Solar-Powered Vehicle-to-Load (V2L) Plug-in Electric Vehicles: Alleviation of the Photovoltaic Power Decay

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Solar-Powered Vehicle-to-Load (V2L) Plug-in Electric Vehicles: Alleviation of the Photovoltaic Power Decay

Item Type Conference Paper

Authors Pervez, Imran;Antoniadis, Charalampos;Ghazzai, Hakim;Massoud, Yehia Mahmoud

Citation Pervez, I., Antoniadis, C., Ghazzai, H., & Massoud, Y. (2023).

Solar-Powered Vehicle-to-Load (V2L) Plug-in Electric Vehicles:

Alleviation of the Photovoltaic Power Decay. 2023 IEEE International Conference on Smart Mobility (SM). https://

doi.org/10.1109/sm57895.2023.10112517 Eprint version Post-print

DOI 10.1109/sm57895.2023.10112517

Publisher IEEE

Rights This is an accepted manuscript version of a paper before final publisher editing and formatting. Archived with thanks to IEEE.

Download date 2024-01-15 17:39:34

Link to Item http://hdl.handle.net/10754/691562

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Solar-Powered Vehicle-to-Load (V2L) Plug-in Electric Vehicles: Alleviation of the Photovoltaic

Power Decay

Imran Pervez, Charalampos Antoniadis, Hakim Ghazzai, and Yehia Massoud

Innovative Technologies Laboratories, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia Email:{imran.pervez, charalampos.antoniadis, hakim.ghazzai, yehia.massoud}@kaust.edu.sa

Abstract—As any alternating current (AC) load, plug-in electric vehicle (PEV) battery when powered by a photovoltaic (PV) source is subject to the power decay problem. To optimize the PV power extraction for a given non-uniform irradiance and temperature, the PV power must be managed by a maximum power extraction (MPE) system. The PV MPE coupled with an inverter and a PEV battery includes a proportional integral (PI) control system which negatively impacts the MPE controller performance. This is unlike a straightforward MPE system without a PI control where the PV power is exclusively controlled by the MPE controller. Metaheuristic algorithms are usually employed to optimize the PV MPE systems for a non-PI controlled MPE system, This study proposes a low exploration metaheuristic- based algorithm to mitigate the problems with the MPE system coupled with a PI control system. The proposed algorithm is contrasted with the low burden narrow search (LBNS) (low exploration) and the Jaya algorithms (high exploration). The findings show that the proposed algorithm performed significantly better than LBNS and Jaya in addressing the aforementioned problems.

Index Terms—Electric vehicle, battery, grid-connected system, photovoltaic, metaheuristic algorithm, maximum power extraction (MPE).

I. INTRODUCTION

Integrating photovoltaic (PV) technology into the grid provides many benefits for producing energy sustainably and economically for commercial and residential loads. The PV can also be used to charge the plug-in electric vehicle (PEV) battery, which can in turn be used as a vehicle-to-grid (V2G) and vehicle-to-load (V2L) to supply power back to the grid and residential or commercial loads, respectively. The power to the load is transferred through PEV battery using V2L connections in the case of deficient power supply through PV. Thus, charging and discharging the PEV battery using the V2L concept provides two-fold benefits: charging the PEV and supplying the load, respectively.

However, charging/discharging a PEV and maintaining the inverter side DC link voltage constant for a constant supply to grid requires a PI control system, which negatively affects the power transfer capability of PV which has been addressed in this work. More specifically, the maximum power extraction (MPE) of a PV, which is a technique to extract maximum amount of power out of a PV is impacted with the inclusion of a PI control system.

Several studies (e.g., [1]- [9]) are available in the literature capable of extracting maximum power through PV. However, none of them to the best of our knowledge tackle the problems with MPE arising as a result of PI control system on the MPE control system. The PI control system impacts the PV MPE control by acting as a variable voltage and current across the MPE system and impacting the MPE converter voltage as opposed to the MPE systems without a PI control. Studies on PV-inverter connections often execute MPE using straightforward conventional/mathematical algorithms under uniform irradiation conditions (e.g., [10]- [13]). These methods might not be affected by the inverter PI control, but under non- uniform irradiation conditions, they are unable to converge to the maximum power point (MPP) for MPE.

