Received February 27, 2018, accepted April 4, 2018, date of publication April 18, 2018, date of current version May 9, 2018.

Digital Object Identifier 10.1109/ACCESS.2018.2828323

Digitization Era for Electric Utilities: A NovelBusiness Model Through an Inter-DisciplinaryS/W Platform and Open Research ChallengesPRODROMOS MAKRIS 1, (Member, IEEE), NIKOLAOS EFTHYMIOPOULOS1, VASSILISNIKOLOPOULOS2, ANDREW POMAZANSKYI3, BORIS IRMSCHER3, KRASSEN STEFANOV4,KATINA PANCHEVA4, AND EMMANOUEL VARVARIGOS1,51Institute of Communication and Computer Systems, National Technical University of Athens, 15773 Athens, Greece2INTELEN Services Ltd., Nicosia, Cyprus3NUROGAMES GmbH, 50676 Cologne, Germany4Faculty of Mathematics and Informatics, Sofia University ‘‘St. Kliment Ohridski’’, 1164 Sofia, Bulgaria5Department of Electrical and Computer Systems Engineering, Monash University, Clayton VIC 3800, Australia

Corresponding author: Prodromos Makris (prodromosmakris@central.ntua.gr)

This work was supported by the European Union’s Horizon 2020 Research and Innovation Program through theSOCIALENERGY Project under Grant 731767.

ABSTRACT The digitization era for electric utilities is tightly coupled with the development of inter-disciplinary software (S/W) platforms aiming at establishing much more efficient communication pathwayswith their clientele and other smart grid market stakeholders. This paper presents the SOCIALENERGYS/Wplatform, which is funded by the EU H2020 work programme. SOCIALENERGY is a user engagement,social networking, gamification, and business management platform aiming at evolving energy markets’operation and educating virtual energy communities. The proposed business model is targeted on electricutilities’ customer segment. The proposed system is modular by design incorporating several subsystemsfrom various disciplines, such as ICT, energy efficiency, behavioral economics, education, and gamification.The proposed system facilitates the easy, rich, and deep communication among individual energy consumers,virtual energy communities, utilities, and other less direct stakeholders (such as electric appliance retailersand building renovators). Finally, the paper provides important research/innovation insights and challengesto be addressed towards the proposed Green Social Response Network concept and the exploitation ofSOCIALENERGY system as part of more complex systems for the 2030 smart grid era and beyond.

INDEX TERMS Digital electric utilities, virtual energy communities, behavioural change, green socialnetwork, demand response programs.

I. INTRODUCTIONAt the retail electricity market side, electric utility compa-nies are gradually following the ‘digitization’ path towardsproviding more effective and attractive energy services totheir clients [1]. This digitization trend is expected to enhancethe active participation of consumers along the energy valuechain, thus changing the nature of consumer engagementacross the customer life cycle. Thus, digitization should beconsidered as part of every progressive utility’s initiative [2]towards reducing its OPEX (e.g., educate consumers, encour-age self-service or create value with new services) and itsCAPEX (e.g., exploit a variety of advanced energy efficiencyservices and products).

As shown in Fig. 1, there are three main driving forcestowards the digitization era of electric utilities, namely:a) regulatory and policy shifts, b) changing market demand,and c) technological innovation. In particular, informationand communication technologies (ICTs), which can cutthrough pre-existing layers of regulatory processes and busi-ness models to directly connect all stakeholders to the goodsand services they want to purchase/sell, are gaining tractionacross liberalized energymarkets. Nowadays, electric utilitiesare keen on prioritizing their CAPEX investments on energyefficiency-related assets, as the latter are much cheaper toimplement than building new generating capacities. As statedin [3], the levelized cost of energy efficiency resource costs

224522169-3536 2018 IEEE. Translations and content mining are permitted for academic research only.

Personal use is also permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

VOLUME 6, 2018

P. Makris et al.: Digitization Era for Electric Utilities: Novel Business Model

FIGURE 1. Confluence of 3 driving forces for the digitization era ofelectric utilities.

for utilities about 2 to 5 cents per kilowatt-hour, which isone-half to one-quarter the cost of other options like coal/gas/nuclear investments or solar/wind installation programs.As a result, the adoption of various digital S/W solutionssounds increasingly appealing for electric utilities as well asfor their clients (i.e. energy consumers), who are increasinglykeen on experiencing personalized energy services [4].

Interestingly, the telecom operator industry’s disruptionparadigm, which took place a few decades ago, has severalremarkable similarities with the various research, innova-tion and commercial trends of the electric utility industry(cf. Fig. 2). In table 1, these similar characteristics are brieflysummarized. Hence, the electric utility industry may adoptthe good practices and ‘‘success stories’’ of the (mobile) tele-com industry and possibly follow-up the respective ‘‘lessonslearned’’ in order to experience an analogous market growthwithin the next years.

FIGURE 2. Research, innovation and commercial trends for new businessmodels in the electric utility sector.

As traditional electric utilities are gradually being trans-formed into Energy Service Providers (ESPs), new businessmodels are required. Fig. 2 presents the basic research, inno-vation and commercial trends that constitute a typical iterativeprocess within a progressive utility’s everyday business.

TABLE 1. Mapping of telecom industry’s with electric utility industry’sdisruption paradigms.

First of all, research is needed in order to drive innovativesolutions, such as the incorporation of artificial intelli-gence algorithms, big data analytics, behavioural economicsapproaches, socio-economic research and optimizationtools, towards the development of advanced Energy Pro-grams (EPs). EPs are contracts with end users that chargethem not only based on the amount of energy they consume,but also according to the level of their energy efficiency (thepattern of their energy consumption curve and/or the levelof their behavioural changes). Innovation means the novel

VOLUME 6, 2018 22453

P. Makris et al.: Digitization Era for Electric Utilities: Novel Business Model

exploitation (e.g. via optimal combination or enhancement)of already existing S/W solutions and systems, such as onlinesocial networks, user engagement platforms, serious games,gamification modules, e-learning platforms, etc. Thus, newproducts and services can be created in order to enhancethe quality of service that the utility provides to its cus-tomers. Finally, innovation provides input for new ideasregarding further commercial exploitation from a strategicbusiness point of view. For example, utilities can collaboratewith a telecom provider to offer converged ICT-energy ser-vices or they can collaborate with various types of ESCOs.Use of e-commerce platforms is also an emerging commercialtrend for utilities, who want to collaborate with cross-sellmarket stakeholders to realize new revenue streams. Ulti-mately, the real-life results from the utility’s business andrelated analytics are then fed back into the research division todesign new and enhanced tools towards meeting the updatedcustomer segment’s needs. This iterative process guaranteesthat the digital electric utility’s business will be sustainablycompetitive in the liberalized electricity markets’ framework.

