A hierarchical, distributed PEV charging control in low voltage ...

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A hierarchical, distributed PEV charging control in low voltage distribution grids to ensure network security Matthias D. Galus Student Member, IEEE, Simon Art, G¨oran Andersson, Fellow, IEEE

Abstract—Eight representative low voltage distribution networks in the city of Zurich are modeled including detailed data on individual consumer loads. Subsequently, the impacts of large scale plug-in electric vehicle (PEV) deployment on the particular networks are simulated. The spatial and temporal PEV transportation behavior is determined using an agent based model which makes use of an energy consumption model for individual vehicles. The different distribution networks, which are scattered over the metropolitan area of Zurich, are mapped to the transportation network. Case studies show that uncontrolled recharging leads to a variety of undesired effects such as asset overloading and excessively low voltages. A distributed, hierarchical demand management scheme is integrated into each distribution network and proves to mitigate the undesired effects, suggesting that an intelligent grid architecture can cope with a large number of PEVs while the infrastructure as it is cannot. Index Terms—PEV, smart charging, distribution networks, load management

I. I NTRODUCTION A large scale adoption of electric vehicles (EVs) and plugin hybrid electric vehicles, commonly referred herein as plugin electric vehicles (PEVs), is forecasted by the International Energy Agency (IEA) [1] and will have impacts on the current power system. Apart from vehicle-to-grid (V2G) [2]–[4], which increases the complexity for precipitating the integration of electric vehicles into power systems, the additional load will affect the power system. The paper investigates the impacts of PEV recharging on low voltage networks. Literature agrees on two possible ways of recharging the vehicles; uncontrolled - referred to as ”dumb” - charging or controlled - often referred to as ”smart” - charging [5], [6]. The extent to which the power system will be affected depends on the manner of how the vehicles are recharged. It was shown, e.g. in [7], [8], that uncontrolled charging can lead to undesired effects in power systems, such as increased peak load, overloading of assets on various voltage levels, and violation of voltage bounds. Many studies focus on either high or medium voltage electricity networks to investigate PEV impacts [8], [9]. However, low voltage distribution networks will face challenges introduced by electric mobility first because EV load will likely be spatially unevenly imposed. This can easily lead to asset stress. To this end, few studies are available and often use artificial network architectures and simplifications on transportation flows, PEV behavior and vehicle energy consumption, which is likely due to lack of actual data [10]–[13]. This paper draws on published studies M.D. Galus, S. Art and G. Andersson are with the Power Systems Laboratory, ETH Zurich (Swiss Federal Institute of Technology), Switzerland. Corresponding author’s email address [email protected]

978-1-4673-2729-9/12/$31.00 ©2012 IEEE

Fig. 1: Geographical position of 8 low voltage grids. They comprise residential areas, business areas and other representative and more heterogenous areas. by investigating the impacts of large scale PEV recharging on eight real, low voltage distribution networks located in the city of Zurich, Switzerland. Their location is shown in Fig. 1. The network loads are modeled using measured load data from individual consumers and the transformers feeding the particular networks. The PEV behavior is simulated by a multi agent transportation simulation tool [14]–[16]. A distributed, hierarchical PEV demand management scheme is integrated in every distribution grid, transforming it into an intelligent electricity network. The paper is structured as follows. Section two describes the modeling of the eight distribution networks. Section three describes the modeling of the electric vehicle fleet and its temporal and spatial behavior. It also includes the description of the mapping procedure between the transportation and the distribution networks. Section four describes the distributed and hierarchical control scheme while section five shows results of a case study performed for one of the eight networks demonstrating advantages of the controlled over the uncontrolled charging scheme. Finally, section six summarizes the paper.

