Modelling the Penetration Effect of Photovoltaics and Electric Vehicles on Electricity Demand and Its Implications on Tariff Structures Mahmoud Shepero

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MSc ET 16004

Examensarbete 30 hp Juni 2016

Modelling the Penetration Effect of Photovoltaics and Electric Vehicles on Electricity Demand

and Its Implications on Tariff Structures

Mahmoud Shepero

Masterprogrammet i energiteknik

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Teknisk- naturvetenskaplig fakultet UTH-enheten

Besöksadress:

Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress:

Box 536 751 21 Uppsala Telefon:

018 – 471 30 03 Telefax:

018 – 471 30 00 Hemsida:

http://www.teknat.uu.se/student

Abstract

Modelling the Penetration Effect of Photovoltaics and Electric Vehicles on Electricity Demand and Its

Implications on Tariff Structures

Mahmoud Shepero

The shift towards more renewable energy sources is imminent, this shift is accelerated by the technological advancement and the rise of environmental awareness. However, this shift causes major operational problems to the current grid that is optimised for unidirectional power flow. Besides the operational problems, there are problems related to the optimal tariff scheme. In this thesis a study on the effect of the adoption of photovoltaic solar panels and the electric vehicles on the households' electricity demand profile is presented. The change on the demand profile is going to affect the current tariffs, this effect is also explored in this thesis. In this thesis real life data on household electricity use and photovoltaic power production was used. For electric vehicle charging simulated data was used. Besides that, a demand response scheme for electric vehicle is proposed in order to estimate the savings potential of this demand response on the electricity bill.

The results show that the change in the demand profile is not merely a change in the total energy consumption, but it is a change in the power peaks as well. The peaks change significantly in condominiums and rental apartments, in this households' type it increases by around 80%, while in detached and row houses little change is noticed on the peaks, yet they still increase by around 10%. The demand response shows around 1- 12% savings in the distribution bill depending on the household, however it showed more incentives for condominiums and rental apartments.

The current distribution tariffs perform asymmetrically with the various households. However, one tariff ensures 11.7 MSEK financial revenue for the distribution system operator, this is higher than the other tariffs' revenue by more than 28.5%. The new prospective situation requires totally different tariffs that ensure a balance between firstly a reasonable revenue for the distribution system operator and secondly incentives for consumers to self produce electricity as well as to reduce their peaks.

Ämnesgranskare: Cajsa Bartusch Handledare: Joakim Munkhammar

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Dedicated to all KIC-InnoEnergy’s

Game Changers

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Nomenclature

GHG Green House Gases COP Coefficient of Performance PV Photovoltaic

DG Distributed Generation EV Electric Vehicle

PHEV Plug-in Hybrid Electric Vehicle PEV Plug-in Electric Vehicle

HEV Hybrid Electric Vehicle V2G Vehicle to Grid

DSO Distribution System Operator FIT Feed-in Tariff

DR Demand Response

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1. Introduction

Humans have always relied on energy in order to fulfil everyday needs like eating and access to warmth. Several energy innovations have changed the human history. This change can be observed in the case of fire which provided the energy source for heating and cooking as well as saved our ancestors from wild animals. Another change in human history took place after the discovery of coal. This discovery changed the entire history for centuries. Worldwide trading flourished as a result of being able to power larger ships and trains, consequently the living standard as well as the life expectancy increased. On the one hand, extraction of oil and natural gas has significantly influenced our lives. As a result, the transportation sector has been dramatically changed.

Nowadays, we see vehicles in the streets that run on gasoline. Planes would not have been possible without this new innovation , and hence all the fast international transportation and the associated economic welfare. On the other hand, nuclear energy has provided countries lacking natural oil reserves with an alternative energy source. Finally, renewable energies promoted the shift towards decentralised generation in the electricity sector and a greener environment.

The shift from one energy source to another has always been full of chal- lenges, and usually external circumstances has motivated this shift. Several energy crises influenced the global energy policies. For example, the energy crisis in 1973-1974 promoted the idea of energy security. Nowadays, energy security and independence is a major field of focus of several political de- cisions. The economic crisis in 2008 and the massive increase in oil prices created economic incentives for the shift towards renewable energy sources.

In 2015, a significant reduction in oil prices took place, this reduction threat- ens the renewable energy sector. However, the WEA argues [1, p.182] that the low oil prices will not affect renewable penetration as long as there is a strong political will supported by generous renewable subsidy programs, yet subsi- dies, which are still the major driving force for renewable energy, are expected to increase as oil prices decrease.

Due to the rise of the environmental awareness and the reduction of the costs of renewable energy technologies, the share of renewable energy among the primary energy sources is increasing. Several countries have implemented measures to promote installations of new renewable energy power plants and to reduce their environmental footprint through the reduction of greenhouse gases (GHG) emissions. The EU planned that by 2020 renewable energy will at least produce 20% of the energy used [2].

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To sum up, the future of energy sector is a major research field that is significantly expanding in order to ensure a sustainable continuous supply to the ever growing energy needs of the world. This thesis addresses several focus points regarding the households’ prospective demand curve, which are important to the success of the renewable energy sources penetration in the grid and essential step in the path to a more sustainable world.