Conclusively, an holistic S/W infrastructure together witha multi-disciplinary business model is required in order tocope with all the afore-mentioned challenges. Therefore,we propose the SOCIALENERGY S/W platform, whichconsists of several systems and S/W modules from variousdisciplines, including the ICT, energy efficiency, behavioraleconomics, socio-economic sciences, online social networks,competence based education, serious games and gamifi-cation sectors. The major contribution points of the pro-posed SOCIALENERGY framework can be summarized asfollows:

• A multidisciplinary S/W platform is proposed in orderto accurately inform and effectively educate end userson energy efficiency in a user-friendly way.

• An innovative hybrid demand response (DR) strategyis developed (towards EPs that offer effective DemandSide Management), which combines incentive-basedand price-based DR through the use of advanced userengagement technologies and exploitation of financialincentives.

• The concept on online social networks is exploitedthrough the development of the Green Social ResponseNetwork (GSRN) concept, combined with behavioraleconomics models (such as peer pressure). These leadto the creation, dynamic adaptation and managementof virtual energy communities (VECs) that triggervery effectively behavioral changes towards energyefficiency.

• The self-evolving SOCIALENERGY game integratesall the aforementioned mathematical models and algo-rithms towards energy efficiency. This allows the emu-lation of a real residential home capable to: i) educateempirically end users in a very effective way and ii) offera ‘‘virtual’’ pilot for experimentation purposes in orderto accelerate development of effective energy efficiencyservices.

• A competence based educational (CBE) framework isintroduced aiming to create the best ‘individual learn-ing plan’ (ILP) for each individual SOCIALENERGYuser and subsequently guide him/her through the wholeonline learning and user engagement process.

The remainder of the paper is organized as follows:Section II describes the state-of-the-art in the various dis-ciplines related to our work and introduces the GSRN con-cept. Section III presents the SOCIALENERGY system as awhole, as well as the functionalities of its five main subsys-tems. In Section IV, initial research findings beyond the state-of-the-art that outperform current solutions are presentedtogether with their integration in the proposed platform.Finally, Section V concludes by providing real market appli-cability of the proposed solution together with interestingresearch and innovation insights and challenges for exploitingSOCIALENERGYplatform as part of more complex systemsfor the 2030 smart grid era and beyond.

II. STATE-OF-THE-ART AND THE PROPOSED GREENSOCIAL RESPONSE NETWORK (GSRN) APPROACHA. RELATED WORKS FROM MULTIPLE DISCIPLINESAs surveyed in [5], there is a plethora of behaviorchange programs implemented by progressive electric util-ities, which can be classified in three major categories(cf. table 2): 1) information-based, 2) social interaction-based, and 3) education-based. Nowadays, a utilitymay selectto adopt one or more programs, but it does so in an ad-hoc manner. The SOCIALENERGY vision is to offer therequired S/W infrastructure substrate for a utility to adopt anycombination of state-of-the-art behavior change programsaccording to its targeted business needs.

Regarding state-of-the-art DR programs, these can becategorized based on [6] as follows: 1) mechanisms usedfor the loads’ control (e.g. centralized vs. distributed control,load sheds vs. load shifts), 2) offered motivations to users(e.g., price-based vs. incentive-based), and 3) key per-formance indicator (KPI) that needs to be optimized(e.g., system’s cost minimization, users’ welfare maximiza-tion, utility’s profits maximization, etc). SOCIALENERGYplatform exploits and advances state of the art and thus dis-poses DR programs able to offer a very attractive trade offamong the afore-mentioned KPIs.

Furthermore, gamification solutions apply game mechan-ics to motivate people to change their energy behavior andhave been recently used by several utilities to improve cus-tomer engagement in energy efficiency and DR programs.According to [7], [8], real-life pilots have indicated energysavings of 3-6% among a sizable number of participants,while savings of more than 10% can be achieved in narrowlytargeted programs.

Community based programs and the exploitation of onlinesocial networks (OSNs) to form virtual energy communi-ties (VECs) are becoming increasingly popular during thelast years. As a result, innovative concepts found in theinternational literature, like integrated community energy

22454 VOLUME 6, 2018

P. Makris et al.: Digitization Era for Electric Utilities: Novel Business Model

TABLE 2. Categorization of behavior change programs.

systems [9], virtual microgrid/prosumer associations[10], [11], prosumer community groups [12], [13] andcooperative/ aggregated demand response [14], [15], haveemerged. Being part of a VEC, the end users realize ben-efits additional to the case that each one optimizes his/herindividual interests. This is mainly due to two reasons: a) thestatistical multiplexing gains that a community offers towardsachieving a collaborative goal, and b) the peer pressureinduced among users within the OSN that inherently incen-tivizes each user to adopt a more energy-efficient behaviorand lifestyle [16], [17]. At the same time, the energy systemcan realize considerable benefits, too, all of them related withthe better matching of energy supply and demand. From abusiness perspective, recent marketing strategies that exploitOSNs have been proven very effective and highly increasedthe revenues of the companies that used them. CommunityBased Social Marketing (CBSM) targets the social context asopposed to the individual. It recognizes that human behavioris never isolated, but occurs under specific circumstances,with historically, culturally, economically and politicallydetermined parameters [18].

Finally, a major lesson learned from recent real-life DRpilots around the globe is that users should be well educated

on the complicated concepts of smart grid and liberalizedelectricity markets’ operation. This is of utmost importance,because for example, it is useless for an utility to sell a newEP to its customers, if the latter do not fully comprehend the‘‘pros and cons’’ of this particular choice. One of the latestadvancements for educational and lifelong learning sectors isthe adoption of competence-based education (CBE) frame-works. Competence means the proven ability to use knowl-edge, skills and personal, social and/or methodological abili-ties, in work or study situations in professional and personaldevelopment [19]. An advanced e-learning platform thatadopts a CBE framework aims at: a) allowing competency-based learning and assessment, based on a well-defined tax-onomy of competencies, b) allowing the use of rich setof learning resources, activities and experiences related toachieving the needed competences, c) supporting variousmodes of assessment of learner’s knowledge/competencesand relevant grading according to achieved results, d) sup-porting individual learning plans (ILPs) in order to optimallyguide the learner throughout the whole personalized learningprocess.

