Evaluation of Two Distribution Grids in Terms of PV Penetration Limits and Effectiveness of Reactive Power Controls

DEGREE PROJECT IN ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2016

Evaluation of two distribution grids in terms of PV penetration limits and effectiveness of reactive power controls

LEONARD HÜLSMANN

KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING

Evaluation of two distribution grids in terms of PV penetration limits and effectiveness of reactive power controls

Leonard Hülsmann

Master thesis No. TRITA-EE 2016:170, 2016 KTH Royal Institute of Technology EIT-KIC Energy for Smart Cities Department of Electrical Engineering SE-100 44 Stockholm, Sweden Abstract The large integration of photovoltaic (PV) systems as well as wind turbines in the distribution grid poses new challenges on various levels. One of them is the increased steady-state voltage in the low voltage (LV) as well as the medium voltage (MV) grid, caused by the distributed generation. This can lead to voltage violations above the maximum allowed value of 110% of nominal voltage. There exist various approaches to mitigate this problem. These range from expensive grid reinforcements, like strengthened lines or the installation of on-load tap changers (OLTCs) in distribution transformers, to voltage controls by the distributed generators themselves. One of these voltage controls is the consumption of reactive power by PV inverters. This control can lower the voltage locally but also has an impact on other nodes of the network. A range of possibilities exist, most commonly using an active power dependent reactive power characteristic (cos φ (P) control) or using a voltage dependent reactive power characteristic (Q(U) control). These can either depend on local inputs or involve some kind of coordinated control, for which usually a communication infrastructure needs to be built up.

In this thesis, two MV feeders located close to Worms, Germany, have been modelled and validated with measurement data provided by the local distribution system operator. Along the two feeders, a total of three low voltage grids has been modelled in detail. The effectiveness of a local Q(U) voltage control with a deadband has been compared with the worst case, where no reactive power control is applied, and with the best case, where all PV plants maximally participate in the reactive power control. These settings were then tested for different scenarios of increased PV shares. Furthermore, the impact of the Q(U) voltage control on other LV grids, located at the same feeder, has been analysed.

The results, in conclusion, demonstrate: i) In general, the examined LV grids have already large PV penetrations and large resulting backflows during times of high PV output. However, the share can still be substantially increased, in particular on LV lines that currently have only low or moderate PV penetration; ii) The Q(U) with deadband regulation can increase the hosting capacity of PV plants typically between 20% and 33% for the investigated networks. The performance can however vary highly between different network configurations and PV distributions; iii) due to the Q(U) control, the reactive power support varies widely between different PV plants in the investigated grids. This decreases the fairness of this method and compensation measures might be needed. However, no quantification of this has been made; iv) The best case shows that good reactive power controls would have a very high potential, so e.g. coordinated voltage controls could significantly increase hosting capacities.

Sammanfattning En stor integration av solceller (PV) och vindkraftverk i distributionsnät innebär nya utmaningar på olika nivåer. En av dem är en ökande spänning i lågspänningsnät (LV) samt på mellanspänning (MV), orsakad av distribuerad generering. Detta kan leda till spänningar över högsta tillåtna värde av 110% av nominell spänning. Det finns olika sätt att mildra detta problem. Dessa sträcker sig från dyra nätförstärkningar, som förstärkta linjer eller installation av lindningskopplare (OLTCs) i distributionstransformatorer, till spänningsstyrning av de distribuerade generatorerna. En av dessa spänningsstyrningar är förbrukningen av reaktiv effekt i solcellernas växelriktare. Denna styrning kan sänka spänningen lokalt men den har också en inverkan på andra noder i elnätet. En rad möjligheter finns, oftast med hjälp av en aktiv- effekt-beroende reaktiv effekt (cos φ(P)-styrning) eller med hjälp av en spännings- beroende reaktiv effekt (Q(U)-styrning). Dessa kan antingen styras med hjälp av lokala insignaler eller innebära någon form av samordnad styrning, som vanligtvis medför att en kommunikationsinfrastruktur måste byggas upp. I denna avhandling har två MV-nät, vilka ligger nära Worms, Tyskland, modellerats och validerats med mätdata som tillhandahållits av det lokala distributionssystemets operatör. Längs de två MV-näten, har totalt tre lågspänningsnät modellerats i detalj. Effektiviteten av en lokal Q(U)-styrning med en död-zon har jämförts med det värsta fallet, där ingen reaktiv effektstyrning tillämpas och med bästa fall, där alla PV-omriktare maximalt har deltagit i den reaktiva effektstyrningen. Dessa inställningar testades sedan för olika scenarier för ökad mängd PV. Dessutom har effekterna av Q(U)-styrning på andra LV-nät, som ligger på samma MV-nät, analyserats. Resultaten visar: i) Generellt har de undersökta LV-näten redan stora PV- installationer och stor resulterande back-matning under tider med hög PV-produktion. Mängden PV kan dock fortfarande ökas väsentligt, framför allt på LV-linjer som för närvarande endast har låg eller måttlig mängd PV.; ii) Q(U)-styrning med dödband kan öka möjlig mängden PV med ca 20 - 33% för de undersökta elnäten. Möjlig ökning kan dock variera mycket mellan olika elnät och olika pridning av PV; iii) på grund av den studerade Q(U)-styrningen så varierar den reaktiva effektproduktionen kraftigt mellan olika PV-anläggningar i de undersökta elnäten. Detta minskar en rättvis fördelning med denna metod och kompensations-mekanismer kan behövas. Emellertid har ingen kvantifiering av detta gjorts, iv) Det bästa fallet visar att bra reaktiv effekt-styrning skulle ha en mycket hög potential, så samordnad spännings-styrning skulle avsevärt kunna öka möjlig mängd PV.

TABLE OF CONTENTS

1. Introduction ...... 8 1.1. Problem definition ...... 8 1.2. Objectives ...... 9 1.3. Overview of the report ...... 9 2. presentation of the field ...... 10 2.1. Description of passive distribution networks ...... 10 2.2. Impact of distributed generation on the voltage profile in the distribution grid .. 11 2.3. Regulations on steady-state voltage control ...... 13 2.3.1. Voltage limitations ...... 13 2.3.2. Regulations for generators connected to the MV and LV grid ...... 14 2.4. Voltage control options in distribution networks ...... 15 2.4.1. Novel assets and grid planning principles ...... 15 2.4.2. Active power control ...... 17 2.4.3. Reactive power control by distributed generators ...... 18 2.4.4. The role of communication infrastructure ...... 22 2.5. Techno-economical comparison of these voltage control options ...... 22 3. Model description ...... 24 3.1. Location and setting ...... 24 3.2. Gundersheim substation model ...... 26 3.3. Overview about available and simulated data ...... 27 3.4. Case study #1 – Feeder ‘Gundersheim’ ...... 28 3.4.1. General information ...... 29 3.4.2. Grid parameters ...... 30 3.4.3. Distributed generation ...... 32 3.4.4. Available measurement data ...... 32 3.4.5. Load modelling ...... 34 3.4.6. PV modelling ...... 37 3.4.7. PowerFactory implementation ...... 39 3.5. Case study #2 – Feeder ‘Flörsheim-Dalsheim’ ...... 41 3.5.1. General information ...... 41 3.5.2. Grid parameters ...... 42 4

3.5.3. Distributed generation ...... 45 3.5.4. Available measurement data ...... 45 3.5.5. Load modelling ...... 46 3.5.6. PV modelling ...... 47 3.5.7. Wind farm modelling ...... 47 3.5.8. PowerFactory implementation ...... 48 3.6. Limitations of the model ...... 49 4. Model validation ...... 50 4.1. Case study #1 – Feeder Gundersheim ...... 50 4.1.1. Active power output at SUS’s ...... 50 4.1.2. Active power output at the feeding point ...... 55 4.1.3. Reactive power output at SUS’s and feeding point ...... 57 4.1.4. Voltage output at SUS’s ...... 60 4.1.5. Reactive power output of large scale PV plant ...... 62 4.1.6. Advanced OLTC control at the primary substation ...... 62 4.2. Case study #2 – Feeder Flörsheim-Dalsheim ...... 63 4.2.1. Active power output at SUS’s ...... 63 4.2.2. Active power output at the feeding point ...... 64 4.2.3. Reactive power output at SUS’s and feeding point ...... 65 4.2.4. Voltage output at SUS’s ...... 67 4.3. Limitations of the model validation...... 68 5. Scenario Descriptions...... 70 5.1. Case study #1 – Freimersheim03 ...... 70 5.1.1. Worst case scenario description ...... 70 5.1.2. Comparison with highest observed voltages ...... 73 5.1.3. Description of PV penetration scenarios and possible reactive power controls 73 5.2. Case study #2 – Monsheim10 & Offstein09 ...... 75 5.2.1. Worst case scenario description ...... 75 5.2.2. Comparison with highest observed voltages ...... 76 5.2.3. Description of PV penetration scenarios and possible reactive power controls 77 6. Results ...... 78

6.1. Freimersheim 03...... 78 6.1.1. Tap changer position ...... 78 6.1.2. Increase of PV penetration – Scenario #1 ...... 79 6.1.3. Increase of PV penetration – Scenario #2 and #3 ...... 80 6.2. Monsheim 09 ...... 82 6.2.1. Increase of PV penetration – Scenario #1 ...... 82 6.2.2. Increase of PV penetration – Scenario #2 and #3 ...... 83 6.3. Offstein 07 ...... 84 6.3.1. Increase of PV penetration – Scenario #1 ...... 84 6.3.2. Increase of PV penetration – Scenario #2 and #3 ...... 85 6.4. Conclusion on Q(U) control ...... 87 6.5. Conclusion on the best case ...... 87 7. Conclusion ...... 89 8. Future Work ...... 91

Abbreviations and Symbols cap. Capacitive (also called “leading power factor” or “under-excited”) cosphi Power factor DG Distributed generation DSO Distribution system operator HV High voltage ind. Inductive (also called “lagging power factor” or “over-excited”) LV Low voltage MV Medium voltage NLTC No-load tap changer NRMSE Normalized Root Mean Square Error OLTC On-load tap changer P Active power PCC Point of Common Coupling PF Power factor PFC Power factor control pu Per unit PV Photovoltaic Q Reactive power RES Renewable energy sources RLM German: Registrierende Leistungsmessung, recorded power measurement RMSE Root Mean Square Error RPC Reactive Power Control SLP Standard Load Profile SNOOPI Smart Network Control with Coordinated PV Infeed SUS Secondary Unit Substation (between MV and LV)

1. INTRODUCTION

In the past decade, the share of renewable energy sources (RES) in the electricity power grid has risen sharply, in particular in countries with extensive support schemes such as Germany. The higher share of RES does not only affect the grid on a system- wide level due to the intermittency of RES – a topic that has been much discussed in research as well as media – but also on the local level. Smaller wind turbine parks and photovoltaic (PV) parks are usually connected to the medium voltage (MV) grid, while small scale PV installations on rooftops are typically connected to the low voltage (LV) grid. As a result, more than 70% of all RES in Germany is currently connected to the distribution grid (MV & LV) [1]. This poses new challenges on the operation of distribution grids: Voltage and power quality needs to be ensured, reverse power flows must be handled and line and transformer loadings must not exceed their limits. Of all these issues, overvoltage in the LV grid is often one of the most important limitations caused by the infeed from PV [2], [3]. Therefore, several studies and experiences of distribution system operators (DSOs) indicate that the PV hosting capacity1 in some regions is already reached [4]. This thesis was conducted as part of the SNOOPI project, Smart Network Control with Coordinated PV Infeed [5]. This is a collaboration between two German companies (Energynautics GmbH & EWR Netz GmbH), an Austrian company (Fronius International GmbH), and KTH Royal Institute of Technology. The goal of this project is to find new solutions to install more PV in the LV grid while avoiding voltage instabilities and overloading.

1.1. Problem definition

In the distribution grid of a traditional power grid without any connected RES the voltage decreases linearly from the source to the load. The source is the primary substations that connects the distribution grid to the transmission grid while the loads are the different customers such as households and industries. Distributed generation (DG), mainly in the form of PV, raises the voltage at its respective location. This is initially a positive effect as it counteracts the voltage decline but a large generation can lead to the opposite effect, namely overvoltage. To limit the voltage increase, appropriate measures have to be taken. There exist numerous options on how to achieve this and this thesis will evaluate some of these options in terms of their effectiveness.

1 The hosting capacity is the maximum amount of decentralized generation, that can be added typically to a low voltage network without endangering the proper operation of the network. 8

1.2. Objectives

This study looks at two feeders in the distribution grid. The aim is to create a model of the MV grid of these two case studies and to a lesser degree also of parts of the LV grid. Using measurement data provided by the local DSO, EWR Netz GmbH, the model should then be validated by comparing the model outputs with the measurement outputs. The successfully tested model should then be used to assess how the RES affect the steady-state voltage profile of the two feeders currently and how much more RES could be added with and without any voltage control strategies. This could then prove to be useful both from a research point of view as well as from the perspective of the DSO as it gives him a better assessment of his controlled area.

1.3. Overview of the report

First, a literature review on static voltage control will be given in Chapter 2. After that, Chapter 3 will describe the model on the basis of the two case studies. Subsequently, the model will be validated in Chapter 4 for the two feeders, respectively. In Chapter 5 the two feeders are then analysed in terms of their characteristics and how much more DG could be connected depending on any reasonable control options. Lastly, Chapter 6 will give a conclusion of the work.

2. PRESENTATION OF THE FIELD

This section will treat the general problems that DG, mainly induced by PV and wind turbine instalments in recent years, has on the steady-state voltage level in distribution grids. To counter these impacts a variety of different strategies and technologies exist that will be described and explained. Some of these have been the traditional way of DSOs to deal with DG while others have been fairly new and commercial applications have been only rarely implemented so far. The recent state of the art will be presented on what is achievable as well as what has been carried out within simulations and field tests.

2.1. Description of passive distribution networks

Typical, passive distribution networks – that is networks that have no DG connected – are usually build up in the following way: The primary substation connects the MV distribution network to the HV transmission network and a number of feeders are connected to this substation. Each of these feeders is in general radial because the protection equipment (e.g. circuit breakers) is designed for this case. As is often the case the lines are connected in loops but these are deactivated by switches so that no loop flows can occur. This is a design feature in order to improve reliability so that a continuous power supply can be ensured when a line is damaged or needs to be maintained. Loads in these radial distribution networks are usually mostly resistant, i.e. the power factor (PF) has a value close to 1. Customers such as big industries, that are connected to the MV, are required to operate within a power factor range of 0.9 inductive to 0.9 capacitive [6]. Larger deviations need to be compensated. Furthermore, lines and transformers have resistive losses and consume/produce reactive power Q because of their reactance. The voltage drop caused by a load can be approximated by the following equation: 푅 ⋅ 푃 + 푋 ⋅ 푄 (2.1) 훥푈 ≈ 푙표푎푑 푙표푎푑 푈푁

With ΔU Voltage change across the line R Resistance of the line Pload Active power consumption of the load (negative) X Reactance of the line Qload Reactive power consumption/injection of the load (negative/positive) UN Nominal voltage 10

The active power consumed by the load results in a negative P while Q is close to zero (since PF ≈ 1). Therefore, the voltage drops depending on the size of the load and the resistance of the line. MV grids have thicker cables with lower resistances. These have therefore smaller voltage drops compared to LV cables, when the same load is applied. Overall, the voltage in passive distribution networks decreases monotonically from the primary substation towards the end of the feeder.

