974 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 2, MARCH 2014
A Decentralized Storage Strategy for ResidentialFeeders With Photovoltaics
Francesco Marra, Member, IEEE, Guangya Yang, Member, IEEE, Chresten Træholt,Jacob Østergaard, Senior Member, IEEE, and Esben Larsen
Abstract—This paper proposes a decentralized storage strategyto support voltage control in low-voltage (LV) residential feederswith high photovoltaic (PV) capacity installed. The proposedstrategy is capable of preventing overvoltage situations duringhigh PV generation periods, by the use of locally controlled batteryenergy storage systems (ESS) at the PV system grid interface. Thetraditional way of operating a domestic ESS is based on chargingthe battery as soon as the PV generation exceeds the consumption,without taking into account overvoltage events during high PVgeneration hours; the proposed storage concept improves thetraditional approach, thanks to the provision of voltage support.A novel method, based on voltage sensitivity analysis, identifies acommon power threshold that triggers the ESSs activation in thefeeder. A Belgian residential LV feeder is used as a case study.Time-series simulations based on 1-year load and generationprofiles verify the method findings and quantify the ESS size interms of storage power and energy level.
Index Terms—Decentralized control, energy storage, photo-voltaic systems, power quality, voltage control.
I. INTRODUCTION
W ITH the increasing penetration of photovoltaic (PV),there is a great potential of relieving the loading of
low-voltage (LV) distribution grids. Meanwhile, the LV grid op-eration encounters more and more uncertainties with regard tovoltage quality [1], [2]. In several European countries and re-gions in Germany, Spain, Belgium and others, several LV gridshave reached high PV penetration levels with consequences onthe quality of the supply voltage [3].High generation and low load conditions are the pre-condi-
tions of power flow inversion in a grid feeder; these situationsare likely on a daily basis and may result in overvoltage eventsat the different buses [4]. Therefore, solutions to increase theconsumption at houses with roof-top PV, during periods of highsolar irradiation, are needed to preserve voltage quality. In Ger-many, the vast majority of PV plants, about 13 GW, are con-nected at the LV grid, causing overvoltage events in differentareas during the year [5]. To avoid such events, from January2012, a fixed limitation of the active power feed-in by eachPV system is mandatory [5]. As of today, the limit is set to the
Manuscript received February 12, 2013; revised June 15, 2013 andAugust 10,2013; accepted September 03, 2013. Date of publication September 26, 2013;date of current version February 14, 2014. Paper no. TSG-00100-2013.The authors are with the Electrical Engineering Department, Technical Uni-
versity of Denmark (DTU), Kongens Lyngby DK-2800, Denmark (e-mail: {fm,gyy, ctr, joe, ela}@elektro.dtu.dk).Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TSG.2013.2281175
Fig. 1. Conventional storage strategy and proposed strategy compared.
70% of the nominal peak-output power of the PV system; thiscorresponds to an inherent curtailment of up to 30% when thepeak-output power is being generated. Some alternative solu-tions to active power curtailment may involve domestic loadshifting, to increase local consumption, and energy storage [7],[8].The state of the art on energy management for houses with
PV has mainly focused on demand side management (DSM), toshape the domestic electric load during periods of high PV gen-eration and to minimize the energy drawn from the mains [9].Since the number of flexible domestic appliances is very lim-ited and because these are not necessarily used on a daily basis(e.g. the washing machine), the local consumption of PV gen-eration can be significantly increased with the deployment ofa battery energy storage system (ESS). The conventional sizeof domestic ESSs for houses with PV is in the range of 5 kWh[9], [10], which allows obtaining a local consumption rate ofabout 55% yearly [10]. The conventional way of performinglocal consumption consists on activating the charging of theESS battery as soon as the PV output power is greater than thehouse electric load, Fig. 1. However, this strategy does not pre-vent overvoltage events during maximum PV generation hours(12:00–14:00 PM), as the ESS battery gets fully charged duringmorning hours of sunny days, well before themaximumPV gen-eration period [9].In this paper, a novel decentralized storage strategy, for res-
idential feeders with PV is proposed, prioritizing the charge ofESS batteries around peak generation hours, as shown in Fig. 1.Each ESS is activated at a certain power threshold, Pth, in Fig. 1,which is identified by the method proposed. The selection of thestrategy to use, whether the “proposed” strategy or the “conven-tional” one, Fig. 1, can be optimized using day-ahead solar irra-diation forecasts [11]; however, these are not used in this paper.
