1380 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 9, NO. 3, AUGUST 2013

Guest EditorialSpecial Section on Information Technologies

in Smart Grids

S MART grids refer to electricity networks that can intelli-gently integrate the behavior and actions of all users con-

nected to it, e.g., generators, customers, and those that do bothto efficiently deliver sustainable, economical, and secure elec-tricity supplies. Smart grids are enabler for smart energy whichrefers to making energy use more efficient by utilizing the inte-gration of advanced technologies such as information and com-munication technologies, electronics, and material engineering,for an environmentally sustainable future. Smart energy encom-passes a wide range of research and development issues suchas industry sector-wide standardization, policy framework andreform, operational technologies and systems, information andsocial technologies and systems for carbon mitigation, grid-to-customer integration, customer behaviors, cross-sector large-scale modeling and optimization.In this context, it is a pleasure for us to introduce this Spe-

cial Section on “Information Technologies in Smart Grids” ofthe IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, whichhas the aim of bringing some of the most recent and interestingconcepts in this area by the worldwide research community andpresenting some of the latest advancements and developmentsin the field of information technologies dedicated to the smartgrids. A reading of the papers in this Special Section highlightsthe importance of flexible and dynamical communication, in-formation and decision support systems. Smart grids, in fact,as highly networked complex systems (involving both humansand machines) cannot operate effectively without automatedassistance empowered by flexible and dynamical informationsystems.To address this issue, in [1], a distributed architecture based

on intelligent nodes is proposed based on grid computing and se-mantic web concepts. This distributed architecture allows gridmanagement activities to occur in a collaborative way. A spe-cific business flow model is defined and the architecture hasbeen verified through the Energy Balancing Verification feedwith real data from the Spanish electrical grid.Another need is sophisticated test platforms where the ideas

and prototype systems can be experimentally verified. In [2],a complete description for building an automation infrastruc-ture of a lab-scale microgrid is provided, which allows testingvarious renewable sources, demand profiles and power manage-ment strategies. An interesting aspect of the setup is incorpora-tion of a hydrogen storage by which hydrogen source can beused to deal with long-term energy mismatch problem.The complexity of modern decision has caused organizations

to become increasingly dependent on advanced information

Digital Object Identifier 10.1109/TII.2013.2271932

technologies to quickly process large quantities of data. Com-puterized decision support has been and will always be thekey. Soft computing has provided sophisticated means such asfuzzy logic systems, artificial neural networks and evolutionarycomputation which provide powerful problem representationand modelling paradigms, and learning and optimization mech-anisms. The decision support using soft computing is alsoneeded in smart grids because of substantial human-machineinteractions required to maintain real-time operations to meetsocial, environmental and commercial objectives. Moreover,due to the high dimensional dynamics and discrete controlof power systems, realizing an intelligent control to supportsystem voltages is one of the main tasks for realizing a smartgrid.In [3], a learning control scheme based on multiobjective op-

timization for optimal voltage control is proposed. The schemecombines the genetic algorithm (GA) with a memory whichsaves nondominated solutions accumulated from past experi-ences and allows achieving a fast and self-healing voltage con-trol. The control that can prevent the system from voltage insta-bility can be improved gradually over time.In [4], a self organizing architecture-based cooperative fuzzy

agents are proposed to effective voltage control in smart grids,which is critical for ensuring reliable electricity supply. Theagents are designed to acquire information from local buses andassess global smart grid operations with distributed consensusprotocols.In [5], a co-simulation environment for hardware-in-the-loop

or software-in-the-loop validation of distributed controls inSmart Grids is described. By using IEC 61499 function blockssmart grid controls are described in a component-orientedmanner while the system(s) under control are provided byMatlab models, connected via IP sockets. Important is thatreal-world phenomena like communication latencies are takeninto account to get realistic behavior. Using three test cases,the system shows it strength: a multiagent system isolates forinstance a fault and restores power. Such hybrid power systemfunctions are hard to analyze without a co-simulation approach.Communication protocols and standards are also very im-

portant for smart grids to operate effectively. The popularIEC 61850 standard is subject of [6]. IEC 61850-based pro-tection and automation systems are tested for their functionsand their interoperability. Large substations are described andmodeled with all its hardware and software and operationalevents are simulated. As the above standard does not onlydescribe the communication but also evaluation criteria forthe communication quality it is used as basis for the test. IEC61850-specific traffic is generated and the quality-of-service is