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II. L OAD DECOMPOSITION TECHNIQUES FOR LOW

1100

VOLTAGE NETWORK ANALYSIS

1000

The distribution system of Zurich incorporates more than 800 low voltage (400V) sub-distribution grids, which supply residential neighborhoods, business areas including office buildings and other commercial areas. The individual subdistribution grids are not interconnected. The grids are fed by transformers connected to the 11kV and 22kV network. As it is hard to model the whole city’s sub-distribution network, 8 representative low voltage sub-distribution networks are chosen in order to investigate the impacts on these networks caused by electric mobility. Due to space limitations, this publication focuses only on distribution network No. 5. As can be seen in Fig. 1, the chosen network is in the middle of city, in an area which is dominated by office buildings. Detailed load data measured at the transformer and individual consumer energy consumption data during different tariff periods is used to model the network load. The load curves of the individual loads are then a synthesis of the measured transformer data and the consumer energy consumption data. The consumption share xL,ht of a load L with respect to the measured consumption of all loads during the high-tariff period ht, Etot,ht , is

900

= =

xL,ht xL,lt

EL,ht,m Etot,ht,m EL,lt,m Etot,lt,m

= = = =

,

xL,ht · Ptrafo (tht ) xL,ht · Qtrafo (tht ) xL,lt · Ptrafo (tlt ) xL,lt · Qtrafo (tlt ) .

(2)

This is simply done by finding a factor sL,ht between the consumption calculated on the basis of the transformer curve and the measured total consumption for the two tariff periods: =

sL,lt

=

Power [kW]

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Fig. 2: Two load curves modeling transformer loading; blue: measured load, red: consumption-corrected load. 2000

Load Duration Curve Minimum Load Maximum Capacity Maximum Load

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Loads of interest make up only parts of the total, recorded load data at the transformer level. The data at the transformer level, additionally, is not very precise. Thus, it is necessary to adjust the load curve of a single load in the network to the metered and billed electricity consumption indicated by subscript m:  tht PL,ht (t)dt = EL,ht,m (3) tlt PL,lt (t)dt = EL,lt,m .

sL,ht

800

400

(1)

where subscripts ht and lt indicate high-tariff and low-tariff periods. The subscript m indicates measured values and EL,ht,m is the measured high-tariff consumption of load L. The corresponding load curves, P and Q, during highand low-tariff periods are determined through the measured transformer loadings of active (Ptrafo (t)) and reactive power (Qtrafo (t)) and the corresponding consumption shares during the different tariff periods: PL,ht (tht ) QL,ht (tht ) PL,lt (tlt ) QL,lt (tlt )

Measured Consumption-corrected



(4)

assuming a constant share of power consumption for each load during the particular tariff period. This finally leads

Fig. 3: Load duration curve for the reference case showing maximum transformer capacity, maximum and minimum load. to consumption-corrected load curves for active and reactive power in high- and low-tariff periods: PL,ht (tht ) QL,ht (tht ) PL,lt (tlt ) QL,lt (tlt )

= = = =

sL,ht · xL,ht · Ptrafo (tht ) sL,ht · xL,ht · Qtrafo (tht ) sL,lt · xL,lt · Ptrafo (tlt ) sL,lt · xL,lt · Qtrafo (tlt ) .

(5)

The stochastic behavior of the loads is not modeled by this approach, but the accuracy achieved should suffice for steady state investigations performed henceforth. The results of this load modeling approach are shown in Fig. 2. The figure illustrates the loading of the transformer which feeds the modeled loads. The load cycle is plotted for one week starting from Saturday and ending again on Saturday. The consumption corrected load plotted in red is marginally lower than the measured load plotted in blue. The load duration curve is shown in Fig. 3. It can be noticed that in the reference case the transformer is only loaded to ca. 50% at peak load times. The maximum capacity of the transformer is 2 MVA, i.e there are two transformers installed, each with a rating of 1 MVA. The calculated load curves are used together with the eight 400 V distribution networks provided by the local utility and distribution network operator of Zurich. The resulting reference loading of the chosen network is shown in Fig. 4. The network incorporates rings and a weakly meshed structure which is common for low voltage distribution networks within the city of Zurich.

3

Link-ID 1

(b) (y)

Link-ID 2

Grid area (b) (y)

(y)

Link-ID 4

(b)

Link-ID 3

(b)

(b)

(b)

(y) (b)

(y)

(y)

(b)

Fig. 4: Topology of grid No. 5; yellow (y): relevant line loadings; blue (b): relevant node voltages.

