A Virtual Microgrid Platform for the Efficient Orchestration of Multiple Energy Prosumers

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1 A Vrtual Mcrogrd Platform for the Effcent Orchestraton of Multple Energy Prosumers Ioanns Mamounaks Computer Technology Insttute 26504, Ro Patras, Greece Dmtros J. Vergados Computer Technology Insttute 26504,Ro Patras, Greece Prodromos Makrs Computer Technology Insttute 26504, Ro Patras, Greece Emmanouel Varvargos Computer Technology Insttute 26504, Ro Patras, Greece Tasos Mavrds Intelen LLC 15124, Marous Athens, Greece ABSTRACT The Smart Energy Grd concept ams to explot Informaton and Communcaton Technologes (ICT) towards makng the energy sector more secure, relable and effcent, whle the electrcty markets are rapdly becomng more lberalzed wth new busness actors/models beng ntroduced. In partcular, passve energy consumers are beng transformed nto actve energy prosumers (.e. both producers and consumers), whle energy aggregaton/servces companes are emergng as ntermedares n the so called Internet of Energy arena. Prosumers need to have ther energy assets effcently managed and partcpate n the market ndependently of ther sze and negotatng power, whle aggregators am at maxmzng prosumers benefts by representng them as a sngle bg power entty n the wholesale energy market. Ths paper ntroduces the Vrtual McroGrd (VMG) concept, n whch multple energy prosumers are orchestrated nto bgger assocatons towards optmzng the assocaton s benefts. An nnovatve decson support system platform s presented showcasng that the management of aggregated energy resources can outperform state-of-the-art solutons that manage resources at the ndvdual prosumer s level. The platform s mplementaton s based on vrtualzaton technques and a wde range of functonaltes are descrbed, tested and valdated. Datasets from 37 real-lfe prosumers are used and results of varous decson-makng algorthms show that under dfferent system operaton contexts, dynamc formaton of prosumers groups (clusterngs) can provde remarkable energy savngs and monetary profts to the end users. Categores and Subject Descrptors H.3.4 [Systems and Software]: Dstrbuted systems, Informaton networks, Performance evaluaton, User profles H.4.2 [Types of Systems]: Decson Support Permsson to make dgtal or hard copes of all or part of ths work for personal or classroom use s granted wthout fee provded that copes are not made or dstrbuted for proft or commercal advantage and that copes bear ths notce and the full ctaton on the frst page. Copyrghts for components of ths work owned by others than ACM must be honored. Abstractng wth credt s permtted. To copy otherwse, or republsh, to post on servers or to redstrbute to lsts, requres pror specfc permsson and/or a fee. Request permssons from Permssons@acm.org. PCI 2015, October 01-03, 2015, Athens, Greece (c) 2015 ACM. ISBN /15/10 $15.00 DOI: General Terms Desgn, Management, Measurement, Performance, Algorthms. Keywords Smart Energy Networks, Mcrogrds, Vrtualzaton, Decson Support System, Energy Prosumer, Aggregator, Genetc Algorthms. 1. INTRODUCTION In the context of a rapdly growng electrcty market, renewable energy s produced and dstrbuted through dfferent medum and small producers, who sell ther renewable energy va approprate electronc platforms operated by market operators (MOs). However, such an approach prevents some of the small or very small producers, from partcpatng n the electrcty market, and requres them to be organzed n bgger energy assocatons. In ths case, the man dffcultes are the admnstraton of the large number of heterogeneous Renewable Energy Source (RES) prosumers (.e. partcpants that both produce and consume energy) and the ablty to succeed nteroperaton as each prosumer contrbutes, an nfntesmally small amount of energy to the electrcty grd tryng to maxmze ts own proft. As a result, the effectve admnstraton and orchestraton of large clusters comprsed of small energy producers necesstates the explotaton of new ICT technologes [1]. In the energy and telecommuncaton sectors, new decentralzed operatonal models must be defned. The man research breakthrough beng proposed n our paper s the dea to explot ICT research and nfrastructure n order for the creaton of Vrtual-McroGrds (VMGs) to be realzed. VMG creaton under a hghly dynamc and dstrbuted framework wll overrde the tradtonal centralzed day-ahead electrcty market [2]. A large varety of energy management platforms combne bll and meter data analyss, to acheve effcent operaton of converged energy/ict busness processes. These mcrogrds related frameworks enable servces that ntegrate and control dstrbuted energy generaton assets to form a hghly responsve ntellgent mcrogrd. In order to realze the challenges and correspondng solutons regardng the aggregaton of dstrbuted energy producers, many frameworks appear n the lterature. As outlned n [3], a servce-orented archtecture s proposed n order to ntegrate MG modelng, montorng, and control. In [4], authors mplement a mcrogrd management system to evaluate