B. THE GREEN SOCIAL RESPONSE NETWORK (GSRN)APPROACHIn Fig. 3, the general idea of the proposed ‘‘Green SocialResponse Network’’ (GSRN) concept is illustrated. There arethree main gamification steps: 1) gamify the user engagementin DR and energy efficiency programs, 2) gamify the process(e.g. VECmanagement, EP selection, etc.), and 3) gamify theresults and feed them back to step (1). As depicted in Fig. 3,there is a perpetual information and knowledge flow amongthe 3 gamification steps in a way that all types of users(i.e., individual energy consumers, VEC leaders, electric util-ities, ESCOs) are continuously educated towards understand-ing their role in the smart grid market/ecosystem and theneeds/interests of all other related market stakeholders in aliberalized electricity market context. The proposed GSRNis called ‘‘Green’’, because it facilitates and enables the useof clean energy and good practices on energy efficiency

FIGURE 3. Green Social Response Network (GSRN) concept.

VOLUME 6, 2018 22455

P. Makris et al.: Digitization Era for Electric Utilities: Novel Business Model

focusing on the residential sector. It is also called ‘‘Social’’,because its users are able to participate in VECs and commu-nicate with other peers who have the same interests, or evencommunicate with other commercial stakeholders to betterunderstand their needs/interests. Finally, GSRN is called‘‘Response Network’’, because it enables/facilitates efficientDR procedures to take place and motivates the individualenergy consumers to change their behavior in terms of theway they consume energy in their everyday lives. The resultof this process will ultimately lead to environment-friendlyuse of energy resources, the efficient operation of liberalizedelectricity markets and the realization of new businesses andrevenue streams from both new and existing smart grid mar-ket stakeholders.

According to several recent surveys undertaken by inde-pendent world-known consultancy companies and policymakers [1], [2], [4], [6], [7], the high-level business strategyobjectives of a progressive utility (or else ESP) are summa-rized in table 3 and each one of them is directly mappedto one of the five main SOCIALENERGY subsystems. Insection III, all SOCIALENERGY subsystems are describedtogether with their main functionalities and innovation points.

TABLE 3. Mapping of ESP’s business objectives with socialenergysubsystems.

III. PROPOSED S/W PLATFORM (SOCIALENERGYSYSTEM AS A WHOLE)SOCIALENERGY is an holistic S/W infrastructure to beused by today’s progressive electric utilities towards the

realization of their first steps in the digitization era of retailelectricity markets.

As illustrated in Fig. 4, SOCIALENERGY system com-prises of six S/W components (subsystems), namely: 1)MeterData Management System (MDMS), 2) the core GSRN S/Wplatform or else SOCIALENERGY’s real world, 3) EnergyEfficiency GAME or else SOCIALENERGY’s virtual world,4) Research Algorithms’ Toolkit (RAT), 5) Learning ContentManagement System (LCMS), 6) Energy Information Distri-bution as a Service (EIDaaS) or else virtual marketplace.

FIGURE 4. SOCIALENERGY architecture.

MDMS is the SOCIALENERGY’s database, where allenergy metering data from all ESP’s customers is collectedtogether with all energy-related data models (e.g., electricappliance consumption models, disaggregation data, homeenergy labeling models, etc.).

A. CORE GSRN S/W PLATFORMAll types of SOCIALENERGY users (e.g. individual con-sumers, VEC leaders/managers, electric utility/retailer user,ESCO user, etc.) are able to log in the system via the coreGSRN S/W platform interface. A single sign-in proceduretakes place and then the user is able to navigate in allSOCIALENERGY subsystems. Indicatively, throughGSRN ,an individual consumer can select an EP or participate in aVEC in order to select a community EP that fits its needs.A utility user (e.g. utility’s CEO) is able to visualize the dataof its entire customers portfolio and perform advanced admin-istrative tasks, such as create new EPs, update reporting/recommendation rules of EP to its users, handle variousbusiness analytics, etc. A VEC leader can only have access toits associated VECmembers’ profiles and perform respectivetasks.

22456 VOLUME 6, 2018

P. Makris et al.: Digitization Era for Electric Utilities: Novel Business Model

GSRN platform consists of several S/W modules.‘Data Analytics’ module visualizes all RAT-API outputs andprovides a visualized KPIs’ dashboard to the users in orderto allow them to check their overall performance. ‘Energymodule’ is connected to theMDM-API and RAT-API in orderto visualize real energy consumption curves (ECCs) fromusers’ meters and billing information respectively. ‘Gamingprofile’module connects directly to the GAME-API and getsall relevant details from the game, regarding each specificuser. User gets badges, leader board, performance, stages,points and all available GAME-API inputs. Finally, ‘Social-ties’ module is also working at the backend and is used toget user’s social network information, as the user logs in thesystem. It is also combined with all other modules to providepersonalization and further analytics. ‘E-learning/training’module is responsible for the integration and visualizationof all educational material and relevant interactions comingfrom the GSRN-LCMS API. The rewarding mechanismworks at the backend and computes the individual pointsfor all users’ activity in the GSRN. It also connects to theGAME-API in order to feed the ‘User Profiling’module withgame leader-board and relevant points from the user’s gameperformance. The mechanism is flexible for the administratorto design the point system based on users’ stage, pointsand performance. Point system consists of two categories:‘Actual points’ and ‘Experience points’, indicating activityand knowledge engagement correspondingly.

B. SOCIALENERGY GAMEAfter the SOCIALENERGY user (i.e. individual consumer)is logged in the GSRN, s/he uses the same credentials todownload and start the game. The SOCIALENERGYGAMEcan be played by the user in a range of platforms, starting froma basic web-based implementation and possibly be extendedto a mobile application, too. The GAME is an applied gameon energy efficiency and combines characteristics from seri-ous games and the classic entertainment industry. The playercreates/enters a virtual world (i.e., virtual house) with allelectric appliances and tries to maximize the energy effi-ciency KPIs by striking to find an optimal trade-off betweenthe energy cost (according to the EP that s/he selected) andthe discomfort incurred through load shedding and shiftingactions. Via the gameplay, the user is seamlessly educatedin best practices about energy efficiency and this is done inan enjoying manner. Furthermore, the users can customizethe GAME’s settings and create the virtual environment thatis close to their real house. As a result, GAME can alsoserve as a (near) real-life testbed to help in quantifying user’sbehavioral change through time, which is very importantfrom both a research and commercial exploitation point ofview. It should be noted that the GAME is also interactingwith the RAT (by integrating all sophisticated mathematicalmodeling that modern EPs dispose, which provides the basisfor the GAME’s long-term success in the market). GAMEalso incorporates references to educational material (e.g. inthe form of small pop-up windows) that the users can find

in the LCMS and search for more details therein. Finally,the multi-player feature of the GAME, through the use ofvirtual users (bots), allows the users to be educated on theoperation of community EPs and the additional benefits thatthe latter can provide to the users.

