- Email: [email protected]
Optimization of energy management of a photovoltaic system by the fuzzy logic technique
Available online at www.sciencedirect.com
Energy Procedia 6 (2011) 513–521
MEDGREEN 2011-LB
Optimization of energy management of a photovoltaic system by the fuzzy logic technique F. Chekired*, A. Mahrane, M. Chikh, Z. Smara Unité de développement des équipements solaires (UDES), route nationale N°11, BP386, Bou Ismail, 42415, Tipaza, Algerie.
Abstract The efficiency of a photovoltaic s ystem depends main ly on its energy management wh ich takes in charge the storage and the distribution of the energy produced by the photovoltaic system in order to feed the load and to avoid any shortage of energy. Our pro ject concerns the energy management of a Stand Alone Photovoltaic System. Th is is done by elaborating an algorith m based on the fuzzy logic technique which allo ws us to optimize the management of the storage system, ensuring a longer battery life, and the energy distribution available fro m the photovoltaic array and the batteries. It appears from the first results obtained that the fuzzy logic control maintains the battery voltage almost stable at the end phase of charge. Key words: Stand-Alone Photovoltaic Systems (SAPVS), Energy Management, Battery, Fuzzy Logic, Fuzzy Controller.
1.
Introduction
Climate change and oil shortage have prompted the ever-growing awareness about the need to use non pollutant energy. This favourable economic background conducted to an impressive growth rate of the photovoltaic industry in the last decade and it is expected to continue in the upcoming years. In this context, the nu mber of PV installations increases year after year. As the in itial cost is high, the users need to be ensured that the PV system installed will be reliable and energetically efficient [1]. The mastering of the performance of a Stand Alone Photovoltaic System (SAPVS) is done through the control and management of the energy of the system. The energy management of a PV system must both allow to: Produce a maximu m power fro m the photovoltaic generator, Protect the battery against overcharge and deep discharge. Satisfy the energy needs of the user by avoiding energy shortage.
* Corresponding author. Tel.:+213777248599; fax: +213240133. E-mail address: [email protected]
1876–6102 © 2011 Published by Elsevier Ltd. doi:10.1016/j.egypro.2011.05.059
514
F. Chekired et al. / Energy Procedia 6 (2011) 513–521
We aim by our study to design and implement an energy management algorithm for SAPVS. After the modelizat ion of the storage stage, we introduce a new technique, namely fu zzy logic which we use to design a fu zzy controller for batteries. That will allow us to optimize the management of the storage system ensuring a longer battery life and min imizing the potential malfunction of the elements constituting the photovoltaic system. 2.
Energy management for Stand Al one Photovoltaic System (SAPVS)
A SAPVS consists of a photovoltaic generator composed of one or more solar panels, a set of batteries for electric energy storage, one or more DC-DC converters to provide the appropriate supply voltages for the batteries and continuous loads and a DC-AC converter for powering other AC devices [2], [3]. Continu Charge Continu Controller =/
Solar Panneau Panels solaire K1
DC-AC Continu Alternati v Converter ==|"
K2
AC LOAD Charge DC Continue LOAD
Batteries Batterie
Fig. 1 A Stand Alone Photovoltaic System configuration
Due to the non-availability of permanent solar energy, for various reasons: weather, t ime of day, season etc..., the use of batteries as buffer of energy is required to guarantee continuous supply energy. In order to avoid any energy shortage and ensure a longer battery life, an energy management strategy is essential in a SAPVS. 3.