2 the management and control under system performance and schedulng. However, n a mcrogrd assocaton t s mportant to acheve the flexblty to re-dstrbute energy resources among each other to compensate for energy producton-dstrbuton and to partcpate n the electrcty market through the respectve assocaton, whch acts smlarly to a bg power generator unt. In the specalzed lterature, there are some works related to energy management platforms for mcrogrds. In [5], Tskalaks and Hatzargyrou descrbe a centralzed control system for a mcrogrd. The controller s used to optmze the operaton of the mcrogrd durng nterconnected operaton. Two market polces are assumed to offer optons for the demand for controllable loads, and ths demand-sde bddng s ncorporated nto the centralzed control system. Dagdougu et al. [6] descrbe a realtme operatonal management tool for a hybrd renewable system, composed of an electrolyzer, a hydroelectrc plant, pumpng statons, a wnd turbne and a fuel cell. The goal s to satsfy the varable electrc, hydrogen, and water demands hourly. Korpas andholen [7] present an energy management system for a hybrd plant wth wnd power and hydrogen storage n order to maxmze the expected proft from power tradng n a day-ahead market. As opposed to [5] we propose a real tme market prcng model whch s based on real market prces. Author s research n [6] s based on the renewable energy producton values, n contrast to our proposed platform whch takes nto account both producton and consumpton measurements. Fnally n contrast to [7] our platform maxmzes the expect proft n two dfferent energy markets (.e. day-ahead, ntra-day). In ths paper we propose a VMGA-DSS Smart Energy Management Platform (proposed wthn the VIMSEN project [8]), whch montors and controls renewable energy prosumers, va a web nterface. The man objectves of the VMGA-DSS platform are to: a) acqure real and forecast data from ndvdual prosumers, b) nterface wth market operators and agree on specfc contracts/servce level agreements (SLAs) to be executed n a pre-defned tmeframe, c) desgn, develop and valdate nformaton management and decson makng technologes for the dynamc VMG nfrastructure creaton, d) communcate the results of the SLA back to each prosumer n order for specfc demand response (DR) actons to be enforced. e) Partcpate n lberalzed electrcty markets followng the EU regulatory framework defned n [9]. In order to valdate the VMGA-DSS platform functonalty we propose and present the expermental results of a genetc algorthm that tres to orchestrate the prosumers n the system nto Vrtual McroGrds (.e. clusterng), n a way that lmts ther lost revenue due to naccurate energy forecasts. The paper s structured as follows: n secton 2, we present an overvew of the proposed VIMSEN archtecture and the system operatons. In secton 3, we descrbe the web platform functonaltes. The energy cost model that s consdered s presented n secton 4. In secton 5, the proposed approach for prosumer clusterng s presented accompaned to the expermental analyss results. Fnally, secton 6 concludes ths paper and presents some future research nsghts regardng our platform s explotaton opportuntes. 2. OVERALL VIMSEN ARCHITECTURE AND SYSTEM OPERATION The DSS platform s responsble for orchestratng and effcently managng the RES of multple energy prosumers called as VIMSEN Prosumers (VPs). It s also responsble for enablng the decentralzed electrcty market supported by VIMSEN project. Furthermore, t provdes the necessary nformaton tools and servce engneerng methodologes (based on the Web servce technology) to choose the mcrogrds and the dstrbuted energy sources wthn the VMG framework, to handle the respectve prcng polces and fnally to actvate the dynamc clusterng framework n order to update and evolve VMG assocatons accordng to the current energy nformaton, forecastng and demands. Fnally, novel algorthms regardng the VMG groups formaton/dynamc adaptaton and VMG profles management are desgned developed and are ntegrated n the DSS platform. These algorthms take advantage of an nnovatve hybrd cloud computng processng nfrastructure (HCCI), whch allows heavyprocessng tasks/jobs to be executed n less tme and thus realtme decson makng procedures to be realzed, whch s very mportant for the successful operaton of the whole VIMSEN system. Fgure 1. The VIMSEN Archtecture The VIMSEN operaton s llustrated n seven (7) basc steps n fgure 1: 1. Real-tme data measurements are sent by All VPs (through ther local VIMSEN Gateway - VGW) to the Energy Data Management System (EDMS), whch acts as a VIMSEN data repostory. 