C. RESEARCH ALGORITHMS’ TOOLKIT (RAT)From the GSRN S/W platform’s web interface, the utilityuser (i.e. system administrator) is able to select the ‘‘RAT’’tab and then a new window navigates him/her to the RAT’sfunctionalities. The RAT subsystem is very important forSOCIALENERGY’s operation because it provides all theintelligence that is required towards making SOCIALEN-ERGY S/W platform competitive enough and commerciallysuccessful in a sustainable manner. It provides all the EPs’modeling and ‘‘data analytics’’ services mainly to GSRN andto the GAME (by integrating the sophisticated mathematicalmodeling in the energy pricing and game score calculations).Various research algorithms are executed regarding: i) thedynamic pricing models that are adopted in the various inno-vative EPs and ii) the VECs’ creation and dynamic adaptationalgorithms (required for the online management of VECs).RAT is also a planning tool for the system administratorto automatically analyze various business/strategy ‘what-if’scenarios by running parameterized system-level simulations.More details about the basic operation of ‘dynamic pricingalgorithms’ and ‘VEC creation and dynamic adaptation algo-rithms’ are provided in section IV.

D. LEARNING CONTENT MANAGAMENT SYSTEM (LCMS)LCMS is the subsystem, where the user/player educates him-self both online (e.g. via the gameplay or by taking variouslearning courses) and offline (e.g. by consuming CBE-basedmaterial) to consolidate the new knowledge about good prac-tices on energy efficiency. LCMS interacts with GSRN. Thus,the latter can provide recommendation services to the useraccording to the educational content that is mostly keen onwatching next based on user’s current educational profile andactions in SOCIALENERGY’s real and virtual worlds. Therole of the LCMS is important, because it provides to theuser the opportunity to better comprehend the new conceptsin the liberalized smart grid markets and inter-relate the‘‘lessons learned’’ from the GAME with the real-life condi-tions. In this way, end users are able to efficiently interact withtheir electric utility company. LCMS adopts a CBE frame-work for energy efficiency. There are four main categoriesof competences: a) Energy-related end-user’s knowledge onvarious theoretical aspects of smart grids, dynamic pricing,energy efficiency, etc. b) Personal willingness to act based oneach user’s activities inside the SOCIALENERGY platform,c) Social interactions behavior based on the user’s activityinside the VEC, and d) Energy-related end-user’s skills basedon the user’s achievements (e.g. % of energy savings, % billreduction, etc.).

VOLUME 6, 2018 22457

P. Makris et al.: Digitization Era for Electric Utilities: Novel Business Model

E. VIRTUAL MARKETPLACE AND ENERGY INFORMATIONDISTRIBUTION AS A SERVICE (EIDAAS)Finally, via the ‘virtual marketplace’ component,SOCIALENERGY bridges the gap between energy con-sumers and multiple other market stakeholders related tothe energy efficiency sector. Via the use of SOCIALEN-ERGY platform, the profile of each energy consumer iscreated (e.g. energy consumption history, social networkingactivities, commercial actions’ history, etc.). This profilinginformation can be exploited from stakeholders in orderto: i) design energy efficiency products and services (e.g.offers for building renovation, insulationmaterials, etc.) moreappealing to their audience, ii) allow VECs to contribute inthe design of new or enhanced products/services by givingtheir opinions, iii) exploit VECs as cells within which grouptrading can be facilitated, and iv) generally sell EnergyInformation Distribution as a Service (EIDaaS) to whomit may concern in the long-term future. SOCIALENERGYhas created an API through which it can commercialize thisidea of ‘‘data monetization’’ service. Moreover, the virtualmarketplace can host products and services from electricappliance vendors/retailers, building renovation companies,etc., so that the user can have an end-to-end experience on theway to achieve his/her energy efficiency targets. Ultimately,this business model can be very beneficial for the electricutility, too.

IV. RESEARCH BEYOND THE STATE-OF-THE-ART &INTEGRATION IN THE PROPOSED PLATFORMA. INNOVATIVE ENERGY PROGRAMS’ (EPS) MODELINGAND KPISTo design a new EP, a dynamic pricing model and algorithmare required. Let us consider a system, which consists of autility and its N clients/energy consumers. Without harm ofgenerality, in the retail market, the utility provides electricityto its clients in order to cover their demand. Thus, utilityparticipates in wholesale electricity markets and purchasesthe required amount of energy at a certain cost, which istime-variant and also a non-linear function of the aggre-gated consumption of all N end users (i.e., each incrementalenergy unit purchased costs more). Generally, the utility canminimize the cost of the energy that it purchases in thewholesale electricity market (i.e., the system cost) by givingincentives to its end users to ‘‘harmonize’’ the aggregatedECC (i.e., the demand curve of its entire customer port-folio) with the wholesale market prices. Utilities and endusers (consumers) can mutually benefit from this system’scost reduction and the stability improvement that behavioralchanges in the energy consumption can bring (see Fig. 5).Modern pricing schemes (or else EPs) should be able totrigger these behavioral changes (e.g., by motivating users toconsume less during peak hours and more during non-peakhours). For example, in Real Time Pricing (RTP), prices areanalogous to the dynamic ratio between the total energy pro-duction cost (i.e. supply) and the total amount of consumption

FIGURE 5. Advanced Energy Programs for behavioral change.