1551-3203 © 2013 IEEE

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 9, NO. 3, AUGUST 2013 1381

evaluated in test cases, using equipment of the top five globalIEDs manufacturers.In [7], process bus networks are examined in an IEC

61850–based substation automation system. An interesting as-pect of the study is to acquire the understanding of the networkcharacteristics of “whole of substation” process buses, whichis proved to be a viable option, potentially resulting in a saferworkplace and less impact on environments.The flexibility of future smart grids is the topic [8]. Recon-

figurable control software would allow for updates in the gridif new technologies or market mechanisms are rolled out: avery important and cost-saving feature. Smart grids are con-sidered periodically changing, where new functions like dis-tributed ancillary services need to be implemented in alreadyinstalled infrastructure. The key point is the verification of cor-rectness, if changes are implemented. By using sophisticatedsimulation testbeds, such updates can be checked, before rolledout into the real world. Combining standards like IEC 61850and IEC 61499, a sustainable software development and a com-ponent-oriented design is achieved.The authors in [9] offer a comprehensive investigation of

wireless LAN for IEC 61850–based smart distribution substa-tion applications. Due to low installation cost, sufficient datarates, and easy to deployment, the industrial-harden wirelessLAN technologies are gaining interest among power utilities,especially for less critical smart distribution network applica-tions. Extensive work was carried out to examine the WirelessLAN technology within a power distribution substation. To ex-amine the real-world field performance, the developed proto-type devices were installed in the switchyard and control roomof a power distribution substation, and testing results of variousapplications are presented.In [10], a cognitive communication-based cross-layer frame-

work is proposed for improving the wireless communicationquality-of-service (QoS) in the harsh smart grid environment,which poses challenges such as electromagnetic interference,equipment noise, multipath effects and obstructions. The paperemploys dynamic spectrum access to mitigate the channel im-pairments, defines multi-attribute priority classes and proposesa distributed control algorithm for data delivery Performanceevaluation in a simulation environment shows that the pro-posed framework do achieve the required QoS. The relevanceof Phasor Measurement Units (PMUs) for interlinking of exten-sive power systems through Wide Area Measurement System(WAMS) is introduced in [11]. The PMUs in WAMS employ asynchronous data collection hierarchy for real time monitoringof power system. Since physical topology affects the wirelesscommunication system, Geographical Information System(GIS) is incorporated. In [12], a combination of WiMAX at5.8 GHz and Ethernet LAN is proposed for monitoring andcontrol of energy meters in a smart grid. Communication signalquality is an important factor to consider as it directly affectsthe reliability of the monitoring and control system. The perfor-mance of the proposed WiMAX-LAN communication systemfor advanced metering infrastructure (AMI) is analyzed.The demand side and the acquisition and identification of

consumer load data is subject of [13]. Taking harmonic mea-surements and artificial neural networks, individual loads like

ventilators or compact fluorescent lights are identified. Thisis valuable data for services like automatic energy efficiencyconsulting.The optimization of energy consumption, with consequent

costs reduction, is one of the main challenges in present andfuture Smart Grid. A Mixed-Integer Linear Programming para-digm-based energymanagement system (EMS) for smart homesis proposed in [14]. The EMS is able to provide an optimal so-lution in terms of management of renewable resources and op-timal task scheduling under dynamic electrical constraints, en-suring at the same time the thermal comfort according to the userrequirements. Simulations based on real data confirm the effi-ciency and robustness of the algorithm also in terms of achiev-able money saving.An EMS for smart grids providing LFC, an economic oper-

