also, these parameters are recalculated according tovariables such as the energy price.
Depending on the type of microgrid unit, iSockets aredivided into three main cases:
a) Generation iSockets: A generation node i will becharacterized by:{
αpi = αpi0 − kEei,c + kp · eβpi = βpi0 − kEei,c + kp · e
{αqi = αqi0
βqi = βqi0(4)
Where
αpi0, βpi0, αqi0, βqi0 are a selectable parameters use toprioritize each power generation source,ei,c is the generation cost [ce/kWh],kE is a multiplier of the generation cost andkp is a multiplier of the energy cost.
b) Storage iSockets: A storage node i will be character-ized by: αpi = αpi0 − kwWi,a + kpe
βpi = βpi0 − kw(Wi,M −Wi,a) + kpeαqi = αqi0
βqi = βqi0
(5)
Where
Wi,M is the maximum storable energy (100%)Wi,a is the available energy (0-100%) andkw is a multiplier of the available energy.
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c) Load iSockets:{αpi = αpi0 − krei,crc + kpe
βpi = βpi0 − krei,crc + kpe
{αqi = αqi0
βqi = βqi0(6)
Where
ei,crc is the cost of load reduction [ce/kWh] andkr is a multiplier of the load reduction cost.
For reactive power control, αqi and βqi parameters can beequal for all units as in the previous expressions or voltagedependant. If they are equal reactive power will be proportion-ally divided between units. This can imply important voltagedeviations if some nodes are located at large distances andthere are important impedances between them. In this cases,αqi and βqi parameters can be voltage dependant, so that thenodes with different voltages can eventually contribute moreor less to the total reactive power reference.
B. Distributed operation mode
In this operational mode iSockets become more independentwithin the operating area. They manage themselves and do notreport their decisions to iNode. As can be seen in Fig. 5 there isno feedback of the metered variables Pi and Qi, from the VSCunit to the iNode since the iSocket manages that information.
iSockets calculate their local active and reactive powerusing the same expressions as in centralized operation modeusing Fp = 0 and Fq = 0, therefore (choosing appropiateα and β parameters) the microgrid will respond to energyprice increases connecting more generation, disconnectingnon-critical loads and selling the energy stored in storageunits. For energy price reductions the microgrid units willrespond storing energy, disconnecting high cost generatorsand connecting loads. The resulting microgrid operating pointsobtained depend on energy prices and the availability of energystorage systems.
1
I-NODEDistributed operation
VSC unit
P1, Q1
P1*, Q1*
P2, Q2
P2*, Q2*
P3, Q3
P3*, Q3*
PN, QN
2
Priority Load
Diesel generator3
VSC unit
VSC unit
Non-priority Load
Pi, Qi
Pi*, Qi*Battery banki
VSC unit
PN*, QN*Wind
turbineN
VSC unit
Fig. 5. Distributed operation block description
IV. SIMULATION STUDY
The proposed control scheme has been simulated usingMatlab/Simulinkr. The scenario presented consists of sixnodes (Figure 6):
TABLE IMICROGRID SIMULATION PARAMETERS
Parameters Generation nodes Storage nodes Load nodesiSocket1 iSocket2 iSocket3 iSocket4 iSocket5 iSocket6
Pi,m [W] -4000 -3000 -4000 -4000 0 0Pi,M [W] 0 0 4000 4000 3000 2000αPi0 40 -60 -180 -180 -120 -80βPi0 80 -20 60 60 -60 -20kp 0 0 6 6 2 2kE 0 0 - - - -kw - - 1.2 1.2 - -kr - - - - 1 0
• Generation nodes:– iSocket1 is managing a wind turbine of a 4kW rated
power. It is a priority generation node due to therenewable nature of the source. In such case theα and β parameters are configured to operate as apriority generator and have a high value, near upperlimit of Fp.
– iSocket2 is a diesel generator of 3kW rated power.Its use has associated a cost of generation.
• Storage nodes: there are two storage nodes. Both controla battery with the same characteristics but different stateof charge. The power profile set to each of them dependson this state.