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2. Background

This chapter presents the general background for the research. The background on energy consumption is presented in Section 2.1. In Section 2.2 background on PV technologies is provided. Section 2.4 provide a background on the existing tariff structures. Research gaps are presented in the end of this chapter in Section 2.5 followed by the aims of the thesis in Section 2.6.

2.1 Energy Consumption

In the last decade, the global primary energy consumption has increased tremen- dously, influenced by the substantial rise in population which has doubled throughout the last four decades. Estimations prospect that the global population will reach nine billion by the year 2040 [1, p.62].

As shown in Figure 2.1, compared to the year 2000, the energy consumption of China, southeast Asia and Latin America has increased. On the other hand, the energy consumption of the USA and EU has nearly stabilised. This resem- bles the industrial production shift from EU and the USA towards developing countries, creating a substantial economic growth in those countries. While in the USA and Europe the economy is still growing, it is shifting towards unindustrialised, energy efficient and service based economy [1, p.61].

In 2013, Sweden relied on nuclear power to supply thirty percent of its energy. Fossil fuels in the form of oil products, natural gas, coal, etc. represented around 30% of the energy supplied. During the first decade of the 21 century the household electricity consumption has decreased. This was a result of the switch from oil heating to electric heating and heat pumps which have high coefficient of performance (COP). This represents a relevant amount of energy reduction since heating and hot water supply accounts for nearly half of the energy consumption [3].

2.2 Photovoltaics

The sun is a huge fusion reactor located in the center of the solar system. It has a diameter of 1.39 ⇥ 10⁹m [4, p.3]. In 2014, the world consumed 1.3 ⇥ 10⁴Mtoe of primary energy [5], while the Earth receives nearly 10000 times as much energy from the sun [6, p.23]. As a result, distributed photovoltaic

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Figure 2.1. Primary Energy Consumption.¹

(PV) generation plays a major role in achieving sustainability in the energy sector [7].

PV represents a simple method to convert sunlight directly to electricity [8].

It is based on the theory of excitation of electrons by photons. As PV lacks the economy of scale it is suited for distributed generation (DG) [6, p.32]. In addition, it is technically an easy technology to implement in smaller scale.

Apart from tilt adjustment and panels cleaning, PV requires virtually no other maintenance during its lifetime, which is around 30 years [8].

PV panels was relatively expensive to produce, consequently, it was mainly implemented by developed countries. Germany and Italy were the largest two PV markets from 2010 until 2012 [9, p.9]. However, the prices of PV has decreased as the technology matured. This created economic incentives to other less rich countries. In 2013 and 2014 , China, Japan and USA were the largest PV markets in the world [9, p.9]. However, Germany still holds the record of total cummulative installed capacity followed by China (see Figure 2.2).

PV is rapidly penetrating the Swedish electricity market. The sales of PV systems in Sweden is increasing by double each year for four consecutive years, in 2014 Sweden installed 36.2MWp [10, p.4]. Alternatively during the same duration Denmark installed 42MWp [11, p.4], while Finland installed 6MW_p [12, p.4], and Norway installed 2.2MW_p [13, p.4]. The decline of the international prices of PV systems as well as the growth of the Swedish PV market induced a decrease in system prices in Sweden. In the year 2014, residential systems costs decreased by nearly 12% [10, p.9].

1Based on IEA data from the World Energy Outlook c OECD/IEA 2015, www.iea.org/

statistics. Licence: www.iea.org/t&c; as modified by Mahmoud Shepero.

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Several studies have investigated the potentials of PV energy for the electricity grid. A study estimated that roof-mounted PV installations in Germany has a potential of 148TW h/year and capacity of 208GW_p [14]. According to this estimation, 32% of the municipalities in Germany can fully cover their yearly electricity consumption by fully utilising the PV potential. However, this estimation is based on total yearly energy and in certain hours the PV will not be able to cover the consumption especially during nights and winter [14].

Despite the enormous potential, this high penetration exposes several problems such as that peak PV generation does not coincide with peak electricity consumption. Hence, further studies analysed the effect of the PV penetration and the residential electricity tariffs on the stability of the grid [15]. High PV penetration requires implementation of other technologies like storage and load shifting in order to minimise the infrastructure costs [7]. This need is more essential in the rural grids as the low population density and the long cables increase the voltage stability threats [15].

2.3 Electric Vehicle

In the EU, 15% of the total emitted carbon dioxide is emitted by light vehicles, i.e. cars and vans [16]. As a result, politicians try to shift towards more sustainable transportation in order to reduce the GHG emissions. Therefore, electric vehicles (EVs) are going to represent the future of the transportation sector, for example, the electric power research institute in the USA predicts that 62% of the vehicles will be plug-in hybrid vehicles (PHEVs) by the end of 2050 [17]. However, the reduction of the GHG emission attributed to EVs is highly dependant on the electricity sources, for example, EVs can reduce more than 85% of the GHG emissions given that they are charged using 80%

electricity from renewable sources [18].

EVs can be categorised based on their drivetrain. On the one hand, plug- in electric vehicles (PEV) use an electric motor powered by batteries to drive the vehicle, the batteries can only be charged form the grid. On the other hand, PHEVs use an internal combustion engine as well as an electric motor to power the vehicle, the batteries can be charged from the grid as well as from an onboard generator coupled with the internal combustion engine. Hybrid electric vehicles (HEVs) are similar to PHEVs, nevertheless, their batteries can only be charged with the onboard generator. In this thesis EVs mean only PEVs and PHEVs.