ation and an electric vehicle integration based on hierarchicalmodel predictive control (HiMPC) is proposed in [15]. TheEMS realizes load-frequency control (LFC), an economicoperation and electric vehicles integration into the smart grid.The EMS is modular and scalable and its design is based ona systematic model and optimization-based procedure. TheHiMPC allows integrating primary, secondary and tertiarycontrol, incorporating constraints and predictions and rejectingdisturbances. An aggregator is proposed as link betweenHiMPC and individual electric vehicle and with the aim toprovide predictions on the availability of electric vehicles forLFC. Simulation results indicate that electric vehicles canconsiderably contribute to balancing fluctuations of renewablegeneration.Voltage sag/swell compensation represents one of the main

problems in smart grids. A novel quasi-Z-source AC/AC con-verter to solve this problem developing a new voltage-basedcommutation strategy, achieved without snubber and by sam-pling only the output voltage, is proposed in [16]. The proposedsolution presents two advantages: (i) the control circuit is verysimple and (ii) compared with conventional AC/AC convertersreduced switching losses due to soft-switching operations, re-sulting in high efficiency. The safe-commutation strategy canbe easily extended to other converters of the proposed family.Smart grids involve techniques related to very different disci-

plines. So far, its study has been done separating the problems,according to discipline. The paper [17] deals with their inte-gration and consequently considers the SG as a whole. This isachieved proposing a Quality of Service-aware ICT with simpleself-management capabilities, provided by a cognitive system,to meet the requirements of Smart Grids. Presented experimen-tations show the feasibility of the proposed solution.The knowledge of consumer electricity consumption is essen-

tial for developing smart grid integration strategies, includingintegration of electrical vehicles and distributed generation. Pre-vious studies tend to fall back on the aggregated data when nodetailed data is available. Valid electricity profiles of householdsare required when simulating voltage problems due to electricvehicle charging at distribution level, when managing a micro-grid with photovoltaic (PV) systems or when estimating the po-tential for battery storage in a distribution grid. Thereofore, atop-down model of the residential electrical load, based on adataset of over 1300 load profiles, is presented in [18]. Load

1382 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 9, NO. 3, AUGUST 2013

profiles are clustered by a Mixed Model to groups similar ones.Within each group, a behavior model is developed using theMarkov model, where its states are based on probability dis-tribution of the electrical power. A second Markov model iscreated to randomize network behavior. A load profile is thencreated by first performing a random-walking of the Markovmodels to get a sequence of states. The inverse of the proba-bility distribution of the electrical power is used to translate theresulting states into electrical power.A combined aggregative short-term load forecasting method

for smart grids is proposed in [19]. The methodology allowsobtaining a global prognosis by summing up the forecasts onthe compounding individual loads. Three methods are used toobtain the overall consumption prognosis by adding up fore-casts on the compounding loads. These newmodels are, namely,bottom-up aggregation, top-down aggregation, and regressionaggregation. Some experiments with two datasets of real datademonstrate the feasibility of aggregative forecast combinationsfor smart grids.Distribution network planning involves development of a

schedule of future additions, thus ensuring the future qualityof energy delivery as well as its lowest cost. On the one hand,the electricity infrastructure must meet the needs of peak loads.Oversized systems can be very expensive, thus, reliable loadmodels are needed to perform distribution network calcula-tions, such as power flow calculations in critical situationsso as to identify poor electricity supply zones for efficientinvestment planning. In [20], a novel individual load estimationmodel based on nonparametric methods is proposed. Makingno hypothesis on the load function and being completelydata-driven, the method is multiobjective, providing both accu-rate maximum and minimum individual load estimations. Thenonparametric models are compared with the current industrialmodel BAGHEERA on the test data. Three different nonpara-metric estimators are proposed, showing that the presentedmethod is more adapted and gives more reliable estimations.The ability to remotely retrieve low-voltage data in smart gridsalso allows applying innovative short-term load forecastingtechniques.It is worth notice that this Special Section covers only a lim-

ited part of ICT issues in Smart Grids, nevertheless, it representsthe current state-of-the-art in discussed topics. Guest Editors ex-pect that these papers will stimulate further investigations andwill represent for quite a long time a reference for the numerousresearchers operating in this complex and exciting area, wherea wide spectrum of knowledge is required and the informationtechnologies are fundamental to link them in a single and verypowerful framework.

