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A decentralized approach towards resolving transmission grid congestion in Germany using vehicle-to-grid technology
Applied Energy 230 (2018) 1435–1446
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
A decentralized approach towards resolving transmission grid congestion in Germany using vehicle-to-grid technology
T
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Philipp Staudt , Marc Schmidt, Johannes Gärttner, Christof Weinhardt Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
H I GH L IG H T S
of uncoordinated electric vehicle charging in German transmission system. • Evaluation of the congestion behaviour of the German transmission grid. • Simulation of a heuristic for redispatch capacity provision by electric vehicles. • Development • Calculation of possible compensation for electric vehicle owners for participation.
A R T I C LE I N FO
A B S T R A C T
Keywords: Electric vehicles Demand flexibility Power transmission system Local markets Charging coordination
The increasing penetration of renewable generation in electricity markets as well as the rising number of electric vehicles pose new challenges for transmission grids. Additional demand and regionally clustered generation force system operators to consider costly expansion plans and employ expensive redispatch measures in the meantime. In this paper, we assess the ability of the expanded German transmission grid to cope with the additional demand of uncoordinated electric vehicle charging using a transportation problem formulation. We then propose local flexibility markets for electric vehicle owners to relieve the grid of congestion and to provide a heuristic that finds feasible solutions. We test our models on empirical data from the German electricity system of 2016. We find that the currently proposed expansion of the German electricity grid will not suffice to cope with increased electricity demand from uncoordinated electric vehicle charging. However, with coordination, electric vehicles can support transmission grid balancing and local flexibility markets can provide reasonable remuneration for electric vehicle owners.
1. Introduction The ambitious environmental objectives of the European Union and Germany induce the need of transforming not only the energy but also the transportation sector [1]. The German rollout plan for electric vehicles (EV) foresees one million EVs by 2020 and six million by 2030 [2]. The need for electricity in the transportation sector will significantly increase the overall electrical load [3]. However, the German electricity market is already struggling with the incorporation of regionally clustered renewable sources, which increase the need for redispatch measures [4]. This is due to the copper plate assumption that comes with a uniform-price electricity market [5]. Transmission network expansion is already underway [6] but it is usually unpopular with the population [7]. Various studies find that if charging is performed in an uncoordinated manner load peaks will increase [8] because charging often occurs in similar times, e.g., after the return from ⁎
work in the early evening. This motivates the question whether the currently planned transmission network expansions will suffice to incorporate uncoordinated charging of an increasing number of electric vehicles. To answer this question, we conduct a case study given the electric vehicle penetration objectives of the German government using empirical data from the German electricity system of 2016 while considering the planned transmission grid expansion. After this analysis, we propose a decentral coordination mechanism using vehicle-to-grid technology to alleviate the impact of electric vehicles on the transmission grid and to actively support the integration of regionally clustered renewable infeed. Vehicle-to-grid (V2G) was originally introduced by [9]. The concept proposes to use EVs bi-directionally as grid resources, i.e. in certain situations the EV takes power from the grid and in other situations it serves as power source for the grid. In this context we calculate the need for redispatch in a simplified German electricity system and introduce a heuristic to reduce the amount of
Corresponding author. E-mail address: [email protected] (P. Staudt).
https://doi.org/10.1016/j.apenergy.2018.09.045 Received 29 April 2018; Received in revised form 14 August 2018; Accepted 5 September 2018 0306-2619/ © 2018 Published by Elsevier Ltd.
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effects on emissions and petrol use. They warn that uncoordinated charging might have negative impacts on the electricity grid. However, they conclude that smart grid charging strategies could mitigate such negative effects to a large extend. In this paper we reproduce the presented results for the German transmission grid and then move further to providing a solution that allows to resolve transmission congestion. A list of numerous papers that find a positive effect of coordinated charging on the effectiveness of existing generation and transmission capacity is provided in [25]. Coordination of EV fleets is used in [26] to decrease the emissions caused by their operation. Smart charging strategies are presented in [27]. Several papers have proposed to use V2G technology to provide ancillary services to TSOs instead of resolving congestion [28]. However, this service can be performed by only a small number of vehicles which will saturate the market [29]. Ancillary service provision has been studied for several national markets as in [30] for the Spanish market. An overview of different results is provided in [31]. The interactions between an aggregator who provides ancillary services through EVs and the connected individual users are modeled in [32]. The authors conclude that using a coordinating pricing mechanism, distributed decisions achieve the same optimality as central coordination. This is an important result for our proposed local flexibility markets. In [33] the others focused on cross-border transmission capacity in Europe and how the need for expansion can be reduced by V2G technology. However, they do not consider redispatch as the cross-border dispatch in Europe is specifically being taken into account at market clearing or the possible compensation of EV owners. While the impact of EVs on the transmission network has attracted less attention, there is a broad range of research on the impact of EV charging on distribution network infrastructure such as [10]. Yet, no research has been carried out to analyze the benefits from using EVs to avoid redispatch needs on the transmission level. This overview shows that spatial components as well as the impact of EV charging on grid infrastructure are an active field of research. Especially, the charging behaviour and the impact on distribution grids have been investigated. However, the specific effect of (no) charging coordination on the transmission grid is still in question. This paper is the first to evaluate the effect of uncoordinated EV charging on the expanded German transmission grid and to propose a coordinated charging approach to relieve the transmission grid of congestion. Furthermore, we calculate the possible compensation for participants which has not been considered previously.
redispatch needed by using EVs as distributed storage. The participating EV owners are being remunerated and we calculate the possible compensation. The exact payment depends on the local need for redispatch and the associated costs. This introduces a spatial component in the electricity system which reduces the need for transmission network expansion. This research is a natural extension of [10] where the authors exclusively consider the load and congestion of the distribution grid in the form of transformer load. Therefore, the contribution of this paper is threefold: (i) We analyse the ability of an expanded German transmission grid to include additional, uncoordinated EV demand, (ii) we develop a V2G heuristic to avoid the need for redispatch and (iii) we calculate the possible spatially differentiated payment resulting from reduced congestion costs to participants in the decentralized V2G mechanism. 2. Related work There is a vast area of research on locational components in electricity markets and congestion management. Usually, this research focuses on the supply side, formulating price rules that respect the physical constraints of the transmission system. Typical market designs for spatial price differentiation are nodal and zonal pricing mechanisms [11]. A framework for spatial differentiation of consumer electricity prices is presented in [12]. In [13] the authors qualitatively look at incentive mechanisms to ensure operational optimality and optimal investment. They conclude that so called smart contracts should be implemented which include a locational component but also incentivize operational optimality. The benefits of introducing spatial components in the form of nodal prices in Germany were investigated in [14]. An extensive overview over the possible applications of V2G mechanisms is given in [15]. In [16] the authors consider V2G technology to better integrate renewable energy generation. We consider this in Section 3. The link between electric vehicles and the electricity sector through the smart grid was established by [17]. The authors argue that it is unclear whether the distribution and transmission system will be able to support uncoordinated charging of a large fleet of electric vehicles. The general usefulness and possible applications of demand side management (DSM) are discussed in [18]. The author specifically argues for load curtailment at distinct locations in the network. In [19] the authors describe how DSM is financially beneficial to customers. A charging strategy to optimally react to time-of-use pricing is proposed in [20]. A similar approach for parking stations is implemented in [21]. A general overview of grid integration of EVs, both technically and economically, is provided in [1]. One important research stream related to EVs is the impact of uncoordinated and coordinated charging on different voltage levels of electricity networks but also on the environmental and economical impact of EVs. The impact on load curves from increased penetration of EVs is discussed in [8]. The authors conclude that consumption peaks would especially increase in the early evening if charging was not coordinated. We use this result in our analysis of the EV impact on transmission networks. In [22] a simulation tool is developed which is able to assess the level of EV penetration that would still be supported by the current grid infrastructure. The authors conclude that only 60% of the vehicle fleet could be replaced by EVs in Switzerland if charged uncoordinatedly. In [23] the impact of uncoordinated EV charging is evaluated. The authors find that a small share of EVs can be handled even without coordination but that infrastructure needs to be upgraded with increasing penetration. Other studies find that the electricity grid will have to be expanded if EV charging is performed in an uncoordinated fashion [24]. The possibility of transforming the vehicle fleet in the U.S. to electric vehicles is analyzed in [3]. They find positive
3. Impact of EV charging on the transmission network First, we analyze the impact of uncoordinated EV charging on the German transmission grid including its intended expansions given the market penetration objectives of the German federal government for EVs. To this end, we use a reduced representation of the German transmission grid ignoring cross-border trading (similar to [14]). We begin by introducing our assumptions regarding the network topology. 3.1. Transmission network topology We abstract from reality by reducing the German transmission network to one node per federal state. Furthermore, we add two nodes for offshore wind generation in the North and Baltic Sea. Doing so, we assume that there is no congestion within the states. This reduces the overall probability for congestion. We model the problem as a transportation model as in [34]. This means that in this part of the study we ignore Kirchhoff’s laws and assume that electricity might be well-directed through the network without loop flows. This increases the
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Fig. 1. Transmission system model.
amount of electricity that can be transmitted through the system as capacity can be used with maximal efficiency. Therefore, if congestion occurs using this formulation it will most likely also occur using actual load flow calculations. The flow patterns themselves would of course not be correct, but at this point we are not interested in exact flows but rather the information if the system can be stable. We use the static network models provided by the four German TSOs TenneT [35]. Amprion [36], TransnetBW [37] and 50Hertz [38] to model the transmission capacity between states and assume that there be no capacity restriction for offshore wind generation. This is reasonable as German regulation forces TSOs to fully connect all renewable generation capacity. We include the transmission capacity that will be added with the latest expansion plans [39]. A schematic representation of the model is shown in Fig. 1.