MARRA et al.: DECENTRALIZED STORAGE STRATEGY FOR RESIDENTIAL FEEDERS 975
Fig. 2. Simplified single-line diagram of LV feeder with PV.
The decentralized storage strategy is implemented and sim-ulated for a residential LV feeder with 23% PV penetration,using measurement-based 1-year load and generation profiles;this time interval is a quite representative for the goal of thepaper, thus the stochastic nature of PV generation is not mod-eled. The advantages of the storage strategy, as opposed to de-centralized reactive power options for voltage support are alsoaddressed.
II. LV FEEDERS WITH PV
LV distribution grids are designed and operated in a radialfashion [4]. The purpose of the LV grid is to provide connectionfrom the medium-voltage (MV) grid to the supply points for theindividual customers. Typically, a European LV grid consistsof a secondary step-down transformer to step down the voltagefrom the MV level to a 0.4 kV line-to-line voltage and of dis-tribution lines or cables. Voltage regulation is passive in mostcases. When PV systems are connected at a feeder bus, Fig. 2,the voltage magnitude at the point of common coupling (PCC)is likely to increase and its expression can be approximated asfollows:
(1)
(2)
(3)
(4)
where
voltage variation vector at the bus;
current vector;
grid voltage vector and its conjugate;
nodal active and reactive power;
active and reactive power feed-in of thePV system;
load active and reactive power;
equivalent resistance and reactance of thefeeder cable.
The voltage equation is obtained assuming negligible cablelosses, thus the R/X ration can be also neglected. To ensure a
correct operation of domestic load appliances, the power qualityStandard EN 50160 is considered, with regard to voltage magni-tude variations [12]. It shall be ensured that during each periodof one week, the 95% of 10-minute average values of the supplyvoltage shall be within the range 10% of , where isthe nominal mean value [12].
III. HOME ENERGY MANAGEMENT
The management of energy storage systems (ESSs) at houseswith PV requires an electrical setup which comprises smart me-ters, smart sockets, for realizing the load-shift of different appli-ances, and a main controller to realize load management [13],[14]. The study in [10] analyzes the local consumption rate fora cluster of households with annual consumption of 4000 kWhand a 5 kW PV system. The local consumption rate amountsto about 25%. To reach higher levels of local consumption, thehouse shall be equipped with an ESS, whose operation is co-ordinated with the PV system. With a 5 kWh battery-ESS, thelocal consumption rate rises of about 30% showing a final localconsumption rate of about 55% [10].The typical AC system in a house with PV and ESS is de-
picted in Fig. 3(a). The PV system is composed of a PV arrayand a PV inverter whose AC output power is monitored by themain controller via a real-time meter. The ESS is connected atthe same point of connection of the PV inverter and its input/output power is also real-time monitored [15]. A wide rangeof battery-ESS technologies can be utilized, such as lead-acidand Li-ion batteries. However, due to the better performances ofLi-ion battery chemistries in terms of lifetime, energy-to-weightratio, self-discharge rate and charge- discharge efficiency, thistechnology is adopted for the ESS in this paper [16].The ESS is modeled with the charge-discharge equations as
expressed in (5) and (6), where is the discharging power andis the charging power of the ESS battery, respectively;
is the energy stored in the battery at time ; is the durationtime of each interval [17]. The two coefficients and arethe discharge and charge efficiencies respectively.
(5)
(6)
The operation of the battery system should also take into ac-count power and energy constraints. Themaximum power limitsduring charging/discharging can be described by (7) and (8) re-spectively:
(7)
(8)
For simplicity, we refer to to indicate the power flow in-outthe ESS, thus including both the phases of charging and dis-charging. Positive values of indicate the provision of voltagesupport. The state-of-charge (SOC) and energy limits of an ESScan be described as follows:
(9)
(10)
where
976 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 2, MARCH 2014
Fig. 3. Decentralized storage concept.
and are the minimum andmaximum energy levels ofthe storage, defining theusable energy window;
and are the minimum andmaximum SOC limits thatshall be set in relation to theapplication.
In this paper, the size of the ESS is not pre-determined, in-stead it is one of the decision variables, in order to comply withthe voltage constraints in the feeder.
