energies
Article Probabilistic Approach to Integrate Photovoltaic Generation into PEVs Charging Stations Considering Technical, Economic and Environmental Aspects
Najmat Celene Branco * and Carolina M. Affonso
Faculty of Electrical and Biomedical Engineering, Federal University of Para, Belem, PA 66075-110, Brazil; [email protected] * Correspondence: [email protected]; Tel.: +55-91-3201-7634
Received: 9 September 2020; Accepted: 27 September 2020; Published: 29 September 2020
Abstract: This paper investigates the integration of a photovoltaic system into plug-in electric vehicles charging stations in a university campus building located in Belem, Brazil, considering technical, economic, and environmental impacts in a probabilistic approach. Monte Carlo method is implemented to probabilistically estimate output variables, representing uncertainties from input data such as solar generation, vehicles demand and building load. Simulations are based on local irradiance data and electricity demand measurements collected by a local monitoring system installed in the building. The analysis comprehends a study time horizon of 10 years and evaluates transformer load and voltage level, carbon emissions avoided, and the financial feasibility of the project. Results show the connection of a PV system with penetration level of 15.6% can significantly reduce transformer overload occurrence by 69% and decrease overload duration time on average from 4 to 1 h at 10th year. PV system can reduce PEV CO2 emission by 97.4% on average compared with internal combustion engine vehicles. From a financial perspective, the project is feasible and economically attractive with a payback time that ranges from 6 to 8 years, being an attractive solution to the Amazon region to support a cleaner energy matrix.
Keywords: probabilistic analysis; Monte Carlo; plug-in electric vehicles; economic feasibility; photovoltaic system; CO2 emissions
1. Introduction The transport sector is a major use of fossil fuel, increasing greenhouse gas emission and the global warming. In 2019, the transport sector produced 8.2 Gt of CO2 emission, representing 24% of direct emissions from fuel combustion [1]. Due to the necessity to reduce air pollution, the number of plug-in electric vehicles (PEVs) has been continuously increasing in the last years, as well as the use of renewable energy sources. In 2019, the global stock of electric cars reached 7.2 million and the number of charging points worldwide was approximately 7.3 million, representing an increase of 60% from the year before [2]. In this same year, renewable energy installed capacity had its highest increase and grew 200 GW, in which more than 50% were from photovoltaic generation [3]. Despite the environmental benefits electric vehicles can bring, their large-scale integration on distribution system can cause several negative impacts such as increased system peak demand, transformer overload operation and power quality problems. Several papers have been proposed addressing the connection of electric vehicles in power system, and most of them analyze their impact on grid, use PEVs on the vehicle-to-grid (V2G) operation mode, and propose smart charging algorithms to mitigate their effects. Reference [4] proposes a charging coordination algorithm using PEVs in both operation modes grid-to-vehicle (G2V) and V2G to reduce power losses, maintain voltage profile
Energies 2020, 13, 5086; doi:10.3390/en13195086 www.mdpi.com/journal/energies Energies 2020, 13, 5086 2 of 18 and balance load current. In [5], PEVs charge/discharge pattern is optimized, and V2G operation is analyzed in an island electric grid in Spain. Reference [6] proposes a smart charging algorithm to reduce electricity consumption costs and prevent transformer overloading integrating a battery energy storage system and photovoltaic generation into the charging station. Although a huge advance has been made, PEVs smart charging and V2G operation mode are at an early stage of development and some barriers still need to be addressed. To effectively apply these concepts, it is essential to have a safe communication technology between PEVs and electric vehicle supply equipment (EVSE), following strict protocols and standards to avoid cyber-attacks and guarantee personal data protection [7]. Another important issue is that V2G operation still requires further advances to reduce battery degradation and gain consumer acceptance [8]. Also, the presence of an aggregator would be required to control the bi-directional power flow in the system. In this scenario, the integration of photovoltaic (PV) generation into PEVs charging station infrastructure can be a possible solution to properly integrate PEVs on grid and reduce their negative impacts. It could reduce carbon emission and system peak demand, avoiding transformer overload condition. Some studies have been developed addressing this issue. Reference [9] proposes the integration of a solar plant to a PEV parking lot to reduce power consumption and losses considering various operating conditions. In [10], authors study the impact of integrating solar photovoltaic panels with charging stations into a residential system in Kuwait considering reactive power compensation. Reference [11] proposes the design of a fast-charging station with storage system, solar and wind generation to improve the profitability and reduce energy consumption from the grid. In [12], authors optimize the use of PEVs, photovoltaic generation and energy storage batteries in a building in the day-ahead electricity market to maximize the local profit. Although some research has been done, the use of PV generation into PEVs charging infrastructure have some challenges that need to be addressed. The output power of photovoltaic generation may have diurnal and seasonal fluctuations and electric vehicles demand also have an uncertain pattern that varies with drivers’ behavior and preferences. Due to the difficulty in supply-demand matching, this problem must be modeled based on stochastic approach exploring different scenarios to account these variabilities. Besides, the massive use of electric vehicles is not a solution itself that should reduce carbon emission, since it depends if the source of electricity used to charge vehicles is green or comes from non-renewables sources. Then, it is important to study the impact of using PV generation and electric vehicles to reduce carbon emission and achieve a sustainable electric mobility [13]. This paper aims to assess the integration of a PV system with electric vehicles charging stations in a university campus building located in Belem, Brazil. A case study is performed based on city irradiance data and building electricity demand measurements collected by a local monitoring system. The study includes technical, economic, and environmental analysis, evaluating the impact on transformer load and voltage, financial feasibility of the project and carbon emissions avoided, constituting a probabilistic problem. Monte Carlo simulation is employed to probabilistically estimate those outputs taking into consideration uncertainties due to PV generation, building and PEVs demand. The originality and main contributions of this paper are:
The study is based on real data collected with an advanced metering infrastructure system installed • in a University Campus located in the Amazon region, in northern Brazil; Unlike other studies, PEVs demand is represented individually instead of aggregated, considering • important parameters to each vehicle, such as arriving time and battery initial state-of-charge; Uncertainties due to solar generation, PEVs and building demand are considered in the model in • a stochastic manner, using Monte Carlo simulation; The analysis simultaneously incorporates technical, economic and environmental issues allowing • a global view of the problem to power systems planning purpose, showing the benefits of using green technologies; Power system is modeled and considered in simulations through power flow analysis. • EnergiesEnergies 20202020,, 1313,, x 5086 FOR PEER REVIEW 33 of of 17 18
The rest of this paper is structured as follows. Section 2 shows the building electricity demand The rest of this paper is structured as follows. Section2 shows the building electricity demand data and the University Campus grid configuration. In Section 3, the probabilistic modeling with data and the University Campus grid configuration. In Section3, the probabilistic modeling with Monte Carlo method is presented. Section 4 discusses simulation results and Section 5 addresses the Monte Carlo method is presented. Section4 discusses simulation results and Section5 addresses the main conclusions of the paper. main conclusions of the paper.