3.3. Electric vehicle characterization
3.2. Demand representation
3.4. Generation mix
We use demand data from 2016 provided by ENTSO-E [40]. This implies the additional assumption that electricity demand will not change significantly until 2030 besides the additional demand for EV charging. We partition the total demand by shares of the total German GDP of the individual states as no specific demand data for individual states is available [41]. This is a common method for analysis of the German electricity market [14]. The total number of EVs in every state is calculated based on a total number of six million EVs and then distributed to the federal states according to the respective share of the total German GDP.
The assumed future generation mix in Germany is based on a scenario developed by the German Federal Network Agency who oversees the activity of the four German TSOs [47]. The agency has proposed
Our simulation is based on the assumption that the German federal government will achieve its objective of six million EVs by the year 2030 [2]. We use an average charging rate of 7.66 kW [42]. Furthermore, we assume that all six million EVs are charged at the same time as a worst case assumption and, following [43], three charging processes per week as a more realistic scenario. We choose the charging time to be 6 pm as [44] finds that charging often starts at this time of day. Other studies also find that charging would peak at 6 pm as company cars are stationed and private vehicles return home around that time [45]. In [46] the authors find that peak charging will occur right after customers return home after work. We also consider charging at 12 pm as solar radiation peaks at that time to assess the load shifting potential. An overview over the scenarios can be found in Table 1.
Table 1 Simulation scenarios.
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Time of day
Charging behaviour
12 pm 6 pm
Daily 3 times per week
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Table 2 Expected generation capacity by technology in 2030 [47]. Technology
Scenario 2030 (GW)
Nuclear Lignite Coal Natural Gas Pumped storage Wind-Offshore Wind-Onshore PV
0.0 9.5 14.8 37.8 11.9 58.5 15.0 66.3
Table 3 Cases without feasible solution.
12 pm 6 pm
|f (i, j )| ⩽ u (i, j )
qic ⩽ cic
∑ i∈I
∑
qic
subject to di = qic + qir + f (i, j )
(5)
∀ (i, j ) ∈ E
qic + qir
(6) (7)
i∈I
∀ i ∈ I, ∀ j ∈ I
(8)
4. Local V2G flexibility markets In the previous section, we showed that the transmission network is not ready to serve the uncoordinated charging demand of a fleet of six million EVs (even with the current expansion plans). Building upon these insights, we now introduce a mechanism to alleviate the grid of congestion using a V2G mechanism. In the following, we demonstrate the effects of the proposed mechanism by using it to solve redispatch situations where the congestion forces the transmission system operator to deviate from the economic dispatch [50]. 4.1. EV flexibility markets For the proposed approach, EV owners submit a charging corridor to the transmission system operator (TSO) in which the state of charge (SOC) of their vehicle can be varied externally, e.g. by 20% around the actually planned SOC. The TSO can steer the SOC in any direction
(2)
∀ i ∈ I , ∀ j ∈ I ⧹ {i }
∑
(4)
We calculate the model over all 366 days of the year 2016 for 12 pm and 6 pm and for each charging behaviour. The results can be found in Table 3. Without additional demand from EVs, we always find a solution to the optimization problem meaning that all demand can be covered. In case of daily charging, meaning that all 6 million EVs are charged at the same time either at 12 pm or 6 pm, there is a considerable share of cases in which no solution can be found, i.e., not all demand can be covered. In the worst-case daily charging scenario, at 6 pm the demand cannot be covered in 43.4% of all cases. This is a considerable result which underlines the importance of implementing local charging coordination schemes. Even in the more realistic scenario of 3/7 charging, demand cannot be covered in all cases. While we do not consider cross-border trading, the transportation modelling of the load flow calculation allows the transmission of more electricity, especially, since we do not take into account the n-1 criteria which would reduce available transmission capacity by about 50% [49]. We find that the load shifting potential is considerable. Under the more realistic charging scenario, shifting the demand from 6 pm to 12 pm leads to exclusively feasible results. However, as a conclusion of this section we find that the expected charging behaviour at 6 pm will create considerable challenges for the transmission grid and the electricity system as a whole. This implies that we need to find ways to coordinate charging such that the additional load from EV charging is more distributed to avoid transmission grid congestion. In the following section, we present an algorithm to coordinate EV charging and calculate the respective possible compensation for EV owners. This way we introduce spatial differentiation of electricity prices which is necessary to reduce the needed transmission grid expansion.
(1)
i∈I
19.9% 43.4%
3.6. Results
The objective of the following optimization model is to check if the balance of supply and demand can be upheld in the transmission grid at all times given the assumed generation and grid structure as well as the demand increased by EV charging. Therefore, various objective functions are possible as no specific optimization result is expected but the general statement that a feasible solution exists. We decide to minimize the amount of conventional generation qic needed over all nodes i ∈ I , thereby giving priority to renewable infeed qir with qc = (q1c , …q|Ic|) and qr = (q1r , …q|Ir|) being the vectors of conventional and renewable generation over all nodes. As the conventional generation can be controlled we introduce it as a variable. The total generation at each node is then characterized as qi = qic + qir with q = (q1, …q|I|) being the vector of generation and the total generation being Q = ∑i ∈ I qi . In case of conventional generation the total generation at each node i is restricted by the conventional capacity cic with c c = (c1c , …c|cI|). Renewable generation cannot be controlled and is therefore assumed to be fixed at qir . The demand at each node is denoted as di with d = (d1, …d|I|) being the vector of demand at each node. The total demand is then D = ∑i ∈ I di . We denote a line as a tuple of two nodes and create the set E = I × I of all possible line connections between the nodes. The capacity of each transmission line is denoted by the capacity function u: E ↦ ⩾ 0, (i, j ) ↦ u (i, j ) and u (i, j ) = u (j, i) . If no transmission links exists it is set to zero. Similarly, the actual flow is denoted by the function f : E ↦ , (i, j ) ↦ f (i, j ) with f (i, j ) = −f (j, i) . The following optimization problem minimizes the conventional generation while ensuring load balance on each node and in the overall system as well as respecting transmission and generation restrictions. As previously pointed out, we are mostly interested in knowing, whether a feasible solution for the problem exists as this would indicate that the transmission system can absorb the additional EV charging demand. qi
0% 1.6%
∀ (i, j ) ∈ E
di , qic , qir , u (i, j ) ⩾ 0
3.5. Simulation model
min Qc = c
di =
Daily charging
∀i∈I
f (i, j ) = −f (j, i) three different scenarios of which we choose the one that assumes a medium speed of renewable generation expansion. The expected capacity for the dominating generation technologies is displayed in Table 2. The distribution of generation over the federal states can be found in the appendix (Table A.6). To distribute future generation capacity among the different states we assume that the relative shares for every technology among the states will not change. The current shares are based on data from the same federal agency [48]. In the simulation we only distinguish between conventional and renewable generation. Conventional generation capacity is assumed to always be available while the generation from renewables is based on the actual generation in 2016. We assume that the relation between capacity and generation from renewables stays the same for the future scenario and scale the production relative to newly developed generation capacity based on the scenario in Table 2.
3/7 charging
(3) 1438
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it by the same amount behind the bottleneck. If this exceeds the limits given by the remaining production or conventional capacity, these limits set the redispatch amount. The addition of γ was included to increase convergence speed. After the generation schedules are adjusted, the algorithm calculates the new resulting load flows and goes back to the first Algorithm 1 to check for bottlenecks. This process is iterated until all bottlenecks are resolved or the heuristic cannot find anymore line options to reduce congestion in which case the algorithm terminates.
needed at any time as long as the corridor limits are not violated. The resulting flexibility, respecting charging time and speed, can then be exploited to balance the grid. EV owners are compensated for the flexibility provision. We will reflect on the possible amount of compensation in our redispatch case study. In the following we denote a power plant in a region from where electricity cannot be sufficiently transmitted as in front of the bottleneck and a power plant in the corresponding area with too little supply as behind the bottleneck. If congestion occurs, the TSO starts coordinated charging of EVs in front of the bottleneck to use the abundant electricity and coordinated discharging of EVs behind the bottleneck to ensure grid balance. When the bottleneck is resolved the EVs return to their original state of charge through recharging or offsetting the next recharge, respectively. This mechanism is described in further detail in Section 4.2. The provision of flexibility of EVs can be differentiated geographically by bidding areas. The remuneration of owners then depends on their position in the grid. This way, we introduce a spatial price component in the electricity market.