2.2. Impact of distributed generation on the voltage profile in the distribution grid

Because of the continuous expansion of primarily PV instalments and wind turbines, many distribution grids have already nowadays a high share of DG that often leads to backflows in the grid, i.e. from the installed renewable energy generators towards the primary substation. This has also an impact on the voltage profile. The additional active and reactive power generation at the end of a line is added to equation 2.1, leading to equation 2.2: 푅 ⋅ (푃 + 푃 ) + 푋 ⋅ (푄 + 푄 ) (2.2) 훥푈 ≈ 푙표푎푑 퐷퐺 푙표푎푑 퐷퐺 푈푁

With PDG Active power injection of the DG (positive) QDG Reactive power consumption/injection of the DG (negative/positive) The active power injection of the DG adds a positive term to the negative active power consumption of the load. If the active power injection of DG on a line surpasses the active power consumption by loads, the power flow reverses. Hence, (푃푙표푎푑 + 푃퐷퐺) becomes positive. If Q is neglected a voltage rise across the line can be observed. However, the reactive power consumption/injection of DG can be utilized. Loads are in general operated at a power factor very close to 1 but PV and wind turbines are usually connected through an inverter. An inverter can adjust its active and reactive power output more or less freely only limited by the DC power of the PV/wind turbine and the nominal power/current limit of the inverter. Hence, the DG unit can either be operated inductive, resistive or capacitive2 (≙ upper, middle, lower operation point in Figure 2-1).

2 Please note: For a generator the terms “capacitive”, “leading power factor” and “under-excited” can be used interchangeably. In all cases it is meant that the generator consumes reactive power Q. To avoid confusion, the term “consuming Q” or “consuming VAr” is used as much as possible. The same applies to the interchangeable terms “injecting Q or VAr”, “inductive”, “lagging power factor” and “over-excited” for a generator. 11

Figure 2-1: Inverter limitations by current limit (red line) and DG output (black line) and possible operation points

A DG unit with a capacitive PF consumes reactive power, so QDG becomes negative and the total voltage rise is reduced. An inductive PF (Q injecting) on the other hand increases ΔU. It is therefore desired to operate DG in capacitive (reactive power consuming) mode in order to limit the voltage rise. Figure 2-2 shows the effect of the different DG settings.

Figure 2-2: Impact of DG on the voltage profile of a power line The effectiveness of reactive power consumption depends very much on the R/X ratio. In MV grids the R/X ratio is typically around 1, so active and reactive power can impact the voltage change in equal manner. In LV networks however, R is usually much greater than X, leading to R/X ratios of up to 10 e.g. in the modelled grids of this thesis. Therefore, each kVAr can only compensate 10% of the voltage rise of each kW.

2.3. Regulations on steady-state voltage control

2.3.1. Voltage limitations The European standard EN 50160 stipulates that the voltage in distribution grids is to be kept between 90% and 110% of its nominal value during 95% of the supplied time [7]. This voltage band is further divided into different sections, defining the respective voltage drop or rise across the MV grid, the MV/LV transformer and the LV grid. A common allocation across European countries is seen in Figure 2-3.

Figure 2-3: Voltage band allocation based on current planning practices [8], [9] EWR Netz GmbH defines the allowable voltage rise for DG in their grid as follows: - Voltage rise for DG (MV): 3% - Voltage rise across transformer: 2% - Voltage rise for DG (LV): 3%

However, this allocation can be inflexible as the entire voltage band up to 110% Unominal as well as down to 90% Unominal is not fully used. For example, the share of MV connected DG might be very low, so a higher share of the voltage band could be available for the LV connected DG. Furthermore, the OLTCs voltage can be somewhat lowered during times of high DG, making also a larger voltage band accessible. Future voltage control options will likely make the grid operation more flexible. Due to this reason the voltage limitation in this thesis has been always set to the upper voltage band limit, 110% Unominal. The other strategy could be to limit the LV and MV connected DG to respectively 3% as commended by the local DSO. However, this limits strongly

13 the hosting capacity of DG and it is also likely that the concept of voltage band allocation is going to be revised in the future. 2.3.2. Regulations for generators connected to the MV and LV grid In Germany, generation units connected to the MV and the LV grid are already since 2008 and 2012, respectively, required to be involved in maintaining static voltage stability [10], [11]. 2.3.2.1. Reactive power capability The two regulations stipulate that generators are able to provide a power factor at the point of common coupling (PCC) of cos φ = 0.95underexcited to 0.95overexcited for LV connections ≤ 13.8 kVA and MV connections cos φ = 0.90underexcited to 0.90overexcited for LV connections > 13.8 kVA Several options are available: - fixed power factor cos φ - active power dependent power factor cos φ(P) - fixed reactive power Q - voltage dependent reactive power Q(U) The practical utilization of any of these control options is however up to the local DSO. The functionality of these controls will be explained in the next chapter. However, with these regulations Germany is ahead of other European countries. In 2013, [12] reported that Italy, Spain and the Czech Republic had no regulations yet in force for reactive power control by PV inverters. According to [12], such regulations are under way though. 2.3.2.2. Three phase connection [11] further sets a limit to 4.6 kVA single phase installations in LV networks. Larger installations need to be three phase connected. This is very important because unsymmetrical infeed only increases the voltage of one phase while not on the others. This can strongly limit the effectiveness of voltage control strategies [8]. Other European countries have already put similar regulations into place or consider doing it [13]. 2.3.2.3. 70% regulation Furthermore, the German Renewable Energy Act from 2012 [14] sets a static cap for small scale (< 30 kWpeak) PV installations. This stipulates a maximum feed-in of 70% of the maximum PV capacity into the grid in order to ensure a stable grid operation. Hence, at full output, 30% must at least be self-consumed. Alternatively, the PV plant owner can decide that the DSO is allowed to limit his active power feed-in via a remote control interface if a safe grid operation requires it.

2.4. Voltage control options in distribution networks

The traditional way of dealing with voltage problems are investments into power grid equipment by the DSO. Increased loads as well as generation can be countered with strengthened cable connections, either by installing parallel lines or replacing existing cables by new ones with higher capacity. Grid reinforcements like these are expensive though and they also require a continuous coordination between DG expansion and power grid upgrades. It is therefore preferable to look for alternatives where voltage control can be achieved in a more economical and, if possible, also easier scalable way. In this chapter, alternative approaches and their current state of the art will be presented. 2.4.1. Novel assets and grid planning principles This part deals with some technologies that were before only used in transmission networks and that now are redesigned for distribution grid purposes. Also some new grid planning approaches have been suggested. 2.4.1.1. VAr control One option is to install reactive power absorbing devices. There exist various technologies such as the static synchronous compensator (STATCOM), the static VAR compensator (SVC) or hybrid solutions. With these it is possible to influence ΔQ in equation 2.2 and therefore control the voltage. Due to their high costs they are however mostly only used in transmission networks. Further research and cost reduction might however make them applicable to some distribution grids. 2.4.1.2. Advanced OLTC control of the HV/MV transformer Typically, the HV/MV transformer, that connects the distribution to the transmission grid, has an on-load tap changer in order to maintain a constant voltage level at the MV busbar. However, it is possible to set the voltage set point below nominal value during times of high generation induced by the RES. This can pose a problem though if the DG is unequally distributed. For example, on one feeder there might be a lot of generation while at another one predominantly loads (due to the lack of DG capacity or lack of DG output because of disparate cloud cover). In this case, a reduced voltage level at the feeding substation could lead to under-voltage in the latter feeder. These potential problems are often incorporated by worst case assumptions, which limits the full utilization of this method. It is possible to improve this by monitoring the voltage at various points in the grid. This poses however also additional costs and is also not achievable in a straightforward way. Another option is to use simulation tools such as the one presented in this thesis, in order to determine possible voltage violations. Still, this option offers only a temporary solution as only a small part of the voltage rise can be compensated.

2.4.1.3. OLTC for MV/LV transformer The transformer connecting the LV grid to the MV grid is commonly operated via a no- load tap changer (NLTC). These tap changers have a fixed tap position than can only be changed manually on-site and the transformer needs to be disconnected for a short time. On-load tap changers (OLTCs) offer the premise of operating the supplied LV network largely independently from the MV grid. During high infeed, the tap changer setting can be set to below nominal voltage. Contrary, at times of high consumption the voltage is set above nominal voltage. Even so, similar issues arise as in the case of the advanced OLTC control: Unequally distributed loads and PVs can result into large voltage ranges within the same grid and effectively limit the operation of the OLTC. Additionally, a larger voltage range within the MV grid can be allowed. This requires however a refurbishment of all or most transformers alongside the feeder. Higher investment and operation costs for OLTCs are the crucial market entry barriers for OLTCs right now: In [15] (German), the prices for OLTC transformers are quoted with three to four times the price of an NLTC transformer (prices in 2015). The DSO involved in this project quoted a price of 30 000€ per OLTC transformer which is also approximately a factor of four times as expensive. In spite of that, [16] assessed that OLTCs would have the highest technical potential for increasing the PV hosting capacity in 40 real LV grids. Thus, it provides a viable solution for very high PV penetration scenarios. 2.4.1.4. Closed loop and meshed operation Another option is to operate the distribution in a meshed way as is done on the high voltage transmission level. Protection devices in the distribution grid are currently designed for radial systems, though, so they would need to be upgraded which comes with its own costs and safety aspects. 2.4.1.5. Extended grid planning and wide area voltage control The grid operator has the possibility to use extended grid planning approaches. This means he assigns more resources and people to analyse different distribution grids in greater detail. Often, the grid is designed for worst case scenarios, meaning that the safety margins are kept high. By installing e.g. additional measurement equipment and assigning people to analyse the resulting data, more information is gathered about the system and uncertainties are diminished. That allows the DSO to operate its grid closer to its physical or allowable boundaries. Also the instalment of smart meters across Europe is a promising development that gives DSOs better monitoring possibilities. However, the instalment of measurement devices and expanded monitoring and analysis increases significantly the capital and operational expenditures of the DSO.

2.4.2. Active power control A straightforward way to reduce the voltage rise in the distribution grid is by reducing the active power output of DG in one of the following ways. 2.4.2.1. Batteries Batteries can store energy when DG infeed and the consequential voltage rise is high in the local grid. Furthermore, they can also provide voltage support against under-voltage by injecting active power when local demand is high. Furthermore, batteries can also be utilized for nation-wide storage. Therefore, batteries can store or inject power as a function of frequency and local voltage. There exist many studies showing this functionality, e.g. [17]. The cost investments in electrical batteries to control the voltage rise are still very high, so curtailment of DG units (see below) offers currently the cheaper alternative [18], [19]. 2.4.2.2. Demand side management Similarly, intelligent load management can utilize flexible loads in order to increase/decrease load consumption during times of high/low voltage or high/low frequency. So far, these concepts have been restricted to large battery system and large (industrial) loads and primarily for frequency control. Regulatory frameworks for voltage control still need to be developed and small customers need to be able to participate in this market (e.g. through so-called virtual power plants). 2.4.2.3. Voltage dependent curtailment of distributed generators The 70% static cap for small scale PV plants, mentioned in Chapter 2.3.2, is a rather unintelligent control. However, a voltage dependent active power control can restrict the power output only in times of over-voltage, effectively increasing the yearly amount of PV generated. In this Volt/Watt control, the active power is gradually decreased, if a certain voltage threshold is exceeded, with the DG unit switching completely off at 110% Unominal. An example is shown in Figure 2-4.

Figure 2-4: Exemplary characteristic for a P(U) control

With this control, the PV hosting capacity of a distribution grid is effectively infinity, as the PV plants will shut down in case of over-voltage. Therefore, economical analyses have been made in several studies. [16] shows for example that the hosting capacity can be increased by 40% implying a power curtailment of less than 1% of the annual yield when considering all installations in the examined LV grid. However, care should be taken as this control discriminates PV plants at technically unfavourable points where often over-voltages occur. These would need to be compensated for which a regulatory framework is still missing [16]. Finally, this control can easily be supplemented by frequency dependent curtailment that reduces the power output during times of system-wide excess production. 2.4.3. Reactive power control by distributed generators In Chapter 2.2 the impact of reactive power control on the voltage level had already been briefly described. This part shall give a more detailed explanation as this thesis will deal with the potential of reactive power controls more in detail. As mentioned before the voltage change across a line can be described by the following equation: 푅 ⋅ (푃 + 푃 ) + 푋 ⋅ (푄 + 푄 ) Δ푈 ≈ 푙표푎푑 퐷퐺 푙표푎푑 퐷퐺 (2.3) 푈푁 This voltage change is made up of the voltage drop induced by the load and the voltage increase induced by the DG unit. The latter can be described separately by

푅 ⋅ 푃퐷퐺 + 푋 ⋅ 푄퐷퐺 Δ푈퐷퐺 ≈ (2.4) 푈푁 In this equation, the effectiveness of any reactive power control depends very much on the R/X ratio. As described in Chapter 2.3.2, generation units connected to the German LV and MV grid must be able to operate between 0.9/0.95 inductive and 0.9/0.95 capacitive. With a power factor of 0.95 cap., the resulting reactive power consumption is 33% of the active power, as shown by the following relationship:

푃 푆 = (2.5) 푃퐹 푆2 = 푃2 + 푄2 (2.6)

푃 2 Q = √( ) − 푃2 (2.7) 푃퐹

1 2 Q = 푃 ⋅ √( ) − 1 (2.8) 0.95

Q = 33% 푃 (2.9)

With P Active power (injected) Q Reactive power (consumed) S Apparent power PF Power factor

Imagine a scenario with an oversized inverter that can facilitate the additional reactive current and an R/X ratio of 1. In this case, one third of the voltage rise caused by the DG can be compensated by the reactive power consumption:

푅 = 푋 (2.10)

푄퐷퐺 = − 0.33 푃퐷퐺 (2.11)

푄 푐표푛푡푟표푙 푅 ⋅ 푃퐷퐺 − 푅 ⋅ 0.33 푃퐷퐺 Δ푈퐷퐺 = (2.12) 푈푁

푄 푐표푛푡푟표푙 푅 ⋅ 0.67 푃퐷퐺 Δ푈퐷퐺 = (2.13) 푈푁

푄 푐표푛푡푟표푙 푛표 푄 푐표푛푡푟표푙 Δ푈퐷퐺 = 67% ⋅ Δ푈퐷퐺 (2.14)

If the inverter is already at its nominal power before the Q control is applied, the active power needs to be decreased by 5%. This lowers the voltage further, but leads on the other hand to decreased revenues for the plant owner. In moderate climates such as Germany, PV inverters are usually undersized. Therefore, it can make economic sense to increase the size of the inverter in order to better utilize the reactive power control. [20] shows that the cost-optimal point is an increased size by up to 4.7%, depending on the reactive power control and considering a power factor of 0.95 capacitive (Q consuming). If, contrary to the above case, an LV cable with an R/X ratio of 5 is considered, the effectiveness of the reactive power control drops is diminished by a factor of 5. This means that the voltage can only be decreased by 6.5% for the above scenario. This is however only considering one line. If reactive power is also consumed in other, independent lines, they contribute to the voltage rise compensation across the transformer and effectively reduce the voltage at the critical line. This phenomenon will be discussed thoroughly in the results of this thesis. Therefore, most studies show a possible increase in hosting capacity of about 20 – 40% (e.g. [4], [8], [21], [22]).