Energy storage for SAPVS
3. 1. Electrical model of battery For the storage stage of photovoltaic system, both types of electrochemical storage battery as leadacid (Pb acid) and nickel-cad miu m (Ni-Cd ) batteries are co mmonly used. Thanks to their sturdiness and stability, the lead-acid batteries are the most used in PV installations [4]. In order to study the operation of the battery, we have used a battery model shown on the figure 2 wh ich is main ly based on: The relation between the state of charge (SOC) and the charging current (IB) [4, 5]. The variation of the voltage (VB ) according to the current and the state of charge (SOC) [4]. The variation of the capacity (CB) versus the current [4]. The models used for each stage of the battery model (figure 2) are described by the following mathematical equations: The capacitor model:
C B (t )
³ tt
0
I C (t )dt C B ,i
(1)
CB (t) is the capacitor of the battery in (Ah) at a time t, CB,i the initial capacitor o f the battery in (Ah). Where:
515
F. Chekired et al. / Energy Procedia 6 (2011) 513–521 gy
I C (t )
I B (t ) I GAZ (t )
(2)
Ic is the current of the capacitor, IB the current of the battery (A) and IGAS is the gasification current of the battery (A). The gasification current model: I GAZ (t )
C10 u I GO u e[ CV (VELE ( t ) 2.23) CT (TB ( t ) 20 )] 100 Ah
(3)
Where C10 is the battery capacity at the rate of ten hours of discharge (Ah), IGO the standard gasification current (A), CV the voltage coefficient (V), VELE the battery element voltage (V). TC : temperature Coefficient (K). TB : Battery temperature (K). The State of Charge (SOC) model: The state of charge of a battery (SOC) is described by the mathemat ical equation: SOC (t )
C B (t ) u 100% C10
(4)
The voltage model: The charging voltage of the battery is described by the following equation:
VB (t )
E B (t ) RO,C u I C (t )
(5)
EB is the internal voltage of the battery [6, 7], RO, C is the internal resistance of charging phase The discharging voltage of the battery expression is:
VB (t )
E B (t ) RO, D u I C (t )
RO, D is the internal resistance of discharging phase [6, 7]. 3. 2. Simulation of the proposed model
Fig. 2 (a) Block diagram of the battery model [6], (b) Battery model under Simulink –M atlab environment.
(6)
516
F. Chekired et al. / Energy Procedia 6 (2011) 513–521
Using the mathemat ical exp ressions related to each stage of the battery model, we have built the battery simu lator in the Matlab-Simu link environment. This battery simulator allo ws us to study the charging and the discharging state of the Lead-acid battery used for storage in the case treated depending on the variation of the different parameters as the current, the voltage and the SOC of the battery. We have investigated the behaviour of the battery in two situations ‘the state charge’ and ‘the discharge state’ for a given fixed values of the battery current and for a constant temperature to 25o C. In the case of the ‘charge status’, the battery current takes the positive values of 0.5, 3, 5 and 9A, for the case of ‘discharge status’, the battery current takes the negative values of -0.5, -3, -5 and -9A. The temperature is fixed to 25O C in both cases. The figure. 3. (a) and figure 3. (b) show the influence of the current battery value on the battery SOC. 30
110
100
25 90
20
IB = 5 A
70
SOC(%)
SOC (%)
IB = 0.5 A
IB = 3 A
80
IB = 9 A
60
50
IB = - 0.5 A 15
IB = - 3 A IB = - 5 A
10
IB = - 9 A
40
5 30
20 0
50
100
150
200
250
Time (s)
0 0
50
100
150
200
250
Time (S) Fig. 3 (a) The battery SOC behaviour during the charging state; (b) The battery SOC behaviour during the discharging state.
We notice that the higher is the current for charging the battery the s maller is the time of charg ing. Thus, for a current of 9A the battery is charging in 20s while it takes 195s for a current of 0.5 A. We obtain for the ‘discharge status’ similar curves for the SOC of the battery than those we got for the ‘charge status’ case except that these curves decrease with time. It should be noted that the depth of discharge was set to 70% as shown as the figure. 3. (b) 4.
Battery Fuzzy Controller
4. 1. Introduction to the Fuzzy Logic technique Several techniques are availab le for the imp lementation of the energy management algorith m [1]. Among them we have chosen the fuzzy logic technique which has low power d issipation, an optimized cost, is reliable and stable. Fuzzy systems (FS) are based on fuzzy set theory and associated techniques pioneered by Lotfi Zadeh [9]. It is a non-linear control method, wh ich attempts to apply the expert knowledge of an expe rienced user to the design of a fuzzy-based controller. Generally, as shown in figure 4, the Fu zzy Logic Controller (FLC) is co mposed by four main co mponents: a) The fuzzifier that maps crisp values into input fuzzy sets to activate rules. b) The rules wh ich define the controller behavior by using a set of IF-THEN statements. c) The in ference engine wh ich maps input fuzzy sets into output fu zzy sets by applying the rules.