2.2. Case Case Study: Study: Building Building Demand Demand Data Data Brazil has high solar potential, with irradiance levels relatively stable through the whole year. Brazil has high solar potential, with irradiance levels relatively stable through the whole year. In 2019, photovoltaic generation installed capacity had an increase of 37.6% (675 MW) compared to In 2019, photovoltaic generation installed capacity had an increase of 37.6% (675 MW) compared to the the previous year, and the solar photovoltaic market is expected to keep growing. The electric previous year, and the solar photovoltaic market is expected to keep growing. The electric automotive automotive market in Brazil is also projected to increase in the next years, although still being market in Brazil is also projected to increase in the next years, although still being dominated by the dominated by the ethanol industry, which accounts for 45.4% of the country’s CO2 emission [14]. ethanol industry, which accounts for 45.4% of the country’s CO2 emission [14]. In this context, this work analyzes the integration effects of a grid-connected photovoltaic system In this context, this work analyzes the integration effects of a grid-connected photovoltaic system with four electric vehicle charging stations installed at the central library of the main campus of with four electric vehicle charging stations installed at the central library of the main campus of Federal Federal University of Para, in the Amazon region of Brazil. Installing charging points on campus is a University of Para, in the Amazon region of Brazil. Installing charging points on campus is a great great addition to any institution from a business point of view, showing social responsibility and addition to any institution from a business point of view, showing social responsibility and environment environment commitment. The main campus structure includes 84 lecture and administrative commitment. The main campus structure includes 84 lecture and administrative buildings, multiple buildings, multiple libraries, and a hospital. Electricity is supplied to the university through an libraries, and a hospital. Electricity is supplied to the university through an overhead power line at overhead power line at 13.8 kV, and the library building is equipped with a 225 kVA 13.8 kV/127 V 13.8 kV, and the library building is equipped with a 225 kVA 13.8 kV/127 V transformer, as illustrated transformer, as illustrated in Figure 1. in Figure1.
Figure 1. Single-line diagram of campus distribution system. Figure 1. Single-line diagram of campus distribution system. The central library building has two floors and occupies an area of 6177.81 m2 as shown in Figure2. It hasThe more central than library 67 employees building andhas two the openingfloors and hours occupies are from an area 8 h of to 6177.81 22 h from m2 as Monday shown toin Figure Friday, 2.and It has from more 8 h tothan 14 67 h onemployees Saturday. and An the advanced opening metering hours are infrastructure from 8 h to 22 system h from named Monday SISGEE to Friday, was and from 8 h to 14 h on Saturday. An advanced metering infrastructure system named SISGEE was
Energies 2020, 13, x FOR PEER REVIEW 4 of 17 Energies 2020,, 13,, 5086x FOR PEER REVIEW 4 of 17 18 installedinstalled atat thethe buildingsbuildings [15][15] andand thethe monthlymonthly peakpeak loadload registeredregistered inin 20182018 isis shownshown inin FigureFigure 3a.3a. TheinstalledThe buildingbuilding at the hashas buildings anan administrativeadministrative [15] and the profileprofile monthly andand itsits peak electricityelectricity load registered demanddemand includes inincludes 2018 is cooling,cooling, shown lighting, inlighting, Figure andand3a. Thethethe buildinguseuse ofof electricalelectrical has an administrative appliancesappliances suchsuch profile asas and computcomput its electricityers,ers, printers,printers, demand andand includes photocphotoc cooling,opiers.opiers. lighting,TheThe demanddemand and the isis influenceduseinfluenced of electrical byby differentdifferent appliances features,features, such as such computers,such asas regularregular printers, classclass and periodperiod photocopiers. andand vacation/holidayvacation/holiday The demand is period.period. influenced TheThe academicbyacademic different yearyear features, calendarcalendar such atat asthethe regular universityuniversity class isis period dividividedded and intointo vacation twotwo semesters.semesters./holiday period. TheThe firstfirst The semestersemester academic classesclasses year usuallycalendarusually startstart at the onon university MarchMarch andand is end dividedend inin June.June. into twoTheThe semesters.secondsecond semestersemester The first classesclasses semester startstart classesonon AugustAugust usually andand start endend on inin December.MarchDecember. and end in June. The second semester classes start on August and end in December.
Figure 2. Central Library building on main campus. Figure 2. Central Library building on main campus.
140 120 140 120 120 100 120 100 100 80 100 80 80 80 60 60 60 60 40 40 40
40 Active (kW) Power Peak Load(kW) 20 Active (kW) Power
Peak Load(kW) 20 20 20 0 0 0 00:15:39 22:52:42 00:09:00 0 02:18:42 04:21:49 06:25:00 08:30:22 10:33:03 12:36:29 14:39:47 16:43:04 18:46:15 20:49:28 Jan Fev Mar Apr May Jun Jul Aug Sept Oct Nov Dec 02:12:00 04:14:57 06:17:54 08:20:57 10:25:18 12:29:31 14:33:24 16:38:04 18:42:40 20:45:53 22:48:58 00:51:49 02:54:46 04:57:40 07:00:33 09:03:30 11:07:51 13:12:13 15:16:39 17:20:47 19:24:41 21:27:58 23:31:02 01:36:12 03:39:13 05:42:08 07:45:08 09:49:06 11:53:52 13:58:18 16:02:58 18:07:17 20:10:47 22:13:43 00:16:47 02:19:49 04:22:50 06:25:44 08:28:37 10:33:08 12:37:40 14:41:31 16:46:55 18:50:49 20:53:57 22:56:58 00:59:56 03:02:52 05:05:34 07:08:22 09:11:40 11:15:12 13:20:14 15:24:06 17:29:00 19:33:06 21:36:09 23:39:09 01:42:04 03:45:13 05:48:13 07:51:16 09:54:17 11:57:29 14:00:32 16:03:43 18:06:51 20:09:43 22:12:44 00:15:39 22:52:42 00:09:00 02:18:42 04:21:49 06:25:00 08:30:22 10:33:03 12:36:29 14:39:47 16:43:04 18:46:15 20:49:28 Jan Fev Mar Apr May JunMonth Jul Aug Sept Oct Nov Dec 02:12:00 04:14:57 06:17:54 08:20:57 10:25:18 12:29:31 14:33:24 16:38:04 18:42:40 20:45:53 22:48:58 00:51:49 02:54:46 04:57:40 07:00:33 09:03:30 11:07:51 13:12:13 15:16:39 17:20:47 19:24:41 21:27:58 23:31:02 01:36:12 03:39:13 05:42:08 07:45:08 09:49:06 11:53:52 13:58:18 16:02:58 18:07:17 20:10:47 22:13:43 00:16:47 Time02:19:49 04:22:50 06:25:44 08:28:37 10:33:08 (hours)12:37:40 14:41:31 16:46:55 18:50:49 20:53:57 22:56:58 00:59:56 03:02:52 05:05:34 07:08:22 09:11:40 11:15:12 13:20:14 15:24:06 17:29:00 19:33:06 21:36:09 23:39:09 01:42:04 03:45:13 05:48:13 07:51:16 09:54:17 11:57:29 14:00:32 16:03:43 18:06:51 20:09:43 22:12:44 Month Monday Tuesday WednesdayTime Thursday (hours) Friday Saturday Sunday Classes Period Vacation Period Average Value during Classes Period Monday TuesdayPhase Wednesday A Phase Thursday B Phase C FridayTotal Saturday Sunday Classes Period Vacation Period Average Value during Classes Period Phase A Phase B Phase C Total ((aa)) ((bb)) FigureFigure 3.3. BuildingBuilding demand demand in in 2018. 