Supervision control for optimal energy cost management in DC microgrid: Design and simulation

Electrical Power and Energy Systems 58 (2014) 140–149

Contents lists available at ScienceDirect

Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes

Supervision control for optimal energy cost management in DC microgrid: Design and simulation Manuela Sechilariu ⇑, Bao Chao Wang, Fabrice Locment Université de Technologie de Compiègne, AVENUES-GSU EA 7284, BP 60203, rue du Docteur Schweitzer, 60203 Compiègne, France

a r t i c l e

i n f o

Article history: Received 30 January 2013 Received in revised form 7 January 2014 Accepted 18 January 2014

Keywords: DC microgrid Energy management Prediction Smart grid Simulation Supervision

a b s t r a c t The development of microgrids could facilitate the smart grid feasibility which is conceived to improve instantaneous grid power balancing as well as demand response. It requires microgrid control functions as power balancing, optimization, prediction, and smart grid and end-user interaction. In literature, these aspects have been studied mostly separately. However, combining them together, especially implementing optimization in real-time operation has not been reported. The difficulty is to offer resistance to optimization uncertainties in real-time power balancing. To cover the research gap, this paper presents the supervision design with predicted powers flow optimization for DC microgrid based on photovoltaic sources, storage, grid connection and DC load. The supervision control, designed as four-layer structure, takes into account forecast of power production and load power demand, storage capability, grid power limitations, grid time-of-use tariffs, optimizes energy cost, and handles instantaneous power balancing in the microgrid. Optimization aims to reduce the microgrid energy cost while meeting all constraints and is carried out by mixed integer linear programming. Simulation results, show that the proposed control is able to implement optimization in real-time power balancing with resistance to uncertainties. The designed supervision can be a solution concerning the communication between loads and smart grid. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Aiming to avoid grid voltage fluctuations [1,2], or even blackout, at any time instant, the electric grid must balance power between the production and the consumption with a small margin of error. The grid capacity is built to satisfy the peak consumption. If the peak consumption can be shifted during a day, referred to as ‘‘peak shaving’’, the power adjustment, often ensured by excess capacities working in stand-by mode, could be largely reduced. To build a more robust utility grid, strategies and means of power management are being developed, as well as information on grid needs and availability [3], which could assist in power balancing by avoiding undesired injection and performing load shaving during peak hours. For this, the smart grid is being created to facilitate information exchange. Smart grid is electric networks that employ innovative and intelligent monitoring, control communication, and self-healing technologies to deliver better services for power producers and distributors, flexible choices for end-users, reliability and security of power supply [4,5]. Smart grid is expected mainly for the following aspects: bidirectional power distribution; bidirec-

⇑ Corresponding author. Tel.: +33 344234964; fax: +33 344235262. E-mail address: [email protected] (M. Sechilariu). 0142-0615/$ - see front matter Ó 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijepes.2014.01.018

tional communication, and reduction mismatching between supply and demand. The concept of microgrid is proposed for better renewable energy penetration into the utility grid and helps energy management to respond to some grid issues, such as peak shaving, and reduces energy cost [6–10]. Microgrids are considered as one of the possible approaches helping to develop the smart grid [11]. By aggregating loads and multi-source, renewable and traditional, microgrid can operate in both off-grid and grid-connected configuration. It is generally considered that microgrid controls on-site generation and power demand to meet the objectives of providing local power, ancillary services, and injecting power into the utility grid if required [8]. Concerning microgrid approach, several main advantages can be given: improving renewable energy penetration level, facilitating the smart grid implementation, better energy supply for remote areas, power balancing at local level with selfsupplying possibility, and maintaining load supply during islanding operation or off-grid mode [12]. Thus, the microgrid controller becomes essential for balancing power and energy management, and facilitates the sources pooling during islanding. Depending on the usage of AC or DC bus for coupling different elements within microgrid, AC microgrid, DC microgrid and hybrid AC/DC microgrid structures exist [13]. At present, the DC grid is not ubiquitous [14,15], but more HVDC transmission lines are being built in MW level, while low voltage DC grid is being adopted,

M. Sechilariu et al. / Electrical Power and Energy Systems 58 (2014) 140–149

141

Nomenclature CG grid energy cost (€) CLS load shedding cost (€) CPVL PV production limitation cost (€) CS storage energy cost (€) Ctotal microgrid energy cost (€) cG grid energy tariff (€/kW h) cNH grid energy tariff for normal hours (€/kW h) cPH grid energy tariff for peak hours (€/kW h) cLS load shedding tariff (€/kW h) cPVL PV production limitation tariff (€/kW h) cS storage energy tariff (€/kW h) CP proportional gain CREF storage nominal capacity (Ah) iPV PV current (A) iPV PV current reference (A) KD distribution coefficient KL load shedding coefficient KL_lim load shedding limit coefficient p* power reference (W) pG grid power (W) pG grid power reference (W) pG_I grid injection power (W) pG_S grid supply power (W) pG_I_lim grid injection power limit (W) pG_S_lim grid supply power limit (W) pG_I_prediction grid injection power prediction (W) pG_S_prediction grid supply power prediction (W) pL load power (W) pL_D load power demand (W) pL_lim load power limit (W) pL_max load maximum power (W) pL_prediction load power prediction (W) pPV PV power (W)

starting with data centers, for the reason of more efficiency, less cost, less occupied space, lower lifetime cost and more reliability [16–18]. Paper [13] presents a three-levels hierarchical control according to ISA-95 and applied to AC or DC microgrids. This general approach of hierarchical control for microgrids is conceived for a large-scale power system, upstream in the utility grid hierarchy. Imitating the behavior of a grid synchronous generator control, the proposed hierarchical control strategy aims at balancing power between multi inverters coupled on the same bus without communication, while controlling the power at the point of common coupling (PCC) at the same time. The proposed hierarchical control is considered as a part of the central control and does not take into account the prediction of the power generation and the energy optimization. In [18], support for autonomous DC microgrid applications is proposed by integrating the device-level service oriented architecture paradigm into the international standard IEC 61850 applications. In order to create self-manageable microgrid with semantic-enabled plug-and-play process for distributed energy resources, this solution provides generic middleware platform required for vertical communication. However, the proposed solution applied to the real microgrid power systems requires additional control and regulation policy. A high-level energy management supervision, by means of multi-agent systems, is presented in [19]. In this work, the authors focus on two-level architecture for multiple interconnected microgrids aiming to manage distributed energy resources in order to match the buyers and sellers in the energy market.

