Impact of Smart Inverters on Feeder Hosting Capacity of Distribution Networks

SPECIAL SECTION ON ADDRESSING CHALLENGING ISSUES OF GRIDS WITH HIGH PENETRATION OF GRID CONNECTED POWER CONVERTERS: TOWARDS FUTURE AND SMART GRIDS Received October 8, 2019, accepted November 4, 2019, date of publication November 11, 2019, date of current version November 20, 2019.

Digital Object Identifier 10.1109/ACCESS.2019.2952569

Impact of Smart Inverters on Feeder Hosting Capacity of Distribution Networks TAHA SELIM USTUN , (Member, IEEE), JUN HASHIMOTO (Member, IEEE), AND KENJI OTANI, (Member, IEEE) Fukushima Renewable Energy Institute, AIST (FREA), Koriyama 963-0215, Japan

Corresponding author: Taha Selim Ustun (ustun@ieee.org) This work was supported by the Ministry of Economy, Trade, and Industry.

ABSTRACT Penetration of renewable energy based distributed generators into power networks is limited.

This is due to several reasons such as intermittency of the generation, low-inertia during disturbances and high variation of local voltage and frequency. Traditionally, distribution networks are designed for a finite voltage drop with distance from feeder connection. When PV panels are deployed at households, their impact on the local voltage profile becomes very substantial. Especially, when there is high solar radiation, generated power increases and the local voltage exceeds the permittable limits. Smart inverters are introduced to tackle this problem. Unlike other inverters, they can follow the local network measurements and provide auxiliary voltage and frequency support. With these features, smart inverters have the ability to push the renewable energy penetration level further. To investigate this phenomenon, custom smart inverter models are developed. Power flow calculation steps are modified to include smart inverter’s active inputs. Then, feeder hosting capacity studies were run with conventional and smart inverters to investigate the improvement.

Developed models, modified power flow algorithm and results of simulations are reported. Findings show renewable energy share can be increased in networks with smart inverter deployments only, without making substantial changes to rest of the network.

INDEX TERMS Impact studies, renewable energy deployments, active distribution networks, advanced control, power system modeling, power system analysis.

I. INTRODUCTION

Grid operation and control have novel operational problems due to increasing renewable energy penetration levels [1].

Different stakeholders, such as policymakers or environment activists, look for ways of deploying more renewable energy-based generators [2]. This requires novel approaches and techniques toward electrical network operation [3], [4].

On the one hand, intermittency of renewable sources creates supply-demand issues for the operators [5]. While the inverter-interfaced topology of these generators brings down the overall inertia of the system and reduces controllability of power system parameters such as voltage and frequency [6].

To meet the renewable energy penetration objectives while keeping the power system operation stable, it is imperative that these generators provide auxiliary support for voltage and frequency control.

The associate editor coordinating the review of this manuscript and approving it for publication was Dinesh Kumar

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In order to tackle the negative impacts of increased PV penetration on the power networks, smart inverters (SIs) with advanced capabilities such as frequency support and VoltVAR control are being deployed [7]. These inverters have the ability to operate in all four quadrants of the P-Q plane.

Therefore, they may inject power into the system for longer periods of time, when conventional inverters (CIs) may have disconnected due to constraints. SI active contribute to grid operation and there are additional concepts that should be taken into account while studying their impacts [8].

It is a known fact that Electrical Power Companies (EPCOs) are notorious about adding any new device in their system. Considering the possible impacts of these devices on the grid, this is understandable. However, these companies adopt technologies that have proven benefits in power system operation or abilities to cut down the costs and increase the profits. Deployment of Flexible AC Transmission Systems (FACTS) to increase transmission capacity can be quoted as a real-life example [9]. Similarly, SIs can help voltage and frequency profiles in power networks while providing

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/

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clean energy. In this fashion, EPCOs can not only meet renewable energy penetration levels stipulated by the regulations but also gain equipment that can support its operation at the edges of the grid, where they are more likely to go out of bounds [10].