2018. (a ()a )Monthly Monthly peak peak load; load; (b ()b Daily) Daily demand demand during during 1-week 1-week in Figure 3. Building demand in 2018. (a) Monthly peak load; (b) Daily demand during 1-week in inNovember. November. November. The electricity consumption during vacation period is considerably lower than during the classes TheThe electricityelectricity consumptionconsumption duringduring vacationvacation periodperiod isis considerablyconsiderably lowerlower thanthan duringduring thethe period, except in July of 2018, since in this year classes were extended until July 17th. Figure3b presents classesclasses period,period, exceptexcept inin JulyJuly ofof 2018,2018, sincesince inin thisthis yearyear classesclasses werewere extendedextended untiluntil JulyJuly 17th.17th. FigureFigure the active power measured in the three phases of the transformer that supplies the building for 1-week 3b3b presentspresents thethe activeactive powerpower measuredmeasured inin thethe ththreeree phasesphases ofof thethe transformertransformer thatthat suppliessupplies thethe period from 19 to 25 November 2018. The energy consumption is higher between 9 h and 22 h during buildingbuilding for for 1-week 1-week period period from from 19 19 to to 25 25 November November 2018. 2018. The The energy energy consumption consumption is is higher higher between between weekdays, and much lower on weekends. The distribution transformer at the central library building 99 hh andand 2222 hh duringduring weekdays,weekdays, andand muchmuch lowerlower onon weekends.weekends. TheThe distributiondistribution transformertransformer atat thethe uses only 55% of its rated capacity but may become overloaded with PEVs demand. The load profile centralcentral librarylibrary buildingbuilding usesuses onlyonly 55%55% ofof itsits ratedrated capacitycapacity butbut maymay becomebecome overloadedoverloaded withwith PEVsPEVs of this week was adopted as the base load to perform all analyses since it follows the average peak demand.demand. TheThe loadload profileprofile ofof thisthis weekweek waswas adoptedadopted asas thethe basebase loadload toto performperform allall analysesanalyses sincesince itit load during the classes period. followsfollows thethe averageaverage peakpeak loadload duringduring thethe classesclasses period.period. 3. Probabilistic Modeling 3.3. ProbabilisticProbabilistic ModelingModeling Since many uncertainties are involved in this problem, Monte Carlo simulation is applied according SinceSince manymany uncertaintiesuncertainties areare involvedinvolved inin thisthis problem,problem, MonteMonte CarloCarlo simulationsimulation isis appliedapplied toaccording Figure4. to All Figure input 4. variables All input with variables inherent with uncertainties inherent uncertainties are represented are represented by a probability by a probability density accordingfunction (PDF) to Figure [16]. 4. Random All input values variables are sampled with inherent following uncertainties their input are PDF represented several times, by a and probability outputs densitydensity functionfunction (PDF)(PDF) [16].[16]. RandomRandom valuesvalues areare sampledsampled followingfollowing theirtheir inputinput PDFPDF severalseveral times,times, valuesand outputs are obtained values and are recorded.obtained and Each recorded. set of samples Each isse calledt of samples an iteration is called and an the iteration result is and a probability the result anddistribution outputs values of possible are obtained outputs. and Monte recorded. Carlo Each simulation set of samples informs is all called possible an iteration output and values the ofresult the isis aa probabilityprobability distributiondistribution ofof possiblepossible outputs.outputs. MonteMonte CarloCarlo simulationsimulation informsinforms allall possiblepossible outputoutput modelvalues andof the their model probability and their of probability occurrence. of Theoccurrence. methodology The methodology applied includes applied technical, includes economic, technical, valuesand environmental of the model analysis.and their Technicalprobability analysis of occurrence. evaluates The the methodology impact on transformer applied includes load and technical, voltage economic,economic, andand environmentalenvironmental analysis.analysis. TechnicalTechnical analanalysisysis evaluatesevaluates thethe impactimpact onon transformertransformer loadload andand voltage voltage profile, profile, and and the the possible possible benefits benefits that that could could be be obtained obtained installing installing PEVs PEVs charging charging stations stations
Energies 2020, 13, 5086 5 of 18
Energies 2020, 13, x FOR PEER REVIEW 5 of 17 profile, and the possible benefits that could be obtained installing PEVs charging stations and PV system.and PV Thesystem. economic The economic approach approach investigates investigates the financial the feasibilityfinancial feasibility of the project of the using project economic using indicators,economic indicators, and the environmental and the environmental analysis estimates analys carbonis estimates emissions carbon avoided emissions with avoided the installation with the of PEVsinstallation charging of PEVs stations charging and PV stations system. and PV system.
Figure 4. Methodology applied.
System voltage is obtained with power flow simulations, and transformer load (Pt ) is obtained System voltage is obtained with power flow simulations, and transformer transload f (𝑃 ) is asobtained in Equation as in Equation (1): (1): t t t t P = P + Ppev P , t T, (1) trans f build − PV ∀ ∈ ∈ 𝑃 = 𝑃 +𝑃 − 𝑃 , ∀ t T, (1) t t t where P is building demand, Ppev is PEV total demand, and P is photovoltaic generation at time where 𝑃build is building demand, 𝑃 is PEV total demand, andPV 𝑃 is photovoltaic generation at t, all given in kW. time Thet, all number given in of kW. iterations needed to achieve convergence in Monte Carlo method was 3000. All steady-stateThe number simulations of iterations are performed needed to usingachieve MATLAB convergence and PSAT in Monte software Carlo [ 17method,18]. Input was 3,000. variables All modelingsteady-state are simulations presentednext. are performed using MATLAB and PSAT software [17,18]. Input variables modeling are presented next. 3.1. Building Electricity Demand 3.1. Building Electricity Demand Based on the average load profile during classes period, building demand is projected 10-years aheadBased as shown on the in average Figure 5load. This profile projection during is classes based onperiod, a yearly building increase demand of 6.0% is projected established 10-years from historicalahead as shown data analysis. in Figure To consider5. This projection uncertainty, is based building on demanda yearly isincrease modeled of by6.0% a normal established probability from densityhistorical function data analysis. for a 24-h To period, consider which uncertainty, is commonly building used todemand model electricityis modeled load, by witha normal mean followingprobability the density hourly function demand for and a standard24-h period, deviation which of is 2% commonly [19]. used to model electricity load, with mean following the hourly demand and standard deviation of 2% [19].
Figure 5. Building demand projected to 10-years ahead.