pPV_lim PV limited power (W) pPV lim PV limited power reference (W) pPV_MPPT PV MPPT power (W) pPV_prediction PV power prediction (W) pS storage power (W) pS storage power reference (W) pS_C storage charging power (W) pS_D storage discharging power (W) soc storage state of charge (%) SOCmax SOC upper limit (%) SOCmin SOC lower limit (%) SOC0 initial soc (%) v DC bus voltage (V) v* DC bus voltage reference (V) vPV PV voltage (V) v PV PV voltage reference (V) v PV lim PV limited voltage reference (V) v PV MPPT PV MPPT voltage reference (V) vS storage voltage (V) Abbreviation ACR automatic current regulator AVR automatic voltage regulator HMI human–machine interface MPPT maximum power point tracking NH normal hours PH peak hours PI proportional–integral PV photovoltaic P&O Perturb & Observe PWM Pulse Width Modulation

A generalized formulation for intelligent energy management of a microgrid is proposed in [20] using multiobjective optimization to minimize the operation cost and the environmental impact. An artificial neural network ensemble is developed to predict renewable energy generation and load demand. In addition, a battery scheduling is proposed as a part of an optimal online energy management, seen as a decision-making process. However, smart grid data exchanges online or dynamic energy pricing are not considered. To increase penetration of small PV production into the grid, a local hierarchical control with energy management is proposed in [22]. The system is presented as multi-layer control structure, each layer with a different function, and is based on an optimal power flow management with predictions, which considers batteries ageing and day-ahead approach into the optimization process. However, the exchange data with the smart grid, such as limitations of the grid capacity, is not taken into account. Moreover, due to uncertainty of prediction and lack of grid information, the grid power could be out of control. Concerning the energy management two main approaches are considered: rule-based and optimization-based approaches. Rulebased approach manages the system according to prefixed rules, such as simple rule base, multi-agent system [19] and fuzzy logic approaches [20,21]. Optimization based approach manages the system by mathematical optimization, carried out with objective function and constraints. The optimization methods include the artificial intelligence joint with linear programming [20], linear programming [21] or dynamic programming [22,23], and genetic algorithms [24].

142

M. Sechilariu et al. / Electrical Power and Energy Systems 58 (2014) 140–149

To sum up the results presented in these works, the rule based system is simple and robust, but not guarantee the optimal performance with given operating conditions. Moreover, rules become complex when facing different scenarios. The optimization gives an optimal solution within given constraints and operation condition. However, optimization is usually treated as separate problem from the power balancing strategy, and optimization is not implemented in real-time operation. Since optimization are preformed based on prediction, errors between prediction and real condition could result in degraded real-time operation by violating certain constraints, or even result in failure. Thus, optimization and power balancing should be designed together and coupled with adequate interface. Hence, in contrast of the above cited works, in this study the goal is to design and develop a microgrid local supervision control which can respond to all following requirements: – to design optimization and power balancing together and couple them through adequate interface; – to implement optimization in real-time operation with resistance to uncertainties; – to process prediction data used for optimization; – to exchange data with the smart grid and with the end-user; – to adapt various data in microgrid operation, such as real-time market pricing and user demand. Thus, this paper focuses on supervisory control of DC microgrid which is supposed to manage the power flow in microgrid and power flow exchanged with the utility grid, with the objective of making full use of each source while respecting their constraints on capacity and power. The microgrid based on PV sources, storage, grid connection and DC load is presented in Section 2. The supervision control, designed as four-layer structure, which is supposed to exchange data with the smart grid, deal with end-user demand, forecast of PV production and load consumption is described in Section 3. Taking into account forecasting data, storage capability, grid power limitations, grid time-of-use tariffs, the predicted powers flow is optimized by mixed integer linear programming and solved by CPLEX solver. The DC microgrid control is simulated for two different cases, based on optimized powers flow and without optimization; the results are presented in Section 4. Concluding remarks are given in Section 5.

2. Microgrid overview The microgrid presented in Fig. 1 is suggested for local PV power generation combined with storage and grid connection,

which feed directly a DC load through their dedicated converters. Concerning the DC bus, following considerations are assumed: for the demand side, 90% of a tertiary building’s electrical load is possible to be DC fed in efficiency manner; for the grid side, the power factor can be controlled at 1 [5]. Aiming to interact with the load environment (user, metadata, smart grid), a supervision system is added, whose main role is to balance instantaneous power in power system following an energy management algorithm. The system operation must keep power balance while respecting constraints of certain elements. Fig. 2 shows the powers flow in the microgrid, using unidirectional powers whose sign convention is always positive. The system operates respecting the available storage level and taking into account the grid connection. In case of insufficient PV energy toward the load, the system security is ensured thanks to the grid connection and by storage, as well as the load shedding program. If any excess PV power, the storage could be charged and the grid connection gives the possibility to trade it back. 2.1. PV sources control PV system is controlled either by a maximum power point tracking (MPPT) method or by an algorithm to output a limited power pPV lim to protect the storage from overcharging or to maintain grid injection power within imposed constraint. In this study, the chosen MPPT method is the very well known Perturb & Observe (P&O) [25,26], according to which PV sources produce a MPPT power pPV MPPT . The PV production could be limited thanks to the limited power reference pPV lim calculated by the supervision with the algorithm described in Section 3.4. The PV power pPV control strategy is shown in Fig. 3. The P&O algorithm and the PV production limiting algorithm give at the same time corresponding voltage reference v PV MPPT and v PV lim to operate PV system. The maximum of these two references is taken as the PV voltage control reference v PV , which represents the minimum power. Following v PV , the PV system is operated by voltage and current double closed-loop control via automatic voltage regulator (AVR) and automatic current regulator (ACR). The output of control, duty cycle of Pulse Width Modulation (PWM) signal, is given to the power electronic devices to control the PV system. During the MPPT operation, if a limited power within the MPPT ability is given, the proportional–integral (PI) controller would increase the v PV lim . For v PV lim > v PV MPPT the v PV lim reference is taken and the MPPT algorithm is stopped. By constrained power closed loop control, the PI controller controls the PV power at the limited level. In case of low solar irradiance, the PV output power ability is less than the limited power reference, so the PI controller will decrease v PV lim until the lower limit, and v PV MPPT is taken to control the PV system. So, the limited power

SUPERVISION SYSTEM System states

pS _ C

Storage

pS

_D

POWER SYSTEM

v PV power through load DC Load PV

Control

DC * vPV

DC

pG*

DC

DC AC

pS*

DC

KL

PV Sources

pG _ S

pG _ I PV Sources

Grid connection

Storage

Fig. 1. DC microgrid overview.