This potential of SIs increases their popularity in the research domain. There are publications that report coordinated droop methods to counter intermittency [11], comparison of different operation modes and their effectiveness under different conditions [12] as well as developing new ones [13].

In these works, SIs connected to transmission networks is considered while the real problem about voltage levels occur in distribution networks. Following from this, SIs benefits will be more immediate and apparent in controlling voltage levels in low-voltage systems, i.e. distribution networks.

Building on this immediate benefit, SIs may enable more renewables to be deployed as their auxiliary support makes voltage more stable and controllable. This is very enticing for EPCOs, since every SI deployment will result in better voltage control and more renewable energy-based generation.

Japanese power grid is of particular importance since the local EPCOs have the tendency to operate alone without much power exchange with other EPCOs. Considering the strategical move Japan has made recently to move from nuclear-dominant power grid to renewable energy-based generation portfolio, SIs may play a critical role in unlocking this potential [14].

There is limited work in the literature focusing on operation and behavior of SIs. Some studies focus on investigating the behavior of SIs under changing voltage conditions [15], [16].

Since SIs have different modes of operation, some researchers focus on prioritizing them for optimal operation [17]. It is also discussed in the literature, how effective the use of SIs as reactive power sources [18] in contrast to traditional power-electronics-based compensators [19], and to manage feeder voltage through them [20]. Distributed control [21] and peer-to-peer operation [22] are very popular in smart grid power studies, and SIs are also investigated with these schemes as well. In addition to these, some impact studies have been performed to investigate what happens to voltage profile or power flow with and without SIs [23]. Some of these studies focus on a particular grid system [24], [25] while others focus on SI impact on a specific functionality, such as islanding detection [26]. It has been reported in the literature that most of the simulation packages lack the ability to model SIs properly [27]. In these studies, there is no detail about how power flow calculations are performed with addition of active components such as SIs. Furthermore, while there have been impact studies to investigate how voltage and power profiles change with SI operation, a thorough Feeder Hosting Capacity (FHC) study has not been performed.

This short communication is addressed to this need in the literature. SIs can provide Volt-Var support to prevent unacceptable voltage levels in the system. This can increase the hosting capacity of a certain network. Therefore, there is an imminent need to study SI’s impact on voltage stability VOLUME 7, 2019

and FH). A commercial power system simulation software was modified to model SIs and include them in power flow iterations. Then, random inverter placement approach is utilized to gradually introduce SIs into a distribution network.

Particle Swarm Optimization (PSO) approach is used to find FHC with CIs and SIs. Results show SIs can support grid operation and provide real benefits, in terms of more captured renewable energy.

The rest of the paper is organized as follows: Section II summarizes the operating modes of SIs and how they benefit in increasing the overall FHC. Section III details the new software models developed for SIs and the modifications that are made to include them in power flow calculations.

Section IV details the feeder hosting capacity (FHC) study with random SI distribution and FHC results. Section 5 provides conclusions and discussions.

II. OVERVIEW OF SMART INVERTER CAPABILITIES

IEC 61850-90-7 defines SIs as inverters with advanced capabilities to support voltage and frequency [7]. Unlike CIs, these devices can provide and receive both real and reactive power, P and Q. Operating in all quadrants of power plane, SIs monitor voltage and frequency at their terminals and change their power outputs accordingly. Despite this general understanding, there is no clear definition of these capabilities for different locations or how a certification procedure should that place. To standardize the devices, IEC/TR 61850-90-7 developed a list of interoperability functions as shown in Table 1. The last two categories relate to information exchange for health check and pricing signals. All the other categories relate to controlling power output based on different control parameters. These parameters may be terminal voltage and frequency, and these can be utilized to change real power, P, or reactive power, Q, output of SI. Research shows that these active control abilities decrease burden of inverter-interfaced generators on the grid both economically and technically [28].

Before going into details of these modes, it is important to make some definitions that are used to characterize the operational behavior of these modes. The terms and their explanations are given in Table 2.

