Sizing of hybrid PMSG-PV system for battery charging of electric vehicles

Abstract

The number of electric vehicles are increasing in the society as they are considered as zero emission vehicles and also because conventional fuels are becoming expensive. Additional electrical power should be produced to meet the energy requirement of this increase in electric vehicle population. To use the existing grid infrastructure without any failure, installing distributed generator at secondary distribution network is essential. In this work, sizing of wind-driven permanent magnet synchronous generator—photovoltaic hybrid distributed generating system has been attempted to meet the energy demand of electric vehicles of a particular residential area. Different feasible combinations for wind generator capacity and photovoltaic capacity are obtained to satisfy the additional energy requirement. Results are analyzed based on energy, financial payback periods and daily power profile of the hybrid system. Based on this analysis, the sizes of wind generator and photovoltaic array have been chosen to meet the energy demand of electric vehicles of that particular residential locality.

Graphical abstract

Keywords

electric vehicles / hybrid PMSG-PV system / smart grid

1 Introduction

Electric vehicles (EVs) are getting more attention from customers as fossil fuels are getting depleted. EVs are considered to be one of the best solutions for reducing greenhouse emission. Even though the initial investment for electric vehicle (EV) is high, it is considered more profitable than conventional vehicles [1]. As EVs require large amount of electrical energy for charging, penetration of EVs will have a significant impact on distribution network. Charging of EVs will lead to undesirable peaks and create heavy load burden in the power system network [2–4]. Regulated and coordinated charging of EVs will also lower the power losses [3]. High urban congestion and high price of conventional fuels have resulted in EVs being targeted as passenger vehicles in India. Low manufacturing cost and established automobile infrastructure make India a profitable market for EV manufacturers [5]. To meet the additional energy requirement of large number of EVs in society, excess power should be generated and transmitted to the consumer end. Nevertheless, with the existing grid infrastructure, transmitting the additional power to the consumer will lead to heavy transmission losses and faults. So, the better choice is to install distributed generators (DGs) based on intermittent sources at consumer end to meet the additional energy requirement of EVs. Distributed generator (DG) systems based on wind-photovoltaic (PV) sources are considered to be more reliable, due to the complementary nature of the resources [6]. At the same time, connecting DG to grid will make the grid unstable because of its unpredictable power fluctuations. Obtaining the right choice of the sizes of generators is important for charging the EVs. Different methodology for sizing hybrid renewable energy system has been overviewed [7,8]. Different sizing procedures for stand-alone hybrid wind-PV systems have been explained [9–14]. Sizing procedures for grid connected hybrid wind-PV system have also been described [15,16]. However, in this paper, a different focus on sizing the generators of grid connected hybrid wind-PV DG system is given to satisfy the additional energy demand of EVs.

In the above context, an attempt has been made for the first time to size the hybrid wind-driven PMSG-PV DG system with the constraint of satisfying the energy requirement of EVs in a particular residential area. Different combinations of PV capacity and wind turbine capacity are analyzed to satisfy the above mentioned constraint. Energy payback period (EPP) and financial payback period (FPP) are also calculated for all the combinations to assess the sustainability of the hybrid system and to justify the investment on the system. Power profiles of all the feasible combinations for a period of 24 h are obtained. The PV and wind turbine capacity are thus obtained based on the required profile which are then regulated or scheduled to charge the EVs.

2 Description of the hybrid scheme

The hybrid scheme considered in this paper is shown in Fig. 1. Several wind-driven PMSG-PV hybrid schemes have been explained in detail [17–19]. Earlier, hybrid DG systems with PMSG-PV had individual power converters for each sources or a battery backup. Further, each converter was controlled using complex algorithms for peak power tracking. To minimize the conduction and switching losses of the devices, it is necessary to have minimum number of power converters (power conversion stage) and the chosen scheme provides this advantage. Two controllers are designed to extract maximum power from both PV and PMSG. Controller 1 varies the duty-cycle of the boost converter to maintain the DC link voltage at maximum power point voltage of PV array. Controller 2 varies the current reference (I_ref) of current controlled inverter to maximize the current from both the sources and to feed the grid. Different status of sources and the corresponding functions of two controllers are summarized in Table 1.

3 Sizing of the hybrid system

The capacity of the PV array and wind turbine should be chosen in such a way that, the total energy produced by the hybrid wind-PV system over a period of a year should be equal to the power consumed by EVs. In this paper, a residential block having 50 EVs is considered. Each vehicle has a battery of 42 kWh of usable capacity (Toyota RAV4EV). The rated capacity of the charger for this EV is 7.2 kW. It is also assumed that each EV is charged once in a day to its usable capacity of 42 kWh. So the energy required to charge these 50 EVs over a period of a year is 756 MWh. Hence, the PMSG-PV hybrid system should produce 756 MWh of energy over a year to meet the energy requirement of EVs. The energy required over a year is taken constant for the sake of sizing the units. The random factors involved in the sizing process are wind speed and solar irradiation. The randomness of solar irradiation and wind-speed are taken into account for sizing the hybrid PMSG-PV system in this paper. It has been reported that sizing the hybrid system from simulated data of solar-irradiation and wind-speed with randomness factor gives better results than sizing the hybrid system from the experimental data [20]. So, the wind speed and solar irradiation of the location (Aralvaimozhi, Tamilnadu, India) has been simulated based on the procedure given in Ref. [21].