Grid connection Fig. 2. Powers flow representation.

DC Load

M. Sechilariu et al. / Electrical Power and Energy Systems 58 (2014) 140–149

143

Supervision algorithm

power pL, to keep power balance. The load limited power pL_lim is controlled by the load coefficient KL defined by Eq. (6):

* pPV _ lim

K L ¼ pL

vPV

STOP P&O

iPV ×

* pPV _ lim

pPV +-

* vPV

PI

* vPV

+-

max

* vPV _ lim

AVR

* iPV

+-

ACR

ð6Þ

where pL_max is a constant as contractual subscribed maximum power. It is supposed that the load could not be supplied with a power that exceeds this limit. Given the constant denominator pL_max, therefore KL e [0, 1] changes according to the available power for supplying the load. If pL_D > pL_lim, the load would be shed within the limit level. These operations should be controlled by the supervision system. Temporarily load partial shedding could be a solution to reduce utility grid mismatching, or to obtain less energy consumption, within the limit agreed by end-user.

≥

* vPV _ MPPT

lim =pL max

PWM

2.5. Power balancing principle According to the powers sign convention (powers always positive), the physical law of power balancing is described by:

Fig. 3. PV system control strategy.

control does not affect MPPT algorithm and MPPT power is produced. Experiment validation of the control is provided in [27]. 2.2. Storage control Lead-acid batteries are selected as storage for the DC microgrid applied to building, because of relatively low cost and mature technology [28]. The storage is operated by current closed-loop control, and the storage power is controlled by supervision system which calculates the corresponding power reference. The storage state of charge soc must be respected to its upper and lower limitations, SOCmax and SOCmin respectively, to protect the storage from overcharging and over discharging, as described by Eq. (1). The soc is calculated by Eq. (2), with SOC0 as initial soc at t0, CREF as storage nominal capacity (Ah) and vS as storage voltage. When the soc limit is not reached, the PV production should not be limited, as in Eq. (3).

SOC min 6 socðtÞ 6 SOC max socðtÞ ¼ SOC 0 þ pPV

lim ðtÞ

¼ pPV

Z

1 3600  v S  C REF

MPPT ðtÞ

ð1Þ t

ðpS C ðtÞ  pS D ðtÞÞdt

ð2Þ

t0

if socðtÞ < SOC max

ð3Þ

2.3. Grid connection control The grid connection is controlled by current closed-loop control. The grid power is controlled by supervision system which calculates the corresponding power reference. Furthermore, as messages transmitted by the smart grid via supervision system, limits for grid supply power pG_S_lim and grid injection power pG_I_lim could be imposed. By these two limitations, grid problems, such as performing peak shaving, avoiding undesired injection or downscaling injection fluctuations caused by intermittent PV productions, can be improved. During microgrid operation, the grid power should be controlled to satisfy Eqs. (4) and (5).

0 6 pG I ðtÞ 6 pG

I lim

ð4Þ

0 6 pG S ðtÞ 6 pG

S lim

ð5Þ

2.4. DC load control The load power demand pL_D should be satisfied; nevertheless, in case of insufficient storage and grid access limits, pL_D cannot be fully met, and the load must be partially shed, forming load

pL ðtÞ þ pG I ðtÞ þ pS C ðtÞ ¼ pG S ðtÞ þ pS D ðtÞ þ pPV ðtÞ

ð7Þ

with pL(t) = min (pL_D(t), pL_lim(t)) and pPV(t) = min (pPV_MPPT(t), pPV_lim(t)). Fluctuations in the DC bus voltage, which is noted v, are caused by the difference between load consumption and PV generation. The required power reference p* for power balancing is calculated by regulating v with a proportional controller as in Eq. (8):

p ðtÞ ¼ pPV ðtÞ  pL ðtÞ  C P ðv  ðtÞ  v ðtÞÞ

ð8Þ

*

where v is the DC bus voltage control reference and CP is the proportional gain. For stabilizing the DC bus voltage, power balance in the system is performed by adjusting storage and grid power. Thus, p* is shared by the storage and the grid as in Eq. (9)

p ðtÞ ¼ pG ðtÞ þ pS ðtÞ

ð9Þ

with pG(t) = pG_I(t)  pG_S(t) and pS(t) = pS_C(t)  pS_D(t). After calculating p*, the grid power reference pG and storage power reference pS are calculated according to a distribution coefficient KD(t), as described in Eqs. (10) and (11).

pS ðtÞ ¼ K D ðtÞp ðtÞ

ð10Þ

pG ðtÞ ¼ p ðtÞ  pS ðtÞ

ð11Þ

The power balancing of studied DC microgrid operation can work with any KD value. To improve efficiency and reduce energy cost, optimization calculations using specifically metadata (power predictions, power limits, real-time grid energy tariffs) are done by the supervision which outputs the distribution coefficient, KD(t), whose time depending values represent the predicted optimized powers flow. 3. Supervision control design The supervision system, proposed in Fig. 4, is designed in fourlayer structure, which consists of human–machine interface (HMI), prediction layer that predicts load consumption and PV production, energy management layer that optimizes the predicted powers flow, and operation layer that balance instantaneous power, based on unique interface parameter KD(t), in power system. The supervision control takes into consideration prediction data and various constraints, such as grid power limits and storage capacity, which can be fully respected for both optimization and real operation. Each layer provides an independent function and thus, the structure is flexible and can be implemented in several microcontrollers or computers so that real-time power balancing control and complicated optimization can be executed at the same time without affecting each other. The multi-layer structure

144

M. Sechilariu et al. / Electrical Power and Energy Systems 58 (2014) 140–149

Supervision User demand

K L _ lim

Human-machine interface

Metadata

Wheather Load scheduling Weather forecast

Prediction layer

Smart grid messages

{

Load management data

Energy management layer Operation layer

p L _ prediction p PV _ prediction

Fig. 6. Prediction layer design.