Algorithm 2. redispatch
Input: f , u, e , qc , qr , c c , l Output: – 1
1 ∊ = min ( 2 (f (i, j )−u (i, j )) + γ , qlc[0] , clc[1] −qlc[1] 2 qlc[0] = qlc[0] −∊ 3 qlc[1] = qlc[1] + ∊ 4 f = calculateLoadFlow (qc , qr ) 5 linefinding (f , u, e , qc , qr , c c ) 6 break 7 return ()
4.2. Model of flexibility markets We model a uniform-price electricity market based on a merit order dispatch [51]. Therefore, demand and supply bids are cleared centrally on one marketplace regardless of transmission constraints. Supply bids usually occur by technology specific marginal costs [14]. The most expensive power plant that is allocated to cover the entire demand then sets the price. Divergent from our approach in Section 3, we implement load flow calculations to determine electrical flows in the transmission grid. To this end, we use the DC approximation and ignore transmission losses [52]. The net injection at every node x i is the difference between the local generation and the local demand x i = qi−di . To dissolve any bottlenecks within the network, we implement a two-staged redispatch heuristic as described in Algorithms 1 and 2. The model is designed with as little complexity as possible to work efficiently, since a large amount of EVs might have to be coordinated (see Section 4.4). The objective of stage 1 is to identify a line where redispatch is technically possible and that contributes most to resolving the congestion. Redispatch is possible at a line (i, j ) if f (i, j ) > 0, qic ≠ 0 and c jc−qjc ≠ 0 . Intuitively, this means that it is possible if there is still capacity left behind the bottleneck and still some conventional power plants running in front of the congestion. To describe the algorithms, we introduce sets of nodes Ki that contain all nodes j to which transmission capacity exists from node i such that j ∈ Ki ⇔ u (i, j ) > 0 . Furthermore, e is the vector of all edges in E such that e [k ] ∈ E ∀ k ∈ {0, …, #E }, f is the vector of all line flows, f (Ki, i) is the vector of all flows from nodes in K to node i and u the vector of all line capacities in the same order as e. The method sorted () returns an array sorted by the first column in descending order and calculateLoadFlow () returns the line flows in an electrical network. In the first stage, the heuristic intends to find a pair of nodes, i.e. a line, where production can be modified to resolve congestion. If such a pair cannot directly be found at a congested transmission line because either there is no additional conventional capacity behind a bottleneck or no conventional generation running in front of a bottleneck, the algorithm intends to first increase the conventional production before and decrease the conventional production behind the bottleneck by moving out to other nodes connected to the ones along the congested line. This line search is described in Algorithm 1.
The cost of redispatching the amount ∊ is calculated at both involved nodes and includes both the cost of ramping down power plant A gd with marginal cost mA and the cost of ramping up power plant B gu with marginal cost mB . With the market price being p calculated from the overall uniform-price merit order, the cost is then calculated as follows.
kd = (p−mA) ∊ ku = (mB−p) ∊
(9)
Using this formulation we calculate the additional cost of the congestion for the entire system. This cost could be redistributed as remuneration to EV owners for resolving the congestion. The same heuristic as described above is applied for the EV-dispatch. In this case, EVs can be seen as conventional power plants with zero marginal cost and a generation capacity that is limited by the borders of the charging corridor and the maximum charging power. In addition, the availability of EVs varies based on driving patterns. In case EVs are used for load balancing, their actual SOC (SOC a) differs from the planned SOC (SOC p). This discrepancy is referred to as the EV gap and describes the amount of energy needed to bring EVs back to their planned SOC.
SOC p = SOC a + EVGap
(10)
One objective of the simulation is to bring EVs back to their planned SOC as fast as possible. Therefore, the EV gap is shifted into the consecutive period as additional demand. 4.3. Simulation To demonstrate the implementability of the proposed model and to analyze possible remuneration amounts for EV owners, we implement the algorithm in a market setup with 5 bidding areas inspired by the German electricity system. Other authors have already investigated market splitting in Germany and our division is close to the division in [53]. The bidding areas correspond to transmission network areas of the four German TSOs where the bidding area of TenneT is divided into two parts as congestion often occurs from north to south in Germany [50]. The transmission system capacities are based on the static network models of the German TSOs. However, we limit the total capacity to 50% on each line to account for the n-1 criteria employed by German TSOs [49]. The bidding areas are displayed in Fig. 2.
Algorithm 1. linefinding The second stage’s objective is to adjust the generation to relieve the system of all congestion. The algorithm is described as Algorithm 2. The amount of energy generation that is decreased before the bottleneck and increased behind the bottleneck is referred to as ∊. The algorithm clears congestion iteratively by always reducing generation before the bottleneck by half of the energy exceeding the line limit and increasing 1439
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respective bidding areas based on the corresponding GDP as in Section 3. The renewable infeed is available on TSO level through the European Energy Exchange (EEX) [58]. The distribution between TenneT North and South is based on the generation capacity of the states covered by the corresponding bidding area. The simulation study at hand not only focuses on the question whether EVs can resolve redispatch situations but also analyzes if cascading effects occur in the sense that redispatch needs are only being pushed from on hour to the next. We also calculate the possible compensation over one year for each EV participant by redistributing saved redispatch expenditures. A flow chart of the simulation procedure is shown in Fig. 4.
We assume the reactance to be the same on all connections. The available generation capacity is assigned to the different geographical bidding areas based on the location of the power plants derived from [48]. Each power plant is assigned technology specific marginal costs which are taken from [14]. These costs would usually have to be frequently updated as the costs for fuel and other input factors constantly vary. However, we proceed with the given numbers to provide some first insights. We assume, that each driver contributes ± 20% of her battery capacity at any time. Furthermore, to determine the total available EV capacity, we assume that all EVs that are not driving participate in the redispatch. As typical driving behaviour, we use the data given by the German mobility panel (MOP) [54]. The MOP is a study on daily driving behaviour of citizens over a time span of 13 years [55]. The MOP is divided by socio-economic background of the drivers. We define the group of drivers as full time employees as these are the most frequent car users [54]. A detailed analysis of the MOP can be found in [56]. The availability over different days of the week is displayed in Fig. 3. It can be seen that there are always at least 80% of EVs available. Furthermore, the graph shows that most EVs are on the road shortly before or after 6 pm and return to charging then. We assume a capacity of 40 kWh per EV battery pack [57]. We simulate the hourly system state based on data from the year 2016. The demand and the total number of EVs is distributed to the
4.4. Evaluation In the following, we first simulate the described model in the absence of any EVs as the base scenario. We calculate the need for redispatch as well as the associated costs. Then, we move on to including EVs and allowing them to participate in the stabilization of the transmission grid. We then distribute the previous redispatch costs per bidding area on the participating EVs to find an upper bound for the remuneration.
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4.4.1. Base scenario In the base scenario, we evaluate how the transmission network behaves without EV flexibility. The redispatch is entirely performed by conventional generation capacity. The results show that roughly 28 TWh have to be redispatched causing costs of 147 million €. We especially want to encourage the local use of EVs. Therefore, we take a closer look at the spatial distribution of redispatch needs and EV compensation. The additional costs are not equally distributed over the transmission grid but rather occur at the north to south borderlines—the link between Tennet North and South as well as the link between 50 Hertz and Tennet South. Consequently, the cost for redispatch are not equally distributed but can mainly be attributed to bidding areas 2, 4 and 5. The distribution of the total redispatch and the additional cost to solve the associated congestion over the bidding areas is displayed in Table 4. Fig. 5 shows the average redispatch needed per bidding area over one day within the year. One can observe that redispatch is mostly needed in the morning at around 8 am and in the early evening at around 6 pm. This corresponds to the time most of the EVs are on the road and, therefore, not available for the EV-dispatch [54] as they are on the way to return home. This creates the situation that as more EVs connect to the grid, some are still on the road and therefore not available. A detailed depiction of the EV availability is shown in Fig. 3. Nevertheless, as shown in the following section, there is always a sufficient number of EVs available to contribute to the reduction of redispatch needs. Furthermore, as we never use the entire fleet, we assume that statistically there are always enough EVs with a remaining charging corridor. We never violate the overall restriction of all charging corridors, however, we do not track individual EVs. A more refined study on V2G scheduling that is complementary to the presented approach can be found in [59].
Fig. 2. German transmission grid.
Fig. 3. Share of EVs available for redispatch. 1441
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Fig. 4. Simulation flow chart.
redispatch of renewable or conventional generation. Therefore, the cost is significantly lower than it was in the German market in 2016 where congestion management costs amounted to 591 million Euro [60]. Another reasons might be that we ignore congestion within the bidding areas and only consider cross-border transmission capacity. Furthermore, using technology specific marginal costs based on [14] leads to lower wholesale electricity prices. While the average hourly wholesale spot price was 30 €/MWh in 2016 [58], the simulated electricity price averaged at 21 €/MWh. Assuming that the underestimation of prices is based on underestimated marginal costs, this would also imply lower redispatch costs. Finally, due to the stepwise supply curve that results from using technology specific marginal costs without considering individual power plants, the additional costs of redispatch are often underestimated as market price and marginal cost coincide. However, given that redispatch costs are underestimated, we would expect higher revenues for EV owners which would increase the willingness to participate. In conclusion, the model clearly succeeds in demonstrating the usefulness and benefits of the approach. The proposed mechanism introduces a spatial component in uniform-priced electricity market and supports the stabilization of the grid. As the approach is very flexible it can easily be applied to support temporary mismatches between the overall generation pattern and the transmission grid infrastructure.