2. The EDMS component acts as a sngle meter data repostory whch sends VMG measurements n varous tmeframes to the Forecastng and Modelng System (FMS) and the Decson Support System (DSS) upon request. 3. The FMS explots VIMSEN external data sources and provdes predctons of future states of the VMG assocatons. Predcton results about weather forecasts are receved from onlne Weather Operators (WO) and

3 sends VIMSEN-related forecasts to DSS for a gven VMG assocaton upon request. 4. The DSS (see outlned area n fgure 1) decdes about the optmal VMG group formatons to satsfy gven objectves and constrants beng set by market/grd operators such as the Market Operator (MO), the Dstrbuton System Operator (DSO), the Transmsson System Operator (TSO), the Balance Responsble Party (BRP), etc. 5. Servce Level Agreements (SLAs) at a VMG level are sent from the DSS to Global Demand Response Management System (GDRMS) to be broken down to ndvdual SLAs per VP. 6. The GRDMS performs automatc control polces at a VMG level and sends DR commands and energy management recommendatons to each VP. 7. The VGW receves the DR acton messages va ts local DR manager module and s responsble for mplementng the actons at a VP level. It should be noted that the Vrtual McroGrd Aggregator (VMGA) acts as an ntermedary market entty between a vast number of VPs (lower level) and the varous market/energy actors (hgher level). Therefore, VMGA s able to nteract wth the: a) Market Operator (MO) n order to partcpate n day-ahead and ntra-day electrcty markets, b) Balancng Responsble Partes (BRPs) n order to partcpate n balancng market and c) DSO/TSO for partcpatng n energy effcency certfcates (or else demand response) market. Upon a DSS user s request, hstorcal data s acqured from the database va a RESTful API. In case real-tme data s requested, the DSS component uses a web API to retreve the approprate data from EDMS. Then, t stores the data to DB and fgures are vsualzed n a Confguraton Panel (CP) va a RESTful API. The data vsualzaton functonaltes of the DSS consst of presentng dfferent types of data to the DSS users. Dfferent functonaltes have been mplemented, to allow for vewng, plottng and extractng relevant prosumpton data, ncludng hstorcal and real-tme vews, ndvdual or aggregated prosumpton profles, and dfferent types of measurements, such as prosumpton, producton and consumpton, as well as at dfferent tmescales (hour/day/week/month/year). As shown n Fgure 2, real-tme data can be streamlned for a specfc tme perod n order for the end user to be able to contnuously montor the real-tme operaton of an ndvdual VP. 3.2 Aggregated data vsualzaton The DSS user s able to request day-ahead forecasts from the FMS regardng a sngle VP or a group (cluster) of VPs. These forecasts are then used as baselne prosumpton profles by DSS to make ts offers n the day-ahead market based on the prcng sgnals generated by the MO. In the automated case, forecast prosumpton profles are used by DSS algorthms for the creaton of a VMG nfrastructure. The result s that aggregaton of VPs can consderably reduce the devatons between forecasts and real-lfe measurements and thus respectve SLAs agreed wth the MO can be better met. The DSS user can vsualze the monetary profts that the aggregaton of VPs can provde to ndvdual prosumers as a result of ther partcpaton n VMG assocatons. 3. WEB PLATFORM FUNCTIONALITIES Ths secton descrbes some basc functonaltes of the VMGA DSS platform. In partcular, four man DSS operatons are showcased, namely: a) DSS user can vsualze aggregated hstorcal and real-tme data for multple VPs, b) DSS user can vsualze forecast vs. real data for multple VPs, c) DSS user can vsualze open energy data publshed by a MO, and d) DSS user can create and adapt a VMG assocaton to accomplsh a gven goal. In the followng subsectons, a bref descrpton s gven regardng each capablty of a DSS user offered by the platform accompaned by respectve llustratve fgures. 