Figure 1 shows VV11 - Volt-Var management control with no impact on active power output. As shown, the control is intended to change reactive power output with changing voltage conditions. When the voltage is +/−1 % of Vref, no reactive power action is taken as this variation is within allowed limits. If voltage drops below 99% of Vref, then SI starts actively injection reactive power to support the voltage profile. If the voltage is less than or equal to 97 % of Vref, the reactive power output is fixed at 50 % of VArAval, this is saturation region. For any voltage level between 97 % and 99 % of Vref, reactive power output changes linearly.

Same observation can be done for overvoltage regions. If the voltage exceeds 103 % of Vref, 50 % VArAval is absorbed to counter the voltage rise.

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TABLE 1. Smart inverter capabilities listed in IEC61850-90-7.

TABLE 2. Terminology used to define SI modes.

As far as maximizing the local renewable generation, VV11 reactive power control mode holds a special place as it can reduce unintended voltage rise and avoid resultant generation capping. As shown in Figure 2, in passive distribution networks, a constant voltage drop is observed with respect to the distance from the feeder connection.

However, renewable energy-based distributed generation alters this paradigm completely. As shown in Figure 3, active power injection by the distributed generation changes voltage drop to voltage rise. Furthermore, this voltage drop is highest in the furthest node, which would be, otherwise, the node with lowest voltage. Traditional measures taken against voltage drop is planned to keep this node above a certain voltage level.

In other words, tap-changers and transformers are adjusted so that the furthest node is lifted.

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This mechanism backfires with distributed generation, as the furthest node causes the highest voltage rise in the system. In order to keep the voltage at acceptable limits, renewable generation is limited, i.e. free and clean energy is not captured, despite the fact that it is available, and the necessary equipment is deployed.

Figure 4 shows how limited renewable generation can be mitigated when VV11 mode is implemented and voltage rise is limited. As shown, deployed SIs implement volt-var curve defined in Figure 1 and actively absorb reactive power, Q, to counterbalance the voltage rise due to active power, P, injection. In this fashion, power grid can operate in a more stable manner and more renewable energy can be captured.

It is evident that every SI deployed contributes to voltage control and increases the amount of renewable

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FIGURE 1. VV11 Volt-Var control.

However, commercial simulation packages. Therefore, in this study, firstly simulation models are developed for SIs and necessary modes are implemented. Then, developed models are verified at different operation regions. Also, traditional power flow approach needs to be updated to include active generation nodes in distribution networks. These nodes do not only generate power but also change its operating point, in terms of power injection. Power flow algorithm needs to be revised so that behavior of SIs for different conditions is taken into account.

A. DEVELOPING SMART INVERTER SIMULATION MODEL

FIGURE 2. Voltage drop profile in passive distribution networks.

energy captured. This changes traditional FHC studies performed for CIs where there is no voltage support. Therefore, there is a need to study how increased penetration of SIs in a distribution network increases its FHC and the ability to capture more renewable energy. This is not only beneficial for grid stability but also for shorter pay-back times for deployments and easily meeting clean energy requirements set by the legislators.

III. MODELING AND ITERATION MODIFICATIONS FOR INCLUDING SMART INVERTERS IN POWER FLOW STUDIES

As expressed above, studying impact of SIs in voltage control and overall renewable energy capture is very important.

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Figure 5 shows SI model verification test for VV11 mode defined in Figure 1. SI is fed from a PV panel and is connected to an infinite bus over a small line impedance. PV panel sources 100 % power without any cloud coverage or interruptions. The voltage of the infinite bus is changed to observe developed SI model’s behavior. It is important to note that SI’s change their Q output according to the voltage observed on their terminals. Figure 5 shows screenshots of different times when a single SI operate in different zones of VV11.

Q values are not manually changed and different manually changed models are not used for different regions of VV11.

Only a single SI model reacts to voltage changes and operate in different regions of VV11 curve. Further information on dynamic operation of SIs can be found in [25].