Fig. 5: Visualization the location and speed of all agents in the middle of the city of Zurich, at around 06:30 in the morning. Agents colored red have a low speed and compared to the free speed of the road and are stuck in a traffic jam [15]. III. S IMULATING B EHAVIORAL PATTERNS OF PEV S AND I NTEGRATION OF P OWER AND T RANSPORTATION S YSTEM Traffic flows can be modeled through the behavior of individual vehicles. Simulating each car individually as an agent is called agent based micro simulation and allows tracking of individual vehicles over time and space. Assigning a utility to each agent allows for individual decision modeling such as choosing the path to drive or choosing the location for refilling gasoline. MATSim is an agent based transport simulation framework with focus on large scenarios involving many cars [16]. Simulations with more than seven million agents on a navigation network with around one million links have already been demonstrated [15], [16]. Figure 5 shows a simulation result for the city of Zurich, Switzerland. The dots, each one representing an agent are at different locations in the city facing different traffic situations such as traffic jams. The latter is visualized through the red dots. The simulation result incorporates the state of charge (SOC) of each individual vehicle, its arrival, departure time and location as well as the desired SOC at departure for all cars over the whole day. The vehicles all incorporate the same design, e.g. mass and energy

Fig. 6: Mapping of the transportation network (linkIDs) and the distribution grid. linkIDs parts lying outside of the grid area affect the number of charging processes. consumption derived from [17], battery size (10 kWh) and connection capacity (3.5 kW). The vehicles park and connect at certain locations, given by linkIDs, in the transportation network. The linkIDs are streets and are defined by Geographical Information System (GIS) coordinates. Using these coordinates allows a mapping of the respective charging location to the electricity infrastructure as described in [14]. Contrary to previous studies on higher voltage levels such as [9], the mapping proves to be challenging for LV grids because the area supplied by the grid does not necessarily correspond well to the area represented by the linkIDs. The size and shape of a linkID depends on the street topology, and the corresponding area might - and in most cases does - continue outside the grid area. This is illustrated in Fig. 6. A linkID represents the area of roughly one street but the resolution of the electricity network model is much finer as, for example, one load often represents a single residential home in the electricity grid. Thus, the charging processes generated by the transportation simulation cannot be assigned directly to one load of the distribution grid and it is likely that an excessive amount of charging processes is assigned to the investigated distribution grid by the mapping process. For a proper assignment of parking cars to the loads a simplistic approach is chosen. First, all vehicles parking in a linkID, which is a part of the grid area, are included. As this leads to an excessive number of vehicles for the grid area under investigation, a rough estimation of the available parking area in the grid area is performed. Assuming an average area of 11.5 m2 per vehicle needed to park the car1 , the capacity of the available parking area in the modeled network area can be calculated. It caps the number of recharging cars. The cars are assigned to the individual loads in the network in proportion to the loads’ relative shares of the peak load. This is in accordance with (1). The parking capacity for the investigated grids comprises between 500 and 900 parking places. IV. H IERARCHICAL D EMAND M ANAGEMENT OF PEV S IN LV G RIDS USING AN U TILITY F UNCTION A PPROACH A demand management scheme based on agent technology has been presented in [18], further developed in [14] and applied to the complete city of Zurich in [9]. The theoretical background and details of the integration of the concept with the agent based transportation simulation are found in [19]. In the following, only a brief recapitulation of the concept is given. 1 This corresponds to the authority rules for parking spaces in the city of Zurich.

4 400 V Network

PEV Manager

PEV Manager PEV Manager PEV Manager PEV Manager

PEV Manager

PEV Manager

Fig. 7: Integration of PEV Managers into power networks. The concept utilized for PEV demand management is based on an optimization platform referred to as PEV Manager. PEV Managers are devices that can be embedded in an intelligent electricity network utilizing a bidirectional information flow architecture, e.g. smart meters. The operational issues of these devices along with implications for a future electricity market framework are discussed in detail in [20]. So far, PEV Managers are envisioned to be operated at the 11/22 kV network while Supervisory PEV Managers (S-PEV Managers) could be included on higher voltage levels. Therefore, the demand management concept includes a hierarchical structure. Here, the PEV Managers are installed at the 400 V level directly at the house, i.e. load, connection lines. This concept is illustrated in Fig. 7. The charging control concept of the PEV Managers is based on the game theoretical concept of mechanism design described in [19], [21]. It is assumed that a PEV agent can be assigned an benefit for each energy level in its battery. Intuitively, the benefit should be low when the battery is empty and be high when the battery is full. The marginal benefit, i.e. the infinitesimal change in benefit due to the infinitesimal change of SOC, should decrease the higher the SOC actually is. That means, the more energy is in the battery, the lower the sensitivity for acquiring additional energy and hence the lower the increase in benefit. This is consistent with well known economic theory in electricity markets [22]. These characteristics of the benefit function are achieved by defining it for PEV v at node n as: B B bv,n (Ωv,n (T )) = αv,n Cv,n Ωv,n (T ) − βv,n Cv,n Ωv,n (T )2 (6) with  Ωv,n (T ) = socv,n (T ) − socmin v,n + q (7) ∀ v ∈ Vn (T ) , ∀ n ∈ N