Energies 2020, 13, x FOR PEER REVIEW 9 of 57
feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbWexLMBbXgBd9gzLbvyNv2CaeHbl7mZLdGeaGqiVCI8FfYJH8 YrFfeuY=Hhbbf9v8qqaqFr0xc9pk0xbba9q8WqFfeaY=biLkVcLq=J Hqpepeea0=as0Fb9pgeaYRXxe9vr0=vr0=vqpWqaaeaabiGaciaaca qabeaadaqaaqaafaGcbaaeaaaaaaaaa8qacaWGqbWdamaaDaaaleaa peGaamiuaiaadAfaa8aabaWdbiaadshaaaaaaa@4320@ is photovoltaic generation at time t, all given in kW. The number of iterations needed to achieve convergence in Monte Carlo method was 3,000. All steady-state simulations are performed using MATLAB and PSAT software [17,18]. Input variables modelingEnergies 2020 are, 13 presented, x FOR PEER next. REVIEW 11 of 57
3.1. Building Electricity Demand
where
Figure 6. Normal PDF of start charging time. Since there is no statistical data of daily distance traveled by domestic vehicles in Brazil, this parameter is modeled by a Weibull PDF, which is commonly used to model driving distance parameter [22,23]. Based on the city’s typical route, mean value of 46 km was adopted as illustrated in Figure7. In this route, the driver leaves the university (point A), stops at point B which could be a grocery store or a mall, and goes home (point C). In the next day, the driver returns to the university (point A) and charges his vehicle. Energies 2020, 13, x FOR PEER REVIEW 6 of 17
3.2. Electrical Vehicle Demand Instead of aggregated, PEVs demand is modeled individually to achieve a more precise representation. PEVs daily demand curve can be obtained from three information: start charging time, the required time to charge each vehicle and the charging power. This study considers that vehicles start charging immediately after arriving at the building. The start charging time (Tstart) is modeled based on the occupation of the building observed over several weeks, which indicated the library has a bigger occupancy rate around 14 h and is open between 8 h and 22 h during the week. Based on that, random numbers are generated from the normal distribution with mean equal to 14 h
and standard deviation of 2 h as shown in Figure 6. The battery initial state-of-charge (𝑆𝑂𝐶 ) can be calculated using the driving distance (d) as in Equation (2) [20,21]:
× 𝑆𝑂𝐶 = 𝑚𝑎𝑥 𝑆𝑂𝐶 ,1− , (2)
where E is the battery energy consumed in kWh/km, Cb is the battery capacity in kWh, and SOCmin is the minimum SOC assumed as 5%.
Figure 6. Normal PDF of start charging time.
Since there is no statistical data of daily distance traveled by domestic vehicles in Brazil, this parameter is modeled by a Weibull PDF, which is commonly used to model driving distance parameter [22,23]. Based on the city’s typical route, mean value of 46 km was adopted as illustrated in Figure 7. In this route, the driver leaves the university (point A), stops at point B which could be a
Energiesgrocery2020 ,store13, 5086 or a mall, and goes home (point C). In the next day, the driver returns to the university7 of 18 (point A) and charges his vehicle.
(a) (b)
FigureFigure 7. Driving7. Driving distance distance modeling. modeling. (a )(a Driving) Driving route; route; (b )(b Weibull) Weibull PDF. PDF.
The energy required Ereq to charge the vehicle can be evaluated by Equation (3), adopting efficiency η equal to 0.8: Ereq = (1 SOC ) (E/η), (3) − i × The necessary time to charge the vehicles (Tcharge) is obtained by Equation (4). PEVs are modeled as constant power loads, charged with P and unity power factor:
Ereq T = (4) charge P
t The aggregated PEV demand (Ppev) can be obtained taking in account each PEV demand j at t time t (PPEV j) as in Equation (5). This study considers PEVs are charged at Level 2 (6.6 kW), which is adequate to public buildings [24,25] Two types of vehicles are adopted with parameters presented in Table1: Nissan Leaf and Chevrolet Bolt [26,27].
XN Pt = Pt , t T, (5) pev j=1 PEV j ∀ ∈
Table 1. PEV Battery Specification.
Vehicle Battery Specification Nissan Leaf Chevrolet Bolt Battery Energy (E) 0.1058 kWh/km 0.1567 kWh/km Battery Capacity (Cb) 40 kWh 60 kWh
3.3. Photovoltaic Generation Solar irradiance is commonly represented by a Beta PDF as shown in Equation (6):
Γ(αt+βt) (αt 1) (βt 1) t − t − t t t t ( t) ( t) s 1 s , 0 s 1, α , β 0 fs = Γ α Γ β × × − ≤ ≤ ≥ , (6) 0 , otherwise where st is solar irradiance in W/m2, Γ is Gamma function, αt and βt are the shape parameters calculated based on solar irradiance mean µt and standard deviation σt at time t, as shown in Equation (7): µt 1 µt t t t t t µ β β = 1 µ − 1 and α = , (7) − × ( t)2 − (1 µt) σ − Energies 2020, 13, 5086 8 of 18
This paper uses data of ambient temperature and solar irradiance from the city of Belem (1◦5010 S, 48◦4490 W), Brazil, available at National Renewable Energy Laboratory (NREL) website [28]. Based on irradiance historical data, the hourly mean and standard deviation are estimated to capture solar irradiance oscillations, obtaining Beta PDF parameters. Then, irradiance random values are generated according to the PDF to each hour of the day. The PV system output power can be obtained by Equations (8) and (9) [29]: ! P st h i Pt st = n 1 + λ Tt 25 , (8) PV 1000 × cell − st Tt st = Tt + (NOCT 20), (9) cell a 800 − t where s is the irradiance at time t, Pn is nominal power of the PV module in W, λ is the temperature t t coefficient of power in %/◦C, Tcell is cell temperature, Ta is ambient temperature, and NOCT is the nominal operating cell temperature (45 ◦C), all temperatures in ◦C. Figure8 shows the monthly solar irradiance and temperature in Belem. The region where Federal University of Para is located is privileged in terms of solar irradiance, with a maximum value of 5.38 kWh/m2/day for the month of September and a minimum of 4.4 kWh/m2/day for the month of February [30]. The ambient temperature has little variation, in a rage of values from 24.5 ◦C to 32.8 ◦C. The number of required PV panels is obtained as follows in Equation (10):
max Ed Energies 2020, 13, x FOR PEER REVIEWN◦. o f Panels = ,8 (10)of 17 η (1 L) P PSH inv × − × kWp × where 𝐸max is the maximum daily energy consumed from PEVs in kWh, 𝜂 is the inverter where Ed is the maximum daily energy consumed from PEVs in kWh, ηinv is the inverter efficiency, efficiency, 𝑃 is the module peak power, L is the losses coefficient, and PSH is the peak-sun hour. PkWp is the module peak power, L is the losses coefficient, and PSH is the peak-sun hour.
6 Annual mean 4
2
Irradiance (kW/m²)Irradiance 0 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Month 35
C) Annual mean o 30 25 20 15 10 5
Temperature ( Temperature 0 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Month Figure 8. Monthly averaged solar irradiance and temperature in Belem, Brazil. Figure 8. Monthly averaged solar irradiance and temperature in Belem, Brazil. The photovoltaic system proposed in this study is composed by BYD 335P6D-36 modules of 335 Wp andThe Froniusphotovoltaic inverter system model proposed 27.0-3-S, in withthis study specifications is composed listed by inBYD Table 335P6D-362[ 31,32 ].modules The average of 335 irradianceWp and Fronius was used inverter to obtain model the peak-sun 27.0-3-S, hourwith (PSH), specif andications around listed 105 in modules Table 2 are [31,32]. required The to average ensure systemirradiance operation was used throughout to obtain the the year. peak-sun The maximum hour (PSH), number and around of PV modules 105 modules in series are in required a string isto calculatedensure system by Equation operation (11), throughout and the maximum the year. number The maximum of strings number connected of PV to themo inverterdules in isseries given in by a thestring Equation is calculated (12) [33 by]: Equation (11), and the maximum number of strings connected to the inverter max is given by the Equation (12) [33]: VINV Nseries < , (11) V oc 𝑁 < , (11) max IINV N = , (12) str 𝑁 = Isc , (12) where 𝑉 is the maximum inverter DC input voltage, 𝑉 is the module open circuit voltage, 𝐼 is maximum inverter DC current, 𝐼 is the module short circuit current.