IV. PROPOSED METHOD
To lower the voltage profiles during overvoltage periods, theESS at each house with PV shall be operated during the max-imum generation interval, as illustrated in Fig. 1. The systemarchitecture to achieve voltage support by decentralized storageis depicted in Fig. 3. Every roof-mounted PV system is cou-pled with an ESS at the same point of connection. The housemain controller controls the activation of the ESS. If the power
reaches a pre-defined threshold (e.g. the 70% of thePV system peak-power), the main controller sends a chargingactivation signal to the ESS control, corresponding to flag setto 1, in Fig. 3(b). The flag is set to 0, otherwise. At the endof the PV generation period, i.e. after sun set, the main con-troller sets the flag to 2, so the ESS can be discharged back tothe initial state. Depending on the flag setting, the ESS controlgenerates an ESS activation signal corresponding to “Chargeon”, “Discharge on”, or Idle, as depicted in Fig. 3(c). The ESSstores the incoming PV energy, by charging with a power level
. The charging process continues as longas is greater than the threshold. The ESS control canbe also improved introducing a power dead-band to avoid fastswitching events.A safe usage of the ESS battery should be ensured at all times,
during charging or discharging operation. This task is achievedby the SOC control, which is the inner control loop of the storagesystem. The charging of the ESS battery should be limited toa SOC window of 20–90%, as determined in [18], for Li-ionbattery types. Other types of batteries may be considered for thesame concept, involving potentially a different usage window.To provide voltage support in the feeder, the main problem to
solve is the identification of the ESS maximum charging powerunder worst case conditions of maximum generation and noload. This problem is solved using linear programming (LP)method, based on voltage sensitivity analysis.
V. PROBLEM FORMULATION
The main problem is to identify the power threshold for acti-vating the ESSs in the feeder, considering that voltage violationsshould be avoided. The objective function in (11) shall mini-mize the ESS size while securing all bus voltages within 1.1p.u. The method identifies a value for , between 0 and 1, thatrepresents the share of PV peak-output power used by the ESSfor charging. For instance, the feed-in power limitation of 70%[5] is equivalent to adopting . The problem of interestcan be solved with linear programming (LP), with the followingformulation:
Objective function:
(11)
MARRA et al.: DECENTRALIZED STORAGE STRATEGY FOR RESIDENTIAL FEEDERS 977
Constraints:
(12)
where is the number of PV plants in the feeder; isthe charging power of the -ESS; and are the min-imum and maximum ESS discharging and charging power, re-spectively; is the column vector containing all bus voltages inthe feeder; is the maximum voltage magnitude allowed.Constraints (12) are: a) the operative constraints that bound thevoltage magnitude at all buses within the limits; b) the operativepower range for each ESS; c) a common value for respec-tively. It shall be noted that the voltage constraint a) includesthe network equations that incorporate the characteristics of ca-bles, load and generation. The expression in (13) describes thevoltage constraint at the generic bus . Reactive power contri-butions are neglected.
(13)where
busbar grid voltage magnitude (p.u.);
voltage magnitude at bus (p.u.);
number of buses in the feeder;
active power feed-in by PV on bus ;
aggregated ESS charging power at bus ;
voltage sensitivity coefficient of bus , to theactive power exchanged (p.u./MW).
Due to the use of load flow equations, the aim of the methodis not to precisely quantify the ESS size for voltage support,but rather to estimate the value of required for a certain PVpenetration level; this value is also dependent on the number ofPV systems in the feeder and on their rated power. With 1-yeartime-series simulation, the value of can be confirmed or ad-justed according to the voltage profiles obtained.
VI. CASE STUDY
The Belgian residential LV feeder [20] is used as case study.The feeder comprises 7 buses, of which 4 out of 7 hostingPV systems: bus 2, 4, 5 and 7, respectively. It is composedof NA2XRY type LV cables and it is part of a larger LV gridwhich includes 9 feeders in total [20]. The feeder supplies 33houses and it embeds 9 single-phase roof-mounted PV systems,with total PV capacity installed of 42.6 kW. The PV capacityper bus is indicated in Table I. On bus 2, 4, and 5, each PVsystem size corresponds to the value indicated in Table I, whilefor bus 7, 6 PV systems contribute as follows: 2 plants of 4.8kW each; 2 plants of 4.25 kW each and 2 plants of 4.4 kW
TABLE IPV IN THE FEEDER—23% PENETRATION, ORIGINAL SCENARIO
Fig. 4. Single-line diagram of the LV grid feeder.
each, respectively. The definition of PV penetration used in thispaper refers to the one given in [21]:
(14)
The PV penetration in the feeder is the ratio between the totalPV installed capacity to the nominal feeder capacity. The feedercapacity is intended as the capacity of the first line section of thefeeder, corresponding to 185 kVA in this case. It follows that aPV penetration of 23% is present in the feeder.
