Impact of operation strategies of large scale battery systems on distribution grid planning in Germany

Please cite this paper as follows: Matthias Resch, Jochen Bühler, Mira Klausen, Andreas Sumper, Impact of operation strategies of large scale battery systems on distribution grid planning in Germany, Renewable and Sustainable Energy Reviews, Volume 74, July 2017, Pages 1042-1063, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2017.02.075. (http://www.sciencedirect.com/science/article/pii/S1364032117302976) ! Impact of Operation Strategies of Large Scale Battery Systems on Distribution Grid Planning in Germany Matthias Rescha,b, Jochen B¨uhlera, Mira Klausena, Andreas Sumperb aReiner Lemoine Institut gGmbH, Ostendstraße 25, 12459 Berlin, Germany bCentre d’Innovaci´o Tecnol`ogica en Convertidors Est`atics i Accionaments (CITCEA-UPC), Departament d’Enginyeria El`ectrica, Universitat Polit`ecnica de Catalunya, ETS d’Enginyeria Industrial de Barcelona, Av. Diagonal, 647, Pl. 2. 08028 Barcelona, Spain Abstract Due to the increasing penetration of fluctuating distributed generation electrical grids require reinforcement, in order to secure a grid operation in accordance with given technical specifications. This grid reinforcement often leads to over-dimensioning of the distribution grids. Therefore, traditional and recent advances in distribution grid planning are analysed and possible alternative applications with large scale battery storage systems are reviewed. The review starts with an examination of possible revenue streams along the value chain of the German electricity market. The resulting operation strategies of the two most promising business cases are discussed in detail, and a project overview in which these strategies are applied is presented. Finally, the impact of the operation strategies are assessed with regard to distribution grid planning. Keywords: grid planing, distribution grid, large scale batteries, community storage, primary frequency control. 1. Introduction The energy system in Germany is currently changing. In the past, electrical energy was injected by large power plants into the transmission system (220 kV and 380 kV) to cover long distances. It was then delivered to costumers via distribution (smaller) grids (1 kV to 110 kV). Since the German Federal Government decided to withdraw from the nuclear energy programme and to reduce the greenhouse gas emissions in order to mitigate climate change, the expansion of renewable energy sources was subsidised by introducing the German Renewable Energy Act (EEG) in 2000. This led to a tripling of the share of renewable energy in the German electricity mix from 7 % in the year 2000 to 25 % in the year 2013 [1]. As a consequence, the sinking levelised cost of electricity (LCOE) of renewable energy sources (RES) led to grid parity. [2]. This trend will probably continue as the German Federal Government committed itself to a RES ratio of 80 % of the gross electricity production in the year 2050 [3]. In contrast to conventional power plants, RES are Email address: [email protected], Tel. +493053042011, Corresponding author (Matthias Resch) Preprint submitted to Renewable & Sustainable Energy Reviews December 1, 2016 mainly realised as distributed generators (DG), as defined by [4,5]. Due to their relatively small installed nominal power they are mainly connected to the distribution grid at medium voltage (MV) and low voltage (LV) levels [6,7]. For example, 80 % of photovoltaic (PV) power plants in Germany are connected to the LV grid [8]. Due to this, the nominal DG power installed in the distribution grid surpassed the power installed in the transportation grid in 2010 [9]. Furthermore, the DG are distributed very inhomogeneous in Germany with wind power plants in the north and photovoltaic systems in the south [10]. This, and the fact that the power feed-in of DG is not necessarily simultaneous to the local load demand, results in a transformation process of the distribution grids. Formerly they were characterised by the consumption whereas now the reverse power flow becomes increasingly common. This means that in some moments of the year there is a power flow from the distribution grid to the transportation grid [11]. As German electricity grids are planned to work uni-directional with a power flow from high to low voltage levels this could lead to several problems. For example the protection concept is designed such as to work for an uni-directional power flow and may not work in a bi-directional way [12]. Furthermore, power quality issues can arise. In some grids the maximum possible PV penetration rate is reached as DG are often installed in rural grids [13]. Therefore, an additional installation of DG is often followed by grid reinforcement in order to solve over-voltage and equipment over-loading issues. The drawback of this traditional grid planning procedure is large investment in infrastructure with a low utilisation rate. Historically, network extension planning has been based on maximum load scenarios, but in the case of a high penetration with DG the grid is dimensioned to deal with maximum generation [14]. In Germany, the number of hours in which PV-systems feed more than 90 % of their nominal power into the grid is below 100 hours a year [15]. Due to this, traditional grid planning may cause inefficient grid operation and higher grid utilisation fees that have to be borne by the general public (cost increase of 9,2 % from 2008 to 2014) [16]. As in [17] predicted, this will lead to a linear cost increase for DG induced grid reinforcement due to over-voltage and over-loading issues of 331 EUR/ kW until 2030. The cost can be designated to different voltage levels (400V:13 % / 1 kV-36 kV:29 % /60 kV −380 kV: 58 %). Therefore, the impact of different operation strategies of microgrids [18], electrical vehicles [19] and residential storage systems [20,21] to increase the hosting capacity of DG in distribution grids have been analysed by the authors. Although [22] provides an overview of (large scale) energy storage technologies suitable for wind power application, the implications of the operating strategies as for example voltage control for distribution grid planning have not been analysed in detail. Extending the previous work of the authors, this paper gives an overview and evaluates alternative possibilities to traditional DG induced grid extension with large scale battery storage systems (BSS). As in most cases, this alternative turns out not to be profitable, if the BSS’s only purpose is to mitigate traditional grid extension [14] additional revenue streams have to be taken into account. Therefore, the objectives of this paper are to review additional applications for BSS in the German electricity market in order to combine them with the task of mitigating grid extension caused by DG and evaluate the impact of the resulting operation strategies 2 on traditional and new approaches of distribution grid planning. The paper is structured as follows: In section 2 the legal framework for the operation of distribution grids in Germany and the challenges that arise with integration of high shares of DG are described briefly. Section 3 covers traditional distribution grid planning and in section 4 new grid reinforcement planning methods are presented. A brief overview of different BSS applications and their possible profit margins for the German energy market is presented in section 5. In the same section, the implementation of large scale battery systems in distributions grids is discussed. The focus lies on BSS that apply self-consumption maximisation and primary control reserve, due to their economical relevance, as well as the possible impact on the grid planning. Finally, the conclusions are summarised in section 6. 2. Legal framework, arising challenges and possible solutions for DG and BSS connected to distribution grids in Germany 2.1. Legal framework for the operation of distribution grids According to the German Energy Act (EnWG) section 14(1) [23] the grid operators are legally bound to ensure a safe and stable energy supply. Especially the power quality issues of over-loading of cables and transformers as well as over-voltage are of major interest. The parameters that should fulfilled regarding over-loading of transformers and LV-cables are defined in DIN EN 60076-2:2011 [24] and DIN VDE 0276-603 [25], respectively. Table 1 shows the load factors of the rated apparent power Srfor different components according to [17] under normal operation conditions that are defined in [26]. For the heavy load flow (HLF) and reverse power flow (RPF) different maximum load factors apply. This is due to the different shape of the profiles in both cases. Furthermore, the (n-1)-criterion as defined in [27] and further specified in [17] applies for MV-cables and HV/ MV transformers for the load case. In the case of a HLF for MV-cables and HV/ MV transformers [17] sets the maximum loading to 120 %. For all other components and scenarios it is set to 100 %. Nevertheless, the maximum loading of MV/ LV transformers depends not only on the profile but is also not consistent in the literature: it ranges from 150 % for oil immersed transformers only[28,29] to 120 % [30,31] and 100 % [17] for all kind of transformers in the case of a RPF caused by PV systems . [Table 1 about here.] Voltage characteristics of electricity in distribution grids are defined in [26]. The most important restrictions are that the frequency has to be kept at 50 Hz ±1 Hz and the 10−minute RMS average of the voltage at the point of common coupling (PCC) has to be kept with in an interval ±10 % of the nominal voltage. To ensure this two technical specifications for DG quantify the permitted voltage rise of 2 % in the MV [32] and of 3 % in the LV, respectively [33]. These technical specifications apply if the MV or the LV are calculated separately, otherwise these thresholds don‘t have to be considered. Furthermore, all generators connected 3 to the electrical grid have to comply with the specifications of [34], [35] and [36], respectively. Furthermore, the technical note [37] has to be considered for BSS connected to the LV. The technical restrictions for over-voltage and over-loading are commonly used to determine the hosting capacity, as defined in [38], to integrate DG into existing grids. An exhaustive international overview of the main technical issues limiting the hosting capacity for DG of distribution feeders is given in [39]. 2.2. Challenges and solutions for electrical grids with fluctuating feed-in of renewable energies In this subsection the challenges that arise from the integration of high shares RES into the electrical grid are discussed. The increasing penetration of DG has, among other issues, led to the following [39,40,41]: For distribution grids in particular: •Thermal over-loading of network equipment •Voltage rise •Increased fault levels, especially for MV grids •Power quality issues •Impact on grid protection due to RPF •Effect on the operation of voltage regulators and tap changers because of RPF •Impact on grid losses For the whole electrical system: •Increased demand of control power •Increase of transmission line bottlenecks •Decreasing spinning reserve The most important challenge in distributions grids on an international level is due to over-voltage issues [39]. In Germany for example, 80 % of the grid reinforcement is due to over-voltage issues in distribution grids [42]. Besides grid reinforcement, ancillary services have to be provided by generators and loads to cope with these issues. These services are defined in [43] and classified as follows for normal operation conditions: •Frequency control •Voltage control 4 •Remote automatic generation control •Grid loss compensation All these ancillary services can be provided by DG and in particular by BSS [44]. Therefore, the technical and economic applications of BSS are analysed hereafter in order to supply ancillary services and as an alternative to traditional grid reinforcement. 