yio

mcteudu D

Keywords:DC microgridEnergy managementOptimizationPrediction

enti

ing undesired injections. The smart grid implementation, which is designed to meet these requirements,is facilitated by microgrids development. This paper presents a DC microgrid (PV array, storage, power

an indu

voltaic (PV) panels, have been being implemented in the utilitygrid. However, the PV production is intermittent and random,and could be strongly uctuating according to weather conditions.Considering the grid regulation time that is much slower than thePV production uctuation time, the high level PV penetration couldincrease the power mismatching in the utility grid and cause sta-bility problems. Thus, renewable power direct injections must be

ours, informationt [6]. For this, theion exchanmunicatio

tocol, supervising the status of the whole system and prolarge-scale real time data are still demanding for innovationmicrogrid, which combines distributed energy sources, enerrages and loads and operates in on-grid and off-grid conguration[812], is being created to facilitate smart grid development[13,14]. For large scale renewable energy integration, microgridplays an important role [15,16]. With communication technology,microgrid can interact with smart grid in order to assist grid powerbalancing by an advanced energy management and so to reducethe cost and to improve power quality [17,18]. To reach this objec-

Corresponding author. Tel.: +33 344234964; fax: +33 344235262.E-mail address: manuela.sechilariu@utc.fr (M. Sechilariu).

Energy Conversion and Management 82 (2014) 110

Contents lists availab

n

lsesumption. However, the distributed generation with renewable en-ergy is supposed to reduce greenhouse gas emissions as well asenhance grid generation. To satisfy the energy requirement andmitigate environmental issues, renewable sources, such as photo-

injection and perform load shaving during peak hon grid needs and availability are very importansmart grid is being created to facilitate informatDespite the technologies of smart devices and comhttp://dx.doi.org/10.1016/j.enconman.2014.03.0100196-8904/ 2014 Elsevier Ltd. All rights reserved.ge [7].n pro-cessing. Thus,gy sto-grid is required to balance the power production and consumptionat any time instant. Unbalanced power would cause the grid volt-age and frequency uctuations [1,2], or even blackout. The gridreliability is ensured by excess capacities working in stand-bymode. The energy demand is increasing fast since decades, andthe grid capacity has grown massively to satisfy the peak con-

current energy storage technologies are limited by capacity, re-sponse time, life cycle cost, specied land form, and environmentalimpact [4,5].

To achieve high level renewable energy penetration into grid,strategies and means of power management should be developedto build a more robust utility grid. Moreover, to avoid undesiredSmart gridSupervision

1. Introduction

As electricity cannot be stored ongrid connection, DC load) with multi-layer supervision control which handles instantaneous power bal-ancing following the power ow optimization while providing interface for smart grid communication.The optimization takes into account forecast of PV power production and load power demand, while sat-isfying constraints such as storage capability, grid power limitations, grid time-of-use pricing and gridpeak hour. Optimization, whose efciency is related to the prediction accuracy, is carried out by mixedinteger linear programming. Experimental results show that the proposed microgrid structure is ableto control the power ow at near optimum cost and ensures self-correcting capability. It can respondto issues of performing peak shaving, avoiding undesired injection, and making full use of locally pro-duced energy with respect to rigid element constraints.

2014 Elsevier Ltd. All rights reserved.

strial scale, the electric

improved to accommodate future high level penetration [3]. En-ergy storage could to be a perfect solution to deal with the inter-mittency and uctuation of renewable energy production, butAccepted 2 March 2014Available online 19 March 2014

limitations for high level PV penetration. It induces additional regulations in grid power balancingbecause of lacking abilities of responding to grid issues such as reducing grid peak consumption or avoid-DC microgrid power ow optimization bcontrol. Design and experimental validat

Manuela Sechilariu a,, Bao Chao Wang a, Fabrice LocaUniversit de Technologie de Compigne, AVENUES-GSU EA 7284, BP 60203, rue du DobUniversit de Technologie de Compigne, HEUDIASYC UMR CNRS 7253, BP 60203, rue

a r t i c l e i n f o

Article history:Received 29 September 2013

a b s t r a c t

Urban areas have great pot

Energy Conversio

journal homepage: www.emulti-layer supervisionn

ent a, Antoine Jouglet b

r Schweitzer, 60203 Compigne, Franceocteur Schweitzer, 60203 Compigne, France

al for photovoltaic (PV) generation, however, direct PV power injection has

le at ScienceDirect

and Management

vier .com/ locate /enconman

tive, both power balancing and energy management, which in-volves different time scale and rigid constraints optimization, needto be elaborated.

In this context, this paper presents a mixed integer linear pro-gramming optimization of a DC microgrid power ow by meansof a multi-layer supervision control. The DC microgrid overviewis given in Section 2, and then the power subsystem, based on PVarray (PVA), storage, power grid connection and DC load, is de-scribed in Section 3. The supervision subsystem is developed inSection 4. It is supposed to exchange data with the smart gridand to deal with end-user demand and data forecast. Energy man-agement layer optimizes power ow off-line, few hours ahead, andoperation layer controls power ow and ensures self-correctingcapability. Optimization results are presented in Section 5. Exper-

sources, for urban areas, DC microgrid may integrate, at higher en-

2 M. Sechilariu et al. / Energy Conversionergy efciency, generators as PV or fuel cells. Unlike an AC powersystem, with a DC microgrid, various DC sources can operate to-gether without regard on phase matching. In a DC microgrid, onlythe voltage needs to be stabilized, whereas AC power system re-imental results shown in Section 6 evaluate the optimization andvalidate the microgrid supervision feasibility. Discussions on opti-mization enhancement possibilities are presented in Section 7.Conclusions are given in Section 8.