Power system states

3.3. Energy management layer

Power system Fig. 4. Design principle of supervision.

simplifies implementation of such complex control strategy. Thus, the control of the power balancing is separated from energy management layer, yet they are linked through one interface parameter KD(t). On one hand, the energy management layer is able to optimize the microgrid energy cost through predictive data and thus obtain the predicted optimized power flow which is then translated into KD(t) sequence. On the other hand, the power balancing control in operation layer is an independent function that can work with any KD(t) value. The distribution coefficient KD(t) is a single interface parameter yet represents the power flow from different sources. Hence, the communication of KD(t) does not need high speed communication between layers.

3.1. Human–machine interface layer The HMI design is presented in Fig. 5. User can specify the lowest limit of the load coefficient, KL_lim. Aiming to maintain power balance, if the system requires KL < KL_lim, the operation is assumed with the necessary KL value, but the user will be notified.

3.2. Prediction layer

Energy management layer (Fig. 7) interacts with the prediction layer and controls the operation layer by calculating the distribution coefficient KD following an optimization method. The optimization goal is to obtain the best power distribution between the grid and the storage, so to reduce energy cost, grid power peak consumption, load shedding and limiting PV production at the same time. In this study, the smart grid message is supposed to provide real-time grid energy tariffs and grid power limits, which assist in reducing peak supply and avoid undesired injection. Furthermore, thanks to the smart grid connection, this layer is able to inform the grid operator of the grid supply power prediction pG_S_prediction and grid injection power prediction pG_I_prediction. The energy cost of system Ctotal consists of grid energy cost CG, storage energy cost CS, PV production limitation cost CPVL and load shedding cost CLS, as in Eq. (12).

C total ¼ C G þ C S þ C PVL þ C LS

By calculating the energy cost for each time duration Dt, CG is defined by Eq. (13). According to this definition, the grid power could be bought or sold.

CG ¼

tF X

1 6

3:6  10

½cG ðti Þ  Dt  ðpG I ðti Þ þ pG S ðt i ÞÞ

t i ¼t 0

This study takes into account the same price for energy purchased or sold, and the grid energy tariff is defined by Eq. (14).



cNH ¼ 0:1€=kW h for t 2 normal hoursðNHÞ cPH ¼ 0:7€=kW h for t 2 peak hoursðPHÞ

CS ¼

tF X

1 6

3:6  10

½cS ðt i Þ  Dt  ðpS C ðt i Þ þ pS D ðt i ÞÞ

ti ¼t 0

with t i ¼ ft0 ; t 0 þ Dt; t 0 þ 2Dt; . . . ; tF g and cs ðtÞ ¼ 0:05€=kW h ð15Þ

{ nvo

Load shedding limit

K L _lim Fig. 5. HMI layer design.

ð14Þ

Storage aging should be considered to give an energy tariff of storage using. For this study, the storage energy cost CS and an arbitrary storage energy tariff are defined in Eq. (15).

Smart grid messages

User demand

ð13Þ

with t i ¼ ft0 ; t 0 þ Dt; t 0 þ 2Dt; . . . ; tF g

cG ðtÞ ¼

The prediction layer design is presented in Fig. 6. It calculates possible PV power and load power evolutions for the next day. Based on solar irradiance and temperature forecasting, and on PV model, built with parameters identification or PV solar irradiance mapping [29,30], the PV predictable power pPV_prediction could be calculated with as less error as possible. The load power supply can be predicted by statistical data and/ or by information of load scheduling from building management system and with respect of user demand [31,32]. The load scheduling, that represents the operating program of building facilities, building energy needs linked to weather, and the partial and full load shedding program are supposed known, and they are implemented and updated continuously by the building management system. According to the value of KL_lim chosen by the user, the load prediction power pL_prediction can be calculated by this layer.

ð12Þ

Grid time-of-use tariffs

pG _ S _ lim , pG _ I _ lim

nvi nvi: network variable input nvo: network variable output

p L _ prediction p PV _ prediction

pG _ I _ prediction pG _ S _ prediction

}

Fig. 7. Energy management layer design.

⎧⎪ pG _ S _ lim ⎨ pG _ I _ lim ⎪⎩ K D

145

M. Sechilariu et al. / Electrical Power and Energy Systems 58 (2014) 140–149

The cost of PV system shedding and an arbitrary PV shedding tariff are defined in Eq. (16).

C PVL ¼

tF X

1 6

3:6  10

½cPVL ðt i Þ  Dt  ðpPV

MPPT ðt i Þ

 pPV

{

pG _ S _ lim , pG _ I _ lim KD

Power system

SOCmin , SOCmax

lim ðt i ÞÞ

p PV , p L , soc, pG , v

t i ¼t 0

with t i ¼ ft 0 ; t0 þ Dt; t 0 þ 2Dt; . . . ; t F g and cPVL ðtÞ ¼ 1€=kW h ð16Þ

* pG* , pS* , K L , pPV _ lim

The cost of load shedding and an arbitrary load shedding tariff are defined in Eq. (17).

C LS ¼

1

tF X

3:6  106 ti ¼t0

½cLS ðti Þ  Dt  ðpL D ðt i Þ  pL

lim ðt i ÞÞ

with t i ¼ ft 0 ; t0 þ Dt; t 0 þ 2Dt; . . . ; t F g and cLS ðtÞ ¼ 1€=kW h ð17Þ Aiming to limit the power grid fluctuations, grid power changing rate limits are introduced as:



pG ðti Þ  pG ðt i1 Þ 6 Limit pG ðti Þ  pG ðt i1 Þ P Limit

ð18Þ

with t i ¼ ft 0 ; t0 þ Dt; t 0 þ 2Dt; . . . ; t F g As PV energy grid injection benefits incentive tariffs, energy grid injection by power grid charged storage is forbidden. Thus, the following limits are imposed in order to ensure storage energy charge and grid injection only from PV production.

