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This accepted-version article has been published in Proceedings of the 2015 IEEE Power & Energy Society General Meeting, Denver, CO, 2015. doi: 10.1109/PESGM.2015.7286510 © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Understanding Photovoltaic Hosting Capacity of Distribution Circuits Anamika Dubey and Surya Santoso

Arindam Maitra

Department of Electrical and Computer Engineering The University of Texas at Austin, Austin, TX Abstract – A widespread integration of residential photovoltaics (PVs) into the distribution system may potentially disrupt the nominal circuit operating conditions and result in power quality issues. The objective of this paper is to investigate the impacts of residential PVs on distribution voltages. A stochastic analysis framework is used to simulate possible PV deployment scenarios. The PV integration limit, termed hosting capacity, is calculated with respect to bus overvoltages, voltage deviations, and voltage unbalance. A thorough interpretation of PV hosting results is presented, and several factors potentially affecting the circuit’s voltage quality are identified. Additionally, the impacts of increasing the circuit’s minimum load on the PV hosting capacity are evaluated. Finally, the voltage quality impacts of PV locations are identified, and, based on the results, useful and generic recommendations for PV integration are presented. Index Terms-- Distributed energy resources, solar energy, photovoltaics, voltage, power quality.

I.

INTRODUCTION

The integration of distributed energy resources (DER), especially distributed solar photovoltaics (PV), has been gaining pace in past decade, making solar the fastest growing renewable energy source in the US [1]. Since utility distribution systems are designed for centralized power generation and are optimized for the unidirectional power flow, the integration of DER may disrupt the normal distribution system operation. A high PV penetration can potentially lead to voltage issues, may result in thermal stress, or could increase system harmonics. Additionally, the variability in power generation due to PVs can affect system controls, resulting in an increased number of capacitor switching and regulator tap changes, thus wearing the equipment and increasing the system losses [2, 3]. This calls for a study to determine how much PV a given distribution circuit can accommodate without violating the nominal system operating conditions. The obtained PV capacity is referred to as the circuit’s PV hosting capacity. The PV hosting capacity can be defined with respect to several impact criteria characterizing the nominal operating conditions of a distribution circuit, for example, system overvoltage, thermal stress, harmonics, etc. [4-9]. For a typical North American residential circuit, ensuring a good voltage quality is of prime concern to the distribution providers. Furthermore, the strict regulations enforced for the bus overvoltage and voltage unbalance [10] make voltage quality

Electric Power Research Institute Knoxville, TN an important factor in determining the PV hosting capacity. Thus, in this work, the hosting capacity is determined with respect to the following three voltage quality criteria: overvoltage, voltage deviation, and voltage unbalance. In this paper, the PV hosting capacity is determined using the stochastic analysis framework developed in [4-7]. Note that several other methods have been proposed in the literature to determine the maximum PV/DG penetrations based on the feeder’s voltage and current limits [11-14]. However, in the prior work, either simplified feeder models were used thus not representing the actual circuit conditions [12-14], or PV penetrations were simulated only at a few specific locations [11-14] thus not considering the stochasticity of residential PV size and location, or the method is tested for small test feeders [12, 13] thus questioning its application for an actual distribution circuit. In this work, a large number of potential PV deployment scenarios are simulated on an actual distribution feeder while considering the unpredictability of future PV deployments, both in terms of size and location. The voltage quality impacts of multiple simulation runs are quantified and statistically representative PV hosting capacities are calculated. The primary contribution of this paper is the detailed interpretation of the results obtained for the PV hosting capacity. It is observed that the PV hosting capacity not only depends on the total PV penetration, but also on the relative locations and sizes of the residential PV systems. In addition to calculating the PV hosting capacity, the effects of specific factors related to residential PV deployment, circuit characteristics, and loading conditions that may impact the circuit’s PV integration limits are also characterized. Also the characteristics of the PV deployment scenario resulting in an overvoltage violation and the primary buses most affected due to PV integration are identified. The impacts of the circuit loading condition on the PV hosting capacity are also assessed. Additionally, the locational impacts of PV systems are determined. Based on the results, several recommendations for distribution providers regarding PV integration are presented. II.

STOCHASTIC ANALYSIS FRAMEWORK TO DETERMINE PV HOSTING CAPACITY

This section presents a summary of the stochastic analysis framework developed by EPRI for determining the impacts of PV systems on a distribution circuit. The developed framework simulates and examines a large variation of PV deployment

scenarios. The stochastic analysis of the distributed solar locations and sizes determines the PV penetration level (kW) likely to cause an adverse impact on the distribution circuit. The developed framework incorporates small-scale residential, commercial, and large-scale (MW) utility PV deployments. First, a base case model of the selected distribution feeder is developed. The existing PV systems are incorporated in the base case model. A multi-phase load flow analysis is conducted for the base case. Next, the simulation steps for the stochastic analysis framework are discussed. Several PV deployment scenarios are simulated and the PV system impacts are accessed by running a multi-phase load flow analysis. Based on the results, the PV hosting capacity is determined. A. Create PV Deployment Scenarios In order to reasonably represent the effects of customerowned small-scale PV systems, multiple PV deployment scenarios are simulated by associating random variations to both locations and sizes of the PVs connected to the customer loads. The locations of customer loads with PV systems are randomly selected from the pool of all customer loads supplied by the distribution circuit. The size of the PV system at each customer location is drawn from the residential or commercial PV distribution curves, depending upon the customer type [15]. The method to select the PV system size is shown in Fig. 1.