– iSocket3 controls a 20% charged battery.– iSocket4 controls a 75% charged battery.
• Load nodes– iSocket5 manages a priority load which need to be
supplied almost uninterruptedly, as a result the α andβ parameters are configured to operate as a priorityload and had a value near the lower limit of Fp.
– iSocket6 controls a conventional dispatching load.Table I shows the description of the parameters used in thesimulation.
FP
EeneP6*
iSocket6
FP
EeneP5*
iSocket5
FP
EeneP4*
iSocket4
FP
EeneP3*
iSocket3
FP
EeneP2*
iSocket2
FP
EeneP1*
iSocket1P*
P1
P2
P3
P4
P5
P6
FP
iNode P
P_sp P
con6
P_sp P
con5
P_sp P
con4
P_sp P
con3
P_sp P
con2
P_sp P
con1
t
conP6
conP5
conP4
conP3
conP2
P1
conP1Eene
P6
P5
P4
P3
P2
FP
Ptot
Clock
Fig. 6. Microgrid Matlab/Simulinkr block description
Static and the dynamic analysis have been conducted. Inboth cases the analysis is focused on active power. Reactivepower would be shared proportionally by all the involved units.The cases studied have focused on changes that occur due tothe variation of energy price.
A. Steady state analysisA case study is presented to illustrate the equilibrium points
of the proposed algorithm. These equilibrium points can be
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calculated using the equations (??)-(6). Fig. 7 shows themicrogrid operating point when Fp = −22 and and e = 10ce/kWh, which corresponds to a generation of 2417 W. Thisvalue is derived from the sum of power set points provided bythe microgrid units. As it can be seen in the right graph, whenFp increases, the total power set to the microgrid increases too.Likewise the left graph shows the layout of the parameters αp
and βp for each iSocket. As an example it can be observed thatthe conventional load, iSocket6, has αp6 = −60 (P6,m =0 W)and βp6 = 0 (P6,M =2000 W) and at the point in the studyit has a reference power consumption of 1267W (P ∗6 =1267W).
-100 -80 -60 -40 -20 0 20 40 60 80 1000
1
2
3
4
5
6
7
-4000 W 0 W
-4000 W 1 Wind turbine
-3000 W 0 W
-150 W 2 Diesel generator
-4000 W 4000 W
-3467 W 3 Battery 75%
-4000 W 4000 W
933W 4 Battery 20%
0 W 3000 W
3000 W 5 Priority load
0 W 2000 W
1267 W 6 Conventional load
FP
iSoc
ket
-100 -50 0 50 100-1.5
-1
-0.5
0
0.5
1
1.5x 10
4
-2417 W
FP
Pow
er [
W]
Fig. 7. Microgrid steady state analysis when e = 10 ce/kWh and Fp = −22
If there is a significant increase in energy price (from10 to 15 ce/kWh), the price sensitive elements respond tosuch a change. In this case (Fig. 8), the load and storageiSockets scroll right. So the microgrid modifies its responseand at the studied point, Fp = −22, increases the generation,P ∗ =5284 W. In this new scenario, the parameters of iSocket6are αp6 = −50 and βp6 =10 and its consumption is reducedto 933 W. Therefore, when the energy price increases a 50%,the conventional load is reduced by a 26,4%.
-100 -80 -60 -40 -20 0 20 40 60 80 1000
1
2
3
4
5
6
7
-4000 W 0 W
-4000 W 1 Wind turbine
-3000 W 0 W
-150 W 2 Diesel generator
-4000 W
-4000 W 3 Battery 75%
-4000 W 4000 W
-1067 W 4 Battery 20%
0 W 3000 W
3000 W 5 Priority load
0 W 2000 W
933 W 6 Conventional load
FP
iSoc
ket
-100 -50 0 50 100-1.5
-1
-0.5
0
0.5
1
1.5x 10
4
-5284 W
FP
Pow
er [
W]
Fig. 8. Microgrid steady state analysis when e = 15 ce/kWh and Fp = −22
B. Dynamic analysis
The system behavior in centralized mode has been studiedwith step changes of the reference power P ∗ in time intervalsof 30s. The energy price is fixed at 10 ce/kWh. Fig. 9 and 10show the system response.