The current grid is not adapted to handle high levels of EV penetration [19]. Several studies have investigated the vehicle to grid (V2G) potentials [17, 19, 20]. V2G technology can reduce power peak demands and hence reduce the power grid operating costs. In V2G, the vehicle battery is capable of consuming and discharging power. Consequently, it can be used to solve the intermittence problem of the renewable energy sources [20]. The advan-

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tages of V2G technology is faced by some serious drawbacks. One of the major drawbacks is the degradation of the battery due to successive charging and discharging [19]. Another study [21] shows that optimising the charging schedule can reduce grid voltage drop, power loss as well as optimise the load profile.

2.4 Tariffs

The electricity sector is divided into generation, transmission, distribution and retailing. Nowadays in most of the countries, including Sweden, where the electric power sector is deregulated, retailing and generation are competitive liberalised sectors. On the contrary, transmission and distribution are still reg- ulated sectors. Distribution in particular is a natural monopoly [22, 23].

2.4.1 Swedish Electricity Tariff Structure

This section provides description of the different distribution tariffs in Sweden.

This section relies on the work produced by SWECO [24].

Fuse subscription

The majority of consumers in Sweden subscribe to this contract. This contract has fixed energy based distribution charge (SEK/kWh) regardless of time of use, and a subscription fee (SEK/yr) that varies according to fuse size (Am- pere). Consumers have incentives to reduce their overall energy consumption,

Figure 2.2. Cumulative PV capacity end 2014 [9, p.9].

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without changing their consumption pattern. Also, hourly metering in Sweden proved that the vast majority of consumers can theoretically reduce their fuse size (Ampere) as their consumption needs can be fulfilled with a lower fuse rating, for further information see: [24].

Svensk Energi power tariff

This tariff is similar to the fuse subscription tariff. However, it introduces monthly power based distribution charge (SEK/kW) which depends on the peak power consumed in each month. Customers are encouraged to flatten their load profile and to stop power peaks. Having high peaks would result in high bill even if the energy consumption is low.

Svensk Energi power tariff with time of use

This tariff introduces a variable energy based distribution charge (SEK/kWh) which varies according to the time of the day which in return reflects the total load on the grid. Two periods of consumption are used: peak hours which occur during the weekdays from 6 a.m. until 10 p.m. during the period from November until March; and the off-peak hours during the rest of the hours. As a result customers are encouraged to flatten their load profile, and to shift their consumption to off-peak hours.

Monthly power tariff

In this tariff a monthly subscription fee (SEK/month) is implemented. In addition, a power based distribution charge (SEK/kW) that varies each month which is high at winter months compared with summer months. Hence, customers are recommended to reduce power peaks.

2.5 Research Gaps

Recent studies [25, 26] have investigated the DG from the system performance perspective. The results shows that the distribution grid can withstand large DG percentage without causing problems in power quality nor causing intermittence in power supply.

Several studies estimated the energy costs savings when implementing different tariff structures in Sweden. However, those studies mainly focus on the effect of the distribution tariff and they did not include the total electricity costs. By making a complete study, it might depict that the savings represent minor percentage in the total electricity bill [27].

Most of the tariff structures in the world has not been designed taking into consideration a high level of DG. As a result, the performance of the current tariff structure at high levels of DG and EV charging is still unexplored. Tariffs has a major influence on the success of the DG penetration as they provide the economic incentives for prosumers. Also, tariffs has to reflect the cost of the

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system from the perspective of the distribution system operator (DSO) as well as insure reasonable amount of economic profit.

2.6 Aims of the Thesis

This thesis is a result of a master degree project. It was carried out as part of a project established by the Swedish energy agency. This thesis aims to investigate the introduction of distributed generation to the distribution grid, and answer several questions which are the research questions addressed in this thesis: what is the electricity demand profile for this dataset for different DG penetration levels for different households’ types? what would be the effect of introducing EV charging on the electricity demand profile for different households’ types? what effects does this change in the demand profile have on different tariffs?

In order to answer the research questions the following has been investigated:

– Electricity demand model

Estimates the consumption pattern and amount of electricity consumption of several households with various levels of PV penetration and EV charging. Real household consumption data will be used in the thesis which will reflect various consumers’ consumption habits.

– Tariff structure model

Perform a study on the existing tariff structures. Explore the implications of the prospective demand profile on existing tariffs and analyse the amounts of savings associated with the different tariffs and consumption habits.

The early phases of this thesis was made in collaboration my master colleague Vladyslav Milshyn [28], together we simulated the demand curve as well as wrote the programming codes needed to estimate the distribution tariffs. After that, I focused on investigating the effects of the penetration of the EVs combined with PVs and he focused on investigating the effects of the penetration of PVs with battery storage.

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3. Methodology

The main aim of the research is to develop a model and simulate real household consumption, PV production, EV charging as well as existing tariffs. In this chapter a presentation of the methodology followed in the thesis is described. Sections 3.1, 3.2, and 3.3 describe the data used in the thesis. A summary of the tariff structures that were modelled is provided In Section 3.4 followed by the correction of the tariff level in Section 3.5. Section 3.6 presents the demand profile estimation approach. Finally, a demand response of the EV charging is described in Section 3.8.