where Ωv,n (T ) describes the relationship between the SOC at the beginning of the time period T and an arbitrary amount of energy q  which is added to the SOC for all v ∈ Vn (T ) = {1, 2, ...NnPEV(T )}∀ n ∈ N

,

(8)

B denotes the battery capacity of PEV v, The parameter Cv,n αv,n and βv,n define the maximal marginal benefit and the slope of the marginal benefit. It is realistic to set αv,n to the value of the current gasoline price, but as both parameters can be tuned [19], the actual value is adjustable and depends on the desired charging behavior in relation to an exogenously transmitted control price signal. A utility function is derived in order to make use of the mathematical concepts of mechanism design and to allow PEV

agents to bid for the potentially scarce good of electric power at a congested distribution grid node. The utility function is constructed from the benefit function by multiplying it with an agent specific parameter θv,n (T ) and subtracting a virtual cost, which is derived from an exogenously transmitted or endogenously determined control price signal πn (T ). If a specific node is congested, the control price signal at this node is endogenously determined by the algorithm [14], [19]. Hence, the utility function is formulated as   uv,n Ωv,n (T ), π(T ), θv,n (T ) =    2 B B Ωv,n (T ) − θv,n (T )βv,n Cv,n Ωv,n (T ) θv,n (T )αv,n Cv,n     B −πn T, Θn (T ) Cv,n qv,n T, θv,n (T )|Θn (T ) (9) with     pv,n T, θv,n (T )|Θn (T ) qv,n T, θv,n (T )|Θn (T ) = , B ς(T )Cv,n (10) where pv,n (·) is the power assigned to PEV v and qv,n (·) is the assigned energy, related to the battery capacity of this PEV. The agents bidding process must be circumvented in order to avoid a time consuming, iterative solution. An optimization can be formulated calculating the outcome of an auction among the agents. The optimization is performed by the PEV Manager. It maximizes the sum of the utility functions uv,n and distributes the available power to PEVs connected at node n in time step T . In this setup, i.e. when the PEV Managers are installed at the household connections, overloading of the household connection lines is avoided. The optimization maximizes    Fn = uv,n Ωv,n (T ), πn (T ), θv,n (T ) (11) v∈Vn (T )

subject to pmin v,n (T )

≤

socmin v,n

≤

Pnmin (T ) ≤

  pv,n T, θv,n (T )|Θn (T ) 

 pv,n

≤ pmax v,n (T ) (a)

≤ socmax v,n (b)  max T, θv,n (T )|Θn (T ) ≤ Pn (T ) (c) socv,n (T )

v∈Vn (T )

∀ v ∈ Vn (T ) , ∀ n ∈ N

.

(12) For each n ∈ N the objective function Fn is maximized. The first constraint ensures that the power with which the PEV is recharged is within the physical limits of the connection. Constraint (12b) ensures that the physical limits of the batteries are obeyed and the last constraint of the PEV Manager gives that the maximum capacity of the actual household line is not violated. This avoids the fuses to be activated which would leave the house without power. The management scheme prevents excessive PEV load and shifts it to later times where the base loading, i.e. the inflexible household load, is lower. In case the particular node is heavily loaded during long times, it is possible that PEVs will not be able to achieve their desired SOC before departure. Hence, in such a case, the excessive PEV load is partly shifted and partly shed. This is assumed to be circumstantial as PEVs could use their gasoline engine.