Table 2. Inverter and PV Module Specifications.
PV Module Peak Power 335 W Rated voltage Vmp 37.35 V Rated current Imp 8.97 A Open circuit voltage Voc 47.28 V Short circuit current Isc 9.39 A Efficiency 17.4% Dimension 1.95 m × 0.98 m Inverter Maximum PV power 37.8 kW Nominal power 27 kW MPPT voltage range 580–850 V DC input voltage range 580–1000 V
According to equipment specification, the proposed PV model arrangement consists of five parallel lines and each line has 21 panels connected in series, with a total peak power of 35.17 kW, using a total area of 200 m2. Operation at unity power factor was assumed, which is normal for PV inverters.
Energies 2020, 13, 5086 9 of 18
max max where VINV is the maximum inverter DC input voltage, Voc is the module open circuit voltage, IINV is maximum inverter DC current, Isc is the module short circuit current.
Table 2. Inverter and PV Module Specifications.
PV Module Peak Power 335 W Rated voltage Vmp 37.35 V Rated current Imp 8.97 A Open circuit voltage Voc 47.28 V Short circuit current Isc 9.39 A Efficiency 17.4% Dimension 1.95 m 0.98 m × Inverter Maximum PV power 37.8 kW Nominal power 27 kW MPPT voltage range 580–850 V DC input voltage range 580–1000 V
According to equipment specification, the proposed PV model arrangement consists of five parallel lines and each line has 21 panels connected in series, with a total peak power of 35.17 kW, using a total area of 200 m2. Operation at unity power factor was assumed, which is normal for PV inverters.
4. Simulation Results
4.1. Technical Analysis This section analyzes potential impacts on distribution transformer load and voltage for a study time horizon of 10 years. The results consider first the connection of PEV charging stations, and then Energies 2020, 13, x FOR PEER REVIEW 41 of 57 the connection of PEV charging stations combined with the proposed PV generation system.
4.1.1. PEV Charging Stations This case supposes the connection of 4 PEV PEV charging charging stations stations at thethe central central library library buildingbuilding withoutwithout thethe installationinstallation of aa PVPV system.system. Figure9 9 shows shows thethe cumulativecumulative distributiondistribution functionfunction (CDF)(CDF) of transformertransformer peak load during a typical weekday, forfor yearyear 11 toto 10.10. TheThe peakpeak hourshours onon building’sbuilding’s loadload coincidescoincides with the periodperiod most PEVs are charging,charging, leading to transformer overload condition. Simulation results show that at year 8, the probabilityprobability of transformertransformer overload is 5.36%,5.36%, at year 9 is 77.9%, and at yearyear 1010 isis 100%,100%, whichwhich isis unacceptable.unacceptable.
Figure 9. CDF of transformer peak load for different years. Figure 9. CDF of transformer peak load for different years.
Since year 10 is the most critical scenario, it will be analyzed in more details. Figure 10 shows transformer peak load boxplot during a typical weekday at year 10. Boxplot is a graphical representation that displays the distribution of variables. The box contains 50% of data, the lower and upper boundaries of the box are the first quartile and the third quartile, and the median is represented by a solid line across the box. The lines that extend from the box to minimum and maximum values are known as whiskers.
Figure 10. Boxplot of transformer load at year 10.
Results indicate transformer load exceeds its nominal capacity between 11 h and 16 h, and in most cases transformer remains overloaded during 4 h according to Figure 11. Transformer overloaded operation is only acceptable during emergency or contingency conditions, and for a small period. In this case, since the time transformer remains overloaded is long, it can cause equipment premature aging, not being encouraged. This restriction may limit the installation of PEVs charging stations at this building if no additional measures are adopted, as upgrading transformer, or installing a PV generation system.
Energies 2020, 13, x FOR PEER REVIEW 41 of 57
4.1.1. PEV Charging Stations This case supposes the connection of 4 PEV charging stations at the central library building without the installation of a PV system. Figure 9 shows the cumulative distribution function (CDF) of transformer peak load during a typical weekday, for year 1 to 10. The peak hours on building’s load coincides with the period most PEVs are charging, leading to transformer overload condition. Simulation results show that at year 8, the probability of transformer overload is 5.36%, at year 9 is 77.9%, and at year 10 is 100%, which is unacceptable.
Energies 2020, 13, 5086 10 of 18 Figure 9. CDF of transformer peak load for different years.
Since year 1010 isis thethe mostmost criticalcritical scenario,scenario, itit willwill bebe analyzedanalyzed inin moremore details.details. Figure 10 showsshows transformertransformer peak peak load load boxplot boxplot during during a typical a typical weekday weekday at year 10. at Boxplot year 10. is a Boxplotgraphical is representation a graphical thatrepre displayssentation the that distribution displays ofthe variables. distribution The of box variables. contains The 50% box of data, contains the lower 50% andof data, upper the boundaries lower and ofupper the boxboundaries are the firstof the quartile box are and the first the thirdquartile quartile, and the and third the quartile, median and is represented the median byis represented a solid line acrossby a solid the line box. across The linesthe box. that The extend lines from that theexten boxd from to minimum the box to and minimum maximum and values maximum are known values asare whiskers. known as whiskers.
FigureFigure 10.10. Boxplot of transformer load at year 10.
Results indicateindicate transformer transformer load load exceeds exceeds its its nominal nominal capacity capacity between between 11 h 11 and h 16and h, 16 and h, inand most in casesmost transformer cases transformer remains remains overloaded overloaded during 4during h according 4 h according to Figure 11 to. Transformer Figure 11. Transformer overloaded operationoverloaded is operation only acceptable is only during acceptable emergency during oremergency contingency or contingency conditions, conditions, and for a small and for period. a small In thisperiod. case, In since this case, the time since transformer the time transformer remains overloaded remains overloaded is long, it can is long, cause it equipment can cause equipment premature aging,premature not beingaging, encouraged.not being encouraged. This restriction This restriction may limit may the installationlimit the installation of PEVs chargingof PEVs charging stations Energiesatstations this 20 building 20 at, 1 3 this, x FOR if building no PEER additional REVIEW if no measures additional are measures adopted, are as upgrading adopted, astransformer, upgrading or transformer, installing42 aof PV57 or generationinstalling a system.PV generation system.
Figure 11. Histogram of number of hours transformer operates overloaded at year 10. Figure 11. Histogram of number of hours transformer operates overloaded at year 10. The voltage on the low side of transformer is not affected even when machine operates overloaded. The voltage on the low side of transformer is not affected even when machine operates The effect of PEV demand on transformer voltage profile is unnoticeable since this bus is electrically overloaded. The effect of PEV demand on transformer voltage profile is unnoticeable since this bus close to the feeding point. To illustrate this, Figure 12 presents the voltage profile at year 10, when is electrically close to the feeding point. To illustrate this, Figure 12 presents the voltage profile at PEVs are connected in two different places: close to the feeding point at location 1 (central library year 10, when PEVs are connected in two different places: close to the feeding point at location 1 building), and far away from the feeding point at location 2. (central library building), and far away from the feeding point at location 2.