3.1 Household Consumption Data

Real household electricity consumption data is used in this project. This data was provided by two DSOs in Sweden: Sollentuna Energi and Saltsjö Boo Energi, and this data from November 2012 until October 2013 on hourly basis.

This duration represents a whole year of data starting from the beginning of the winter season, which starts in the beginning of November, and ending in the end of October, which is the end of the summer season. Winter and summer seasons are determined based on the electricity use and the household heating in Sweden. These seasons have also different tariffs since the households’

consumption is different between the seasons. The households’ consumption data are anonymised to ensure complete privacy of consumers’ details.

This data was measured for different types of houses and in two different locations in Sweden: Sollentuna and Saltsjö Boo. The types of houses and the locations are presented in table 3.1.

3.2 EV Data

A previous study [29] has developed a model for EV charging in Sweden. This model , Grahn-Munkhammar model for EV charging, is a Markov chain based model for generating synthetic household electricity use and EV charging [30, 31]. For this thesis only EV charging was obtained using this model. This model provided a year of EV charging data for an 24kW h electric vehicle. It also assumes that the EV is charged at home using the single phase 2.3kW (230V , 10A) charging outlets. The choice of the charging outlets is based

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Table 3.1. Description of data for household electricity use

Location House Code Number of

type data samples

Sollentuna

Condominium apartments BR SA 490 Rental apartments HR SA 491 Single family houses V SA 422

Row houses RH SA 69

Saltsjö Boo Condominium apartments BR SB 487 Rental apartments HR SB 418 Single family houses V SB 518

on the fact that this outlet power is the easiest to install for the most of the Swedish households. Furthermore, The model assumes that the optimal depth of discharge for the Li-ion batteries to be 60% as suggested by [32]. A car which consumes on average 0.2kW h/km and drives with speed of 46km/h for 38km/day is modelled (for further information see: [29]).

For this thesis EV charging of 24kW h was used. In the Grahn-Munkhammar this battery capacity represents the standard battery capacity for which the model was calibrated. This battery capacity also simulates pure EVs with battery capacities above that level, such as e.g. Tesla 70 90kW h battery capacity, accurately. Only for lower battery capacities the input battery capacity has to be altered.

3.3 PV Data

The PV data used in the research is measured by using a pyranometer on a panel located above Ångström Laboratory. The panel is tilted with an angle of 45 and directed towards south. Ångström Laboratory is located in 59 50⁰ N and 17 38⁰ E. This data will be applied to two locations in Sweden: Sol- lentuna which is located in 59 26⁰ N and 17 56⁰ E, and Saltsjö Boo which is located in 59 19⁰N and 18 17⁰ E. Since this study focuses particularly on the performance of the various tariffs, instead of adjusting the PV data according to the different locations and panel orientations of each household, which will be difficult to estimate given the lack of data on the household rooftop orientations, it was assumed that the difference in PV production will be mi- nor and that the simulation of two PV installation peak powers will reflect the differences.

Estimating the available rooftop area for households in Sweden was not possible therefore a reasonable estimation of the installed capacity was made based on the available data for Sweden. M. Ruppert et al. used data from the german publicly accessible ’renewable energy plant register’, where a record

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of the installed PV capacities is available [15]. A similar approach was pur- sued in this thesis. Data from the green electricity certificate system was used even though this data represents nearly one third of the PV installations in Sweden. Based on Figure 3.1 capacities of 5kWp and 10kWp was simulated for the detached and row houses. For the condominiums and rental apartments capacities of 0.5kW_p and 1kW_p was simulated. The reference capacities are 10kWpand 1kWpdepending on the house type.

Figure 3.1. Histogram of the certified PV systems in Sweden.

3.4 Simulated Tariffs

One of the aims of this thesis is to analyse the performance of various tariff structures taking in consideration the variety of the electricity consumption habits. Consequently, three distribution tariff structures were compared in this thesis, The First one is the time of use tariff Vattenfall T4, the second one is Sala Heby Energi’s simple tariff, and the third one is the Sollentuna Energi’s demand based power tariff. The differences between the distribution tariffs might be small when the total cost of electricity is estimated (see: Section 2.5). Hence, a retail contact was added to the simulation.

3.4.1 Vattenfall T4

This tariff is based on two components: an annual fixed access charge (SEK/yr) which depends on the fuse size (Ampere) and whether the household is apart- ment or detached house; and a distribution charge (SEK/kWh) which depends

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on the energy consumption regardless of their energy use pattern. The distribution charge varies depending on whether the energy is consumed during peak or off-peak hours. The peak hours are defined as the duration between 6 a.m. and 10 p.m. in the working days during the winter months which are January, February, March, November and December. Otherwise, the rest of the year is considered to be off-peak hours [33].

3.4.2 Sala Heby Energi Simple Tariff

This tariff consists of only distribution charge (SEK/kWh). This tariff is currently introduced only to the condominiums and rental apartments. In addition, it gives incentives to only reduce the energy consumption regardless of the time of use [34].

3.4.3 Sollentuna Energi Power Tariff

This tariff charges consumers annually based on annual fixed access charge (SEK/yr) which depends on the fuse size (Ampere). Moreover, a distribution charge (SEK/kW) is added to the bill. The distribution charge is calculated by calculating the average of the five highest meter measurements measured during peak hours. Peak hours are the hours between 7 a.m. and 7 p.m. during working days. The distribution charge varies between summer and winter months. Winter is defined as the months between November to March, while summer is defined as the duration between April and October [35].