2. DC microgrid system

Smart grid is expected mainly for bidirectional power distribu-tion, bidirectional communication, and reducing mismatching be-tween supply and demand. In this study, the smart grid messageis supposed to provide grid power limits, which assist in reducingpeak supply and avoiding undesired injection. As an element ofsmart grid, a microgrid should respond to local power balancingwith power ow optimization in order to reduce peak consump-tion. Aiming at improving penetration level of PV with less impacton grid and optimized local power ow, a DCmicrogrid system, ap-plied to tertiary buildings, which general overview is given inFig. 1, is studied in this work.

The multisource power subsystem consists of PVA, storage andutility grid connection, all connected on a DC bus through theirdedicated converters. In most of the cases, PVA is controlled by amaximum power point tracking (MPPT) algorithm, but could becut off or run to output a constrained power to protect storagefrom overcharging, or to maintain control of grid power injection.

The DC load of the multisource power subsystem is supplied bythe DC bus for the following reasons. For the demand side, 90% of atertiary buildings electrical load is possible to be DC fed in ef-ciency manner without AC/DC conversions [19,20]. Taking into ac-count that other renewable energy generators are intrinsically DCFig. 1. DC microgrid overview.quires each element to have almost identical wave shapes in orderto operate. In case of a DC microgrid, only one inverter is requiredto connect the public power grid, while an ACmicrogrid power sys-tem requires more inverters. Compared to DC/DC conversions, DC/AC inversion stage introduces more EMI issues requiring additionaltreatment due to higher voltage changing rate in switching tran-sient of power bridges. Compared to AC microgrids, DC microgridcan control power transfer at PCC (point of common coupling) withAC grid more easily and precisely. However, in transmission level,AC systems have advantages with mature technology of transform-ers and circuit breakers. In distribution level, DC microgrids havemore advantages in incorporation of renewable energies, simpli-ed control and efciency.

The proposed advanced local management of both supply anddemand may be seen as buffer to the grid that tends to reduce peakpower and smooth uctuations. To interact with smart grid, thesupervision subsystem is designed in multi-layer structure, whichconsists of communication layer, prediction layer, energy manage-ment layer, and operation control layer; more details on the archi-tecture of this supervision are given in [21]. Communication withend-user and smart grid, through dedicated interfaces, introducesenergy management criteria and constraints. Prediction layer pre-dicts energy load consumption and renewable energy production.Based on predictive model, energy management layer optimizesthe power ow with an aim of making full use of produced energyand responding to utility grid issues. The operation control layerfunction is to balance power instantaneously in multisource powersubsystem.

Each layer can run a complete operating function and respondsto a different time scale, from microseconds to days. With dedi-cated interface, interaction between layers does not need highspeed communications so that they can be arranged in differentcontrollers to form a more exible control structure.

3. Power subsystem

Fig. 2 shows the detailed power ow in the microgrid, usingunidirectional parameters. The power sign convention for parame-ters in Fig. 2 is always positive. The system operation must keeppower balancing while respecting constraints of certain limits.

PVA is mostly controlled by an MPPT algorithm, as Per-turb&Observe (P&O) [22], and produces MPPT power pPV_MPPT. Withrespect to storage overcharging and grid power injection limitsfrom smart grid message, the PVA production may be partiallyshed by constrained PVA production algorithm. The PVA shedpower is noted as pPV_S, and it is considered pPV_S = 0 in MPPT algo-rithm. Thus, PVA production pPV is described by Eq. (1):

pPV t pPV MPPTt pPV St 1The control strategy of PVA production is described in [23]. Thiscontrol takes into consideration two algorithms: P&O and con-strained PVA production. The constrained PVA production powerreference pPV CONS is calculated by operation layer with the algo-rithm described in Section 4.1. Thus, P&O and constrained PVA pro-duction algorithms give at the same time corresponding voltagereferences vPV MPPT and vPV CONS to operate PVA. The maximum ofthese two references is taken as the PVA voltage control referencevPV , which represents the minimum power.

The load should be satised according to end-user demand. Incase of insufcient storage and limited grid access, the load powerdemand pL_D cannot be fully met, and the load must be partiallyshed to maintain the operation of the critical load. The proportionin load power that must be shed is noted as pL_S, and the load

and Management 82 (2014) 110power pL is following Eq. (2):

pLt pL Dt pL St 2

voltage.

n u

sionTo calibrate soc accumulation error, regulated charging (con-stant voltage and/or constant current charging) operation can beperformed to fully charge storage from time to time.

