Comput Sci Res DevDOI 10.1007/s00450-016-0308-5

SPECIAL ISSUE PAPER

GridBox pilot projectA platform for monitoring and active control of distribution grids

Alain Brenzikofer1 · Florian Müller1 · Alexandros Ketsetzis2 · Florian Kienzle2 ·Marco Mangani2 · Marc Eisenreich3 · Yamshid Farhat3 · Rainer Bacher4

© The Author(s) 2016. This article is published with open access at Springerlink.com

Abstract GridBox is an open platform for monitoring andactive control of distribution grids. It is based on an innova-tive concept that comprehensively addresses the challengesDSOs will be exposed to in the context of increasing amountof decentralized and often fluctuating generation as well asthe electrification of the heat and transportation sector. In thispaper, we outline the principles of the GridBox concept, wedescribe its key elements in terms of hardware and softwareand we specify functionalities and applications. The practi-cal implementation of the concept is illustrated by presentingan overview of the results from field tests in two differentregions in Switzerland one in an urban grid area in the cityof Zurich and one in a rural grid area in the canton of Bern.Results from the evaluation campaigns for state estimationand optimization algorithms are presented.

Keywords Distribution grid · State estimation ·Optimal power flow · Topology estimation

1 Motivation

With the increasing amount of decentralized and often fluc-tuating generation and the electrification of the heat and

The GridBox pilot project was co-funded by the Swiss Federal Officeof Energy.

B Alain Brenzikoferalain.brenzikofer@scs.ch

1 Supercomputing Systems AG, Zurich, Switzerland

2 ewz, Zurich, Switzerland

3 BKW, Bern, Switzerland

4 Bacher Energie AG, Zurich, Switzerland

transportation sector, the planning and operation of distribu-tion grids has to be adapted. DSOs will have to balance theirinvestment in the new network infrastructure and in smarttechnology for active network management to ensure a sta-ble operation of the grid in a cost-efficient way. In addition,the regulatory requirements for distribution system operatorsnecessitate more and more detailed documentation on relia-bility of supply and power quality. Therefore,DSOswill needbetter information and control of the state of their distribu-tion grids especially on the medium- and low-voltage level.On these two voltage levels, the usage of monitoring andcontrol devices is currently very limited. As a consequence,the observability and controllability of the distribution gridson these voltage levels is typically low. This represents a bigchallenge for DSOs as most of the new generation capacitiessuch as photovoltaic (PV) orwind power plants are connectedto these voltage levels.

Several studies show the potential impact of distributedgeneration (DG) capacities on the needs for distribution gridreinforcements and extensions [1,2]. These studies come tothe conclusion that the costs ofDGgrid integration canbe sig-nificantly reduced by adopting innovative grid componentslike regulated distribution transformers and line voltage reg-ulators or active/reactive power controlling devices as wellas information and communication technologies. It is exactlythese issues which the GridBox concept addresses.

2 The GridBox concept

The GridBox concept represents an open platform whichprovides solutions to the challenges mentioned above. Atthe core of the GridBox concept is a highly distributed net-work of real-time measurement devices synchronized byGPS. All devices within this network measure voltage and

123

A. Brenzikofe et al.

current synchrophasors with a high accuracy in real timeand communicate the measurement data to hierarchicallylayered masters. Thus, in contrast to purely decentralizedcontrol approaches, the GridBox concept allows for an opti-mal balance between local control functions and centralizedfunctions being activated by theGridBoxmaster. The central-ized monitoring and control is enabled by a continuous gridstate estimation carried out by the GridBox master. The stateestimation uses the real-time phasor measurements commu-nicated by all GridBox devices in the network. In this way,appropriate control signals can be sent to the actuators ofcomponents that are controllable by the GridBox devices inthe case of a critical grid state. Examples of grid compo-nents that can be controlled in such a way are low voltageregulation systems for controlling the voltage in an individ-ual distribution feeder or controllable local grid transformerswith on-load tap-changers. Distributed generation such asPV plants, controllable loads such as water boilers or storagedevices such as battery energy storage systems (BESS) canalso be controlled by theGridBox devices. Thus, theGridBoxplatform aims at integrating any type of controllable powergeneration, load, storage and intelligent building technologyand offers a flexible framework for grid monitoring and opti-mizing algorithms. In this way, the GridBox concept enablesan active networkmanagement byDSOs. This endowsDSOswith the opportunity to optimally balance the use of SmartGrid technologies with conventional grid development mea-sures in the short-, medium- and longterm. Furthermore, theopen platform principle shall guarantee interoperability andthe seamless integration of third party applications.