F. Chekired et al. / Energy Procedia 6 (2011) 513–521
d) The defuzzifier that maps output fuzzy values into crisp values.
Fig. 4 Fuzzy inference system
The rules describing the FLC operation are expressed as linguistic variables represented by fuzzy sets. The controller output is obtained by applying an inference mechanis m [9, 10]. 4. 2. Designing methodology of a battery Fuzzy controller As the storage stage of the SAPV, in other words the battery, is one of the most sensitive block of the PV system, we have focused, as a first step, by elaborating an energy management for a SAPVS, on the energy storage management and battery controller. We propose ‘an intelligent battery controller’ based on a fuzzy algorith m. The fuzzy controller wh ich uses the Mamdani's FLC approach [11, 12, 13] has two fuzzy inputs and two fu zzy outputs (fig. 5). The two inputs are the voltage battery VB and the change of the battery voltage ΔVB , wh ich are defined by: (7) 'VB (n) VB (n) VB (n 1) Where: n is the sampling time, and VB (n) is the instantaneous corresponding voltage. The output variables are the control signal of the switch K1 located between the photovoltaic generator and the battery and the control signal of the switch K2 located between the battery and the load (Fig. 1). After applying the rules, which corresponds to the fuzzification step, the method of the center of gravity is used for the defuzzication in order to find the actual values of K1 and K2 [11, 12, 13].
Fig. 5 Diagram of fuzzy controller for battery.
517
518
F. Chekired et al. / Energy Procedia 6 (2011) 513–521
To imp rove this fuzzy algorith m, we will insert in one hand VR the required voltage for connecting the load while the battery is charging, on the other hand, VF the overcharging voltage for disconnecting the PV array while the battery is discharging to the load. The battery voltage VB varies between 10.7 V and 14 V. The linguistic variables considered are: BUD for battery under-discharged, BD battery discharged BIC battery in charge, BC battery charged and BOC battery over-charged. The variation of the battery voltage ΔVB varies in the range of -50V to +50V. The linguistic variables associated to ΔVB are: NC for negative change, ZC for zero change and PC for positive change. The range of the K1 output variable is (0 to 4) and its linguistic variables are chosen as K1 Off , K1 Off temporary, K1 On and K1 On temporary. The range of the K2 output variable is (0 to 4) and its linguistic variables are identical to those of signal K1 . The battery voltage is always fluctuating. As the lead-acid batteries are very sensitive to the time of the load it is necessary to charge them with a current corresponding to a voltage VB wh ich is between two thresholds: the maximu m threshold (the gasification voltage) and the min imu m threshold (the sulphating voltage). In our work, we used a battery of 12V [6]. Figures 6 and 7 show the membership functions of the two inputs VB , ΔVB and the two outputs K1 , K2 for the battery fuzzy controller.
Fig. 6 (a) M embership functions of input variable VB; (b) M embership functions of input variable Δ VB.
Fig. 7 (a) M embership functions of output variable K1; (b) M embership functions of output variable K2.
519
F. Chekired et al. / Energy Procedia 6 (2011) 513–521 gy
Table 1 shows the rule table of fu zzy controller where the inputs of the matrix are fuzzy sets of voltage (VB) and the change of the voltage (ΔVB ). The output of this ru les table is the state of t wo switches K1 and K2 . Table.1. Fuzzy inference of fuzzy controller inputs /outputs.
VB
BDD
ΔVB NC
ZC
PC
BD
BIC
BC
BOC
K1
On
On
On
On_temp
Off
K2
Off
Off_temp
On
On
On
K1
On
On
On
On
Off
K2
Off
Off
On
On
K1
On
On
On
Off_temp
Off
K2
Off
Off_temp
On
On
On
On
In order to understand how to use this table, we give the following examp le: If we assume that the battery is deeply discharged (BDD) and the change of voltage is negative (NC) then the switch K1 will be off and K2 On. 5.
Simulati on and results
5. 1. Implementation of the battery fuzzy controller in a Stand Alone Photovoltaic System For a given photovoltaic system, taking into account the conditions of the irradiance, the temperature and the SOC o f the battery, the FLC orders the opening or the closing of both switches K1 and K2 such that to ensure an optimal management and distribution of the energy available fro m the photovoltaic field and the battery. Figure. 8 presents the implementation of the algorithm translated in table .1 under Matlab/ Simu link.