0 50 100 150-1
-0.5
0
0.5
1x 10
4
Time [s]
Pow
er [
W]
Ptot
P*
0 50 100 150-50
0
50
Time
FP
T0
T0
Fig. 9. Microgrid dynamic set points when e = 10 ce/kWh
As can be seen in Fig. 9 the sum of powers of themicrogrid, Ptot, fits the sequence of values entered, P ∗ ={8000, 0,−5500} W. Likewise, the control signal Fp, changesits operating point according to the PI output of the iNode.Figure 10 shows the power provided by each microgridunit, Pi. According the sign criteria established in (1), thegeneration units (iSocket 1 and 2) remain at the bottom of thegraph and the loads (iSocket 5 and 6) at the top. The windturbine (iSocket1), presents a fluctuating power curve due tothe turbulent wind flow implemented in the simulation. On theother hand, the priority load is almost supplied uninterruptedlyand remained stable at 3000W.
Comparing the results of the steady state analysis and thedynamic analysis at Fp = −22 and e =10 ce/kWh (Figure7 and 10), it can be seen that the metered values of eachiSocked match reference values, except for the wind turbine(P ∗1 = −4000W and P1 = −1647W) because its generationdepends on available wind power.
0 50 100 150
-4000
-3000
-2000
-1000
0
1000
2000
3000
4000
Time [s]
Pow
er [
W]
1 Wind turbine2 Diesel generator3 Battery 75% 4 Battery 20% 5 Priority load6 Conventional load
T0
P6=1267W
Fig. 10. Microgrid Pi response for a e = 10ce/kWh during the dynamicsimulation
V. SYSTEM DESCRIPTION
The microgrid experimental platform under investigation(Fig. 11) is composed of two main systems: the communi-cation system and the power system.
A. Power system description
The microgrid power system is composed of a three con-figurable units:
6
Wide Area Network
Microgrid comunication bus
I-SOCKET 1Generation unit
I-NODE
I-SOCKET 2Storage system
I-SOCKET 3Consumption unit
CONTROL CENTER
IEDs
IED
Ethernet
IEC 61850
CAN Bus CAN Bus CAN Bus
ACDC
DCAC
ACDC
DCAC
ACDC
DCAC
LV grid
Emulator 2Emulator 1 Emulator 3
Eolic generation group
Energy storage group
Load group
400V/400V4kVA
400V/400V4kVA
400V/400V4kVA
VSC
VSC VSC
VSC
VSC
VSC
Fig. 11. Microgrid functional block description
• Generation unit: emulates different types of generationsuch as wind and solar, reproducing the real behaviour,and in the case of renewable energy sources, reproducingthe variable nature and dependence on external climato-logical factors.
• Load unit: emulates the real behaviour of different typesof consumption based on sensitive-loads and/or non-sensitive-loads using various load profiles.
• Energy storage unit: emulates a storage system which,according to needs, can be either a battery or an electricvehicle.
The use of these configurable units (shown in Fig. 12)allows reproducing the scenarios that are deemed of interestwithout having to wait for appropriate weather conditions.The reconfigurability property of them allows emulating anysituation generating or consuming real power.
Each unit of the microgrid power system has two VSCcomposed of a three-phase inverter, AC and DC transducersand protective devices. Its design enables utilization as aconfigurable renewable generation emulator, a battery bankor a load. The converters are connected in a back to backconfiguration, so while one acts like a bidirectional boost-rectifier, the other one transfers power to the grid according tothe set point given by the iSocket. Therefore, there is a powerflow through these devices in which only converter losses areconsumed.