There exist various possibilities to control the reactive power at the inverters. Common methodologies and their (dis-)advantages are as followed. 2.4.3.1. Static control - Constant Q With this method, a constant amount of reactive power is consumed, independent of the voltage or the power output of the DG unit. Hence, it unnecessarily increases the reactive currents in the grid, leading to higher network losses and a low PF at the primary substation [23]. - Constant PF A constant PF at 0.9 cap. or 0.95 cap is more suitable than the previous method as no or only little reactive power is consumed when the generation is low. However, it still provides reactive power when not needed, which decreases the revenues for the plant owner and increases the network losses. 2.4.3.2. Active power dependent control - cos φ (P) Making the power factor control dependent on the active power output is another improvement to the constant PF control. During low infeed the reactive power consumption is reduced while during high infeed the generation unit compensates some of its voltage rise. However, this control also springs into action when the voltage is not close to its upper limit. This might be often the case when for example clouds cover only part of the PV plants. Figure 2-5 shows a cos φ (P) characteristic with a deadband that starts consuming reactive power at 50% of nominal P. This is also the recommended characteristic from the current German directive [11].

Figure 2-5: Exemplary characteristic of an active power dependent power factor control cos φ (P)

2.4.3.3. Voltage dependent control - Q(U) This method consumes reactive power in function of the voltage (also called Volt/VAr control). For voltage levels across a certain threshold, e.g. above 1.07 p.u., the reactive power consumption is gradually increased. The higher the voltage, the higher is the compensating effect of this method. Additionally, it is possible to inject reactive power to increase voltage if it falls below a certain threshold. This is known as Q@night-capability [24]. In between those two thresholds a deadband can be applied. Figure 2-6 shows the resulting characteristic.

Figure 2-6: Q(U)-Characteristic, adapted from [25] The clear advantage of this control is that reactive power is only consumed when it is really necessary. Therefore, this method offers the lowest reactive power flows in the network, which also leads to the least associated losses in the distribution grid. This has to be weighed against the disadvantage, that the technical effectiveness is decreased because inverters across the LV grid experience different voltage magnitudes and contribute to the voltage rise compensation in various degrees. This results into the same problem as with the P(U) control: Inverters at unfavourable (weak) points of the system will experience a high voltage more often and need to consume reactive power more often. Therefore, their economic viability is decreased and they should be compensated for this grid support. However, also the Q@night-capability can offer new and interesting possibilities. Not only the upper voltage band could be better used by the Q(U) control but also the lower voltage band could be utilized to a higher degree. This could for example be achieved by operating the network at a slightly lower voltage. PV plants could then inject reactive power in order to raise that level, should the voltage drop below a certain threshold.

2.4.4. The role of communication infrastructure Utility scale PV systems typically come with a remote control interface that allows the DSO to transmit reactive power set values to the PV plant. However, typical applications for residential scale PV systems do usually not require any communication capability and focus instead on the above described autonomous and local control of reactive power [26]. The use of communication could significantly improve the utilization of these control options, though. For example, in the case of the Q(U) control other PV plants could also increase their reactive power consumption if they know that a nearby PV unit is experiencing a critical voltage close to 110% Unominal. The issue here is the cost/benefit ratio as the communication infrastructure comes with high up-front costs as well as privacy concerns. In any case, it is commended to keep the local control procedures as a backup setting at least for stable and maintenance free operation [27].

2.5. Techno-economical comparison of these voltage control options

A few studies have assessed the techno-economical potential of different voltage control options in order to increase the PV hosting capacity. For this, many aspects need to be taken into consideration: - Investment costs (e.g. grid reinforcement, OLTC installation, increased PV inverters costs for additional functionalities, communication infrastructure) - Grid losses (e.g. due to increased reactive power flows) - Maintenance (e.g. OLTCs) - Grid operation costs (e.g. communication, monitoring) - Reduced PV infeed (e.g. P(U) control) These assessments show that grid reinforcements can sometimes be completely replaced or at least substantially delayed. [28] analysed two different LV networks. Amongst other options he compared a combined Q(U)/P(U) control with the installation of an OLTC. In the first LV network the cost effectiveness was approximately the same (5% difference), while in the second LV network the Q(U)/P(U) option was considerably cheaper (26% difference). Both options were by a factor 2 to 4 cheaper than traditional grid reinforcements. The MetaPV project [23] compiled a large evaluation of reactive power control options for PV inverters and the cost effectiveness between different voltage control options (no comparison to OLTCs though). Their conclusions for the different reactive power control strategies can be seen in Figure 2-7.

Figure 2-7: Qualitative comparison of reactive power control schemes (++ criterion fulfilled, + criterion approached, - criterion not fulfilled) [23] For the cost comparison they drew the following conclusions: - Reactive power control + power curtailment is cheaper than reactive power control + storage - Central (coordinated) control is cheaper than local control - The cheapest coordinated control is standard control over the internet, enabled through inverter integrated communication With an appropriate control (Coordinated reactive power control + curtailment with inverter integrated communication) the PV hosting capacity can be more than doubled before grid reinforcement becomes economically more viable. Lastly, also the expected increase of PV capacity in the area needs to be taken into account. If the network is already at its limits but only a moderate increase is expected of about 20-40%, reactive power controls could be sufficient. If much higher PV penetrations are predicted then transformer upgrades or coordinated controls might be needed. Even higher PV penetrations are then only possible through traditional grid reinforcements as the cables and transformers reach their thermal limits. These considerations show that large differences can exist between LV networks. A transformer replacement with an OLTC might proof more economical if the transformer is already at the end of its lifetime or the costs for a communication infrastructure might be higher in some areas than others. It is therefore a challenge to develop standard tools that can help grid planners for their work. In the end, the best option may vary for different networks.

3. MODEL DESCRIPTION

In the course of this thesis work, a model has been developed to study steady-state voltage problems in the distribution grid. For this, the power system simulation toolbox PowerFactory 15.2 by DIgSILENT has been used. A first version of the model was created by S. Geidel [29] from Energynautics, one of the collaborators of the SNOOPI project. This model was then further improved and expanded by me, the author. In this chapter, only the final version of the model will be described. The model examines two MV feeders as case studies, which are part of the distribution grid of a German distribution system operator and are located at the same primary substation although on independent busbars. On one of these feeders, one LV network is modelled in detail, while at the other feeder two LV networks are modelled in detail. These make up three LV networks in total, which will be used to analyse different reactive power controls. This chapter will describe the model setup in detail. First, some general information about the area will be given. Then, the primary substation will be shortly described to which the two MV feeders are connected. Next, an overview about the available data will be presented. Subchapter 4 will then deal in detail with the description of the first feeder. This includes: General information; power grid related data; the role of DG at the feeder; available measurements; load and PV modelling; and finally, the implementation into PowerFactory. Subchapter 5 will do the same for the second feeder albeit shortened only to the essential parts and the differences to the first feeder. Lastly, the limitations of the model will be summarized.

3.1. Location and setting

The distribution grid with the two case studies is located in German North Rhine- Westphalia, west/north-west of the city of Worms, a middle-sized town with 80 000 inhabitants about 50 km south of Frankfurt am Main.

Figure 3-1: Location of the connecting primary substation (Map data ©2016 Geobasis-DE/BKG (©2009), Google) As indicated in Figure 3-2, the primary substation is located in Gundersheim. It connects the 110 kV HV grid to the 20 kV MV distribution grid. From there, two distribution feeders branch off, one to the west and one to the south, as indicated by the white and brown circles, respectively. These will provide the two case studies to be examined in this thesis. On these two feeders, three LV networks will be examined in detail: fre03 (feeder #1) and mon09/ofs07 (feeder #2). Their location is indicated in the figure. Both feeders are purely radial, with the white and brown circles representing secondary unit substations (SUS), each with a transformer that connects the respective 0.4 kV LV network to the MV feeder. The supplied villages typically comprise of 1000-3000 inhabitants. The area surrounding those villages is mostly agricultural and part of it is used for winegrowing. Therefore, most of the customers are households, complemented by some small scale commerce and industry as well as by a few bigger industrial customers. A significant part of these customers has rooftop or ground-mounted PV panels installed. Furthermore, a large scale PV power plant is connected on the westward leading feeder and the feeder going southward includes a large wind farm. These make up a considerable amount of distributed power generation and their dominant impact on the voltage level will be seen later on.

Figure 3-2: Geographical location of the feeders of the two case studies with the common substation in Gundersheim (Map data © OpenStreetMap contributors)

3.2. Gundersheim substation model

First of all, the substation that connects the 110 kV HV grid with the 20 kV MV grid in Gundersheim shall be described. Two 45 MVA transformers are connected in parallel to the MV double busbar. To these two busbars a total of five feeders are connected. Furthermore, five wind farms are connected via separate lines (without any additional generation or loads) to the double busbar. The two busbars are decoupled from each other and only in case of maintenance or fault the coupling might be closed. Therefore, each of the transformers supplies a part of the feeders and wind farms independently from each other. A simplified sketch of the busbar system can be seen in Figure 3-3.

Figure 3-3: The primary substation system in Gundersheim Each of the two 45 MVA transformers has an on-load tap changer that can regulate the medium voltage in 19 steps with a 1.772% nominal voltage change per step. The connected wind farms have a total capacity of 41 MW while the maximum load of the five feeders was only 17 MW in 2015. Together with the installed PV power along the feeders, this results into large backfeeding which is why for the two HV/MV transformers the active power ranged from approximately -32 to 10 MW in 2015. The feeders ‘Gundersheim’ and ‘Flörsheim-Dalsheim’ will form the two case studies and will be examined individually. In both cases the reference point will be set to the start of each feeder. This means that the rest of the primary substation is not considered in the model and, further, that the busbar serves as the slack bus and balances the active and reactive power flow from the feeder. This is described in further detail in Chapter 3.4.7.

3.3. Overview about available and simulated data

The goal of the model is to achieve a good representation of the actual network. In order to validate this network model, simulations are carried out over a longer period of time. This makes sure that the model does not only provide good results for single points in time. During the thesis work, periods of several days’ duration for different seasons have been analysed. For reasons of clarity however, only one 3-day period has always been depicted in this thesis. This results in a large variety of information that needs to be collected. Most of the needed data was provided by the DSO. However, a lot of information was not available and had therefore to be simulated or assumed.

Equipment data about lines, transformers, etc. were easily obtained by spreadsheets from the DSO, as well as load values of big industrial customers and large DG units. But as Smart Meters are not yet widely adopted in Germany, high resolution load data is not available. Instead, it is a common method to approximate loads through Standard Load Profiles (SLPs). The same holds true for small scale PV installations whose output could otherwise as well be measured by Smart Meters. Chapter 4 will deal with these issues and describe how their values have been simulated. Table 3-1 summarizes all the various data sets, their origin and where they are treated in this thesis.

Table 3-1: Overview about the different data sets, their sources and their respective sections in the thesis

Data See Unit Availability Source set/Information section Load kW (15min average), Simulated DSO 3.4.5 characteristics kVAr (15min average) (with SLPs) Large-scale PV MW (15min average), Measured DSO 3.4.6 characteristics MVAr (15min average) Small-scale PV kW (15min average), Simulated - 3.4.6 characteristics kVAr (15min average) MW (15min average), Wind generation Measured DSO 3.5.7 MVAr (15min average) Line impedances Ohm/km, mH/km, Provided DSO 3.4.2 (R, L, B) µS/km (by line type) Approximated Line lengths km DSO 3.4.2 (with network maps)

Feeding voltage kV (15min average) Measured DSO 3.4.7

Size (kVA), Transformer data Provided DSO 3.4.2 tap changer position, etc.

3.4. Case study #1 – Feeder ‘Gundersheim’

The first feeder we will look at is called Gundersheim3. Here, a special emphasis will be given to one of the SUS's (‘fre03’) in the village Freimersheim. It was selected by the SNOOPI project due to its higher share of small scale PV than the other substations at the feeder. In the following, general and power system specific attributes of the feeder will be described and a special focus will be given to the DG at the feeder and in Freimersheim in particular.

3 Unfortunately, the name of the feeder and the primary substation is both ‘Gundersheim’. Please read carefully to avoid confusion. 28

3.4.1. General information In order to get a general feeling of the local situation a few facts are described. There is a total number of 47 MV/LV SUS's at the feeder Gundersheim. On the 400 Volt LV level, a total of 3700 customers are connected to the grid of which 12 customers are categorized as so called RLM customers (German: Registrierende Leistungsmessung, recorded power measurement). This means they are obligated to measure and record their power consumption, which is only done for customers with a power consumption of more than 100 MWh per year [30]. For the remaining customers, standard load profiles (SLPs) are created depending on their customer type (household, industry, etc.). In the area of interest, fre03, a total of 87 customers is supplied. Figure 3-4 shows a simplified layout of the entire feeder, with the location of fre03 and the large PV plant. Table 3-2 summarizes the basic information about the feeder.

Figure 3-4: Sketch of the feeder Gundersheim, including the area of interest, fre03, and the location of the solar farm 29

Table 3-2: General information about case study #1

# of substations 47 # of SLP customers 3700 # of RLM customers 12 Average # of customers per substation 79 fre03: # of customers 87

3.4.2. Grid parameters As is common in distribution grids, the feeder is built up completely radial. Loop connections exist but they are kept open and are only closed in case of maintenance or fault. The furthest substation is at a distance of about 22 km. Most of the feeder is supplied via underground cables apart from a few overhead lines, interconnecting villages. The fre03 LV network consists only of cables. The MV/LV transformers at the SUS's are besides one exception all equipped with off- load tap changers. This means the tap position can only be changed manually on-site, no automatic tap changing exists. All tap changers have three possible tap positions with one of the following settings: 20.8/20.0/19.2kV to 400V or 20.8/20.4/20.0kV to 400V. The fre03 transformer has a rated power of 400 kVA and a tap changer position of 20.8kV/400V, so it is downregulating the voltage at the LV side. This will be of importance for the results in Chapter 6. However, in the PowerFactory model the LV grids including the respective transformer have not been modelled in detail. Instead, the total load per SUS has been aggregated into a single load and all small scale PV has been aggregated into a single generator. This is illustrated in the following figure:

Figure 3-5: Loads and PVs are aggregated at their respective SUS Only for the area of interest, the SUS fre03, the low voltage grid was modelled in detail. A screenshot of the model can be seen in Figure 3-6.