3. Traditional distribution grid planning Although, there are different guidelines for distribution grid planning on a national [45]and an international level [46], as well as recommendations like [47] , every DSO has a different planning process because of the different characteristics of each distribution grid and DSO [48]. To standardise the different planning approaches a study was conducted that summarises the methodology of 17 DSO covering more than 50 % of all distribution grids in Germany [17] and which can be regarded as the state-of-the-art approach. Fig. 1 describes the conventional distribution grid planning schematically: [Fig. 1 about here.] One problem of this approach lies in the input data, since the LV load is usually not measured and has to be estimated. The estimated LV load may be gained from the (measured) annual maximum load of the secondary transformers [49], the rated power of these transformers [50] or structural data as the degree of electrification or population density [49,45]. Also, approaches employing combinations of these datasets are possible and described in [17]. On the generation side the rated power of the generators are usually well known and published [51]. To evaluate whether a certain threshold is reached (as described in section 2) a power flow calculation is conducted in which the power of the load and the generator are adjusted to certain worst case scenarios, specified in subsection 3.1. If a threshold is passed, the grid will be reinforced according to the methodology described in subsection 3.2. 3.1. Assumed scenarios - worst case parameters Distribution grids are traditionally planned in a deterministic manner [46]. The traditional scenario to conduct a power flow only considers maximum demand, whereas the generation is assumed to be constant. As aforementioned, the higher penetration rate of DG leads to two worst case considerations: the heavy load flow (HLF) and the reverse power flow (RPF) scenario. On an international level they are parametrised according to [46]: 5 1) Heavy load flow: Max load; no generation. 2) Reverse power flow: Min load; max generation. These extreme parameters do not consider the time variability of demand and generation. Thus, a simple probabilistic determination of the worst case scenario parameters that covers all possible grid states for Germany sets the scenarios closer to the reality. For loads, this scenario parameter is called coincidence factor and is defined in [46] as the average power absorbed related to the installed power. For generators, this factor is referred to as diversity factor by [52], and is defined as the quotient of the actual and the installed capacity. To quantify the coincidence and the diversity factor taking into account the simultaneity of generation and consumption, several studies have been conducted [7,17,53,54]. The diversity factors of [7] apply for ten generators of the same type. The same study presents that diversity factor differs if the correlation between the generators is taken into account. The results are listed in Table 2 and Table 3. [Table 2 about here.] [Table 3 about here.] The coincidence and diversity factors all apply to the maximum/rated power of the generators and loads. In case of PV this factor refers to the installed module power PST C [53,54]. In the reverse power flow case the factor for the load of the MV is higher, as higher blending of the stochastic behaviour of the loads is taken into account. Some bigger costumers/loads have their own secondary transformer and are connected directly to MV (C. load). The maximum power of these loads can be assumed as 40 % of the rated apparent power Sr,t of the secondary transformer [55]. Based on experience, these simple worst case parameters cover all possible grid states. These worst case scenarios are therefore commonly used, e.g. in [56,57,17], as this method provides a high level of reliability without measurements in the LV [45]. The likelihood of these extreme grid states however, is not considered with this practice, and may never occur in reality [58]. Furthermore, no time interdependencies of the assets are considered. As a consequence, the distribution grids tend to be over-dimensioned. Thus [59] and [46] claim that new planning approaches should be taken into account as they may use infrastructure more efficiently [47], as well as avoid redundant investments and minimise O&M costs [60]. There are plenty of different approaches to come to a more realistic assessment of the scenario parameters, as for example [55,52,61]. In general, there is a wide field of different new planning approaches for different applications which are analysed in subsection 4.1. 3.2. Grid reinforcement methodology Hereinafter the methodology of grid reinforcement for distribution grids, especially for low and medium voltage grids, is described. The methodology is depicted in the figures for radial grid structures in the 6 LV and for open ring structures in the MV. Nevertheless, these methodologies are transferable to other grid topologies and can be considered as state-of-the-art in Germany [17]. As described before, triggers for grid reinforcement are either local over-voltages or over-loadings of a cable or a transformer. First, the over-loading measures are implemented, then another load-flow is conducted. If there are still over-voltage problems in the grid, the measures to solve these apply. Methodology for low voltage grids: As depicted in Fig. 2, an over-voltage is solved by installing a parallel cable (type see Table 4) from the distribution substation to the next distribution cabinet over 2/3 of the line length. A critical over-loading of a line is solved by installing a parallel line till the next distribution cabinet, starting to search from half of the line on. [Fig. 2 about here.] If more than one line is affected, as shown in Fig. 3, all affected lines are divided at the distribution cabinet that lies closest behind one half of the line. The lines of the second half are connected to a new secondary substation. The rated apparent power Sr,t of the additional MV/LV transformer is the same as the one that was formerly feeding the entire LV-grid. If there is an over-loading in a transformer and its apparent power Sr,t ≤400 kV A, it is replaced by the next bigger standard transformer (630 kVA). If the over-loading is not solved, a parallel 630 kVA transformer is installed. [Fig. 3 about here.] Methodology for medium voltage grids: Similar to the LV a parallel line is installed in the case of over-loading or over-voltage. In case of overvoltage the length of the new line is 2/3rd of the length of the affected feeder, whereas for over-loading the parallel line is installed between the primary substation and the DG that causes the trouble (see Fig. 4). It applies for both measures that no secondary substations are installed on the parallel MV line which is connected to the bus bar of the primary substation. At the connection points an additional breaker is installed in the affected feeder. [Fig. 4 about here.] If the parallel cable does not solve the issue, a new MV ring is installed according to Fig. 5. By this measure the critical part of the affected open MV ring is transferred to two uncritical open MV rings by separating the DG that causes the problems with a parallel MV line. The costs for the earthworks apply only once, as it is assumed that both lines share the same trench. 7 [Fig. 5 about here.] If the HV/MV transformer is over-loaded it is replaced with a 40 MVA transformer. If the over-loading still remains a parallel 40 MVA transformer for the same feeder is installed. In case all the aforementioned measures do not solve the problems, a new primary substation is installed as depicted in Fig. 6. In this case, the placement of the new substation and new breakers is done manually in order to solve all occurring issues in the MV-grid manually. [Fig. 6 about here.] Other studies [62,63,64,30,31] suggest slightly different approaches. For example [62,63,31] regard low voltage exclusively, whereas [64] focuses only on the medium voltage and [30] considers both voltage levels. Another difference in [62,31] is that the new parallel line is installed from the secondary transformer to the distribution cabinet closest to the critical node within the feeder. According to [17,62,31] all new lines are supposed to be underground cables, instead of overhead lines due to the higher acceptance of the general public. For an easier automation the reinforcement equipment is standardised but differs from case to case as shown in Table 4. [Table 4 about here.] According to [13] who conducted a statistical analysis of distributions grids in southern Germany, the NAYY 4x240mm2is the most commonly used cable type in LV (36 % in rural grids, 84 % in villages and 38 % suburban grids) and is used twice as often as any other cable type. 4. New planning methods and definitions for BSS 4.1. New planning methods for integrating DG and BSS in distribution grids The aim of the reviewed studies in this section is to determine, besides other network parameters, the optimal number, location and size of DG and BSS units. This is achieved by optimising the total capital expenditures (CAPEX) and operational expenditures (OPEX) including DG and BSS. Several objectives have been pursued via this optimisation of DG integration in distribution grids. Some of the most common objectives are: minimisation of energy losses, maximisation of DG capacity or energy via sizing and allocation of DG, minimising curtailment losses, minimising costs, as well as the minimisation of the grid reinforcement cost associated with DG [65]. The planning process can be described as a non-linear mixed integer optimisation problem. There are several comprehensive reviews for new distribution grid planning approaches. While [66,67,68] describe and classify the planning approaches generally, [65,69,70] concentrate on DG integration. Hereafter, the criteria and definitions as well as the three-level tree-structure 8 applications. In energy related applications, the storage is charged and discharged during several hours, reaching one cycle a day. In contrast to this, for power applications the BSS is cycled several times a day and discharged and charged in shorter periods (typically seconds and minutes). The type of application directly affects the range in which the rated power range of the BSS tends to be and might be used as an indication, as listed in Table 6 according to[101]. [Table 6 about here.] In subsection 5.1 the market potential of large scale BSS in German distribution grids according to the definition mentioned above is estimated. The operation strategies of the two most promising business cases are analysed in subsection 5.2 and 5.3 and the impact of the operation strategies is concluded in subsection 5.4. 