B. Communication system description
The communication system is composed of four nodes: oneiNode and three iSockets. Each node is implemented in a
Fig. 12. Microgrid power units configuration
Linux embedded control board with the following character-istics:• ARM9 based microcontroller, 180MHz• 32MByte flash memory• 32MByte SDRAM memory• Isolated 10/100 Base-T Ethernet• Isolated CAN• RTC• 12 isolated digital inputs• 2 isolated digital outputsDepending on the communication layer, a different commu-
nication protocol is used:• Communication between iNode and iSocket is done with
the standard IEC 61850 [16], [17]. Fig. 13 shows the setof objects defined in the microgrid using this standard[18]. The IEC 61850 series defines an abstract com-munication architecture that provides a hierarchical setof classes associated to services. These services, calledACSI (Abstract Communication Service Interface), aremapped to MMS (Manufacturing Message Specification).Furthermore, it uses a configuration language and anobject model to describe the system components, knownas IEDs (Intelligent Electronic Device).
M
LN DRCT: DER unit controller characteristics
LN DRCS: DER unit status
LN DRCC: DER unit control actions
LN FSEQ: Sequencer
LN DCCT: DER ecnonomic dispatch parameters
LN MMTR: Metering
LN CSWI: Switch controller
LN XCBR: Circuit creaker
LN ZINV: Inverter
LN ZRCT: Rectifier
LN MMDC: DC Measurements
LN MMXU: AC Measurements
LD LOAD
M
LN DRCT: DER unit controller characteristics
LN DRCS: DER unit status
LN DRCC: DER unit control actions
LN FSEQ: Sequencer
LN DCCT: DER ecnonomic dispatch parameters
LN MMET: Metereological conditions
LN MMTR: Metering
LN CSWI: Switch controller
LN XCBR: Circuit creaker
LN ZINV: Inverter
LN ZRCT: Rectifier
LN MMDC: DC Measurements
LN MMXU: AC Measurements
LD WIND TURBINE
GLN DGEN: DER unit generator
LN DRAT: DER generator ratings
M
LN DRCT: DER unit controller characteristics
LN DRCS: DER unit status
LN DRCC: DER unit control actions
LN FSEQ: Sequencer
LN DCCT: DER ecnonomic dispatch parameters
LN MMTR: Metering
LN CSWI: Switch controller
LN XCBR: Circuit creaker
LN ZINV: Inverter
LN ZRCT: Rectifier
LN MMDC: DC Measurements
LN MMXU: AC Measurements
LN ZBAT: Battery
LN ZBTC: Battery charger
LD BATTERY
Fig. 13. Set of objects defined in the microgrid test bench with IEC 61850standard
• Communication between iSocket and its appropriate VSCuses a CAN proprietary protocol. The iSocket transmitsa data frame that contains a command word, a P ∗i and
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Q∗i to be set to the VSC. The VSC answers with threeCAN messages, which contain the status of the unit andother information such as Pi and Qi.
Data exchange between iNode and iSockets will be mon-itored in a IEC 61850 SCADA. Fig. 14 shows the graphicdisplay of the remote microgrid viewed by the operator. In thisapplication the software system is only monitoring the remotestatus and alarm conditions of the microgrid. The monitoredvalues of the microgrid units are: the active and reactivepowers, the ac and dc voltages and currents, the temperatureand the supplied energy among others. The SCADA has thenecessary data to analyse the response of the microgrid to thevarious tests performed.
Fig. 14. Microgrid IEC 61850 SCADA
VI. EXPERIMENTAL RESULTS
On the basis of the previously described experimentalsystem, experimental test were performed. To develop thesetests, the microgrid units are configured to emulate a dis-patching load (managed by iSocket3), a battery (managed byiSocket2) and a micro wind turbine (managed by iSocket1).The battery is characterized by a Ni-Cd model described inTable II (to study changes in the battery SoC during testing,its capacity has been reduced to 1 A/h). The wind power curveimplemented in the wind turbine emulator is shown in TableIII. Control parameters of iSockets are in Table IV, resultingin the control equations of Table V.
The proposed control strategy is validated by experimen-tal tests in two different operational modes: centralized anddistributed.