ACKNOWLEDGMENT

The Guest Editors would like to express their deep gratitudeto all the authors that have submitted their valuable contribu-tions and to the numerous and highly qualified anonymous re-viewers. We think that the selected contributions, which rep-resent the current state-of-the-art in the field, will be of greatinterest to the industrial electronics community. They wouldlike to thank Prof. D. Wilamowski, Editor-in-Chief of the IEEETRANSACTIONS ON INDUSTRIAL INFORMATICS, for giving us the

opportunity to organize this Special Section and for all the en-couragement, help and support given throughout the processand Ms. L. Pattillo, for her professional support and assistanceduring the whole preparation of this Special Section.

CARLO CECATI, Guest EditorDepartment of Electrical/Information EngineeringUniversity of L’AquilaL’Aquila, 67100 Italyc.cecati@ieee.org

GERHARD HANCKE, Guest EditorDepartment of Electronic and ComputerEngineering

University of PretoriaPretoria, 0002 South Africag.hancke@ieee.org

PETER PALENSKY, Guest EditorDepartment of EnergyAustrian Institute of TechnologyVienna, 1210 Austriapalensky@ieee.org

PIERLUIGI SIANO, Guest EditorDepartment of Industrial EngeneeringUniversity of SalernoFisciano (SA), 84084 Italypsiano@unisa.it

XINGHUO YU, Guest EditorPlatform Technologies Research InstituteRMIT UniversityMelbourne, 3001 Australiax.yu@rmit.edu.au

REFERENCES[1] A. Espinoza, Y. Penya, J. C. Nieves, M. Ortega, A. Pena, and D. Ro-

driguez-Alvarez, “Supporting business workflows in smart grids: Anintelligent nodes-based approach,” IEEE Trans. Ind. Informat., vol. 9,no. 3, pp. 1384–1397, Aug. 2013.

[2] L. Valverde, F. Rosa, and C. Bordons, “Design, planning and manage-ment of a hydrogen-based microgrid,” IEEE Trans. Ind. Informat., vol.9, no. 3, pp. 1398–1404, Aug. 2013.

[3] H. M. Ma, K. W. Chan, and M. B. Liu, “An intelligent control schemeto support voltage of smart power systems,” IEEE Ind. Informat., vol.9, no. 3, pp. 1405–1414, Aug. 2013.

[4] V. Loia, A. Vaccaro, and K. Vaisakh, “A self organizing architecturebased on cooperative fuzzy agents for smart grids voltage control,”IEEE Trans. Ind. Informat., vol. 9, no. 3, pp. 1415–1422, Aug. 2013.

[5] C. Yang, G. Zhabelova, C. Yang, and V. Vyatkin, “Co-simulation envi-ronment for event-driven distributed controls of smart grid,” IEEE Ind.Informat., vol. 9, no. 3, pp. 1423–1435, Aug. 2013.

[6] G. Manassero, E. L. Pellini, E. C. Senger, and R. M. Nakagomi,“IEC61850 based systems—Functional testing and interoperabilityissues,” IEEE Ind. Informat., vol. 9, no. 3, pp. 1436–1444, Aug. 2013.

[7] D. M. E. Ingram, P. Schaub, R. R. Taylor, and D. A. Campbell, “Per-formance analysis of IEC 61850 sampled value process bus networks,”IEEE Ind. Informat., vol. 9, no. 3, pp. 1445–1454, Aug. 2013.