5

S-PEV Manager

(b) (y)

Uz

(b)

(y)

11/22 kV

(y)

400 V (b) (b)

Fig. 8: Zone fed by a 11/22 kV-400 V tap changing former. A Supervisory PEV Manager (S-PEV ager) controls the underlying PEV Managers the PEV Managers at each node control the connected at the 400 V voltage level.

transManwhile PEVs

(y)

(b)

(b)

(y) (b) (y) (b)

Obviously, network wide challenges such as overloaded transformers, overly low voltages or overloaded lines are not mitigated by this configuration of PEV demand management. A hierarchical scheme can be used to mitigate the latter issues. An S-PEV Manager is installed at the transformer (or at the busbar to which the transformers are connected) and is able to control the underlying PEV Managers. This concept is illustrated in Fig. 8. Note that the transformer between the 11/22 kV level and the 400 V is a tap changing transformer whose tap is usually left unchanged. The S-PEV Manager is able to mitigate transformer overloading and line overloading. The mitigation of line overloads is described in detail in [23]. The mitigation of overly low voltage is achieved by using a Thevenin equivalent for the distribution network fed by the transformer. The Thevenin equivalent allows to find the maximum load which can be supplied by the network without violating a lower voltage bound, usually chosen to 0.95 p.u. The S-PEV Manager utilizes a similar optimization algorithm as the PEV Manager. The formulation of the S-PEV Manager optimization is found in [9]. It establishes a secure network state considering the inputs of its underlying PEV Managers.

V. C ASE S TUDY OF PEV IMPACTS ON L OW VOLTAGE G RIDS The case studies have been performed for all eight distribution grids. However, due to space limitations only the results for distribution network No. 5 will be discussed. Two cases are differentiated, an uncontrolled and a controlled recharging case. In the uncontrolled case, the PEVs arrive and connect according to the MATSim simulation. They start to recharge immediately. If network problems arise, they are not mitigated. The controlled case utilizes the PEV demand management concepts discussed above. Figure 9 shows a loadflow result of the distribution network for the uncontrolled case during peak time. Voltages of some important nodes are displayed in the blue (b) boxes while line loadings are given in the yellow (y) boxes. It can clearly be seen that the network security is heavily endangered. Numerous distribution lines exceed their maximum allowed rating. Two lines exhibit power flows which are close to double their maximum rating. Furthermore, many voltages fall under the minimum allowed value of 95%. This heavily deteriorates

Fig. 9: Load flow results for uncontrolled charging; yellow (y): relevant line loadings; blue (b): relevant node voltages. (b) (y)

(b)

(y)

(y)

(b)

(b)

(b)

(y) (b)

(y) (b) (y) (b)

Fig. 10: Load flow results for controlled charging; yellow (y): relevant line loadings; blue (b): relevant node voltages. power quality in the supplied area2 . Figure 10 displays a loadflow result of the network for the controlled recharging case during peak load time. It can be seen that the network is relieved. None of the former critical, overloaded lines violates its maximum rating. All line loadings are comprehensively displayed in Fig. 11. In the uncontrolled recharging case, 7 lines face loadings which are higher than their maximum allowed loading during peak load time. In the controlled case, all line loadings are substantially reduced, which is mainly due to the demand management scheme and the network configuration. Line No. 6, 7, 8 create a heavy bottleneck. Decreasing their loading leads to a reduced loading of many other lines. In this particular network, however, the overall limiting factor is the voltage level at the household connection points. 2 Please note that the PEV load is modeled as constant power load. Modeling it as some other type of load might mitigate some of the issues. The chosen modeling approach aims to create a worst case scenario in order to show the possible impacts of large scale PEV adoption on low voltage distribution networks.

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Load in Reference Case Load in PEV Case (uncontrolled) Load in PEV Case (controlled)

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Fig. 13: Load plot of different charging scenarios. Blue: reference load; dotted, red: uncontrolled PEV load; red: uncontrolled total load; green: controlled total load; dotted green: controlled PEV load. 700

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Fig. 11: Line loadings in the distribution network. blue: reference case; red: uncontrolled; green: controlled.