Figure 12. Voltage at low-voltage side of transformer.
4.1.2. PEV Charging Stations with PV Generation This case considers the installation of four electric vehicles charging stations and a PV system in the central library parking lot. Results are referred to the most critical year (year 10) and a sensibility analysis is performed considering two cases with different photovoltaic penetration levels (PL): • Case 1: PL = 15.63% (35.17 kW); • Case 2: PL = 23.52% (52.93 kW). The first case refers to the PV system projected to supply 100% of PEVs electricity demand. The second case refers to a bigger PV system, projected to supply 100% of PEVs electricity demand and part the building’s electricity demand. This study defines the PV penetration level as the ratio of PV peak power to the transformer nominal capacity, being calculated by Equation (13) [34].
Energies 2020, 13, x FOR PEER REVIEW 10 of 17 overloaded operation is only acceptable during emergency or contingency conditions, and for a small period. In this case, since the time transformer remains overloaded is long, it can cause equipment premature aging, not being encouraged. This restriction may limit the installation of PEVs charging stations at this building if no additional measures are adopted, as upgrading transformer, or installing a PV generation system.
1200
1000
800
600
400
200
0 1234567 Hours transformer operates overloaded
Figure 11. Histogram of number of hours transformer operates overloaded at year 10.
The voltage on the low side of transformer is not affected even when machine operates overloaded. The effect of PEV demand on transformer voltage profile is unnoticeable since this bus is electrically close to the feeding point. To illustrate this, Figure 12 presents the voltage profile at
Energiesyear 10,2020 when, 13, 5086 PEVs are connected in two different places: close to the feeding point at location11 of 181 (central library building), and far away from the feeding point at location 2.
Figure 12. VoltageVoltage at low-voltage side of transformer.
4.1.2. PEV Charging Stations with PV Generation This case considers the installation of four electric vehicles charging stations and a PV system in the central library parking lot. Results Results are are referred to the most critical year (year 10) and a sensibility analysis is performed considering two cases with didifferentfferent photovoltaicphotovoltaic penetrationpenetration levelslevels (PL):(PL): • Case 1: PL = 15.63%15.63% (35.17 (35.17 kW); kW); • • Case 2: PL = 23.52%23.52% (52.93 (52.93 kW). kW). • The first first case case refers refers to to the the PV PV system system projected projected to supply to supply 100% 100% of PEVs of PEVs electricity electricity demand. demand. The Thesecond second case case refers refers to a to bigger a bigger PV PV system, system, projected projected to tosupply supply 100% 100% of of PEVs PEVs electricity electricity demand demand and part the building’s electricity demand. This study de definesfines the PV penetration level as the ratio of PV peak power to the transformer nominalnominal capacity,capacity, beingbeing calculatedcalculated byby EquationEquation (13)(13) [[34].34]. 𝑃𝐿 = 100 × PV Peak Power , (13) PL = 100 , (13) × Trans f ormer Rated Capacity Figure 13 shows the boxplot of transformer peak load for both PV penetration levels during 24 h at year 10. The probability of transformer overloading is considerably reduced as PV peak power increases. In Case 1, transformer overloading probability is 31.2% and in Case 2, it is 8.2%. Figure 14 illustrates the cumulative distribution function of transformer daily peak load for both PV penetration levels. In both cases, transformer operates overloaded only for 1 h in most scenarios, as shown in TableEnergies3 .20 Thus,20, 13, bothx FOR PVPEER penetration REVIEW levels are acceptable, and the installation of a PV system of44 35.17 of 57 kW to 52.9 kW can help to reduce transformer overload occurrence.
Figure 13. Boxplot of transformer load at year 10. Figure 13. Boxplot of transformer load at year 10.
Figure 14. Transformer peak load CDF for two PV levels at year 10.
Table 3. Probability of Number of Hours Transformer Overloads.
Number of Hours Transformer
Operates Overloaded PV Level 1 h 2 h 3 h PL = 15.6% 88.5% 11.1% 0.4% PL = 23.5% 97.2% 2.8% 0%
4.2. Economic Analysis In this section, cash flow analysis is applied, and to check the viability of the investment some financial indicators are evaluated: net present value (NPV), internal rate of return (IRR) and payback (PB) [35]. Net present value is a financial indicator extensively used across finance to analyze the profitability of an investment, accounting the difference between all cash inflows and cash outflows during a period of time, as shown in Equation (14):
Energies 2020, 13, x FOR PEER REVIEW 44 of 57
Energies 2020, 13, 5086 12 of 18 Figure 13. Boxplot of transformer load at year 10.
Figure 14. Figure 14. Transformer peak load CDF for two PV levelslevels atat yearyear 10.10. Table 3. Probability of Number of Hours Transformer Overloads. Table 3. Probability of Number of Hours Transformer Overloads. Number of Hours Transformer Operates Overloaded Number of Hours Transformer
PV Level 1 hOperates Overloaded 2 h 3 h PL = 15.6%PV Level 88.5%1 h 2 h 11.1%3 h 0.4% PL = 23.5%PL = 15.6% 97.2%88.5% 11.1% 2.8% 0.4% 0% PL = 23.5% 97.2% 2.8% 0% 4.2. Economic Analysis 4.2. Economic Analysis In this section, cash flow analysis is applied, and to check the viability of the investment some financialIn this indicators section, arecash evaluated: flow analysis net present is applied, value and (NPV), to check internal the viability rate of return of the (IRR) investment and payback some (PB)financial [35]. indicators Net present are valueevaluated: is a financial net present indicator value (NPV), extensively internal used rate across of return finance (IRR) to and analyze payback the profitability(PB) [35]. Net of present an investment, value is accounting a financialthe indicator difference extensively between used all cash across inflows finance and to cash analyze outflows the duringprofitability a period of an of investment, time, as shown accounting in Equation the difference (14): between all cash inflows and cash outflows during a period of time, as shown in Equation (14): XT t NPV = C0 + CFt(1 + r)− , (14) where C0 is the investment cost in $, T is the number of years the investment is analyzed, r is the discount rate in %, and CFt is the cash flow in
XT t C0 + CFt(1 + IRR)− = 0, (15) − t=1 The duration time of the project is 25 years, which corresponds to solar panels lifetime. Equipment’s costs and rates were collected from manufacturers datasheet and website, which are listed in Table4[37–40] . The electricity time-of-use (TOU) rate adopted by local utility company is presented in Table4. Free parking is available on campus throughout the day, which is a common practice in Brazil. This study considers university will also offer free charging to electric vehicles as a financial incentive to encourage its use and reduce carbon footprint. Energies 2020, 13, 5086 13 of 18
Table 4. Equipment Costs and Financial Parameters.
