Summary
This paper focusses on analyzing the possibility of increasing electric energy self-consumption in an office building (OB) utilizing a photovoltaic (PV) installation. The main reason for investigating this topic is the increasing role of climate and weather driven renewable energy sources (RES) such as photovoltaics and wind turbines in the energy sector. In general a significant mismatch between power availability from those sources and power demand is observed. In this paper present possible scenario of managing variable energy yield from PVs in case of an OB where a significant part of car fleet consists of electric vehicles (EV) has been presented. Suggested operation scenario has been tested based on a yearlong hourly times series covering energy de-mand and irradiation values. Obtained results indicate soundness of the proposed solution from the perspective of energy management.
Keywords: photovoltaics, peak-shaving, demand side management Introduction
There is observed an increasing impact of both renewable energy sources (RES) as well as electric vehicles (EV) on the operation of the national power system. In the Polish case the role of EVs is still marginal however, several RES such as wind turbines start to play an important role in covering the national energy demand. The biggest problem with some of the RES (mainly wind and solar) is their highly intermittent nature which leads to unwanted disruption on the energy market. In literature there is a multitude of papers which investigate the possibilities of facilitating the pro-cess of RES (more precisely VRES where V – variable) to the energy system. Some of the point to the potential of exploiting the complementary in spatial and temporal domain solar and wind re-sources [3]. Another suggest coupling renewable energy re-sources with various form of energy storage [4, 7]. Some authors point to the need of choosing suboptimal orientations of PV modules in order to increase their energy yield correlation with power demand [1]. Alternative approach is a modifi-cation of power demand by using so called demand side management (DMS) potential [5]. Considering the possible scenario that EVs will become common it is important to underlie that they will significantly impact the structure of the energy system [8]. On the one hand they be a significant energy consumer but on other they may become a mobile energy storages. This potential should be used to facilitate the process of VRES integration, especially considering the fact that EVs can be charged not straight after completing their journey. To sum up this paper aims at combing the above presented approaches by proposing an approach for increasing on-site energy consumption from a PV installation by means of an EV’s battery charging schedule.
1. Problem description
The aim of this paper is to analyze the possibility of increasing the on-site energy consumption in an office building which is equipped with a photovoltaic installation an several of its occupant are using electric vehicles. Their batteries are charged from the office building grid, meaning that from the perspective of the power system operator this additional demand is perceived as building energy demand. In the considered building the PV installation is covering a significant part of the overall energy demand. However, due to the intermittent nature of solar radiation and the varying energy demand in the building there exists a significant mismatch between demand and supply. In consequence not all available energy from a PV installation is consumed on site. Such situation is not desirable from a power system point of view since unexpected energy surpluses from PV leads to disruptions on the energy market. On the other hand the owners of the EVs want the batteries to be charged before the end of the working day. Usually they will start charging their vehicles just after arriving to office. However, this is not a good solution because this will only boost the morning peak load and will not use the energy surpluses from the PV installation. In the following paragraphs the considered case study as well the whole procedure and related problems and concepts will be described.
1.1. Case study and input data
For the purpose of this study an office building located in southern Poland was selected for which the energy demand has been measured over the whole year. The energy demand values were available with an hourly time step. The building overall energy demand amounted to 82 MWh out of which 33% was used for heating, 30% for powering interior equipment and the remaining part for fans, cooling and lights – as shown on Fig. 1.
Figure 1. Share of individual energy consuming appliances in overall (82 MWh) energy demand over the considered year
The considered building is operating six day in a week from Monday to Saturday. During Sat-urday the energy demand is slightly smaller whereas on Sunday only the basic processes are realized such as interior lights or server operation. The typical energy demand patterns are presented on Fig. 2.
Figure 2. Weekly power demand in considered office building
From the perspective of this study and the data availability (irradiation and temperature has been obtained from [6] it is important to highlight how the energy demand is determined by the outside temperature. Here it is important to add that temperature strongly positively correlates with the irradiation values. This is beneficial because increasing temperature, lead to greater power de-mand but usually simultaneously the power availability from the photovoltaics is also bigger. This phenomena has been presented on Fig. 3.
