# Hybrid Photovoltaic Systems with Accumulation—Support for Electric Vehicle Charging

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

_{2}emission limits on vehicles imposed in EU are the main driving force in this sector). A dynamic growth in the area of alternative drives/fuels can today be witnessed in Compressed Natural Gas (CNG) and electric vehicles. Contemporary developmental trends indicate that CNG will be increasing its share in the market but cannot be expected to become one of the principal fuels. This assumption is based on the lower potential of CNG as regards the reduction of CO

_{2}. The development of electromobility in the CR is hindered by several factors such as limited supply of electric vehicles, low number of charging stations or limited customer experience.

- Application of integrated charger for charging with 3–43 kW power, with one-phase or three-phase connection.
- Application of an external charger which rectifies alternating current and charges EVs with a power of 50 kW or more.

#### Hybrid Energy Systems–State of the Art

_{4}technologies have recently been on the increase. Further storage possibilities can be seen, for example, in storing energy in a hydrogen storage and subsequent conversion via fuel cells or in accumulators of the type of Vanadium Redox Battery (VRB) [8].

- Capacity of the storage system.
- Chosen technology.
- Method of operation—depth of discharge and cycling.

## 2. Concept of Proposed Solution

^{−1}at the installation site, the exploitation of this kind of energy would be a considerable limiting factor when localizing individual charging stations.

## 3. Description of Mathematical Model

#### 3.1. Photovoltaic Panel

- I
_{pv} - is the input current of PV panel (A),
- I
_{ph} - is the current generated by photodiode (A),
- I
_{s} - is the saturation current (A),
- q
- is the electron charge (q = 1.602 × 10
^{−9}C), - V
- is the output voltage of panel (V),
- N
_{s} - is the number of panel cells in series (-),
- k
- is the Boltzmann constant (k = 1.38 × 10
^{−23}J·K^{−1}), - T
- is the cell temperature (K),
- A
- is the diode ideality factor (-),
- R
_{s} - is the series resistance of panel (Ω),
- R
_{sh} - is the shunt resistance of panel (Ω).

_{s}and R

_{sh}can be determined analytically (on the assumption of choosing the parameter A) from the values given in the data sheet for the particular panel, using the Lambert W function [11]. Another possibility of determining the required parameters is the application of the numerical Newton-Raphson iteration method [15]. In this case it is not necessary to choose the parameter A but the method is strongly dependent on the chosen initial approximation. In case the initial condition is not chosen suitably, divergence will occur in the calculation. The PV panel parameters considered for the simulation are given in Table 1.

_{NR}, X

_{L}) of the measured values (I

_{SC,M}) from the simulated ones is due to the overall impurity shading of the panel during measurement, which shows in the decreased value of short-circuit current against the value given in the manufacturer’s datasheet. As is obvious from Figure 5, the differences between the results for simulated panel values and the parameters obtained using the Newton-Raphson iteration method and the Lambert W function are minimal and they can be used to determine the required parameters (A, R

_{s}and R

_{sh}).

^{−2}) and the particular voltage on the panel output, i.e., the reference voltage V

_{ref}determined by the maximum power point tracker (MPPT).

#### 3.2. DC-DC Converter

_{DC-DC}= 96% is considered.

- P
_{in,DC-DC} - is the controller input power (W), i.e., the output power of PV field P
_{out,PV}(W), - P
_{out,DC-DC} - is the output power of DC-DC controller (W),
- Q
- is the control coefficient (-).

_{ref}, i.e., PV field loading, at which the maximum power can be drawn [13,14]. This fact is the consequence of the non-linearity of the I-V curve. Present-day algorithms for finding MPP differ mostly in the speed and accuracy of finding MPP and also in the demands made on the implementation. The tracking algorithms can generally be divided into three types: Perturb and Observe (P&O), incremental conductance (IC) and Temperature gradient techniques, the first two being the most widely used [16,17,18].

#### 3.3. Battery Energy Storage and Control System

- P
_{out,DC-DC} - is the output power from DC-DC converter (W),
- P
_{load,DC} - is the input power into DC-AC inverter for load feeding (W),
- SOC
_{batt} - is the state of battery charging (%),
- P
_{batt} - is the power on accumulator interface–positive value means battery discharging (W),
- P
_{grid, LT} - is the power drawn from grid in LT periods (W),
- P
_{grid, HT} - is the power drawn from grid in HT periods (W).