As noted in the figures, maximum installed capacity, VAMax, is 50 kVA, while 40 kVA can be dedicated to real power, Wmax, and 30 kVAR to reactive power, VArMax. Furthermore, the characteristic points, P1-P4, in Figure 1 are also defined. This implementation varies from Figure 1 as the saturation regions are below 95 % and above 105 % Vref. While the linear zones are between 95-97 % Vref and 103-105 % Vref. Naturally, the dead zone is between 163529

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FIGURE 3. Voltage rise profile in active distribution networks.

97 and 103 % Vref. IEC 61850-90-7 standard defines the volt-var curve but does not stipulate voltage and reactive power levels. These can be adjusted to local implementations.

In this study, voltage levels are changed to meet local grid code.

Since PV panel is providing 100 % power, the real power output of SI is always 40 kW. VV11 is designed to provide VAR support without effecting the real power output. Therefore, it is expected that real power output stays at 40 kW for all operating conditions. This is verified in Figure 5.

At 94% Vref, (top left), SI is overexcited saturation mode and provides 50 % of the available VARs. Real power output is 40 kW and the installed capacity is 50 kVA, therefore, 30 kVAR is the available VARs. As expected, 15 kVARs is provided by the SI to support the voltage and bring it up.

At 96% Vref, (top right), SI still provides VARs to lift the grid voltage up. However, this time, it operates in the linear region and only provides 7.49 kVARs instead. At 100 % Vref, (bottom left), the voltage is in dead zone and no VAR output is required or provided. Final verification is for high voltage region, 104 % Vref, (bottom right) where SI provides negative VARs in the linear region to help reduce the voltage rise.

In a similar fashion, other modes are also implemented.

To illustrate the difference in operation, VV12 mode as well as verification studies of the developed model are 163530

given in Figure 6 below. It can be observed that this mode is designed to support voltage drops only. Furthermore, the reactive power output is represented as 100 % of Wmax, and not as 50 % of VArMax as in V11. At 103 % Vref, (bottom left) the maximum voltage level according to grid code is already reached and reactive power injection needs to be ceased. Real power output stays at 40 kW. On the other hand, for low voltage levels such as 95 % Vref, (bottom right) (e.g. voltage drop due to long lines), 100 % of Wmax which is 40 kVARs. Since SI’s capacity is only 50 kVA, real power output is reduced to 30 kW as VAR support takes precedence.

Same is repeated in the linear region, 100 % of Vref (top right), but this time VAR output is much less and real power output is not reduced.

B. CHANGES IN POWER FLOW ITERATIONS TO ACCOUNT FOR SI IMPAC

After simulation models are validated successfully, the power flow approach is tweaked to include SIs in the studies. Since SI is a component that actively contributes to power exchange in the system, it disrupts natural flow of power flow iterations.

In order to accommodate these, general power flow iteration approach has been modified as follows: 1. Solve the system with normal Power Flow Iterations 2. Solve for V, P and Q values for all nodes VOLUME 7, 2019

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FIGURE 4. SI support to mitigate voltage rise due to active power injection.

3. For nodes where an SI is present, use these initial V, P, Q values to determine its reaction (e.g. an SI may inject Q due to volt-var control setting) 4. Re-run steps 1 and 2 with new values of nodes, this time considering the changes in 3.

5. Check whether SIs reached their cut-off voltages (absolute minimum or maximum). If so, shut them down and exclude them in next iterations.

6. Repeat above process until the power flow calculations accommodate all SI reactions and a steady state is reached.

It goes without saying that it is possible to run these SIs as traditional inverters and such cases can be used as benchmark cases to compare with other scenarios where advanced inverter capabilities are activated.

IV. FHC ANALYSIS WITH SMART INVERTER DEPLOYMENTS

A typical distribution network that is widely used for similar impact studies, along with its circuit parameters, is shown in Fig.7 [25]. It is a 210 V system connected to a 6.6 kV bus and has 61 nodes. The nodes that are furthest are used for sampling as shown.