Figure 3-6: Screenshot of the fre03 LV network, as modelled in PowerFactory (Map data © OpenStreetMap contributors) The lines are directly drawn in the program and the length calculated from that. Furthermore, in Chapter 2.4.3 it was described how the R/X ratio impacts the effectiveness of voltage controls. Therefore, some further technical information about prevalent R/X ratios along the feeder is given in Table 3-3.

Table 3-3: R/X ratios for different parts of the feeder Gundersheim

R/X ratio in MV grid – very close to the feeding point 0.9 (0–1 km distance) R/X ratio in MV grid - further away from the feed point 1.7 (1–22 km distance) R/X ratio in LV grid - distribution grid 2.6 (mostly) R/X ratio in LV grid - customer connections4 8.2

4 With this, the short cable connection to the individual household is meant. It is usually only a few meters long. 31

3.4.3. Distributed generation Alongside the feeder Gundersheim no wind turbines exist. Hence, apart from the load, the feeder is only characterized by PV generation. However, there is a huge PV power plant at about 1 km west of Freimersheim. This PV power plant has an installed capacity of 7.3 MW and a maximum power output of 6 MWpeak. With that, it produces more energy during peak output than is consumed within the whole feeder. Additionally, a considerable amount of small scale PV exists. This is mostly roof mounted although also some ground mounted installations can be found. Along the feeder, a total of about 278 PV installations exist, with 6 of them in fre03. The total installed capacity of all small scale PV plants is 4400 kW and, according to the DSO, they are not providing any reactive power for voltage support yet, so the power factor is assumed to be 1. The possibility for VAr control exists however, as described in Chapter 2.3.2. All PV plants have been modelled by the standard static generator model for PV provided by PowerFactory [31]. This essentially means that each generator is a PQ- node with a fixed active and reactive power output. It can also be described as a negative load (injecting rather than consuming P). Table 3-4 summarizes some basic information about the DG and its role at the feeder Gundersheim.

Table 3-4: Basic facts about the distributed generation at the feeder Gundersheim

# of wind farms 0 # of small scale PV plants 278 Small scale PV capacity 4.4 MW Large scale PV plant capacity 7.3 MW Total PV capacity 11.7 MW PV capacity per customer in fre03 1.4 kW/customer Maximum load (year 2015) 3.7 MW Active power range at feeding point (year 2015) -7.2 to 3.7 MW Percentage of customers with an installed PV plant 7.5%

As can be observed the 7.3 MW PV plant has a big impact on the overall generation along the feeder as it makes out 62% of the total installed PV capacity. The large amount of PV results into backfeeding to the substation in Gundersheim that is up to twice as much as the maximum load. Furthermore, with currently only 7.5% of the customers having installed a PV plant, there seems to be still a considerable expansion of PV power within the region possible. 3.4.4. Available measurement data It is not only necessary to model the physical layout of the grid but also reasonable values for loads and generators have to be inserted. These represent then the total model with which load flow calculations can be performed. The results can subsequently

32 be compared with actual measurements that have been recorded on-site. The next three subchapters will deal with this, starting with the available measurement data. All measurements at hand come in a 15-minute average format and in all cases the following properties have been recorded:  Active power P  Reactive power Q  Apparent power S  Voltage U  Current I  Power factor cosphi Measurement devices are only sparsely distributed across the network. On the one hand, measurements are available for the power flow and the voltage level at the feeding point of the Gundersheim feeder. Furthermore, at seven SUS’s measurement devices are installed in the transformer (on the LV side). Their position is shown in Figure 3-7. They are deployed in all six SUS's in Freimersheim (one of them being the solar farm) as well as in one SUS in a neighbouring village, indicated by the dotted arrow. The measurement devices measure the power flow through the respective MV/LV transformer. Hence, this power flow represents the total power of the respective LV grid, that is supplied by the SUS.

Figure 3-7: Location of the available measurement data for the feeder Gundersheim 3.4.5. Load modelling 3.4.5.1. Active power With the data available by the DSO, three things are known about each customer:  The total yearly electricity consumption  The categorization of the customer  The SUS, to which he is connected The customer is categorized either as an RLM- or an SLP-customer. SLP-customers are further classified into households, industries, etc. Figure 3-8 shows the breakdown of all customer types for the whole feeder, while Table 3-5 shows this only for the example of fre03 (the area of interest). In both cases, the bulk of electricity consumption is made up by residential consumers. Altogether, the SLP customers are broken down into 17 different customer types (e.g. industry has seven subcategories). 34

Figure 3-8: Share of electricity consumption per customer category for the feeder Gundersheim

Table 3-5: Customer categories for the SUS fre03 in Freimersheim, the amount of customers per category and their respective accumulated yearly electricity consumption

Customer # of Accumulated yearly Share of electricity Share type customers electricity [kWh] consumption Household 57 66% 207 777 64% Industrial 13 15% 37 146 11% Heating profile 8 9% 30 799 9% Agriculture 8 9% 25 759 8% Street lighting 1 1% 24 428 7% Total 87 100% 325 909 100%

As mentioned before, 15-minute average values of RLM-customers are continuously recorded. This data was available so no approximation of these loads had to be done. However, in order to get 15-minute average values for SLP-customers, their respective Standard Load Profile can be used. These are not exact measurements but instead 15- minute average values over a large sample of customers of a common type and available for the whole year. Figure 3-9 shows a typical household vs. an industrial SLP, for an exemplary week.

Figure 3-9: Exemplary residential and industrial Standard Load Profile for a summer week With this information, it is possible to generate a load profile for each customer section. For this, first the respective SLP is selected depending on the type of customer and then this profile is scaled by the yearly consumption of all customers that fall into this particular customer category. Example: For the household sector in fre03 a yearly consumption of 207 777 kWh was recorded (see Table 3-5). The Standard Load Profile for households has a yearly electricity consumption of 3700 kWh. In order to project the real electricity consumption of all residential customers onto the residential 208 777 푘푊ℎ SLP, each 15-minute value will be multiplied by the factor { }. This 3700 푘푊ℎ step is done for all customer sections5 in fre03, and further repeated for all other SUS’s alongside the feeder. The load profiles for the different customer segments are then summed up and form the aggregated load profile for the secondary substation. Just like the individual SLPs come in 15-minute average format, also the final aggregated load profile is comprised of 15- minute average values. Since in the case of fre03 the LV network has been modelled in detail, this approach is not possible. Instead, the same average 15-minute load is assigned to each customer. An individual load profile would have been possible for each customer but the integration in PowerFactory would have been very cumbersome and slow and was therefore omitted. 3.4.5.2. Reactive power The reactive power of loads is very difficult to model. Depending on the appliance, the load can be slightly inductive (Q consuming) or capacitive (Q injecting). Usually, the power factor is close to 1. In this model, a good power factor for the aggregated loads was found by trial and error that resulted into a power factor of 0.98 inductive (VAr

5 There exists only one SLP for residential customers. However, the other sectors are further divided into more subcategories. For example: Industrial customers have seven subcategories, and there exist three different heating profiles. 36 consuming). This value includes the reactive power flow induced by cable and transformer impedances. For fre03 a unitary power factor has been chosen for all loads. More about the reactive power modelling will be described in Chapter 4.1.3. 3.4.6. PV modelling 3.4.6.1. Active power For the modelling of the PV generation, there is on the one hand the large scale 7.3 MW PV plant next to Freimersheim, and on the other hand all the small scale PV installations in the various villages. The large PV plant is connected to a separate SUS and the 15-minute average values for its active power output were measured and available. These values served as a direct input for the active power values in PowerFactory. The same is not possible for small scale PV due to the lack of capable measurement devices such as Smart Meters. Furthermore, the individual PV installations are dispersed over the area and their active power output convoluted with the customer loads. However, also here crucial data was provided by the DSO, which included:  The PV capacity of every PV installation  The SUS, to which it is connected In order to generate the PV profile for a PV installation, the output of the large PV plant is used as a reference. Example: If the average power output of the 7.3 MW power plant was measured to be 50% for a certain 15-minute time period (3.65 MW), the PV output of a 20 kW PV installation would be assumed to be 50% as well, ergo 10 kW. This methodology is reasonable for PV installations that are a short distance from the 7.3 MW PV plant as differences in the PV output through sunrise/sunset and cloud cover changes happen nearly at the same time or at least at a smaller time scale than the 15- minute average measurement values. The village Freimersheim for example is only about 1 km next to the big PV plant. However, with increasing distance the approximation gets less accurate as in particular cloud cover changes will not happen simultaneously but with a slight time shift. Furthermore, the PV infeed to PV capacity ratio might be different for small scale PVs compared to the large scale PV plant. The big PV plant reaches a maximum of 82% of its installed capacity (e.g. due to inverter limits) but lower or higher values could be obtained for small PV plants. Also, different panel angles and orientations result in deviating output profiles. All these issues will be addressed in the following chapter when the model is compared with measurements. Similar to the loads, all small scale PV installations of one SUS get aggregated to a single PV profile. First, the total PV capacity is calculated for each SUS and then the result projected onto the PV output profile of the large PV plant. Figure 3-10 shows this 37 principle. The exception is fre03 where the PV installations could be directly added at their respective position in the grid, without the need of aggregation.

Figure 3-10: Illustration of the aggregation and generation of a PV profile for the SUS fre03 3.4.6.2. Reactive power - Small scale PV units As mentioned before, the small scale PV generators should operate at unitary power factor according to the DSO, hence not injecting or consuming any reactive power. As it turned out this doesn’t seem to be the case, though. Through trial and error, a power factor value has been found that approximates the final reactive power profile at the feeding point reasonably well. The value chosen for the power factor is 0.98 capacitive (VAr consuming). More on this will be described in Chapter 4.1.3. - Large scale PV plant For the large scale PV plant measurement data is available for the reactive power. However, the reactive power output is dependent on a cosphi(U) characteristic. In Chapter 5, a worst case scenario will be developed that changes the voltage profile at the solar farm and therefore result in a different reactive power output of the PV plant. Therefore, this cosphi(U) characteristic has been implemented in the model. The characteristic can be seen in Figure 3-11. The performance of this voltage control will be verified in Chapter 4.1.5, where the resulting reactive power flow is compared to the measurements.

Figure 3-11: cosphi(U) characteristic of the 7.3 MW PV plant 3.4.7. PowerFactory implementation The previous chapters described a methodology to formulate all loads and generations in terms of PQ buses. In order to solve the resulting power-flow problem a slack bus needs to be defined where the active and reactive power of the system is balanced. In this study, the MV busbar of the primary substation is defined as the slack bus. Therefore, the bus voltage at the feeding point of the Gundersheim feeder needs to be fixed to a certain value. The voltage at the busbar is not kept constantly at 20 kV. This has several reasons: - The HV/MV transformer of the primary substation uses an on-load tap changer with discrete steps. Therefore, the voltage is always only in the range of the desired voltage. - The DSO operates the voltage usually at 20.5 kV in order to reduce line losses. - If the DG is very high at the risk of over-voltages in the distribution grid, the voltage control of the OLTC comes into effect and reduces the voltage to up to 20 kV (treated in Chapter 4.1.6). Therefore, the voltage levels at the busbar are instead taken from measurements (again in 15-minute average format). An exemplary voltage profile for the same summer days as previously is shown in Figure 3-12.

Figure 3-12: Voltage profile for the Gundersheim substation at the busbar, to which the Gundersheim feeder is connected Ultimately, all the information is now available that is needed to run the model. All input values come in 15-minute average values and their sources are a final time summarized in Table 3-6.

Table 3-6: Input values for the model

Location Input value Source Substation U (slack bus) Measured Gundersheim PLoad Modelled SUS fre03 PPV Modelled SUS fre08 PPV Measured (7.3 MW PV) QPV Modelled PLoad, aggregated Modelled QLoad, aggregated Modelled All other SUS’s PPV, aggregated Modelled QPV, aggregated Modelled

The measured and modelled input values are now used from the first 15-minute time period and a balanced load flow calculation is performed. Balanced means that all loads and generators are equally distributed across all three phases. The resulting P, Q and U values are recorded. Then, the values for the second 15-minute time period are inserted and a new load flow calculation performed. The P, Q and U values for the second time step are recorded. This process is repeated for all 15-minute time points of a given series, e.g. three days. The process is also illustrated in the following figure:

Figure 3-13: Illustration of the model implementation in PowerFactory

3.5. Case study #2 – Feeder ‘Flörsheim-Dalsheim’

The second feeder, which has been modelled, is the feeder called 'Flörsheim-Dalsheim'. Here, two areas of interest had been selected by the SNOOPI project: The SUS ‘mon09’ in the village ‘Monsheim’, and the SUS ‘ofs07’ in the village ‘Offstein’. The description of the feeder will follow in a similar manner as for the feeder Gundersheim. 3.5.1. General information The feeder Flörsheim-Dalsheim covers an area with almost 3000 customers. 20 customers thereof are RLM customers (see Chapter 3.4.1) and in total, 39 substations exist along the feeder. The first area of interest, mon09, has 124 customers while the second one, ofs07, has a larger transformer and includes 237 customers. As in the case of fre03, these two SUS's comprise of a slightly higher PV penetration than the surrounding SUS's. Figure 3-14 shows the layout of the grid with the position of the two areas of interest and the big wind farm. Table 3-7 shows some basic information about the examined area.

Figure 3-14: Sketch of the feeder Flörsheim-Dalsheim, including the areas of interest, mon09 and ofs07, and the wind park

Table 3-7: General information about case study #2

# of substations 39 # of SLP customers 3000 # of RLM customers 20 Average # of customers per substation 76 mon09: # of customers 124 ofs07: # of customers 237

3.5.2. Grid parameters As in the case of the first case study, the MV grid structure is purely radial with open loops. The furthest SUS is 19.5 km away from the primary substation. Contrary to the first case study, a number of loop connections exist in the two LV networks that enable loop flows. Also contrary to the first feeder, the entire MV grid is supplied by underground cables. The LV grid is also purely connected through cable connections with the exception of ofs07, where a large part of the LV grid is still supplied via overhead lines but over time those will also be replaced by underground cables. The

42 transformers at the SUS's are all equipped with off-load tap changers with the same possible tap positions as described in Chapter 3.4.2. Mon09 is supplied by a 400 kVA transformer, for ofs07 it is a 630 kVA transformer. Both have their tap changer in the neutral (20kV/400V) position. The aggregation of the LV grids has been done in the same way as for the first feeder (see Figure 3-5) with the exception of mon09 and ofs07. Figure 3-15 shows the PowerFactory model of those two LV grids while Table 3-8 shows some additional information on R/X ratios for different sections of the grid.