Fig. 8 Simulink block of the battery fuzzy controller.
520
F. Chekired et al. / Energy Procedia 6 (2011) 513–521 gy
(
)
5. 2. Simulation of the battery fuzzy controller Figure 9 represents the states of both switches K1 and K2 after applying the battery fuzzy controller,
Signal K 1
Signal K 2
Fig. 9 States of the switches K1 and K2 controlled by the fuzzy controller.
Controlled voltage V B
Not controlled voltage V B
The figure 9 is a dynamical representation of the states of the K 1 and K2 switches in response to the variations of VB and Δ VB accord ing to the inference rules previously established and managed by the FLC in order to better regulate the battery voltage. The opening and closing of these switches controls the link between the generator and the battery and the link between the batt ery and the load.
Time (s)
Time (s)
Fig. 10 (a) Variation of the battery voltage not controlled; (b) Variation of the battery voltage set by the fuzzy algorithm.
In order to evaluate the FLC performance, we have simulated, in a first stage , the energy management of a SAPVS which uses the on/off technique for the management. As it is shown on the figure 10(a), the range of the battery voltage variation is between 12V and 14V.
F. Chekired et al. / Energy Procedia 6 (2011) 513–521 gy
The figure 10(b) shows the result obtained by inserting the FLC. The voltage delivered to the load at the end phase of charge is compris ed in a narro w range situated between 11.7V and 12.5V. As expected the FLC allo ws avoiding the successive overloads and deep discharge of the battery and then prevents any malfunction of the PV system. 6.
Conclusion
We have presented in this paper the first version of a photovoltaic energy management system based on Fuzzy Logic Control technique. Th is technique allows us to manage with accuracy the switching mode of K1 and K2 wh ich respectively control the battery charging mode and the feeding of the load. The transition time between the charging and discharging mode using this technique is very short comparing to the other techniques used in this field. The next step of our work, concerns the use of other battery models wh ich take into account several parameters like the efficiency of the battery, the State of Charge o f the battery lin ked to the battery voltage. 7.
Acknowledgement We would like to thank Mr S. Elmetnani (UDES) for his valuable advices on the writings of this article
8. [1]
References
A. Mahrane, M. Chikh, A. Chikouche. Energy management for stand alone PV systems. Jordan Journal of Mechanical and Industrial Engineering. Volume 4, Number 1, Jan. 2010. [2] K Sobaihi. Study and realization of voltage feedback MPPT converter. Memory of master. ENP, Algeria, 2003. [3] T Obeidi. Application of the genetic algorithms in the control of MPPT converter., Memory of master. ENP, Algeria 2006 [4] A Cherif, M Jraidi, A Dhouib. A battery ageing model using in stand-alone PV systems, power sources, 112; 49-53, 2002. [5] F. W. Anthony, Modelling and simulation of lead-acid batteries for photovoltaic systems, ph, D. Thesis, 1983. [6] B. Wichert, Control of Photovoltaic-Diesel Hybrid Energy Systems; Doctor of Philosophy of the Curtin University of Technology, April 2000. [7] Caisheng Wang, Modeling and control of hybrid wind/photovoltaic/ fuel cell distributed generation systems, Doctor of Philosophy in Engineering, Montana state university, Bozeman, July 2006. [8] T imothy, J. Ross. Fuzzy Logic with Engineering Applications, second ed. John Wiley & Sons Ltd., 2004. [9] A. Messai, A. Mellit, A. Guessoum, and S.A. Kalogirou, Maximum power point tracking using a GA optimized fuzzy logic controller and its FPGA implementation, Solar energy, 2010. [10] Jiménez, C.J., Sanchez Solano, S., Barriga, A. Hardware implementation of a general purpose fuzzy controller. IFSA’95, pp. 185–188, 1995. [11] F. Chekired, Implementation of MPPT neuro-fuzzy Controller on FPGA, Memory of Magister, ENP, Algeria 2008. [12] Larbes C., Aït Cheikh S. M., Obeidi T., Zerguerras A. Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system, Renewable Energy, 34: 2093-2100. 2009
521
Energy Procedia 6 (2011) 513–521
MEDGREEN 2011-LB
Optimization of energy management of a photovoltaic system by the fuzzy logic technique F. Chekired*, A. Mahrane, M. Chikh, Z. Smara Unité de développement des équipements solaires (UDES), route nationale N°11, BP386, Bou Ismail, 42415, Tipaza, Algerie.