3.1 Real tme data vsualzaton Fgure 2. Real-tme data nterface for an ndvdual VP Fgure 3. Real vs forecast aggregated data Furthermore, the DSS user s able to request short-term forecasts from the FMS. The tmeframe of these forecasts s consdered to be 15 mnutes beng thus explotable by DSS user to manually adapt exstng VMG nfrastructure n order for VPs to be able to partcpate n the varous varants of the ntra-day market (e.g. Italan MO wth whch VMGA nteracts, realzes fve dfferent types of ntra-day markets [10]). In fgure 3, all profles of ndvdual VPs (both forecast and real) are llustrated accompaned by aggregated profles for the randomly selected group of VPs. As shown at the upper sde of the fgure, the selected VPs are located n Greece. 3.3 Open Energy Market data capabltes Regardng market-related data, ths s retreved from the MO (DRrelated data could be retreved from an energy system operator, too) upon the recept of a new electrcty market event (e.g. day-

4 ahead, ntra-day, etc). The market operator s responsble for provdng the necessary data that allow the system to partcpate n buyng and sellng energy. In order to partcpate n the market, the DSS platform may submt bd requests to the MO s Internet platform accordng to the forecast prosumpton profles of ts portfolo. DSS s able to acqure hstorcal data regardng the volume of exchanges and the bd/ask prces n the electrcty market, whle DSS can also be nformed about the market clearng prces publshed by MO. Other relevant data from the market may be the total demand and supply of energy, ncludng hstorcal data, as well as real tme nformaton. In partcular a DSS user can select va CP: a) an MO (e.g. Italan, Greek, Irsh, Nordc Pool, etc), b) specfc geographcal regon (e.g. Sardna n Italy), c) a specfc tmeframe (day, week, month, year, etc), and d) an electrcty market varant (day-ahead, ntra-day, energy effcency certfcates market, etc). Ths open data s stored n a database (DB) and s manly used as nput for many DSS algorthms. Upon a market-related event publshed by MO, DSS platform should be able to respond accordngly by provdng RES offers va the aggregaton of ndvdual VPs n the resdental sector. The DSS acqures the notfcaton from the MO and a message s also dsplayed at DSS user s sde (CP). In fgure 4, day-ahead and ntra-day prces examples are gven for Sardna n Italy by the Italan MO [10]. Fgure 4. Open data acquston from a Market Operator 3.4 VMG assocaton management Fgure 5. Lst of varous clusters (.e. VMG assocatons) that were created usng our genetc algorthm. A DSS user can create a VMG nfrastructure by selectng and confgurng a plethora of parameters va the Confguraton Panel (CP). The prosumer organzaton nto vrtual mcrogrds (VMGs), conssts of ntellgent algorthms executon that wll a) determne the optmal tmng perods for buyng and sellng energy n the market, b) determne when s the approprate quantty and approprate prce to partcpate n the market, n a way that maxmzes the revenue from producng renewable energy, whle c) mantan hgh-standard energy securty. The creaton of a VMG assocaton may be subject to a regulatory constrant (e.g. the entry barrer constrant s used n many countres a mnmum amount of energy s requred for a VMG to enter the market). The user can vsualze the results of dfferent VMG nfrastructure creaton scenaros and compare them to conclude to the most effcent one for each type of event. Fnally, as shown n fgure 5, DSS user s able to edt an exstng VIMSEN assocaton (e.g. add/remove a VP or adapt some VMG confguraton parameters). The DSS user can vsualze the results n real-tme and conclude to an effcent VMG nfrastructure that meets all hs/her manageral/techncal pre-requstes. 4. THE MARKET PARTICIPATION MODEL For prosumer < N, N s the number of prosumers assocated wth the VMGA aggregator, we have the real prosumpton n each hourly block t denoted as: r (t) cons (t) prod (t) (1) cons (t) s the amount of energy consumed by prosumer at hourly block t, and prod (t) s the correspondng energy producton. The forecasted prosumptons are f () t at each hourly block t. The forecast s calculated 24 hours pror to the correspondng hourly block. 