When the soc limit is not reached, the PVA production shouldnot be limited, as in Eq. (5).

pPV St 0 if soct < SOCMAX 5In order to integrate with the smart grid operation, limits for

grid power supply pG_S_LIM and grid power injection pG_I_LIM are im-posed. These limits are considered as messages from the smart gridcommunication. The grid connection is controlled by currentclosed-loop control and the grid power should be controlled to sat-isfy Eqs. (6) and (7).

0 6 pG It 6 pG I LIM 6Temporarily load partial shedding could be a solution to reduceutility grid mismatching, or to obtain less energy consumption, ifagreed by end-user. Nevertheless, these operations should be con-trolled by energy management layer (minimizing or avoiding loadshedding . . .).

Considering relatively low cost and mature technology, lead-acid battery is selected as storage for the building-integrated DCmicrogrid. The storage is operated by current closed-loop control,and the storage power can be controlled by giving correspondingcurrent reference. To protect the storage from overcharging andover-discharging, the storage state of charge soc limitations,SOCMAX and SOCMIN, as upper and lower limit respectively, haveto be respected as in Eq. (3), while soc is calculated by Eq. (4).

SOCMIN 6 soct 6 SOCMAX 3

soct SOC0 13600 uS CREF

Z tt0

pS Ct pS Dtdt 4

with CREF as storage nominal capacity (A h) and uS as storage

Fig. 2. Power ow representatioM. Sechilariu et al. / Energy Conver0 6 pG St 6 pG S LIM 7

4. Supervision subsystem

The supervision subsystem, as interface between the powersubsystem and the smart grid, optimizes local power ow, with re-spects of grid needs and element constraints, to reduce energy cost,avoid undesired injection and reduce peak grid power supply.

4.1. Operation layer design

Operation layer aims at balancing power in the power subsys-tem while respecting the energy management performed in upperlayer. PVA production and the load power may be shed; so they arepartially controllable elements in the power subsystem. On theother hand, the storage and the grid can be fully controlled. Thepower balancing is described by Eq. (8):

pLt pG It pS Ct pG St pS Dt pPV t 8with powers always positive as sign convention.

The difference between load consumption and PVA generationcauses uctuations in the DC bus voltage, which is noted v. Stablev signies well-balanced power in the power subsystem. Thus,power balance is performed by adjusting storage and grid powerfor stabilizing the DC bus voltage. The required power referencep for power balancing is calculated by regulating v with a propor-tionalintegral (PI) controller as in Eq. (9):

pt pPV t pLt CPvt vt CIZ

v vdt 9

where v is the DC bus voltage control reference, CP and CI are pro-portional and integral gain respectively.

According to the following denitions pG(t) = pG_I(t) pG_S(t)and pS(t) = pS_C(t) pS_D(t), p is provided by storage and grid asin Eq. (10).

pt pGt pSt 10where grid power reference pG and storage power reference p

S are

calculated according to a distribution coefcient KD, as given inEqs. (11) and (12).

pSt KDtpt; with KD 2 0;1 11pGt pt pSt 12

The distribution coefcient KD is time varying and, in our case,its values are calculated before operation, i.e. off-line, by optimiza-tion algorithm in energy management layer with forecast data andconstraints of each element. Thanks to the proposed supervisioncontrol structure, it is also possible to re-perform optimizationon-line, i.e. during operation, and update KD sequence withoutinterrupting power balancing, which takes full advantage of latestprevision and real-time system status. Thus, as key interface

sing unidirectional parameters.

and Management 82 (2014) 110 3parameter between the energy management layer and the opera-tional layer, KD, allows self-correcting capability for both powerbalancing and energy management. KD provides possibility to con-trol the power ow at near optimal cost while respecting all con-straints. The power subsystem can operate with any KD value,which facilitates robust power balancing strategy while providingrobust interface with optimization. The robustness of the powercontrol strategy (power balancing with any KD value) is ensuredby PVA constrained production and load shedding. By load shed-ding, load power is controlled equal to or less than pL_CONS, whichis calculated as in Eq. (13).

pL CONS pPV pG S LIM 13The DC load consists of critical load and interruptible load. The

critical load requires a continuous power supply, such as comput-ers. The interruptible load can be shed temporarily, such as cooling

and heating appliances. Some lighting can also be partially shed. Asthe load is mainly demanded by the end-user, the power systemcan only control the load by load shedding. To keep safe supplyfor the critical load, the power system control can send load shed-ding signal to disconnect some appliances; therefore, load powerdemand can only reach a limited power level pL_CONS. In order todescribe the limit imposed to the load, despite system scale for dif-ferent building applications, the load shedding coefcient KL = pL_-CONS/PL_MAX is dened, with PL_MAX as maximum or contractual loadpower. Therefore, many values of KL could be physically possible,with KL 2 [0, 1].

Thus, KL changes according to the available power for supplyingthe load. Then, the power subsystem compares the current loadpower with pL_CONS, if the load power is greater than the limit,the load would be shed within the limit. When the storage is fulland the grid injection limit does not permit absorbing all surplusesof PVA productions, the PVA constrained production is performedby calculating pPV CONS following Eq. (14):

pPV CONS pL pG I LIM 14

To sum up the above described power balancing, and takinginto consideration the limits of storage and grid given in Eqs. (3)and (5-7), the overall algorithm of the operation layer is shownin Fig. 3.