Abstract The efficiency of a photovoltaic s ystem depends main ly on its energy management wh ich takes in charge the storage and the distribution of the energy produced by the photovoltaic system in order to feed the load and to avoid any shortage of energy. Our pro ject concerns the energy management of a Stand Alone Photovoltaic System. Th is is done by elaborating an algorith m based on the fuzzy logic technique which allo ws us to optimize the management of the storage system, ensuring a longer battery life, and the energy distribution available fro m the photovoltaic array and the batteries. It appears from the first results obtained that the fuzzy logic control maintains the battery voltage almost stable at the end phase of charge. Key words: Stand-Alone Photovoltaic Systems (SAPVS), Energy Management, Battery, Fuzzy Logic, Fuzzy Controller.
1.
Introduction
Climate change and oil shortage have prompted the ever-growing awareness about the need to use non pollutant energy. This favourable economic background conducted to an impressive growth rate of the photovoltaic industry in the last decade and it is expected to continue in the upcoming years. In this context, the nu mber of PV installations increases year after year. As the in itial cost is high, the users need to be ensured that the PV system installed will be reliable and energetically efficient [1]. The mastering of the performance of a Stand Alone Photovoltaic System (SAPVS) is done through the control and management of the energy of the system. The energy management of a PV system must both allow to: Produce a maximu m power fro m the photovoltaic generator, Protect the battery against overcharge and deep discharge. Satisfy the energy needs of the user by avoiding energy shortage.
* Corresponding author. Tel.:+213777248599; fax: +213240133. E-mail address: [email protected]
1876–6102 © 2011 Published by Elsevier Ltd. doi:10.1016/j.egypro.2011.05.059
514
F. Chekired et al. / Energy Procedia 6 (2011) 513–521
We aim by our study to design and implement an energy management algorithm for SAPVS. After the modelizat ion of the storage stage, we introduce a new technique, namely fu zzy logic which we use to design a fu zzy controller for batteries. That will allow us to optimize the management of the storage system ensuring a longer battery life and min imizing the potential malfunction of the elements constituting the photovoltaic system. 2.
Energy management for Stand Al one Photovoltaic System (SAPVS)
A SAPVS consists of a photovoltaic generator composed of one or more solar panels, a set of batteries for electric energy storage, one or more DC-DC converters to provide the appropriate supply voltages for the batteries and continuous loads and a DC-AC converter for powering other AC devices [2], [3]. Continu Charge Continu Controller =/
Solar Panneau Panels solaire K1
DC-AC Continu Alternati v Converter ==|"
K2
AC LOAD Charge DC Continue LOAD
Batteries Batterie
Fig. 1 A Stand Alone Photovoltaic System configuration
Due to the non-availability of permanent solar energy, for various reasons: weather, t ime of day, season etc..., the use of batteries as buffer of energy is required to guarantee continuous supply energy. In order to avoid any energy shortage and ensure a longer battery life, an energy management strategy is essential in a SAPVS. 3.