4.1 The energy cost of an ndvdual prosumer We assume that each prosumer partcpates n the day ahead energy market, buyng or sellng energy dependng on the forecasted prosumpton. The revenue/cost caused by the partcpaton n the day-ahead market s calculated based on the amount of energy that s traded. Thus, the assocated cost s: p * () t c f ( t) p ( t) (2) da( t) * s the energy prce per unt at hourly block t, obtaned from the day-ahead market. If the forecasts always reflected accurately the true net amount of energy that s produced or consumed, eq. (2) would gve the actual cost/proft for each prosumer. When there s a dfference between the amount of energy traded n the day-ahead energy market and the amount that was actually produced or consumed, then the prosumer s charged wth a penalty. We assume that two penalty factors are beng used: a) p, when the real prosumpton exceeds the forecast, and b) s p, when the prosumpton s below the forecasted value. v Thus, accordng to our model, the actual cost may be calculated usng the followng formula: c t f t p t r t f t p p t f r ( t) f ( t) * ( ) ( ) ( ) ( ( ) ( )) (1 v) ( ) or by (3)

5 c t r t p t f t r t p p t f r ( t) f ( t) * ( ) ( ) ( ) ( ( ) ( )) s ( ) pt () s the prce of energy n the spot market. In case the real prosumpton exceeds the forecasted one, the prosumer s charged for the amount of energy he forecasted to produce, at the day-ahead market prce plus a penalty that s proportonal to the devaton, and to the spot market prce. In case the real prosumpton s lower than the forecasted one, the prosumer s charged for the net energy that he consumed, at the day ahead market prce, plus a penalty that s proportonal to the devaton, and to the spot market prce. We defne the estmaton error as the dfference between the real and the forecasted amounts of energy: e ( t) r ( t) f ( t) (4) Then we defne the penalty functon as: e ( t) pv f e ( t) 0 u( e ( t)) e ( t) ps f e ( t) 0 Fnally, f we assume that the energy prce for each hourly block n the day ahead market s equal to the cost the spot market, then equaton (3) can be smplfed to: (5) c ( t) d ( t) u( e ( t)) p( t), (6) d ( t) f ( t) p( t) s the cost of energy f no penaltes were appled. 5. A GENETIC ALGORITHM APPROACH FOR PROSUMER CLUSTERING In ths secton we present an algorthm for generatng optmal clusters usng genetc algorthms. We wll present the defnton of a chromosome for the specfc problem that we are examnng, and we wll defne the ftness functon that s used to determne how sutable each canddate soluton s. 5.1 The genetc representaton In order to use a Genetc Algorthm for solvng the prosumer clusterng problem we frst have to defne the genetc representaton,.e. the structure of the chromosome that wll be used to solve the optmzaton problem. In the case of prosumer clusterng, we represent each soluton as a vector of sze N, N s the number of prosumers that are to be clustered nto vrtual mcrogrds. Thus, soluton Sj may be wrtten as: g j S g, g,..., g (7) j j1 j2 jn K s the d of the cluster prosumer k s assgned, and K s the maxmum number of clusters. 5.2 The ftness functon The ftness functon n a genetc algorthm determnes how good or bad one soluton s compared to another one. It should be defned n a way that better solutons have a hgher ftness value than worse solutons. The objectve of the clusterng algorthm s to orchestrate the prosumers nto clusters n a way that mnmzes ther costs, by reducng the uncertanty n the estmaton of ther prosumpton, and thus reducng the penaltes that they wll be charged wth. Thus the ftness of a soluton wll depend on the percentle mprovement n the penalty costs that each cluster n the system may acheve for ts prosumers. A mcrogrd wll have the most beneft f t manages to make the term: f a (8) t mk as small as possble n relaton to term e (t) u( e ( t)) b f u( e (t)) t mk (9) s the error n forecastng the prosumpton of prosumer for hourly block t, and m k appled, through the followng equaton: Fnally, the ftness functon s defned as: k j defnes the clusterng that s m g k (10) b a f f f (s j ) (11) b f 1 k K mk The mutaton functon The mutaton functon s essental for mantanng genetc dversty from one generaton of a populaton of chromosomes to the next one. The method that s appled n our genetc algorthm mplementaton swaps the poston of two consecutve genes. 5.4 The crossover functon The crossover functon s used to combne two chromosomes (solutons) nto a sngle new chromosome. There are several ways to combne two chromosomes, such as one pont crossover, twopont crossover, cut-and-splce, edge recombnaton, etc. We used the two-pont crossover to combne our chromosomes. Accordng 1 x, x K are selected. to ths method, two random numbers 1 2 The new chromosome conssts of the frst parent s genes, for ndexes between x 1 and x 2 ndexes outsde the above nterval., and the second parent s genes for 5.5 Evaluaton In order to evaluate the performance of the genetc clusterng algorthm, we tested t on data from 37 prosumers located n Greece. The prosumpton data were provded by Intelen [11]. Fgure 6. The evoluton of the genetc algorthm