Note that storage power pS is updated once more if grid powerreaches its limit, signifying that the storage, if available, is usedrst in power balancing before performing load shedding or PVAconstrained production. So, unless extreme case, by applying KDthe power balancing can be affected but always satised. Concern-ing self-correcting ability in power balancing, the grid power rep-resents the most important degree of exibility, but PVAconstrained production and load shedding are also performed ifnecessary.

4.2. Energy management layer design

Energy optimization are usually solved by linear programming[24] or dynamic programming [25,26] technique. Dynamicprogramming can solve non-linear problem, while linear program-

4 M. Sechilariu et al. / Energy Conversion and Management 82 (2014) 110Fig. 3. Flowchart of operation layer control.

sionming solves problems satisfying linear forms. Linear programmingcan be more efciently solved with less time and memories.

Energy management layer optimizes off-line the power ow ofthe DC microgrid based on forecast information provided by pre-diction layer. Then, the optimization result is transformed intotime varying optimal KD sequence, which is given to control theoperating layer. Based on meteorological forecast, PVA model,and load consumption prevision, few hours ahead the operationon-line, prediction layer provides the PVA power prediction pPV_-PDCT and load power prediction pL_PDCT. The optimization goal isto obtain the best power distribution between the grid and thestorage, so to reduce energy cost, grid power peak consumption,load shedding and PVA shedding. By prediction metadata, the en-ergy management layer can estimate off-line the optimum powerow, i.e. the evolution of pS_D, pS_C, pG_S, pG_I. This time varyingpower ow is translated into KD control parameter, i.e. optimumKD values, according to (11) and (12), aiming to run the operationlayer in the coming hours. KD is given by Eq. (15).

KD pS C pS DpS C pS D pG S pG I15

The optimum KD sequence represents optimum power ow in oneparameter. KD is the interface parameter between the energy man-agement layer and the operational layer. The advantage of using KDlies in coupling easily power balancing and energy management, sothat robust power balancing and optimization can be achieved atthe same time. The power balancing strategy is designed to satisfyall constraints and is parameterized with KD. Energy managementlayer gives only KD through low speed communication to controlthe operation, instead of updating all power references in real time.Optimization can be re-perform on-line and update KD sequencewithout interrupting power balancing, which takes full advantageof latest prevision and real-time system status. Moreover, sinceoperation layer can keeps power balancing with any KD value, theoperation is robust to prediction uncertainties.

4.2.1. Optimization problem formulationEnergy management layer optimizes power ow by means of

minimizing the energy cost of system Ctotal. The objective cost func-tion is subject to many constraints such as grid time-of-use tariff,grid access limits, storage capacity and life cycle, load sheddingand PVA shedding together, rather than only calculating global en-ergy cost. The energy cost of the system consists of grid energy costCG, storage energy cost CS, PVA shedding cost CPVS and load shed-ding cost CLS, as in Eq. (16).

Minimize Ctotal CG CS CPVS CLS 16Taking into consideration multi-criteria such as peak shaving,

avoiding undesired injection, making full use of available storagecapacity and avoiding possible load shedding and PVA shedding to-gether, it is difcult to refer to real energy tariffs. In this study, tar-iff and cost function are rather a technique for optimizing multi-criteria at the same time, than only calculating energy invoices.On the other hand, concerning PVA shedding, load shedding andstorage, their energy tariffs calculation is quite complex and de-pends on the chosen technology. This is why energy tariffs givenin this study are somewhat arbitrary; however they are chosento highlight the logic of management strategy which seems in en-ergy trend for the next twenty years.

By calculating the energy cost for each time duration Dt, CG isdened by Eq. (17). According to this denition, the grid powercould be bought or sold.

CG 1XtF

cGti Dt pG Iti pG Sti

M. Sechilariu et al. / Energy Conver3:6 106 tit0with ti ft0; t0 Dt; t0 2Dt; . . . ; tFg

17Although today the PV energy grid injection benets high tar-iffs, knowing that there is a heavily subsidized part, this studytakes into account a single rate for energy purchased or sold, andthe grid energy tariff is dened by Eq. (18):

cGt cNH 0:1 =kW h for t 2 normal hours NHcPH 0:7 =kW h for t 2 peak hours PH

18

For normal hours, an average energy tariff, close to that offeredby most providers, is considered. In contrast, for peak hours, a verypenalizing purchase tariff is chosen which is not proposed today.The reason for this assumption lies in providing demand response,for most critical hours, to perform power peak shaving.

The cost of storage CS is dened in (19) and an arbitrary storageenergy tariff is dened in Eq. (20).