- The system will primarily use the available power P
_{out, DC-DC}to charge the battery and feed the load P_{load,DC}. - In the case the SOC
_{batt}drops below the value of 20% and the load power P_{load,DC}exceeds the available power P_{out,DC-DC}, the system is charged up from the grid by a constant power P_{grid}, which is an optional simulation parameter. - In HT periods, the power drawn is registered as P
_{grid,HT}and the charging stops when the limit of 30% SOC_{batt}is reached. - In LT periods, charging begins independently of the value of SOC
_{batt}. Power from the grid is registered as P_{grid,LT}and charging only stops when the limit of 100% SOC_{batt}is reached.

#### 3.4. DC-AC Inverter

_{load,DC}and the output quantity is the power P

_{load,AC}. In practical applications, the converter efficiency changes in dependence on the current load; for the assembled model the DC-AC inverter efficiency was at η

_{DC-AC}= 95%. The simulation block is formed by a three-phase voltage source which generates voltage for the connected load and is supplemented with voltage and current measuring blocks. Similar to the DC-DC converter, no switching processes were considered.

#### 3.5. Load

## 4. Validation of Assembled Model

- AC current on the DC-AC inverter output, using the Hioki 9272-10 clamp probe, range 20 A.
- DC current on the DC-DC controller output, using the Hioki 3274 clamp probe, range 150 A.
- DC current on accumulator interface, using the Hioki 9709 pull-through sensor, range 500 A.

#### Simulation Results for the Day Measured

## 5. Simulation of Results of Concept of Supporting Storage System for Fast Charging Stations for EVs

_{EV}to 80% SOC

_{EV}of the one EV’s capacity.

#### 5.1. Operating Scenario A—Description and Assessment

_{EV}range of the EV accumulator. One charging cycle thus represents ca 20 kWh of electric energy and takes about half an hour [10].

_{batt}of the support accumulator decreasing to 20%, the charging should start irrespective of the low/high tariff. As is obvious from the algorithm of internal logic given in Figure 9, if grid charging takes place in the HT period, the charging is terminated when 30% SOC

_{batt}is reached and the full charging of the system is preferentially performed in the LT period. For every day, the considered LT period in simulation is from 8 p.m. to 4 a.m., in keeping with the valid regulations of the operator of the electricity grid of CR.

_{batt}to the stipulated level it was necessary to start charging even in the HT period (Figure 15). As expected, HT charging was terminated when 30% SOC

_{batt}was reached. Thus the indispensable reserve is provided for completing the charging cycle of EVs in case the station is unexpectedly disconnected from the grid. The minimum 20% SOC

_{batt}was chosen with a view to accumulator lifetime and also as a reserve for supplying the system’s own consumption.

^{−2}) for night hours, which may be due to a measurement error but it is probably the effect of ambient illumination and moonlight. The model thus responds to input values while MPPT tries to set the value of operating voltage. But because of insufficient irradiation, no current is generated and the power of PV field remains zero.

#### 5.2. Operating Scenario B—Description and Assessment

_{batt}does not drop to the 20% level and HT charging is not applied. The different load diagram thus has a direct impact on the ratio of energy amount E_GRID_LT to E_GRID. From the viewpoint of reduced SOC

_{batt}, a more uniformly distributed load diagram is more favourable.

## 6. Evaluation—Advantages and Disadvantages of Proposed Concept

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Nomenclature

A | diode ideality factor (-), |

E_AC_LOAD,meas | measured value of energy drawn by load on AC side of inverter (Wh), |

E_AC_LOAD,sim | simulation result of energy drawn by load on AC side of inverter (Wh), |

E_battminus,meas | measured value of energy drawn from battery (Wh), |

E_battminus,sim | simulation result of energy drawn from battery (Wh), |

E_battplus,meas | measured value of energy fed into battery (Wh), |

E_battplus,sim | simulation result of energy fed into battery (Wh), |

E_DC_DC,meas | measured value of energy fed into system by DC-DC converter (Wh), |

E_DC_DC,sim | simulation result of energy fed into system by DC-DC converter (Wh), |