5.1. BSS applications and German energy market In broad terms, there are two ways to gain monetary benefits along the electricity value chain with existing BSS applications in the German electricity market: first, revenues received by the storage owner or operator and second, cost reduction or avoidance by the storage owner or operator [102]. Generally, revenues can be achieved through existing markets and bilateral contracts. Cost reduction or avoidance on the other hand is highly based on individual use cases. Some important application analyses have been summarised for the German electricity market in [103,104,105,106] and are shortly presented in the next sections together with their potential benefit estimations: (a) Market revenues (i) Power exchange markets: As electricity is a homogeneous commodity and the majority of the power supply must be consumed at time of production, electricity prices show a high volatility. In addition, the short-term demand is not very price elastic [107]. These circumstances allow intertemporal arbitrage transactions at the EPEX-Spot (day-ahead and intraday market). Arbitrage contains purchases of electricity in times of low energy prices (off-peak prices) and sales of electricity when prices are comparatively high (peak prices) [108]. The attractiveness of the application depends on price spreads and the frequency of price spreads in these markets. On the day-ahead market, 24 hour single contracts and diverse block contracts are traded for the next day via a daily static auction. The intraday market starts shortly after the dayahead market (trades for the following day start at 3 pm and end 30 minutes before the actual physical delivery of the respective contract) and is organised by continuous trading. (ii) Control reserve markets: A stable operation of the power supply system at a system frequency of 50 Hz requires that the system 15 balance of feed-in, off-take and losses are balanced at any time or that it will be balanced in case of any deviations in a short period of time [109]. An increase or decrease in net output of BSS can ensure a real-time system balance [110]. Since 2001, the German TSOs procure their needs for different control reserves (primary, secondary and tertiary control reserve) on an open, transparent and nondiscriminatory market. The main differences between the three control reserve forms are the tender time and period, the product time-slice, the award criteria and the remuneration. In addition, positive and negative SCR and TCR are separately marketed, whereas in the case of PCR the power increase and decrease must be ensured by a single offer, but the forms of control reserve can be provided by various technical units (also known as pooling). (b) Revenues based on bilateral contracts (i) Voltage support: In order to maintain stable network operation, the voltage level must be kept in certain ranges. The static voltage support can, among others be achieved by a local offset of reactive power [111]. BSS with an inverter and a corresponding power electronic can principally provide reactive power [112]. A compensation of reactive power is exclusively paid on the high and extra high voltage level by the respective TSO. On the distribution level the requirements are part of the FNN-guidelines but there is no monetary compensation [95]. (ii) System restoration: BSS can be used to energise transmission and distribution lines and have the ability to synchronise sub-systems as well as back-up other black start units [105]. In Germany, each of the four TSOs in cooperation with the DSOs are obliged to have a sufficient capacity of black start units plus a concept for the restoration of supply in their control area. The black start capability is not explicitly defined in the Transmission Code. The requirements for the type, scope and remuneration are negotiated bilaterally. (iii) Redispatch: In many areas in Germany, transmission capacities are not keeping pace with the changing feed-in and off-take infrastructure. In order to ensure security of supply, TSOs with the help of DSOs take redispatch measures, adjusting feed-in from particular generating and storage facilities [113]. A transparent market for redispatch does not exist. The selection of generators for redispatching is based on their location in the network, their generation form and their size, which determines either the cost-based (where the adequacy of costs is regulated) or market-based (based on individual bids submitted by the generators) redispatch [114]. (c) Cost reduction or avoidance (i) Uninterrupted power supply (UPS): 16 Large and long power cuts (>3 min) arise relatively arbitrarily in Germany. However, voltage dips (<1 min) as well as short interruptions (<3 min) occur 10 to 100 times per year [114]. Therefore, depending on the specific outage times and individual power quality needs (e.g. voltage, frequency, harmonics), a UPS system can consist of a BSS in combination with a generation unit like a diesel or gas generator or of a battery only [106]. (ii) Balancing group management (BGM): With the liberalisation of electricity markets in Europe and Germany, the balancing group system was established. Accordingly, each producer or consumer must belong to a balance group and all balance groups must be levelled at a quarter-hourly basis. The German TSOs are liable for determining and settling the amounts of balancing energy in their control area, using a common symmetric imbalance price for each 15-minute time period (German: regelzonen¨ubergreifender einheitlicher Bilanzausgleichsenergiepreis, reBAP) [115]. Consequently, a BSS can optimise the individual energy balancing costs. (iii) Energy cost management (ECM): The benefit area is similar to arbitrage at power exchange markets. In this case not wholesale prices but individual end-user tariffs are relevant. The BSS can avoid high price energy purchases during peak demand hours for residential and commercial/industrial users [116]. Since 2010, according to section 40(5) EnWG energy suppliers are obliged to offer load-variable and daytime dependent tariffs. The tariff-structure and -spreads depend mainly on the respective supplier and individual electrical demand amounts (e.g. industrial, residential). (iv) Reactive power management (RPM): Producers and network operators need to transfer the apparent power according to the active and reactive power demand of the end user. Common supply contracts in the industry allow that 50 % of the active energy can be obtained free of charge as reactive energy, which corresponds to a cosϕ of 0.89 [117]. In case of a higher demand for reactive power an additional fee must be paid, which is subject to individual negotiations. This inductive reactive power demand can be covered amongst others by a BSS. (v) Demand management: As standard load profiles are applied in the customer segment and only annual energy consumptions are measured, no tariffs with power limits or incentives are available at the moment. This can potentially change with the roll out of smart meters. However, industrial consumers typically have two price components: expenses of the peak power demand and expenses for the consumed energy [103]. Usually, demand management is done by the retraction of running processes. Therefore, a load-shift via BSS may have (alongside with economic aspects) production-related benefits. (vi) Renewable energy self-consumption (RESC): End-consumers with generation capacity (e.g. photovoltaics) can increase the amount of self-consumed 17 energy by adding BSS. With the increasing difference between cost of generation and purchase price BSS become more and more attractive to end-consumers. For instance, PV-generation costs and feed-in tariffs have dropped well below purchase prices from the grid, whereas purchase prices have increased continuously [103]. It is noteworthy that due to the EEG amendment from 2014, newly installed systems over 10 kW or 10.000 kWh/a are surcharged for own consumption. Overall, the attractiveness of RE self-supply depend highly on electricity fee regulations. (vi) Grid expansion relief: Due to the growing energy demand, decoupled supply and demand regions, as well the fluctuating nature of most renewable energy generation, further investment in new lines, transformers and substations may become necessary [118]. According to the usual load characteristics, the available transmission capacity limits only the maximum transmittable power, but not the energy [119]. BSS can help defer or avoid grid expansions by storing energy. Nevertheless, BSS in general are more cost intensive and the current incentive regulation (ARgeV) does not consider alternative and perhaps more expansive infrastructure investments. According to a German market analysis based on data from 2013 the benefits can be grouped in accordance to their market potential (see Table 7). The market potential consists of three core aspects: conceivable revenue, applicability for BSS and a favourable legal framework. Only a low potential for BSS benefits lies in grid expansion relief, voltage support and system restoration; redispatch, demand management and reactive power management hold a medium benefit potential. A high market potential is given by energy trading at the day-ahead and intra-day market, frequency support, un-interruptible power supply, balancing group management, energy cost management and renewable energy self consumption. The highest revenue potential for the market based applications lies in the primary control reserve market whereas the highest cost reduction potential can be seen in maximising the self consumption using renewable energies, especially for households. Therefore, many BSS projects, especially in Germany, but also world-wide focus on these two applications [120]. An up-to-date world-wide database on energy storage systems and their applications is maintained by the US Department of Energy [121], which confirms that these two applications are the most common. Ergo, the focus of this work lies on operating strategies for the maximisation of self consumption (subsection 5.2) and primary frequency control (subsection 5.3). Another approach is to combine complementary business models, this may increase the profit compared to a single revenue stream [122]. [Table 7 about here.] 18 5.2. Detailed overview of operating strategies for self-consumption With the rise of DG the idea of the prosumer (entities that consume and produce), first mentioned in 1980 [123], became more popular. The main motivation to become an electrical prosumer as defined in [124], is that self-consumption of locally generated electricity, as defined in [125], is more profitable than drawing it from alternative supplies. This is the case if the levelised costs of electricity (LCOE) of the DG can compete with the cost to draw electricity from the power grid (electricity retail price). A comprehensive manual to calculate the LCOE for renewable energies was first presented by [126] and has further been discussed by [127] [128] and [129]. To incorporate the cost of storage [130] proposed to calculate the levelised cost of stored energy. A comprehensive overview on grid parity world-wide is given by [131]. It is shown that Europe was the first main market world-wide where grid parity was achieved in 2010. It is quite likely that the market volumes for self-consumption business cases will grow in the future as the trend of falling LCOE of DG and BSS continues. The LCOE of PV, for example, are assumed to decrease by 30-50 % from 2014 to 2030 [132]. An even more drastic price decline is foretold for BSS, especially for lithium-ion batteries (LIB). The lowest battery cell price for utility scale LIB could decrease by 64 % from 2014 to 2020 [133]. Although normally only addressed as LIB, there are at least four promising types of LIB suitable energy storage applications with different cell chemistries [134] and price reduction potentials till 2020 [133]: lithium manganite (39 %), lithium nickel cobalt aluminum oxide (50 %), lithium-iron phosphate (37 %) and lithium titanate (25 %). A more conservative meta-study conducted by Nykvist et al. indicates that the costs of LIB for battery electric vehicles could fall below 150 USD/kWh