CS 13:6 106

XtFtit0

cSti Dt pS Cti pS Dti 19

cst 0:05 =kW h 20The cost of PVA shedding is dened by Eq. (21) and an arbitrary

PVA shedding tariff is dened in Eq. (22):

CPVS 13:6 106

XtFtit0

cPVSti Dt pPV Sti 21

cPVSt 1 =kW h 22The cost of load shedding is dened by Eq. (23) and an arbitrary

load shedding tariff is dened in Eq. (24):

CLS 13:6 106

XtFtit0

cLSti Dt pL Sti 23

cLSt 1 =kW h 24In order to limit the power grid uctuations, grid power chang-

ing rate limits are introduced as in Eq. (25):

pGti pGti1 6 LimitpGti pGti1P Limit

25

The limits expressed by Eq. (26) are imposed in order to ensurestorage charging and grid injection only from PVA production.

pGtiP 0;pStiP 0 if pPV ti pL DtiP 0pGti 6 0;pSti 6 0 if pPV ti pL Dti < 0

26

Considering the discrete time instant ti, from initial time t0 to -nal time tF, with the time interval Dt, Eq. (27) gives the problemformulation:

Minimize Ctotal CGCSCPVSCLSwith respect to :

pLtipG ItipS Cti pG StipS DtipPV tiSOCMIN 6 soct6 SOCMAX

socti SOC0 13600uSCREFXtFtit0

pS CtipS DtiDt

06pG Iti6pG I LIM06pG Sti6 pG S LIMpGtipGti16 LimitpGtipGti1PLimitpGtiP0;pStiP0 if pPV tipL DtiP0pGti>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>:

and Management 82 (2014) 110 5ti ft0;t0Dt;t02Dt; . . . ;tFg27

ito 0 if the SOCMAX is not reached at time ti, and to 1 in the other

MAX

tity soc(ti) + e is greater than SOCMAX, variable xi can take the value 1.

as CPLEX, LP_SOLVE and GUROBI. In this study, our optimization

rst experimental test, the PVA predictable power pPV_PDCT is calcu-

Prediction layer gives to the energy management layer the fore-

ionproblem is solved using the IBM ILOG CPLEX solver [29], which isa powerful tool for solving different types of optimization prob-lems. However, any other mixed integer linear programming solveralso can be used. While mixed integer linear programs are com-monly more difcult to solve than linear programs due the integervariables, our problem can be solved very efciently by such a sol-ver, even with a huge set of time points, as it can be seen in the re-sult shown in Section 5. In fact, in order to express our problem inthe syntax of the solver and to call the solving algorithm of the sol-pPV Sti 6 xi pPV ti pL Dti 30

expressing the fact that pPV_S(ti) = 0 when xi = 0 (i.e. if the SOCMAX isnot reached at time ti) and pPV_S(ti) can take a value no greater than(pPV(ti) pL_D(ti)) when xi = 1 (i.e. if the SOCMAX is reached at time ti).

Note that the xi variable data type is integer (more preciselybinary), that is why the previous formulation is now said to be amixed integer linear program. The direct consequence is that thevery efcient Simplex algorithm cannot be used directly anymoresince it works only with real variables. Instead, more advancedtechniques (often using Simplex as subroutines), like cutting planemethods, branch and bound algorithms, branch and cut or branchand price methods, are used to solve these problems [28].

Nevertheless, numbers of mixed integer linear programmingsolvers implementing these optimization techniques exist, suchFinally, the following constraint (Eq. (30)) is used to ensure thatconstraint given by Eq. (5) can be fully respected:xi 6socti eSOCMAX

29

where e is a small real constant (0.005 in this application) allowingacceptable margin around the value of SOC . As soon as the quan-case:4.2.2. Solving the problemLinear programming techniques are common approaches to

solve optimization problems which can be expressed in the stan-dard form given by Eq. (28). The Simplex algorithm, proposed byDantzig [27], is widely used to solve such problems very efcientlywhen all variables are real. It can be veried that, mathematicalformulation given by Eq. (27) follows standard form expressedby Eq. (28), except for the last constraint which does not take a lin-ear form.

MaxMin Z c1x1 c2x2 cnxn

subject to

a11x1 a12x2 a1nxn 6 ;Pb1a21x1 a22x2 a2nxn 6 ;Pb2

..

. ... ..

.

am1x1 am2x2 amnxn 6 ;Pbmx1; x2; . . . ; xn P 0 6 0; freedom

8>>>>>>>>>>>>>:

28

However, this constraint can be easily linearized by introducinga variable array xi for each time point ti. Different from the othervariables of the formulation which take continuous real value, vari-ables xi have to take their value in 0;1gf as binary variables. Thefollowing constraints (Eq. (29)) are used to ensure that x equals

6 M. Sechilariu et al. / Energy Conversver, a procedure written in C++ is used. This procedure outputs theoptimal power ow in a le for control parameter translation, KD,which is then transmitted to the operation layer.casts of hourly evolutions of the PVA power and the load power.The others powers values are based on the experimental DC micro-grid platform whose details are given in [20]. According to theprediction information, the energy management layer solves theoptimization problem by CPLEX and gives the optimum power owevolution, as presented in Fig. 6.

Based on optimum power ow evolution, as result of theoptimization solving, corresponding optimum KD sequence iscalculated by (15). So, optimum KD, as controller parameter, aimslated from hourly solar irradiance forecasts, assuming the simpli-cation that pPV_PDCT is a solar irradiance proportional function. Fig. 4shows the solar irradiance forecast, which is provided as hourlydata at 3:00 a.m. on 25th of October 2012.