3 Key elements of the Gridbox concept

The GridBox devices are the basic components of the con-cept. They measure voltage phasors at the nodes wherethey are installed as well as current phasors for all linesattached to that node. The form of the GridBox is chosento fit distribution cabins. Rigid PCB-based as well as flexi-ble Rogowski current sensors have been developed to fit insmaller house connections boxes. The GridBox base mod-ule measures voltage on 3 phases and current on 4 phases(R,S,T,PEN). Additional current modules are attached foreach line to be measured.

Figure 1 shows the hardware setup for a cable distributioncabinet, to be extended according to the number of feederspresent. Apart frommeasuring voltage and current, the Grid-Box devices can interface low-voltage relays, DG invertersor storage devices to be able to control load and generationat the grid nodes. Thus, the GridBox devices represent thelocal physical components of the grid management system.

Another crucial component of the GridBox system is thecommunication network. GridBox supports several low- as

Fig. 1 GridBox base module, current module and Rogowski currentsensors

well as high-bandwidth communication channels to mini-mize operational cost. For the two pilot grids, three com-munication types are implemented: fibre optics, broadbandpowerline (BPL) as well as GPRS/UMTS. First experienceshave shown that GPRS/UMTS solutions are not suitable forfuture applications because of their high cost to handle highdata volumes.Nowadays, fibre optics are not yetwidely avail-able. BPL has a good potential for future application, but isstill quite power-hungry and it is restricted by law to gridsconsisting only of underground cables. Further possibilitiesare RF transmission in multi-hop mesh networks or narrow-band powerline communication (PLC).

Instead of using standardized, generic protocols like IEC61850, a custom communication protocol based on SCTPneeded to be developed to reduce latency, overhead andminimize bandwidth as well as data volume. Moreover, theprotocol and data handling needs to be robust against packetloss asGPRS/UMTSaswell asBPL are unreliable communi-cation channels. The ‘GridNet’ protocol is optimized for thetransfer of GridBox measurements in intervals of one secondand time-limited prioritized retransmission to enforce config-urable delay limits and for sending commands to controllablegrid components. As an extension, GridBox is designed toallow for protocol tunneling for third party devices. Eachgrid region is managed by a GridBox master that collectsand processes all the data. A real-time state estimation isperformed, followed by an optimal power flow algorithm.Those algorithms are described in subsequent sections.

The federalistic approach has several advantages. Dataand control is kept as local as possible. This increases robust-ness and also reduces data security and privacy violationrisks. Regions operate semi-independently in a hierarchicalway as shown in Fig. 2.

One region only needs to share aggregated grid state indi-cators with their superordinate region master. So a N7 regionrepresents nothing more than (controllable) prosumers forthe N5 region master. This helps reducing data traffic and

123

GridBox pilot project

GridBox GridBox regionGridBox master

N3

N5

N7

Fig. 2 GridBox system federalistic hierarchy

ensures scalability. However, the two pilot grids only imple-ment one region each with their respective master.

Purely local control of grid-connected devices basedon local voltage measurements (as presented in [3]) hasthe disadvantage that it is not guaranteed to be stable assoon as many such controllers are deployed. When tap-changing transformers or reactive-power-controlled invertersare present, the local voltage level is often an insufficientindicator of the severity of the grid state. A GridBox Mas-ter employs regional control based on state estimation of theentire sub-grid to overcome this problem. This concept couldeventually be extended to peer-to-peer control without theneed for a GridBox master as suggested in [4]. Furthermore,a peer-to-peer alternative of the GridBox project is discussedin [5].