Figure 4. Hourly cooling demand with regard to observed outdoor temperature
Figure 5. Observed irradiation and outside temperature values
The analysis which has been performed based on Fig. 3–6 proofs that observed in considered office building power demand is strongly dependent on the temperature. This results from the fact that usually when the temperature are greater than 15C there is a need for air-conditioning and simultaneously when the temperatures are low it is mandatory to use electricity powered heating system. In the considered location (Fig. 5) it is clear that PVs have significant potential to cover some of the power demand resulting from the high temperature. Two visible on Fig. 6 vertical lines correspond to non-working days when only some basic processes are being realized.
1.2. Energy generation, objective function
If irradiation and temperature values are known the hourly energy yield from a photovoltaic installation can be estimated based on (1).
(
STC)
PV PV STC PVP
T
T
C
H
H
E
µ
»
⋅
⋅
η
¼
º
«
¬
ª
−
⋅
°
⋅
−
⋅
=
1
1
(1)where: EPV – energy yield from a PV installation [kWh], PPV – installed capacity in PV generator [kW], t – time here [h], H – irradiation [kWh/m2], HSTC – irradiation in standard testing conditions [1000 kWh/m2] T– air temperature [°C], µ – temperature-dependent efficiency reduction factor [0.5
%], TSTC – cell temperature in standard testing conditions [25C], PV – performance ratio of all remaining system components (inverter, wire losses, shading, bird droppings, etc.) [80 %].
In the considered case study the office building energy demand has been aggregated on an hourly level. It is important to underline that no differentiation between: lighting, ventilation, heat-ing, air-conditionheat-ing, water heating and other energy consuming processes, despite the fact that such data was available. However, it is important to note that in case of Poland it has been observed that the changing energy demand patterns (especially those changes resulting from increasing utilization of air-conditioning) create a new market niche for photovoltaics [3, 4]. Therefore, also from the perspective of OB in which air-conditioning has significant share in energy consumption, it will be essential in future works to consider this phenomena.
Considering the aggregated energy demand (ED) it is now easy to calculate how much energy from PV installation will be used to cover (EPV_D) it and when the energy surpluses (ES) will occur. Those two values can be calculated based on following formulas (2), (3):
¯
®
≥
−
=
otherwise
E
E
for
E
E
E
PV D D PV S0
(2) S PV D PVE
E
E
_=
−
(3)Having calculated the energy surpluses it is now possible to determine how much of the energy generated by a PV installation is going to be send to the grid in the baseline scenario. Naturally, if the building is equipped with energy storage (like lithium-ion batteries or other ones) this energy can be stored and used when the energy supply from PVs will be smaller than the demand. However, at the current electricity prices investing in energy storing technologies is not usually economically justified [7].
Those energy surpluses can be used to charge the batteries of the electric vehicles. Usually, as shown on Fig. 6, the majority of them will occur during midday. This is very beneficial from two main reasons:
− charging EVs can be postponed. Meaning that there is no need to charge them fully before the midday. Instead the charging process can be speedup during midday;
− even if the charging process has been postponed and the energy surpluses from PV instal-lation were not sufficient it is still possible to finish it before the end of the working hours.
Figure 7. Hourly profile of energy demand, production from PVs and balance for a selected day. Installed capacity in PVs was 83 kW
Knowing the share of energy demand covered by a PV installation it is also possible to calculate the so called energy deficits EDef, here understood as an energy demand not covered from the PV installation. In general their value can be estimated based on following equation (4):
D D PV
Def
E
E
E
=
_−
(4)Having established the energy surpluses and deficits it is now possible to formulate the objective function expressed in the following equation (5):
(
)
¦ ¦
= =+
=
n i m j Def j i S j iE
E
Z
1 1 , ,min
(5)In the above presented equation indices (i, j) refer respectively to following days of the year and hour within day. Therefore, i = 1,…, 365 and j = 1,…, 24. Considering the fact that the energy deficits if occurring will have a negative value it is now clear that the minimal possible value of the objective is zero. If such value is achieved it corresponds to the fact that the energy source (in this case a PV installation) has generated as much energy surpluses as deficits. It is important to underlie that the mentioned deficits and surpluses result directly from the non-dispatchable nature of PV energy source and the variability of the energy demand.