[8] T. Strasser, F. Andren, F. Lehfuss, M. Stifter, and P. Palensky, “Onlinereconfigurable control software for IEDs,” IEEE Ind. Informat., vol. 9,no. 3, pp. 1455–1465, Aug. 2013.

[9] P. P. Parikh, T. S. Sidhu, and A. Shami, “A comprehensive investiga-tion of wireless LAN for IEC 61850 based smart distribution substationapplications,” IEEE Trans. Ind. Informat., vol. 9, no. 3, pp. 1466–1476,Aug. 2013.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 9, NO. 3, AUGUST 2013 1383

[10] G. A. Shah, V. C. Gungor, and O. B. Akan, “A cross-layer QoS-awarecommunication framework in cognitive radio sensor networks forsmart grid applications,” IEEE Trans. Ind. Informat., vol. 9, no. 3, pp.1477–1485, Aug. 2013.

[11] D. Ghosh, T. Ghose, and D. K. Mohanta, “Communication feasibilityanalysis for smart grid with phasor measurement units,” IEEE Trans.Ind. Informat., vol. 9, no. 3, pp. 1486–1496, Aug. 2013.

[12] B. Sivaneasan, P. L. So, H. B. Gooi, and L. K. Siow, “Performancemeasurement and analysis of WiMAX-LAN communication operatingat 5.8 GHz,” IEEE Trans. Ind. Informat., vol. 9, no. 3, pp. 1497–1506,Aug. 2013.

[13] R. A. S. Fernandes, I. N. da Silva, and M. Oleskovicz, “Load pro-file identification interface for consumer online monitoring purposes insmart grids,” IEEE Ind. Informat., vol. 9, no. 3, pp. 1507–1517, Aug.2013.

[14] F. De Angelis, M. Boaro, D. Fuselli, S. Squartini, F. Piazza, and Q.Wei, “Optimal home energy management under dynamic electrical andthermal constraints,” IEEE Ind. Informat., vol. 9, no. 3, pp. 1518–1527,Aug. 2013.

[15] F. Kennel, D. Görges, and S. Liu, “Energy management for smartgrids with electric vehicles based on hierarchical MPC,” IEEE Ind.Informat., vol. 9, no. 3, pp. 1528–1537, Aug. 2013.

[16] L. He, S. Duan, and F. Peng, “Safe-commutation strategy for the novelfamily of quasi-Z-source AC/AC converter,” IEEE Ind. Informat., vol.9, no. 3, pp. 1538–1547, Aug. 2013.

[17] J. Navarro, A. Zaballos, A. Sancho-Asensio, G. Ravera, and J. E. Ar-mendariz-Inigo, “The information system of INTEGRIS: INTelligentElectrical GRId Sensor communications,” IEEE Ind. Informat., vol. 9,no. 3, pp. 1548–1560, Aug. 2013.

[18] W. Labeeuw and G. Deconinck, “Residential electrical load modelbased on mixture model clustering and Markov models,” IEEE Ind.Informat., vol. 9, no. 3, pp. 1561–1569, Aug. 2013.

[19] C. E. Borges, Y. K. Penya, and I. Fernàndez, “Evaluating combinedload forecasting in large power systems and smart grids,” IEEE Ind.Informat., vol. 9, no. 3, pp. 1570–1577, Aug. 2013.

[20] N. Ding, Y. Besanger, F. Wurtz, and G. Antoine, “Individual non-parametric load estimation model for power distribution network plan-ning,” IEEE Ind. Informat., vol. 9, no. 3, pp. 1578–1587, Aug. 2013.

Carlo Cecati (M’90–SM’03–F’06) is a Professor ofIndustrial Electronics and Drives with the Universityof L’Aquila, L’Aquila, Italy, where he is a Rector’sDelegate and the Coordinator of the Ph.D. course onrenewable energy and systainable building. His re-search and technical interests cover several aspects ofpower electronics, distributed generation, and smartgrids.Prof. Cecati was a corecipient of the 2012 Best

Paper Award from the IEEE TRANSACTIONS ON

INDUSTRIAL INFORMATICS and from the IEEE In-dustrial Electronics Magazine. He has been an active member of IEEE-IESsince the 1990s and currently is a Senior AdCom Member and is serving as theEditor in Chief of IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS.