4 Functionalities and applications

The core functionality, which has been demonstrated withcontinuous operation of about 150 devices during 1 yearsince March 2015, consists of sampling every second themeasurement data of eachGridBox device and collecting thisinformation in quasi real-time on a GridBox Master. Basedon this functionality, two types of applications are possible.On the one hand, real-time applications, so called smart gridapplications (SGAs), are executedwith the set of informationavailable every second. These include power system stateestimation, topology estimation, monitoring and prosumercontrol. On the other hand, ex post applications that requirearchived measurement data can be run. The interplay of theSGAs and the archive service within the GridBox systemis illustrated in Fig. 3. Here, the measurement accumulatorand the governor services provide an interface for the SGAsto receive measurements from and send control signals tothe GridBox devices, respectively. Topology estimation ofSect. 4.1 tries to determine the connectivity graph or eventhe entire model of the corresponding grid. Assuming that agrid model is given, the state estimation of Sect. 4.2 com-

GridBox GridBox GridBox

BESS

StateEstimator

Optimizer

Archive

TopologyEstimator

MeasurementAccumulator

Governor

Master

PV Boiler

GridBox

Fig. 3 Smart grid applications (SGA) architecture

pletes and adjusts the GridBox measurements which can beinaccurate or missing altogether. The estimates of the stateestimation can then be used to formulate an optimizationproblem. The solution to this optimization problem deter-mines a set of control signals which, after sending them tothe corresponding GridBox devices, improve the overall gridstate with respect to a certain objective. This optimal powerflow control SGA is discussed in Sect. 4.3. Furthermore, amonitoring SGA and the data archive service are presentedin Sects. 4.4 and 4.5, respectively.

4.1 Topology estimation

Automated topology identification is a subsequent appli-cation which will be tested during the pilot project. Theobjective is to draw a connectivity graph, i.e. to determinewhich nodes are connected with each other. Therefore, analgorithm matches node currents on both sides of a line.Especially in ameshed distribution network, the connectivitygraph may be subject to frequent changes by switching oper-ations. Even though a distribution system operator normallypossesses at least one network diagram for planning andoperation purposes, the automated identification can replacemanual interventions for the initial input of network dataor updates in the case of topology switches. Adding nodevoltages to the current phasor data used in the topology identi-fication, the electrical parameters of the lines can be obtainedso that load flow calculations can be performed. For a dis-cussion of algorithm approaches and results, the reader isreferred to [6,7].

4.2 State estimation

The elementary real-time application consists of a linearthree-phase state estimator based on concepts presented in[8,9] capable of detecting and treating bad data such as mea-surement deviations and of handling missing data. Missing

123

A. Brenzikofe et al.

YR

YS

YT

YRS

YST

YRT

GT/

2

GS

/2

GR

/2

GS

T/2

GR

S/2

GRT/2

GT/

2

GS

/2

GR

/2

GS

T/2

GR

S/2

GRT/2

1:rmn

1:rmn

1:rmn

rnm:1

rnm:1

rnm:1

Fig. 4 The line model used for state estimation and optimization. Athree-phase pi model with mutual coupling for both series and shuntadmittance

data can be of temporary nature in case of communicationinterruption. However, more relevant are nodes that are notmeasured at all. The line model used is shown in Fig. 4.

Besides technical reasons such as inaccessible cable junc-tions in the ground, it is also an economical motivation todeploy only as many GridBoxes as necessary. In both pilotnetworks, most accessible nodes are equipped with a Grid-Box device in order to be able to evaluate the confidencemetric of the state estimator by deliberately considering onlya subset of the available measurement points. Section 6.1explains the outcomes of this analysis. The state estima-tion associates to each node estimated values and confidenceintervals for current and voltage.

4.3 Optimal power flow control

In order to dispatch all controllable prosumers (actors) inan optimal way, an application was developed that solvesthe three-phase optimal power flow (OPF) problem for theentire grid region with objectives like voltage stability orreducing line or transformer strain at multiple locations inthe grid. Further goals could be minimization of grid losses.With that, the DSO will be able to choose among severalobjective functions.

In order to be reactive enough for voltage stabilityimprovement, the computational load needs to be kept low.This has been achieved by implementing a single-timestepOPF using sequential linear programming (after conductingsome preliminary studies based on semidefinite program-ming, see [10]). Actors with storage capabilities are oftencontrolled using model predictive control (MPC) [11]. Asthis project focuses on the geographical extent as well as highcontrol reactivity in the order of seconds, such a computation-heavy approach has been neglected.

In the pilot grids, the optimizer can control PV plants bycurtailing active power or requesting a certain power factor.A BESS located in the ewz pilot grid can be controlled inactive as well as reactive power. Water boilers as well as

Fig. 5 GridBox Cockpit measurement view showing instantaneousmeasurement values as well as prosumer states and historical data

heatpumps can be switched on or off in order to shift theirload.