3.4.4 Vattenfall Retail Contracts

Several retail contracts are offered by Vattenfall in Sweden. Vattenfall has two main retail contracts fixed and variable tariff contracts. The fixed one has a fixed charge (SEK/kWh) plus a yearly subscription fee (SEK/yr). In the variable tariff contract the fixed tariff is replaced by a variable one that varies according to the Nord pool spot price at the region of consumption in addition to the fixed tariff Vattenfall charges a yearly fixed fee (SEK/yr). In this research the fixed contract is simulated in order to estimate the percentage of savings in the total electricity bill. The choice of the fixed contract is based on the fact that still 36.1% of the customers in Sweden subscribe to a fixed price contract.[23, p.26].

3.4.5 Feed-in Tariffs

Two feed-in tariffs (FIT) are used in Sweden. The first one is the FIT adopted by the energy retail companies. The second one is the one adopted by the

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DSOs and is called remuneration. Sollentuna Energi as well as Sala Heby Energi, unlike Vattenfall, do not have a FIT for the DSO. Vattenfall however has FIT for the DSO [36] and another for the retail [37]. In order to promote PV installations, and during the first year of selling electricity to the grid, Vattenfall retail pays customers a FIT equal to 0.4SEK/kW h added to the Nord Pool spot price, after the first year customers receive only the Nord Pool spot price as a FIT [37].

3.5 Tariff Level Correction

Various tariffs are levelled differently, this is due to the differences in the network physical layout, losses, and capacity, all those factors induce differences in costs [22]. However, for a single network different tariffs should produce nearly the same revenue since the costs are nearly equal. Consequently in order to evaluate the performance of each tariff after the change in the demand profile a correction is needed to eliminate the effect of the variation in the levels of the compared tariffs.

The factor is calculated by estimating the revenue of each current customer segment based on the currently applied tariff which is Sollentuna Energi power tariff and then calculate the revenues according to the remaining tariffs. Then the factor is obtained by comparing the variation of the revenue obtained by applying each tariff and the one obtained by the reference tariff.

3.6 Demand Profile

The demand profile of the household is affected by the PV production as well as the EV charging and the household appliances consumption pattern. The total grid consumption of the household P(t) at time t is the difference between the load and the PV production P_PV(t). The load is divided into two parts: the first part is consumed by the household PHousehold(t), and the second part is the EV charging P_EV(t). The grid consumption can be expressed by:

P(t) = P_Household(t) + PEV(t) PPV(t). (3.1) However, due to the fact that the PV data is for a panel with peak power of 1.164kWp, which is not necessarily the case in all the households, a correction for the general equation is made and coefficient PP was added to represent the peak power of the PV panel in this specific household category:

P(t) = P_Household(t) + P_EV(t) PP

1.164 ⇥P_PV(t). (3.2) Each household is simulated solely, as shown in Figure 3.2a, in order to take into consideration the dependence of the results on both: the consumption

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(a) (b)

Figure 3.2. (a) Flow chart of the demand profile estimation. (b) Flow chart of the costs estimation.

pattern, and the monthly peak power of each specific household. This simulation method ensured that the costs are correctly associated with households.

Alternatively, any simulation based on an average demand profile would have resulted in an inaccurate cost estimation. This inaccuracy will occur due to the fact that an average demand profile smoothes the peaks which are considered the key player in some tariff structures.

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3.7 Hourly Costs

The estimation of hourly costs provides an opportunity to see the costs distribution on each hour. Consequently, an estimation of hourly costs was made for each household. Several assumptions were assumed in this calculation which depend on the tariff structure. Equation 3.3 presents the procedure followed to estimate the cost in each hour. The energy fee EF(t) at time t in (SEK/kWh) is multiplied by the hourly energy consumption P(t) at time t in (kWh) and added to the subscription fee SF in (SEK/yr) divided by the total hours in a year. This equation is valid for all tariff structures except Sollentuna Energi power tariff because in the later tariff their is no hourly energy fee.

Cost(t) =

(P(t) ⇥ EF(t) +₈₇₆₀^SF if consuming P(t) 0,

P(t) ⇥ FIT(t) +₈₇₆₀^SF if producing P(t) < 0. (3.3) A modification to the general cost equation is made in order to adapt the equation to the different tariff structure adopted by Sollentuna Energi power tariff. In this adaptation a monthly fee MF is used to represent the tariff monthly fee described in Section 3.4. This monthly fee is then distributed on the number of hours n, which is the number of hours of consumption that occurs during peak hours in each month:

Cost(t) =

(P(t) ⇥^MF(t)_n +₈₇₆₀^SF if consuming P(t) 0,

P(t) ⇥ FIT(t) +₈₇₆₀^SF if producing P(t) < 0. (3.4) Figure 3.2b shows the steps of simulation followed to estimate the hourly costs of each household when applying the different tariffs.

3.8 Demand response

Several tariffs charges higher fee during the peak hours. These tariff structures incentivise customers to shift their consumptions during off-peak hours. How- ever, regarding the simulated tariffs Vattenfall considers the peak hours to be 16 hours on a winter day, which makes it extremely difficult if not impossible to shift consumption to off peak hours. Consequently, the demand response of the EV charging is adapted instead to the Sollentuna Energi power tariff which offers a more reasonable tariff scheme that allow for load shifting.