(i.e. demand) [20], [21]. A pricing scheme has to achieve anattractive trade-off among the following requirements (KPIs):i) the end user’s satisfaction, ii) the stability of the energyproduction/ transmission/consumption system, and iii) theutility’s financial profitability. The first requirement is alsoreferred to as ‘user’s welfare’ and is formulated as the differ-ence between a utility function that expresses how much anend user values a specific consumption pattern and the costof energy that s/he consumes. In the context of comparingdifferent pricing schemes, the user’s welfare expresses whichpricing scheme leads to more competitive services in the openmarket [22], [23]. The second requirement is also denotedas ‘behavioral efficiency’ and expresses the capability of apricing scheme to achieve the objectives that motivated it inthe first place (e.g. load curtailments and shifts). Intuitively,behavioral efficiency of a pricing scheme expresses howfriendly it is to a TSO/DSO (addressing issues related toenergy network stability, efficiency and costs) and implicitlyaffects several financial metrics (e.g. investments in RES,energy storage and network upgrades). Usually, it is linkedwith minimizing the system’s energy cost, as in [24] and [25].The third requirement is also referred to as ‘profit dynamics’and represents the profit percentage per energy unit andthe total revenues of the utility company. In other words,it expresses the financial growth potential of the company thatexploits a specific pricing scheme (or else EP) [20], [26].

A wide range of innovative EPs are integrated inSOCIALENERGY platform. In particular, SOCIALEN-ERGY conducts research on the improvement of the behav-ioral efficiency of the EPs without sacrificing the rest ofthe aforementioned KPIs. For example, as shown in Fig. 5,a behavioral change in the aggregated ECC can providereduced energy cost for the system without sacrificing users’welfare due to the fact that some of them are flexible enoughto undertake the changes in their individual ECCs and inreturn get reimbursed by the utility. Through SOCIALEN-ERGY platform, the administrative user can perform exhaus-tive system-level simulations before deciding to release a

22458 VOLUME 6, 2018

P. Makris et al.: Digitization Era for Electric Utilities: Novel Business Model

new EP in the retail market. Similarly, an end user can alsoexploit SOCIALENERGY platform to dynamically invest(if it is beneficial for her/him) on a new EP that fitshis/her updated needs. Finally, an end user can also playthe SOCIALENERGY GAME in order to comprehend theoptimal behavior that one should have towards harvesting themaximum benefits from a certain EP.

B. MANAGEMENT OF MULTI-PARAMETRIC VIRTUALENERGY COMMUNITIES (VECS)In SOCIALENERGY system, VECs can be created in abottom-up (and thus manual) way from the users themselvesjust like in traditional social network platforms. AVEC leadermay also be the one that initiates and coordinates the processjust like in web forums and other web 2.0 tools. However,VECs can also be created and dynamically adapted in anautomated way via the use of clustering algorithms in orderfor both users and the utility to optimally exploit the benefitsof VEC concept. In particular, a utility’s portfolio can becategorized in several VECs based on qualitative characteris-tics such as demographics, geographical, socio-economic andother social norms-based metrics [27]–[29]. Given an alreadyexisting social graph, the goal of a clustering algorithm mayalso be to find such VECs that the total power consumptionin each group of users achieves minimum variance [17].VECs can also be created in a way that users’ satisfaction,social network dynamics and the peer pressure that VECmembers induce to each other are taken into considera-tion [16]. Other algorithmsmay take into account quantitativemetrics for VEC creation problem. For example, the domi-nant VEC creation criterion can be the similarity factor ofEnergy Consumption Curves (ECCs) and/or the FlexibilityCurves (FCs) of the users. In other words, users with similarECCs and FCs increase the probability of performing betterin a community-based EP. Another criterion would be toput together users that have the minimum deviation betweentheir forecast and real consumption in order to minimize theimbalance penalties of a utility’s portfolio, as we proposein our prior works [11], [30]. Finally, for billing purposes,there are also intra-clustering algorithms, which can allocatethe costs among the members of a certain VEC by applyingvarious policies as shown in the work of some of the authorsin [10].

All the above-mentioned multi-parametric approaches forVECs’ creation can be easily customized and integrated inSOCIALENERGY platform. A few clustering examples thatwe currently use in the SOCIALENERGY platform are illus-trated in Fig. 6. What’s more interesting is that the admin-istrative user can set specific thresholds based on which anend consumer can be recommended to switch to a differentVEC that better fits his/her updated interests and needs. Userscan also play the multi-player mode of the GAME in orderto be seamlessly educated about the potential benefits andoperation of community-based EPs.

FIGURE 6. Multi-parametric VEC creation and dynamic adaptation.

C. CONTEXT-AWARE DATA ANALYTICS SERVICES,E-COMMERCE AND BOTTOM-UP ORGANIZATIONALSTRUCTURES FOR SOCIAL INNOVATION ANDE-GOVERNANCEThe vision of SOCIALENERGY platform is to use therecent innovative concepts on e-commerce in order to trig-ger e-governance and consequently social innovation. Thereare five promising innovation fields towards this goal.The first is the exploitation of information beyond thee-commerce retailer’s site towards personalized and accuraterecommendations for products and services. In more detail,SOCIALENERGY envisages the exploitation of informationfrom OSNs (e.g. activity, relationships, etc.) and contentconsumption platforms (e.g. YouTube). Its aim is to process,structure and annotate this information in a way that is tunedfor the requirements of e-commerce personalization services.

Secondly, SOCIALENERGY envisages cross-domaine-commerce hyper personalized services that will offer agreat opportunity to retailers to dispose their products/services beyond their company’s site in a targeted/efficientand non-intrusive manner. In more detail, SOCIALEN-ERGY envisages to provide: i) hyper personalized and non-intrusive e-mail personalization and couponing, ii) injectionof e-commerce content (e.g. audiovisual content, couponing)and product/service recommendations through the environ-ment of SOCIALENERGY to relevant virtual communities.

Thirdly, SOCIALENERGY targets the increase ofe-commerce transactions through automatic and intelli-gent product/service assortment recommendation servicesfor portfolio extensions that will be highly beneficial fore-commerce retailers. Towards this goal, there will be usedinformation from: i) social network relationships’ and activ-ities’ analysis, ii) communities in GSRN, and iii) LCMS.

Fourthly, an innovative service that SOCIALENERGYenvisages to offer through GSRN is to advance existinge-commerce paradigm through collective e-commerce ser-vices and bottom-up collaborative crowd-funding services.

VOLUME 6, 2018 22459

P. Makris et al.: Digitization Era for Electric Utilities: Novel Business Model

This can be done through the development of a moderncrowd-funding/investment system able to: i) ensure the scal-able, self-organized and secure financial transactions, ii) offerto its users the capability to provide conditional funding(e.g. donate/invest in case that the number of investors andthe total amount is higher than a threshold), iii) give thefinancial incentives to the members of SOCIALENERGYcommunities to participate in these activities, and iv) adver-tise the crowd-funding efforts through the SOCIALENERGYplatform.