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Fig. 12: Node voltages in the distribution network. Blue: reference case; red: uncontrolled; green: controlled. Figure 12 shows all node voltages for the uncontrolled and the controlled case. It can be seen that in the uncontrolled case the voltages of all nodes are under the minimal allowed voltage level of 95 %. At some nodes, voltages even fall under 90 %. In contrast, the voltages in the controlled case are all above the minimum allowed voltage level. Hence, power quality is not jeopardized in the network. The fact that the voltages are the limiting factor is underlined by Fig. 13. The figure displays load curves including the reference load curve in blue. The load imposed by PEVs in the uncontrolled and controlled charging scenario is illustrated by the dotted red and green lines, respectively. The resulting total loads are then plotted in red and green, respectively. The reference load and the PEV load curve exhibit the typical load shape of a business area. Most of the PEV load is imposed on the network during the day time, between 07:00 and 19:00, i.e. during working hours. When the PEV load is added to the reference load of the network, the maximum transformer capacity is exceeded. This is striking as Fig.3 shows that the transformer is only relatively weakly loaded to about 50 % during peak load times. The PEVs add a substantial load to the network. The peak PEV load is found to be 1500 kW. This load alone would fill the transformer capacity to 75 %. Hence, uncontrolled charging overloads all assets of this specific distribution network. Controlled charging reduces the PEV load massively as the maximum PEV load is then found to be about 800 kW, roughly half of the PEV peak load in the uncontrolled case. After the maximum network load is reached at 07.30 in the morning, the PEV load is reduced to about 300 kW, which is 20 %

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Fig. 14: Number of PEVs broken down for different activities such as charging, arriving, departing and parking. of the PEV load at the same time in the uncontrolled case. The hierarchical charging control ensures compliance with the lower voltage bound at all times and therefore creates a plateau of PEV load during the working hours. This valley corresponds well to the shape of the reference load. As the reference load increases the PEV load is reduced. The PEV load is not significantly increased during the late evening hours and early morning hours. This implies that a lot of PEV load is not shifted to later times but completely shed. The slight difference between the uncontrolled and controlled case in the PEV load during those hours results from PEVs which park in the area of the distribution grid for a rather long time, staying until the evening hours. These PEVs are recharged by the charging control mainly during the low load hours in the evening or early morning. PEVs which are only parked in the grid area during the peak hours are just partly recharged before their departure. As grid security is always kept, the tradeoff is that these vehicles are not able to attain their desired SOC at departure. In order to recharge all vehicles appropriately, this network would need to be reenforced. The implications of the control algorithm on the individual cars are visualized in Fig. 14. It depicts arrivals and departures through lines in cyan and magenta, respectively. The number of parked cars is plotted by the blue line. During the periods in which the number of arrivals is bigger than the one of departures, the number of parked cars increases and vice versa. The red line illustrates the number of cars charging in an uncontrolled manner. The green line plots the number of cars which are recharged using the described control scheme.

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Fig. 15: Uncontrolled (a) and controlled (b) distribution of charging PEVs.

Fig. 16: Uncontrolled (a) and controlled (b) load distribution of charging PEVs.

Comparing the red and the blue line, it can be seen that the number of charging cars is always lower than the number of parked cars because vehicles, which are already fully charged and still parked, are considered. The large gap between the number of parked and charging cars between 13:00 and 18:00 indicates that many cars are already fully charged at this time because they started to charge as soon as they arrived. The green line follows the blue line well. Here, the charging power of each car is reduced by the control algorithm in order to avoid network stress. Therefore, the cars need to charge longer. Between 13:00 and 18:00 all parked cars recharge. Later, some of the vehicles are fully recharged, which gives that the green line lies under the blue line. The red line and the green line are congruent between 04:00 and 09:00 as all vehicles which arrive start to recharge. However, their recharging takes place with a different power rating. This finally cumulates in the gap between the red and the green line, since many cars are fully recharged earlier when being charged in uncontrolled manner. Figure 15 shows the distribution of charging cars over time and location, i.e. node in the network. The effects of the control algorithm which have been discussed before are seen here in more detail. The number of charging cars in the controlled case is always bigger than for the uncontrolled case. This is due to the different charging power of the individual cars which is determined by the control scheme. The temporal distribution of the number of charging cars is also different for the two cases. It is clearly seen that a large number of cars charges later in the day for the controlled case. The cars do not move strategically between the nodes in order to attain energy. Figure 16 shows the temporally and spatially differenti-