1) Centralized operation mode: To test the microgrid re-sponse in centralized operation mode, two case studies areconsidered: In the first the energy price is constant and in thesecond it changes.
a) Case Study 1 constant energy price: Consideringconstant energy price of e = 10 ce/kWh, different active andreactive power references has been applied according to TableVI. The microgrid response is shown in Fig. 15. The batterystate of charge plot shows the evolution of the battery state ofcharge. The power plot, shows the active power evolution ofthe three units: wind turbine, battery and load. The active andreactive total power plot shows the whole microgrid active
TABLE IICONFIGURATION CHARACTERISTICS OF THE BATTERY EMULATOR
Characteristics SoC
Capacity = 25 A/h Cut voltage = 541,6 V 0%Nominal voltage = 650 V Discharge voltage = 650 V 10%
Charge voltage = 715 V 80%Overcharge voltage = 845 V 100%
Battery state of charge
650
700
750
800
850
dc Voltage
500
550
600
650
700
750
800
850
1 11 21 31 41 51 61 71 81 91
dc Voltage
Battery State of Charge [%]
TABLE IIICONFIGURATION CHARACTERISTICS OF THE WIND TURBINE EMULATOR
Characteristics
Sweep area= 3.8m2 ρ = 1.2kg ·m2
Micro wind turbine available power
‐3500
‐3000
‐2500
‐2000
‐1500
‐1000
‐500
0
0 100 200 300 400 500 600 700 800 900 1000Time [s]
‐4500
‐4000
‐3500
‐3000
‐2500
‐2000
‐1500
‐1000
‐500
0
0 100 200 300 400 500 600 700 800 900 1000
Power [W]
Time [s]
TABLE IVMICROGRID TEST PARAMETERS
Parameters iSocket1 iSocket2 iSocket3Pi,m [W] -4000 -4000 0Pi,M [W] 0 4000 3000Qi,m [VAr] -3000 -3000 -3000Qi,M [VAr] 3000 3000 3000αPi0 40 -180 -100βPi0 80 60 -30αQi0 -100 -100 -100βQi0 100 100 100kp 0 6 2kE 0 - -kw - 1.2 -kr - - 0
and reactive powers. The control signals plot illustrates thecontrollers outputs Fp and Fq . Finally, the Eene plot the energyprice, which is constant during the 600 seconds analysed.
It can be seen in Power plot (Fig. 15) that the power curvesshape of the wind turbine and the battery are mirrored (thebattery compensates the wind power fluctuation) to ensurethat the overall power reference is tracked. In the Active andReactive total power plot (Fig. 15), it can be checked that thevalue corresponding to the active and reactive power, Ptot andQtot, is in accordance with the reference values, P ∗ and Q∗.The control signals sent from the iNode to the iSocket areshown in the control signals plot.
In the time interval t0 7→ t1 the active power reference is 0W, the battery SoC decreases slowly because the wind turbinecan almost supply the load and few power from the batteryis needed. In the interval t1 7→ t3, the microgrid reference isP ∗ =5000 (t1 7→ t2) and 3000 W (t2 7→ t3). The controller
8
Fig. 15. Centralized operation Microgrid response (Case study 1)
charges the battery to have the demanded P ∗ resulting inincreased battery SoC . As the SoC of the battery increases, itis more reluctant (α and β parameters change) to charge. Thus,to keep P ∗, the control signal Fp is increased and the powergenerated by the wind turbine is reduced. When the battery isfully charged, t3 7→ t4, the unique consumption is the load andto maintain the P ∗ set point, the wind turbine must disconnect.In the time interval t4 7→ t5 the microgrid generates 4000 Wby discharging the battery, reducing the load consumption andreconnecting the wind turbine. The battery SoC steady dropsto a 38%. At t4 7→ t5 there is an inductive reactive powerreference. Finally, in t5 7→ t6 there is a capacitive reactivepower reference.
b) Case Study 2 non-constant energy price: To analysethe effect of energy price change, Figure 16 and Table VIImust be considered. Table VII shows that the active powerreference is maintained at 1000 W during all the test, whilethe energy price is increased in t1 7→ t2. The energy priceincrease implies a new equilibrium that makes the controllerincrease Fp in order to maintain the active power reference.