Gerhard Hancke (M’88–SM’00) received theB.Sc., B.Eng., and M.Eng. degrees from the Univer-sity of Stellenbosch, Stellenbosch, South Africa, andthe D.Eng. degree from the University of Pretoria,Pretoria, South Africa, in 1983.He is currently with the University of Pretoria,

as a Professor and Coordinator of the ComputerEngineering Program and Head of the AdvancedSensor Networks Research Group, a joint initiativebetween the Department of Electrical, Electronicand Computer Engineering and the Meraka Institute

at the Council for Scientific and Industrial Research. His research interests

are primarily in advanced distributed sensors and actuators networks, andespecially in the industrial application thereof.Prof. Hancke is a Senior AdCom Member and past Secretary of the IEEE In-

dustrial Electronics Society. He served as Guest Editor for a number of SpecialSections in the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS and IEEETRANSACTIONS ON INDUSTRIAL ELECTRONICS, as well as other scholarly jour-nals. He has been actively involved in organizing IEEE Conferences, in manyoccasions as General Chair. He also served on other IEEE Committees, amongstothers as Chair of the IEEE Ethics and Member Conduct Committee and Chairof the IEEE Admission and Advancement Committee.

Peter Palensky (M’03–SM’05) received the Ph.D.degree from the Vienna University of Technology,Vienna, Austria.He is Principal Scientist at the Energy Department,

Austrian Institute of Technology (AIT). Before thathe was HEad of the Business Unit for SustainableBuilding Technologies, AIT, CTO of EnvidatecCorporation, Hamburg, Germany, Associate Pro-fessor at the Department of Electrical, Electronicand Computer Engineering, University of Pretoria,South Africa, University Assistant at the Vienna

University of Technology, Austria, and Researcher at the Lawrence BerkeleyNational Laboratory, CA. His main research fields are complex energy systemsand smart buildings.Dr. Palensky is an elected AdCom Member of the Industrial Electronics So-

ciety of the IEEE. He is active in international committees like ISO, IEEE, andCEN.

Pierluigi Siano (M’09) received the M.Sc. degree inelectronic engineering and the Ph.D. degree in infor-mation and electrical engineering from theUniversityof Salerno, Salerno, Italy, in 2001 and 2006, respec-tively.He is currently an Aggregate Professor with the

Department of Industrial Engineering, University ofSalerno. His research activities are centered on theintegration of renewable distributed generation intoelectricity networks and smart grids. In these fields,he has coauthored more than 100 papers including

more than 40 international journals.Dr. Siano is an Associate Editor of the IEEE TRANSACTIONS ON INDUSTRIAL

INFORMATICS.

Xinghuo Yu (M’92–SM’98–F’08) received B.Eng.and M.Eng. degrees from the University of Scienceand Technology of China, Hefei, China, in 1982 and1984, and the Ph.D. degree from South-East Univer-sity, Nanjing, China in 1988, respectively.He is now with RMIT University (Royal Mel-

bourne Institute of Technology), Melbourne, Aus-tralia, where he is currently the Founding Directorof RMIT Platform Technologies Research Institute.His research interests include variable structure andnonlinear control, complex and intelligent systems,

and industrial applications.Prof. Yu is serving as an Associate Editor of the IEEE TRANSACTIONS ON

CIRCUITS AND SYSTEMS PART I, the IEEE TRANSACTIONS ON INDUSTRIALINFORMATICS, the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, andseveral other scholarly journals. He is an IEEE Distinguished Lecturer andVice-President (Publications) of the IEEE Industrial Electronics Society(2012–2013). He is a Fellow of the Institution of Engineers, Australia, a Fellowof the Australian Computer Society, and a Graduate of the Australian Instituteof Company Directors.