Energy storage for SAPVS
3. 1. Electrical model of battery For the storage stage of photovoltaic system, both types of electrochemical storage battery as leadacid (Pb acid) and nickel-cad miu m (Ni-Cd ) batteries are co mmonly used. Thanks to their sturdiness and stability, the lead-acid batteries are the most used in PV installations [4]. In order to study the operation of the battery, we have used a battery model shown on the figure 2 wh ich is main ly based on: The relation between the state of charge (SOC) and the charging current (IB) [4, 5]. The variation of the voltage (VB ) according to the current and the state of charge (SOC) [4]. The variation of the capacity (CB) versus the current [4]. The models used for each stage of the battery model (figure 2) are described by the following mathematical equations: The capacitor model:
C B (t )
³ tt
0
I C (t )dt C B ,i
(1)
CB (t) is the capacitor of the battery in (Ah) at a time t, CB,i the initial capacitor o f the battery in (Ah). Where:
515
F. Chekired et al. / Energy Procedia 6 (2011) 513–521 gy
I C (t )
I B (t ) I GAZ (t )
(2)
Ic is the current of the capacitor, IB the current of the battery (A) and IGAS is the gasification current of the battery (A). The gasification current model: I GAZ (t )
C10 u I GO u e[ CV (VELE ( t ) 2.23) CT (TB ( t ) 20 )] 100 Ah
(3)
Where C10 is the battery capacity at the rate of ten hours of discharge (Ah), IGO the standard gasification current (A), CV the voltage coefficient (V), VELE the battery element voltage (V). TC : temperature Coefficient (K). TB : Battery temperature (K). The State of Charge (SOC) model: The state of charge of a battery (SOC) is described by the mathemat ical equation: SOC (t )
C B (t ) u 100% C10
(4)
The voltage model: The charging voltage of the battery is described by the following equation:
VB (t )
E B (t ) RO,C u I C (t )
(5)
EB is the internal voltage of the battery [6, 7], RO, C is the internal resistance of charging phase The discharging voltage of the battery expression is:
VB (t )
E B (t ) RO, D u I C (t )
RO, D is the internal resistance of discharging phase [6, 7]. 3. 2. Simulation of the proposed model
Fig. 2 (a) Block diagram of the battery model [6], (b) Battery model under Simulink –M atlab environment.
(6)
516
F. Chekired et al. / Energy Procedia 6 (2011) 513–521
Using the mathemat ical exp ressions related to each stage of the battery model, we have built the battery simu lator in the Matlab-Simu link environment. This battery simulator allo ws us to study the charging and the discharging state of the Lead-acid battery used for storage in the case treated depending on the variation of the different parameters as the current, the voltage and the SOC of the battery. We have investigated the behaviour of the battery in two situations ‘the state charge’ and ‘the discharge state’ for a given fixed values of the battery current and for a constant temperature to 25o C. In the case of the ‘charge status’, the battery current takes the positive values of 0.5, 3, 5 and 9A, for the case of ‘discharge status’, the battery current takes the negative values of -0.5, -3, -5 and -9A. The temperature is fixed to 25O C in both cases. The figure. 3. (a) and figure 3. (b) show the influence of the current battery value on the battery SOC. 30
110
100
25 90
20
IB = 5 A
70
SOC(%)
SOC (%)
IB = 0.5 A
IB = 3 A
80
IB = 9 A
60
50
IB = - 0.5 A 15
IB = - 3 A IB = - 5 A
10
IB = - 9 A
40
5 30
20 0
50
100
150
200
250
Time (s)
0 0
50
100
150
200
250
Time (S) Fig. 3 (a) The battery SOC behaviour during the charging state; (b) The battery SOC behaviour during the discharging state.
We notice that the higher is the current for charging the battery the s maller is the time of charg ing. Thus, for a current of 9A the battery is charging in 20s while it takes 195s for a current of 0.5 A. We obtain for the ‘discharge status’ similar curves for the SOC of the battery than those we got for the ‘charge status’ case except that these curves decrease with time. It should be noted that the depth of discharge was set to 70% as shown as the figure. 3. (b) 4.
Battery Fuzzy Controller
4. 1. Introduction to the Fuzzy Logic technique Several techniques are availab le for the imp lementation of the energy management algorith m [1]. Among them we have chosen the fuzzy logic technique which has low power d issipation, an optimized cost, is reliable and stable. Fuzzy systems (FS) are based on fuzzy set theory and associated techniques pioneered by Lotfi Zadeh [9]. It is a non-linear control method, wh ich attempts to apply the expert knowledge of an expe rienced user to the design of a fuzzy-based controller. Generally, as shown in figure 4, the Fu zzy Logic Controller (FLC) is co mposed by four main co mponents: a) The fuzzifier that maps crisp values into input fuzzy sets to activate rules. b) The rules wh ich define the controller behavior by using a set of IF-THEN statements. c) The in ference engine wh ich maps input fuzzy sets into output fu zzy sets by applying the rules.