1.3. Charging electric vehicles – general assumptions and principles
Without electric vehicles the profile of energy demand and supply as well as the differences between them may look as presented on Fig. 7. It is clear that in general the discrepancy between energy availability and demand is relatively big. Especially when one considers also the annual var-iability of solar energy (over 70% of solar energy is available from April to September whereas only 36% of the cumulative demand is observed over this period). This implies that energy surpluses from a PV installation will be in general available almost only during summer period and over spe-cific no-working days. As it can be observed on Fig. 6 (which is a visualization of a spespe-cific day) the significant energy deficits will usually occur during the first (6–8) and last (18–20) working hours. Minor deficits will naturally be observed during the night when the energy generation from a PV installation is not possible. Considering above and owning to the fact that the energy surpluses will occur mostly during midday, it is promising to postpone the electric vehicles charging from morning (where people arrive at work) to the midday and afternoon.
In the presented in this paper approach every single calculation is based on an assumption that the values of solar irradiation and energy demand in the office building are known with a 100% certainty at least over next 12 hours when the building is operating. This makes the whole model a deterministic one, whereas in reality those values would have to be forecasted. Additionally it has been assumed that the fleet of EV consists of 10 vehicles which rated battery capacity amounts to 40 kWh and on average the worker arrives to the job with a battery state of charge (SOC) equal to 50%. It means that to the mean 224 kWh of electricity consumed in this building per day one has to add additional 200 kWh (neglecting the charging losses) used for charging EV’s batteries. For the purpose of this study it has been additionally assumed that the charger capacity is 40 kW – meaning that mentioned fleet of vehicles can be charged in 5 hours or 5.5 hours if the charging efficiency is equal to 90%. The whole procedure of charging and discharging battery banks has been described in [2]. For the considered case the whole procedure of decision making with regard to charging EV’s batteries depending on the energy availability and imposed constraints has been presented on Fig. 8.
if Xi,jD = 1 The EV charging is not necessary or possible if iא൏Ǣͳ͵ No Yes Yes No
Charge the EV with the avialble energy surplus from
PVs but do not exceed the charger rated capacity (Pmax)
and avoid overcharging Charge the batteries using
energy surplus from PVs but also the energy from the grid. If energy from the grid is about
to be used aim at maintaining equal level of charing power
Figure 8. Decision making process during charging EV’s batteries regarding the source of energy The considered in the first decision bloc variable (
X
iD,j) is a binary variable used to decide whether during given day and hour the EV’s batteries will be charged ( D, =1j i
X ) or not (XiD,j =0). In this study all Sundays and holidays have been excluded from the calculations. Also the no-work-ing hours (18:00 to 07:00) are considered as a no-chargno-work-ing time. The second decision bloc refers to specific period. Here an assumption has been made that considering the fact that the energy surpluses from the PVs will not always be available it is mandatory the reserve some hours for charging the EVs from the grid (here from 14:00 to 18:00). For such assumption it is now obvious that from 07:00 to 13:00 the EV’s batteries will be charged only from available surpluses and from 14:00 to 18:00 from both surpluses and the grid. Therefore, the cars will be always fully charged at the end of the working day.
2. Results and discussion
First of all the optimal installed capacity in PVs has been estimated based on Eq. 5. For this purpose an available in MS Excel Solver has been used. The presented in Eq. 5 objective function reaches its optimum when the volume of energy surpluses and deficits is equal. Such value has been found for the installed capacity in PVs equal to PPV = 83 kW. This capacity has been calculated for a case when no electric vehicles are being considered.
The second step was the analysis of the system behavior when a fleet of EVs is about to be charged by means of the energy available from the grid as well as the variably occurring energy surpluses from PVs. The calculations performed based on the assumptions and procedure presented in section 1.3. have shown that:
− additional energy demand resulting from the need to charge of fleet of EVs is equal to 62.4 MWh what means that building overall energy demand will increase by 76% to 144 MWh per annum;
− in baseline scenario (no EVs) the energy surpluses from PVs amounted to almost 45 MWh, which corresponds to 55% of the overall energy yield from this source. It means that in considered building 45% of consumed energy will be coming from own PV installation, but remaining 55% will be send to the power grid;
− the required by EVs 62.4 MWh of electricity per annum were covered by PVs and the grid, respectively 27.6 MWh and 34.8 MWh. What translates into 44% and 56%;
− the use of energy from PVs to charge the batteries reduced the energy surpluses from 45 MWh per annum to 17.3 MWh per annum. Meaning that the on-site energy consumption increased to 79%;
− however, from the perspective of the power grid the building energy consumption increased to 79.6 MWh, which is close to the original energy consumption observed in considered building without EVs fleet.