Finally, SOCIALENERGY envisages the combinationof collective e-commerce services with Community BasedSocial Marketing (CBSM) [18], which is an alternative to twopervasive models about behaviour change (i.e. the attitude-behaviour model and economic self-interest model). Theformer suggests that informing individuals and convincingthem to adopt a positive attitude towards a particular actionwill suffice for them to change behaviour. The latter assumesthat individuals will always change their behaviour to max-imize financial benefit. Neither of these older models hassufficed to close the gap between the energy savings weknow are out there and the participation levels necessary toaddress them. CBSM, in contrast, targets a community (thesocial context) with all the respective benefits as analysed insections II and IV.B.

Fig. 7 illustrates the advanced e-commerce servicesthat SOCIALENERGY system envisages to implement andrelease in the market. Of course, cross-domain partnershipsare required with various market stakeholders by followingan appropriate business model, which is out of scope of thispaper.

FIGURE 7. Advanced e-commerce and social innovation services.

V. REAL MARKET APPLICABILITY AND FUTURECHALLENGES TOWARDS 2030 ENERGY MARKETSIn its current form, SOCIALENERGY platform can bedirectly exploited by today’s electric utilities and ESPs in theretail market as a stand-alone product based on the businessmodel proposed in this paper. Its ‘‘modularity-by-design’’feature offers the flexibility to the targeted customer segmentto customize its own S/W platform based on the business

strategy and the type of its customer portfolio’s needs. Forexample, a utility may opt only for the GSRN-GAME solu-tion, while another one may opt for a GSRN-RAT-LCMSsolution, etc. SOCIALENERGY aims to constitute the plat-form that will not only mediate the future energy market, butwill also harmonize demand and production in it through itsvery innovative and advanced features like the game, the sup-port of sophisticated research algorithms for dynamic energy

TABLE 4. SOCIALENERGY’s market applicability in the 2030 smartgrid era & beyond.

22460 VOLUME 6, 2018

P. Makris et al.: Digitization Era for Electric Utilities: Novel Business Model

pricing and management of VECs, and the development ofLCMS, which guarantees the long-term user engagement andcontinuous learning of good practices on energy efficiency.

Furthermore, regarding SOCIALENERGY system’s mar-ket applicability in the future, it is designed in a waythat it can be easily integrated as a S/W component inmuch more complex systems of the 2030 smart grid eraand beyond. Table 4 provides a summary of possibleSOCIALENERGY system’s extensions in order to serve vari-ous business models of several emerging market stakeholdersin the future.

SOCIALENERGY S/W platform can serve as a perfectsubstrate of a future transactive energy or P2P energy trad-ing platform operated by a progressive electric utility/ESP.As today’s DSOs are gradually transformed into distribu-tion service orchestrators, SOCIALENERGY could also beintegrated in an Advanced Distribution Management Sys-tem (ADMS), whose responsibility will be to continuouslyprovide ‘stability as a service’ by deploying various tac-tics such as: interrupting the electricity supply to electricvehicle recharging stations, sourcing power from embeddedcombined heat and power generation, tapping into grid-scalestorage, sending RTP signals to consumers, applying Volt/VAR/frequency control methods, etc. SOCIALENERGYcould also be a part of a cyber-physical system via the use ofa mixed/augmented reality application deployed by a smartBMS in collaboration with an IoT/telecom service provider.The proposed ‘virtual marketplace’ component could alsobe considerably enhanced and be integrated in e-commerceservice provider’s platform. Policy/decision makers couldalso exploit SOCIALENERGY as part of an e-governanceand policy modeling platform for energy efficiency. Otherforms of ‘‘social innovation’’ can also be boosted such as theopportunity for end users to collaboratively participate in thedesign of a new policy, EP, product, EIDaaS, etc. or learnabout good practices on energy efficiency in a collaborativeand socially inclusive manner. Finally, as private investmentfunds need strong techno-economic tools to be able to secureand manage the risks of the whole investment lifecycle,SOCIALENERGY could be a good basis for providing suchkind of services in the future.

REFERENCES[1] MIT Energy Initiative, ‘‘Utility of the future: An MIT energy initiative

response to an industry in transition,’’ Massachusetts Inst. Technol., Cam-bridge, MA, USA, Tech. Rep., Dec. 2016.

[2] Navigant Research, ‘‘Defining the digital future of utilities: Grid intelli-gence for the energy cloud in 2030,’’ Navigant Res., Chicago, IL, USA,White Paper, Jun. 2017.

[3] M.Molina, P. Kiker, and S. Nowak, ‘‘The greatest energy story you haven’theard: How investing in energy efficiency changed theUS power sector andgave Us a tool to tackle climate change,’’ ACEEE Res., Washington, DC,USA, Tech. Rep. 1604, Aug. 2016.

[4] Accenture Consulting Report. (Jun. 2017). New Energy Consumer: ThisTime it Gets Personal. [Online]. Available: https://www.accenture.com/ca-en/_acnmedia/Accenture/next-gen-5/insight-new-energy-consumer-2017/Accenture-NEC2017-Gets-Personal-POV.pdf

[5] R. Sussman and M. Chikumbo, ‘‘Behavior change programs: Status andimpact,’’ Amer. Council Energy-Efficient Econ., Washington, DC, USA,Tech. Rep. B1601, Oct. 2016.

[6] J. S. Vardakas, N. Zorba, and C. V. Verikoukis, ‘‘A survey on demandresponse programs in smart grids: Pricing methods and optimizationalgorithms,’’ IEEE Commun. Surveys Tuts., vol. 17, no. 1, pp. 152–178,1st Quart., 2015.

[7] F. Grossberg, M. Wolfson, S. Mazur-Stommen, K. Farley, and S. Nadel,‘‘Gamified energy efficiency programs,’’ ACEEE, Washington, DC, USA,Tech. Rep. B1501, 2015.

[8] Y.-K. Chou. (2015). Actionable Gamification: Beyond Points, Badges, andLeaderboards. [Online]. Available: http://www.amazon.com/Actionable-Gamification-Beyond-Points-Leaderboards-ebook/dp/B00WAOGY4U

[9] B. P. Koirala, E. Koliou, J. Friege, R. A. Hakvoort, and P. M. Herder,‘‘Energetic communities for community energy: A review of key issues andtrends shaping integrated community energy systems,’’ Renew. Sustain.Energy Rev., vol. 56, pp. 722–744, Apr. 2016.