Table 3-8: R/X ratios of different parts of the feeder Flörsheim-Dalsheim

R/X ratio in MV grid - up to a large RLM customer close 0.9 to Monsheim (0 – 8 km distance) R/X ratio in MV grid - after Monsheim 1.7 (8 – 19.5 km distance) R/X ratio in LV grid - UG cables - distribution grid 2.6 (mostly) R/X ratio in LV grid - UG cables - customer connections 8.2 (mostly) R/X ratio in LV grid - OH lines – majority of customer 5.6 connections (83% of all cases) R/X ratio in LV grid - OH lines - customer connections at 10.6 the end of some distribution grid lines (17% of all cases)

Figure 3-15: Screenshots of the mon09 (above) and the ofs07 (below) LV networks, as modelled in PowerFactory 44

3.5.3. Distributed generation The second case study varies from the first one insofar that there doesn’t exist a large PV plant but instead a large wind farm. This changes the general behaviour of the feeder. While the feeder Gundersheim was following a certain pattern (load during the night, generation and bidirectional power flow during the day), this is not the case for the feeder Flörsheim-Dalsheim. Depending on the availableness of wind, there can either be a high consumption in the feeder or a large backfeed to the primary substation. The wind farm has a size of 9.6 MW and is regulated with a constant power factor of 0.95 capacitive (consuming VAr). The wind farm is modelled by a simple PQ node as in the case of the PV plants, hence, the standard static generator model of PowerFactory [31] is used again. Furthermore, a total of 196 small scale PV installations exist in the area that account for an installed capacity of around 3300 kW. With a customer-owned share of 6.6%, the penetration of PV plants is slightly lower than the one at the Gundersheim feeder with 7.5%. Table 3-9 summarizes the findings at the Flörsheim-Dalsheim feeder.

Table 3-9: Basic facts about the distributed generation at the feeder Flörsheim-Dalsheim

# of wind farms 1 Wind farm capacity 9.6 MW # of small scale PV plants 196 Small scale PV capacity 3.3 MW PV capacity per customer in mon09 0.8 kW/customer PV capacity per customer in ofs07 1.4 kW/customer Maximum load in 2015 5.7 MW Active power range at feeding point in 2015 -7.9 to 5.7 MW Percentage of customers with an installed PV plant 6.6%

3.5.4. Available measurement data As for the other feeder, all measurement data is available in 15-minute average format. Values are recorded for the following locations: - The feeding point of the Flörsheim-Dalsheim feeder - One SUS in Monsheim (mon01) - The location of the 9.6 MW wind turbine (SUS wah06) Compared to the first case study, there is clearly a lack of available measurement data. Instead of measurement data for the big PV plant there is measurement data available for the large wind farm. And while in the first case there are a number of measurement devices installed in SUS’s, here it is only one (at mon01, seen in the middle of Figure 3-16). This results in a much harder and suboptimal model validation as will be seen in Chapter 4.

Figure 3-16: Location of the available measurement data for the feeder Flörsheim-Dalsheim 3.5.5. Load modelling 3.5.5.1. Active power The distribution of SLP customers is similar to the other feeder. But at the start of the feeder, a large industry complex accounts for a large share of the total feeder load. This customer is therefore categorized as an RLM customer. Figure 3-17 shows the electricity consumption for the different sectors.

Figure 3-17: Share of electricity consumption per customer category for the feeder Flörsheim-Dalsheim 46

The same methodology is used to model these loads: The SLP & RLM loads are aggregated to their specific SUS. In the case for mon09 and ofs07, the total load is distributed out equally across all customers. 3.5.5.2. Reactive power For the Flörsheim-Dalsheim feeder, the same power factor for aggregated loads has been taken as for the Gundersheim feeder. This also provides a good modelling of the reactive power flow at the feeding point which will be discussed in Chapter 4. The power factor value is 0.98 inductive (VAr consuming). Equally, the power factor values for the loads located in mon09 and ofs07 has been kept at 1. 3.5.6. PV modelling 3.5.6.1. Active power Contrary to the first case study, there is no nearby large scale PV plant with measured output values available. It was planned to receive measurement data from newly installed measurement devices but due to some problems no data could be obtained. Due to this lack and no alternative, the output data of the large scale PV plant has nevertheless been taken. The distance of the SUS’s of the second feeder is between 10 and 16 km from the PV plant. Therefore, much higher deviations from the real PV profile are to be expected. However, for very sunny or very cloudy days the output should still resemble the actual measured output. But particularly days with a dispersed cloud cover are difficult to model. 3.5.6.2. Reactive power Similar to the Gundersheim feeder, a power factor of 0.98 capacitive (VAr consuming) obtained the best results for the reactive power modelling at the feeding point. Please refer to Chapter 4.1.3 where the model validation demonstrates this result. The PV plants in mon09 and ofs07 have been kept at a power factor of 1. 3.5.7. Wind farm modelling 3.5.7.1. Active power The active power values for the wind farm are directly taken from measurements. Figure 3-18 shows the profile for the wind farm.

Figure 3-18: Measured active power output of the wind farm 3.5.7.2. Reactive power The 9.6 MW wind farm operates at a fixed power factor of 0.95 capacitive (VAr consuming). It mitigates therefore part of its voltage decrease. However, it would be commended to change this setting to either a cos φ (P) or Q (U) control (with a minimum PF of 0.95 capacitive) in order to decrease reactive power flows at the feeder. 3.5.8. PowerFactory implementation With the information of the previous chapters, all nodes can be described again as PQ- nodes. The busbar at the primary substation is again selected as the slack bus. This busbar is not the same one as for the first feeder, so a different voltage profile will be observed. The methodology for the PowerFactory implementation is the same as described in Chapter 3.4.7. Table 3-10 summarizes the different input values and their origin.

Table 3-10: Input values for the model

Location Input value Source Substation U (slack bus) Measured Gundersheim SUS’s mon09 & PLoad Modelled ofs07 PPV Modelled SUS wah06 PWind Measured (9.6 MW wind farm) QWind Modelled PLoad, aggregated Modelled QLoad, aggregated Modelled All other SUS’s PPV, aggregated Modelled QPV, aggregated Modelled 48

3.6. Limitations of the model

In the course of the modelling, a number of simplifications have been made which limit the accuracy of the output of the model. These simplifications need to be weighed up against the additional effort and increased computing times, that would result from a more complex model. Most prominently, at all the SUS's apart from the areas of interest the loads and small scale PV plants have been aggregated and directly connected to the MV grid without the addition of the transformer. This way, the impedances of the lines and the transformer are neglected which affects  On the one hand, grid losses. The resulting active power should be slightly lower than the one used in the model. It was determined that this is however only in the magnitude of about 2-3% and therefore negligible compared to other limitations.  On the other hand, generation of reactive power. This will be discussed in Chapter 4.1.3. Furthermore, regarding the PV and load modelling of the two feeders, there are some drawbacks in:  Using Standard Load Profiles. Especially load modelling in the LV grid is significantly hampered by this, as real loads are much more unpredictable  Using one reference as the input for all PV generators. This leads therefore to synchronism between PV units. The real output should be much smoother. Also, PV units far away only reflect to a small degree the reference output, depending on the weather conditions.  Using balanced load flow calculations. In real networks, single-phase loads and infeed lead to voltage distortions between the phases. Unsymmetrical load flow computations can take this into account. However, appropriate data or good assumptions need to exist. Also it was stated in Chapter 2.3.2 that generators above 4.8 kVA need to be three-phase connected since 2012 in Germany. This means that only small scale PV plants and PV plants constructed before 2012 can still result in unsymmetrical infeed. It was pointed out by [8] that these assumptions can usually be used for the modelling of MV networks; on the LV level, though, they can limit the conclusions that can be drawn from the model outcomes. On the other hand, an accurate representation of LV networks is difficult to obtain and requires extensive resources, e.g. in the form of measurement equipment. Therefore, it needs to be considered by which means the model could be improved as much as possible without exceeding the required human and capital resources. The roll-out of smart meters could significantly help in this regard.

4. MODEL VALIDATION

This chapter will validate the described model with the help of the available measurement data. By performing load flow calculations for each 15-minute time step, the simulation results are obtained over a duration of time. The graphs shown in this thesis will always contain a comparison of a three-day period between the simulation results and the measurements. The actual validation was done for a six-day period and also for other seasons of the year. However, the depicted time period is in summer which provides more insights into the behaviour of the PV plants. The chapter follows again the same structure by examining first feeder 1 (Gundersheim) and then feeder 2 (Flörsheim-Dalsheim). For each feeder first the active power output at the secondary substations is compared. Then, the same comparison is done for the feeding point. Subsequently, the reactive power at the substations and the feeding point is analysed, followed by the voltage profiles at the SUS’s. Lastly, and this is only valid for the first feeder, the Q(U) control of the large solar farm and the advanced OLTC control at the primary substation are verified.

4.1. Case study #1 – Feeder Gundersheim

The validation will be done on the basis of the seven SUS’s and the feeding point. These were pointed out in Chapter 3.5.4 as the locations of the currently installed measurement devices. 4.1.1. Active power output at SUS’s In the previous two chapters the aggregated profiles for the load and generation at an SUS has been described. These profiles can now be added to reflect the total active power flow through the transformer.

Figure 4-1: Creation of the active power flow through the transformer by summing up the load and generation within the LV grid at the example of fre02 The impact of the PV is arguably quite remarkable as during the day the power flow direction is reversed and sometimes up to 1.5x as high as the maximum load. The result of this aggregation can now be compared with actual measurements. In Chapter 3.4.4 it was mentioned that all SUS’s in Freimersheim have measurement units installed, with one of them being the SUS of the PV plant. Apart from that, also one SUS further away is equipped. The SUS’s in Freimersheim are very close (ca. 1 km), so also a good conformity of the PV impact should be observed. The SUS in Ober-Flörsheim has a distance of 6 km, so some time shift might be observed. Figure 4-2 shows the comparison for fre03 and a time period of three days.

Figure 4-2: Comparison of modelled and measured active power flow at the SUS fre03 for a period of three days As seen, the congruence of the two graphs is very well for most of the time points. There are however some incongruences: During night (~9 p.m. till ~6 a.m.) there are only minor differences. The measured profile shows some considerable higher load peaks during the late evening that can’t be seen in the simulated profile. Also the measured profile shows much more variability in contrary to the simulated profile since there is only a small number of customers at the SUS. This number is not large enough so the behaviour of individual customers has still a relatively strong impact on the total power flow. During day (~6 a.m. till ~ 9 p.m.) the congruence is also remarkably good but there exist a number of outliers. For example, on June 19th the simulated profile shows a peak minimum of -60 kW during noon while the measured value is only around -20 kW. This is most likely due to dispersed cloud cover that in this case was leading to a cloud covering most of the rooftop PVs but less so the big PV plant. Still, even with these limitations, the result is quite exceptional. Figure 4-3 shows the comparison for the four other SUS’s in Freimersheim. The first three show also promising results. The SUS fre07 has bigger differences, in particular during the day, which can be partly deduced to the fact that fre07 has the biggest distance to the 7.3 MW PV plant. But also in the night there are some bigger differences which highlight the imperfection of the modelling.

Figure 4-3: Comparison of modelled and measured active power flow at the SUS's fre02, fre05, fre06 and fre07 for a period of three days Lastly, Figure 4-4 shows the comparison also for the SUS in one of the neighbouring villages (ofl01). While here the simulated profile follows the measured one to a certain degree during the night, the differences during the day are quite substantial and quite another profile is observed. The frequent deviations in the PV output infer that during this three-day period the cloud cover was rather dispersed, whereby the PV panels connected to this SUS experienced very solar irradiation values.

Figure 4-4: Comparison of modelled and measured active power flow at the SUS ofl01 for a period of three days In order to get a quantified measure of the congruence between the two graphs of each SUS, the root mean square error (RMSE) and the normalized RMSE are calculated. The RMSE is a frequently used measure of the difference between values. It’s defined as followed:

푇 1 푅푀푆퐸 = √ ∑(푦 − 푦̂ )2 (4.1) 푛 푡 푡 푡=1 With yt Simulated value for time t ŷt Measured value for time t n Number of data points T End point of time period The normalized RMSE can be either divided by the mean of the dataset or by the range of the dataset. The former method is not suitable because there are positive and negative value, so the average might turn out to be close to zero and give an unreasonable result. Therefore, the normalized RMSE is used, defined as:

푅푀푆퐸 푁푅푀푆퐸 = (4.2) 푦푚푎푥 − 푦푚푖푛

With ymax Maximum simulated value 6 ymin Minimum simulated value

Furthermore, the errors are examined for a period of six days (not only the three days shown in the figures) and individually for day and night. Therefore, the range is also taken in the one case for the night, and the other case for the day. Therefore, the two NRMSE are not comparable with each other as the two ranges differ usually quite substantially. The results are obtained in the following table.

Table 4-1: Error margin for all SUS's with measurements

PV NIGHT DAY SUS capacity RMSE NRMSE RMSE NRMSE fre02 163 kW 5.8 kW 23% 15.0 kW 11% fre03 125 kW 5.9 kW 21% 13.6 kW 14% fre05 155 kW 4.4 kW 15% 14.3 kW 12% fre06 40 kW 5.3 kW 21% 8.2 kW 19% fre07 87 kW 5.8 kW 22% 15.7 kW 20% ofl01 60 kW 4.7 kW 18% 12.1 kW 22%

First of all, the absolute error (RMSE) is much smaller during the night than during the day. This is due to the fact that the load has been modelled quite well while the fluctuating PV generation can only be modelled imperfectly. While the RMSE value for ofl01 is quite low, the NRMSE value is not. This is due to the low share of PV in ofl01 which doesn’t result in high absolute errors but the relative error is nevertheless high. Also the higher NRMSE for fre06 and fre07 can be attributed to lower PV penetrations, as the load fluctuations have a larger influence. Furthermore, fre07 is the SUS farthest away from the 7.3 MW PV plant. The other three SUS’s show relatively good results. 4.1.2. Active power output at the feeding point As written before, at the feeding point (where the slack bus is located) the active and reactive power output of the distribution system is balanced. Figure 4-5 shows the comparison between the measured and the simulated active power output at the start of the Gundersheim feeder for the same three-day period as before.

6 The max/min simulated value has been chosen instead of the max/min measured value. This is due to the fact that the measurement values can fluctuate quite substantially, which leads to misleading NRMSE results 55

Figure 4-5: Comparison of the active power flow at the Gundersheim feeding point As can be seen the congruence is very good, seemingly better than in the previous comparisons at SUS level. Only in a few cases the difference between the modelled and the measured profile is large. Those differences can especially be observed during the morning time. Furthermore, as the same PV profile is adopted for all PV installations along the feeder, the synchronism of this stacks up and this leads to some outliers as for example at noontime of the second day. There, the model gives an output of almost -7.0 MW while the measurements just shows a moderate power flow of about -3.5 MW (it is the value right after the peak minimum value of ~4.0 MW). These discrepancies have a much bigger influence on the active power flow than minor PV output differences due to varying panel orientation or different PV output to capacity ratios. Therefore, it has not been tried to improve these issues of secondary importance. Regardless of that, the large number of customers leads to better simulation results as opposed to the results from individual SUS’s: Both nightly and daily normalized RMSE are considerably lower than in Table 4-1.