Equipment costs (PL = 15.6%) R$ 141,993.24 Equipment costs (PL = 23.5%) R$ 217,359.67 O&M costs 1%/year Inflation rate 5%/year Energy price increase rate 8%/year Panel degradation rate 0.8%/year TOU Rate (peak hours 19 h–22 h) 2.63 R$/kWh TOU Rate (off-peak hours) 0.31 R$/kWh
The Monte Carlo method was applied to evaluate the financial indicators and results are presented in Figure 15. In Case 1 (PL = 15.6%), the implementation of the system will yield an average NPV value of R$385,499, with minimum and maximum values of R$217,640 and R$516,970 respectively, and IRR has an average value of 22.3%. In Case 2 (PL = 23.5%), NPV has an average value of R$289,789, whose minimum and maximum values are R$121,930 and R$421,260 respectively, and IRR of 15.5% as an average value. Payback results are summarized on Table5. In Case 1, payback time has an average value of 7 years, and in Case 2 an average value of 9 years. In both cases considered, NPV is always positive and IRR is greater than the minimum required rate of return of 7%, which confirms both projects are economically robust and attractive.
Table 5. Payback Statistical Results.
PLPV = 15.6% PLPV = 23.5% Minimum 6 years 8 years Mean 7 years 9 years Energies 2020, 13, x FOR PEER REVIEWMax. 8 years 11 years 13 of 17
Figure 15. NPV and IRR. (a) Case 1: PL = 15.6%; (b) Case 2: PL = 23.5%. Figure 15. NPV and IRR. (a) Case 1: PL = 15.6%; (b) Case 2: PL = 23.5%. 4.3. Environmental Analysis 4.3. Environmental Analysis When analyzing vehicles environmental impact, it is important to perform its life cycle assessment (LCA),When accounting analyzing from vehicles production environmental to use, and impact, ultimately it is itsimportant end of life to recyclingperform its or disposal,life cycle asassessment illustrated (LCA), in Figure accounting 16. These from considerations production to are use, essential and ultimately to assess theits end sustainability of life recycling of such or technologies.disposal, as illustrated Regarding in the Figure using 16. phase, These PEVs consider haveations zero are tailpipe essential carbon to assess emission the sustainability (tank-to-wheel of contribution)such technologies. since they Regarding run only the on electricity.using phase, However, PEVs have they mayzero contribute tailpipe carbon indirectly emission with upstream (tank-to- emissionswheel contribution) (well-to-tank since contribution) they run only depending on electricit on they. electrical However, energy they may source contribute used to charge indirectly vehicles. with Countriesupstream thatemissions generate (well-to-tank electricity usingcontribution) mostly renewable depending energies on the willelectrical have energy a much source lower carbonused to footprintcharge vehicles. when charging Countries PEVs, that comparedgenerate electricit with countriesy using thatmostly extract renewable energy energies primarily will from have burning a much fossillower fuels. carbon Brazil footprint has a when low-carbon charging intensity PEVs, compared (CI) grid electricity with countries since that its energy extract matrixenergy is primarily largely from burning fossil fuels. Brazil has a low-carbon intensity (CI) grid electricity since its energy matrix is largely dominated by renewables sources, according Figure 17 [41]. In 2019, the average carbon intensity of Brazilian grid was 75 gCO2/kWh [42]. As a comparison, UK grid carbon intensity was 241 gCO2/kWh in 2019 [43].
Figure 16. PEVs life cycle stages.
Energies 2020, 13, x FOR PEER REVIEW 13 of 17
Figure 15. NPV and IRR. (a) Case 1: PL = 15.6%; (b) Case 2: PL = 23.5%.
4.3. Environmental Analysis When analyzing vehicles environmental impact, it is important to perform its life cycle assessment (LCA), accounting from production to use, and ultimately its end of life recycling or disposal, as illustrated in Figure 16. These considerations are essential to assess the sustainability of such technologies. Regarding the using phase, PEVs have zero tailpipe carbon emission (tank-to- wheel contribution) since they run only on electricity. However, they may contribute indirectly with upstream emissions (well-to-tank contribution) depending on the electrical energy source used to charge vehicles. Countries that generate electricity using mostly renewable energies will have a much lowerEnergies carbon2020, 13 ,footprint 5086 when charging PEVs, compared with countries that extract energy primarily14 of 18 from burning fossil fuels. Brazil has a low-carbon intensity (CI) grid electricity since its energy matrix is largely dominated by renewables sources, according Figure 17 [41]. In 2019, the average carbon intensitydominated of Brazilian by renewables grid was sources, 75 gCO according2/kWh [42]. Figure As a17 comparison,[ 41]. In 2019, UK the grid average carbon carbon intensity intensity was 241 of gCOBrazilian2/kWh grid in 2019 was 75[43]. gCO 2/kWh [42]. As a comparison, UK grid carbon intensity was 241 gCO2/kWh in 2019 [43].
Energies 2020, 13, x FOR PEER REVIEW Figure 16. PEVs life cycle stages. 14 of 17 Figure 16. PEVs life cycle stages.
Figure 17. Brazilian energy matrix in 2019.
This section estimates carbon emissions avoided with the installation of 4 PEVs charging stations and a PV system at the central library parking lot. This analysis is limited to assess emission during the using phase. Manufacturing Manufacturing and end of life life ph phasesases are are not not considered. considered. The The following following assumptions assumptions are adopted: • 15 PEVs are daily charged atat thethe centralcentral librarylibrary parkingparking lotlot duringduring weekdays;weekdays; • • The PV emission factor is 21 gCO 2/kWh/kWh [[44];44]; • 2 • The well-to-wheelwell-to-wheel (WTW)(WTW) emission emission factor factor of of internal internal combustion combustion engine engine vehicles vehicles is 178 is gCO178 gCO[45];2 • 2 [45]; PEVs can be fully powered using electricity from grid or using electricity produced by PV system. In thePEVs latter can case, be itfully is important powered to using consider electricity situations from where grid PVor using will not electricity fully meet produced PEVs demand, by PV whichsystem. will In the becomplemented latter case, it is with important energy to from consider grid. Bothsituations cases where with di PVfferent will PV not penetration fully meet levelsPEVs demand,are analyzed: which Case will 1: PLbe =complemented15.63% and Case with 2: PLener= 23.52%.gy from The grid. annual Both CO cases2 emission with different produced PV by penetrationPEVs can be levels evaluated are byanalyzed: Equation Case (16): 1: PL = 15.63% and Case 2: PL = 23.52%. The annual CO2 emission produced by PEVs can be evaluated by Equation (16): ( ) = grid + PV EmPEV gCO2 Nd.EPEV .Fgrid Nd. EPEV .FPV, (16) 𝐸𝑚 (𝑔𝐶𝑂 ) =𝑁 .𝐸 .𝐹 +𝑁 .𝐸 .𝐹 , (16) grid where Nd refersrefers to to the the number number or or days days in in the the year year excluding excluding weekends, weekends, 𝐸E PEV is the daily energy PV consumed from grid to charge PEVs inin kWh,kWh, FFgridgrid isis the the grid grid emission factor factor in in gCO 2/kWh,/kWh, 𝐸E PEV is the daily energy consumed from PV toto chargecharge PEVsPEVs inin kWh,kWh, andand FFPVPV isis the PV emission factor in gCO2/kWh./kWh. Emissions from internal combustion engine vehicles (ICEV) are estimated based on Equation (17), as a reference for comparison purposes:
𝐸𝑚 (𝑔𝐶𝑂 ) =𝑁 .𝑁 .𝑑.𝐹 , (17)
where Nv is the number of vehicles per day, d is the daily distance traveled, and FICEV is the carbon emission of combustion engine vehicles in gCO2/km. The probability density function of CO2 emissions produced by PEVs and ICEV after the 10-year period are presented in Figure 18. Results show an average of 20.4 tonCO2 produced when PEVs are charged entirely from grid. When PEVs are charged from PV generation and complemented from grid, an average value of 7.9 tonCO2 is obtained in Case 1 and 7.1 tonCO2 in Case 2, where PV generation is bigger. Comparing with the average value of 299.5 tonCO2 produced when using only internal combustion engine vehicles, a substantial reduction of 93.2% is obtained when PEVs charged entirely from grid and 97.4% when PEVs are charged with local PV generation.