by 2025, and therefore decrease by more than 50 % [135]. The lowest battery cell price for utility scale flow batteries is predicted to decrease by 48 % until 2020, making them the second most interesting battery type concerning the price reduction potential [133]. The liberalisation of the energy market since the 1990s has not lead, as theoretically predicted, to a decline of the electricity price for household consumers due to more competition, but to an increase in all 27 member countries of the EU-27, except Finland, between 1998 and 2008 [136]. As electricity prices are much harder to predict than, for example, the LCOE of PV a large variety of methods have been applied over the past 15 years [137], indicating that the electricity price for households will further rise all over Europe [136]. Keeping in mind the big uncertainty of predicting these prices the electricity retail price in Germany is likely to increase until 2030 according to a technical report commissioned by the Federal Ministry for Economic Affairs and Energy [138]. In countries with lower LCOE of PV compare to Germany like Spain for instance, self-consumption systems might have a positive NPV, but a possible back-toll fee could turns a profitable system to a negative NPV[139]. Therefore a favourable legislative framework, as it is the case in Germany, is mandatory for this business case. By analysing the Italian market, one can deduce which size is more profitable in a post feed-in market. It can be concluded that small residential PV systems have higher net present values than bigger 19 systems, as the economy of scale does not compensate the benefits of smaller systems [140]. Therefore, the trend of installing PV systems in LV grids in Germany is likely to continue. PV systems in southern Germany reached PV grid parity in 2012 [2]. With only a PV-system to match the demand, the achievable self-consumption rates are limited, and can only be increased by demand side management (DSM) and BSS come into play. It is shown by [141] that BSS have a higher potential to increase self-consumption than DSM [141]. Consequently, self-consumption increase is mainly realised with residential energy storages (RES), as this business case became profitable in 2013 in Germany [142]. As described before, the benefit in 2013 results from the PV LCOE, which are currently between 9.8 and 14.2 EURct./kWh in Germany [143], and the electricity costs for households, which amount to 28.9 ct./kWh [144]. It is noteworthy that due to the EEG amendment from 2014 newly installed systems over 10 kW or 10.000 kWh/a are surcharged for own consumption (currently with 6.2 EURct./kWh). Therefore, the theoretically achievable profit margin lies between 8.5 and 19.1 EURct./kWh. This led to an installation of more than 4600 residential storage systems for self consumption in Germany until June 2015[145]. In the industry segment the PV generation costs are generally 2 EURct./kWh lower than in household applications because of the larger systems sizes and lie between 7.8 and 14.2 EURct./kWh [143]. The power purchase costs for large customers with a consumption of 100 GWh/a range between 4.1 and 15.6 EURct./kWh. Thus, the theoretical realisable value range (considering the EEG surcharge) is 0 to 5 ct./kWh. But could it be economically feasible to pool the prosumer and instead of having a BSS and PV-system in every household share and scale them up? [146] showed that the pooling of prosumer generators and loads has been beneficial in all calculated scenarios in the UK compared to a single prosumer. This is due to the combination of PV systems, wind turbines and loads. By doing this the self-consumption level could be raised up to 17,5 % , wherefore the economics in case of grid parity improve significantly. However, BSS were not considered in this study. Large scale or pooled BSS that apply a self-consumption maximisation can be addressed as community electricity storage (CES), as defined in [147,148]. A more detailed definition of CES is given in [149]. Parra et al. [150] conducted a study in which the LCOE of single households in the UK using PV residential storage systems and using a CES instead were compared. It has been shown, that the LCOE could be lowered by 37 % for a 10-household community and 66 % for a 60-household community. In Germany, CES, diverging from the definition in [148] cannot be operated or owned by the DSO using the CES to participate in the energy market because of the unbundling. The CES has to be owned and operated by a citizen cooperative or an external storage operator, for example. In Germany, no similar calculations considering the potential of lowering the LCOE have been conducted, but [151] showed that by applying CES the losses caused by the grid-compatible storage operation can be lowered by 50 % on average compared to RES. With the existing legal framework the business models of residential storages and CES cannot be directly compared because of the additional burden of extra fees and taxes for CES. Nevertheless, the studies mentioned before seem to indicate that CES have some advantages over residential storages. 20 The operation strategies however, can be transferred and classified into the following four categories [95,98]: direct loading, schedule mode, peak shaving and based on a prognosis. A more detailed description and quantitative comparison of the control strategies for residential systems can be found in [20]. Although being very similar, the control strategies for CES are different, as the incentive programme introduced by the German government only supports storage systems for grid connected PV systems up to 30 kW [98]. As a consequence, CES do not have to limit the rated power of the DG PrDG. For the following graphs it is assumed that the CES is connected to the low voltage and the yearly energy consumption is equal to the energy production of the DG in the LV grid. It is assumed that all DG are PV systems. As suggested in [152] the ratio between capacity and the rated power of the PV system is 1:1. The implementation of the different operating strategies of the German CES projects listed in Table 8 are sorted in the four categories and described briefly. (a) Direct loading The generated energy is directly stored in the BSS if the residual power Pres of load and generation is positive. This simple strategy maximises the self consumption rate as it ensures that the BSS is loaded as soon as possible. Drawback of this strategy are the steep gradients depicted in Fig. 7 and that, depending on the battery capacity, an excessive feed-in to the grid might occur during peak irradiation around noon, if there is no PV power limitation on the power of the PV systems. [Fig. 7 about here.] A grid compatible operating strategy using direct loading to ensure a maximal renewable energy selfconsumption rate (RESCR) is used by [153] and [154]. In [153] the BSS is placed in the LV side of a micro grid with DG, which is connected to the public grid via one MV/ LV transformer. The charging and discharging of the battery is calculated in 1-h steps, from measured and synthesised time series. The main differences in the CES project of [154] are that the generation and load of every participating prosumer is measured every 5-7 seconds and that the BSS is not necessarily placed at the same location as the DG and consumers. The idea of this project is that every participant may use a part of the battery that is virtually partitioned to increase the individual RESCR. (b) Schedule In this strategy, the time to charge the battery will be shifted to a typical time with high radiation. The schedule mode with constant charging power is depicted in Fig. 8 showing a more favourable behaviour from grid perspective because feed-in peaks as in the direct loading strategy are prevented. [Fig. 8 about here.] 21 Nevertheless, the self-consumption rate might be reduced, as in days with lower radiation the BSS might not be fully loaded. Several strategies have been proposed for this purpose. The main differences are that [155] and [156] propose a starting point around noon and charge the battery with full power whereas others, for example [157], suggest a constant charging power over a larger period. Currently there is no CES project in Germany known to the authors using this strategy. (c) Peak-shaving (load levelling) The main objective of the peak shaving strategy (Fig. 9) is to avoid over-voltage and equipment overloading issues by limiting the power at the PCC and using the remaining residual power to charge the battery [158,152,159]. [Fig. 9 about here.] The limitation of power at the PCC should be based on the voltage at the PCC, the power range of the battery, and the PV penetration of the grid [158]. The main objective of this strategy is to not surpass a certain level of Pres/PrDG at the PCC of the BSS. There are mainly three possibilities to achieve this aim: (i) The battery is sized for the worst case, e.g. the day with the highest irradiation and no load, as in [160]. (ii) The power of the DG is curtailed in case of a full battery, as depicted in Fig. 9 and described in [159]. (iii) Instead of curtailing the DG an additional load is used to reduce the residual load by using, for example, power-to-head [161]. This grid supportive operating strategy is applied to large scale BSS by [14], [160], [162] and [163]. The focus of IRENE Project lies on grid expansion relief. Therefore, one or several BSS are dimensioned and placed strategically in the LV to mitigate the total feeder RPF to 70 % of the cumulated PrDG of the respective feeder in which the BSS are installed. Additionally to this active power control, a reactive power control is implemented. The calculation of the set-points of P and Q are calculated externally and not by the BSS itself. [160] Similar to the aforementioned project the BSS in Fechheim limits the power to 40 % of the cumulated PrDG of feeder in which the BSS is allocated with an active power control and uses a reactive power control to reduce the voltage in the case of a fully loaded storage [162]. The aim of the SmartOperator project is to minimise voltage deviations and line utilisation. The BSS is dimensioned to enable a peak shaving of 50 % for a period of 5 h [163] based on initial studies by 22 [164]. A learning algorithm is used to calculate the forecast of generation and load as well as future grid states, based on real time data of voltage and current [165]. This forecast is used to calculate the active and reactive power flows of the BSS to ensure peak shaving of the PV systems and that the voltage values of the grid nodes stay within given thresholds. The advantage of peak shaving is that critical voltages might be avoided by limiting the feed-in power. The voltage can be further reduced by absorbing reactive power. From the point of view of self consumption maximisation, a problem is raised during cloudy or foggy days, when there is not enough radiation to charge the battery. Consequently, the self-consumption rate will be reduced. On the other hand, during high irradiance days, the power curtailment is high as can be seen in Fig. 9 for case (ii). For case (i) and (iii), however the additional investment costs have to be considered critically. This applies in particular for case (i) in a distribution grid with many wind generators as in this case the energy to power ratio of the BSS needs to be higher as for PV systems [164]. To avoid these losses or additional invest, an optimisation of the power flow based on a prognosis is proposed in the next strategy. (d) Prognosis based strategy This strategy uses load and weather forecast data to adjust the charging power and feed-in power to get a