K L < K L _ lim ⇒ alert user

Fig. 8. Operation layer design.

ent types of optimization problems. However, any other mixed integer linear programming solver also can be used (LP_SOLVE, GUROBI, . . . ). In addition, in order to express our problem in the syntax of the solver and to call the solving algorithm of the solver, a procedure written in C++ is used. This procedure output to a file the optimal powers flow, which is the evolution of pS_D(t), pS_C(t), pG_S(t), pG_I(t). The estimated optimum powers flow is then translated into a control parameter that is the optimum distribution coefficient KD(t). Taking into account Eqs. (9) and (10), the optimum KD values are calculated by Eq. (21):

K D ðtÞ ¼

pS C ðtÞ  pS D ðtÞ pS C ðtÞ  pS D ðtÞ þ pG I ðtÞ  pG S ðtÞ

ð21Þ

3.4. Operation layer

pG ðti Þ P 0; pS ðt i Þ P 0 if pPV ðt i Þ  pL D ðt i Þ P 0 pG ðti Þ 6 0; pS ðt i Þ 6 0 if pPV ðt i Þ  pL D ðti Þ < 0 with t i ¼ ft 0 ; t0 þ Dt; t 0 þ 2Dt; . . . ; t F g

ð19Þ

Finally, by considering the discrete time instant ti, from t0 to tF, with the time interval Dt, the optimization problem can be completely mathematically expressed by (20):

Minimize C total ¼ C G þ C S þ C PVL þ C LS with respect to : 8 pL ðti Þ þ pG I ðt i Þ þ pS C ðti Þ ¼ pG S ðti Þ þ pS D ðt i Þ þ pPV ðt i Þ > > > > > SOC min 6 socðt i Þ 6 SOC max > > > > tF > X > > > ðpS C ðti Þ  pS D ðt i ÞÞDt > socðt i Þ ¼ SOC 0 þ 3600v1S C REF > > > ti ¼t0 > > > > > < 0 6 pG I ðt i Þ 6 pG I lim 0 6 pG S ðt i Þ 6 pG S lim > > > > p ðt i Þ  pG ðt i1 Þ 6 Limit > > > G > > pG ðt i Þ  pG ðt i1 Þ P Limit > > > > > pG ðt i Þ P 0; pS ðt i Þ P 0 if pPV ðt i Þ  pL D ðti Þ P 0 > > > > > > pG ðt i Þ < 0; pS ðt i Þ < 0 if pPV ðti Þ  pL D ðt i Þ < 0 > > : pPV MPPT ðtÞ  pPV lim ðtÞ ¼ 0 if socðtÞ < SOC max

According to the energy management layer output (KD(t) and grid power limits), the operation layer presented in Fig. 8 aims at balancing power in the power system while meeting all constraints. If there is not sufficient power to supply the load (insufficient PV production when the grid power is limited and the storage is empty), the load shedding is performed. In such cases, the load power has to be limited to pL_lim, which is calculated as in Eq. (22).

pL

ð20Þ

ti ¼ ft 0 ; t0 þ Dt; t 0 þ 2Dt; . . . ; t F g Energy optimization are usually solved by linear programming [21] or dynamic programming [22,33] technique. Dynamic programming can solve non-linear problem, while linear programming solves problems satisfying linear forms. Linear programming can be more efficiently solved with less time and memories. It can be verified that, mathematical formulation given by Eq. (20) follows standard linear programming form, except for the last constraint which does not take a linear form. However, this constraint can be easily linearized by introducing for each time point ti, a variable array which is integer; this is why the optimization formulation is a mixed integer linear program [34]. In this study, the optimization problem is solved using the IBM ILOG CPLEX solver [35], which is a powerful tool for solving differ-

lim

¼ pPV þ pG

S lim

ð22Þ

The system compares the actual load power pL with the load power limit pL_lim; if pL > pL_lim the load would be shed within the limit level. When the storage is full and the grid injection limit does not permit absorbing all excess of PV production, the PV limited production is performed by calculating pPV lim :

pPV

lim

¼ pL þ pG

I lim

ð23Þ

To sum up the above described power balancing, and taking into consideration the limits of storage and grid given in Eqs. (1), (3), (4), and (5), the overall algorithm of the operation layer is shown in Fig. 9. Experiment validation of the technical feasibility of the operation layer for a constant value of KD is provided in [15]. As the prediction layer requires available and reliable forecast data, for the PV sources geolocation and for few hours ahead, in this study, the optimized DC microgrid is tested by simulation. 4. Simulation results The microgrid control is simulated for an operation on 23rd of April 2011 in Compiegne, France. The objective of this study is more to validate a comprehensive approach rather than purely numerical results. For this reason we do not give the numerical values of various system components studied, yet powers values are based on our experimental multisource system platform [12,15,36]. The simulation results of the DC microgrid were obtained under the MATLAB-Simulink. All the implemented auto-

146

M. Sechilariu et al. / Electrical Power and Energy Systems 58 (2014) 140–149

Read from the energy management layer: K D , pG _ S _ lim , pG _ I _ lim Read from the power system: v, pG , pPV , pL , soc, SOCmin , SOCmax p* = pPV − pL − CP (v* − v) Yes

Yes

No

soc ≤ SOCmin

pS* = 0

No

p* ≤ 0

Yes

pS* = 0

pS* = K D ⋅ p*

pG* = p* − pS* with pG* _ S = − pG*

Yes * G_S

p

pG* _ S ≥ pG _ S _ lim

No

soc ≥ SOCmax

pS* = K D ⋅ p*

pG* = p* − pS* with pG* _ I = pG*

No

Yes

No

pG* _ I ≥ pG _ I _ lim

p = pG _ I _ lim

= pG _ S _ lim

* G

Update load shedding control

Yes

soc ≤ SOCmin No

pL _ lim = pPV + pG _ S _ lim

Yes

soc ≥ SOCmax K L = pL _ lim pL _ max

Update PV limited power reference

No * pPV _ lim = pPV _ MPPT

* pPV _ lim = pL + pG _ I _ lim

* pPV _ lim = pPV _ MPPT

KL = 1

KL = 1

Fig. 9. Flowchart of operation layer control.