Firstly, three scenarios are simulated on the system with CIs: (i) under full-load, no generation; (ii) no-load VOLUME 7, 2019

full-generation and (iii) normal loading and generation conditions. As shown in Fig.8, these results give the worst low-voltage, worst high-voltage and normal operating voltage conditions. It is clearly observed that high PV generation causes over-voltage problems and needs to be capped. SIs with Volt-VAR control can help reduce the voltage and maximize the PV generation by avoiding capping.

FHC studies are performed to see impact of this capability of SIs. For FHC studies, a simple Particle Swarm Optimization (PSO) approach has been implemented. PV locations are randomly selected for a given penetration level. The penetration level is defined as the ratio of installed PV capacity to maximum load, Loadmax . For instance, if the penetration level is 30 %, the installed capacity of PV in the network is only 30 % of the Loadmax . This is randomly distributed to nodes. The nodes closer to the feeder can accommodate more PV penetration with less voltage deviations. Also, such a small amount of PV penetration is easy to accommodate in the feeder since the load is much higher. It is also more probable that a small amount is randomly distributed in the grid in a convenient way where the risk of voltage deviations is small.

When penetration level is increased, the reverse happens.

Most of the power is injected to the utility grid as the local consumption becomes small. Furthermore, more PV capacity 163531

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FIGURE 5. Implementation and verification of VV11 var support mode.

FIGURE 6. Implementation and verification of VV12 var support mode.

is distributed in the grid which increases the risk of voltage deviations.

After the simulation is run for a given penetration level, 30% of the ones with the worst voltage profile are passed on 163532

to next random list. In this way, it is ensured that more relevant cases with voltage issues are simulated. Scenarios are run until all the nodes exceed the voltage limit or the maximum penetration level is reached. Voltage limit is set to 105% VOLUME 7, 2019

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FIGURE 7. Simulated distribution network and its parameters (sampling nodes indicated).

FIGURE 8. Voltage profiles of the distribution network.

nominal voltage, Vn , and penetration range is swept from 30% to 2000% with initial steps of 4%. When node voltage reaches values close to 105%, penetration step is reduced to 1% for higher resolution.

In order to see the impact of loading, three different loading values (LDs) are considered; these are 40, 70 and 100 % which represent low, medium and full load conditions. LD is defined as the ratio of the current load to the maximum possible load. Normally, there are more potential loads in a grid than those currently drawing power. In order to investigate the impact of different loading in a grid, these three different loading conditions are investigated. As expected, higher loading would mean more local consumption and less impact on the grid.

At any node, the PV penetration to load ratio, PV/loadmax cannot exceed a predefined ratio, as might be stipulated by the utility. In this work, PV/loadmax is set to 2 which is a practical value used by distribution operators.

The process engine follows the methodology shown in Figure 9. Its operation is summarized as a pseudo-code, below: 1. Search for buses with inverter/PV Array, and calculate the total PV (Ptotal−gen ).

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FIGURE 9. FHC analysis approach.

2. Sum up all operating loads and multiply with LD to work out system load, Pload , and maximum permitted PV size, PV/loadmax .

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FIGURE 10. Voltage rise with increasing CI capacity for different LDs.

FIGURE 11. Voltage Rise with increasing SI capacity for different LDs.

3. Start with CIs and perform PV penetration sweep from 30% of Pload and repeat until all nodes exceed 105% of Vn or maximum PV size is reached in every node: 3.1. For each penetration level, generation buses are randomly selected, and total penetration power is distributed to every bus up to their generation capacity. This continues until penetration power is completely distributed.

3.2. Once the random distribution is finished, run power flow to observe local voltages and power generation.

3.2.1. If SIs are used, introduce SI’s reaction as explained in Section II and repeat power flow studies in 3.2.

3.2.2. The randomization stops once all generation buses are covered in a scenario.

3.2.3. When penetration power is larger than Ptotal−gen , the power is distributed proportionally to all buses.

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3.3. When a sweep finishes, increase last penetration by 4 % and repeat 3.