F. Chekired et al. / Energy Procedia 6 (2011) 513–521
d) The defuzzifier that maps output fuzzy values into crisp values.
Fig. 4 Fuzzy inference system
The rules describing the FLC operation are expressed as linguistic variables represented by fuzzy sets. The controller output is obtained by applying an inference mechanis m [9, 10]. 4. 2. Designing methodology of a battery Fuzzy controller As the storage stage of the SAPV, in other words the battery, is one of the most sensitive block of the PV system, we have focused, as a first step, by elaborating an energy management for a SAPVS, on the energy storage management and battery controller. We propose ‘an intelligent battery controller’ based on a fuzzy algorith m. The fuzzy controller wh ich uses the Mamdani's FLC approach [11, 12, 13] has two fuzzy inputs and two fu zzy outputs (fig. 5). The two inputs are the voltage battery VB and the change of the battery voltage ΔVB , wh ich are defined by: (7) 'VB (n) VB (n) VB (n 1) Where: n is the sampling time, and VB (n) is the instantaneous corresponding voltage. The output variables are the control signal of the switch K1 located between the photovoltaic generator and the battery and the control signal of the switch K2 located between the battery and the load (Fig. 1). After applying the rules, which corresponds to the fuzzification step, the method of the center of gravity is used for the defuzzication in order to find the actual values of K1 and K2 [11, 12, 13].
Fig. 5 Diagram of fuzzy controller for battery.
517
518
F. Chekired et al. / Energy Procedia 6 (2011) 513–521
To imp rove this fuzzy algorith m, we will insert in one hand VR the required voltage for connecting the load while the battery is charging, on the other hand, VF the overcharging voltage for disconnecting the PV array while the battery is discharging to the load. The battery voltage VB varies between 10.7 V and 14 V. The linguistic variables considered are: BUD for battery under-discharged, BD battery discharged BIC battery in charge, BC battery charged and BOC battery over-charged. The variation of the battery voltage ΔVB varies in the range of -50V to +50V. The linguistic variables associated to ΔVB are: NC for negative change, ZC for zero change and PC for positive change. The range of the K1 output variable is (0 to 4) and its linguistic variables are chosen as K1 Off , K1 Off temporary, K1 On and K1 On temporary. The range of the K2 output variable is (0 to 4) and its linguistic variables are identical to those of signal K1 . The battery voltage is always fluctuating. As the lead-acid batteries are very sensitive to the time of the load it is necessary to charge them with a current corresponding to a voltage VB wh ich is between two thresholds: the maximu m threshold (the gasification voltage) and the min imu m threshold (the sulphating voltage). In our work, we used a battery of 12V [6]. Figures 6 and 7 show the membership functions of the two inputs VB , ΔVB and the two outputs K1 , K2 for the battery fuzzy controller.
Fig. 6 (a) M embership functions of input variable VB; (b) M embership functions of input variable Δ VB.
Fig. 7 (a) M embership functions of output variable K1; (b) M embership functions of output variable K2.
519
F. Chekired et al. / Energy Procedia 6 (2011) 513–521 gy
Table 1 shows the rule table of fu zzy controller where the inputs of the matrix are fuzzy sets of voltage (VB) and the change of the voltage (ΔVB ). The output of this ru les table is the state of t wo switches K1 and K2 . Table.1. Fuzzy inference of fuzzy controller inputs /outputs.
VB
BDD
ΔVB NC
ZC
PC
BD
BIC
BC
BOC
K1
On
On
On
On_temp
Off
K2
Off
Off_temp
On
On
On
K1
On
On
On
On
Off
K2
Off
Off
On
On
K1
On
On
On
Off_temp
Off
K2
Off
Off_temp
On
On
On
On
In order to understand how to use this table, we give the following examp le: If we assume that the battery is deeply discharged (BDD) and the change of voltage is negative (NC) then the switch K1 will be off and K2 On. 5.
Simulati on and results
5. 1. Implementation of the battery fuzzy controller in a Stand Alone Photovoltaic System For a given photovoltaic system, taking into account the conditions of the irradiance, the temperature and the SOC o f the battery, the FLC orders the opening or the closing of both switches K1 and K2 such that to ensure an optimal management and distribution of the energy available fro m the photovoltaic field and the battery. Figure. 8 presents the implementation of the algorithm translated in table .1 under Matlab/ Simu link.