4.4.2. EV scenarios We now analyze the impact of 2, 4, 6 and 8 million EVs on the transmission grid in Germany. In line with the previous allocation of demand, these vehicles are assigned to each bidding area using the share of the GDP of the corresponding federal states. In Table 5 one can see that using the flexibility of 2 million EVs already reduces the need for conventional redispatch significantly. As the savings are high and the number of EVs low, the average monetary benefit of individual EVs is the highest over all scenarios analyzed. With an increasing number of EVs, the total cost for redispatch can be further reduced. However, intuitively, the benefit of an individual EV decreases, too. The duration of consecutive timeslots in a bidding area with an EV gap unequal to zero on the other hand is not influenced by the number of EVs. On average it took a bidding area 7 h to return to the planned SOC with a maximum between 142 and 161 h for the different bidding areas. Even in the worst case the SOC returned back to the planned SOC in less than a week. A representation of the EV gap during the month of December can be found in Fig. 6. It can be observed how the EV gap always returns back to zero. The evaluation shows that EV owners can receive an attractive compensation for participating in flexibility programs. The remuneration in practice might even be higher. In our model, the cost for redispatch seems comparably low. We do not differentiated between
5. Discussion & application Table 4 Redispatch needs and additional costs. Bidding area
Redispatch (GWh)
Additional costs (thousand €)
TransnetBW TenneT South Amprion TenneT North 50 Hertz Total
98 11,125 124 3861 12,969 28,179
28 117,856 74,327 6591 22,701 147,252
As described in Section 2, there is a variety of research regarding charging strategies and V2G applications. However, the given paper fills important gaps. Firstly, different from other approaches, we consider EVs considering the constraints given by the vehicle itself and the mobility pattern of EV drivers as a means to resolve redispatch in the transmission grid. Other studies such as [61] focus on using V2G services for ancillary services. This can be an additional income source for EVs and is complementary to our research. Secondly, few studies have considered the impact of EV charging on the transmission grid and to 1442
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Fig. 5. Avg. daily redispatch per node.
Table 5 Redispatch cost and benefit for every EV per scenario per year. Transnet
TenneT South
Amprion
TenneT North
50Hertz
Total
2 M EVs
Redispatch cost (k€) Benefit EV (€)
0 0.09
27,924 167.43
7 0.12
1010 17.41
5672 56.63
34,613 56.32
4 M EVs
Redispatch cost (k€) Benefit EV (€)
0 0.05
6304 103.84
0 0.07
156 10.04
1133 35.86
7593 34.91
6 M EVs
Redispatch cost (k€) Benefit EV (€)
0 0.03
747 72.68
0 0.05
26 6.83
150 25,00
924 24.39
8 M EVs
Redispatch cost (k€) Benefit EV (€)
0 0.02
24 54.84
0 0.03
0 5.14
11 18.86
36 18.40
them to be active resources in resolving transmission grid congestion. The presented approach is based on a set of assumptions which influence the results such as the configuration of bidding zones as well as the marginal cost of power plants. However, this study can greatly contribute to providing first insights of the potential of EVs to reduce redispatch need on the transmission level. We therefore abstract from the problems of lower voltage levels with integrating EVs. The current discussion mostly focuses on problems for distribution grids as do the authors of [64]. The transmission grid will of course only be affected, once the problems on the distribution level are resolved. However, such consideration need to be made today. Practitioners have already identified the opportunities of V2G coordination for the transmission grid
our knowledge no one has conducted a worst case analysis. The authors in [62] considered the transmission grid but assumed average charging behaviour and already found an increase in bottlenecks. Special events, such as football games, can easily lead to a more coordinated behaviour with devastating effects on the transmission system. Thirdly, we calculate concrete monetary values for possible compensation. Most other research relies on synthetic cost functions as in [63]. The authors work on pricing schemes to ideally guide EV owners to an optimal behaviour. Again, this research is complementary to ours as we calculate concrete possible compensation but without considering the actual willingness of consumers to participate. Overall, the presented approach does not only solve the problem of integrating EVs in the transmission grid but allows
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Fig. 6. EV gap per node in December 2016.
occurs in uniform-price electricity markets with transmission network congestion. At first, we show that the German transmission system—even with planned expansions—will not be able to support the integration of six million electric vehicles without relying on neighbouring connected electricity grids. If cross-border trade will suffice to guarantee a stable grid operation remains to be shown. This analysis can be transferred to other transmission grids if the according empirical data can be gathered. After demonstrating that an uncoordinated charging behaviour will endanger grid stability, we introduce a mechanism to relieve the grid of congestion by contracting battery corridors from electric vehicle owners. Then, we show the applicability of the model by simulating the electricity flow and necessary redispatch on a split German electricity market for 2016. We find that electric vehicles can play an active role in resolving congestion situations in transmission grids as almost the entire redispatch need can be covered by the coordinated use of vehicle-to-grid technology. Electric vehicle owners can be financially compensated for providing flexibility. A further research avenue that would extend our study, is to investigate communication interfaces between electric vehicle owners, aggregators and transmission system operators. Furthermore, the willingness of electric vehicle owners to participate in such programs needs to be evaluated. This study contributes to the further integration of electric vehicles into energy systems. As their market penetration increases, infrastructure providers need to find physical or market based solutions to ensure their smooth integration. The proposed approach is one building block towards such solutions. Referring to the previously introduced research objectives, we briefly summarize the
and the Dutch transmission system operator is experimenting with such applications.1 We used a redispatch heuristic rather than optimizing the result which might be an avenue for improvement. Furthermore, the individual compensation is based on participation rather than actual contribution in terms of kWh. Future work might concentrate on the effect of the mechanism for individual vehicle owners. Of course, such applications can only be realized with the help of an aggregator who coordinates EV owners. We did not consider an aggregator in this study as our objective is to show that EVs can in fact provide sufficient capacity and energy to resolve the redispatch in Germany rather than providing a guide to the actual implementation of such a process. Furthermore, the strategic behaviour of an aggregator might distort the overall result. A similar argument has been made in [31]. There is however ample research of business models for such market actors as in [65]. The authors study responsive and unresponsive customers which again complements the presented study.
6. Conclusion In this paper, we propose an approach to integrate electric vehicles into the electricity system in which they are used for stabilization of the transmission grid. We focus on reducing the need for redispatch as it 1 https://www.tennet.eu/news/detail/electric-vehicles-replace-powerplants-to-maintain-supply-demand-balance-on-high-voltage-grid/.
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results. We find that (i) the expanded German transmission grid will not suffice in all cases to support uncoordinated charging of the envisioned number of electric vehicles. However, (ii) if coordinated, these electric vehicles can even support grid stability by providing temporary mobile storage capacity using the presented heuristic and reduce the conventional redispatch. Finally, (iii) EV owners can receive an attractive financial compensation in regions which suffer from high transmission grid congestion.
Acknowledgements This work was supported by the German Research Foundation (DFG) as part of the Research Training Group GRK 2153: Energy Status Data - Informatics Methods for its Collection, Analysis and Exploitation. We would also like to thank the reviewers for their feedback and constructive comments.
Appendix A See Table A.6. Table A.6 Share of generation capacity by federal state in 2016. Federal state
Conventional capacity
Renewable capacity
Lignite
Coal
Natural gas
Nuclear
Pumped storage
Oil
Others
Biomass
Hydro
Wind offshore
Wind onshore
PV
Others
BW BY BE BB HB HH HE MV NI NW RP SL SN ST SH TH North sea Baltic sea
0 0 0.8 21.1 0 0 0.2 0 1.7 50.0 0 0 20.7 5.5 0 0 0 0
19.5 3.0 2.7 0 3.2 6.3 2.7 1.8 10.3 40.1 0 7.8 0 0 2.6 0 0 0
4.1 17.6 3.7 2.9 0.7 0.6 6.3 1.2 16.1 31.2 7.5 0.4 2.6 3.0 0.1 1.9 0 0
25.1 36.9 0 0 0 0 0 0 25.0 0 0 0 0 0 13.1 0 0 0
29.5 8.5 0 0 0 0 9.8 0 3.5 4.8 0 0 17.1 1.3 1.9 23.7 0 0
18.1 25.1 8.5 8.6 2.3 1.0 0.6 0 1.5 13.0 0 0 0.4 6.0 14.9 0 0 0
1.4 3.9 0.5 5.2 5.8 0.3 2.3 0.3 9.3 57.4 3.0 4.4 0.2 3.8 2.0 0.2 0 0
11.1 20.6 0.6 6.2 0.1 0.6 3.5 5.0 19.4 10.6 2.4 0.3 4.2 6.0 5.9 3.6 0 0
22.3 55.0 0 0.1 0.3 0 1.8 0.1 1.7 4.2 6.4 0.3 6.0 0.7 0.1 0.9 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 90.2 9.8
1.8 4.4 0 14.1 0.4 0.2 3.1 6.9 20.5 9.8 7.1 0.7 2.8 11.1 13.9 3.1 0 0
13.0 28.8 0.2 7.6 0.1 0.1 4.6 3.6 9.1 11.1 4.9 1.1 4.1 5.0 3.8 3.0 0 0
5.8 24.5 1.3 6.7 3.5 0.9 8.1 1.5 4.6 24.4 5.1 1.0 1.4 8.1 2.2 0.9 0 0
Total
100
100
100
100
100
100
100
100
100
100
100
100
100
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Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
A decentralized approach towards resolving transmission grid congestion in Germany using vehicle-to-grid technology
T
⁎
Philipp Staudt , Marc Schmidt, Johannes Gärttner, Christof Weinhardt Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
H I GH L IG H T S
of uncoordinated electric vehicle charging in German transmission system. • Evaluation of the congestion behaviour of the German transmission grid. • Simulation of a heuristic for redispatch capacity provision by electric vehicles. • Development • Calculation of possible compensation for electric vehicle owners for participation.