A. Distributed operation mode
Fig.17 shows the system response with respect to thechanges on energy price, during a time interval of 200 seconds.It can be seen that the battery is sensible to the changesin the energy price. As it increases (t0 7→ t1 to t1 7→ t2),the microgrid reduces the consumption (P 07→1
tot = 908W toP 17→2tot = −886W) and the battery tends to discharge. If there
is a drop on energy price the microgrid consumes more powerfrom the grid and charges the battery (Table VIII).
VII. CONCLUSIONS
This paper has presented a control algorithm of a utility con-nected microgrid. The control scheme is based on independentcontrol of active and reactive power. The microgrid operationmodes include centralized and distributed operation. The mi-crogrid is controlled by a central iNode and several iSocketsfor each unit. The controllers to be implemented in each nodehave been discussed along with the required communicationsystems. The proposed scheme has been validated by means
9
TABLE VMICROGRID TEST FUNCTIONS
iSocket1 iSocket2 iSocket3
Graphical instruction definition
-100 100
4000
-4000
P1* [W]
Fp
βP1=80αP1=40
-100 100
4000
-4000
P2* [W]
FpβP2=60
αP2=-60-100 αP3=-80
3000 W
100
4000
-4000
P3* [W]
FpβP3=-10
αpi and βpi functions
{αp1 = 40
βp1 = 80
αp2 = −180 + 6e
+1.2Waβp2 = 60 + 6e
−1.2(100−Wa)
{αp3 = −100 + 2e
βp3 = −30 + 2e
αqi and βqi functions
{αq1 = −100
βq1 = 100
{αq2 = −100
βq2 = 100
{αq3 = −100
βq3 = 100
P ∗i =
0
−4000+ 4000βp1−αp1
(Fp−αp1)
−4000
4000
−4000+ 8000βp2−αp2
(Fp−αp2)
4000
3000
3000βp3−αp3
(Fp −αp3)
0
Q∗i =
3000
−3000+ 9000βq1−αq1
(Fq−αq1)
−3000
3000
−3000+ 9000βq2−αq2
(Fq−αq2)
−3000
3000
−3000+ 9000βq3−αq3
(Fq−αq3)
−3000
TABLE VICENTRALIZED OPERATION MICROGRID RESPONSE
Interval P ∗[W] Q∗[VAr]t0 7→ t1 0 0t1 7→ t2 5000 0t2 7→ t3 3000 0t3 7→ t4 3000 0t4 7→ t5 -4000 675t5 7→ t6 -4000 -350t6 7→ t7 -1000 0t7 7→ 1000 0
TABLE VIICENTRALIZED OPERATION MICROGRID RESPONSE UNDER A ENERGY
PRICE CHANGE
Interval P ∗[W] e[ce/kWh] Fp
t0 7→ t1 1000 10 [-10, 12]t1 7→ t2 1000 15 [24, 37]t2 7→ 1000 10 [-10, 0]
TABLE VIIIMICROGRID DISTRIBUTED OPERATION RESPONSE
Interval e [ce/kWh] Ptot [W] P2 [W]
t0 7→ t1 10 908 0t1 7→ t2 15 -886 -1650t2 7→ t3 5 2551 2330t3 7→ 20 -2844 -3580
of simulations and experimentally. The experimental microgridis composed of three configurable units: a generation unit, astorage unit and a load unit. These units are interfaced with
Fig. 16. Centralized operation microgrid response under a energy pricechange (Case study 2)
2000
4000
Power [W]P3: Load [W]
P2: Battery [W]
t1 t2 t3t0
214 52
266
‐4000
‐2000
0
2000
4000
0 50 100 150 200
Power [W]P3: Load [W]
P2: Battery [W]
P1: Wind turbine [W]
t1 t2 t3
2000
6000Total Power Ptot: Metered
real power [W]
t0
‐4000
‐2000
0
2000
4000
0 50 100 150 200
Power [W]P3: Load [W]
P2: Battery [W]
P1: Wind turbine [W]
t1 t2 t3
10
15
20Eene [c€/kWh]
Energy price
‐6000
‐2000
2000
6000
0 50 100 150 200
Total Power
Time [s]
Ptot: Metered real power [W]
t0
203 40,87
243,87
1,27232689
‐4000
‐2000
0
2000
4000
0 50 100 150 200
Power [W]P3: Load [W]
P2: Battery [W]
P1: Wind turbine [W]
t1 t2 t3
0
5
10
15
20
0 50 100 150 200
Eene [c€/kWh]
Time [s]
Energy price
t3t2t1t0
‐6000
‐2000
2000
6000
0 50 100 150 200
Total Power
Time [s]
Ptot: Metered real power [W]
t0
134,1 65,4
199,5
107 49,6
156,6
1,31854839
Fig. 17. Microgrid distributed operation response
the microgrid through a Voltage Source Converter (VSC) andare controlled by the nodes of the communication system bymeans of IEC 61850. Results have shown good performancefor different scenarios.