[10] N. Doulamis, A. Doulamis, and E. Varvarigos, ‘‘Virtual associations ofprosumers for smart energy networks under a renewable split market,’’IEEE Trans. Smart Grid, to be published.

[11] D. J. Vergados, I. Mamounakis, P. Makris, and E. Varvarigos, ‘‘Pro-sumer clustering into virtual microgrids for cost reduction in renewableenergy trading markets,’’ Elsevier Sustain. Energy, Grids Netw., vol. 7,pp. 90–103, Sep. 2016.

[12] A. J. D. Rathnayaka, V. M. Potdar, T. Dillon, O. Hussain, and S. Kuruppu,‘‘Goal-oriented prosumer community groups for the smart grid,’’ IEEETechnol. Soc. Mag., vol. 33, no. 1, pp. 41–48, 2014.

[13] A. J. D. Rathnayaka, V. M. Potdar, T. S. Dillon, O. K. Hussain,and E. Chang, ‘‘A methodology to find influential prosumers in pro-sumer community groups,’’ IEEE Trans Ind. Informat., vol. 10, no. 1,pp. 706–713, Feb. 2014.

[14] G. Ye, G. Li, D. Wu, X. Chen, and Y. Zhou, ‘‘Towards cost minimizationwith renewable energy sharing in cooperative residential communities,’’IEEE Access, vol. 5, pp. 11688–11699, Jul. 2017.

[15] K. Ma, G. Hu, and C. J. Spanos, ‘‘A cooperative demand response schemeusing punishment mechanism and application to industrial refrigeratedwarehouses,’’ IEEE Trans. Ind. Inf., vol. 11, no. 6, pp. 1520–1531,Dec. 2015.

[16] A. Mani, I. Rahwan, and A. Pentland, ‘‘Inducing peer pressure to promotecooperation,’’ Nature Sci. Rep., vol. 3, Apr. 2013, Art. no. 1735.

[17] Q. Huang, X. Li, J. Zhao, D.Wu, and X.-Y. Li, ‘‘Social networking reducespeak power consumption in smart grid,’’ IEEE Trans. Smart Grid, vol. 6,no. 3, pp. 1403–1413, May 2015.

[18] C. M. Frantz, B. Flynn, S. Atwood, D. Mostow, C. Xu, and S. Kahl,‘‘Changing energy behavior through community based social marketing,’’in The Contribution of Social Sciences to Sustainable Development atUniversities (World Sustainability Series), F. W. Leal and M. Zint, Eds.Cham, Switzerland: Springer, 2016, pp. 259–272.

[19] M. Mulder, Competence Based Vocational and Professional Education:Bridging the Worlds of Work and Education. Cham, Switzerland: Springer,2017.

[20] P. Samadi, A.-H. Mohsenian-Rad, R. Schober, V. W. S. Wong, andJ. Jatskevich, ‘‘Optimal real-time pricing algorithm based on utility max-imization for smart grid,’’ in Proc. 1st IEEE Int. Conf. Smart GridCommun. (SmartGridComm), Gaithersburg, MD, USA, Oct. 2010,pp. 415–420.

[21] N. Li, L. Chen, and S. H. Low, ‘‘Optimal demand response based on utilitymaximization in power networks,’’ in Proc. IEEE Power Energy Soc. Gen.Meeting, Detroit, MI, USA, Jul. 2011, pp. 1–8.

[22] N. Yaagoubi and H. T. Mouftah, ‘‘User-aware game theoretic approach fordemandmanagement,’’ IEEE Trans. Smart Grid, vol. 6, no. 2, pp. 716–725,Mar. 2015.

[23] Z. Wang and R. Paranjape, ‘‘Optimal residential demand response formultiple heterogeneous homes with real-time price prediction in a multia-gent framework,’’ IEEE Trans. Smart Grid, vol. 8, no. 3, pp. 1173–1184,May 2017.

[24] W. Tang, S. Bi, and Y. J. A. Zhang, ‘‘Online coordinated charging decisionalgorithm for electric vehicles without future information,’’ IEEE Trans.Smart Grid, vol. 5, no. 6, pp. 2810–2824, Nov. 2014.

[25] S. Althaher, P. Mancarella, and J. Mutale, ‘‘Automated demand responsefrom home energy management system under dynamic pricing andpower and comfort constraints,’’ IEEE Trans. Smart Grid, vol. 6, no. 4,pp. 1874–1883, Jul. 2015.

[26] L. P. Qian, Y. J. A. Zhang, J. Huang, and Y. Wu, ‘‘Demand responsemanagement via real-time electricity price control in smart grids,’’ IEEE J.Sel. Areas Commun., vol. 31, no. 7, pp. 1268–1280, Jul. 2013.

VOLUME 6, 2018 22461

P. Makris et al.: Digitization Era for Electric Utilities: Novel Business Model

[27] H. Allcott, ‘‘Social norms and energy conservation,’’ Elsevier J. PublicEcon., vol. 95, nos. 9–10, pp. 1082–1095, 2011.

[28] S. N. A. U. Nambi and R. V. Prasad, ‘‘Toward the development of a techno-social smart grid,’’ IEEE Commun. Mag., vol. 54, no. 11, pp. 202–209,Nov. 2016.

[29] A. Cassidy, M. Strube, and A. Nehorai, ‘‘A framework for exploring socialnetwork and personality-based predictors of smart grid diffusion,’’ IEEETrans. Smart Grid, vol. 6, no. 3, pp. 1314–1322, May 2015.

[30] G. Tsaousoglou, P. Makris, and E. Varvarigos, ‘‘Electricity market policiesfor penalizing volatility and scheduling strategies: The value of aggrega-tion, flexibility, and correlation,’’ Elsevier Sustain. Energy, Grids Netw. J.,vol. 12, pp. 57–68, Dec. 2017.

[31] USA GridWise Architecture Council, ‘‘GridWise transactive energyframework, v1.0,’’ USA Nat. Inst. Standards Technol., Gaithersburg, MD,USA, Tech. Rep., Jan. 2015.

[32] C. Zhang, J. Wu, C. Long, and M. Cheng, ‘‘Review of existing peer-to-peer energy trading projects,’’ Elsevier Energy Procedia, vol. 105,pp. 2563–2568, May 2017.