A R T I C LE I N FO
A B S T R A C T
Keywords: Electric vehicles Demand flexibility Power transmission system Local markets Charging coordination
The increasing penetration of renewable generation in electricity markets as well as the rising number of electric vehicles pose new challenges for transmission grids. Additional demand and regionally clustered generation force system operators to consider costly expansion plans and employ expensive redispatch measures in the meantime. In this paper, we assess the ability of the expanded German transmission grid to cope with the additional demand of uncoordinated electric vehicle charging using a transportation problem formulation. We then propose local flexibility markets for electric vehicle owners to relieve the grid of congestion and to provide a heuristic that finds feasible solutions. We test our models on empirical data from the German electricity system of 2016. We find that the currently proposed expansion of the German electricity grid will not suffice to cope with increased electricity demand from uncoordinated electric vehicle charging. However, with coordination, electric vehicles can support transmission grid balancing and local flexibility markets can provide reasonable remuneration for electric vehicle owners.
1. Introduction The ambitious environmental objectives of the European Union and Germany induce the need of transforming not only the energy but also the transportation sector [1]. The German rollout plan for electric vehicles (EV) foresees one million EVs by 2020 and six million by 2030 [2]. The need for electricity in the transportation sector will significantly increase the overall electrical load [3]. However, the German electricity market is already struggling with the incorporation of regionally clustered renewable sources, which increase the need for redispatch measures [4]. This is due to the copper plate assumption that comes with a uniform-price electricity market [5]. Transmission network expansion is already underway [6] but it is usually unpopular with the population [7]. Various studies find that if charging is performed in an uncoordinated manner load peaks will increase [8] because charging often occurs in similar times, e.g., after the return from ⁎
work in the early evening. This motivates the question whether the currently planned transmission network expansions will suffice to incorporate uncoordinated charging of an increasing number of electric vehicles. To answer this question, we conduct a case study given the electric vehicle penetration objectives of the German government using empirical data from the German electricity system of 2016 while considering the planned transmission grid expansion. After this analysis, we propose a decentral coordination mechanism using vehicle-to-grid technology to alleviate the impact of electric vehicles on the transmission grid and to actively support the integration of regionally clustered renewable infeed. Vehicle-to-grid (V2G) was originally introduced by [9]. The concept proposes to use EVs bi-directionally as grid resources, i.e. in certain situations the EV takes power from the grid and in other situations it serves as power source for the grid. In this context we calculate the need for redispatch in a simplified German electricity system and introduce a heuristic to reduce the amount of
Corresponding author. E-mail address: [email protected] (P. Staudt).
https://doi.org/10.1016/j.apenergy.2018.09.045 Received 29 April 2018; Received in revised form 14 August 2018; Accepted 5 September 2018 0306-2619/ © 2018 Published by Elsevier Ltd.
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effects on emissions and petrol use. They warn that uncoordinated charging might have negative impacts on the electricity grid. However, they conclude that smart grid charging strategies could mitigate such negative effects to a large extend. In this paper we reproduce the presented results for the German transmission grid and then move further to providing a solution that allows to resolve transmission congestion. A list of numerous papers that find a positive effect of coordinated charging on the effectiveness of existing generation and transmission capacity is provided in [25]. Coordination of EV fleets is used in [26] to decrease the emissions caused by their operation. Smart charging strategies are presented in [27]. Several papers have proposed to use V2G technology to provide ancillary services to TSOs instead of resolving congestion [28]. However, this service can be performed by only a small number of vehicles which will saturate the market [29]. Ancillary service provision has been studied for several national markets as in [30] for the Spanish market. An overview of different results is provided in [31]. The interactions between an aggregator who provides ancillary services through EVs and the connected individual users are modeled in [32]. The authors conclude that using a coordinating pricing mechanism, distributed decisions achieve the same optimality as central coordination. This is an important result for our proposed local flexibility markets. In [33] the others focused on cross-border transmission capacity in Europe and how the need for expansion can be reduced by V2G technology. However, they do not consider redispatch as the cross-border dispatch in Europe is specifically being taken into account at market clearing or the possible compensation of EV owners. While the impact of EVs on the transmission network has attracted less attention, there is a broad range of research on the impact of EV charging on distribution network infrastructure such as [10]. Yet, no research has been carried out to analyze the benefits from using EVs to avoid redispatch needs on the transmission level. This overview shows that spatial components as well as the impact of EV charging on grid infrastructure are an active field of research. Especially, the charging behaviour and the impact on distribution grids have been investigated. However, the specific effect of (no) charging coordination on the transmission grid is still in question. This paper is the first to evaluate the effect of uncoordinated EV charging on the expanded German transmission grid and to propose a coordinated charging approach to relieve the transmission grid of congestion. Furthermore, we calculate the possible compensation for participants which has not been considered previously.
redispatch needed by using EVs as distributed storage. The participating EV owners are being remunerated and we calculate the possible compensation. The exact payment depends on the local need for redispatch and the associated costs. This introduces a spatial component in the electricity system which reduces the need for transmission network expansion. This research is a natural extension of [10] where the authors exclusively consider the load and congestion of the distribution grid in the form of transformer load. Therefore, the contribution of this paper is threefold: (i) We analyse the ability of an expanded German transmission grid to include additional, uncoordinated EV demand, (ii) we develop a V2G heuristic to avoid the need for redispatch and (iii) we calculate the possible spatially differentiated payment resulting from reduced congestion costs to participants in the decentralized V2G mechanism. 2. Related work There is a vast area of research on locational components in electricity markets and congestion management. Usually, this research focuses on the supply side, formulating price rules that respect the physical constraints of the transmission system. Typical market designs for spatial price differentiation are nodal and zonal pricing mechanisms [11]. A framework for spatial differentiation of consumer electricity prices is presented in [12]. In [13] the authors qualitatively look at incentive mechanisms to ensure operational optimality and optimal investment. They conclude that so called smart contracts should be implemented which include a locational component but also incentivize operational optimality. The benefits of introducing spatial components in the form of nodal prices in Germany were investigated in [14]. An extensive overview over the possible applications of V2G mechanisms is given in [15]. In [16] the authors consider V2G technology to better integrate renewable energy generation. We consider this in Section 3. The link between electric vehicles and the electricity sector through the smart grid was established by [17]. The authors argue that it is unclear whether the distribution and transmission system will be able to support uncoordinated charging of a large fleet of electric vehicles. The general usefulness and possible applications of demand side management (DSM) are discussed in [18]. The author specifically argues for load curtailment at distinct locations in the network. In [19] the authors describe how DSM is financially beneficial to customers. A charging strategy to optimally react to time-of-use pricing is proposed in [20]. A similar approach for parking stations is implemented in [21]. A general overview of grid integration of EVs, both technically and economically, is provided in [1]. One important research stream related to EVs is the impact of uncoordinated and coordinated charging on different voltage levels of electricity networks but also on the environmental and economical impact of EVs. The impact on load curves from increased penetration of EVs is discussed in [8]. The authors conclude that consumption peaks would especially increase in the early evening if charging was not coordinated. We use this result in our analysis of the EV impact on transmission networks. In [22] a simulation tool is developed which is able to assess the level of EV penetration that would still be supported by the current grid infrastructure. The authors conclude that only 60% of the vehicle fleet could be replaced by EVs in Switzerland if charged uncoordinatedly. In [23] the impact of uncoordinated EV charging is evaluated. The authors find that a small share of EVs can be handled even without coordination but that infrastructure needs to be upgraded with increasing penetration. Other studies find that the electricity grid will have to be expanded if EV charging is performed in an uncoordinated fashion [24]. The possibility of transforming the vehicle fleet in the U.S. to electric vehicles is analyzed in [3]. They find positive
3. Impact of EV charging on the transmission network First, we analyze the impact of uncoordinated EV charging on the German transmission grid including its intended expansions given the market penetration objectives of the German federal government for EVs. To this end, we use a reduced representation of the German transmission grid ignoring cross-border trading (similar to [14]). We begin by introducing our assumptions regarding the network topology. 3.1. Transmission network topology We abstract from reality by reducing the German transmission network to one node per federal state. Furthermore, we add two nodes for offshore wind generation in the North and Baltic Sea. Doing so, we assume that there is no congestion within the states. This reduces the overall probability for congestion. We model the problem as a transportation model as in [34]. This means that in this part of the study we ignore Kirchhoff’s laws and assume that electricity might be well-directed through the network without loop flows. This increases the
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Fig. 1. Transmission system model.
amount of electricity that can be transmitted through the system as capacity can be used with maximal efficiency. Therefore, if congestion occurs using this formulation it will most likely also occur using actual load flow calculations. The flow patterns themselves would of course not be correct, but at this point we are not interested in exact flows but rather the information if the system can be stable. We use the static network models provided by the four German TSOs TenneT [35]. Amprion [36], TransnetBW [37] and 50Hertz [38] to model the transmission capacity between states and assume that there be no capacity restriction for offshore wind generation. This is reasonable as German regulation forces TSOs to fully connect all renewable generation capacity. We include the transmission capacity that will be added with the latest expansion plans [39]. A schematic representation of the model is shown in Fig. 1.