ated consumed power of the charging PEVs. The effects of the charging control are eminent. While in the uncontrolled recharging case the shape of the consumed power follows the shape of the of the number of charging cars, the shape of consumed power in the controlled charging case is quite different. In fact, it barely relates to it. The values of consumed power are much smaller as than in the uncontrolled charging case. The plateau during the working hours, which is also shown in Fig. 13, can be seen well. The total consumed power is much smaller than in the uncontrolled case but the number of charging cars is equal or even bigger. This underlines that scarcely available power is distributed to all connected vehicles. This is performed optimally, i.e. based on the individual utility functions, assuming that the utility functions are an appropriate measure. VI. S UMMARY The paper investigates the impacts of large scale PEV recharging on eight low voltage distribution networks. The networks are modeled using real network and detailed consumption data provided by the utility company of the city of Zurich. The PEV behavior is simulated using an agent based transportation simulation tool resulting in spatially and temporally detailed data outputs including energy demand. Using a precise mapping of transportation and power network, the impacts of uncontrolled charging can be evaluated. It is found that, depending on the network architecture and its base loading, various physical network constraints are violated. For the network investigated in detail, the voltage bounds are the limiting factor. However, overloads on lines as well as overloading of transformers feeding the networks are found.

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This is especially striking since a lot of spare capacity is found in the reference case. The other seven networks face similar challenges. In four cases the transformer is not overloaded. All networks face overloading of lines although usually in a small range. The networks are found to face severe voltage level issues. The suggested distributed hierarchical charging control is able to mitigate all negative network impacts of excessive PEV recharging. However, this comes at the cost of some individual vehicles not acquiring enough energy to depart with their desired SOC. This can only be solved by expanding the network accordingly and allowing for a larger load during load peak times. However, controlled recharging is able to utilize the infrastructure in place to its full potential. Furthermore, together with previously published findings, the approach of PEV Managers, i.e. intelligent, distributed, management devices, on different voltage levels suggests that all possible negative impacts of PEV recharging on current electricity infrastructure can be effectively mitigated. A transformation of the infrastructure to include more intelligence is a crucial necessity. This does, however, not mean that network expansion becomes superabundant. VII. ACKNOWLEDGEMENTS The work is sponsored by the ETH Zurich (Swiss Federal Institute of Technology Zurich) under research grant TH 22073. The authors would like to thank especially R.A. Waraich from the IVT at ETH Zurich for performing the transportation simulation in MATSim, E. Iggland from the PSL at ETH for proof reading the paper and ewz, the local utility of Zurich, Switzerland, and there especially F. Dietlicher, for providing the network data and participating in very helpful discussions. R EFERENCES [1] International Energy Agency (IEA). Technology roadmap, electric and plug-in hybrid electric vehicles. Technical report, June, 2011. [2] W. Kempton and J. Tomic. Vehicle-to-grid power fundamentals: Calculating capacity and net revenue. Journal of Power Sources, 144(1):268– 279, 2005. [3] S. L. Andersson, A. K. Elofsson, M. D. Galus, L. G¨oransson, S. Karlsson, F. Johnsson, and G. Andersson. Plug-in hybrid electric vehicles as regulating power providers: Case studies of Sweden and Germany. Energy Policy, 38(11):2751–2762, 2010. [4] M.D. Galus, S. Koch, and G. Andersson. Provision of load frequency control by PHEVs, controllabel loads and a co-generation unit. IEEE Transactions on Industrial Electronics, 58(10):4568–4582, 2011. [5] J. A. Pecas Lopes, F. J. Soares, and P. M. R. Almeida. Integration of electric vehicles in the electric power system. Proceedings of the IEEE, 99(1):168–183, 2011. [6] M.D. Galus, M. Zima, and G. Andersson. On integration of PHEVs into existing power system structures. Energy Policy, 38(11):6736–6745, 2010. [7] S. W. Hadley. Evaluating the impact of plug-in hybrid electric vehicles on regional electricity supplies. In Proceedings of the VII. Bulk Power System Dynamics and Control (iREP) Symposium, Charleston, SC, USA, August 2007. [8] J.A. Pecas Lopes, F.J. Soares, and P.M. Rocha Almeida. Identifying management procedures to deal with connection of electric vehicles in the grid. In Proceedings of IEEE PowerTech, Bucharest, Romania, June 2009. [9] M. D. Galus, R. A. Waraich, and G. Andersson. Predictive, distributed, hierarchical charging control of PHEVs in the distribution system of a large urban area incorporating a multi agent transportation simulation. In Proceedings of the Power Systems Computation Conference (PSCC), Stockholm, Sweden, August 2011. [10] K. Clement-Nyns, E. Haesen, and J. Driesen. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Transactions on Power Systems,, 25(1):371–380, 2010.