In this thesis a demand response (DR) model is made on the EV charging load in order to estimate the potential of the DR in costs saving. The EV charging is based on 2.3 kW charging power which if coincides with the peak monthly consumption will result in large increase in the distribution bill. The most common DR schemes are peak shaving, valley filling and load shifting from peak to off peak hours [38]. The proposed DR scheme is a load shifting

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scheme, this ensures no change in the energy used throughout the year nor in the EV driving distance. Hence, the scheme postpones the EV charging if it occurs during the highest monthly peaks which Sollentuna Energi power tariff charges for, i.e. peaks that occur from 7 a.m. to 7 p.m. during working days. In addition the DR can be tuned to work on a specific number of peaks.

Sollentuna Energi power tariff charges customers based on the mean of the highest three peaks in the months, and the DR can be tuned to work on the highest 5,10 15 and 20 peaks of the month. Figure 3.3 presents how the DR scheme shifts the EV charging if it coincide with one of the highest peaks of the month, in this example the scheme is tuned on the highest 10 peaks. This scheme shifts only the EV charging data until the end of the current charging session, i.e. until the user stops charging, otherwise postponing one hour will postpone the charging schedule of the whole year.

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Figure 3.3. Flow chart of the DR code tuned to operate on the highest 10 peaks of the month.

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4. Results

In this chapter the thesis results are presented. The categorisation method used in presenting the results is described in Section 4.2. Secondly in Section 4.1, the change in the demand profile is presented, followed by Section 4.3 where a discussion of the performance of the various distribution tariffs based on the new demand profile is presented. Section 4.4 discusses the relevance of the savings associated with the distribution tariffs in the total electricity bill.

Finally, the results of the DR are presented in Section 4.5.

4.1 Categorisation of Results

In each household category and location the yearly energy consumption varies significantly from household to another. Hence, a further categorisation was made to better present the existing variety. Instead, any results obtained without this categorisation will be unreflective of the variety in the demand profiles of the households and consequently will represent the average user in each household category and location.

The proposed categorisation is made based on the yearly energy consumption. As shown in Figure 4.1, the households consumption is essentially possible to represent as a normal distribution. Based on the previous observation a decision was made to categorise each type household into three categories.

The three categories represents the high consumption households, the average consumption households, and the low consumption ones. Firstly, the high consumption households are the ones whose yearly consumption are above the mean + standard deviation of the households’ set. Secondly, the low consumption households are the ones whose yearly consumption are lower than the mean standard deviation of the households’ set. Finally, the mean category is the most common one. The borders of the categories are drawn in vertical lines in Figure 4.1. In all the cases of normal distribution consumption the mean category will represent 68% of the houses, and the high and low categories will represent nearly 16% of the houses each.

4.2 Demand Profile

According to the simulated data, 1kW_pPV solar panels produce 1.11MW h/yr, since they are on a tilted plane with a 45 tilt, and the EV consumes 1.06MW h/yr.

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Figure 4.1. Propability distribution curve of the RH SA type households’ energy consumption. The household classifications are defined in Table 3.1.

Adding both PV and EV consumption data to the current household demand profile causes a change in the peak power as well as the total yearly energy consumption. Figure 4.2 shows the influence of both the PV and EV on the demand profiles of two sample households representing condominiums and detached houses, in the figure an average demand profile is estimated which separately represents the average winter and summer days demand profile.

These sample houses have suffered an increase in their peaks, a large increase up to 88% in average winter day peaks is noticed in case of the condominium, while in case of the detached house the average winter day peak increased by only 8.3%. Consequently, a substantial increase in the distribution bill is noticed for condominiums and rental apartments, while detached and row houses notice a slight increase, this increase is only noticeable in Sollentuna Energi power tariff as shown in Figure 4.3. Since Vattenfall T4 and Sala Heby Energi simple tariff use an energy based distribution charge (SEK/kWh), the distribution bill costs decrease especially in the cases of high annual PV production in detached and row houses as shown in Figures 4.4, 4.5.

4.3 Tariff Performance

One of the aims of this thesis is to study the implications of the PV and EV on the current tariff structures and how the current tariffs will react to the prospective PV and EV penetration. In this simulation a comparison between the various tariffs and the currently applied Sollentuna Energi power tariff and

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Figure 4.2. Average day of winter and summer. (a) Sample house from BR SB type household (b) Sample house from V SB type household. Reference PV installation is presented. Note the y-axis differs between plots. The household classifications are defined in Table 3.1.

an estimation of the percentage of the difference was made. In this study an estimation of the influence of changing the PV capacity was performed.

In this estimation two PV capacities was simulated, the first capacity is the reference PV capacity and the second capacity represents 50% of the reference PV capacity. The smaller capacity of PV was chosen assuming that a limited rooftop area directed towards south is available for each household.

Figure 4.6a shows that in the high consumption category the condominiums and rental apartments tend to have high peaks compared with their total energy consumption. Consequently, Vattenfall T4 is the cheapest. The condominiums and rental apartments have high enough yearly energy consumption to make Sala Heby Energi simple tariff more expensive than the rest of the tariffs, and this is attributed to the fact that they charge the highest distribution charge (SEK/kWh) among the simulated tariffs. On the contrary, the detached and row houses have low peaks in comparison with their yearly energy consumption, and this results in the increase of the costs associated with the tariffs that charge distribution charge (SEK/kWh) in comparison with the reference tariff. Besides that, Sala Heby Energi simple tariff is still more expensive than Vattenfall T4 even though Vattenfall T4 has high annual fixed access charge (SEK/yr) for detached and row houses.