4.4 Monitoring

The actual state of the distribution network is calculated andevaluated against the permissible operational limits at everyinstant. To visualize the actual state of the grid as well asconstraint violations and historic data, a cockpit has beendeveloped as a web-service as shown in Fig. 5. Three-phasevoltages as well as power flows are animated in real time,providing the DSOwith a today lacking level of transparencyfor the lower grid regions.

4.5 Data archive

The second major set of possible functionalities can be seenas a by-product of the aforementioned real-time applications.An archive with measured values, like synchrophasors andharmonics of voltages and currents as well as outputs of thereal-time algorithms, like estimated values, switching statesand optimizer set-point values, can be used to improve exist-ing algorithms and develop new applications with respectto realistic conditions. The synchrophasor measurementscould be used to develop sophisticated state estimation algo-rithms based on machine-learning techniques. As anotherexample, the load and voltage profiles could lead to moreefficient network planning. Furthermore, the capability ofthe GridBox to measure synchronized phasors would allowthe implementation of innovative future applications suchas fault detection (i.e. locate the exact place where a faulthas appeared), transient analysis, detection of unintentionalislanding or management and operation of micro-grids (i.e.reconnect a part of the grid without injecting disturbances).

The GridBox platform is designed as open as possible inorder to be able to address future questions that are not yetforeseeable today or that require longterm data.

123

GridBox pilot project

5 Development project and field tests

The GridBox system has been developed within a 3-yearsresearch project by the project partners BKW (DSO in theSwiss cantons of Bern and Jura), ewz (DSO of the city ofZurich and part of the Grisons), Supercomputing Systems(development service provider), Bacher Energie (consultingand project support) and co-funded by the Swiss FederalOffice of Energy. The GridBox system has been tested in twopilot installations: one in an urban low-voltage grid area in thecity of Zurich and one in a rural medium- and low-voltagearea in the canton of Bern. In total, roughly 150 GridBoxdevices have been installed in the beginning of 2015. ewzuses a low voltage grid area in the periphery of Zurich (dis-trict Affoltern) for the project. This pilot grid is illustrated inFig. 6.

The chosen area provides opportunities for numeroustests. The goal in the field test is to install GridBox mea-suring instruments with a complete coverage of all nodes inthe network area in order to be able to extensively and reliablyvalidate the concept. As illustrated in Fig. 6, there are severalcontrollable loads and PV plants with controllable invertersin the ewz pilot grid area. GridBox devices can send controlsignals to the PV inverters so that they provide reactive powerfor voltage control. Furthermore, tests for active power cur-tailment of the PV plants are carried out. In addition, thereis a battery energy storage system (BESS) installation in thepilot grid area with a power rating of 120 kW and a nominalcapacity of 720 kWh.

Most of the GridBox devices at ewz are placed within thedistribution grid, either in transformer stations or in distrib-ution cabinets. Some GridBox devices are installed next tohouse connection boxes. Data security is a crucial issue. Thehandling of the measured data has to be considered carefully,

Fig. 6 Urban pilot grid in the city of Zurich (ewz)

Fig. 7 Rural pilot grid in the canton of Bern (BKW)

Fig. 8 GridBox installed at a distribution cabin

especially where the GridBoxes are installed at house con-nection boxes and the measurement data could be attributedto individuals. Therefore, adequate measures for guarantee-ing the protection of customers privacy have been adopted.

The BKW pilot network is located in a rural area ofthe canton of Bern. It comprises a part of the mediumvoltage network as well as an entire low voltage network(see Fig. 7).

In the low-voltage network,GridBoxes havebeen installedat almost all accessible points, i.e. on the low-voltage feed-ers of the transformer stations, the cable distribution cabinetsin the streets, and the house connection boxes of the endcustomers. Figure 8 shows a GridBox installation in a distri-bution cabinet. In the medium-voltage network, GridBoxeshave been installed in selected transformer stations along onesubstation feeder. While the GridBox base modules can beconnected directly to the LV connection points for voltagemeasurement and power supply, the MV switchgears havebeen equipped additionallywith sensored cable terminations,housing a resistive voltage sensor and a Rogowski-type cur-rent sensor.