3.3.1. When a node voltage exceeds 105%, reduce penetration sweep step to 1 %.

3.3.2. Keep 30 % of the nodes where the local voltage exceeded 105% of Vn and pass these nodes to next randomization.

4. Repeat Step 3 with SIs. Use same random patterns generated in 3.1 to compare cases with CIs and SIs. V. RESULTS AND DISCUSSIONS

Fig.10. shows FHC results for CI. It is easy to observe that FHC is scaled by system loading since local consumption increases and less energy is injected to the grid. FHC results for SI, shown in Fig.11., show how the voltage support

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TABLE 3. Minimum-Maximum FHC for CI and SI under 3 LDs.

provided by SI improves FHC. As listed in Table 3, even with 50% available VAR absorption, distribution network with SIs can have significantly more hosting capacity for PV. SIs also have greater impact over minimum FHC than maximum FHC. It can be seen that the relation between inverter capacity and system voltage is almost linear, with a turning point where the slope becomes steeper. This happens when the maximum voltage node in the system is replaced. In this test system, at the start of the simulation, the maximum voltage node has a high initial voltage but low voltage increasing speed when PV penetration increases. When a node with low initial voltage but high voltage rising speed catches up, this node will become the new maximum voltage node, and the voltage rising speed will be replaced the previous one as well.

In this system, Node 59 replaces Node 21 at that point.

Results show that use of SI can increase FHC by almost 200%. This means more renewable energy can be captured, more capping can be avoided. In addition to benefits to the grid operation such as voltage support, SIs provide financial and environmental motivations with faster return-oninvestment and higher renewables use.

VI. CONCLUSION

Popularity of renewable energy is increasing, and the policymakers are enforcing requirements for clean energy in the overall generation mix. Electrical power companies need to meet these requirements while ensuring safe and stable operation of the power network. Since the grid has been developed assuming a passive distribution network, having distributed generation at its fringe ends creates novel problems such as voltage rises due to power injection. The most common solution to this issue is limiting renewable energy generation to keep voltage below acceptable limits. However, this reduces the benefit from renewable investments and elongates payback period due to limited generation income. Smart Inverters, inverters with advanced capabilities, have the ability to both inject real power and help grid operation in terms of voltage and frequency support. These devices can solve the stalemate between the desire to increase renewable energy generation and the need to keep voltage below certain levels.

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[28] Communications Working Group: Position Paper on Self Consumption of PV Electricity, EPIA Policy, Brussels, Belgium, Jul. 2013.

TAHA SELIM USTUN received the Ph.D. degree in electrical engineering from Victoria University, Melbourne, VIC, Australia. He was an Assistant Professor of electrical engineering with the School of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. He is currently a Researcher with the Fukushima Renewable Energy Institute, AIST (FREA), where he leads the Smart Grid Cybersecurity Laboratory.

He has edited several books and special issues with international publishing houses. His current research interests include power systems protection, communication in power networks, distributed generation, microgrids, electric vehicle integration, and cybersecurity in smart grids. Dr. Ustun is a member of the IEEE 2004 and 2800 Working Groups and the IEC Renewable Energy Management Working Group 8. He is also an Associate Editor of IEEE ACCESS and a Guest Editor of the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Energies, Electronics, and Information journals.

He has been invited to run specialist courses in Africa, India, and China. He is also a Reviewer in reputable journals and has taken active roles in organizing international conferences and chairing sessions. He has delivered talks for the Qatar Foundation, the World Energy Council, the Waterloo Global Science Initiative, and the European Union Energy Initiative (EUEI).

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JUN HASHIMOTO joined AIST (FREA), in 2017, where he has been contributing to PV research, especially PV system performance testing and characterization. He is currently involved in IEC TC82 WG2, 3, 6, and 7 to develop international standards as an expert on PV modules, systems, and CPV. His current research interest includes to study smart grid systems, especially advanced inverter interoperability for PV and energy storage.

KENJI OTANI currently leads the Energy Network Team, Renewable Energy Research Center, Fukushima Renewable Energy Institute, AIST (FREA). His current research interests include the system integration technologies of PV and hybrid power systems.

VOLUME 7, 2019