Energies 2020, 13, 5086 15 of 18
Emissions from internal combustion engine vehicles (ICEV) are estimated based on Equation (17), as a reference for comparison purposes:
EmICEV (gCO2) = Nd.Nv.d.FICEV, (17) where Nv is the number of vehicles per day, d is the daily distance traveled, and FICEV is the carbon emission of combustion engine vehicles in gCO2/km. The probability density function of CO2 emissions produced by PEVs and ICEV after the 10-year period are presented in Figure 18. Results show an average of 20.4 tonCO2 produced when PEVs are charged entirely from grid. When PEVs are charged from PV generation and complemented from grid, an average value of 7.9 tonCO2 is obtained in Case 1 and 7.1 tonCO2 in Case 2, where PV generation is bigger. Comparing with the average value of 299.5 tonCO2 produced when using only internal combustion engine vehicles, a substantial reduction of 93.2% is obtained when PEVs charged entirely Energies 2020, 13, x FOR PEER REVIEW 15 of 17 from grid and 97.4% when PEVs are charged with local PV generation.
Figure 18. PDF of CO2 emissions at year 10. (a) PEVs are fully powered with electricity from grid; Figure 18. PDF of CO2 emissions at year 10. (a) PEVs are fully powered with electricity from grid; (b) (b) PEVs powered with electricity from PV and grid; (c) ICEV. PEVs powered with electricity from PV and grid; (c) ICEV. 5. Conclusions 5. Conclusions Electric vehicles are a viable option to reduce fossil fuel consumption and CO2 emission in the road transportationElectric vehicles sector. Givenare a viable the high option solar to potential reduce infossil Brazil fuel and consumption the early state and of marketCO2 emission development in the roadof electric transportation vehicles, the sector. integration Given ofthe photovoltaic high solar systemspotential into in PEVsBrazil charging and the infrastructure early state of can market be an developmentattractive solution of electric to the region,vehicles, encouraging the integratio theirn use of and photovoltaic reducing carbon systems dioxide into emissions,PEVs charging with a infrastructurecleaner energy can matrix. be an attractive solution to the region, encouraging their use and reducing carbon dioxideThis emissions, paper proposes with a a cleaner probabilistic energy approach matrix. to investigate the impact occasioned by the integration of fourThis PEV paper charging proposes stations a probabilistic and a photovoltaic approach system to investigate in a university the campusimpact buildingoccasioned located by the in integrationBelem, Brazil. of four Monte PEV Carlo charging method stations is applied and toa phot accountovoltaic the uncertaintiessystem in a university effects on campus PEV demand, building PV locatedgeneration in Belem, and building’s Brazil. Monte electricity Carlo load. method Simulations is applied are to based account on historicalthe uncertainties load data effects collected on PEV by a demand,local monitoring PV generation system and installed building’s in the electricity building. load. Two Simulations different PV are penetration based on levelshistorical are analyzed,load data collectedand a study by a time local horizon monitoring of 10 system years isinstalled adopted. in the Analysis building. are Two conducted different evaluating PV penetration the eff ectlevels on aretransformer analyzed, load and anda study voltage time profile,horizon carbonof 10 years emissions is adopted. avoided Analysis and theare financialconducted feasibility evaluating of the effectproject. on The transformer achievement load ofand this voltage study profile, led to the carbon following emissions conclusions: avoided and the financial feasibility of the project. The achievement of this study led to the following conclusions: Even though distribution transformer has currently a low utilization factor of only 55% of its • Evenrated though capacity, distribution the connection transformer of four Levelhas currently 2 charging a low stations utilization in the buildingfactor of parkingonly 55% lot of will its ratedresult capacity, in overloaded the connection operation of with four a probabilityLevel 2 charging of occurrence stations of in 77.9% the building in a future parking scenario lot will of 9 resultyears-ahead. in overloaded This probability operation increases with a probability to 100% in of year occurrence 10; of 77.9% in a future scenario of 9The years-ahead. installation This of a probability PV generation increases system to with 100% penetration in year 10; level of 15.6% can reduce transformer • Theoverloading installation probability of a PV from generation 100% to 31.2%, system and almostwith penetration eliminate violations level of reducing 15.6% overloadingcan reduce transformer overloading probability from 100% to 31.2%, and almost eliminate violations reducing overloading probability to 8.2% when a penetration level of 23.5% is adopted. The overload duration is also reduced, from an average value of 4 h to 1 h; • A substantial reduction of 93.2% tonCO2 is obtained when PEVs are fully powered with electricity from grid, and 97.4% tonCO2 when PEVs are powered with electricity from PV generation; • In both photovoltaic systems projects (PL = 15.6% and 23.5%), NPV is always positive and IRR is greater than the minimum required rate of return of 7%, with payback periods varying from 6 years to 11 years, which confirms both projects are economically robust and attractive.
Author Contributions: Conceptualization, N.C.B.; methodology, N.C.B. and C.M.A.; software, N.C.B. and C.M.A.; validation, C.M.A.; formal analysis, N.C.B.; investigation, N.C.B.; data curation, N.C.B.; writing— original draft preparation, N.C.B.; writing—review and editing, N.C.B and C.M.A.; visualization, N.C.B. and C.M.A.; supervision, C.M.A.; project administration, C.M.A.; funding acquisition, C.M.A. All authors have read and agreed to the published version of the manuscript.
Funding: This research was supported by PROPESP/UFPA and CAPES, Brazil.
Energies 2020, 13, 5086 16 of 18
probability to 8.2% when a penetration level of 23.5% is adopted. The overload duration is also reduced, from an average value of 4 h to 1 h; A substantial reduction of 93.2% tonCO is obtained when PEVs are fully powered with electricity • 2 from grid, and 97.4% tonCO2 when PEVs are powered with electricity from PV generation; In both photovoltaic systems projects (PL = 15.6% and 23.5%), NPV is always positive and IRR is • greater than the minimum required rate of return of 7%, with payback periods varying from 6 years to 11 years, which confirms both projects are economically robust and attractive.
Author Contributions: Conceptualization, N.C.B.; methodology, N.C.B. and C.M.A.; software, N.C.B. and C.M.A.; validation, C.M.A.; formal analysis, N.C.B.; investigation, N.C.B.; data curation, N.C.B.; writing—original draft preparation, N.C.B.; writing—review and editing, N.C.B and C.M.A.; visualization, N.C.B. and C.M.A.; supervision, C.M.A.; project administration, C.M.A.; funding acquisition, C.M.A. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by PROPESP/UFPA and CAPES, Brazil. Acknowledgments: This research was supported by CEAMAZON—Center of Excellence on Energy Efficiency in the Amazon and by UFPA—Federal University of Para. Conflicts of Interest: The authors declare no conflict of interest.
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