3.3. Electric vehicle characterization
3.2. Demand representation
3.4. Generation mix
We use demand data from 2016 provided by ENTSO-E [40]. This implies the additional assumption that electricity demand will not change significantly until 2030 besides the additional demand for EV charging. We partition the total demand by shares of the total German GDP of the individual states as no specific demand data for individual states is available [41]. This is a common method for analysis of the German electricity market [14]. The total number of EVs in every state is calculated based on a total number of six million EVs and then distributed to the federal states according to the respective share of the total German GDP.
The assumed future generation mix in Germany is based on a scenario developed by the German Federal Network Agency who oversees the activity of the four German TSOs [47]. The agency has proposed
Our simulation is based on the assumption that the German federal government will achieve its objective of six million EVs by the year 2030 [2]. We use an average charging rate of 7.66 kW [42]. Furthermore, we assume that all six million EVs are charged at the same time as a worst case assumption and, following [43], three charging processes per week as a more realistic scenario. We choose the charging time to be 6 pm as [44] finds that charging often starts at this time of day. Other studies also find that charging would peak at 6 pm as company cars are stationed and private vehicles return home around that time [45]. In [46] the authors find that peak charging will occur right after customers return home after work. We also consider charging at 12 pm as solar radiation peaks at that time to assess the load shifting potential. An overview over the scenarios can be found in Table 1.
Table 1 Simulation scenarios.
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Time of day
Charging behaviour
12 pm 6 pm
Daily 3 times per week
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Table 2 Expected generation capacity by technology in 2030 [47]. Technology
Scenario 2030 (GW)
Nuclear Lignite Coal Natural Gas Pumped storage Wind-Offshore Wind-Onshore PV
0.0 9.5 14.8 37.8 11.9 58.5 15.0 66.3
Table 3 Cases without feasible solution.
12 pm 6 pm
|f (i, j )| ⩽ u (i, j )
qic ⩽ cic
∑ i∈I
∑
qic
subject to di = qic + qir + f (i, j )
(5)
∀ (i, j ) ∈ E
qic + qir
(6) (7)
i∈I
∀ i ∈ I, ∀ j ∈ I
(8)
4. Local V2G flexibility markets In the previous section, we showed that the transmission network is not ready to serve the uncoordinated charging demand of a fleet of six million EVs (even with the current expansion plans). Building upon these insights, we now introduce a mechanism to alleviate the grid of congestion using a V2G mechanism. In the following, we demonstrate the effects of the proposed mechanism by using it to solve redispatch situations where the congestion forces the transmission system operator to deviate from the economic dispatch [50]. 4.1. EV flexibility markets For the proposed approach, EV owners submit a charging corridor to the transmission system operator (TSO) in which the state of charge (SOC) of their vehicle can be varied externally, e.g. by 20% around the actually planned SOC. The TSO can steer the SOC in any direction
(2)
∀ i ∈ I , ∀ j ∈ I ⧹ {i }
∑
(4)
We calculate the model over all 366 days of the year 2016 for 12 pm and 6 pm and for each charging behaviour. The results can be found in Table 3. Without additional demand from EVs, we always find a solution to the optimization problem meaning that all demand can be covered. In case of daily charging, meaning that all 6 million EVs are charged at the same time either at 12 pm or 6 pm, there is a considerable share of cases in which no solution can be found, i.e., not all demand can be covered. In the worst-case daily charging scenario, at 6 pm the demand cannot be covered in 43.4% of all cases. This is a considerable result which underlines the importance of implementing local charging coordination schemes. Even in the more realistic scenario of 3/7 charging, demand cannot be covered in all cases. While we do not consider cross-border trading, the transportation modelling of the load flow calculation allows the transmission of more electricity, especially, since we do not take into account the n-1 criteria which would reduce available transmission capacity by about 50% [49]. We find that the load shifting potential is considerable. Under the more realistic charging scenario, shifting the demand from 6 pm to 12 pm leads to exclusively feasible results. However, as a conclusion of this section we find that the expected charging behaviour at 6 pm will create considerable challenges for the transmission grid and the electricity system as a whole. This implies that we need to find ways to coordinate charging such that the additional load from EV charging is more distributed to avoid transmission grid congestion. In the following section, we present an algorithm to coordinate EV charging and calculate the respective possible compensation for EV owners. This way we introduce spatial differentiation of electricity prices which is necessary to reduce the needed transmission grid expansion.
(1)
i∈I
19.9% 43.4%
3.6. Results
The objective of the following optimization model is to check if the balance of supply and demand can be upheld in the transmission grid at all times given the assumed generation and grid structure as well as the demand increased by EV charging. Therefore, various objective functions are possible as no specific optimization result is expected but the general statement that a feasible solution exists. We decide to minimize the amount of conventional generation qic needed over all nodes i ∈ I , thereby giving priority to renewable infeed qir with qc = (q1c , …q|Ic|) and qr = (q1r , …q|Ir|) being the vectors of conventional and renewable generation over all nodes. As the conventional generation can be controlled we introduce it as a variable. The total generation at each node is then characterized as qi = qic + qir with q = (q1, …q|I|) being the vector of generation and the total generation being Q = ∑i ∈ I qi . In case of conventional generation the total generation at each node i is restricted by the conventional capacity cic with c c = (c1c , …c|cI|). Renewable generation cannot be controlled and is therefore assumed to be fixed at qir . The demand at each node is denoted as di with d = (d1, …d|I|) being the vector of demand at each node. The total demand is then D = ∑i ∈ I di . We denote a line as a tuple of two nodes and create the set E = I × I of all possible line connections between the nodes. The capacity of each transmission line is denoted by the capacity function u: E ↦ ⩾ 0, (i, j ) ↦ u (i, j ) and u (i, j ) = u (j, i) . If no transmission links exists it is set to zero. Similarly, the actual flow is denoted by the function f : E ↦ , (i, j ) ↦ f (i, j ) with f (i, j ) = −f (j, i) . The following optimization problem minimizes the conventional generation while ensuring load balance on each node and in the overall system as well as respecting transmission and generation restrictions. As previously pointed out, we are mostly interested in knowing, whether a feasible solution for the problem exists as this would indicate that the transmission system can absorb the additional EV charging demand. qi
0% 1.6%
∀ (i, j ) ∈ E
di , qic , qir , u (i, j ) ⩾ 0
3.5. Simulation model
min Qc = c
di =
Daily charging
∀i∈I
f (i, j ) = −f (j, i) three different scenarios of which we choose the one that assumes a medium speed of renewable generation expansion. The expected capacity for the dominating generation technologies is displayed in Table 2. The distribution of generation over the federal states can be found in the appendix (Table A.6). To distribute future generation capacity among the different states we assume that the relative shares for every technology among the states will not change. The current shares are based on data from the same federal agency [48]. In the simulation we only distinguish between conventional and renewable generation. Conventional generation capacity is assumed to always be available while the generation from renewables is based on the actual generation in 2016. We assume that the relation between capacity and generation from renewables stays the same for the future scenario and scale the production relative to newly developed generation capacity based on the scenario in Table 2.
3/7 charging
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it by the same amount behind the bottleneck. If this exceeds the limits given by the remaining production or conventional capacity, these limits set the redispatch amount. The addition of γ was included to increase convergence speed. After the generation schedules are adjusted, the algorithm calculates the new resulting load flows and goes back to the first Algorithm 1 to check for bottlenecks. This process is iterated until all bottlenecks are resolved or the heuristic cannot find anymore line options to reduce congestion in which case the algorithm terminates.
needed at any time as long as the corridor limits are not violated. The resulting flexibility, respecting charging time and speed, can then be exploited to balance the grid. EV owners are compensated for the flexibility provision. We will reflect on the possible amount of compensation in our redispatch case study. In the following we denote a power plant in a region from where electricity cannot be sufficiently transmitted as in front of the bottleneck and a power plant in the corresponding area with too little supply as behind the bottleneck. If congestion occurs, the TSO starts coordinated charging of EVs in front of the bottleneck to use the abundant electricity and coordinated discharging of EVs behind the bottleneck to ensure grid balance. When the bottleneck is resolved the EVs return to their original state of charge through recharging or offsetting the next recharge, respectively. This mechanism is described in further detail in Section 4.2. The provision of flexibility of EVs can be differentiated geographically by bidding areas. The remuneration of owners then depends on their position in the grid. This way, we introduce a spatial price component in the electricity market.