Metaheuristic algorithms can solve the MPE problem under non-uniform irradiation conditions but their performance is impacted when a PI control system is used to regulate DC link voltage and PEV battery power. By performing a com- prehensive search for MPP for extended periods of time, the metaheuristic algorithms with high search space exploration capability (e.g., [7], [8]) can reduce the PI control effect.

However, this causes slow convergence and power oscillations that lead to power loss. Contrarily, the low or moderate exploration MPE algorithms (e.g., [3]- [5]) reduces power fluctuations and have faster convergence speed, but it might not attenuate the impact of PI control on power. In order to apply the metaheuristic algorithms for inverter-connected PV MPE, a trade-off between power convergence efficiency, power convergence speed, and power losses must be achieved.

However, the trade-off may result in decreased performance, either in terms of convergence speed and power fluctuations (high exploration), or in terms of power convergence accuracy (low exploration).

In this paper, we propose a novel exploratory proportional pulsing hybrid technique with gradient descent (EPPG) for PV MPE control to prevent this trade-off. To decrease the impact of PI control in addition to maintaining the convergence speed and accuracy, the proposed algorithm resort to a low exploration metaheuristic-based algorithm as will be explained in the later sections. The low burden narrow search (LBNS) [5]

and Jaya algorithms [7], which are considered to be low and high exploration algorithms, respectively, are compared to the proposed algorithm. The outcomes demonstrate a substantial

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I^PV

Duty ratio control for impedance variation

PI control system PI control

system I^bat

Vbat

To AC load and grid through

inverter VPV

IPV Duty ratio

control for impedance variation

(a) (b)

Load Buck-boost equivalent impedance

Boost equivalent impedance

Boost equivalent impedance PV MPE

control PV MPE

control

PV MPE control PV MPE

control V^PV

PEV battery

Fig. 1: PV MPE (a) without PI control, and (b) with PI control for PEV battery and inverter control.

advancement in addressing the different challenges related to these algorithms. The rest of the paper is irganized as follows:

Section II describes the PV MPE control with and without PI control systems, Section III explains the proposed algorithm for MPE control, Section IV is the results and discussion, and Section V concludes the paper.

II. PV MPEWITH AND WITHOUTPI CONTROL

The MPE techniques for PV with and without a PI system are described in this section.

A. PV MPE without PI Control

Fig. 1(a) shows the PV MPE system without a PI control system which is mostly studied and enhanced in the literatures.

The DC-DC boost converter acts as a variable impedance connected in series with the PV and the load adjusted by the duty ratio pulses, as described in [14]. The PV MPE control system sends the duty ratio signals to the boost converter to vary its impedance correspondingly and the process continues until the PV starts generating the maximum power that corresponds to a specific impedance for a given irradiance. Evidently, for such a system, the system impedance is varied only through MPE control system as the load is constant and cannot affect the impedance variation.

B. PV MPE with PI Control

The PV-based grid-connected system for supplying electric- ity to the AC load and PEV battery is shown in Fig. 1(b).

The inverter receives the DC output voltage from the PV and converts it to AC so that it can be sent to the AC load. The PI control system illustrated in Fig. 1(b) is used to maintain the inverter DC side voltage at a steady 650V. Moreover, it is also used to charge or discharge the PEV battery in the case of deficient and excess supply of power from PV respectively.

Fig. 2: Illustration of PI control effect on the PV MPE control.

The MPE control concludes in the metaheuristic exploration phase by converging to an optimum duty ratio. In the next phase, only PI control regulates the inverter DC voltage at a constant MPE duty ratio, which degrades the converged power.