fully charged battery at the end of the day and/or avoid over-voltage and asset over-loading (Fig. 10). [Fig. 10 about here.] A control loop within the day corrects the deviation from the forecast data. This strategy reaches the highest self consumption rate after the direct loading strategy while still being grid supportive, this is due to the lower curtailment losses compared to other strategies [20,125]. The main differences of this strategy are the forecast techniques. Principally, the previously published studies can be divided into four classes: (i) Studies using a perfect forecast [152,166]. (ii) Studies using synthetic forecasts (modified measured time series) [167,168,169]. (iii) Studies based on external weather-based forecast from meteorological services [170,171,161,172] (iv) Studies that base their forecast on a persistence method based on values measured by the PV- system [167,125,173] Obviously, no prediction errors apply to a perfect forecast. The only difference is the time resolution, which in the case of [152] is 1 min and in the case of [166] is 15 min. One proposition for modelling synthetic forecast which has been presented by [168] and also used by 23 [169], uses the Spherical Harmonic Discrete Ordinate Method [174]. In this model measured data is used to generate the global solar irradiation at ground level for the next days. By forecasting the weather data a minute-based PV power is calculated taking into account the orientation and angle of the power plant. An error analysis of the model has shown that the average error (rRMSE) of the weather forecast for the next day is 32.5 % for one site. This is very close to the accuracy of approximately 30 % of current numerical weather prediction models for Central Europe [175]. The value increases for a longer forecast horizon. Instead of a physical model, [167] uses a noise sequence to fabricate a forecast based upon the hourly average of the measured data. This results in an hourly forecast for the next day with an rRMSE of 30 %. Several studies use external weather forecasts and calculate the AC power profile of the PV system according to predicted irradiance instead of synthesising the forecast data. A simple forecast method in which the historical data of the solar irradiance and the predicted weather conditions (sunny, cloudy, rainy) are used to calculate the PV profile in 1 h steps is presented by [170]. Also on an hourly basis, [171] predicts the PV power output for different region sizes in Germany based on forecasts for up to three days ahead that are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). For a single site and day ahead forecast an rRMSE of 36 % could be quantified. As the rRMSE decreases as the examined area rises for the whole of Germany the accuracy of the rRMSE is 13 %. Another study uses the irradiance forecast based on the Weather Research and Forecasting (WRF) Model [176] and evaluated the deviation of the measured irradiance values of a pyranometer (5-8 %) and the PV power output (3-5 %) on a 15 minute base for a PV plant in Italy [172]. Historical forecast data of irradiance and temperature in 1-h steps from Meteotest [177] has been used by [161] to calculate the PV output power and it is shown that the RESCR decreases by 15 % if forecast errors are taken into account instead of assuming a perfect forecast . Another approach is to use persistence weather forecast. The forecasting method is based on extrapolating the current or recent PV power plant output taking into account the changing of the sun angle. Since the persistence is based on stochastic learning technique from historical pattern, the accuracy highly depends on the forecast horizon due to the change of cloudiness [178]. The forecast method is suitable for minute based forecasts for one location. For simulation purposes, an autonomy forecasting using a learning algorithm is more preferable compared to the one that depends on the global weather data. The differences of the different persistence forecasts arise in the algorithms used to predict the load and PV output and the values that are used to correct the intra-day deviation from the forecasted values. As described before, [167] uses a synthesised forecast data with a noise and a learning algorithm based on historical data to adapt the charging algorithm to the PV output and load within the day. Also [169] uses a synthesised PV forecast; concerning the load an easier method is proposed by predicting it based on the load profile of the past five days. In this method, the day is divided into three periods: 24 According to [181], the maximum deployment time for PCR increases linearly with the requested primary control power. Starting from a value of zero the maximum offered power by a PCR provider must be fully activated after 30 seconds at the latest. However, BSS that are able to provide the requested power much faster are allowed to use this characteristic as a degree of freedom. This means that battery operators are allowed to use the whole “permissible operating range” depicted in Fig. 17 to readjust the SOC of their storages. [Table 10 about here.] 5.4. Impact of BSS maximising self-consumption and applying PCR on distribution grid planning In this section the impact of the operating strategies derived from the business cases of self-consumption maximisation and primary control reserve as shown in subsection 5.2 and 5.3 on distribution grid planning are discussed. How BSS can be implemented in traditional grid planning as presented in subsection 2.2 is subject to ongoing research. However, [198] gives some hints by showing that DSO only consider active power flows, which seems a viable proposition as they are responsible for the revenue stream for the two business cases and a reactive power control is not yet mandatory for large scale BSS. Therefore, in the fist part of this section only the active power flows are evaluated using the worst case approach of traditional grid planning and the resulting diversity factors for BSS are listed in Table 11. Secondly, the effect of reactive power control on the planning is discussed briefly as it can be considered independent of the business case, given that the power electronics is able to provide a four quadrants operation. In the last part deficiencies of the traditional planning methodology are presented and possible steps to new planning approaches, as explained in subsection 4.1, are discussed. •grid compatible self consumption The worst case is that the battery is fully loaded for the RPF scenario and fully discharged in the HLF-case. The resulting diversity factors for implementing BSS in the grid planning are listed in Table 11 and result in a neutral behaviour of the BSS. The operation strategy direct loading and schedule as used in the projects Strombank and MSG EUREF (see Table 8) can be mentioned as an example. •grid supportive self consumption For the HLF the same as for grid compatible BSS applies, as the battery might be fully discharged as well. The difference arises for the RPF. In this case the battery is used to mitigate the reverse power flow caused by DG with peak shaving. The peak shaving threshold can be either fixed or adaptive as in the case of forecast based charging and discharging. For the projects listed in Table 8 that use a forecast based operation mode a peak shaving functionality is implemented. Nevertheless, the rated power of the BSS might be higher than the power used to mitigate the power at the PCC, which is the case in the EEBatt project where the energy to power ratio of the BSS is 1:1. In this case, the diversity 31 factor is the quotient of the power used for peak shaving purposes and the rated charging power of the BSS. For example, for the project SmartOperator (pure peak shaving) the diversity factor is 1, but it is <1 in the EEBatt project (forecast based SC). This operating strategy can solve over-voltage (cable) and thermal issues (cable and secondary transformer) if the BSS is installed in the same LV feeder as the DG causing them. The thermal load of the primary transformer is reduced in any case independently of the allocation of loads, DG and BSS, as the peak of the RPF is mitigated in any case, if a diversity factor of >0 for the BSS is reached. •system compatible primary control reserve It can be deduced from Fig. 18, that a BSS providing PFC might discharge or charge with its full rated power at any moment. Depending on the system architecture, some BSS have the capability to be overloaded, as reported in [190] for VRF (100 % over-loading), in [184] for LIB (30 % over-loading for 15 min), and in [199] (25 %) also for LIB. In a worst-case scenario, the normal operation together with the application of the degrees of freedom as described in subsection 5.3 can lead to a diversity factor >1. Depending on the allocation of the BSS it might reduce the hosting capacity of DG of the affected grid as this operation strategy tightens the over-voltage and over-loading issues. All projects listed in Table 10, except the SmartPowerFlow project, where the BSS behaves in a system supportive way fall into this category. [Fig. 18 about here.] •system supportive primary control reserve The diversity factor for this operating strategy is the same as for the gird compatible behaviour, as the active power flows are the same. The difference here is that a reactive power control is used to solve over-voltage issues. [Table 11 about here.] In the traditional distribution grid planning reactive power control is usually not considered and only a fixed cosϕ can be taken into account as only one time-step for the two worst case scenarios is calculated. In a grid/ system compatible behaviour cosϕ may be set to 0 and in a grid/ system supportive behaviour to the maximum favourable value from grid perspective. Nevertheless, this issue has not been analysed systematically yet and may lead to wrong results if the method of the traditional planning is applied. For an accurate simulation of a reactive power control, a load flow analysis based on time series has to be applied. It can be concluded that the traditional planning method of passive distribution systems for large scale BSS will lead to over-capacities and uncertainties concerning the reactive power flows. Therefore, CIGRE promotes the shift to active distribution systems as defined in CIGRE WG C6.11 [200], which will incorporate 32 DG and BSS in a more active way than the fit-and-forget approach which is currently used and will allow to apply new planning approaches more efficiently. This transition is described in detail by [46]. As discussed in subsection 4.1, BSS, as well as DG and the distribution grids need to be modelled to calculate time-series and derive suitable probability density functions. Depending on the application and technology different time-steps need to be realised in these models [46]. In [201] it is shown that for SC the operation strategy should be simulated at least in one minute time-steps to avoid short-term feed-in peaks. For PCR the resolution has to be even higher and one second time-steps seem appropriate, in order to incorporate all degrees of freedom described in subsection 5.3 properly. As for the reactive power control current studies focus on two main directions: a central approach using an AC OPF, such as [71], or an autonomous voltage control, as for example a Q(V) control [41]. It seems as if autonomous voltage control strategies are the more favoured solution at the moment as the technical standard for connecting BSS and DG in MV and LV are aiming in this direction [31]. The challenges of future investigation lie in modelling BSS to calculate active and reactive power time series for different applications in order to apply them for new planning approaches in active distribution systems. 