123

A. Brenzikofe et al.

At BKW, both voltage levels (MV and LV) are character-ized by a radial structure. In accordance with a considerableshare of overhead lines and pole-mounted transformers, thisnetwork was chosen as it is typical for rural areas. Further-more several DG units, here photovoltaic systems and onesmall hydro plant, and controllable loads like water boilersare available. Due to the low customer density in the chosengrid area, the technical options for high volume data com-munication are very limited. The choice was between thelandline DSL and the mobile phone network. In order to beas independent as possible from end customers and becausethis communication channel is already in use inside the com-pany for remote meter reading, data is sent via the mobilephone network.

Accuracy for all measurement channels was verified bymeans of a laboratory installation with defined frequency,voltage, current and harmonics content. It can be highlightedthat the total vector error for the voltage phasor measurementat nominal frequency and nominal voltage is in the range of0.04 %, dominated by the magnitude error.

6 Results

6.1 State estimator performance

As the true state of the grid is unknown, there is no way toquantify the accuracy of estimation by itself. The analysisof state estimator performance has therefore been taken outby comparing estimated voltage and current magnitudes totheir corresponding measurements. Table 1 shows the accu-racy achieved for 95 % of all sensor locations. As shown in[12], model errors are a limiting factor for estimator accu-racy, causing a tradeoff between voltage and current accuracywhen configuring the state estimator. Further research isneeded to develop a model-adaptive state estimator.

The pilot grids have been equipped with a very large num-ber of sensors. For economical reasons it is of interest, howmany sensors are needed to achieve a desired accuracy. Toanswer this question, archived measurement data has beenused to re-run different scenarios only using a subset of allavailable sensor data and comparing the output to the origi-nal estimation. Figure 10 shows the degradation of accuracyfor different scenarios for the case of the radial BKW lowvoltage grid shown in Fig. 9. Dropping redundant measure-

Table 1 State estimation accuracy

BKW (radial grid) ewz (meshed grid)

Voltage accuracy 1 Vrms 2.5 Vrms

Current accuracy 10 Arms 5 Arms

Fig. 9 BKW low voltage grid topology. Household (HAK) mea-surements are marked in green, distribution cabins (VK) are red thetransformer station (TS) is blue. Redundant measurements are high-lighted in yellow (color figure online)

10

1

0.1

0.01

estimation quality (95-percentile) vs number and placement of devices

no re

dund

ancy

using

34 G

B

TS & H

AK only

using

54 G

B

no sy

nchro

phas

ors

using

61 G

B

no cu

rrent

sens

ors

using

61 G

B

TS & V

K only

using

9 GB

no vo

ltage

sens

ors

using

61 G

B

Diff

eren

ce to

vol

tage

est

imat

ion

usin

g or

igin

al s

enso

r set

[%]

0.5

Diff

eren

ce to

cur

rent

est

imat

ion

usin

g or

igin

al s

enso

r set

[Arm

s]

1

2

5

10

20

50

Fig. 10 State estimator accuracy comparison for different sensorplacement scenarios. TS, HAK, VK and GB stand for transformationstation, Hausanschlusskasten or house connection, Verteilkabine or dis-tribution cabin and GridBoxes, respectively

123

GridBox pilot project

ments makes only little difference but allows to use only 34out of 61 GridBoxes. Omitting measurements at distributioncabins has only little impact (less than 0.5 %) on voltageaccuracy whereas omitting house connection measurements(only using 9 GridBoxes) shows surprisingly good currentaccuracy (6A) but isweak for voltage accuracy (6%).Assum-ing that the measurement devices are not time-synchronized(no-synchrophasors) shows that voltagemagnitude estimatesdepend only little on synchrophasor information, yet thequality of current estimates suffers significantly. Omittingcurrent measurements entirely would be an attractive cost-saver on low voltage grids. The effect on voltage accuracymight be acceptable, but if currents are of interest, one shouldinvest on a better voltage measurement accuracy as well as

a very accurate grid model. Finally, omitting voltage sensors(except the measurement of the slack voltage) leads to unac-ceptable voltage accuracy. Further research could apply thesame analysis tomid-voltage gridswhere there is an econom-ical interest to omit voltage sensors in favor of many currentsensors. Moreover, additional efforts are needed in order tobetter understand and explain these results.