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Alba Colet-Subirachs received the Industrial Engi-neering degree from the school of Industrial Engi-neering of Barcelona (ETSEIB), Technical Univer-sity of Catalonia (UPC), Barcelona, Spain, in 2009.She worked at the Centre of Technological Inno-vation in Static Converters and Drives (CITCEA-UPC) in a cooperation project in which she gotthe experience in programming DSP applied tothe power electronics converters. She is currentlyinvolved in Smart Electrical Grid projects at theCatalonia Institute for Energy Research (IREC). Her
research interests include renewable energy integration in power systems andpower electronics.
Albert Ruiz-Alvarez received the degree in in-dustrial engineering from the School of IndustrialEngineering of Barcelona (ETSEIB), UniversitatPolitecnica de Catalunya (UPC). He obtained dis-tinction in his Electrical Final Dissertation entitled:Design and implementation of a device for powertransformers monitoring, in December 2009. In May2009, he became a member of the Catalonia Institutefor Energy Research (IREC), in the Power Electron-ics and Electrical Power Grids research area, wherehe works as a Project Engineer. In IREC he has
carried out projects related to smart grids, IEC 61850 and renewable energies.
Oriol Gomis-Bellmunt (S’05-M’07) received thedegree in industrial engineering from the Schoolof Industrial Engineering of Barcelona (ETSEIB),Technical University of Catalonia (UPC), Barcelona,Spain, in 2001 and the PhD in electrical engineeringfrom the UPC in 2007. In 1999 he joined EngitrolS.L. where he worked as project engineer in theautomation and control industry. In 2003 he de-veloped part of his PhD thesis in the DLR (Ger-man Aerospace center) in Braunschweig (Germany).Since 2004 he is with the Electrical Engineering
Department of the UPC where he is lecturer and participates in the CITCEA-UPC research group. Since 2009 he is also with the Catalonia Institute forEnergy Research (IREC). His research interests include the fields linkedwith smart actuators, electrical machines, power electronics, renewable energyintegration in power systems, industrial automation and engineering education.
Felipe Alvarez-Cuevas Figuerola is responsiblefor Telecontrol projects of Endesa network Factory.He was recently strongly involved in the processof standardization of Telecontrol protocols in theEndesa Group. Within this project, he has beenworking since the last eight years in the automationof a few hundreds of hydro power plants and HVsubstations and a few thousands of MV substations.Currently he is responsible for integration of theEndesa?s Smart City Project in Malaga.
Antoni Sudria-Andreu (M’95,SM’05) receivedM.Sc.E.E and Ph.D. degrees from UniversitatPolitecnica de Catalunya (UPC), Barcelona, Spain,in 1979 and 2005. In 1979 he joined ElectronicsEngineering Department, UPC, as Assistant Pro-fessor. He joined the Power Electronics ResearchDepartment of a railway industry in 1980. In 1985he joined the Electrical Engineering Department asAssociate Professor. In 2001 he founded the Centerof Technological Innovation in Static Convertersand Drives (CITCEA-UPC). He is the head also of
Electrical Engineering research area of the Catalonia Institute for EnergyResearch (IREC) since January 2009.
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