6. Conclusion In this paper, traditional approaches and recent advances in distribution grid planning alongside with alternative possibilities to traditional grid extension with large scale battery storage systems are described. In addition the German energy storage market is analysed and the operation strategies of the two most profitable applications, self-consumption maximisation and primary frequency control, are described in detail after an extensive literature review. The main findings and contributions of the paper are: •A clear methodology for grid extension measures in distribution grids has been presented. •Most of the new approaches for distribution grid planning use deterministic models and do not consider reliability issues. There is a great variety of these models with their respective pros and cons that have to be considered for the given planning task. Nevertheless, it is shown that the over-sizing problem remains even for advanced grid planning methods if worst case scenarios are applied. Therefore, there is a great need for detailed models to generate combined active and reactive power flows of BSS that are market-driven and grid/ system supportive at the same time. •An analysis of 20 potential revenue streams for BSS shows that the primary control reserve market holds the highest revenue potential for market based applications, whereas the highest cost reduction potential lies in the maximisation of the self-consumption using renewable energies, especially for households. 33 •As suitable options for the maximisation of self consumption the operation strategies direct loading, schedule mode, peak shaving, and prognosis based loading were identified. Additionally, several large scale BSS projects in Germany applying those strategies were presented. The prognosis based operation strategy with a peak limit restriction seems to be the most promising, as it leads to manageable curtailment losses, especially if the feed-in limit is reduced in the future. Within the forecast based strategies the adaptive forecast algorithm combines the advantages of autonomy from external forecasts with their accuracy. Nevertheless, due to additional fees and taxes applying for community electricity storages, this business case is hard to transfer from residential to large scale storages. Besides a need of revising the existing legal framework in order to make CES economically feasible, there is also a need for future research regarding this application. Nonetheless, it seems especially interesting as in the near future PV systems, that have reached the end of their 20 year period of feeding into the grid with a fixed feed-in tariff, can be used for this applications with an extreme low LCOE. •Primary frequency control seems to be the most promising business case for BSS in Germany at the moment. Although the net present value is just becoming positive, there is still a great challenge to make it profitable. Although the degrees of freedom help to achieve this goal, research is still necessary to determine the different benefits of these options, especially for VRFB, since most of the BSS used for primary frequency control are lithium-ion batteries. •The task to implement BSS in (traditional) distribution grid planning is also subject to ongoing research. At the moment many of the studies only consider active power flows and worst case assumptions are applied. If traditional planning methods for passive distribution systems are applied for large scale BSS, over-capacities will probably be the result. In order to evaluate the potential of BSS to behave in a grid supportive manner, power flow simulations considering operation strategies for active and reactive power control for different time scales depending on the application have to be conducted. In conclusion, it is worth pointing out that large scale BSS are becoming economically feasible in Germany, however there is a lack of planning guidelines for DSO to integrate the BSS in their grid. Furthermore, not all the applications and operating strategies are mitigating the problems of the DSO that arise with increasing penetration of DG. Future studies should concentrate on combining a profitable and a grid supportive behaviour into one operation strategy, otherwise the implementation of BSS in distribution grids might lead to further grid extension instead of grid relief. 7. Acknowledgement This work was supported by the German Federal Ministry of Economics and Technology (BMWi) and the Projekttr¨ager J¨ulich GmbH (PTJ) within the framework of the project “SmartPowerFlow” (FKZ0325523A). 34 The author would also like to thank Mrs. H. Krauth and Mr. A. Penzkofer for the kind collaboration in the proofreading. References [1] D. B¨ohme, W. D¨urrschmidt, M. Van Mark, Erneuerbare Energien in Zahlen, Tech. rep., Federal Ministry for Economic Affairs and Energy (2014). 1 [2] E. Karakaya, A. Hidalgo, C. Nuur, Motivators for adoption of photovoltaic systems at grid parity: A case study from Southern Germany, Renewable and Sustainable Energy Reviews 43 (2015) 1090–1098. doi:10.1016/j.rser.2014.11.077. 1,20 [3] Federal Ministry of Economic Affairs and Energy (BMWi), Federal Ministry for the Environment Nature Conservation, Building and Nuclear Safety (BMU), Energiekonzept f¨ur eine umweltschonende, zuverl¨assige und bezahlbare Energieversorgung (2010). 1 [4] A. Keane, M. O’Malley, Optimal Allocation of Embedded Generation on Distribution Networks, IEEE Transactions on Power Systems 20 (3) (2005) 1640–1646. doi:10.1109/TPWRS.2005.852115.2 [5] T. Ackermann, G. Andersson, L. S¨oder, Distributed generation: a definition, Electric Power Systems Research 57 (3) (2001) 195–204. doi:10.1016/S0378-7796(01)00101-8.2 [6] C. Gonzalez, R. Ramirez, R. Villafafila, A. Sumper, O. Boix, M. Chindris, Assess the impact of photovoltaic generation systems on low-voltage network: software analysis tool development, in: 2007 9th International Conference on Electrical Power Quality and Utilisation, IEEE, 2007, pp. 1–6. doi:10.1109/EPQU.2007.4424183.2 [7] S. Nykamp, A. Molderink, J. L. Hurink, G. J. Smit, Statistics for PV, wind and biomass generators and their impact on distribution grid planning, Energy 45 (1) (2012) 924–932. doi:10.1016/j.energy.2012.06.067.2,6,66,68 [8] A. von Oehsen, Y.-M. Saint-Drenan, T. Stetz, M. Braun, Vorstudie zur Integration großer Anteile Photovoltaik in die elektrische Energieversorgung - Erg¨anzte Fassung vom 29.05.2012, Tech. Rep. November 2011, Frauenhofer IWES, Studie im Auftrag des BSW - Bundesverband Solarwirtschaft e.V., Kassel (2012). 2 [9] A. Mohring, J. Michaelis, Techno-¨okonomische Bewertung von Stromspeichern im Niederspannungsnetz, Tech. rep., Fraunhofer ISI, Karlsruhe (2013). 2 [10] C. Breyer, B. M¨uller, C. M¨oller, E. Gaudchau, L. Schneider, K. Gajkowski, G. Pleßmann, Vergleich und Optimierung von (de-)zentral orientierten Ausbaupfaden zu einer Stromversorgung aus EE in Deutschland, Tech. rep., Reiner Lemoine Institut gGmbH, Berlin (2013). 2 [11] T. Stetz, M. Kraiczy, K. Diwold, M. Braun, B. Bletterie, C. Mayr, R. Br¨undlinger, B. Noone, A. Bruce, I. Macgill, High Penetration PV in Local Distribution Grids Outcomes of the IEA PVPS Task 14 Subtask 2, Tech. Rep. July (2014). 2 [12] T. Stetz, M. Rekinger, I. Theologitis, Transition from Uni-Directional to Bi-Directional Distribution Grids, Tech. rep. (2014). 2 [13] G. Kerber, Aufnahmef¨ahigkeit von Niederspannungsverteilnetzen f¨ur die Einspeisung aus Photovoltaikkleinanlagen, Phd thesis, TU M¨unchen (2011). 2,8 [14] B. Meyer, H. Mueller, R. Koeberle, M. Fiedeldey, C. Hoffman, J. Bamberger, Impact of large share of renewable generation on investment costs at the example of aw distribution network, in: 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013), no. 1241, Institution of Engineering and Technology, 2013, p. 4. doi:10.1049/ cp.2013.1137.2,13,22,74 [15] E. Wieben, T. Kumm, E. Hohn, M. Rohr, M. Stadler, The 5% approach as building block of an energy system dominated by renewables, in: 28th Conference on Environmental Informatics - Informatics for Environmental Protection, Sustainable Development and Risk Management, BIS-Verlag, Oldenburg, 2014, pp. 85–92. 2 35 [16] Federal Network Agency (BNetzA), Monitoringbericht 2014, Tech. rep., Bundesnetzagentur, Berlin (2014). 2,73 [17] German Energy Agency (dena), dena-Verteilnetzstudie. Ausbau- und Innovationsbedarf der Stromverteilnetze in Deutschland bis 2030. (2012). 2,3,5,6,7,8,13,47,48,66,67,68,69,70,73 [18] P. Wlodarczyk, A. Sumper, M. Cruz, Voltage Control of Distribution Grids with Multi-Microgrids Using Reactive Power Management, Advances in Electrical and Computer Engineering 15 (1) (2015) 83–88. doi:10.4316/AECE.2015.01012.2 [19] N. Leemput, F. Geth, J. Van Roy, P. Olivella-Rosell, J. Driesen, A. Sumper, MV and LV Residential Grid Impact of Combined Slow and Fast Charging of Electric Vehicles, Energies 8 (3) (2015) 1760–1783. doi:10.3390/en8031760.2 [20] M. Resch, B. Ramadhani, J. B¨uhler, A. Sumper, Comparison of control strategies of residential PV storage systems, in: 9th International Renewable Energy Storage Conference (IRES 2015), 2015, p. 18. doi:10.13140/RG.2.1.3668.2084.2, 21,23 [21] O. C. Rascon, M. Resch, B. Schachler, J. Buhler, A. Sumper, Increasing the hosting capacity of distribution grids by implementing residential PV storage systems and reactive power control, in: 2016 13th International Conference on the European Energy Market (EEM), IEEE, 2016, pp. 1–5. doi:10.1109/EEM.2016.7521338.2 [22] F. D´ıaz-Gonz´alez, A. Sumper, O. Gomis-Bellmunt, R. Villaf´afila-Robles, A review of energy storage technologies for wind power applications, Renewable and Sustainable Energy Reviews 16 (4) (2012) 2154–2171. doi:10.1016/j.rser.2012. 01.029.2 [23] Federal Ministry of Justice and Consumer Protection, Law on electricity and gas supply (Energiewirtschaftsgesetz-EnWG) (2015). 3,26,29 [24] Association for Electrical; Electronic & Information Technology (VDE), Power transformers - Part 2: Temperature rise for liquid-immersed transformers (IEC 60076-2:2011); German version EN 60076-2:2011, Tech. rep. (2012). 3 [25] Association for Electrical; Electronic & Information Technology (VDE), Power cables - Part 603: Distribution cables of rated voltage 0,6/1 kV; German version HD 603 S1:1994/A3:2007, parts 0, 1, 3-G and 5-G (2010). 3 [26] German Institute for Standardisation (DIN), Voltage characteristics of electricity supplied by public distribution networks; German version EN 50160: 2010 + Cor.: 2010 (2011). 3,26 [27] Association of German Grid Operators (VDN), TransmissionCode 2007 - Netz- und Systemregeln der deutschen ¨ Ubertragungsnetzbetreiber, Tech. rep. (2007). 3,27,29,66,75 [28] G. Kerber, R. Witzmann, Loading Capacity of Standard Oil Transformers on Photovoltaic Load Profiles, in: 10th World Renewable Energy Congress and Exhibition, 2008, pp. 1198–1203. 3 [29] M. Labed, M. Brand, H. Rose, A Cost-Effective Approach for The Grid Integration of Distributed Renewable Resources, International Journal of Emerging Technology and Advanced Engineering 4 (3) (2014) 249–252. 3 [30] T. Ackerman, M. Koch, H. Rothfuchs, N. Martens, T. Brown, Verteilnetzstudie Rheinland-Pfalz, Tech. rep. (2014). 3,8, 70 [31] B. Engel, S. Laudahn, O. Marggraf, A. Schnettler, Vergleich von technischer Wirksamkeit sowie Wirtschaftlichkeit zeitnah verf¨ugbarer Verfahren zur Sicherung der statischen Spannungshaltung in Niederspannungsnetzen mit starker dezentraler Einspeisung, Tech. rep., TU Braunschweig, RWTH Aachen, TU M¨unchen, FGH (2014). 3,8,33 [32] German Association of the Energy and Water Industry (BDEW), Technische Richtlinie Erzeugungsanlagen am Mittelspannungsnetz (2008). 