As the maximum total output power of the used 8 PV panels, atstandard test condition 1000 W/m2, is 1000 W, the PVA predictablepower pPV_PDCT is identical as solar irradiance forecasts curve.

Load power prediction is considered data are supposed to begiven by building management system, which implies additionaluncertainties. Regarding the load, in this work a simple arbitraryload power evolution is considered, whose prediction, pL_PDCT,implies uncertainties as shown in Fig. 5.4.3. Prediction layer

Interface with external information/data for optimization isneeded. So, using forecast data and based on PV model [30], thislayer calculates the PVA predicted power pPV_PDCT.

Regarding the load, its power supply can be predicted by statis-tical data and/or by information of operating program from build-ing management system [31]. Depending on criteria chosen by theuser, the load prediction power, pL_PDCT, can be given by this layer.

4.4. Humanmachine interface layer

By humanmachine interface (HMI), end-users can specify en-ergy management criteria for the DC microgrid. End-user can alsodene critical load in load shedding program, assign different pri-orities for each physical device that would be shed automatically.The necessary minimum power level is also dened for the safeoperation of the building critical load that requires continuouspower supply. Modify the optimization criteria or overwrite con-straints is also possible in this layer. At this stage, HMI is not devel-oped, but this layer will be elaborated in further work.

5. Optimization results

The power ow optimization is calculated for an operation on25th of October 2012, at Compigne in France. Since 2008 a PVAis installed on the roof of the university building, whose geographiccoordinates are 4924000.9800N/247059.1900E. An image of the uni-versity building equipped with PVA appeared in Fig. 2 like DC load.The forecasts of solar irradiance (W/m2) for the given geographiccoordinates are provided by the national weather service METEOFrance. So, the solar irradiance forecast are not calculated on thebasis on historical data of irradiation, but it is calculated by METEOFrance in real time for the specic day and the specic geographi-cal coordinates. Accordance with the service plan, the forecast dataare transmitted daily at 3:00 a.m. In this work, the PVA predictablepower pPV_PDCT is calculated on the basis of the transmitted forecastdata and a PVA of 8 PV panels. As this paper aims at demonstratingfeasibility of the designed supervision energy management, for this

and Management 82 (2014) 110to control the power ow near the optimum.Taking into consideration the above power predictions, in our

case study, the optimized power ow and KD evolution, are

sion0

100

200

300

400

500

600

700

800

0

100

200

300

400

500

600

700

800Solar irradiance (W/m2)PVA power prediction (W)

9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00

M. Sechilariu et al. / Energy Convercalculated a few hours before the operation day of 25th of October,as shown in Fig. 7 and in Fig. 8 respectively.

Grid and storage constraints, as arbitrary values, are imposedfor the system operation. Grid power injection limit is imposedas 300W, and supply limit as 400 W. Peak hours during theday are assumed 11:0013:00 and 16:0018:00. To mitigate gridpower strong uctuations, grid power changing limit is imposedas 1 W/s.

Arbitrary soc limits are considered as 49% and 52%, while theinitial SOC is 50%. Considering the storage capacity of our experi-mental platform, these soc limits are selected to show the systembehavior with relevant storage events (full, empty) in a day run.

Taking into account that the optimization program must com-pute the power ow for 9 h operation, the data resolution is chosenat 10 s/point, i.e. 3240 points each power curve. For a normal

Fig. 4. Solar irradiance forecast at 3:00 and PVA power prediction.

9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:000

100

200

300

400

500

600

700

800Prediction (W) Experiment (W)

Fig. 5. Load power demanded during the test and load power prediction.

Fig. 6. Flowchart of optimization solving.

Fig. 8. Optimum KD and soc evolution by energy management layer.computer with CORE i5 processor, the execution time of the opti-mization program is within 3 s. At the end, the optimal cost calcu-9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00-600

-400

-200

0

200

400

600

800

1000Power (W) PPV_PDCT PL_PDCT PS PG PL PPV

Fig. 7. Optimized power ow by energy management layer.

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and Management 82 (2014) 110 7lated by CPLEX is 0.108 .

6. Experimental results and discussions

The experimental test is based on platform whose main param-eters and picture are given in [20].

The control of each component of DC microgrid is achieved by aclassical linear PWM control [32]. The experimental test is oper-ated on 25th October under real solar irradiance and PVA temper-ature as illustrated in Fig. 9.

The calculated optimum KD time varying sequence is given tothe operation layer a few hours ahead. Based on KD and operatingalgorithms, the operation layer controls power subsystem in real-time, and experimental results are given in Figs. 10 and 11.

During this day operation, grid and storage share power for sup-plying or receiving energy according to control parameter KD givenby energy management layer. Fig. 10 gives also the PVA MPPT pro-duction, pPV_MPPT, aiming at comparison with measured pPV duringthe PVA constrained production (around 15:15).

Concerning the load, the real-time load demanded power, pL_Dwhich is different of pL_PDCT, is not always satised and implies loadshedding. In this test, the real-time supplied power, pL, is differentfrom pL_D as measured around 17:20. So, prediction uncertaintiesmake power ow in Fig. 10 different from Fig. 7.