As the consumption category goes down from high to mean, it becomes cheaper to be charged based on an energy based distribution charge (SEK/kWh) compared to the power charge (SEK/kW) applied by Sollentuna Energi, especially for the condominiums and rental apartments, since, they have high peaks compared to their total energy consumption. Meanwhile, the high annual fixed

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(a)

(b)

(c)

Figure 4.3. Sollentuna Energi power tariff costs before and after PV and EV penetration. (a) Represents the high category. (b) Represents the mean category. (c) Represents the low category. Note the y-axis differs between plots. The household classifications are defined in Table 3.1.

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(a)

(b)

(c)

Figure 4.4. Vattenfall T4 costs before and after PV and EV penetration. (a) Represents the high category. (b) Represents the mean category. (c) Represents the low category.

Note the y-axis differs between plots. The household classifications are defined in Table 3.1.

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(a)

(b)

(c)

Figure 4.5. Sala Heby Energi simple tariff costs before and after PV and EV penetration. (a) Represents the high category. (b) Represents the mean category. (c) Represents the low category. Note the y-axis differs between plots. The household classifications are defined in Table 3.1.

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(a)

(b)

(c)

Figure 4.6. Comparison between various tariffs and Sollentuna power tariff on the distribution bill savings. (a) Represents the high category. (b) Represents the mean category. (c) Represents the low category. 50% and 100% of the reference PV capacity are presented. The household classifications are defined in Table 3.1.

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access charge (SEK/yr) charged by Vattenfall T4 makes it cheaper for the detached and row houses to be charged based on Sala Heby Energi simple tariff as shown in Figure 4.6b.

The pattern continues in the low consumption category, as presented in Fig- ure 4.6c, in this category the cheapest tariff is the Sala Heby Energi simple tariff due to the absence of the annual fixed access charge (SEK/yr) which suppresses the effect of the high distribution charge (SEK/kWh) charged by this tariff.

The costs of the three distribution tariffs (SEK/yr) are presented in Fig- ure 4.7. Sollentuna Energi power tariff will ensure the highest revenue for the DSO in the case of the new demand profile, the revenue is estimated to be 11,7MSEK in case of the reference PV capacity, Sala Heby simple tariff will ensure 9.1MSEK, while Vattenfall T4 will ensure the lowest revenue 9,08MSEK for the same demand profile.

Since the DR is adapted to Sollentuna Energi power tariff, it reduces the Sollentuna Energi power tariff costs, the results of the distribution tariff performance with DR, consequently, become slightly different. In case of the cheaper tariffs, they tend to be less cheaper. While in case of the more expensive tariffs they tend to be more expensive as shown in Figure 4.8.A more detailed discussion of the results of the DR is presented in Section 4.5

Tariffs perform differently depending on each household demand profile.

As shown in Figure 4.9a, households with high peaks and low energy consumption tend to save more while adopting an energy based tariff like Vatten- fall T4, while households with high energy consumptions and relatively low peaks tend to save more with the power based tariffs like Sollentuna Energi power tariff as sown in Figure 4.9b.

4.4 Total Electricity Costs

The distribution tariff represents nearly half of the electricity bill if taxes and VAT are equally divided between retail and distribution bills [23, p.24]. Con- sequently, the amounts of savings associated with different distribution tariffs as well as DR which are presented in Sections 4.3 and 4.5 are reduced to nearly half its value as shown in Figure 4.10. However, the low consumption category witness an increase in the percentage of savings. This increase is achieved due to the high energy production in this category, since the retail tariff include FIT for surplus PV production. This incident is shown in Figure 4.10c where it is obvious in case of detached and row houses which have large PV installation capacity.

Even though the installed PV capacity has shown little impact on the distri- butions tariffs performance in Section 4.3, it expresses significant role in the total electricity bill, especially in the low consumption category where there is an existing surplus energy. This role is attributed to the existence of FIT in the

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(a)

(b)

(c)

Figure 4.7. Costs of various distribution tariffs. 50% and 100% of reference PV capacities are presented. (a) Represents the high category. (b) Represents the mean category. (c) Represents the low category. Note the y-axis differs between plots. The household classifications are defined in Table 3.1.

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retail contract. The total costs of electricity (SEK/yr) are presented in Figure 4.11.

4.5 Demand Response

Sollentuna Energi power tariff charges customers for the highest three peaks in a month that occur during peak hours. Based on this tariff scheme the DR is simulated at various levels. The levels determine the number of peak hours that the DR reacts to and starts shifting the EV charging. The higher the DR level the higher the savings, nevertheless, the higher the inconvenience. This inconvenience is due to the postponed charging which delay the EV drivers in the following mornings. The highest simulated level of DR is made based on 20 peaks DR which will result in the worst case scenario, worst case means that all the 20 monthly peaks coincide with EV charging, in a postponed charging for 20 hours each month. In this DR study the PV capacities used are the reference capacities which are 1kW_pfor condominiums and rental apartments and 10kWpfor detached and row houses. Figure 4.12 presents the effect of the model on peak shaving which will in result save money.