PRODROMOS MAKRIS (M’09) was bornin Samos, Greece, in 1985. He received theB.Sc., M.Sc., and Ph.D. degrees in context-aware resource management for mobile and fixednetworking systems from the University of theAegean, Greece, in 2007, 2009, and 2013, respec-tively. He is currently a Senior Researcher with theNational Technical University of Athens, Greece.From 2013 to 2016, he pursued his post-doctoralresearch on context-aware resource management

in smart energy networks being also the Project and Technical CoordinatorAssistant of the FP7-ICT VIMSEN Project. During the last decade, he hasbeen actively participating in several other national and EC-funded projects(e.g., FP6-IST-UNITE, FP7-ICT-HURRICANE, FP7-ICT-PASSIVE,FP7-ICT-COGEU, H2020-ICT-SOCIALENERGY, and so on.). He has over35 publications in international conferences and journals. He is a member ofthe Technical Chamber of Greece.

NIKOLAOS EFTHYMIOPOULOS was bornin 1980. He is currently pursuing the Ph.D.degree in computer science. He is also a SeniorResearcher with ICCS, National Technical Uni-versity of Athens, Greece. Since 2004, hehas been participating in various ICT projects(e.g., FP7-ICT-VITAL++, ICT-FP7-STEER, andH2020-ICT-SOCIALENERGY). His researchactivities are optimization, theory of dynamicalsystems, pattern recognition, big data, distributed

searching, social systems, smart grids, and pricing scheduling. He has around30 publications in these areas.

VASSILIS NIKOLOPOULOS received thedegree (Hons.) in electrical engineering as aValedictorian of three Engineering Departments(Civil, Electrical, and Mechanical) from the Poly-technic School, University of Dundee, Scotland,in 2000, the M.Sc. and Diploma degrees (Hons.)in control from Imperial College, certificates inmanagement and marketing from LSE and afterpreparing the French classe preparatoire and suc-ceeding in entrance exams he entered the military

Ecole Polytechnique of Paris (X99), from where he received the Majeuresd’Ingenieur Diplome de l’ Ecole polytechnique in applied mathematics andinformatics, with mention Honorable, and the Ph.D. degree from the Mul-timedia Technology Laboratory, National Technical University of Athens,with a focus on the domain of Optimal Knowledge Engineering, EnergyICT and hypercubic KDD Fusion. He was a Research and DevelopmentManager and an Innovation officer/PM in big EU (FP6, eContent, eTEN,and IEE) and National projects (GSRT). He is currently the Co-Founderand a Chief Innovation Officer of Intelen and responsible for the Innovationdevelopment/transition and the creation of new advanced services for thefuture markets.

ANDREW POMAZANSKYI was born in 1991.He received the B.A. degree in business admin-istration and the M.Sc. degree in economicpolicy. He has a broad previous experience indifferent fields (aerospace, agricultural, chemicaltrade, and media industry), which helped him tobring Nurogames to new, not yet realized markets(research-wise). He has been a Lead Project Man-ager of Nurogames GmbH since 2012. Dealingmainly with Research and Development projects

on international scale, he acquired and managed a number of projects deal-ing with AI and game-based interventions toward behavioral change. Hisspecific fields of interest are eHealth and eLearning domains. His strongparticipation in Research and Development Projects (FP7, H2020, BMBF,BMWI, CreateMEDIA, and Leitmarktwettbewerb.NRW) and holding hispulse on innovation helps Nurogames to bring the most innovative productsto the market.

BORIS IRMSCHER was born in 1972. Hereceived the Diploma degree in audiovisual mediafrom the University of Fine Arts in Frankfurt/Mainand the Academy of Media Arts Cologne.His 14 year experience covers Art Direction,Game-Conception, Nintendo DS, Nintendo Wii,Character Design, Storyboards, 2-D Animation,Illustration, Artworks, Texturing, Image editing,Flash, and Actionscript. He worked in severalgame companies as a digital artist and a game

designer, including Ubisoft and Electronic Arts. Since 2006, he has beenan Art Director and a Leading Game Designer with Nurogames. Amonghis notable achievements one can mention a scholarship from the stategovernment for his exceptional achievements and a prize for the animationfilm Drawing Rhythms in 2000.

KRASSEN STEFANOV was a Specialist with theUniversity of Amsterdam (AI research project),the University of Twente, and the University ofBirmingham. He is currently a Professor withthe Faculty of Mathematics and Informatics,the Director of the Centre for Information SocietyTechnologies, and the Head of the IT Department,Sofia University. He is also the Head of the Mas-ter of Science Program Distributed Systems andMobile Technologies. He has long experience in

participating in over 20 EC-funded research projects and has over 120 publi-cations. He was involved in the Program committee of over 15 Internationalconferences.

22462 VOLUME 6, 2018

P. Makris et al.: Digitization Era for Electric Utilities: Novel Business Model

KATINA PANCHEVA MA in Public Policy,Decentralized Governance, CEU and Projectmanager at Centre of Information Society Tech-nologies at Faculty of Mathematics and Informat-ics, Sofia University. Since 2007 she has beeninvolved in the application process and implemen-tation of projects funded by the Structural Funds,National Science Fund, LLP, EuropeAid etc. in thefield of e-learning (e-learning research, DoctoralSchool for specialization and vocational training in

e-learning), establishment of digital platforms for community cohesion andintegrated urban development, technical assistance to the public administra-tion (analytical support, capacity building and training), regional and sustain-able development on local and national level (energy efficiency measures inmunicipal education infrastructure, prevention of floods etc).

EMMANOUEL (MANOS) VARVARIGOS wasborn in 1965. He received the Ph.D. degree inelectrical engineering and computer science fromMIT, Cambridge, MA, USA, in 1992. He has beena Researcher with Bell Communications Research,Morristown, NJ, USA, and an Associate Professorwith the University of California at Santa Barbara,Santa Barbara. He has also been an AssociateProfessor with the Delft University of Technol-ogy, The Netherlands. In 2000, he became a Full

Professor with the Department of Computer Engineering and Informatics,University of Patras, where he is currently the Head of the CommunicationNetworks Laboratory. He is also the Scientific Director of the NetworkTechnologies Division, CTI, which is responsible for the planning and oper-ation of the Greek School Network, the biggest public network in Greece,interconnecting over 15 000 school labs in Greece and providing educationalservices to the secondary education community. In 2015, he joined as a FullProfessor with the School of Electrical and Computer Engineering, NationalTechnical University of Athens. He has over 300 publications in internationaljournals and conferences in the areas of algorithms, networking, and cloudcomputing.

VOLUME 6, 2018 22463