6.2 Voltage stability optimization

Various campaigns have been taken out to assess the per-formance of the developed optimizer. The most challengingoptimization goal is voltage stability as it requires a veryreactive control loop. As shown in [12], the GridBox system

Fig. 11 Undervoltage on onephase during evening timeswhen the optimizer is notrunning

Fig. 12 Voltages of all threephases during evening timewhen the optimizer is running

123

A. Brenzikofe et al.

allows to control a prosumer with a lag of 5–12 s, dependingon complexity of the objective functions and the size of thegrid region.

Figure 11 shows the daily undervoltage events on one ofthe three phases during evening time at a node in the ewzgrid before any optimization takes place. This node hostsa large battery energy storage system (BESS) that can becontrolled by the GridBox system. Figure 12 shows that theoptimizer is capable of keeping the voltage within a targetband. Plotted are voltages reported by the state estimator aswell as voltages reported by the optimizer (newton-raphsonreconstruction based on optimal prosumer setpoints, to be

200 205 210 215 220 225 230 235 240 245 250 255 2600

20

40

60

80

100

120

140

160

180

Vrms

occu

rren

ces

voltage levels with and without optimization

lower lim

it (-10%)

upper limit (+10%

)

Optimizer:offOptimizer:on

Fig. 13 Histogram of 10-minute-average (according to EN50160)voltage levels with (30.11.2015 until 6.12.2015) and without(30.10.2015 until 5.11.2015) optimization. Undervoltages are success-fully mitigated by the optimizer controlling BESS active power

understood as a prediction of what will happen as soon asprosumers implement the desired setting). The fact that esti-mation and prediction stay close together proves that the gridstate may be assumed stationary for the duration of our con-trol lag of few seconds. It is worth noting the big voltagedifference among the three phases. Unfortunately, the BESSwasn’t capable of controlling the phases independently, somitigating an undervoltage on phase S led to a slight over-voltage on phase T.

Figure 13 shows the voltage histogram for several dayswith and without GridBox control. The lower mean value ismainly caused by seasonal changes in electricity demand andonly little by battery losses. It can be concluded that GridBoxis able to keep the voltages within a desired band by using aBESS.

Controlling active power curtailment and power factor ofPV plants has been tested in the BKW grid. Figures 14, 15and 16 show how the optimizer reacts to overvoltages causedby PV injection. As a first measure, the PV plant is told to‘inject’ negative reactive power. Modern PV inverters allowcontrolling the power factor within the limits of a PF of 0.85.For theGridBox system itwouldhowever bedesirable to havethe ability to go down to a PF of zero. Active power curtail-ment is implemented only in the case where the injection ofnegative reactive power does not mitigate the problem.

Finally, a campaign including all available prosumer types(BESS, PV and boiler) to optimize voltage stability has beencarried out. The objective function is a desired voltage bandat the node where the BESS and 6 PV plants are installed(3 of which are curtailable by GridBox, one is used as anuncontrolled reference plant). Water boilers throughout thegrid region are controlled aswell. One of the challengeswhencontrolling boilers is the fact that the state-of-charge (SoC) is

Fig. 14 PV campaign: voltageat PV injection node

123

GridBox pilot project

Fig. 15 PV campaign: activeand reactive power P and Q,respectively, at PV injectionnode

Fig. 16 PV campaign: activecontrol signals in terms ofrelative active power and phasorangle phi at PV injection node

not known. A very simple linear state model has been imple-mented for that case: the boiler’s energy content is chargedaccording to the supplied active powerwhereas the consump-tion of hotwater ismodeled such that in the absence of supplypower, a fully charged boiler is depleted within 24 h. Due tolimitations of this model and other limitations such as unre-liable communications and not taking into account predictedfuture boiler states, cold showers are prevented by a localsecurity measure implemented on the GridBox devices thatcan overrule the optimizer to guarantee a minimum on-time

for each boiler within 24 h. To compensate for the lack ofconstraints with respect to predicted future boiler SoCs, acost term incentivizes boiler SoCs of 50 % at all times withlow weight.