1500

(a)

P PV_MPPT_measurement

P PV_hourly average_

(W)

1000

0 9:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

18:00

Time 1500

(b)

PPV_measurement MeasurePPV_prediction Prediction

(W)

1000 PV

Nowadays services can be found providing worldwide PV power forecast according to location and weather information. However, public data of solar irradiance (W/m2) forecasting are not yet precise enough for a specified location. This is why is very difficult to calculate the PV power prediction following real solar irradiance forecasting. The measured PV MPPT power, whose values have been recorded by our experimental device, is shown in Fig. 10a: the green curve shows the real time PV power evolution, while the gray bars are the hourly average PV power. As this paper aims at demonstrating feasibility of the designed supervision, to overcome the lack of real solar irradiance forecasting, the PV power prediction data are calculated from the real measurement data. PV power hourly prediction data given in Fig. 10b are assumed having random ±10% error with the hourly average of the measurement data. Load prediction data are supposed to be given by load management system, which implies additional uncertainties. In this study, a simple arbitrary load power evolution is considered. The difference of load power and load power prediction is shown in Fig. 11. In order to perform an optimized operation for the next day, prediction layer is supposed to give to the energy management layer the forecasts of the PV power and the load power hourly evolutions. Peak hours during the day are assumed 11:00–13:00 and 16:00–18:00. Grid and storage constraints, as arbitrary values,

500

p

4.1. Optimization results

P

PV

matic controls, described earlier, are working satisfactorily. The proportional controller, whose proportional gain CP is used in Eq. (8), provides a wide control bandwidth adapting to simulation step. However, synthesis for parameter tuning is not realized in this work.

500

0 9:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

18:00

Time Fig. 10. PV MPPT power evolution (a), hourly PV power measurement and prediction (b).

are imposed for the system operation. To mitigate grid power strong fluctuations, grid power changing limit is imposed as 20 W/s. Arbitrary soc limits are considered as 45% and 55%, while SOC0 = 50% and CREF = 130 Ah. Considering the storage capacity of

147

M. Sechilariu et al. / Electrical Power and Energy Systems 58 (2014) 140–149 1500

age has to be proposed to supply the load. During the peak hours the soc decreases continually, as shown in Fig. 12b. Hence, the storage is used to supply the load as much as possible with respect to its soc lower limit. However, as the storage energy is not enough, the grid power is also used to supply the load. This is why, during peak hours, storage power and grid power are proposed to share the necessary power to supply the load in an optimized manner while respecting all constraints. This sharing power is proposed in an intermittent manner by the used solver.

PL_measurement PL_prediction Measure Prediction

(W)

1000

500

0 9:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

4.2. Powers flow simulation controlled by KD(t) optimum evolution

18:00

Time Fig. 11. Load power measurement and day-ahead prediction.

our experimental platform, whose parameters values are used in the simulation study, these soc limits are selected to show the system behavior with relevant storage events (full, empty) in a day run. Grid power injection limit is imposed as 700 W, and supply limit as 600 W. Based on the prediction information, the energy management layer calculates the optimization problem by CPLEX and gives the optimum powers flow evolution, as presented in Fig. 12a. Corresponding KD(t) sequence is calculated by Eq. (21) from the optimum powers flow evolution, as presented in Fig. 12b. For performing a day optimization during 9 h, the data resolution is chosen at 10 s/point, i.e. 3240 points each power curve. The optimization program execution time is within 10 s for a computer with CORE i5 processor. The optimum cost estimated by CPLEX is 0.517 €. Fig. 12 shows the optimization calculation results. It can be observed that for peak hours the PV production is not sufficient to supply the load; so, the storage and the grid have to supply the load for the remain part of power. The considered optimization problem is formulated to minimize the global energy cost for the whole period from 9:00 to 18:00, while respecting all constraints. So, as for the peak hours the grid energy tariff is very high and largely superior to storage energy tariff, it seems normal that the stor-

The calculated KD(t) optimum evolution is given to the operation layer to run the power system following the conditions given by 23rd of April 2011 (meteorological and load). The operation powers flow, as real situation, simulated by MATLAB, is shown in Fig. 13a. During this day operation, grid and storage share power for supplying energy or for receiving energy at the same time. In the first off-peak hours (9:00–11:00), grid mainly supplies the load for reserving storage for peak hour supply. During the first peak hours (11:00–13:00), the load is supplied by storage and grid, the sharing proportion is determined by optimization calculation which aims also to reserve storage for supplying during second period of peak hours. Just before 13:00 the surplus of the PV production is injected into the grid in order to make the maximum profit. Aiming to reduce the energy cost by avoiding grid to supply during peak hours, in the second peak hour period (16:00–18:00), the storage is mainly used for supplying the load. During 13:00–15:00 with the excess PV production, the storage is charged for supplying in the second peak hour. Grid power injection limit and supply limit are respected. Short time load shedding can be seen in the operation after 17:00, when the battery is empty. The load shedding is performed based on instantaneous power information. To avoid load shedding fluctuations in PV power fluctuating circumstances, it is also possible to impose duration for load shedding in CPLEX optimization, and optimized load shedding information could be

2000

2000

(a)

pPV

pG_I

pG_S

pS_C

pS_D

pPV_MPPT

pL

(a)

1500

Peak hours

Power (W)

1500

1000

pL_D

pPV_MPPT

pG_I

pG_S

pS_C

pS_D

pPV

pL

Peak hours

1000

500

500 0 9:00

0

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

16:00

56 soc 55 54 53 52 51 50 49 48 47 46 45 44 17:00 18:00

(b)

10:00

12:00

13:00

14:00

15:00

12:00

13:00

14:00

15:00

430 425 420 415 410 405 400 395 390 385 380 375 370 9:00

(b)

(V)

KD

11:00

11:00

16:00

17:00

18:00

Time

Time

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 9:00

10:00

18:00

10:00

DC bus voltage

11:00

12:00

13:00

14:00

15:00

16:00

17:00

56 55 54 53 52 51 50 49 48 47 46 45 44 18:00

soc

%

9:00

%

Power (W)

pL_D

Time

Time Fig. 12. Optimized powers flow (a), optimized KD(t) and soc evolution (b).

Fig. 13. Simulated powers flow (a), DC bus voltage and soc evolution (b) for optimum KD(t).