Initially, wind turbine capacity and PV capacity are chosen randomly. Then, the wind speed and solar irradiation pattern of the given location are fed to the simulation model to calculate the energy yield of the system over a year. If the total energy produced by the chosen combination of wind-PV capacity is 760 MWh, then this size of wind-PV is considered as one of the feasible solution. Likewise, other possible combinations of wind-PV which can also generate 756 MWh of energy over a year are found. The proposed initial sizing procedure is illustrated as a flow chart in Fig. 2. This procedure gives different possible combinations of wind-PV size to satisfy the energy requirement of EVs.

Then EPP and FPP is obtained for all feasible combinations. Steps to calculate EPP and FPP for the hybrid PMSG-PV are explained clearly in the subsequent sections.

4 EPP calculation

EPP is a widely used indicator to determine the sustainability of renewable energy systems [22]. EPP is given as [23]

(1)\mathrm{EPP}={\displaystyle \frac{E_{\mathrm{PV}}+E_{\mathrm{W}}+E_{\mathrm{BOS}}}{E_{\mathrm{G}}}},

where E_PV is the embodied energy of the PV panel, E_W is the embodied energy of the wind turbine, E_BOS is the embodied energy in the balance of the system (BOS) components, and E_G is the amount of energy generated by the hybrid system in a year. The components of BOS include support structure, wiring, and power converters (DC-DC converter and inverter in this case). The embodied energy in the PV array and other BOS components are calculated as explained in Ref. [24]. Because of the adopted topology, the inverter is chosen with the rated power to handle the total maximum power of the PMSG and PV. But the DC-DC converter is chosen with the rated power to handle the total power of the PMSG alone.

The calculation of energy content and EPP for wind turbines are explained in detail [25–27]. Due to the lack of inventory data, the embodied energy content of wind turbine is approximated to 1.414 MJ, if that wind turbine produces 8.76 MWh of energy over a year [28]. The EPP for the different possible combinations of wind-PV size are calculated using Eq. (1). The energy content of various components of the hybrid system and the EPP of different possible combinations of wind-PV are listed in Table 2.

5 FPP calculation

Net present value (NPV) and FPP are power tools which help to assess the investment decision [29]. FPP can be calculated by evaluating the NPV of the hybrid PMSG-PV system for every year as explained below [30]. NPV can be expressed as

(2)\mathrm{NPV}=-\left({\mathrm{CC}}_{\mathrm{PV}}+{\mathrm{CC}}_{\mathrm{W}}\right)+{\displaystyle \sum _{i=\mathrm{1}}^n{\displaystyle \frac{{\mathrm{CF}}_i}{{\left(\mathrm{1}+r\right)}^i}}},

where CC_PV and CC_w are the capital cost investment for the PV system and wind energy system, respectively, and r is the nominal rate of interest (8%)

And the cash flow (CF) for the year i can be expressed as

(3){\mathrm{CF}}_i={\left(\mathrm{cash}\hspace{0.17em}\mathrm{input}\right)}_i-{\left(\mathrm{cash}\hspace{0.17em}\mathrm{output}\right)}_i

(4)={\left(P_{\mathrm{C}}{E}_{\mathrm{C}}+P_{\mathrm{S}}{E}_{\mathrm{S}}\right)}_i-{\left(C_{\mathrm{OMPV}}+C_{\mathrm{OMW}}\right)}_i,

where P_C and P_S are the price of the energy consumed and sold respectively, E_C and E_S are the energy consumed (i.e. energy not bought from the grid) and energy sold during the ith year, And C_OMPV and C_OMW are the operation and maintenance costs for the PV system and wind turbine system respectively. The total energy produced in this hybrid system is completely utilized by the EVs for their charging. So Eq. (4) becomes

(5){\mathrm{CF}}_i={\left(P_{\mathrm{C}}{E}_{\mathrm{C}}\right)}_i-{\left(C_{\mathrm{OMPV}}+C_{\mathrm{OMW}}\right)}_i.

The values of the different parameters used in Eqs. (2) to (4) are tabulated in Table 3.