Figure 17 shows the voltage at the BESS node during oneday of the all-in campaign. The voltage tolerance band for theoptimizer is chosen such that both over- and undervoltagesoccur. As expected with PV plants, there is a rise in voltagearound midday. The corresponding PV injections are shownin Fig. 18. The optimizer decided to curtail PV injection to

123

A. Brenzikofe et al.

Fig. 17 All-in campaign:voltage at the BESS node

Fig. 18 All-in campaign: PVreference irradiation andcurtailment

mitigate overvoltages. In order to minimize PV curtailment,the BESS is charging during midday as can be seen in Figs.19 and 20. During evening times, the BESS avoids an under-voltage on phase S by injecting power.

Figure 21 shows switch states for three boilers. Because ofthe single-timestep optimization a lot of switching happens

during slight overvoltage times. This behavior stresses theswitch relays more than necessary and reduces their lifetime.Future work should implement an MPC controller for boilerdispatch optimization. Duringmidday, the boilers stay turnedon and the internal SoC estimate rises until it approaches100 % as Fig. 22 shows.

123

GridBox pilot project

Fig. 19 All-in campaign:BESS active power injection

Fig. 20 All-in campaign:BESS SoC

7 Conclusions and outlook

The GridBox system is a novel approach for monitoringand active control of distribution grids which addresses awhole set of challenges that DSOs will face in the nearfuture. The GridBox concept represents an open platformwhere the hardware and software elements are specificallydeveloped for covering the practical needs of a DSO. Theplatform is designed such that it can flexibly integrate abroad set of applications in the future. In order to test theapplications under typical circumstances, two different pilot

regions in the distribution grids of ewz and BKW werechosen.

The rollout of 150 devices and their operation for an entireyear has successfully taken place. The GridBoxes whichhave been installed in the medium and low voltage networkmeasure and send realtime measurements to the GridBoxMaster.

Several campaigns were carried out to demonstrate thepotential functionalitieswith a strong emphasis on evaluatingstate estimator performance and voltage stability optimiza-tion. It could be shown that three-phase state estimation

123

A. Brenzikofe et al.

Fig. 21 All-in campaign: boileractive settings

Fig. 22 All-in campaign: boilerSoC

achieves an accuracy around 1 % of Unom and the degra-dation of this accuracy when using less sensors has beenquantified. The optimizer campaigns showed the feasibilityof jointly optimizing the dispatch of several actors of differ-ent kinds, distributed among different locations, to improvevoltage stability in the low-voltage grid.

The pilot project ended by March 2016. A commercial-ization of the GridBox platform is planned.

Open Access This article is distributed under the terms of the CreativeCommons Attribution 4.0 International License (http://creativecomm

ons.org/licenses/by/4.0/), which permits unrestricted use, distribution,and reproduction in any medium, provided you give appropriate creditto the original author(s) and the source, provide a link to the CreativeCommons license, and indicate if changes were made.

References

1. consentec (2012) Auswirkungen eines verstärkten Ausbaus derdezentralen Erzeugung auf die Schweizer Verteilnetze Study forthe Swiss Federal Office of Energy

2. IAEW, OFFIS (2014) Moderne Verteilernetze für Deutschland,Study for the Federal Ministry for Economic Affairs and Energyin Germany

123

GridBox pilot project

3. BfE (2013) Swiss2G Pilot- and Demonstration Project; an inno-vative concept for the decentralized management of distrib-uted energy generation, storage and consumption and consumeracceptance—Rapport annuel

4. Bolognani S, Zampieri S (2011) A gossip-like distributed opti-mization algorithm for reactive power flow control. In: Proc. IFACWorld Congress

5. Vogel B (2016) Augen im Stromnetz. VSE-Bulletin6. Xu O (2014) Automated topology identification in smart gridap-

plications. Master Thesis at SCS AG7. Katsoulakos N (2016) Automated topology identification in smart

grid. Master thesis at EPFL and SCS AG8. Zanni L, Sarri S, Pignati M, Cherkaoui R, Paolone M (2014)

Probabilistic assessment of the process-noise covariance matrixof discrete Kalman filter state estimation of active distribution net-works. In: 13th probabilistic methods applied to power systems(PMAPS), Durham

9. Pignati M, Zanni L, Sarri S, Cherkaoui R, Le Boudec J-Y et al(2014) A pre-estimation filtering process of bad data for linearpower systems state estimators using PMUs. In: 18th Power sys-tems computation conference, Wrocaw