A. Method Implementation
The method proposed is demonstrated on the grid feeder ofFig. 4. To reproduce a worst case scenario for voltage magni-tude variations, the assumption of maximum generation and noload in the feeder is made; this should ensure a margin from thevoltage limit of 1.1 p.u. with real load and generation profiles.The method identifies a value of , leading to a maximumESS charging power of about 1 kW per house with PV system.A second scenario assuming 50% PV penetration is investi-
gated with the method. The PV installed capacity for this sce-nario is indicated in Table II. With this PV penetration level, themethod identifies a value of , leading to a maximumcharging power per ESS of about 6 kW. It is observed that, bydoubling the PV penetration in the feeder, the ESS power re-quirement increases by 6 times. As a consequence, it is also ex-pected that the ESS energy level is significantly increased in the
978 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 2, MARCH 2014
TABLE IIPV INSTALLED CAPACITY IN THE FEEDER—50% PENETRATION
Fig. 5. Load in the LV feeder.
Fig. 6. PV generation in the LV feeder.
Fig. 7. “Generation-load” in the LV feeder.
second scenario; both scenarios are investigated via time-seriessimulations using 1-year load and generation profiles.
B. Load and Generation in the Feeder
The ESS operation is analyzed with time-series simulationsfor a 12-month period using 15-minute average load and gen-eration profiles. A previous study performed for the feeder hasshown overvoltage events along the feeder buses [22].The aggregated load and generation in the feeder, from the
1st of January till the 31st of December, are depicted in Fig. 5and Fig. 6, respectively. The 12-month simulation shows thatvoltage rise events at bus 7 are the most critical, due to voltagemagnitude violations in 98 days.In Fig. 7, the difference “PV generation (G)-load (L)” in those
days with voltages magnitude above 1.1 p.u. is depicted. Forgraphical reasons, for each of these days, only one value of G-Lis depicted, though this valuemay change during the entire over-voltage period. Some major findings are: 1) overvoltage eventsoccur with a positive “G-L” difference that corresponds to the
Fig. 8. Power losses in the LV feeder.
TABLE IIIENERGY QUANTITIES IN THE LV FEEDER
power range of 15 to 35 kW; 2) the highest concentration of dayswith voltage above 1.1 p.u. is observed in spring and summer.With regard to grid losses, the total energy losses in the feeder
are calculated considering the losses on all feeder cables; thetotal power losses profile, during the 12-month period, is de-picted in Fig. 8. It is observable that losses are lower in springand summer and higher in winter and autumn.With regard to energy levels, the total energy consumption,
energy production and energy losses are calculated and summa-rized in Table III.
VII. DECENTRALIZED STORAGE
The method identified a value of for the scenariowith 23% PV penetration and a value of for 50% PVpenetration. With time-series simulations, the following casesare investigated:Case 1) which considers , as obtained by the method
application with 23% PV penetration; all privateESSs are activated when .
Case 2) which considers ; all private ESSs are acti-vated with , as required bythe Renewable Energy Sources Act, EEG 2012, inGermany [6].
Case 3) which considers , as obtained by themethod application with 50% PV penetration; allresidential ESSs are activated with
.While for case 1 and case 3 are applied as a result of the
proposed method, the value of for case 2 is predetermined, asit is based on an existing grid connection requirement [6].
A. Case 1
With , the feed-in power by each PV system is at mostthe 80% of . The 1-year charging activity of the 9 ESSsis depicted in Fig. 9. The maximum charging power is about 1.1kW, which is in line with the method results. The phase voltageprofiles , and of the most critical feeder location, bus7, are depicted in Fig. 10. The profiles are clearly below the
MARRA et al.: DECENTRALIZED STORAGE STRATEGY FOR RESIDENTIAL FEEDERS 979
Fig. 9. ESS charging power for voltage support, .
Fig. 10. Phase voltage profiles on bus 7, with .
Fig. 11. ESS charging power for voltage support, .
Fig. 12. Phase voltage profiles on bus 7, with .
voltage magnitude of 1.1 p.u., showing a margin of about 0.01p.u. from the limit, which is acceptable.