FPP (N) can be expressed as in Ref. [30]

(6)-\left({\mathrm{CC}}_{\mathrm{PV}}+{\mathrm{CC}}_{\mathrm{W}}\right)+{\displaystyle \sum _{i=\mathrm{1}}^N{\displaystyle \frac{{\mathrm{CF}}_i}{{\left(\mathrm{1}+r\right)}^i}}}=\mathrm{0.}

By using Eqs. (2) to (6) and the values from Table 3, the FPP for different combinations of wind-PV system is computed and given in Table 4. For simplicity, the inflation rate and escalation in C_OMPV and C_OMW costs are not considered.

6 Discussion based on EPP and FPP

EPP and FPP are the simplest and best tools to assess sustainability and to justify the investment in renewable energy systems. From Tables 3 and 4, it is observed very clearly that the PMSG-PV combination of 60 kW of the PV array and 120 kW of the wind turbine will be the best option in terms of both EPP and FPP. But, another major issue in connecting such distributed generators to grid is transmission congestion. The power profile of different combinations of the PMSG-PV system for a period of 24 h are demonstrated in Fig. 3. It can be observed from Fig. 3 that combination 4 and combination 5 which have a higher wind generation capacity give unpredictable and heavy power fluctuations. This heavy power fluctuation will lead to transmission congestion. It is very difficult to coordinate or schedule the charging of EVs even in the smart-grid environment with this wide power fluctuation.

Combinations 1, 2, and 3 seem to have almost stable power delivery for nearly 6 h in a day. The EVs can be regulated to charge their batteries during this 6 h in smart-grid scenario which will greatly reduce transmission congestion. Moreover, charging the EVs from the power generated by this hybrid distributed generator will reduce the transmission losses to a greater extent. Apart from charging EVs, normal house appliances also can be scheduled to work during this period to avoid transmission congestion. Even though combinations 4 and 5 have shorter EPP and FPP than other three combinations, it is better to avoid these combinations, due to their unpredictable power fluctuation, to avoid transmission congestion. Among combinations 1, 2 and 3, combinations 1 and 2 have longer EPP and FPP than combination 3. So, combination 3 seems to be a better option which satisfies the energy requirement of EVs charging. Besides, it also has a better EPP and FPP and mainly enables the scheduling of EVs charging, consequently reducing transmission congestion.

To validate this decision, the power generation profile of combination 3, the number of EVs under charging mode and the power consumed by EV batteries for an hour intervals are matched and depicted in Fig. 4. It is noticed clearly from Fig. 4 that most of the time, the number of EVs for charging can be regulated in such a way that, the power drawn from the utility for charging purpose is insignificant. Normally, EVs will be charged during night-time in residential blocks and will be used during day-time. But, for the chosen capacity (combination 3) of the PV and wind turbine, the EVs can be scheduled to charge during day-time only. However, this will not affect the EV movement in the day-time, since stationary energy storage (batteries) may be installed in the chosen residential block. During day-time, the energy from the PMSG-PV hybrid system is stored in these stationary batteries and subsequently used to charge the EVs during night-time. This will also greatly reduce the impact of EV charging on the utility grid. Further, domestic loads of residential blocks can be scheduled in the smart-grid environment during day-time to bring down the grid loading during the absence of charging the storage devices for the EVs.

The cumulative energy produced by the system over 24 h is 2400 kWh and the energy demand for 50 EVs is 2100 kWh. So, it is evident that the chosen PV capacity of 255 kW (peak) and wind capacity of 50 kW (combination 3) can satisfy the energy requirement and make the charging schedule convenient for a smart-grid environment.

7 Conclusions

Different possible combinations of a hybrid PMSG-PV distributed generator system are analyzed to satisfy the energy requirement of EVs in a particular residential area. These systems are designed in this paper by incorporating uncertainties of wind speed and irradiation, and hence the systems satisfy the energy requirement of EVs, even if there are small deviations in wind speed and irradiation. The combinations of the PMSG-PV system obtained will make the net energy consumed by the EVs over a period of year to zero. The EPP and FPP for all combinations are calculated to assess the sustainability of the systems. The hybrid PMSG-PV system which has a high wind capacity and a low PV capacity seems to be the economic choice as it has a lower FPP and a shorter EPP. But this choice makes it difficult to schedule the charging of EVs because of the unpredictable and wide range of fluctuation in generation. But, the PMSG-PV system with the PV capacity dominating over the wind capacity results in easier scheduling of EVs charging in smart-grid environment. Thus for the residential block taken for the case study, the capacity of the PV is 255 kW (peak) and the wind generator is 50  kW.

The sizing of renewable sources such as PV and wind for EV charging established in this paper is complex, since it needs to take into account the energy yield, the seasonal variation of this resources, the energy and financial payback period of the generators and the transmission congestion. A method incorporating all of these has been successfully implemented in this paper. The proposed methodology can be easily adopted to any geographical locality and EV population.

Notations

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