6 The confguraton parameters of the genetc algorthm are followng: the ntal populaton was 200 randomly created chromosomes, and the genetc system evolved for 100 generatons. The number of clusters was set to 5. The penalty factors ps and pv were set to 0.2 and 0.3 respectvely. The ftness of the best soluton at each generaton of the tranng s depcted n Fg. 6. We used the prosumpton data of the week 24/3/3015 to 31/3/2015 n order to tran the clusterng algorthm. The clusterng that was formed conssted of the allocaton shown n Table I. Table 1. The clusterng of the prosumers nto clusters, usng the proposed genetc algorthm Cluster name Cluster members Gen 0 3, 16, 18, 19, 20, 23, 25, 28, 29, 30, 31 Gen 1 1, 8, 17, 21, 24, 26, 32, 33 Gen 2 4, 7, 27, 36, 37 Gen 3 2, 5, 6, 9, 11, 12, 13, 14, 34, 35 Gen 4 10, 15, 22 For the followng 5 weeks, we tested the above clusterng wth new forecasted and real prosumpton data. For each of the followng weeks, we calculated the cost accordng to our model consderng the clusterng that was calculated usng data from week 0. The results are depcted n Fgure 7. Fgure 7. The penalty reducton of each cluster for the weeks followng the tranng g perod More specfcally, we can see the performance of the clusters that were created usng the genetc algorthm, for the 5 weeks followng the tranng, n terms of the percentle reducton n the penaltes for each cluster. We observe that the penalty reducton s largely retaned for the followng weeks n most cases. The hgher reducton that s observed n ths experment reaches up to 50%, Whereas even n the worst case, we observe a postve reducton of a few percentage ponts. 6. CONCLUTION In ths paper we presented an nnovatve decson support platform for optmzng the partcpaton of dstrbuted energy prosumers nto the future smart grd. A testbed platform that was developed for ths purpose was presented, and a demonstraton of the platform s capabltes was showcased through the example of a genetc algorthm, that tres to reduce penaltes caused by forecastng errors. The testbed results show that, gven the prosumpton model presented, the proposed clusterng method may acheve sgnfcant benefts n cost reducton for the end users. In future work we plan to ntegrate n our ICT platform an even larger set of real prosumer data combned wth more and more datasets from MOs and BRPs. Fnally to extend the platform s capabltes n order to support a large varety of optmzaton algorthms, for optmzng varous aspects of the mcrogrd operaton. 7. ACKNOWLEDGMENTS The work presented n ths paper has been undertaken n the context of the project VIMSEN (VIrtual Mcrogrds for Smart Energy Networks). VIMSEN s a Specfc Targeted Research Project (STREP) supported by the European 7th Framework Programme, Contract number ICT REFERENCES [1] X. Fang, S. Msra, G. Xue, and D. Yang, Smart Grd The New and Improved Power Grd: A Survey, IEEE Communcatons Surveys & Tutorals, vol. 14(4), pp , [2] G. Lyberopoulos, E. Theodoropoulou, I. Mesogt, P. Makrs and E. Varvargos, A Hghly-Dynamc and Dstrbuted Operatonal Framework for Smart Energy Networks, proceedngs of IEEE CAMAD 2014, pp , 1-3 December, Athens. [3] A. Vaccaro, M. Popov, D. Vllacc, and V. V. Terzja, An ntegrated framework for smart mcrogrds modelng, montorng, control, communcaton, and verfcaton, Proc. IEEE, vol. 99, no. 1, pp ,2011. [4] W. Sh, E.-K. Lee, R. Huang, C.-C. Chu, R. Gadh, Evaluatng Mcrogrd Management and Control wth an Implementable Energy Management System, IEEE Smart Grd Communcatons, Nov [5] A. Tskalaks and N. Haatzargyrou, Centralzed control for optmzng mcrogrds operaton, IEEE Trans. Energy Convers., vol. 23(1), pp , [6] H. Dagdougu, R.Mncard, A. Ouamm, M. Robba, and R. Sacle, A dynamc decson model for the real-tme control of hybrd renewable energy producton systems, IEEE Syst. J., vol. 4(3), pp , Sep [7] M. Korpas and A. Holen, Operaton plannng of hydrogen storage connected to wnd power operatng n a power market, IEEE Trans. Energy Convers., vol. 21 (3), pp , [8] EU FP7-ICT VIMSEN STREP Project webste, [9] EU Drectve 2009/28/EC of the EU Parlament and of the Councl of 23 Aprl 2009 on the promoton of the use of energy from renewable sources and amendng and subsequently repealng Drectves 2001/77/EC and 2003/30, [10] Italan Market Operator portal, Gestore Mercat Energetc (GME), [11] Intelen Company,