10. Hauswirth A (2015) Applications of AC optimal power flow indistribution networks. Master Thesis at ifa, ETHZ and SCS AG

11. Kwon WH, Bruckstein AM, Kailath T (1983) Stabilizing statefeedback design via the moving horizon method. Int J Control37:631–643

12. BfE (2016) GridBox; Netzbasierte Echtzeit-Erfassung des Verteil-netzzustandes und erste Praxistests - Schlussbericht (Publikation291129). http://www.bfe.admin.ch/forschungnetze/01246/03569/index.html?lang=de&dossier_id=06621

Alain Brenzikofer received hisM.Sc. degree from the FederalInstitute of Technology in Zurich(ETH) in the field of Electri-cal Engineering and Informa-tion Technology in 2005. HeWorked at Anocsys AG (2005)as research & development engi-neer in the field of acoustics andsignal processing and at CSEMSA (2006) in the field of opto-electronics, embedded HW/FWdesign and image processingalgorithms. Since 2012 he worksfor SCS AG as project manager

and research&development engineer in the Energy andEmbedded Sys-tems department with a focus on embedded systems design and signalprocessing algorithms.

Dr. Florian Müller receivedhis M.Sc. degree in Mechani-cal Engineering and his PhD inComputational Fluid Dynamicsboth from ETH Zürich. Since2015, he is working for Super-computing SystemsAG as a soft-ware development engineer inthe Energy and Embedded Sys-tems department with a focus onenergy systems simulation andoptimization algorithms.

Alexandros Ketsetzis receivedhis Diploma Degree in Electricaland Computer Engineering fromthe National Technical Univer-sity of Athens inGreece, in 2013.Since 2014, he is a master stu-dent at the Energy Science andTechnologyMSc, at ETHZurich.His current interests include stateestimation and optimization inelectrical power grids.

Dr. Florian Kienzle studiedenergy engineering at theUniver-sity of Magdeburg (Germany).After that he did a PhD atthe Power Systems Laboratoryof ETH Zurich under the guid-ance of Prof. Göran Andersson.Since 2012 he has been work-ing for ewz, the utility of the cityof Zurich–initially as smart gridspecialist and now as head of theteam New Grid Solutions.

123

A. Brenzikofe et al.

Marco Mangani Bachelor ofScience, studied electrical engi-neering at the HSR TechnicalUniversity in Rapperswil. Hejoined ewz in 2009 and nowworks in the Network Designdepartment, where his responsi-bilities include studies for theintegration of renewable energysources and pilot projects forsmart grids.

Marc Eisenreich received hisdegree in Business Administra-tion and Electrical Engineering(Dipl.-Wirtsch.-Ing.) from Tech-nischeUniversitätDarmstadt andhis degree in Engineering fromEcole Centrale de Lyon in 2008.He joined BKW’s Smart GridEngineering department in 2013where he is responsible for pilotprojects with new componentsand concepts in a future intelli-gent distribution grid.

Yamshid Farhat received hisM.Sc. degree from the Polytech-nic University of Catalonia inEnergy Engineering focusing onthe Electric System. Mr. Farhatworked for his Master Thesis atthe Energy Department at theAalborg University in the field ofMicro-grids. Mr. Farhat is work-ing for BKW Energie AG since2013 in the Smart Grid Engineer-ing department with the focuson the development of innovativeproducts for theDistributionNet-work. He is supervised by Marc

Mürner, head of the Smart Grid Components group.

Dr. Rainer Bacher studiedElectrical Engineering at theETH Zürich (Dipl. El.-Ing. ETH,1982; Dr. sc. techn. ETH, 1986).After employments in theUS andat ETH Zürich (Switzerland) asprofessor, in 2002 he was at theSwiss Federal Office of Energyas section leader “ElectricityGrids” and project leader ofthe Swiss Stromversorgungsge-setz (StromVG). He founded hisown consulting company BacherEnergy LTD in 2008. There hasbeen advising key energy policy

as well as research and innovation related governmental bodies suchas the Swiss Federal Office of Energy, the Swiss SmartGrids Projectsfunding agency, Members of the Swiss National Platform on Smart-Grids, the Funding Agencies of 21+ national European governmentson defining and establishing strategies for innovative Smart grids andMarket based electricity/energy systems projects. He continues to be alecturer at ETH Zürich.

123