Algorithm 2. redispatch
Input: f , u, e , qc , qr , c c , l Output: – 1
1 ∊ = min ( 2 (f (i, j )−u (i, j )) + γ , qlc[0] , clc[1] −qlc[1] 2 qlc[0] = qlc[0] −∊ 3 qlc[1] = qlc[1] + ∊ 4 f = calculateLoadFlow (qc , qr ) 5 linefinding (f , u, e , qc , qr , c c ) 6 break 7 return ()
4.2. Model of flexibility markets We model a uniform-price electricity market based on a merit order dispatch [51]. Therefore, demand and supply bids are cleared centrally on one marketplace regardless of transmission constraints. Supply bids usually occur by technology specific marginal costs [14]. The most expensive power plant that is allocated to cover the entire demand then sets the price. Divergent from our approach in Section 3, we implement load flow calculations to determine electrical flows in the transmission grid. To this end, we use the DC approximation and ignore transmission losses [52]. The net injection at every node x i is the difference between the local generation and the local demand x i = qi−di . To dissolve any bottlenecks within the network, we implement a two-staged redispatch heuristic as described in Algorithms 1 and 2. The model is designed with as little complexity as possible to work efficiently, since a large amount of EVs might have to be coordinated (see Section 4.4). The objective of stage 1 is to identify a line where redispatch is technically possible and that contributes most to resolving the congestion. Redispatch is possible at a line (i, j ) if f (i, j ) > 0, qic ≠ 0 and c jc−qjc ≠ 0 . Intuitively, this means that it is possible if there is still capacity left behind the bottleneck and still some conventional power plants running in front of the congestion. To describe the algorithms, we introduce sets of nodes Ki that contain all nodes j to which transmission capacity exists from node i such that j ∈ Ki ⇔ u (i, j ) > 0 . Furthermore, e is the vector of all edges in E such that e [k ] ∈ E ∀ k ∈ {0, …, #E }, f is the vector of all line flows, f (Ki, i) is the vector of all flows from nodes in K to node i and u the vector of all line capacities in the same order as e. The method sorted () returns an array sorted by the first column in descending order and calculateLoadFlow () returns the line flows in an electrical network. In the first stage, the heuristic intends to find a pair of nodes, i.e. a line, where production can be modified to resolve congestion. If such a pair cannot directly be found at a congested transmission line because either there is no additional conventional capacity behind a bottleneck or no conventional generation running in front of a bottleneck, the algorithm intends to first increase the conventional production before and decrease the conventional production behind the bottleneck by moving out to other nodes connected to the ones along the congested line. This line search is described in Algorithm 1.
The cost of redispatching the amount ∊ is calculated at both involved nodes and includes both the cost of ramping down power plant A gd with marginal cost mA and the cost of ramping up power plant B gu with marginal cost mB . With the market price being p calculated from the overall uniform-price merit order, the cost is then calculated as follows.
kd = (p−mA) ∊ ku = (mB−p) ∊
(9)
Using this formulation we calculate the additional cost of the congestion for the entire system. This cost could be redistributed as remuneration to EV owners for resolving the congestion. The same heuristic as described above is applied for the EV-dispatch. In this case, EVs can be seen as conventional power plants with zero marginal cost and a generation capacity that is limited by the borders of the charging corridor and the maximum charging power. In addition, the availability of EVs varies based on driving patterns. In case EVs are used for load balancing, their actual SOC (SOC a) differs from the planned SOC (SOC p). This discrepancy is referred to as the EV gap and describes the amount of energy needed to bring EVs back to their planned SOC.
SOC p = SOC a + EVGap
(10)
One objective of the simulation is to bring EVs back to their planned SOC as fast as possible. Therefore, the EV gap is shifted into the consecutive period as additional demand. 4.3. Simulation To demonstrate the implementability of the proposed model and to analyze possible remuneration amounts for EV owners, we implement the algorithm in a market setup with 5 bidding areas inspired by the German electricity system. Other authors have already investigated market splitting in Germany and our division is close to the division in [53]. The bidding areas correspond to transmission network areas of the four German TSOs where the bidding area of TenneT is divided into two parts as congestion often occurs from north to south in Germany [50]. The transmission system capacities are based on the static network models of the German TSOs. However, we limit the total capacity to 50% on each line to account for the n-1 criteria employed by German TSOs [49]. The bidding areas are displayed in Fig. 2.
Algorithm 1. linefinding The second stage’s objective is to adjust the generation to relieve the system of all congestion. The algorithm is described as Algorithm 2. The amount of energy generation that is decreased before the bottleneck and increased behind the bottleneck is referred to as ∊. The algorithm clears congestion iteratively by always reducing generation before the bottleneck by half of the energy exceeding the line limit and increasing 1439
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respective bidding areas based on the corresponding GDP as in Section 3. The renewable infeed is available on TSO level through the European Energy Exchange (EEX) [58]. The distribution between TenneT North and South is based on the generation capacity of the states covered by the corresponding bidding area. The simulation study at hand not only focuses on the question whether EVs can resolve redispatch situations but also analyzes if cascading effects occur in the sense that redispatch needs are only being pushed from on hour to the next. We also calculate the possible compensation over one year for each EV participant by redistributing saved redispatch expenditures. A flow chart of the simulation procedure is shown in Fig. 4.
We assume the reactance to be the same on all connections. The available generation capacity is assigned to the different geographical bidding areas based on the location of the power plants derived from [48]. Each power plant is assigned technology specific marginal costs which are taken from [14]. These costs would usually have to be frequently updated as the costs for fuel and other input factors constantly vary. However, we proceed with the given numbers to provide some first insights. We assume, that each driver contributes ± 20% of her battery capacity at any time. Furthermore, to determine the total available EV capacity, we assume that all EVs that are not driving participate in the redispatch. As typical driving behaviour, we use the data given by the German mobility panel (MOP) [54]. The MOP is a study on daily driving behaviour of citizens over a time span of 13 years [55]. The MOP is divided by socio-economic background of the drivers. We define the group of drivers as full time employees as these are the most frequent car users [54]. A detailed analysis of the MOP can be found in [56]. The availability over different days of the week is displayed in Fig. 3. It can be seen that there are always at least 80% of EVs available. Furthermore, the graph shows that most EVs are on the road shortly before or after 6 pm and return to charging then. We assume a capacity of 40 kWh per EV battery pack [57]. We simulate the hourly system state based on data from the year 2016. The demand and the total number of EVs is distributed to the
4.4. Evaluation In the following, we first simulate the described model in the absence of any EVs as the base scenario. We calculate the need for redispatch as well as the associated costs. Then, we move on to including EVs and allowing them to participate in the stabilization of the transmission grid. We then distribute the previous redispatch costs per bidding area on the participating EVs to find an upper bound for the remuneration.
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4.4.1. Base scenario In the base scenario, we evaluate how the transmission network behaves without EV flexibility. The redispatch is entirely performed by conventional generation capacity. The results show that roughly 28 TWh have to be redispatched causing costs of 147 million €. We especially want to encourage the local use of EVs. Therefore, we take a closer look at the spatial distribution of redispatch needs and EV compensation. The additional costs are not equally distributed over the transmission grid but rather occur at the north to south borderlines—the link between Tennet North and South as well as the link between 50 Hertz and Tennet South. Consequently, the cost for redispatch are not equally distributed but can mainly be attributed to bidding areas 2, 4 and 5. The distribution of the total redispatch and the additional cost to solve the associated congestion over the bidding areas is displayed in Table 4. Fig. 5 shows the average redispatch needed per bidding area over one day within the year. One can observe that redispatch is mostly needed in the morning at around 8 am and in the early evening at around 6 pm. This corresponds to the time most of the EVs are on the road and, therefore, not available for the EV-dispatch [54] as they are on the way to return home. This creates the situation that as more EVs connect to the grid, some are still on the road and therefore not available. A detailed depiction of the EV availability is shown in Fig. 3. Nevertheless, as shown in the following section, there is always a sufficient number of EVs available to contribute to the reduction of redispatch needs. Furthermore, as we never use the entire fleet, we assume that statistically there are always enough EVs with a remaining charging corridor. We never violate the overall restriction of all charging corridors, however, we do not track individual EVs. A more refined study on V2G scheduling that is complementary to the presented approach can be found in [59].
Fig. 2. German transmission grid.
Fig. 3. Share of EVs available for redispatch. 1441
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Fig. 4. Simulation flow chart.
redispatch of renewable or conventional generation. Therefore, the cost is significantly lower than it was in the German market in 2016 where congestion management costs amounted to 591 million Euro [60]. Another reasons might be that we ignore congestion within the bidding areas and only consider cross-border transmission capacity. Furthermore, using technology specific marginal costs based on [14] leads to lower wholesale electricity prices. While the average hourly wholesale spot price was 30 €/MWh in 2016 [58], the simulated electricity price averaged at 21 €/MWh. Assuming that the underestimation of prices is based on underestimated marginal costs, this would also imply lower redispatch costs. Finally, due to the stepwise supply curve that results from using technology specific marginal costs without considering individual power plants, the additional costs of redispatch are often underestimated as market price and marginal cost coincide. However, given that redispatch costs are underestimated, we would expect higher revenues for EV owners which would increase the willingness to participate. In conclusion, the model clearly succeeds in demonstrating the usefulness and benefits of the approach. The proposed mechanism introduces a spatial component in uniform-priced electricity market and supports the stabilization of the grid. As the approach is very flexible it can easily be applied to support temporary mismatches between the overall generation pattern and the transmission grid infrastructure.