148

M. Sechilariu et al. / Electrical Power and Energy Systems 58 (2014) 140–149

given to operation layer to override the operation layer load shedding control. The energy cost is 0.512 €, which is close to the optimization cost. The soc evolution with the optimum KD(t) and the DC bus voltage are illustrated in Fig. 13b. The DC bus voltage fluctuations are negligible compared to the value of 400 V, signifying the power is well balanced. By comparing the results presented in Figs. 12 and 13, it can be seen that the simulated powers flow is slightly different from the optimization due to the uncertainties of solar irradiance prediction and load power prediction. During the solar fluctuations between 16:00 and 17:00, the storage provided more powers. However, in this case, the storage is still able to be the main load supply during the second period of peak hours, but a slight load shedding occurs when the soc reaches its low limit. 4.3. Powers flow simulation controlled by constant KD In order to further analyze, a simulation case for a constant KD is presented in Fig. 14. It is chosen as constant value KD = 0.5885 which is the average value of optimum KD(t) evolution shown in Fig. 12b. In this case, the obtained energy cost, 0.652 €, the difference with optimization is larger compared with using optimal KD(t), and longer load shedding can be seen during this operation. The soc evolution, illustrated in Fig. 14b, is very different from the optimum soc evolution shown in Fig. 13b. Even optimization effect is affected, the power balancing is robust. Regarding the DC bus voltage illustrated in Fig. 14b, it can be seen that the DC bus voltage remains stable with very slight fluctuations, signifying the power is well balanced. 4.4. Simulation results: comparison and discussion Table 1 shows the energy cost of the microgrid Ctotal given by Eq. (12) and occurrences of load shedding for these three cases: optimized operation by energy management layer with ±10% uncertainties prediction data, simulated operation in case of a real PV production with the calculated optimum KD(t), and simulated

2000

(a)

Power (W)

1500

pL_D

pPV_MPPT

pG_I

pG_S

pS_C

pS_D

pPV

11:00

12:00

13:00

14:00

15:00

16:00

17:00

18:00

Time

(V)

(b)

10:00

DC bus voltage

11:00

12:00

13:00

14:00

15:00

16:00

17:00

56 55 54 53 52 51 50 49 48 47 46 45 44 18:00

soc

%

430 425 420 415 410 405 400 395 390 385 380 375 370 9:00

KD

Ctotal cost (€)

Load shedding duration

Optimization

Optimum KD(t) as given in Fig. 12b Optimum KD(t) as given in Fig. 12b KD = 0.5885

0.517

No shedding

0.512

7 min

0.652

37 min

Simulation Simulation

operation with constant KD = 0.5885. It can be seen that simulated cost for the optimum KD(t) is close to the prediction cost, and the error is within 1%, which is from the uncertainties of prediction. For the constant KD case, the cost is 26% more than the prediction case; moreover, longer load shedding can be seen in this case. Even with some uncertainties, the optimized KD(t) operates the microgrid with respect of the utility grid requirements and storage capacity. This comparison validates the presented simulation case for the proposed supervision of the proposed DC microgrid. As previously mentioned, the communication of KD(t) does not need high speed communication between layers. So, the supervision control provides the possibility of re-performing optimization and updating the KD(t) sequence during the actual operation without interrupting the power balancing. Thus, hourly or more frequent optimization that updates KD(t) sequence, with latest prediction and power system status, is expected to give better energy performance of the supervision control. However, the limit of the supervision control is that optimizing effectiveness is affected on the prediction precision. Predictions uncertainties do not influence power balancing but the optimal energy cost is affected. Future research should focus on enhancing optimization performance, especially facing low prediction precision. An optimization technique that able to optimize power flow with consideration on uncertainties of the prediction combined with a rule based algorithm in operation layer that corrects KD(t) in real time with respect of power system status can be developed as one solution. Besides, a second storage can be installed as backup for correcting the errors between optimized power and real operation. 5. Conclusions

Peak hours

500

10:00

Case operation

pL

1000

0 9:00

Table 1 Comparison of different cases.

Time Fig. 14. Simulated powers flow (a), DC bus voltage and soc evolution (b) for constant KD = 0.5885.

Facing the advent of smart grid context, a microgrid control combining power balancing, optimization and smart grid interaction is proposed through a multi-layer supervision control structure. The research issue of implementing optimization in realtime operation is particularly addressed. Based on PV sources, storage, power grid connection and DC load, the microgrid aims at self supply with limited access to grid. Taking into account forecast of PV production and load power demand, the four-layer supervision system performs optimization and implements optimization in instantaneous power balancing through simple interface. It handles also constraints such as storage capability, grid power limitations, energy grid tariffs, grid peak hours. The optimization is based on mixed integer linear programming, solved by CPLEX. Simulation results, even with uncertainties of prediction and arbitrary energy tariffs, taken as assumptions in this study, show that the proposed supervision design is able to perform efficiency and cost effective powers flow in real-time operation with respect to constraints such as grid power limits and storage capacity. Load shedding and PV power limiting ensures power balancing in any case. The simulation shows that the optimization gives better energy performance while minimizing load shedding and PV production limitation and the operation layer respects all constrains of power system elements.