Table 4-2: Error margin for the active power flow at the Gundersheim feeding point

Comparison of RMSE NRMSE P @ feeding point during night 0.14 MW 10.4% P @ feeding point during day 0.72 MW 7.9%

4.1.3. Reactive power output at SUS’s and feeding point For the modelling of loads and PVs a power factor of 1 has been assumed. When using these settings for the load flow calculation the reactive power flow at the fre03 SUS can be compared with the actual measurements. This leads to the result in Figure 4-6. The measurements are on the LV side, while the simulations are actually obtained on the MV side.

Figure 4-6: Measured (on LV side) vs. simulated (on MV side) reactive power flow at the SUS fre03 Despite including both cable and transformer reactances, the simulated profile shows clearly that almost no reactive power is either consumed or produced by any cables or the transformer. Meanwhile, the measurements (only including cable impedances) show a highly fluctuating reactive power output far from zero, both for the night and the day. During night, when no PV contribution is possible, the values fluctuate between +4 kVAr (Q consumed) and -2 kVAr (Q produced). The negative values occur especially during the evening and the early morning while positive values persist through most of the night. Hence, in the evening and morning the loads seem to be mostly inductive (Q consuming) while during the night they are capacitive (Q injecting). During day the additional effect of PV can be seen. Though their power factor should be 1, a number of spikes can be seen that coincide with the spikes from PV generation. It is possible, that some inverter manufacturers parameterized their products with the commonly used cos φ (P) control (see Figure 2-5) by default, even though EWR Netz GmbH doesn’t require it yet. A clear answer could not be given to this issue.

Even though the modelling of the reactive power is difficult its relative impact is rather small. Figure 4-7 shows the measured active and reactive power at fre03. The reactive power is so small in comparison that during night the power factor at the transformer never drops below 0.98. During the day this becomes more convoluted as both loads and PV inverters are sometimes consuming reactive power while their active power cancels each other out. Hence, P can be close to zero while Q not and the resulting power factor is far from 1. Still, it can be safely assumed that both loads and PVs are usually in a close range to 1.

Figure 4-7: Measured active and reactive power flow at the SUS fre03 (on LV side) In order to come up with reasonable power factors for both loads and PV generators, in particular for the aggregation at the SUS, different power factor have been tried out and the result compared with the reactive power flow at the feeding point. A good outcome was achieved with the following values and the comparison between measurements and simulation can be seen in Figure 4-8:

- Power factor of PV: 0.98 capacitive (VAr consuming) - Power factor of loads: 0.98 inductive (VAr consuming)

Figure 4-8: Comparison of the reactive power flow at the Gundersheim feeding point During day as well as during night a good approximation of the reactive power flow can be achieved. It should also be noticed that a low resolution of the measured values leads to steps in the graph, so measurement inaccuracies can also play a role. Table 4-3 shows the error margins.

Table 4-3: Error margin for the reactive power flow at the Gundersheim feeding point

Comparison of RMSE NRMSE Q @ feeding point during night 0.09 MVAr 28.2%7 Q @ feeding point during day 0.30 MVAr 8.7%

The MV cables produce a high amount of reactive power. This is the reason why during night reactive power is overall produced. If loads and PV plants operated both with a power factor of 1, the reactive power production would be around 1 MVAr. However, this value is slightly increased in the night because of the loads consuming a bit of reactive power, since their power factor is 0.98 inductive. During day, also the PV plants consume reactive power and in particular the large PV plant contributes to that reactive power consumption as it can operate to a power factor of up to 0.90 capactive (Q consuming). Therefore, the reactive power consumption by loads/PVs outweighs the reactive power production by cables often during day and the overall network consumes reactive power instead of producing it. This produces this particular behaviour.

7 This value is exceptionally high. On the one hand, the range is very small, so the denominator in the NRMSE formula is very low. On the other hand, the measurement values are inaccurate due to being rounded off, so the error margins are increased. 59

4.1.4. Voltage output at SUS’s The measurement units that are installed at some of the SUS’s do not only measure P and Q but also the voltage each. The respective voltage profiles can now be compared with the output values from the model. Figure 4-9 shows the resulting graph for the SUS fre03.

Figure 4-9: Comparison of the voltage at the SUS fre02 Clearly, the modelled and the measured voltage profile match each other almost perfectly. The biggest differences can be observed at the time instances where there is a high mismatch between simulated and measured PV generation. These are the times were the synchronized profile of all PV generators leads to large deviations from the actual reality. The voltage reacts therefore strongly to the deviations in active power flow. Simulations showed that there is only a minor influence of reactive power which is also in accordance with the theory. The other SUS’s, shown in Figure 4-10, show likewise results, also the SUS in the neighbouring village (ofl01).

Figure 4-10: Comparison of the voltage at the SUS's fre05, fre06, fre07 and ofl01 Table 4-4 shows the margin of error for all seven substations, which are very low.

Table 4-4: Error margin for the voltage of the six SUS’s with measurement data

SUS RMSE NRMSE fre02 0.021 kV 1.5% fre03 0.014 kV 1.0% fre05 0.037 kV 2.8% fre06 0.033 kV 2.5% fre07 0.041 kV 3.0% fre08 0.031 kV 2.3% ofl01 0.034 kV 3.6%

4.1.5. Reactive power output of large scale PV plant As just shown the voltage profile at the SUS of the large scale PV plant (fre08) is very accurate. When implementing the cosphi(U) control into the model the following comparison arises:

Figure 4-11: Comparison of modelled and measured reactive power flow at the SUS fre08 (Location of the 7.3 MW PV plant) for a period of three days The slight variations during the day are due to the fact that the simulated voltage profile deviates from the measured voltage profile. However, it appears that the PV plant induces reactive power during the night. Why this is happening could not be resolved and has been ignored since the primary concern are over-voltages during the day. These results successfully validate the correct functioning of the cosphi(U) control at the PV plant. 4.1.6. Advanced OLTC control at the primary substation Lastly, there is also a voltage control implemented in the OLTC of the primary substation. As pointed out before, the voltage at the primary substation is often kept above its nominal value in order to reduce line losses. However, at times of high wind and PV generation this risks the occurrence of over-voltages in the grid. Therefore, an internal U(I) control is implemented in the OLTC. In Figure 4-12 the corresponding characteristic can be seen with the maximum allowed deviation and the measured U(I) values mapped onto it. The range results from the fact that the tap changer has discrete steps so the voltage cannot be kept always exactly to the desired voltage (the black line). As can be seen, apart from a very few exceptions the control fulfils its purpose. 62

Figure 4-12: U(I) characteristic of the OLTC voltage control and measured data points of the transformer This voltage control has not been implemented in the final model as such. However, a given amount of DG indicates to which voltage the OLTC sets its control, which will be important for the worst case scenario description in Chapter 5. In the above figure, the lowest voltage (blue data points) is reached with a backfeed of approximately 7.9 MW (from the feeders plus the wind farms). The validation of the advanced OLTC control will not be repeated for the Flörsheim- Dalsheim feeder and this chapter concludes the validation for the Gundersheim feeder.

4.2. Case study #2 – Feeder Flörsheim-Dalsheim

This chapter follows the same structure as the first case study: The active and reactive power will be compared at the only SUS with available measurement data and the feeding point. Then, the voltage profile will be analysed for the same SUS and additionally at the wind farm. The chapters will be kept relatively short as the methodology has been already described for case study #1. Whenever ambiguities arise, it is advised to refer to the chapters of the first case study. 4.2.1. Active power output at SUS’s The comparison between measured and simulated active power output of the only SUS mon01 can be seen in Figure 4-13. Two things stand out: - During night, the active power flow is still reasonably well modelled - During day, there exist now large deviations from the actual profile due to the suboptimal PV modelling

Figure 4-13: Comparison of modelled and measured active power flow at the SUS mon01 for a period of three days Table 4-5 shows the error margins. The day-time values are substantially higher compared to the SUS’s at the Gundersheim feeder (see Table 4-1). This is in line with the high distance from the reference PV plant. The nightly RMSE value is also higher which comes from the fact that the load is 3x higher. Therefore, the NRMSE is actually lower.

Table 4-5: Error margin for the active power flow at the mon01 SUS

SUS PV capacity RMSE NRMSE P @ mon01 during night 9.8 kW 12.3% 147 kW P @ mon01 during day 31.4 kW 22.3%

4.2.2. Active power output at the feeding point As for the Freimersheim feeder, the active power modelling of the total feeder, that can be compared at the feeding point, is substantially better than the one at the single SUS. However, the deviations should be smaller compared to the feeder Freimersheim because there is only 3.3 MW of PV capacity while the 9.6 MW from the wind farm come directly from measurements. The model still shows high RMSE values in Table 4-6, though, also in the night. It was identified that the model lacks here some load information. So while the model itself works well, the input demand is faulty and leads to deviating power flows.

Figure 4-14: Comparison of the active power flow at the Flörsheim-Dalsheim feeding point

Table 4-6: Error margin for the active power flow at the Flörsheim-Dalsheim feeding point

Comparison of PV capacity RMSE NRMSE8 P @ feeding point during night 0.35 MW 3.61% 3.3 MW P @ feeding point during day 0.75 MW 7.37%

4.2.3. Reactive power output at SUS’s and feeding point Mon01 is not one of the areas of interest, so there is no detailed layout of the grid modelled. Instead, the load and PV is also here aggregated. As described before, the best reactive power modelling is achieved with the following values: - PF = 0.98 inductive for aggregated loads (VAr consuming) - PF = 0.98 capacitive for aggregated PVs (VAr consuming) On the SUS level (for mon01) the comparison can be seen inFigure 4-15. For the entire feeder (at the feeding point) the comparison is displayed in Figure 4-16.

8 The NRMSE is here lower than for the Freimersheim feeder. This is however due to the highly fluctuating wind generation. This is not bound to daily cycles, like the sun for the PV, so therefore the range in the denominator is much larger than for the Freimersheim case, and hence the NRMSE lower. 65

Figure 4-15: Measured vs. simulated reactive power flow at the SUS mon01

Figure 4-16: Comparison of the reactive power flow at the Flörsheim-Dalsheim feeding point

Table 4-7: Error margin for the reactive power flow at the Flörsheim-Dalsheim feeding point

Comparison of RMSE NRMSE Q @ feeding point during night 0.22 MVAr 5.7% Q @ feeding point during day 0.29 MVAr 7.1%

4.2.4. Voltage output at SUS’s The voltage can be compared with the measurements taken at the SUS mon01 as well as at the wind farm (wah06). The mismatches in active power flow do not affect the voltage so strongly. Therefore, the simulated voltage profile follows the measurements more smoothly and also the error margins in Table 4-8 show better results.

Figure 4-17: Comparison of the voltage at the SUS mon01

Figure 4-18: Comparison of the voltage at the wind farm (SUS wah06) The margin of error is the following for the two substations:

Table 4-8: Error margin for the voltage at the SUS’s mon01 and wah06

Comparison of RMSE NRMSE Voltage @ mon01 0.081 kV 12.7% Voltage @ wah06 (wind farm) 0.081 kV 7.7%

4.3. Limitations of the model validation

As is often the case with research projects such as this one, there are some limitations when it comes to the model validation. Typically, it is a challenge to use the limited amount of data in the best way possible in order to still achieve a meaningful outcome. In this thesis work, the data was not only limited but also sometimes coming from different sources and in different formats. For example, some of the measurement data obtained came from 15-minute average values while other was coming from 10-minute average values. Simplifications have to be made in such cases that lead to a slightly different model validation outcome. Furthermore, during the course of the thesis work a number of errors in the measurement data acquisition was found and had to be corrected. In some cases, the measurement values were multiplied by a wrong factor or had other discrepancies, while

68 in other cases the time stamp was incorrect, so the measurement data had to be time shifted. Another key aspect was that the SLP data used in this model was from the year 2013. RLM data and obtained measurements, meanwhile, were obtained in the year 2015. Therefore, the model was using SLP profiles from 2013, to reflect the year 2015. This leads also to discrepancies in various forms: For example, weekdays and weekends are not on the same dates, while electric heating demand can differ quite substantially if the ambient temperature for a specific date was very different in the one year to the other. However, this drawback can be turned into an advantage. Despite the lack of up-to-date load data, the model validation results were still very acceptable. Hence, this shows a good applicability for forecasting. The model could use load data from the previous year in order to forecast active power flows and voltage levels for a future time period. Lastly, the model does not provide a clear validation on the amount of PV. In the previous chapters, it had been shown that the active power flow at the feeding points resembles quite accurately the real, measured behaviour. However, it could be better to analyse a day with a clear blue sky, so that full PV output would be expected. In this case, differences between measured and simulated active power flow could be detected easily and would allow better conclusions about the performance of the PV modelling. Due to a late detection of this possible model improvement and limited time availability, this comparison has not been done though. This concludes the model validation.

5. SCENARIO DESCRIPTIONS

After successfully validating the model (however, for the feeder Flörsheim-Dalsheim less so), the model shall now be used to analyse the current condition at the two feeders in order to answer the following questions:  How large are the voltage deviations already caused by DG on the MV and LV level?  How much more DG could be integrated on the LV level?  What is the potential impact of reactive power controls on the LV level? For that, the three modelled LV networks were chosen: fre03 at the feeder Gundersheim and mon09/ofs07 at the feeder Flörsheim-Dalsheim. In order to conceptualize the maximum impact that DG has and can have on the two feeders, a worst case scenario has been developed for both cases. This will then be followed by a description of different future PV penetration scenarios in combination with reactive power control strategies. As before, this will be first done for the first feeder, then for the second one.

5.1. Case study #1 – Freimersheim03

First, the conditions are described to define the worst case scenario. The results are shortly discussed. Afterwards, the scenarios for prospective PV expansions and reactive power controls are described.

5.1.1. Worst case scenario description

For the worst case scenario, the maximum possible voltage will be assumed. For this, it is necessary that every PV plant at the Gundersheim feeder produces at its maximum output. This can be any day between 11.00 am and 15.45 pm, as in this time range the maximum power output has been observed for the large scale PV plant that serves as reference. To have the largest voltage rise possible also the complementary lowest load in fre03 must be assumed. By looking at the calculated yearly SLP profile for fre03, the lowest load during noon time happens on the 22nd of August 2015, 15.30-15.45. The total demand in the entire feeder is 1424 kW (74% of the average load) in this 15- minute interval, while the total PV infeed is 9619 kW. This results in a backfeed of about 7.7 MW (considering grid losses). Note that the observed maximum backfeed in 2015 was 7.2 MW (see Table 3-4), so this is a very unlikely case. It is assumed that, combined with potential production from wind farms and PVs from other feeders, the HV/MV transformer already operates at the left end of its advanced 70

OLTC control (blue data points in Figure 4-12). There, the worst possible state is assumed, so the upper side of the deadband where the voltage is 20.48 kV or 1.024 p.u. This results into a voltage distance diagram as seen in Figure 5-1. The figure depicts the voltage level across the whole feeder Gundersheim. Due to the large 7.3 MW PV plant, the voltage continuously rises alongside the feeder. After that the MV voltage is relatively constant because the small scale PV capacity is not high enough to have a big influence on the MV. But as indicated, the voltage within the LV network of fre03 rises substantially. This is due to the high resistance of the LV cables. The PV infeed has a high influence on the voltage there. As can further be observed, there is one critical node in the LV network that comes close to the maximum voltage limit. A relatively large PV plant with 49 kW is connected to a comparatively long line (= high resistance). The combined effect results into a high voltage rise, up to 1.094 p.u. or 109.4%. However, the tap changer position of the fre03 transformer is not in its neutral position but instead at the position 20.8kV/400V. Therefore, the voltage at the LV side is heavily decreased, which can be seen in Figure 5-2. The voltage of the critical node is with 105.3% now actually lower than the voltage of the MV grid. The situation with the adjusted tap changer position will be used as the base case for the different PV penetration scenarios.