The PV MPE behavior in conjunction with the battery PI control mechanism is shown in Fig. 2.

The MPE method first searches MPP during the metaheuristic update phase by dispersing duty ratios throughout the search space, as seen in the duty ratio plot in Fig. 2. The boost converter output voltage is initially influenced by both the MPE control algorithm and the PI control because they modify the duty ratios for the accompanying boost and buck-boost converter switching, respectively. Consequently, the inverter DC voltage is altered (see the voltage plot in Fig. 2). The MPE algorithm searches for MPP for a while before getting closer to MPP. The MPE algorithm’s further influence on the boost converter output voltage (inverter DC voltage) will be stopped

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by switching through a fixed duty ratio, leaving the voltage primarily under the control of the PI control system. The boost converter output voltage will thereafter be continuously adjusted by the PI control to keep it at 650V. As the voltage continues to deviate from the MPP voltage at the duty ratio determined by the MPE algorithm, the continual change in the output voltage at a constant boost converter duty ratio will continue to reduce the PV power (Fig. 2). As a result, there will be significant power losses as the PV power ultimately converges to a lower value.

Algorithm 1 Explorative Proportional Pulsing hybrid with Gradient Descent (EPPG) Algorithm for MPE

1:#Metaheuristic Initialization

2:D1←0.2, D2←0.4, D3←0.6, D4←0.8 3:D^∗← argmax

{D1,D2,D3,D4}

Pw(x) 4:#Solution Redistribution 5:D^∗₊←D^∗+δD 6:D₄← argmax

{D∗ +, D∗ }

P OW ER(x) 7:ifD4=D^∗₊then

8: D_temp←D^∗+c 9: D^∗_new← argmax

{Dtemp, D∗ }P OW ER(x) 10: ifD_new^∗ =Dtempthen

11: D^∗←Dtemp+c^′ 12: else

13: D^∗←^Dtemp₂^+D^∗ 14: end if

15: else

16: Dtemp←D^∗−c 17: D_new^∗ ← argmax

{Dtemp, D∗ }P OW ER(x) 18: ^if^Dnew^∗ =D_tempthen

19: ^D^∗←D_temp−c^′ 20: else

21: D^∗←^Dtemp₂^+D^∗ 22: end if

23: end if

24: #Convergence criterion of metaheuristic-update 25: D^∗₁← argmax

{D∗, D∗ new}

P OW ER(x) 26: D^∗₂← {D^∗, D^∗_new} − {D^∗₁} 27: D^∗₃←Update (1) for two iterations 28: while ^{|P OW ER(D}

∗1 )−P OW ER(D∗ 2 )| P OW ER(D∗

2) ≥6%do

29: D₃^∗←Update (1) 30: D₁^∗←argmax

{D∗ 1,D∗

3}

P OW ER(Di) 31: ifD₁^∗=D^∗₃then

32: D^∗₂←D^∗₁ 33: else

34: D^∗₂←argmax

{D∗ 2,D∗

3}

P OW ER(Di) 35: end if

36: end while

37: #Power balancing phase 38: D^∗_new←Update (2) 39: D^∗← argmax

{D∗

new ,D∗ }P OW ER(Di) 40: while|Vo−Vt| ≥10do

41: ifD^∗=D^∗_neworF U=truethen 42: F U←f alse

43: D^∗_new←Update (2) 44: D^∗← argmax

{D∗

new ,D∗ }P OW ER(Di) 45: else

46: D^∗_new←D^∗ 47: F U←true 48: end if 49: end while

50: #Gradient descent algorithm

51: D^∗←Update rule in eq. (3) beginning fromD^∗ 52:

III. PROPOSEDEXPLORATORYPROPORTIONALPULSING

HYBRIDALGORITHM WITHGRADIENTDESCENT

To deal with the different challenges mentioned earlier with the PV MPE when connected to the grid, we propose an algorithm that synergizes three different steps in its updating method as shown in Algorithm 1. The algorithm starts with the metaheuristic based updates whose exploration capability has been further reduced by using the advanced limited search strategy (ALSS) proposed in [3].