M. Sechilariu et al. / Electrical Power and Energy Systems 58 (2014) 140–149

On the other hand, the optimization efficiency is based on prediction precision, which may limit the final performance. The designed operation layer can work with any KD value, so the prediction errors and non-optimum KD does not affect the power balance. However, if uncertainties are higher than ±10%, the energy cost and the load shedding duration could be severely affected. The developed simulation will permit in further work to design an additional supervision layer aiming to mitigate the differences between the optimized powers flow and the real one. To sum up, the feasibility of the proposed DC microgrid supervision control structure, that combines grid interaction and energy management with power balancing, is proved by simulation results. Experimental test is going be carried out in real conditions once the PV power prediction service is ready. Although the microgrid only refers to a building scale and involves only a few sources, the idea of parameterize power balancing and interfacing with optimization, as well as smart grid interaction, can be generalized and thus can be used as solution for advanced energy management for other microgrids to optimize local power flow and improve future PV penetration. References [1] Lee TL, Hu SH, Chan YH. D-STATCOM with positive-sequence admittance and negative-sequence conductance to mitigate voltage fluctuations in high-level penetration of distributed-generation systems. IEEE Trans Ind Electron 2013;60(4):1417–28. [2] Wei-Lin H, Chia-Hung L, Chao-Shun C, Hsu CT, Te-Tien K, Cheng-Ta T, et al. Impact of PV generation to voltage variation and power losses of distribution systems. In: Proc 4th international conference on electric utility deregulation and restructuring and power technologies; 2011. p. 1474–8. [3] Liserre M, Sauter T, Hung JY. Future energy systems, integrating renewable energy sources into the smart power grid through industrial electronics. IEEE Ind Electron Mag 2010;4(1):18–37. [4] Peeters E, Belhomme R, Batlle C, Bouffard F, Karkkainen S, Six D, et al. ADDRESS: scenarios and architecture for active demand development in the smart grid of the future. In: Proc of CIRED 20th international conference on electricity distribution; 2009. p. 1–4. [5] Sechilariu M, Wang BC, Locment F. Building-integrated microgrid: advanced local energy management for forthcoming smart power grid communication. Energy Build 2013;59(1):236–43. [6] Lasseter RH, Eto JH, Schenkman B, Stevens J, Vollkommer H, Klapp D, et al. CERTS microgrid laboratory test bed. IEEE Trans Power Delivery 2010;26(1):2531–40. [7] Hatziargyriou N, Asano H, Iravani R, Marnay C. Microgrids. IEEE Power Energy Mag 2007:78–94. [8] Guerrero JM, Chandorkar M, Lee TL, Loh PC. Advanced control architectures for intelligent microgrids—Part I: decentralized and hierarchical control. IEEE Trans Ind Electron 2013;60(4):1607–18. [9] Georgilakis PS. Integration of Distributed Generation in the Power System, M. Bollen, F. Hassan. Wiley–IEEE Press, New Jersey (2011). Int J Electr Power Energy Syst 2013;48:69–70. [10] Alvarez E, Campos AM, Arboleya P, Gutiérrez AJ. Microgrid management with a quick response optimization algorithm for active power. Int J Electr Power Energy Syst 2012;43(1):465–73. [11] Lasseter RH. Smart distribution: coupled microgrids. Proc IEEE 2011;99:1074–82. [12] Sechilariu M, Wang BC, Locment F. Building integrated photovoltaic system with energy storage and smart grid communication. IEEE Trans Ind Electron 2013;60(4):1607–18.

149

[13] Guerrero JM, Vasquez JC, Matas J, de Vicuna LG, Castilla M. Hierarchical control of droop-controlled AC and DC microgrids—a general approach toward standardization. IEEE Trans Ind Electron 2011;58(1):158–72. [14] Shenai K, Shah K. Smart DC micro-grid for efficient utilization of distributed renewable energy. In: Proc of IEEE Energytech; 2011. p. 1–6. [15] Wang BC, Sechilariu M, Locment F. Intelligent DC microgrid with smart grid communications: control strategy consideration and design. IEEE Trans Smart Grid 2012;3(4):2148–56. [16] AlLee G, Tschudi W. Edison Redux: 380 Vdc brings reliability and efficiency to sustainable data centers. IEEE Power Energy Mag 2012;10:50–9. [17] Patterson BT. DC, come home: DC microgrids and the birth of the ‘‘Enernet’’. IEEE Power Energy Mag 2012;10:60–9. [18] Sucic S, Havelka JG, Dragicevic T. A device-level service-oriented middleware platform for self-manageable DC microgrid applications utilizing semanticenabled distributed energy resources. Int J Electr Power Energy Syst 2014;54:576–88. [19] Kumar Nunna HSVS, Doolla S. Multiagent-based distributed-energy-resource management for intelligent microgrids. IEEE Trans Ind Electron 2013;60(1):1678–87. [20] Chaouachi A, Kamel RM, Andoulsi R, Nagasaka K. Multiobjective intelligent energy management for a microgrid. IEEE Trans Ind Electron 2013;60(1):1688–99. [21] Chakraborty S, Weis MD, Simoes MG. Distributed intelligent energy management system for a single-phase high-frequency AC microgrid. IEEE Trans Ind Electron 2007;54:97–109. [22] Riffonneau Y, Bacha S, Barruel F, Ploix S. Optimal power flow management for grid connected PV systems with batteries. IEEE Trans Sustain Energy 2011;2(3):325–32. [23] Bo G, Mills JK, Dong S. Energy management control of microturbine-powered plug-in hybrid electric vehicles using the telemetry equivalent consumption minimization strategy. IEEE Trans Vehicular Technol 2011;60:4238–48. [24] Bustos C, Watts D, Ren H. MicroGrid operation and design optimization with synthetic wind and solar resources. IEEE Trans Latin America 2012;10:1550–62. [25] Houssamo I, Locment F, Sechilariu M. Maximum power tracking for photovoltaic power system: development and experimental comparison of two algorithms. Renew Energy 2010;35(10):2381–7. [26] Houssamo I, Locment F, Sechilariu M. Experimental analysis of impact of MPPT methods on energy efficiency for photovoltaic power systems. Int J Electr Power Energy Syst 2013;46:98–107. [27] Wang BC, Houssamo I, Sechilariu M, Locment F. A simple PV constrained production control strategy. In: Proc of IEEE international symposium on industrial electronics; 2012. p. 969–74. [28] Tan X, Li Q, Wang H. Advances and trends of energy storage technology in microgrid. Int J Electr Power Energy Syst 2013;44(1):179–91. [29] Houssamo I, Wang BC, Sechilariu M, Locment F, Friedrich G. A simple experimental prediction model of photovoltaic power for DC microgrid. In: Proc of IEEE international symposium on industrial electronics; 2012. p. 963–8. [30] Lorenz E, Hurka J, Heinemann D, Beyer HG. Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J Select Topics Appl Earth Observ Remote Sens 2009;2:2–10. [31] Amjady N, Keynia F, Zareipour H. Short-term load forecast of microgrids by a new bilevel prediction strategy. IEEE Trans Smart Grid 2010;1:286–94. [32] Ren P, Xiang Z, Qiu Z. Intelligent domestic electricity management system based on analog-distributed hierarchy. Int J Electr Power Energy Syst 2013;46:400–4. [33] Fuselli D, De Angelis F, Boaro M, Squartini S, Wei Q, Liu D, et al. Action dependent heuristic dynamic programming for home energy resource scheduling. Int J Electr Power Energy Syst 2013;48:148–60. [34] Franco JF, Rider MJ, Lavorato M, Romero R. A mixed-integer LP model for the optimal allocation of voltage regulators and capacitors in radial distribution systems. Int J Electr Power Energy Syst 2013;48:123–30. [35] IBM.com. IBM ILOG CPLEX Optimizer. . [36] Locment F, Sechilariu M, Houssamo I. DC load and batteries control limitations for photovoltaic systems. Experimental validation. IEEE Trans Power Electron 2012;27(9):4030–8.