Fig. 8 Simulink block of the battery fuzzy controller.
520
F. Chekired et al. / Energy Procedia 6 (2011) 513–521 gy
(
)
5. 2. Simulation of the battery fuzzy controller Figure 9 represents the states of both switches K1 and K2 after applying the battery fuzzy controller,
Signal K 1
Signal K 2
Fig. 9 States of the switches K1 and K2 controlled by the fuzzy controller.
Controlled voltage V B
Not controlled voltage V B
The figure 9 is a dynamical representation of the states of the K 1 and K2 switches in response to the variations of VB and Δ VB accord ing to the inference rules previously established and managed by the FLC in order to better regulate the battery voltage. The opening and closing of these switches controls the link between the generator and the battery and the link between the batt ery and the load.
Time (s)
Time (s)
Fig. 10 (a) Variation of the battery voltage not controlled; (b) Variation of the battery voltage set by the fuzzy algorithm.
In order to evaluate the FLC performance, we have simulated, in a first stage , the energy management of a SAPVS which uses the on/off technique for the management. As it is shown on the figure 10(a), the range of the battery voltage variation is between 12V and 14V.
F. Chekired et al. / Energy Procedia 6 (2011) 513–521 gy
The figure 10(b) shows the result obtained by inserting the FLC. The voltage delivered to the load at the end phase of charge is compris ed in a narro w range situated between 11.7V and 12.5V. As expected the FLC allo ws avoiding the successive overloads and deep discharge of the battery and then prevents any malfunction of the PV system. 6.
Conclusion
We have presented in this paper the first version of a photovoltaic energy management system based on Fuzzy Logic Control technique. Th is technique allows us to manage with accuracy the switching mode of K1 and K2 wh ich respectively control the battery charging mode and the feeding of the load. The transition time between the charging and discharging mode using this technique is very short comparing to the other techniques used in this field. The next step of our work, concerns the use of other battery models wh ich take into account several parameters like the efficiency of the battery, the State of Charge o f the battery lin ked to the battery voltage. 7.
Acknowledgement We would like to thank Mr S. Elmetnani (UDES) for his valuable advices on the writings of this article
8. [1]
References
A. Mahrane, M. Chikh, A. Chikouche. Energy management for stand alone PV systems. Jordan Journal of Mechanical and Industrial Engineering. Volume 4, Number 1, Jan. 2010. [2] K Sobaihi. Study and realization of voltage feedback MPPT converter. Memory of master. ENP, Algeria, 2003. [3] T Obeidi. Application of the genetic algorithms in the control of MPPT converter., Memory of master. ENP, Algeria 2006 [4] A Cherif, M Jraidi, A Dhouib. A battery ageing model using in stand-alone PV systems, power sources, 112; 49-53, 2002. [5] F. W. Anthony, Modelling and simulation of lead-acid batteries for photovoltaic systems, ph, D. Thesis, 1983. [6] B. Wichert, Control of Photovoltaic-Diesel Hybrid Energy Systems; Doctor of Philosophy of the Curtin University of Technology, April 2000. [7] Caisheng Wang, Modeling and control of hybrid wind/photovoltaic/ fuel cell distributed generation systems, Doctor of Philosophy in Engineering, Montana state university, Bozeman, July 2006. [8] T imothy, J. Ross. Fuzzy Logic with Engineering Applications, second ed. John Wiley & Sons Ltd., 2004. [9] A. Messai, A. Mellit, A. Guessoum, and S.A. Kalogirou, Maximum power point tracking using a GA optimized fuzzy logic controller and its FPGA implementation, Solar energy, 2010. [10] Jiménez, C.J., Sanchez Solano, S., Barriga, A. Hardware implementation of a general purpose fuzzy controller. IFSA’95, pp. 185–188, 1995. [11] F. Chekired, Implementation of MPPT neuro-fuzzy Controller on FPGA, Memory of Magister, ENP, Algeria 2008. [12] Larbes C., Aït Cheikh S. M., Obeidi T., Zerguerras A. Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system, Renewable Energy, 34: 2093-2100. 2009
521