The duty ratios are initially spread in the search space using the initialization strategy in [3] and the initial best duty ratio is evaluated (lines 3-4). The ALSS phase then starts by slightly incrementing the initial best duty ratio to compare its fitness (power) with the initial best (lines 6-7). If the incremented duty ratio has higher (lower) power than the initial best, it is further incremented (decremented) by a constant valuec= 0.05(lines 8-9 and lines 16-17) to compare its power with the previous duty ratio (line 18). If the power for the new (incremented by c) duty ratio is higher (lower), it is further incremented (decremented) by a constant c^′ = 0.025 else, it is assigned a value between the new (incremented by c) and the previous duty ratio (lines 11-15 and lines 19-23). The duty ratios are updated as follows:

D_i^(t+1)=D^(t)_sbest−(D^(t)_sbest−D^(t)_best)

1 +r

(P1−P2)

|P1−P2|

, (1) with

D^(t+1)_best P1

D^(t+1)_sbest P2

!

=











D_i^(t+1) P_sbest^(t+1) D^(t)_best P_best^(t+1)

!

, ifP_i^(t+1)> P_best^(t) D^(t)_best P_best^(t+1)

D_i^(t+1) P_sbest^(t+1)

!

, otherwise

where D^(t+1)_i represents the duty ratio in the next iteration, D_i^(t) is the current duty ratio, Dbest denotes the best (most optimum) duty ratio among all spreaded ones, Dsbest is the second-best duty ratio, r denotes a random number between 0 and 0.25,Piis the power corresponding to Di,Pbest is the power corresponding to Dbest andPsbest denotes the power corresponding to Dsbest.

After the ALSS phase, the algorithm evaluates the best and the second best duty ratios (lines 26-27) to update (1) (proposed in [8]) for two consecutive iterations (line 28) followed by a meta-heuristic convergence verification step that keeps on updating the duty ratio through (1) until the difference between the best and the second best duty ratio powers reduces to below 6% (lines 29-37).

After the metaheuristic update phase, the algorithm starts the power balancing phase (line 38) in which the duty ratio is updated initially through the following modified boost converter provided in (2) and compared with the previous duty ratio (lines 38-40):

D_i^(t+1)= 1− 1 +γ

t

Vpv

V_o

, (2)

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where the termγis the convergence rate control constant kept equal to 0.05 in this study,Vpvis the photovoltaic (PV) output (boost converter input) voltage, andVo is the boost converter output (inverter input) voltage.

The duty ratio is continuously updated through (2) until the difference between the current voltage (Vo) and the target voltage (Vt) reduces to less than 10. The term ^γ_t

controls the duty ratio update rate with initial faster rate that decays with time/iterations. This is in conjunction with the faster initial voltage update that reduces with time. The modified duty ratio equation keeps the duty ratio in synchronization with the output voltage to maintain the power at MPP. This resolves the power decaying problem but may lead to non- synchronized updates after a few iterations that reduces power.

To solve this, the algorithm starts a verification step that continues through normal (2) updates if the duty ratio update is synchronized withVo (lines 42-45) otherwise it assigns the previous best value to the duty ratio (lines 46-49). Moreover, continuous updating through the previous best value (line 47) may again lead to non-synchronized updates. To solve this, a force updating (FU) parameter is defined to forcefully update the duty ratio through (2) (line 42) by bringing it out of the updating method of assigning previous best values (line 48).

After the power balancing phase, the algorithm continues its final updates through GD algorithm using:

D_i^t+1=D_i^t−α× ∆P

∆V

, (3)

to improve the power convergence accuracy (lines 51-52).

In (3), α denotes the step size, while ∆P and ∆D denote the difference between current and previous power levels and duty ratios, respectively.