B. Case 2
With , the feed-in power of the PV system is at mostthe 70% of . The charging activity of the residentialESSs is depicted in Fig. 11. In this case, the maximum chargingpower is about 1.7 kW and the ESSs operate for 147 days. Thephase voltage profiles , and of bus 7 are depicted inFig. 12. Compared to the voltage profiles obtained for ,a wider margin from the limit of 1.1 p.u. is observed.
C. Case 3
With 50% PV penetration, the total feed-in power by PV sys-tems is at most 92 kW. Such power amount leads to 231 dayswith voltage magnitude violations. While the method applica-tion found a value for of 0.55, simulations show that a value
Fig. 13. ESS energy required for voltage support, with .
Fig. 14. ESS energy required for voltage support, with .
of is required to comply with the voltage limit of 1.1p.u.The assumptions made to increase the PV penetration level
from 23% to 50% have been the following:— same number of PV systems as in the original case, withaugmented installed capacity per system;
— twice the number of PV systems than the original case, i.e.18 PV systems instead of 9.
With 9 PV systems in the feeder, the maximum chargingpower of each ESS is on average 6 kW; while, with 18 PV sys-tems, the maximum charging power results of about 3 kW.
VIII. ENERGY LEVELS QUANTIFICATION
The ESS energy levels are calculated based on the power pro-files obtained for case 1, 2 and 3. With 23% PV penetration and
, the required ESS energy is 1.1 kWh, Fig. 13. Thisvalue is obtained by selecting the day with the highest storagepower and energy content. Whereas, with , the requiredESS energy is 3.8 kWh, Fig. 14.With 50% PV penetration and , the required ESS
energy level with 9 ESSs in the feeder is 28 kWh per system,whereas 14 kWh, if we consider twice the number of ESSs.It shall be considered that the identified energy levels are re-
ferred to the usable energy window (in kWh) of the ESSs. Ac-cording to [18], to operate the ESS battery within a linear SOCregion and to avoid deep discharge cycles, the battery shouldbe oversized in relation to the usable energy window identifiedwith simulations. For each ESS battery, a usable SOC windowof 20% to 90% is considered in this paper, complying with therecommendations made on the use of Li-ion batteries in [18].The nominal battery capacity , the minimum and maximumenergy constraints and can be obtained and theseare indicated in Table IV.With 23% PV penetration, voltage support with re-
quires a nominal battery capacity of about 1.6 kWh; instead,
980 IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 2, MARCH 2014
TABLE IVESS OPERATIVE CONSTRAINTS
Fig. 15. Estimated ESS battery lifetime for the different scenarios.
with , a battery capacity of 5.4 kWh should be used.It is also evident that the maximum charging and dischargingpower of the ESS is not a critical requirement for the provisionof voltage support; in fact, the power level of 2 kW is enoughfor both -cases.Significant differences are observed in power and energy
levels with the 50% PV penetration scenario. With either 9ESSs or 18 ESSs in the feeder, the storage option is not eco-nomically feasible by private PV owners.
A. ESS Battery Lifetime
The estimation of the battery lifetime is limited to the solevoltage support operation of the ESS, regardless of the ESS op-eration in days without voltage problems. The number of cyclesperformed during the 12-month period to support voltage con-trol, and the declared battery lifetime (number of cycles) at 80%depth-of-discharge (DOD), are considered for the estimation of. Considering a Li-ion battery with 1500 declared cycles [23],is approximated as:
(15)
For the three scenarios of , the diagram of Fig. 15 is obtained.Under the 23% PV penetration scenario, it appears that
voltage support using decentralized storage does not impactsignificantly the battery lifetime, if we consider that today’sLi-ion batteries are warranted for about 8–10 years [23]. Aconsiderably shorter lifetime is obtained with the 50% PVpenetration scenario; this is due to the higher number of cyclesrequired for voltage support during one year.
TABLE VENERGY LOSSES IN THE LV FEEDER FOR DIFFERENT SCENARIOS
B. Energy Losses in the Feeder
A comparison of energy losses in the feeder during the12-month period with 23% PV penetration is performed ac-cording to the cases of:1) No storage;2) Decentralized storage (DS) with ;3) Decentralized storage (DS) with ;The results obtained are indicated in Table V. With ,
the energy losses are reduced by 6.6% compared to the casewithout storage. If voltage support is operated with ,the energy losses are reduced of about 7.3%. Losses can be fur-ther reduced if the ESSs operate also in those days without over-voltage problems. In [22], reactive power solutions are used forthe same feeder to provide voltage support.In all cases, a significant reactive power import is observed,
with increased energy losses. Furthermore, with reactive poweroptions by PV units, the PV active power output shall be reducedaccording to the rated capacity of the unit; this aspect representsan indirect active power curtailment.