The DR savings are more relevant in case of condominiums and rental apartments than in detached and row houses with only exception the low consumption category as shown in Figures 4.13a, 4.13b, 4.13c. Even though the percentage of revenue that the DSO gains from fixed costs is slightly higher in case of condominiums and rental apartments compared to detached and row houses, the peaks occur more during the EV charging in case of condominiums and rental apartments, while in detached and row houses the EV charging did not occur during their consumption peaks. In the case of the low consumption category, the EV charging peaks are more relevant in the monthly peaks of the condominiums and rental apartments, and consequently the DR is not saving as much as with the previous two categories. However, the detached and row houses tend to benefit more from the DR in this low category of consumption.

This means that their peaks contribute slightly more to the bill compared with the higher consumption categories.

The DR in this thesis presents a difficulty that may hinder its practical implementation. This DR presupposes the knowledge of the monthly peaks in order to postpone the EV charging which is not the real case. In reality it is seldom the case that the customers know their monthly peaks. Besides if customers were allowed to select the DR peaks, customers would tend to overes- timate their peaks, in fact customers tend to oversize their fuses [24] and they might react similarly with the DR.

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(a)

(b)

(c)

Figure 4.8. Comparison between various tariffs and Sollentuna power tariff on the distribution bill savings. 50% and 100% of reference PV capacities are presented. DR simulations are bordered with red. (a) Represents the high category. (b) Represents the mean category. (c) Represents the low category. Note the y-axis differs between plots. The household classifications are defined in Table 3.1.

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Figure 4.9. Duration diagram, based on Equations 3.3 and 3.4, of the various tariffs for household types (a) BR SB. (b) RH SA. Both represent the high consumption category with 50% of reference PV capacity. Note the y-axis differs between plots.

The household classifications are defined in Table 3.1.

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(a)

(b)

(c)

Figure 4.10. Comparison between various tariffs and Sollentuna power tariff on the total electricty bill savings of the low category. 50% and 100% of reference PV capacities are presented. The household classifications are defined in Table 3.1.

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(a)

(b)

(c)

Figure 4.11. Costs of the electricity bill including the electricity retail costs with the various distribution tariffs. 50% and 100% of reference PV capacities are presented.

(a) Represents the high category. (b) Represents the mean category. (c) Represents the low category. Note the y-axis differs between plots. The household classifications are defined in Table 3.1.

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Figure 4.12. Sample of the DR on a household demand profile.

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(a)

(b)

(c)

Figure 4.13. Distribution bill savings due to DR. (a) Represents the high category.

(b) Represents the mean category. (c) Represents the low category. The household classifications are defined in Table 3.1.

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5. Conclusions

In this thesis a study of the potential change in the demand profile of households when installing PV panels on the roof as well as having an EV. In addition a study of the implications of this change on the various distribution tariffs.

The results show that the household demand profile plays a major role in the savings associated with each tariff scheme. Condominiums and rental apartments tend to have high consumption peaks that coincide with EV charging, while detached and row houses tend to have slightly smoother peaks compared to condominiums and rental apartments. The households total electricity bill savings that can be achieved through selecting the optimal distribution tariff scheme is for most of the cases reasonable to incentivise customers to select the most suitable distribution tariff. Sollentuna Energi power tariff tend to provide the highest revenue among all the other tariffs to the DSO, which means that other DSOs have to estimate their future costs. Estimating costs of DSO is an essential step in calculating the revenue as well as designing the tariff scheme.

The DR model proposed by this thesis shows difficulty in implementation besides it does not have high financial incentives to adopting households, particularly the detached and row houses, whom are most probably going to be the early EV adopters.

The design of future distribution tariffs should focus on the balance between the subscription fee (SEK/yr), the energy based distribution charge (SEK/kWh), and the power based distribution charge (SEK/kW). This balance is needed to insure optimum profit to the DSO as well as incentivise customers to reduce their peaks and shift consumptions to off-peak hours.

Further research can investigate the effect of the new demand profile on the nodal voltages in the distribution grid as well as the grid losses. DR might show significant role in decreasing grid operation costs. Another area of research is to estimate the DSO costs in the new situation and based on that design suitable tariff scheme.

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6. Acknowledgements

This research has been carried out as the degree project of master degree in Energy Technology. This degree project was performed in the department of industrial engineering and management in Uppsala university. This research was part of a larger research team aiming at designing new distribution tariff structures as well as creating financially profitable business models for prosumers in case of high PV penetration.

I would like to sincerely thank the everyone who helped me in obtaining this master degree. Special thanks to my subject reader Cajsa Bartusch for her guidance as well as her active help in the thesis writing process. I would like to thank my supervisor Joakim Munkhammar for his guidance in the thesis as well as his continuous important feedback. Also, I would like to thank Simon Strandberg for his important tips that I would keep forever. Special thanks to Isak Öhrlund for his progressive discussions that helped form the thesis work.

I also would like to thank my colleague Vladyslav Milshyn for the pleasant working environment as well as the collaboration. I hope you all the best.

Last but not least my beloved family. Special thanks to my parents Mo- hamed and Rim as well as my sisters Nehal and Hadir, and the rest of the family for their support without which this achievement would not have been accomplished.

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