IV. RESULTS& DISCUSSION

This section assesses how well the suggested algorithm performs in increasing PV MPE’s power efficiency when it is connected to the grid. The open circuit voltage (V_oc) of 48.97V, the short circuit current (I_sc) of 9.6A, the maximum power point voltage (V_mpp) of 39.66V, and the maximum power point current (I_mpp) of 8.97A are chosen as the PV module ratings. The PV arrangement consists of 14 parallel PV strings with 12 series-connected modules on each string. The battery system includes thirty-five PEV batteries connected together each with a rating of 12V nominal voltage and 48Ah capacity.

The voltage of the phase-to-phase rms grid was selected to be 230V. The required AC load was 50kW. The proposed algorithm is compared to both high exploration (Jaya) and low exploration (LBNS) algorithms to demonstrate its efficacy in eliminating any trade-offs and outperforming other algorithms in terms of power convergence efficiency, convergence speed, and power fluctuations.

The outcomes of the low exploration (LBNS) method are shown in Fig. 3(a). As can be observed, the algorithm begins to search the domain and initially affects the boost converter output voltage. The PI control system now totally controls the voltage after the algorithm has reached the converging

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(b)

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Fig. 3: Performance comparison between (a) Jaya algorithm (high-exploration), (b) LBNS algorithm (low exploration), and (c) proposed EPPG algorithm.

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TABLE I: Convergence speed and power convergence comparison between Jaya, LBNS, and proposed EPPS algorithms

Algorithm

Convergence time (seconds)

Achieved power

(kW)

True MPP (kW)

Convergence efficiency

(%)

Jaya 5.4 53.51

55.75

95.9

LBNS 0.45 44.5 79.8

Proposed

EPPS 0.52 53.52 96

phase. The PV power continues to decline until the voltage has reached a constant value because the PI system keeps driving the voltage to settle at 650V. Even though the convergence speed is quite rapid, the converged power, as shown in Table I, is obviously significantly lower than the genuine MPP value, resulting in a convergence power convergence efficiency of only 79.8%.

The results for the high exploration (Jaya) algorithm are shown in Fig. 3(b). Unlike LBNS, the algorithm explores the search space for longer periods of time, allowing it to control the inverter DC voltage simultaneously with the PI controller.

Despite the fact that the algorithm converged to near MPP (95.9% power convergence efficiency), the convergence speed was very slow, due to several large and small size power fluctuations, resulting in power losses and slow convergence.

The outcomes of the proposed EPPG algorithm are shown in Fig. 3(c). The search is only explored for a very little time by the LBNS-like algorithm. The ALSS and metaheuristic phase ends once it reaches close to MPP, and the power balance phase then begins. The suggested approach, in contrast to the LBNS algorithm, continues to maintain control simultaneously with the PI control even after the exploration phase has con- cluded. Through the improved duty boost converter equation, the algorithm keeps updating the duty ratio proportionally. The improved boost converter equation initially moves in larger steps with larger voltage increases before deteriorating over time with smaller voltage changes. The algorithm in contrast to Jaya and LBNS keep the power maintained around MPP without power fluctuations. The algorithm finally update through GD algorithm to maintain power convergence accuracy. The overall proposed algorithm thus significantly outperformed the LBNS and the Jaya algorithms in terms of power convergence efficiency, power fluctuations, and convergence speed. Table I summarizes the results of all the algorithms in terms of convergence speed and power convergence efficiency.

V. CONCLUSION

This work proposed an efficient algorithm for PV-MPE grid connected system supplying power to AC load and a plug-in

Electric Vehicle (PEV) battery which was utilized as a vehicle to load (V2L). The proposed algorithm addresses the power degradation problem of PV that occurs due to the influence of PI control on the PV MPE control. The proposed algorithm is capable of performing low exploration metaheuristic search, synchronous duty ratio update with inverter DC voltage, and gradient descent updates. The results proved a significant improvement in addressing the aforementioned problems compared to existing algorithms while offering a fast convergence speed.

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