3 [33] Association for Electrical; Electronic & Information Technology (VDE), VDE-AR-N 4105 Generators connected to the low-voltage distribution network - Technical requirements for the connection to and parallel operation with low-voltage distribution networks (2011). 3 [34] ENTSO-E, Requirements for Grid Connection Applicable to all Generators (2013). 4 [35] European Committee for Electrotechnical Standardization (CENELEC), Requirements for generating plants to be connected in parallel with distribution networks - Part 1: Connection to a LV distribution network above 16 A (2015). 36 4 [36] European Committee for Electrotechnical Standardization (CENELEC), Requirements for generating plants to be connected in parallel with distribution networks - Part 2: Connection to a MV distribution network (2015). 4 [37] Forum Network Technology / Network Operation in the VDE (FNN), Anschluss und Betrieb von Speichern am Niederspannungsnetz (2014). 4 [38] C. Schwaegerl, M. H. J. Bollen, K. Karoui, A. Yagmur, Voltage control in distribution systems as a limitation of the hosting capacity for distributed energy resources, in: CIRED 2005: 18th International Conference And Exhibition on Electricity Distribution, IET, Turin, 2005, pp. 6–9. 4 [39] S. Papathanassiou, N. Hatziargyriou, P. Anagnostopoulos, L. Aleixo, Capacity of Distribution Feeders for Hosting DER (2014). 4 [40] German Energy Agency (dena), dena-Studie Systemdienstleistungen 2030. Sicherheit und Zuverl¨assigkeit einer Stromversorgung mit hohem Anteil erneuerbarer Energien, Tech. rep. (2014). 4,27,28 [41] T. Stetz, Autonomous Voltage Control Strategies in Distribution Grids with Photovoltaic Systems: Technical and Economic Assessment, Phd thesis, University of Kassel (2014). 4,33 [42] Agora Energiewende, Stromverteilnetze f¨ur die Energiewende, Tech. rep., Berlin (2014). 4 [43] Union of the Electricity Industry (EURELECTRIC), Ancillary Services Unbundling Electricity Products - an Emerging Market, Tech. Rep. February (2004). 4 [44] M. Braun, Provision of Ancillary Services by Distributed Generators, Phd thesis, Kassel University (2008). 5 [45] Hermann Nagel. Hrsg. Rolf R. Cichowski, Systematische Netzplanung, 2nd Edition, Berlin : VDE-Verl.; Frankfurt, M. : VWEW-Energieverl., 2008. 5,6 [46] F. Pilo, S. Jupe, F. Silvestro, K. E. Bakari, C. Abbey, Planning and Optimization Methods for Active Distribution Systems, Tech. Rep. August, CIGRE (2014). 5,6,33 [47] ETG-Task Force Aktive EnergieNetze, Aktive Energienetze im Kontext der Energiewende: Anforderungen an k¨unftige ¨ Ubertragungs- und Verteilungsnetze unter Ber¨ucksichtigung von Marktmechanismen, Tech. rep., Energietechnische Gesellschaft im VDE (ETG) (ed.) (2013). 5,6 [48] J. Schlabbach, K.-H. Rofalski, Power System Engineering, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, 2008. doi:10.1002/9783527622795.5 [49] W. Kaufmann, Planung ¨offentlicher Elektrizit¨atsverteilungs-Systeme, VDE-Verlag, 1995. 5 [50] H . Lee Willis, Power Distribution Planning Reference Book, 2nd Edition, CRC Press, 2004. 5 [51] Deutsche Gesellschaft f¨ur Sonnenenergie e.V., EEG Anlagenregister, http://www.energymap.info, (accessed 2015-05-20). 5 [52] S. Nykamp, Integrating Renewables in Distribution Grids : Storage, regulation and the interaction of different stakeholders in future grids (2013). doi:10.3990/1.9789036500579.6 [53] G. Wirth, Modellierung der Netzeinfl¨usse von Photovoltaikanlagen unter Verwendung meteorologischer Parameter, Phd thesis, Carl von Ossietzky Universit¨at Oldenburg (2014). 6,66,68 [54] R. Pardatscher, R. Witzmann, G. Wirth, G. Becker, M. Garhamer, J. Brandtl, Research on the impact of photovoltaic power generation in low and medium voltage grids, in: Internationaler ETG-Kongress 2011, W¨urzburg, 2011. 6,66,68 [55] M. Resch, J. B¨uhler, H. Huyskens, A. Sumper, Optimale Positionierung von Großbatterien in Verteilnetzen, in: 30. Symposium Photovoltaische Solarenergie, OTTI e.V., 2015, p. 37. doi:10.13140/RG.2.1.1308.9123.6,66,69 [56] J. B¨uchner, O. Fl¨orcken, S. Dierkes, L. Verheggen, M. Uslar, Moderne Verteilernetze f¨ur Deutschland, Tech. Rep. 44, BMWi (2014). 6 [57] Agora Energiewende, Stromspeicher in der Energiewende, Tech. rep., Agora Energiewende, Berlin (2014). 6 [58] V. Liebenau, J. Schwippe, S. Kuch, C. Rehtanz, Network extension planning considering the uncertainty of feed-in from 37 renewable energies, 2013 IEEE Grenoble Conference (2013) 1–6doi:10.1109/PTC.2013.6652382.6 [59] T. Schmidtner, Probabilistische Methoden in der Netzplanung ”Niederspannung”, in: VDE-Kongress 2012 - Intelligente Energieversorgung der Zukunft, VDE-Verlag, 2012, p. 6. 6 [60] V. Neimane, On development planning of electricity distribution networks, Phd thesis, KTH (2001). 6,12 [61] K. Jan, Agent-based Simulation Environment for Improving the Planning of Distribution Grids, Phd, Technischen Universit¨at Dortmund (2014). 6 [62] T. Stetz, K. Diwold, M. Kraiczy, D. Geibel, S. Schmidt, M. Braun, Techno-economic assessment of voltage control strategies in low voltage grids, IEEE Transactions on Smart Grid 5 (4) (2014) 2125–2132. doi:10.1109/TSG.2014.2320813. 8,70 [63] B. Idlbi, K. Diwold, T. Stetz, H. Wang, M. Braun, Cost-benefit analysis of central and local voltage control provided by distributed generators in MV networks, in: 2013 IEEE Grenoble Conference, IEEE, 2013, pp. 1–6. doi:10.1109/PTC. 2013.6652333.8,11,12,71 [64] B. Idlbi, A. Scheidler, T. Stetz, M. Braun, Preemptive network reinforcement at LV level considering uncertainty in prediction of PV penetration scenarios, in: 2015 IEEE Eindhoven PowerTech, IEEE, 2015, pp. 1–6. doi:10.1109/PTC. 2015.7232793.8,70 [65] A. Keane, L. F. Ochoa, C. L. T. Borges, G. W. Ault, A. D. Alarcon-Rodriguez, R. a. F. Currie, F. Pilo, C. Dent, G. P. Harrison, State-of-the-Art Techniques and Challenges Ahead for Distributed Generation Planning and Optimization, IEEE Transactions on Power Systems 28 (2) (2013) 1493–1502. doi:10.1109/TPWRS.2012.2214406.8 [66] S. Ganguly, N. C. Sahoo, D. Das, Recent advances on power distribution system planning: A state-of-the-art survey, Energy Systems 4 (2) (2013) 165–193. doi:10.1007/s12667-012-0073-x.8,9,11 [67] P. S. Georgilakis, N. D. Hatziargyriou, A review of power distribution planning in the modern power systems era: Models, methods and future research, Electric Power Systems Research 121 (2015) 89–100. doi:10.1016/j.epsr.2014.12.010.8 [68] a. R. Jordehi, Optimisation of electric distribution systems: A review, Renewable and Sustainable Energy Reviews 51 (2015) 1088–1100. doi:10.1016/j.rser.2015.07.004.8 [69] A. Alarcon-Rodriguez, G. Ault, S. Galloway, Multi-objective planning of distributed energy resources: A review of the state-of-the-art, Renewable and Sustainable Energy Reviews 14 (5) (2010) 1353–1366. doi:10.1016/j.rser.2010.01.006. 8 [70] R. Viral, D. Khatod, Optimal planning of distributed generation systems in distribution system: A review, Renewable and Sustainable Energy Reviews 16 (7) (2012) 5146–5165. doi:10.1016/j.rser.2012.05.020.8 [71] S. Koopmann, M. Scheufen, A. Schnettler, Integration of stationary and transportable storage systems into multistage expansion planning of active distribution grids, in: IEEE PES ISGT Europe 2013, IEEE, 2013, pp. 1–5. doi: 10.1109/ISGTEurope.2013.6695339.9,10,12,25,33,71,74 [72] S. Wong, K. Bhattacharya, J. Fuller, Electric power distribution system design and planning in a deregulated environment, IET Generation, Transmission & Distribution 3 (12) (2009) 1061. doi:10.1049/iet-gtd.2008.0553.10,12,71 [73] W. El-Khattam, Y. Hegazy, M. Salama, An Integrated Distributed Generation Optimization Model for Distribution System Planning, IEEE Transactions on Power Systems 20 (2) (2005) 1158–1165. doi:10.1109/TPWRS.2005.846114.10, 12,71 [74] K. Zou, A. P. Agalgaonkar, K. M. Muttaqi, S. Perera, Distribution system planning with incorporating DG reactive capability and system uncertainties, IEEE Transactions on Sustainable Energy 3 (1) (2012) 112–123. doi:10.1109/TSTE. 2011.2166281.10,11,12,71 [75] V. Vahidinasab, Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design, Renewable Energy 66 (2014) 354–363. doi:10.1016/j.renene.2013.12.042.10,12, 71 38 [76] S. Haffner, L. Pereira, L. Pereira, L. Barreto, Multistage Model for Distribution Expansion Planning with Distributed Generation - Part II: Numerical Results, IEEE Transactions on Power Delivery 23 (2) (2008) 924–929. doi:10.1109/ TPWRD.2008.917911.10,12,71 [77] S. Haffner, L. L. Pereira, L. L. Pereira, L. Barreto, Multistage Model for Distribution Expansion Planning With Distributed Generation -Part I: Problem Formulation, IEEE Transactions on Power Delivery 23 (2) (2008) 915–923. doi:10.1109/TPWRD.2008.917916.10,12,71 [78] E. Naderi, H. Seifi, M. S. Sepasian, A Dynamic Approach for Distribution System Planning Considering Distributed Generation, IEEE Transactions on Power Delivery 27 (3) (2012) 1313–1322. doi:10.1109/TPWRD.2012.2194744.10,12, 71 [79] C. L. T. Borges, V. F. Martins, Multistage expansion planning for active distribution networks under demand and Distributed Generation uncertainties, International Journal of Electrical Power & Energy Systems 36 (1) (2012) 107–116. doi:10.1016/j.ijepes.2011.10.031.10,12,71 [80] A. Bagheri, H. Monsef, H. Lesani, Integrated distribution network expansion planning incorporating distributed generation considering uncertainties, reliability, and operational conditions, International Journal of Electrical Power & Energy Systems 73 (2015) 56–70. doi:10.1016/j.ijepes.2015.03.010.10,12,71 [81] H. Falaghi, C. Singh, M.-R. Haghifam, M. Ramezani, DG integrated multistage distribution system expansion planning, International Journal of Electrical Power & Energy Systems 33 (8) (2011) 1489–1497. doi:10.1016/j.ijepes.2011.06. 031.10,12,71 [82] V. F. Martins, C. L. T. Borges, Active Distribution Network Integrated Planning Incorporating Distributed Generation and Load Response Uncertainties, IEEE Transactions on Power Systems 26 (4) (2011) 2164–2172. doi:10.1109/TPWRS. 2011.2122347.10,12,71 [83] M. Sedghi, M. Aliakbar-Golkar, M.-R. Haghifam, Distribution network expansion considering distributed generation and storage units using modified PSO algorithm, International Journal of Electrical Power & Energy Systems 52 (0) (2013) 221–230. doi:10.1016/j.ijepes.2013.03.041.11,12,13,71 [84] H. Chen, Z. Wang, H. Yan, H. Zou, B. Luo, Integrated Planning of Distribution Systems with Distributed Generation and Demand Side Response, Energy Procedia 75 (51322702) (2015) 981–986. doi:10.1016/j.egypro.2015.07.314.11, 12,71 [85] H. Saboori, R. Hemmati, V. Abbasi, Multistage distribution network expansion planning considering the emerging energy storage systems, Energy Conversion and Management 105 (2015) 938–945. doi:10.1016/j.enconman.2015.08.055.11, 12,13,71 [86] E. Kaempf, M. Braun, Expert Systems as Support to Strategic Network Planning, Tech. rep., Fraunhofer IWES Kassel (2015). doi:10.13140/RG.2.1.3473.7760.11,12,71 [87] S. Khator, L. Leung, Power distribution planning: a review of models and issues, in: IEEE Transactions on Power Systems, Vol. 12, 1997, pp. 1151–1159. doi:10.1109/59.630455.11 [88] T. Gorien, Distribution-system planning using mixed-integer programming 128 (2) (1981) 70–79. 11 [89] S. Abapour, K. Zare, B. Mohammadi-ivatloo, Dynamic planning of distributed generation units in active distribution network 9 (2015) 1455–1463. doi:10.1049/iet-gtd.2014.1143.11,12,71 [90] J. Partanen, A modified dynamic programming algorithm for sizing, locating and timing of feeder reinforcements, IEEE Transactions on Power Delivery 5 (1) (1990) 277–283. doi:10.1109/61.107285.12 [91] A. Barin, L. F. Pozzatti, L. N. Canha, R. Q. Machado, A. R. Abaide, G. Arend, Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning, International Journal of Electrical Power & Energy Systems 32 (10) (2010) 1157–1164. doi:10.1016/j.ijepes.2010.06. 