4.4.2. EV scenarios We now analyze the impact of 2, 4, 6 and 8 million EVs on the transmission grid in Germany. In line with the previous allocation of demand, these vehicles are assigned to each bidding area using the share of the GDP of the corresponding federal states. In Table 5 one can see that using the flexibility of 2 million EVs already reduces the need for conventional redispatch significantly. As the savings are high and the number of EVs low, the average monetary benefit of individual EVs is the highest over all scenarios analyzed. With an increasing number of EVs, the total cost for redispatch can be further reduced. However, intuitively, the benefit of an individual EV decreases, too. The duration of consecutive timeslots in a bidding area with an EV gap unequal to zero on the other hand is not influenced by the number of EVs. On average it took a bidding area 7 h to return to the planned SOC with a maximum between 142 and 161 h for the different bidding areas. Even in the worst case the SOC returned back to the planned SOC in less than a week. A representation of the EV gap during the month of December can be found in Fig. 6. It can be observed how the EV gap always returns back to zero. The evaluation shows that EV owners can receive an attractive compensation for participating in flexibility programs. The remuneration in practice might even be higher. In our model, the cost for redispatch seems comparably low. We do not differentiated between
5. Discussion & application Table 4 Redispatch needs and additional costs. Bidding area
Redispatch (GWh)
Additional costs (thousand €)
TransnetBW TenneT South Amprion TenneT North 50 Hertz Total
98 11,125 124 3861 12,969 28,179
28 117,856 74,327 6591 22,701 147,252
As described in Section 2, there is a variety of research regarding charging strategies and V2G applications. However, the given paper fills important gaps. Firstly, different from other approaches, we consider EVs considering the constraints given by the vehicle itself and the mobility pattern of EV drivers as a means to resolve redispatch in the transmission grid. Other studies such as [61] focus on using V2G services for ancillary services. This can be an additional income source for EVs and is complementary to our research. Secondly, few studies have considered the impact of EV charging on the transmission grid and to 1442
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Fig. 5. Avg. daily redispatch per node.
Table 5 Redispatch cost and benefit for every EV per scenario per year. Transnet
TenneT South
Amprion
TenneT North
50Hertz
Total
2 M EVs
Redispatch cost (k€) Benefit EV (€)
0 0.09
27,924 167.43
7 0.12
1010 17.41
5672 56.63
34,613 56.32
4 M EVs
Redispatch cost (k€) Benefit EV (€)
0 0.05
6304 103.84
0 0.07
156 10.04
1133 35.86
7593 34.91
6 M EVs
Redispatch cost (k€) Benefit EV (€)
0 0.03
747 72.68
0 0.05
26 6.83
150 25,00
924 24.39
8 M EVs
Redispatch cost (k€) Benefit EV (€)
0 0.02
24 54.84
0 0.03
0 5.14
11 18.86
36 18.40
them to be active resources in resolving transmission grid congestion. The presented approach is based on a set of assumptions which influence the results such as the configuration of bidding zones as well as the marginal cost of power plants. However, this study can greatly contribute to providing first insights of the potential of EVs to reduce redispatch need on the transmission level. We therefore abstract from the problems of lower voltage levels with integrating EVs. The current discussion mostly focuses on problems for distribution grids as do the authors of [64]. The transmission grid will of course only be affected, once the problems on the distribution level are resolved. However, such consideration need to be made today. Practitioners have already identified the opportunities of V2G coordination for the transmission grid
our knowledge no one has conducted a worst case analysis. The authors in [62] considered the transmission grid but assumed average charging behaviour and already found an increase in bottlenecks. Special events, such as football games, can easily lead to a more coordinated behaviour with devastating effects on the transmission system. Thirdly, we calculate concrete monetary values for possible compensation. Most other research relies on synthetic cost functions as in [63]. The authors work on pricing schemes to ideally guide EV owners to an optimal behaviour. Again, this research is complementary to ours as we calculate concrete possible compensation but without considering the actual willingness of consumers to participate. Overall, the presented approach does not only solve the problem of integrating EVs in the transmission grid but allows
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Fig. 6. EV gap per node in December 2016.
occurs in uniform-price electricity markets with transmission network congestion. At first, we show that the German transmission system—even with planned expansions—will not be able to support the integration of six million electric vehicles without relying on neighbouring connected electricity grids. If cross-border trade will suffice to guarantee a stable grid operation remains to be shown. This analysis can be transferred to other transmission grids if the according empirical data can be gathered. After demonstrating that an uncoordinated charging behaviour will endanger grid stability, we introduce a mechanism to relieve the grid of congestion by contracting battery corridors from electric vehicle owners. Then, we show the applicability of the model by simulating the electricity flow and necessary redispatch on a split German electricity market for 2016. We find that electric vehicles can play an active role in resolving congestion situations in transmission grids as almost the entire redispatch need can be covered by the coordinated use of vehicle-to-grid technology. Electric vehicle owners can be financially compensated for providing flexibility. A further research avenue that would extend our study, is to investigate communication interfaces between electric vehicle owners, aggregators and transmission system operators. Furthermore, the willingness of electric vehicle owners to participate in such programs needs to be evaluated. This study contributes to the further integration of electric vehicles into energy systems. As their market penetration increases, infrastructure providers need to find physical or market based solutions to ensure their smooth integration. The proposed approach is one building block towards such solutions. Referring to the previously introduced research objectives, we briefly summarize the
and the Dutch transmission system operator is experimenting with such applications.1 We used a redispatch heuristic rather than optimizing the result which might be an avenue for improvement. Furthermore, the individual compensation is based on participation rather than actual contribution in terms of kWh. Future work might concentrate on the effect of the mechanism for individual vehicle owners. Of course, such applications can only be realized with the help of an aggregator who coordinates EV owners. We did not consider an aggregator in this study as our objective is to show that EVs can in fact provide sufficient capacity and energy to resolve the redispatch in Germany rather than providing a guide to the actual implementation of such a process. Furthermore, the strategic behaviour of an aggregator might distort the overall result. A similar argument has been made in [31]. There is however ample research of business models for such market actors as in [65]. The authors study responsive and unresponsive customers which again complements the presented study.
6. Conclusion In this paper, we propose an approach to integrate electric vehicles into the electricity system in which they are used for stabilization of the transmission grid. We focus on reducing the need for redispatch as it 1 https://www.tennet.eu/news/detail/electric-vehicles-replace-powerplants-to-maintain-supply-demand-balance-on-high-voltage-grid/.
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results. We find that (i) the expanded German transmission grid will not suffice in all cases to support uncoordinated charging of the envisioned number of electric vehicles. However, (ii) if coordinated, these electric vehicles can even support grid stability by providing temporary mobile storage capacity using the presented heuristic and reduce the conventional redispatch. Finally, (iii) EV owners can receive an attractive financial compensation in regions which suffer from high transmission grid congestion.
Acknowledgements This work was supported by the German Research Foundation (DFG) as part of the Research Training Group GRK 2153: Energy Status Data - Informatics Methods for its Collection, Analysis and Exploitation. We would also like to thank the reviewers for their feedback and constructive comments.
Appendix A See Table A.6. Table A.6 Share of generation capacity by federal state in 2016. Federal state
Conventional capacity
Renewable capacity
Lignite
Coal
Natural gas
Nuclear
Pumped storage
Oil
Others
Biomass
Hydro
Wind offshore
Wind onshore
PV
Others
BW BY BE BB HB HH HE MV NI NW RP SL SN ST SH TH North sea Baltic sea
0 0 0.8 21.1 0 0 0.2 0 1.7 50.0 0 0 20.7 5.5 0 0 0 0
19.5 3.0 2.7 0 3.2 6.3 2.7 1.8 10.3 40.1 0 7.8 0 0 2.6 0 0 0
4.1 17.6 3.7 2.9 0.7 0.6 6.3 1.2 16.1 31.2 7.5 0.4 2.6 3.0 0.1 1.9 0 0
25.1 36.9 0 0 0 0 0 0 25.0 0 0 0 0 0 13.1 0 0 0
29.5 8.5 0 0 0 0 9.8 0 3.5 4.8 0 0 17.1 1.3 1.9 23.7 0 0
18.1 25.1 8.5 8.6 2.3 1.0 0.6 0 1.5 13.0 0 0 0.4 6.0 14.9 0 0 0
1.4 3.9 0.5 5.2 5.8 0.3 2.3 0.3 9.3 57.4 3.0 4.4 0.2 3.8 2.0 0.2 0 0
11.1 20.6 0.6 6.2 0.1 0.6 3.5 5.0 19.4 10.6 2.4 0.3 4.2 6.0 5.9 3.6 0 0
22.3 55.0 0 0.1 0.3 0 1.8 0.1 1.7 4.2 6.4 0.3 6.0 0.7 0.1 0.9 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 90.2 9.8
1.8 4.4 0 14.1 0.4 0.2 3.1 6.9 20.5 9.8 7.1 0.7 2.8 11.1 13.9 3.1 0 0
13.0 28.8 0.2 7.6 0.1 0.1 4.6 3.6 9.1 11.1 4.9 1.1 4.1 5.0 3.8 3.0 0 0
5.8 24.5 1.3 6.7 3.5 0.9 8.1 1.5 4.6 24.4 5.1 1.0 1.4 8.1 2.2 0.9 0 0
Total
100
100
100
100
100
100
100
100
100
100
100
100
100
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