Aiming to reduce the energy cost by avoiding grid to supplyduring peak hours, in the rst off-peak hours (9:0011:00), theload is supplied mostly by the grid for reserving storage. The sur-plus PVA power (after 10:00) is injected into the grid and chargedin the storage, which lead to total energy cost reduction by sellingenergy as well as charging storage. However, due to uncertainty ofprevision, difference between optimization and experimental testcan be seen just before 10:00, grid and storage supply the load in-stead of absorbing energy. Regarding the rst peak hours (11:00

measurement. Finally, to reduce the energy cost during the secondpeak hours (16:0018:00), the storage is mainly used for supplyingthe load. As a consequence of prediction uncertainties and experi-mental power loss, the storage capacity is unable to supply theload exactly until 18:00 as expected by optimization. After SOCMINis reached around 17:10, grid is unable to supply the load for thedemanded power PL_D due to the imposed grid supply power limit.

ion and Management 82 (2014) 1100

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Fig. 9. PVA cell temperature and solar irradiance on 25th October.

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1000Power (W) PPV_MPPT PL_D PS PG PL PPV

8 M. Sechilariu et al. / Energy Convers13:00), the load is mainly supplied by storage, and then, the sur-plus PVA production is injected into the grid in order to reducingpeak cost and make the maximum prot. Around 12:50, the sur-plus PVA power is greater than the grid injection limit, so, the stor-age is charged given that its upper soc limit is not reached yet.

During the second off-peak hours (13:0016:00), the excessPVA production is used to charge storage for supplying in the sec-ond peak hours (16:0018:00), as well as injecting energy into thegrid. The amount of power shared between grid and storage isdetermined by optimization with a goal of fully charging the stor-age and keeping grid injection power within limits.

However, due to uncertainties from PVA power prediction andload prediction, unexpected PVA shedding can be observed. After15:00, as the load prediction uncertainty involves more surplusPVA power than predicted, and the storage is already fully chargedearlier before expected, PVA shedding is performed to keep gridpower under the power injection limit. However, grid injectionpower is less than grid injection limit due to an additional securitymargin applied in PVA shedding algorithm, which aims at keepingpower balancing in even in EMI environment and/or with errors of

cases off-line optimization and experimental test.The experimental cost with the optimum KD is higher than the

optimization cost. This error is mainly due to the cost of PVA and9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00-600

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Fig. 11. Experimental DC bus voltage and soc evolution with optimum KD.load shedding, as well as the energy absorption during highlypenalizing tariff during peak hours. These three elements accountfor about 75% of the error between off-line optimization and exper-imental operation. So, despite prevision uncertainties and powerloss, the power ow obtained experimentally shows optimizationfunction. The experimental results have demonstrated the feasibil-ity of proposed control in respecting rigid constraints and optimiz-ing the power ow at the same time, which is the issue forimplementing optimization in real time operation.

7. Discussions and optimization enhancement

To reduce the error between experimental cost and optimizedcost, two kinds of solutions could be proposed: physical and algo-rithmic. A physical solution can be represented by a second storagewhose objective is to act on the difference between the power ref-erences, given by Eq. (9), obtained during the off-line optimization,on one hand, and during on-line operation on the other hand. Byintroducing additional storage, the error due to previsionuncertainties can be compensated, but the optimization of overallsystem cannot be guaranteed. Moreover, the microgrid global costbecomes greater.

As purely algorithmic solution, the optimization can be re-per-formed with latest prevision data and real-time system status, andcan update KD optimal values, which improve optimization perfor-mance. Concerning latest prevision data it is necessary to provideforecast data updated every hour at least. Theoretically it is quite

Table 1Results of optimization off-line and experimental test.

Case operation Optimization off-line Experiment

KD sequence Optimum KD (Fig. 7) Optimum KD (Fig. 10)Cost () 0.108 0.056PVA shedding 0 39.17 W h; 0.039 So, the load shedding algorithm sheds the load to a lower level, PL,to ensure power balancing. After a short load shedding, the grid isable to supply the full load power demand again.

Fig. 11 shows DC bus voltage and soc evolution. The DC bus issteady with slight uctuations (less than 3% of rated DC bus volt-age), signifying the power in the system is well balanced. The socevolution is consistent with optimization with a little difference,as analyzed above.

During this experimental operation, element constraints suchas grid power limits and storage capacity are fully respected. Thepower ow shows consistence with optimization and the objectiveof reducing grid peak consumption, avoiding undesired injection,making full use of produced energy with regard to all imposed con-straints. Nevertheless, load and PVA shedding occur, but ensure thepower balancing. The total energy cost is 0.056 .

Table 1 summarizes total energy cost, occurrences of load shed-ding and PVA shedding, as well as grid peak hour usage for bothLoad shedding 0 9.10 W h; 0.009 Grid peak usage 146.09 W h; 0.102 9.02 W h; 0.075

possible; however access to the data of hourly updated solar irra-diance forecasts, for a specic location, remains difcult.

Regarding the real-time system status, in case of signicantdifference of predicted status, the optimal operation cannot beguaranteed since lack of information of real optimal operationbeforehand. Aiming to highlight the difference between non-opti-mized and optimized operation, as well as to provide furtherdiscussion on possible techniques to enhance optimization, thissection provides three simulation cases based on the sameexperimental condition with differences concerning KD values.