E_GRID | total amount of electric energy drawn from the grid (Wh), |

E_GRID_HT | amount of electric energy drawn in the high-tariff (Wh), |

E_GRID_LT | amount of electric energy drawn in the low-tariff (Wh), |

E_PV | energy potential of PV system (Wh), |

G | intensity of solar radiation (W·m^{−2}), |

I_{mpp} | maximum power point current of PV panel at STC (A), |

I_{ph} | current generated by photodiode (A), |

I_{pv} | input current of PV panel (A), |

I_{s} | saturation current (A), |

I_{sc} | short circuit current of PV panel at STC (A), |

N_{s} | number of panel cells in series (-), |

P_{batt} | power on accumulator interface (W), |

P_{grid} | power drawn from grid—max. limit (W), |

P_{grid, HT} | power drawn from grid in high-tariff periods (W), |

P_{grid, LT} | power drawn from grid in low-tariff periods (W), |

P_{in,DC-DC} | DC-DC converter input power (W), |

P_{load,AC} | DC-AC inverter output power (W), |

P_{load,DC} | DC-AC inverter input power (W), |

P_{out,DC-DC} | DC-DC converter output power (W), |

P_{out,PV} | PV field output power (W), |

Q | control coefficient (-), |

R_{s} | series resistance of PV panel (Ω), |

R_{sh} | shunt resistance of PV panel (Ω), |

SOC_{batt} | system’s battery state of charge (%), |

SOC_{EV} | electric vehicle’s battery state of charge (%), |

T | PV cell temperature (K), |

V | output voltage of PV panel (V), |

V_{mpp} | maximum power point voltage of PV panel at STC (V), |

V_{oc} | open circuit voltage of PV panel at STC (V), |

V_{ref} | reference voltage determined by the MPPT (V), |

X_{L} | deviation of results obtained using Lambert W function (A), |

X_{NR} | deviation of results obtained using Newton-Raphson method (A), |

k | Boltzmann constant (k = 1.38 × 10^{−23} J·K^{−1}), |

t | PV panel temperature (°C), |

q | electron charge (q = 1.602 × 10^{−9} C), |

ΔE_battminus | difference of energy drawn from battery (%), |

ΔE_battplus | difference of energy fed into battery (%) |

ΔE_AC_LOAD | difference of energy drawn by load on AC side of inverter (%), |

ΔE_DC_DC | difference of energy fed into system by DC-DC converter (%), |

α | short circuit current temperature coefficient (A·K^{−1}), |

η_{DC-DC} | DC-DC converter efficiency (%), |

η_{DC-AC} | DC-AC inverter efficiency (%). |

## References

- Czech Energy Regulatory Office. Decree No 16/2016: Conditions for Connection to the Public Electricity Grid in Czech Language; Czech Energy Regulatory Office: Jihlava, Czech Republic, 2016. [Google Scholar]
- New Green Savings Programme—Directive No. 1 NZU 2014 and Its Annexes. Czech Republic, 2014. Available online: http://www.novazelenausporam.cz/en/ (accessed on 12 December 2016). (In Czech).
- Richardson, D.B. Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration. Renew. Sustain. Energy Rev.
**2013**, 19, 247–254. [Google Scholar] [CrossRef] - Darabi, Z.; Ferdowsi, M. Aggregated Impact of Plug-in Hybrid Electric Vehicles on Electricity Demand Profile. IEEE Trans. Sustain. Energy
**2011**, 2, 501–508. [Google Scholar] [CrossRef] - Automotive Industry Association (AIA). Czech Vehicle Parc—November 2015. Available online: http://www.autosap.cz/zakladni-prehledy-a-udaje/slozeni-vozoveho-parku-v-cr/ (accessed on 18 November 2016). (In Czech).
- IEC 61815-1:2017. Electric Vehicle Conductive Charging System—Part 1: General Requirements; IEC: Geneva, Switzerland, 2017. [Google Scholar]
- Nema, P.; Nema, R.K.; Rangnekar, S. A current and future state of art development of hybrid energy system using wind and PV-solar: A review. Renew. Sustain. Energy Rev.