[11] S. Acha, T. Green, and N. Shah. Effects of optimised plug-in hybrid vehicle charging strategies on electric distribution network losses. In Proceedings of the IEEE PES Transmission and Distribution Conference and Exposition, New Orleans, LO, USA, April 2010. [12] C. Farmer, P. Hines, J. Dowds, and S. Blumsack. Modeling the impact of increasing PHEV loads on the distribution infrastructure. In Proceedings of the 43rd Hawaii International Conference on System Sciences (HICSS), Hawaii, HI, USA, January 2010. [13] K. Clement, E. Haesen, and J. Driesen. Coordinated charging of multiple plug-in hybrid electric vehicles in residential distribution grids. In Proceedings of IEEE/PES Power Systems Conference and Exposition, Seattle, WA, USA, March 2009. [14] M. D. Galus, R. A. Waraich, F. Noembrini, K. Steurs, K. Boulouchos, K. W. Axhausen, and G. Andersson. Integrating power system, transportation systems and vehicle technology for electric mobility impact assessment and efficient control. IEEE Transactions on Smart Grid, Special Issue on Intelligent Distribution Systems, 2011. [15] MATSim-T. Multi Agent Transportation Simulation Toolkit, 2008. [16] M. Balmer, K. Meister, M. Rieser, K. Nagel, and K.W. Axhausen. Agent-based simulation of travel demand: Structure and computational performance of MATSim-T. In Proceedings of the 2nd TRB Conference on Innovations of Travel Modeling, Portland, OR, USA, June 2011. [17] M. D. Galus and G. Andersson. Power system considerations of plugin hybrid electric vehicles (PHEVs) based on a multi energy carrier model. In Proceedings of the IEEE Power & Energy Society (PES) General Meeting, Calgary, AB, Canada, July 2010. [18] M. D. Galus and G. Andersson. Demand management for grid connected plug-in hybrid electric vehicles (PHEVs). In Proceedings of the IEEE Energy 2030 Conference, Atlanta, GA, USA, November 2008. [19] M. D. Galus. Modeling of large scale electric mobility in power systems using agent-based transportation simulation. PhD thesis, ETH Zurich, 2012. [20] M. D. Galus, P. Jonas, M. Weiss, E. Iggland, and G. Andersson. EV Aggregation in Power System Operation: Legal Frameworks and Communication Needs. submitted to IEEE Transactions on Smart Grids, 2011. [21] D. Fudenberg and J. Tirole. Game Theory. The MIT Press, Cambridge, Massachusetts, USA, London, England, 1991. [22] D. Kirschen and G. Strbac. Fundamentals of Power System Economics. John Wiley & Sons Inc., 2004. [23] S. Art. PHEV Management and power system simulation - a case study (Semester thesis, available online). ETH Zurich, 2011. Matthias D. Galus was born in Swientochlowitz, Poland. He received a Dipl.-Ing. degree in electrical engineering and a Dipl.-Ing. degree in industrial engineering from the RWTH Aachen, Germany, in 2005 and in 2007, respectively. He joined the Power Systems Laboratory of ETH Zurich, Switzerland in 2007 where he is working towards a PhD. His research is dedicated to modeling, optimization and efficient integration of PEV into power systems. He is a student member of the IEEE and VDE (German society of electrical engineers). Simon Art was born near Passau, Germany. He received his Bachelor of Science degree in Environmental Natural Sciences at ETH Zurich in 2009. Currently, he is pursuing his Master degree in Energy Science and Technology at ETH Zurich. His interests are in the field of markets for gridbound energy carriers and related technical as well as environmental issues.

G¨oran Andersson was born in Malm¨o, Sweden. He obtained his MSc and PhD degree from the University of Lund in 1975 and 1980, respectively. In 1980 he joined ASEA, now ABB, HVDC division in Ludvika, Sweden, and in 1986 he was appointed full professor in electric power systems at the Royal Institute of Technology (KTH), Stockholm, Sweden. Since 2000 he is full professor in electric power systems at ETH Zurich, Switzerland, where he heads the Power Systems Laboratory. His research interests are in power system analysis and control, in particular power system dynamics and issues involving HVDC and other power electronics based equipment. He is a member of the Royal Swedish Academy of Engineering Sciences and Royal Swedish Academy of Sciences and a Fellow of IEEE.