Figure 5-1: Voltage distance graph for the worst case scenario at the Gundersheim feeder, without adjusted tap changer position

Figure 5-2: Voltage distance graph for the worst case scenario at the Gundersheim feeder, with adjusted tap changer position 72

5.1.2. Comparison with highest observed voltages In order to put the simulated values for the worst case scenario into perspective, they have been compared with the maximum voltage measurements obtained in 2015. The comparison has only been done for fre03 and the large scale PV plant. The measurements for fre03 are from the LV side and therefore correspond to the case as depicted in Figure 5-2. The measurements for the other SUS’s in Freimersheim have not been included as they are also measured at the LV side and have adapted tap changer positions. Therefore, they are not well comparable with the simulated MV voltage.

Table 5-1: Comparison between maximum voltage measurements and simulated values for the worst case scenario of feeder #1 Voltage measured [p.u.] Voltage simulated [p.u.] fre03 (LV side) 1,033* 1,031 fre08 (large scale PV plant) 1,08** 1,07

*10 min average, 1-phase measurements **15 min average, 3-phase measurements

As can be seen, the measured voltage is by 0.01 p.u. higher than the simulated voltage. This is despite using 15-minute average values for all three phases. Instantaneous values, measured only on one phase, would result in an even higher measured voltage. The voltage at the LV side of fre03 corresponds better with the simulated voltage, with only 0.002 p.u. difference. This is despite using single phase measurements (single phase measurements were not available for fre08). However, also here the instantaneous voltage value would be higher. This shows, hence, that the real network has somewhat higher voltage values that the model doesn’t take into account. 5.1.3. Description of PV penetration scenarios and possible reactive power controls A good way to compare different scenarios and possible voltage controls, is by determining the hosting capacity for each scenario and comparing the results. The underlying principle of the hosting capacity is to gradually increase the PV penetration levels until some violation occurs. These violations can either be the upper voltage limit (110% Unominal) or the maximum loading of the transformer or the cables. However, in this study only the maximum voltage limit was considered as a restriction. 5.1.3.1. Tap changer position Two possibilities have been examined: The tap changer position in its neutral (20kV/400V) or in its currently adjusted (20.8kV/400V) position. This comparison has been only done for one scenario, though. 5.1.3.2. Increase of PV penetration Three different scenarios are considered for possible PV expansions:

- The output power of the existing PV units is gradually increased, until the upper voltage limit is reached. There are currently six PV installations out of 85 customers in fre03 (7% penetration). - The PV capacity in fre03 is doubled by randomly adding new PV installations for 20% of the remaining customers. This means an average PV size of 7.8 kW. Afterwards, the output power of all PVs is gradually increased. - The PV capacity in fre03 is quadrupled (x4) by randomly adding new PV installations for 60% of the remaining customers. The average size is again 7.8 kW. Afterwards, the output power of all PVs is gradually increased. 5.1.3.3. Reactive power control Three scenarios will be examined: - No reactive power control (PF = 1 for all PVs) - Q(U) regulation with deadband - Best case (PF = 0.95 cap. (Q consuming) for all PVs) The applied Q(U) control can be seen in Figure 5-3. It is assumed that the inverter has available capacities so that the active power doesn’t have to be reduced. The best case represents a situation where all PV units take fully part in the reactive power control. This scenario could be achieved through different control options: The cos φ(P) control would result in this behaviour but only if all PV plants have maximum power output. The actual voltage rise could be worse if some PV plants on parallel lines are partly covered by clouds and don’t contribute by compensating the voltage rise across the transformer. More on this is described in the results. Other options that reach the best case scenario are coordinated controls where the most critical node in the whole network determines the power factor for all PV units. So should there be a critical situation with a overvoltage at one node in the system, this node could communicate to all other PV inverters in the local grid to reduce their power factor to 0.95 cap. (Q consuming).

Figure 5-3: Applied Q(U) control with deadband

5.2. Case study #2 – Monsheim10 & Offstein09

Similarly, the worst case scenario will be described and analysed for the second feeder as well as the different investigated scenarios described. 5.2.1. Worst case scenario description In the worst case scenario for the second feeder, not only the residential PV units produce at full power output (with 3.3 MW) but also the 9.6 MW wind farm. The lowest demand occurred as well on the 22nd of August 2015 between 15.30 and 15.45 pm. For this 15-minute interval the total feeder load is 1839 kW (58% of the average load). The resulting backfeed is 10.4 MW. This is compared to a minimum observed backfeed of 7.9 MW in 2015. For the busbar voltage, again the voltage at the upper limit of the HV/MV tap changer deadband has been taken (20.48 kV or 1.024 p.u.). The tap changers of the two areas of interest, mon09 and ofs07, are both in neutral position. Figure 5-4 shows the voltage-distance plot for the Flörsheim-Dalsheim feeder.

Figure 5-4: Voltage distance graph for the worst case scenario at the Flörsheim-Dalsheim feeder As can be observed the large wind farm is located at the end of one of the MV side branches and increases therefore substantially the voltage across the feeder, raising the 75 voltage above 1.08 p.u. Especially residential PV installations close to the wind farm have only a narrow voltage band left, so the hosting capacity in this region should be very low. However, the two areas of interest are located at the other MV side branch. In mon09, the PV penetration and distribution seems to be unproblematic as the maximum voltage is only at about 106.4% Unominal. In ofs07, however, one particular line sticks out with a voltage of 108.7%. At this critical node a 79 kW PV plant is installed. For future work in this project, it is commended to focus rather on substations located closely to the wind farm. Also there a similar amount of PV exists and voltage limitations should be reached far more often. Therefore, voltage controls could be tested more easily at those locations. 5.2.2. Comparison with highest observed voltages Also for the second feeder, the voltages from the worst case scenario are compared with real measurements. Also here the measurements come from different data sources. Some are averaged for three phases, while others are available for each phase. In ofs07 and mon09 phasor measurement units were installed in the beginning of 2016. These measured the instantaneous single-phase voltage at a few locations. The values obtained in the summer 2016 have been compared with the simulations. The comparison can be seen in Table 5-2.

Table 5-2: Comparison between maximum voltage measurements and simulated values for the worst case scenario of feeder #2 Voltage measured [p.u.] Voltage simulated [p.u.] mon01 (LV side) 1,064* 1,057 wah06 (large scale wind farm) 1,081** 1,082 mon09: Cable distributor #06 1,063*** 1,064 mon09: Cable distributor #09 1,063*** 1,064 ofs07: Cable distributor #21 1,061*** 1,062 ofs07: Critical PV plant9 1,084*** 1,090

*15 min average, 3-phase measurements **15 min average, 1-phase measurements ***Instantaneous, 1-phase measurement from phasor measurement unit

As in the case of the first feeder, also here the mon01 voltage is slightly higher, even though 15-minute, 3-phase measurement values are used and the instantaneous, 1- phase value would be even higher. Similarly, the measured value comes from the LV side while the simulated value is the MV value. Therefore, it doesn’t take into account a possible voltage change across the transformer. At the wind farm the voltage is almost the same, though also here the instantaneous value would be higher. This stands in contrast to the measurement values from the phasor measurement units. At all units the measured voltage, despite being instantaneous and single-phase, was always slightly lower than in the simulations. Therefore, the model seems to show good conformity here.

9 This is the location of the 79 kW PV plant where the highest voltage was observed in the simulations 76

5.2.3. Description of PV penetration scenarios and possible reactive power controls The scenarios for the two examined LV networks at the second feeder are exactly the same as for the feeder Gundersheim. Each network has been assessed separately, so the hosting capacity is always only increased for one of the LV grids. The only difference is that the tap position was kept in the neutral position as this reflects the current state of the network. 5.2.3.1. Increase of PV penetration The same three scenarios are considered as in case study #1: - The output power of the existing PV units is gradually increased, until the upper voltage limit is reached. In mon09, there are currently 19 PV installations out of 134 customers (14% penetration). In ofs07, there are 27 PV installations out of 240 customers (11% penetration). - The PV capacity in mon09 / ofs07 is doubled by randomly adding new PV installations for 20% of the remaining customers. This means an average PV size of 6.5 kW / 6.4 kW, respectively. Afterwards, the output power of all PVs is gradually increased. - The PV capacity in mon09 / ofs07 is quadrupled (x4) by randomly adding new PV installations for 60% of the remaining customers. The average size is again 6.5 kW / 6.4 kW. Afterwards, the output power of all PVs is gradually increased. 5.2.3.2. Reactive power control The same reactive power controls are applied: - No reactive power control (PF = 1 for all PVs) - Q(U) regulation with deadband (see Figure 5-3) - Best case (PF = 0.95 cap. (Q consuming) for all PVs) Voltage dependent controls could be particularly effective at this feeder, since the highest voltage is only reached by a combination of high enough wind speeds and high solar irradiation. These conditions are less likely to occur, so the control doesn’t come into effect so often. Hence, also reactive power flows and associated losses are reduced in the feeder.in the feeder.

6. RESULTS

In this chapter, the results of the different reactive power control strategies are presented. No reactive power control, Q(U) control with deadband and an optimal reactive power control are each investigated under different PV penetration scenarios. Their effectiveness is analysed in terms of increasing the hosting capacity. This has been evaluated for each of the three modelled LV networks at the two feeders. Therefore, each LV network is examined by a separate chapter in which the different scenarios are discussed one by one. The final chapter gives a conclusion about the different control options. During this process, it will be discussed what implications reactive power control has on the voltage of the affected node as well as on other nodes in the network.

6.1. Freimersheim 03

The first LV network to be examined is fre03, located at the feeder forming the first case study. 6.1.1. Tap changer position The comparison between neutral and adjusted tap changer position has only been done for one scenario: No newly added PV, no reactive power control. For all subsequent scenarios (Increase of PV penetration & Reactive power controls), the hosting capacity will only be computed for the case of an adjusted tap changer. If we gradually increase the PV output power of the existing PV plants, the PV penetration can only be increased by 24% for the neutral position case. By adjusting the tap changer position, a large part of the voltage band is made available and the SUS can take up an additional 178% of PV hosting capacity (seen in Figure 5-2). The following hosting capacity values compared to the base case are reached:

Table 6-1: Hosting Capacity for fre03, with and without adjusted tap changer position (PV penetration scenario #1, no reactive power control)

Hosting Increase compared

Capacity to base case Base case 110 kW - Neutral tap changer position 136 kW 24% Adjusted tap changer position 305 kW 178%

Hence, by adjusting the tap changer position, the hosting capacity can be increased by 124% (278% - 1). This would also be the increase in hosting capacity if an OLTC is 124% installed that has the same tap changer positions. 6.1.2. Increase of PV penetration – Scenario #1 With a 20.8kV/400V tap changer setting and no reactive power control, the Hosting Capacity can be increased to 278% as just described. For this scenario, Figure 6-1 shows the impact that a Q(U) control has.

Figure 6-1: Voltage distance graph for fre03 in case of PV penetration scenario #1, with and without Q(U) control applied As can be seen not only drops the maximum voltage by about 0.01 p.u. but also the voltage at the transformer drops by 0.0036 p.u. Hence, 36% of the voltage drop happen across the transformer while 64% of the voltage drop happen across the line. This voltage drop across the transformer also effects the rest of the network: The voltage at every node drops somewhat. However, as can be observed the Q(U) control is very ineffective because only the critical node is above the 1.07 p.u. threshold. No other PV plant in the network contributes by consuming reactive power. Still, the voltage rise compensation translates into a possible increase in hosting capacity of 30.6% due to the Q(U) control. Would all PV plants contribute by setting their 79 power factor to 0.95 cap. (= best case) the hosting capacity can however be increased by 54.3%. Table 6-2 summarizes the different possibilities.

Table 6-2: Impact of reactive power controls on the hosting capacity; for fre03 and PV penetration scenario #1

Increase Control Hosting Effectiveness of compared to strategy capacity Q(U) / Best case base case Base case 110 kW - - PV No RPC 305 kW +178% - scenario Q(U) control 398 kW +263% +30.6% #1 Best case 470 kW +429% +54.3%

6.1.3. Increase of PV penetration – Scenario #2 and #3 If new PV units are added first to the LV network a higher overall PV capacity can be reached. This relates to the fact that many of these new PVs are added to other lines than the one with the critical node. While a new PV plant at the line with the critical node impacts not only the transformer voltage but also the line voltage, a PV plant at a separate line impacts solely the transformer voltage. Therefore, the latter has a smaller effect on the critical node per kW produced. Table 6-3 shows the different increases in hosting capacity for the two PV scenarios.

Table 6-3: Impact of reactive power controls on the hosting capacity; for fre03 and PV penetration scenario #2 and #3

Increase Control Hosting Effectiveness of compared to strategy capacity Q(U) / Best case base case Base case 110 kW - - PV No RPC 522 kW +376% - scenario Q(U) control 662 kW +504% +26.9% #2 Best case 1011 kW +822% +93.7% PV No RPC 851 kW +676% - scenario Q(U) control 1079 kW +884% +26.8% #3 Best case No maximum No maximum No maximum

There are two curious observations to be made: First of all, while the overall hosting capacity has increased, the Q(U) effectiveness actually decreased from PV scenario 1 to PV scenario 3. To explain this a look should be taken at Figure 6-2.

Figure 6-2: Voltage distance graph for fre03 in case of PV penetration scenario #3, with Q(U) control applied The PV installations indicated by the green arrows contribute to the voltage rise at the critical node while their Q(U) contribution is relatively weak as most of the PV plants operate below 1.07 p.u. To put it differently: The share of PV plants located at the line with the critical node and participating at the Q(U) control has decreased, compared to the first PV scenario. The second observation is that in the third PV scenario the hosting capacity is actually unlimited if all PV plants operate at a PF of 0.95 cap. This is due to PV plants that are located relatively close to the transformer and not at the line of the critical node. For these plants, the reactance of the transformer is comparably much higher than the resistance of the line. Therefore, the R/X ratio is actually much smaller than 1. Hence, their voltage compensation effect actually outweighs their voltage rise effect induced by active power infeed. The overall effect of many of these PV plants seems to predominate in the LV network, so that with increasing PV penetration the voltage actually declines. Therefore, the upper voltage limit is never reached and an infinite amount could theoretically be added. Of course, this is only possible if thermal loading is ignored. If the maximum loading of the transformer and the lines is taken into account, the maximum PV penetration will likely be also already reached in some of the other scenarios. However, this has not been investigated.