**2009**, 13, 2096–2103. [Google Scholar] [CrossRef] - Henninger, S.; Jaeger, J.; Rubenbauer, H. Dimensioning and control of energy storage systems for renewable power leveling. In Proceedings of the 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Dallas, TX, USA, 2016; pp. 1–5. [Google Scholar] [CrossRef]
- Yunus, K.; De La Parra, H.Z.; Reza, M. Distribution grid impact of Plug-In Electric Vehicles charging at fast charging stations using stochastic charging model. In Proceedings of the 2011 14th European Conference on Power Electronics and Applications, Birmingham, UK, 2011; pp. 1–11. [Google Scholar]
- Mastny, P.; Moravek, J.; Vrana, M. Concept of Fast Charging Stations with Integrated Accumulators—Assessment of the Impact for Operation. In Proceedings of the 2016 17th International Scientific Conference on Electric Power Engineering (EPE), Prague, Czech Republic, 16–18 May 2016; pp. 194–199, ISBN 978-1-5090-0907-7. [Google Scholar]
- Cubas, J.; Pindado, S.; de Manuel, C. Explicit Expressions for Solar Panel Equivalent Circuit Parameters Based on Analytical Formulation and the Lambert W-Function. Energies
**2014**, 7, 4098–4115. [Google Scholar] [CrossRef] - Chan, D.S.H.; Phang, J.C.H. Analytical methods for the extraction of solar-cell single- and double-diode model parameters from I-V characteristics. IEEE Trans. Electron. Devices
**1987**, 34, 286–293. [Google Scholar] [CrossRef] - Sera, D.; Teodorescu, R.; Rodriguez, P. PV panel model based on datasheet values. In Proceedings of the 2007 IEEE International Symposium on Industrial Electronics, Vigo, Spain, 2007; pp. 2392–2396. [Google Scholar] [CrossRef]
- Orioli, A.; Di Gangi, A. A procedure to calculate the five-parameter model of crystalline silicon photovoltaic modules on the basis of the tabular performance data. Appl. Energy
**2013**, 102, 1160–1177. [Google Scholar] [CrossRef] - Siddique, H.A.B.; Xu, P.; De Doncker, R.W. Parameter extraction algorithm for one-diode model of PV panels based on datasheet values. In Proceedings of the 2013 International Conference on Clean Electrical Power (ICCEP), Alghero, Italy, 2013; pp. 7–13. [Google Scholar] [CrossRef]
- De Brito, M.A.G.; Sampaio, L.P.; Luigi, G., Jr.; e Melo, G.A.; Canesin, C.A. Comparative Analysis of MPPT Techniques for PV Applications. In Proceedings of the 2011 International Conference on Clean Electrical Power (ICCEP), Ischia, Italy, 14–16 June 2011. [Google Scholar]
- Piegari, L.; Rizzo, R.; Spina, I.; Tricoli, P. Optimized Adaptive Perturb and Observe Maximum Power Point Tracking Control for Photovoltaic Generation. Energies
**2015**, 8, 3418–3436. [Google Scholar] [CrossRef] - Batunlu, C.; Alrweq, M.; Albarbar, A. Effects of Power Tracking Algorithms on Lifetime of Power Electronic Devices Used in Solar Systems. Energies
**2016**, 9, 884. [Google Scholar] [CrossRef] - Femia, N.; Petrone, G.; Spagnuolo, G.; Vitelli, M. Optimization of perturb and observe maximum power point tracking method. IEEE Trans. Power Electron.
**2005**, 20, 963–973. [Google Scholar] [CrossRef] - Moravek, J.; Mastny, P. Hybrid renewable energy system—Configuration and control. Recent Res. Electr. Power Energy
**2013**, 22, 87–92. [Google Scholar] - Wu, Y.; Shen, C.; Liu, C. Implementation of solar illumination system with three-stage charging and dimming control function. In Proceedings of the 2011 International Conference on Electric Information and Control Engineering, Wuhan, China, 15–17 April 2011; pp. 2260–2263. [Google Scholar] [CrossRef]
- The MathWorks, Inc. Available online: http://www.mathworks.com/help/physmod/sps/powersys/ref/powergui.html?searchHighlight=powergui (accessed on 16 December 2016).
- The MathWorks, Inc. Available online: http://www.mathworks.com/help/physmod/sps/powersys/ug/choosing-an-integration-method.html (accessed on 16 December 2016).
- The MathWorks, Inc. Available online: http://www.mathworks.com/help/simulink/ug/choosing-a-solver.html (accessed on 16 December 2016).