IX. CONCLUSION
In this paper, a decentralized storage concept for feeders withhigh PV penetration has been set forth that can be potentiallyused as a storage planning tool.The main contribution of the work is based on the qualita-
tive results obtained with the method, particularly applicable tosituations of LV feeders with high PV penetration. The deploy-ment of private ESSs at houses with PV appears a promisingoption for accommodating increased PV penetration levels. Thestorage strategy proposed improves the traditional way of con-trolling ESSs at houses with PV: battery charging is activatedaccording to an optimized power threshold, instead of “as soonas ”. The strategy allows also a reduction of energylosses in the feeder, compared to the scenario without storageand to scenarios using reactive power options.For sizing the domestic ESSs, a method based on voltage sen-
sitivity analysis has been proposed, which identifies the powerthreshold for the charging activation of the ESS battery. Bytesting the method on a simulated feeder with 23% PV pene-tration, it is found that all ESSs shall be activated starting fromthe 80% of the peak-output power of each PV system. Instead,the application of the 70% power threshold, as required by EEG2012, would lead to over sizing the ESS batteries. A battery lifeof 15 years is obtained with the 80% power threshold, while 10years is obtained with 70%.By scaling up the PV penetration level to 50%, the method
highlights that the power limitation of 70% does not guaranteethe required voltage support in the feeder. Instead, a power limi-tation of 50% from each PV system is required. In the latter case,
MARRA et al.: DECENTRALIZED STORAGE STRATEGY FOR RESIDENTIAL FEEDERS 981
the required ESS size results amplified of about 6 times com-pared to the original penetration level; this would make voltagesupport by private PV owners uneconomical.Though the decentralized storage strategy proposed is applied
to a specific case study, other distribution networks with dif-ferent topologies and load patterns have been analyzed by theauthors, obtaining similar qualitative results. Distribution net-work operators can potentially benefit from the findings of thepaper, as grid reinforcement and active power curtailment canbe postponed.
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Francesco Marra was born in Copertino, Italy, in 1984. He received the B.Sc.and M.Sc. degrees in electronic and mechatronic engineering from Polytechnicof Turin, Turin, Italy, in 2006 and 2008, respectively. Since June 2013, he hasbeen pursing the Ph.D. degree in electrical engineering at the Technical Univer-sity of Denmark, Lyngby, Denmark.His fields of interest include renewable energy integration and control engi-
neering.
Guangya Yang received the B.Sc. and M.Sc. degrees, all in electrical engi-neering, from the Shandong University, China, in 2002 and 2005, respectively.In 2008, he received the Ph.D. degree in electrical engineering from the Univer-sity of Queensland, Australia.Currently, he is a Research Scientist with the Department of Electrical Engi-
neering of the Technical University of Denmark, Lyngby, Denmark. His fieldsof interest include power system operation and control, renewable energy inte-gration and wide-area system monitoring and protection.
Chresten Træholt received the M.Sc. degree in electrical engineering in 1987and the Ph.D. degree in materials science in 1994, both from the Technical Uni-versity of Denmark, Lyngby, Denmark.Since then, he has spent several years on electron microscopy and materials
research at the Technical University Delft, the Netherlands as well as severalyears of experience with the superconductor cable industry. His current fields ofinterest include smart grids, renewable energy, PV, wind power, electric vehiclesand superconductivity.
Jacob Østergaard is head of the Centre for Electric Technology and head ofsection for Electrical Energy Systems at DTU Electrical Engineering.He is also head of the experimental platform for electricity and energy, Pow-
erLabDK. He is member of the Advisory Council of the EU Technology Plat-form SmartGrids. He is coordinator of the M.Sc. program inWind Energy (elec-tric). His research focuses on the development of future intelligent power systemwith increased share of decentralized and environmentally friendly electricity,including wind energy.
Esben Larsen received the M.Sc. degree in electrical engineering from theTechnical University of Denmark (DTU), Lyngby, Denmark, in 1977.He is currently Associate Professor at DTU. His main areas of interest in-
clude: high voltage engineering, wind power, photovoltaic, geothermal, hydropower, micro combined heat and power. He has been manager of “Informationand Knowledge Center of Electric Vehicles” at DTU, in 2000–2003.