On Fig. 9 the modified daily power demand for three considered cases has been presented. Please note how significantly PVs reduce power demand during midday and how the demand in-crease during afternoon because of a need to charge the EVs. The inin-crease of power demand from 14:00 to 18:00 results from the decreasing power availability from PVs.
Figure 9. Mean daily power demand profile
Finally, a partial sensitivity analysis of the considered system has been performed. The impact of additional installed capacity in EVs charger and installed capacity on the energy self-consumption and self-sufficiency were analyzed. The results of the conducted analysis (Fig. 10 and 11) revealed that considering the presented in Fig. 8 rules of operation the installed capacity in PVs charger does not impact neither the building energy self-sufficiency nor the share of energy surpluses. The further analysis has shown that the building energy self-sufficiency (SS) in function of installed capacity in PVs can be described by a following logarithmic function: SS = 0.258ln(PPV) – 0.701 and the energy
surpluses (ES) as: ES = 0.0009 PPV ^ (1.24). Both approximations exhibited significant values of R2
Figure 10. Building energy self-sufficiency in function of charger and PVs installed capacity
Figure 11. Share of energy surpluses from PV in total energy generation from PVs 3. Conclusions
Presented in this paper analysis are first undertaken with regard to observed in Poland solar conditions and variability of energy demand in building. Conducted research aimed at proving that an increasing role of EVs may be beneficial and an appropriate management of their charging (and
discharging) processes may be one of possible solutions for facilitating the process of variable re-newable energy sources (mainly PVs) integration to the national power grid. Performed calculations has shown that by appropriately scheduling the charging process of EVs it is possible to increase the energy self-consumption from a photovoltaic installation. This paper only slightly illuminated the complexity of the whole problem. The whole concept of solar powered EVs in an office building requires a systematic approach which would have to consider both economic, environmental and also technical aspect of proposed solution. What is more the presented here approach is based on a deterministic model whereas a probabilistic one would give more precise results. Mentioned above problems open a multitude of interesting research directions and some of them will be solved in the prepared by the author doctoral dissertation at the AGH University.
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ZWIKSZENIE KONSUMPCJI WŁASNEJ ENERGII ELEKTRYCZNEJ W BUDYNKU BIUROWYM WYPOSAONYM W INSTALACJ FOTOWOLTAICZN ORAZ FLOT
SAMOCHODÓW ELEKTRYCZNYCH Streszczenie
W artykule podjto prób analizy moliwoci zwikszenia konsumpcji własnej energii elektrycznej w budynku biurowym wykorzystujcym instalacj fotowoltaiczn (PV). Głównym czynnikiem skłaniajcym autork do podjcia tego tematu jest rosnca rola zalenych od warunków klimatycznych i pogodowych odnawialnych ródeł ener-gii (OZE) (takich jak fotowoltaika i generacja wiatrowa) w sektorze energetycznym. Na wstpie naley zaznaczy, i obserwuje si znaczc rozbieno w poday energii z instalacji OZE w stosunku do zapotrzebowania na ni. W artykule zaprezentowano moliwe podejcie do zarzdzania energi elektryczn pochodzc ze ródła niedy-spozycyjnego (fotowoltaika) w wypadku budynku biurowego, w którym znaczc cz floty pojazdów stanowi samochody elektryczne. Zaproponowany scenariuszy pracy takiego układu (odbiorników i ródeł energii) został przetestowany w oparciu o roczny szereg czasowy godzinowego przebiegu zmiennoci zapotrzebowania na energi elek-tryczn oraz nasłonecznienia. Otrzymane wyniki wskazuj na zasadno proponowanego rozwizania z perspektywy zarzdzania energi elektryczn.
Słowa kluczowe: fotowoltaika, wyrównanie zapotrzebowania szczytowego, zarządzanie stroną popytową
Magdalena Krzywda Faculty of Management
AGH Universtiy of Science and Technology Ul. Mickiewicza 30 Av., 30-059 Kraków, Poland e-mail: magda.krzywda01@gmail.com