**Figure 2.**Concept of a charging station with renewable energy source (RES) support and accumulation [10].

**Figure 4.**Equivalent schematic of one-diode model of photovoltaic (PV) panel acc. to [11].

**Figure 8.**Basic P&O algorithm, according to [16].

**Figure 12.**Intensity vs. time profile of solar radiation incident on PV field and PV panel temperature.

**Figure 17.**Resulting profiles of simulation of operating states of photovoltaic system (PVS), scenario A.

**Table 1.**Parameters of simulated photovoltaic (PV) panel. Standard Test Conditions (STC); MPP: maximum power point.

Parameter | Value | Note |
---|---|---|

Short circuit current at STC | I_{sc} = 8.66 A | from datasheet |

Open circuit voltage at STC | V_{oc} = 37.9 V | from datasheet |

MPP current at STC | I_{mpp} = 8.14 A | from datasheet |

MPP voltage at STC | V_{mpp} = 31.1 V | from datasheet |

Number of cells in series | N_{s} = 60 | from datasheet |

Short circuit current temperature coefficient | α = 0.0051 A·K^{−1} | from datasheet |

Ideality factor | A = 1.102 | Calculated using the Newton-Raphson iteration method |

Series resistance | R_{s} = 0.277 Ω | Calculated using the Newton-Raphson iteration method |

Shunt resistance | R_{sh} = 1600 Ω | Calculated using the Newton-Raphson iteration method |

Series resistance | R_{s} = 0.228 Ω | Calculated using the Lambert W function |

Shunt resistance | R_{sh} = 625 Ω | Calculated using the Lambert W function |

Name | Quantity Designation | Value |
---|---|---|

Energy drawn by load on AC side | E_AC_LOAD,meas | 7.021 kWh |

Energy fed into system by DC-DC converter | E_DC_DC,meas | 6.823 kWh |

Energy drawn from battery | E_battminus,meas | 2.333 kWh |

Energy fed into battery | E_battplus,meas | 1.548 kWh |

Name | Quantity Designation | Value |
---|---|---|

Energy drawn by load on AC side | E_AC_LOAD,sim | 7.021 kWh |

Energy fed into system by DC-DC converter | E_DC_DC,sim | 6.818 kWh |

Energy drawn from battery | E_battminus,sim | 2.361 kWh |

Energy fed into battery | E_battplus,sim | 1.789 kWh |

Name | Quantity designation | Value |
---|---|---|

Energy drawn by load on AC side | ΔE_AC_LOAD | 0% |

Energy fed into system by DC-DC converter | ΔE_DC_DC | −0.07% |

Energy drawn from battery | ΔE_battminus | +1.2% |

Energy fed into battery | ΔE_battplus | +15% |

Name | Quantity Designation | Value |
---|---|---|

Overall energy drawn from grid | E_GRID | 1333 kWh |

Energy drawn from grid during LT | E_GRID_LT | 1289 kWh |

Energy drawn by load on AC side | E_AC_LOAD | 1824 kWh |

Energy drawn by load on DC side | E_DC_LOAD | 1915 kWh |

Energy potential of PV system | E_PV | 630 kWh |

Energy fed into system by DC-DC converter | E_DC_DC | 590 kWh |

Energy fed into battery | E_battminus | 1698 kWh |

Energy drawn from battery | E_battplus | 1687 kWh |

Balance of stored energy | E_batt | 11 kWh |

Name | Quantity Designation | Value |
---|---|---|

Overall energy drawn from grid | E_GRID | 1303 kWh |

Energy drawn from grid under LT | E_GRID_LT | 1303 kWh |

Energy drawn by load on AC side | E_AC_LOAD | 1824 kWh |

Energy drawn by load on DC side | E_DC_LOAD | 1915 kWh |

Energy potential of PV system | E_PV | 630 kWh |

Energy fed into system by DC-DC converter | E_DC_DC | 600 kWh |

Energy fed into battery | E_battminus | 1408 kWh |

Energy drawn from battery | E_battplus | 1420 kWh |

Balance of stored energy | E_batt | −12 kWh |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Mastny, P.; Moravek, J.; Vojtek, M.; Drapela, J.
Hybrid Photovoltaic Systems with Accumulation—Support for Electric Vehicle Charging. *Energies* **2017**, *10*, 834.
https://doi.org/10.3390/en10070834

**AMA Style**

Mastny P, Moravek J, Vojtek M, Drapela J.
Hybrid Photovoltaic Systems with Accumulation—Support for Electric Vehicle Charging. *Energies*. 2017; 10(7):834.
https://doi.org/10.3390/en10070834

**Chicago/Turabian Style**

Mastny, Petr, Jan Moravek, Martin Vojtek, and Jiri Drapela.
2017. "Hybrid Photovoltaic Systems with Accumulation—Support for Electric Vehicle Charging" *Energies* 10, no. 7: 834.
https://doi.org/10.3390/en10070834