Firstly, in order to emphasize the efciency of this optimizedmicrogrid, Fig 12 shows the simulated power ow of the microgridfor which KD = 0.338 as average value of the optimum KD sequencegiven in Fig. 8. The second simulation case concerns the microgridoperation based on optimum KD sequence calculated off-line as gi-ven in Fig. 8. Obtained power ow for the case is shown in Fig. 13.For the third simulation case, KD sequence is updated hourly duringoperation by re-performing optimization, i.e. on-line optimization.Power ow is illustrated in Fig. 14. However, optimization is up-dated only with regard to system real-time status and predictiondata remain unchanged.

Table 2 summarizes these three simulation cases, and shows to-tal energy cost, occurrences of load and PVA shedding, as well asgrid peak hour usage.

Compared with experimental case, due to lack of power loss, forthese three simulation cases microgrid operates with more PVAshedding and without load shedding. For constant KD case the glo-bal cost is the highest, even compared with the experimental case

balancing. Both latest prevision data and real-time system statuscan help to achieve better optimization and improve microgridoperation.

Concerning prevision data, uncertainties can be taken in theframework of robust optimization [33] in which data can takeany value in given intervals knowing that at most a given propor-tion of these data will reach their worst value. During the optimi-zation process, all scenarii entering in the space of possibilitiesdened by the above data will be taken into consideration in sucha way that, even for the worst scenarii, the proposed operationalsolution is pertinent. Hence, the uncertainties consideration shouldlead to KD control parameter correction which should better ap-proach the optimal operating point of the system.

Another possibility to enhance optimization could be rule-based KD correcting algorithm. Such algorithm can be applied dur-ing the operation which adjusts the KD value received from optimi-zation. The rules can be based on optimization results or/andpower system status. The disadvantage is that such algorithmcan be very complex when considering microgrid real-time statusand multiple constraints such as grid peak hour, energy dynamicpricing, and power grid limitations. Moreover, implementing suchalgorithm cannot ensure optimal operation.

8. Conclusions

Future high level renewable energy penetration requires opti-mized microgrid by local intelligent control involving power bal-ancing and grid interaction to enhance overall grid performance.

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M. Sechilariu et al. / Energy Conversioncost. This is due to not optimized storage usage which induces PVAshedding and much more grid power supply during the secondpeak hours. It can be seen that, even if forecast data remain un-changed and involve uncertainties, the on-line optimization canbetter optimizes the operation by taking into consideration real-time power system status. Compared with the off-line optimiza-tion, re-performing optimization during operation, i.e. hourly up-

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Fig. 13. Simulated power ow with optimum KD calculated off-line.dated KD, permits to obtain less overall cost, less PVA sheddingand better grid peak usage. So, it is expected that with both real-time power system status and updated prevision data, performingon-line optimization can be the solution to enhance the optimiza-tion. Furthermore, the proposed multi-layer control structure per-mits on-line optimization without interrupting the power

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Fig. 14. Simulated ow with optimum KD hourly calculated on-line.

Table 2Comparison of different simulation cases.

KD caseoperation

ConstantKD = 0.338

Optimum off-line

Optimum on-line hourlyupdated

Cost () 0.221 0.071 0.030PVA

shedding131.26 W h;0.131

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0 0 0

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and Management 82 (2014) 110 9Based on a single DC microgrid aiming at high-level urban PV pen-etration and consisting of PV array, storage, power grid connectionand DC load, this paper proposed a multi-layer supervision control

combining power balancing, optimization and interface for smartgrid and user. Taking into account forecast of PV power productionand load power demand, and constraints as storage capability, gridpower limitations, grid time-of-use pricing, and grid peak hour,optimization is formulated as mixed integer linear programmingand is solved by CPLEX. The optimized power ow is translatedinto one interface parameter KD and then transmitted to powerbalancing algorithm (operation layer) to control and optimizereal-time power ow. It was experimentally validated the feasibil-ity of implementing optimization in real-time operation whilerespecting rigid constraints by proposed control. Even with uncer-tainties, the experimentally obtained real-time power ow has

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10 M. Sechilariu et al. / Energy Conversion and Management 82 (2014) 110show the features of optimization in reduce grid peak consump-tion, avoid undesired grid injection and make full use of the pro-duced energy with respect to all constraints.

However, the optimization effect relies largely on the precisionof prevision. Discussions optimization enhancement was provided.It is highlighted that proposed control can re-perform optimizationduring operation with real-time system status without interrupt-ing power balancing, thus enhancing optimization effect. In addi-tion, the idea of parameterizing power balancing to interfacewith energy management layer provides advantages in imple-menting optimization in real-time operation.

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DC microgrid power flow optimization by multi-layer supervision control. Design and experimental validation1 Introduction2 DC microgrid system3 Power subsystem4 Supervision subsystem4.1 Operation layer design4.2 Energy management layer design4.2.1 Optimization problem formulation4.2.2 Solving the problem

4.3 Prediction layer4.4 Humanmachine interface layer

5 Optimization results6 Experimental results and discussions7 Discussions and optimization enhancement8 ConclusionsReferences