015.12,71 39 [92] K. Engels, H.-J. Haubrich, Probabilistic evaluation of voltage stability in MV networks, in: 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134), Vol. 4, IEEE, 2000, pp. 2075–2080. 12,71 [93] S. Koopmann, S. Nicolai, A. Schnettler, Multifunctional operation of a virtual power plant in an active distribution grid: Modelling approaches and first field test experiences from the SmartRegion Pellworm project, in: IEEE PES Innovative Smart Grid Technologies, Europe, IEEE, 2014, pp. 1–6. doi:10.1109/ISGTEurope.2014.7028949.13,25,74 [94] Union of the Electricity Industry (EURELECTRIC), Decentralised Storage : Impact on Future Distribution Grids, Tech. Rep. june (2012). 13 [95] M. Sterner, F. Eckert, M. Thema, F. Bauer, Der positive Beitrag dezentraler Batteriespeicher f¨ur die stabile Stromversorgung, Tech. rep., FNES, OTH Regensburg (2015). 13,16,21,25 [96] Forum Network Technology / Network Operation in the VDE (FNN), Connecting and operating storage units in low voltage networks (2013). 13,14 [97] T¨ UV S¨ud, Grid compabitility certified by T ¨ UV S¨ UD (2015). 13 [98] K.-P. Kairies, D. Magnor, D. U. Sauer, Scientific Measuring and Evaluation Program for Photovoltaic Battery Systems(WMEP PV-Speicher), Energy Procedia 73 (2015) 200–207. doi:10.1016/j.egypro.2015.07.672.14,21 [99] H. Ibrahim, A. Ilinca, J. Perron, Energy storage systems - Characteristics and comparisons, Renewable and Sustainable Energy Reviews 12 (5) (2008) 1221–1250. doi:10.1016/j.rser.2007.01.023.14 [100] A. Poullikkas, A comparative overview of large-scale battery systems for electricity storage, Renewable and Sustainable Energy Reviews 27 (2013) 778–788. doi:10.1016/j.rser.2013.07.017.14 [101] G. L. Soloveichik, Battery Technologies for Large-Scale Stationary Energy Storage, Annual Review of Chemical and Biomolecular Engineering 2 (1) (2011) 503–527. doi:10.1146/annurev-chembioeng-061010-114116.15,66,72 [102] G. Corey, J. Eyer, Energy Storage for the Electricity Grid : Benefits and Market Potential Assessment Guide A Study for the DOE Energy Storage Systems Program, Tech. Rep. February, Sandia National Laboratories, New Mexico (2010). 15 [103] P. T. Moseley, J. Garche, Electrochemical Eenrgy Storage for Renewable Sources and Grid Balancing, 2015. doi: 10.1016/B978-0-444-62616-5.00010-3.15,17,18,29,47,65 [104] B. Battke, Multi-purpose technologies, lock-in and efficiency - Policy implications from the case of stationary electricity storage, Phd thesis, ETH Z¨urich (2014). 15 [105] H.-P. Beck, B. Engel, L. Hofmann, R. Menges, T. Turek, H. Weyer, Eignung von Speichertechnologien zum Erhalt der Systemsicherheit, Tech. rep., Energie-Forschungszentrum Niedersachsen, Gosslar (2013). 15,16 [106] G. Fuchs, B. Lunz, M. Leuthold, D. Sauer, Technology Overview on Electricity Storage, Tech. Rep. June (2012). 15,17 [107] F. Genoese, Modellgest¨utzte Bedarfs- und Wirtschaftlichkeitsanalyse von Energiespeichern zur Integration erneuerbarer Energien in Deutschland, Phd thesis, Karlsruhe (2013). 15 [108] H. Ibrahim, R. Beguenane, A. Merabet, Technical and financial benefits of electrical energy storage, in: Electrical Power and Energy Conference, EPEC 2012, 2012, pp. 86–91. doi:10.1109/EPEC.2012.6474985.15 [109] Consentec GmbH, Description of load-frequency control concept and market for control reserves, Tech. rep. (2014). 16 [110] R. Sioshansi, P. Denholm, T. Jenkin, Market and Policy Barriers to Deployment of Energy Storage, Economics of Energy & Environmental Policy 1 (2) (2012) 47–64. doi:10.5547/2160-5890.1.2.4.16 [111] M. Sterner, M. Thema, F. Eckert, A. Moser, A. Sch¨afer, T. Drees, Stromspeicher in der Energiewende, Tech. rep., Agora Energiewende, Berlin (2014). 16,27 [112] A. A. Akhil, G. Huff, A. B. Currier, B. C. Kaun, D. M. Rastler, S. B. Chen, D. T. Bradshaw, W. D. Gauntlett, Electricity storage handbook, Tech. Rep. July (2013). doi:SAND2013-5131.16 [113] S. Spiecker, P. Vogel, C. Weber, ¨ Okonomische Bewertung von Netzengp¨assen und Netzinvestitionen, uwf 17 (2009) 321–331. 16 40 List of Figures 1 Distribution grid schematic [202,17](adapted) .......................... 48 2 LV grid reinforcement via a parallel line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3 LV grid reinforcement via an additional secondary substation . . . . . . . . . . . . . . . . . . 50 4 MV grid reinforcement via a parallel line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5 MV grid reinforcement via an additional MV ring . . . . . . . . . . . . . . . . . . . . . . . . . 52 6 MV grid reinforcement via an additional primary substation . . . . . . . . . . . . . . . . . . . 53 7 Operating strategy direct loading (generator perspective) . . . . . . . . . . . . . . . . . . . . 54 8 Operating strategy schedule mode (generator perspective) . . . . . . . . . . . . . . . . . . . . 55 9 Operating strategy peak-shaving (generator perspective) . . . . . . . . . . . . . . . . . . . . . 56 10 Prognosis based operating strategy (generator perspective) . . . . . . . . . . . . . . . . . . . 57 11 Relation between frequency deviation and provided primary control reserve . . . . . . . . . . 58 12 Starting and deployment times of primary (PCR), secondary (SCR) and tertiary control reserve(TCR)............................................. 59 13 Degree of freedom “optional overfulfillment” . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 14 Degreeoffreedom“dead-band”................................... 61 15 Degree of freedom “schedule transactions” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 16 Schematic SOC profile for “schedule transactions”. . . . . . . . . . . . . . . . . . . . . . . . . 63 17 Degree of freedom “permissible operating range” . . . . . . . . . . . . . . . . . . . . . . . . . 64 18 Statistical requests of PCR power in the UCTE grid [103](adapted) .............. 65 47 Grid expansion Load and generation forecast Solution Performance criteria fulfilled? Change system configuration Acceptable costs No YesYes No No Yes Fig. 1. Distribution grid schematic [202,17] (adapted) 48 Grid extension: parallel cable over half of the feeder MV-grid Over-loading Over-voltage G G G G MV-grid G G G G MV-grid G G G G MV-grid G G G G Grid extension: parallel cable over 2/3 of the feeder Cabinet Joint Additional cable Critical current Critical voltage Fig. 2. LV grid reinforcement via a parallel line 49 Grid extension for both cases: new MV/ LV substation MV-grid Over-loading Over-voltage Cabinet Joint Grid extension G GGG G GGG G GGG G GGG MV-grid MV-grid G GGG G GGG Critical current Critical voltage Fig. 3. LV grid reinforcement via an additional secondary substation 50 HV-grid Over-loading Over-voltage Grid extension: parallel cable to DG Grid extension: parallel cable over 2/3 of the feeder Additional cable G GHV-grid G G HV-grid G GHV-grid G G Additional breakerCritical current Critical voltage Fig. 4. MV grid reinforcement via a parallel line 51 HV-grid Over-loading Over-voltage Grid extension: parallel cable to DG + new MV ring G GHV-grid G G HV-grid G G Additional cable Additional breaker Critical current Critical voltage Fig. 5. MV grid reinforcement via an additional MV ring 52 HV-grid Over-loading and over-voltage Grid extension: new substation G GGG G G G G HV-grid G GGG G G G G HV-grid Additional breakerCritical current Critical voltage Fig. 6. MV grid reinforcement via an additional primary substation 53 -0,2 0,0 0,2 0,4 0,6 0,8 1,0 036912151821 Pres / PrDG Time [h] E feed-in E import E battery, charge E battery, discharge P res P res with battery Fig. 7. Operating strategy direct loading (generator perspective) 54 -0,2 0,0 0,2 0,4 0,6 0,8 1,0 0 3 6 9 12151821 Pres / PrDG Time [h] E feed-in E import E battery, charge E battery, discharge P res P res with battery Fig. 8. Operating strategy schedule mode (generator perspective) 55 -0,2 0,0 0,2 0,4 0,6 0,8 1,0 036912151821 Pres / PrDG Time [h] E feed-in E import E battery, charge E curtailment E battery, discharge P res P res with battery Fig. 9. Operating strategy peak-shaving (generator perspective) 56 100 8:00 Time [h] SOC [%] 8:15 8:30 8:45 9:00 9:15 9:30 9:45 Schedule transaction Critical SOC Fig. 16. Schematic SOC profile for “schedule transactions”. 63 0 ±100 t1 Time [s] Offered power [%] t1+30s t2 t2+30s ±50 Permissible operating range Minimum requirement Fig. 17. Degree of freedom “permissible operating range” 64 Provided active power/ nominal power [%] -100 -80 -60 -40 -20 0 20 40 60 80 100 Empirical probability [%] 10-6 10-5 10-4 10-3 10-2 10-1 100 101 102 Fig. 18. Statistical requests of PCR power in the UCTE grid [103] (adapted) 65 List of Tables 1 Equipment load factors [17] ..................................... 67 2 Diversity factors for generators connected in MV or LV [7,17,53,54] ............. 68 3 Coincidence factors for loads connected in LV and MV [17,55] ................. 69 4 Standard equipment for grid extension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5 New distribution grid planning approaches with DG integration . . . . . . . . . . . . . . . . . 71 6 Energy and power related applications for BSS [101] ....................... 72 7 Potential benefit estimations for the German electricity market in 2014 . . . . . . . . . . . . . 73 8 Overview of recent large scale BSS projects to maximise self-consumption and peak shaving inGermany.............................................. 74 9 Key parameters for the provision of primary control reserve [27,226,227] ........... 75 10 Overview of recent large scale BSS projects for primary frequency control in Germany, based on [121] and contact with the BSS owners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 11 Diversity factors for BSS applied for SC and PCR . . . . . . . . . . . . . . . . . . . . . . . . 77 66 Table 1. Equipment load factors [17] Equipment Load factor of SrLoad factor of Sr Heavy load flow Reverse power flow LV-cable max. 100 % max. 100 % MV/LV tran. max. 100 % max. 100 % MV-cable max. 60 % max. 100 % HV/MV tran. max. 60 % max. 100 % 67 Table 2. Diversity factors for generators connected in MV or LV [7,17,53,54] Wind PV BM Water 0 [17] 0 [7,17,53,54] 0 [17] 1 [17] HLF 0.6 [7] 0.95 [7] 0.85[17,53,54] 0.98[7] 1[17] RLF 1 [17] 0.89 [7] 1 [17] 68 Table 3. Coincidence factors for loads connected in LV and MV [17,55] Load (LV) Load (MV) C. load (MV) HLF 1 [17] 1 [17] 1 [55] RPF 0.1 [17] 0.15 [17] 0.5 [55] 69 Table 4. Standard equipment for grid extension Equipment dena [17] Stetz et al. [62] Idlbi et al. [64] Ackermann et al.[30] LV-cable (NAYY) 4x150 mm2(3x150; 3x240) mm24x150 mm24x150 mm2 MV/LV tran. (Sr,t) 630 kVA (400; 600; 800) kVA (400; 600; 800; 1000) kVA 630 kVA MV-cable (NA2XS2Y) 3x1x185 mm2- - 3x1x240 mm2 HV/MV tran. (Sr,t) 40 MVA - - - 70 Table 5. New distribution grid planning approaches with DG integration without reliability with reliability under with reliability under normal conditions contingency conditions deterministic uncertain deterministic uncertain deterministic uncertain [73]b, [77]c, [76]c, [72]a,b [75]b,z, [91]y[85]e, [83]e[82]d,z [74]e,z mixed integer [78]d, [86]f, [63]e, [71]a,d [92]y[84]e[79]d,z - [80]d,z continuous [72]h, [71]h[89]g,z - - [81]daMILP, bMINLP, cBD, dGA, ePSO, fES, gDP, hNLP, ypossibilistic, zprobabilistic 71 Table 6. Energy and power related applications for BSS [101] Application Nominal power P Energy related: Peak shaving 0.1 MW to 10 MW Load levelling 1 MW to 100 MW Energy arbitrage 50 MW to 500 MW Power related: Frequency control 1 MW to 30 MW Voltage regulation 1 MW to 30 MW Power quality regulation 1 MW to 30 MW Bridging power 1 MW to 30 MW 72