6.2. Monsheim 09

6.2.1. Increase of PV penetration – Scenario #1 The SUS mon09 has a very different PV distribution than fre03. While fre03 had a single, big PV plant at the end of a long line, in mon09 it is actually the other way around. Most PV plants are located relatively close to the MV/LV transformer and their size is more evenly spread out. Therefore, the PV penetration can actually be quadrupled before a voltage violation occurs (see Table 6-4). The resulting voltage- distance profile is seen in Figure 6-3.

Figure 6-3: Voltage distance graph for mon09 in case of PV penetration scenario #1, without Q(U) control applied In addition, the voltage at the transformer is already close to 1.07 p.u. Therefore, almost all PV plants operate in the voltage range of the Q(U) control, with actually three nodes reaching the upper voltage limit. This is an unusual setup and leads to a very effective Q(U) control: The hosting capacity can be increased by more than 50%. If all PV inverters operate at 0.95 capacitive (Best case), no maximum can be reached. Again, the compensating effect seems to have a bigger influence at the voltage.

Table 6-4: Impact of reactive power controls on the hosting capacity; for mon09 and PV penetration scenario #1

Increase Control Hosting Effectiveness of compared to strategy capacity Q(U) / Best case base case Base case 162 kW - - PV No RPC 645 kW +299% - scenario Q(U) control 1007 kW +523% +56.1% #1 Best case No maximum No maximum No maximum

6.2.2. Increase of PV penetration – Scenario #2 and #3 Again, new PV units are first added to the LV network. This results in an actual decrease in hosting capacity for the second PV scenario, as opposed to the case for fre03. The reason for this is that the initial PV distribution was very close to the substation. Therefore, their cable resistance is comparably low and hence, comparatively more active power can be added for the same voltage rise (see equation 2-2). By adding randomly new PV plants, this balance is tilted to the worse. Particularly new plants added to the upper right feeder in Figure 6-3 increase the voltage there more strongly because the line is longer (= increased resistance). This effect is revised when the PV penetration is increased even further (Scenario #3, +60% PV penetration). This is due to the positive effects resulting from the higher distribution of PV plants. If they are spread out over more lines, also the voltage rise is spread out over more lines (compare with Figure 6-4) and more PV can be added overall.

Table 6-5: Impact of reactive power controls on the hosting capacity; for mon09 and PV penetration scenario #2 and #3

Increase Control Hosting Effectiveness of compared to strategy capacity Q(U) / Best case base case

Base case 162 kW - - PV No RPC 569 kW +252% - scenario Q(U) control 737 kW +356% +29.5% #2 Best case No maximum No maximum No maximum PV No RPC 776 kW +380% - scenario Q(U) control 1028 kW +536% +32.5% #3 Best case No maximum No maximum No maximum

Furthermore, the effectiveness of the Q(U) control decreases to about 30% as only one single line reaches the voltage limit. Figure 6-4 shows the voltage in mon09 for PV scenario #3 with and without Q(U) control.

Lastly, as in the first PV scenario, both cases do not show any voltage violation if an appropriate control would be chosen that enables a maximum reactive power consumption.

Figure 6-4: Voltage distance graph for mon09 in case of PV penetration scenario #3, with and without Q(U) control applied

6.3. Offstein 07

6.3.1. Increase of PV penetration – Scenario #1 In Offstein 07 there is a similar situation as in Freimersheim 03. There is one large PV plant that leads to a large voltage rise at its node. However, compared to fre03, the PV plant is much closer located to the substation. Table 6-6 shows again the hosting capacity for the first PV scenario.

Table 6-6: Impact of reactive power controls on the hosting capacity; for ofs07 and PV penetration scenario #1

Increase Control Hosting Effectiveness of compared to strategy capacity Q(U) / Best case base case Base case 276 kW - - PV No RPC 359 kW +30% - scenario Q(U) control 403 kW +46% +12.3% #1 Best case 464 kW +68% +29.2%

The voltage at the critical node is already close to 110% Unominal, therefore, only another 30% can be added compared to the base case. Furthermore, with the Q(U) control only another 12.3% in PV power can be added which is very low compared to the other LV networks. This can be mostly attributed to the high R/X ratio of the ofs07 LV grid. A large part of the grid is still connected through overhead lines with R/X values of about 5.55 (compared to R/X ratio of 2.55 in fre03 and mon09, see Table 3-3 and Table 3-8). Also the larger sized PV plant is located in that part. Additionally, most of the other PVs are below the 1.07 p.u. threshold and do not contribute to the voltage compensation. Hence, the unusually low value for both the Q(U) control and the optimal case. 6.3.2. Increase of PV penetration – Scenario #2 and #3 Since the voltage at the critical node was already exaggerated, the randomly added PVs were only added at lines other than the critical one. This was done in part because there were not many other customers (only five) on the same line, so the DSO might take this into consideration and either not allow any new connections there or try to find another solution, e.g. reconfigure the grid in order to separate the critical node from other nodes with PV plants. With this setting, the PV hosting capacity can again be substantially increased, up by 260% for PV scenario #3, see Table 6-7. Furthermore, the effectiveness of the Q(U) control increases. This is the opposite effect of what was happening in fre03. Because of the fact that all PVs are added on other lines than the critical one their voltage decreasing effect due to VAr consumption is bigger than their voltage increasing effect due to active power infeed. This can also be illustrated by the following figure:

Figure 6-5: Effect of pure P infeed (green line) vs. P infeed combined with Q consumption (blue line) on the voltage The R/X ratio of the MV/LV transformer is very low, with X >> R. Therefore, pure active power infeed raises the voltage across the voltage almost not at all. If, additionally, Q is consumed by the PV plant, two effects come into place: The voltage reduction across the line due to the line reactance and the voltage reduction across the transformer due to the high transformer reactance. These two effects add up on each on the line end, where the PV unit is located. But also at the LV side of the transformer the voltage is decreased. Therefore, the voltage at the LV side of the transformer is decreased with every other PV plant that is consuming reactive power. If this PV plant is now at another line than the one with the critical node, it effectively decreases the voltage at the critical node. Still, the effectiveness of the Q(U) regulation stays below the one in fre03 (12-20% compared to 25-30% in fre03). This is due to the higher R/X ratio of the overhead lines in the ofs07 LV network. In the future, these overhead lines will however be replaced by underground cables, which will bring this scenario closer to the other ones.

Table 6-7: Impact of reactive power controls on the hosting capacity; for ofs07 and PV penetration scenario #2 and #3

Increase Control Hosting Effectiveness of compared to strategy capacity Q(U) / Best case base case Base case 276 kW - - PV No RPC 630 kW +128% - scenario Q(U) control 724 kW +162% +14.9% #2 Best case 1045 kW +278% +65.8% PV No RPC 995 kW +260% - scenario Q(U) control 1194 kW +332% +20.0% #3 Best case 2465 kW +792% +147.8%

6.4. Conclusion on Q(U) control

The effectiveness of the Q(U) control has a diverse range of values between 12% and 56%. However, in particular the 56% value was identified as an outlier and usually not to be expected. Most values lie therefore in a range between 20% and 33% which coincides well with the literature on Q(U) control.

Table 6-8: Increase of hosting capacity by the Q(U) control for all three LV networks

Increase of Hosting Capacity by Q(U) PV Scenario fre03 mon09 ofs07 #1 30.6% 56.1% 12.3% #2 26.9% 29.5% 14.9% #3 26.8% 32.5% 20.0%

If PV penetration is strongly concentrated on very few lines, single weak lines can pose a problem for the Q(U) control, as most other PV plants operate below the voltage threshold of 1.07 p.u. This is a general problem for the Q(U) control that has been clearly demonstrated. This also carries with it the problem of inequality: In some scenarios, one single PV plant was the sole consumer of reactive power. This can mean decreased revenue for the PV plant operator, so compensation mechanisms would probably need to be developed, if a Q(U) control gets widely adopted. If PV penetration is distributed more widely, as in the case of the third PV penetration scenario, the obtained increases of hosting capacity by the Q(U) control even out into more expected and average values. Mon09 and fre03 have about the same R/X ratios in the respective networks, but for mon09 a higher share of PV plants operates above the 1.07 p.u. voltage threshold and lead therefore to a better performance. Meanwhile, the effectiveness of the Q(U) control shows lower values for ofs07. This can clearly be attributed to the higher R/X ratios there. Still, also here the hosting capacity could be increased by 20% if the PV penetration is not concentrated on only a few lines.

6.5. Conclusion on the best case

The best case shows in turn the full potential that could be achievable with an appropriate control. Curiously, a number of the scenarios lead to the fact, that no voltage maximum could be reached. This is due to the voltage decrease across the high reactance of the transformer, which prevails over the voltage increases caused by the PV active power and the resistance of lines. However, clearly the control option will be limited by the maximum loading of either lines or transformer. Comparing the values, that have been obtained, with the values from the Q(U) control, we can see that the Q(U) control does by far not exploit the full potential it

87 could have. The best case shows that the effectiveness could be increased by a factor between 2 up to 7 (not considering thermal loadings).

Table 6-9: Increase of hosting capacity by the best case for all three LV networks

Increase of Hosting Capacity by best case (all PF = 0.95 cap.) PV Scenario fre03 mon09 ofs07 #1 54.3% No maximum reached 29.2% #2 93.7% No maximum reached 65.8% #3 No maximum reached No maximum reached 147.8%

7. CONCLUSION

The increasing amount of renewable energy connected largely to the medium and low voltage grid poses new challenges for the operation of the distribution grid. One of the most important issues that arise are voltage violations and line/transformer loading limits due to the high active power infeed. Currently, different measures are investigated to evaluate their effectiveness and cost-efficiency because traditional grid reinforcements are an expensive alternative. During the course of this thesis, a model has been developed of two medium voltage feeders, at which a total of three low voltage networks was modelled in detail. The model utilized largely standard load profiles for the load modelling and measurement data from large DG units for the DG modelling, that were also used as reference to simulate other DG units in the network. Under these circumstances, the model was validated and very good results were obtained for one of the feeders. For the other feeder, the validation was however hampered by insufficient DG modelling and faulty input data. This led to larger errors, but it can still be argued that the feeder shows a good enough representation of the real network. On the basis of these two models, worst case scenarios have been developed. These show for some of the LV networks that the maximum voltage might still be below the 110% Unominal threshold but coming very close to it. Hence, measures should be undertaken in these grids. This situation is by a large part amplified by large scale DG units that are connected to the medium voltage (in one case a solar farm, in the other a wind farm). At these DG units, critical voltage levels are already obtained so it doesn’t take much to result into voltage violations in nearby LV grids where smaller DG units can cause larger voltage deviations. Building up on the worst cases, different PV penetration scenarios have been developed on which the following three reactive power controls are tested: No reactive power control, Q(U) control with deadband, and a best case where all DG participate with their maximum reactive power consumption (assuming a power factor of 0.95 cap.). Most scenarios show that even without any reactive power control, the PV capacity could still be substantially increased. However, care should be taken with these values because the model uses oversimplifications such as: balanced load flow calculations; standard load profiles; and 15-minute averages. Hence, the actual network might be closer to its limits. Regardless, the potential of the Q(U) control and the fully achievable potential (through the best case) could be shown. The Q(U) control shows a typical range of 20-33% effectiveness, meaning that the hosting capacity can be increased by that amount. Lower values were obtained for a network with high R/X ratios which is in conformation with research that the effectiveness depends largely on the R/X ratio. Nevertheless, some outliers were observed, with one value as low as 12% and another one as high as 56%, so extreme network setups can result into a broad range of effectiveness due to 89 badly or, in the opposite case, perfectly distributed generation. Furthermore, it has been shown that the Q(U) control leads to highly diversified reactive power contributions. This means that some PV owners would be highly disadvantaged and compensation measures would need to be developed. The effectiveness has however a big potential upwards, as shown by the best case. This one showed that the effectiveness could be often increased by a multiple than what is achieved by the Q(U) control. This can even lead to the possibility that a voltage rise is technically not reachable. However, for all of these scenarios, the thermal loading limits of the lines and transformer are fully omitted. More simulations could also conclude at which point those limitations affect the three LV networks under the various scenarios. It was further shown that an equally distributed PV penetration is essential for achieving high increases in hosting capacity and higher effectiveness of reactive power controls. This is due to the effect that the reactive power control at non-critical lines can support the voltage of critical lines. To conclude, this study affirmed that the Q(U) control and other reactive power controls are effective and economic measures to increase the hosting capacity in LV grids. However, this increases the reactive currents in the networks and it needs to be evaluated if the network is already close to its thermal limits. However, just by a reactive current increase of maximum 5% (an exact value was not determined), due to setting the power factor to up to 0.95 capacitive (Q consuming), the hosting capacity could be increased by 20-30% in the investigated grids. The results will hopefully be used to provide guidance in future work of the SNOOPI project and to develop effective measures to counter both voltage rise and loading limitations caused by distributed generation.

8. FUTURE WORK

There are different pathways that could be explored more and better and this chapter will give an overview on them, that would be interesting according to the author’s perspective. First of all, a number of model improvements can be made. These include: - Modelling of LV networks with individual SLPs for each customer (instead of taking the average) or finding other means of better load modelling in the LV grid - Better PV modelling by using more reference points or implementing models that include meteorological inputs, such as the movements of clouds - Using unbalanced load flow calculations - Using more and better measurement data as well as real life tests in order to better validate the model The model could then be replicated on other grids, in order to see if it achieves a similar performance. Furthermore, by making appropriate changes to the model it could be also used for forecasting purposes. Another topic would be the improvement and expansion of the model results: - Different Q(U) characteristics could be compared to show their performance - The associated reactive power flows for a period of time, e.g. one year, could be quantified. From this, the grid losses could be also calculated. - The impact of a P(U) control could be quantified. How much overall power reduction would lead to an increase of how much hosting capacity? - The impact of an OLTC or, equivalently, a changed tap changer position could be investigated for more LV networks and different scenarios. - Thermal limits of transformers and lines could and should be included. These would for example also show if reactive power controls would still be necessarily if an OLTC/changed tap changer position is applied. These could even be counterproductive as thermal limits might be reached before voltage limitations. In this regard, it should be also considered to look at the secondary substations close to the wind farm at the feeder #2. Here, the voltage is already close to its maximum limits in the MV grid and voltage violations are likely to occur much easier. However, both wind speeds and solar irradiation need to be high, which limits the periods in time where voltage violations could occur. Lastly, an interesting prospect for LV networks might be reducing the voltage by e.g. an adapted tap changer position, but then support the voltage through Q@night-capability, i.e. PV plants inject reactive power during times of low voltage. Investigations could be made if it is easy to integrate this capability in small scale PV inverters and what other requirements (e.g. a certain minimum distribution of PV plants in the system, so that single lines are not disadvantaged) are needed.

All these options (Q(U)/P(U) control, OLTC, Q@night-capability, etc.) could then be economically compared, e.g. by performing cost-benefit analyses. This would advise the project DSO, EWR Netz GmbH, about the best course of action and also other grid planners could draw important lessons from this.

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