penetration is increased in a step of 2% and additional PVs are deployed in addition to the existing PVs. In this paper, 100 such PV deployment scenarios are simulated. Note that each scenario is unique in the order that PVs are deployed. B. Quantify Feeder Impacts The load flow analysis is done for each PV deployment scenario under different loading conditions. Depending on the distribution circuit, the impact criteria are identified. A threshold for the impact criteria is defined based on which the PV hosting capacity is determined. In this paper, we are concerned with the voltage quality impacts of PV systems, and therefore, the PV hosting capacity is calculated with respect to following three impact criteria: bus overvoltage, voltage deviation, and voltage unbalance. Here, voltage deviation is defined as the difference between the bus voltages during full PV generation and no PV generation. The threshold for each impact criteria is defined in Table 1 [10]. TABLE I.

SELECTED IMPACT CRITERIA AND THRESHOLDS

Impact Criteria

Defined thresholds

Overvoltage

>1.05 pu

Voltage Deviation

>0.03 pu

Voltage Unbalance

>0.03 pu

C. Determine PV Hosting Capacities The PV hosting capacity of a distribution feeder is equal to the maximum amount of PV generation that can be integrated without violating the defined threshold for a given impact criteria. Based on the results obtained from the load-flow analysis, the circuit’s PV hosting capacity is calculated. Since hundreds of PV deployment scenarios are simulated, three statistically representative PV hosting capacities are characterized: 1st violation, 50% violation, and all violation (see Table 2). Note that, the use of PV-based reactive power or other PV-based means of regulating voltage is not considered in this study. Thus, the simplest PV models with no capability for generating or absorbing reactive power are simulated. TABLE II.

Figure 1. Flowchart to identify PV size.

PV Hosting Capacity st

Deploy PV Identify PV Locations Determine PV System Sizes

In this study, M = 100 and N= 50, resulting in a total of 5000 Scenarios.

Scenario 1 Penetration N

Unique deployments Scenario M Penetration 1

Scenario M Penetration N

Construct M X N PV Deployment Scenarios

Figure 2. Stochastic analysis framework.

The methodology to systematically simulate stochastic PV deployment scenarios is as follows. For a particular PV deployment scenario, the customer penetration level is increased from 0% - 100% in a step of 2%. The customer penetration defines the percentage of the total customers equipped with PV systems. At a given customer penetration level, PV systems are allocated using Fig. 2. The customer

Number of scenarios violated

1 violation PV hosting

Total PV generation for which the first scenario violates the threshold

50% violation PV hosting

Total PV generation for which at least 50% scenarios violate the threshold

All violation PV hosting

Total PV generation for which all 100 scenarios violate the threshold

Additional PV Scenario 1 Penetration 1

PV HOSTING CAPACITIES

III.

CHARACTERIZING DISTRIBUTION CIRCUIT

An actual 12.47-kV distribution circuit, supplied by a 24MVA substation transformer, is selected for the analysis. The feeder is also connected to a total of 1.196 MW of existing PV. The schematic of the distribution circuit is shown in Fig. 3. The three-phase circuit model of the distribution circuit, starting from the substation down to the individual customer load location, is simulated in OpenDSS. Next, using the yearly load demand measured at the substation for the year 2013, the representative maximum and minimum loads for the distribution circuit are obtained. The

maximum load for the circuit is considered to be equal to the peak load demand recorded for 2013, which was equal to 10.23 MW, recorded on 5th May at 2:00 pm. The minimum load condition is obtained using a statistical analysis carried on the yearly load demand for the feeder. First, the monthly average sunrise and sunset times are identified [16] and, using this data, the daily minimum daytime load demand is obtained. Next, a histogram plot for the minimum daytime load demand is generated (see Fig. 4), and the mean and the median of the distribution are calculated. The analysis yields a mean value equal to 6.1792 MW and a median value equal to 6.13 MW. Based on the statistical analysis, a minimum load equal to 6 MW is selected for the analysis. Distance measured from the meter

condition. This is because during the minimum load the bus voltages are already high, so adding PVs further increases the bus voltages. For the voltage deviation case, however, the circuit is affected most during the maximum load condition. Note that the voltage deviation is measured with respect to the base case voltage, which is lower for the maximum loading case. The rate of change of the voltage due to additional generation is inversely proportional to the base case voltage. Since the base case voltage is lower for the maximum load, a higher voltage deviation is recorded for the same amount of additional PV generation. As for the voltage unbalance, under the maximum load condition, the largest voltage unbalance is about 0.05 pu, which is already higher than the threshold. TABLE III.

1.273 miles

1.612 miles Feeder End 2

PV HOSTING RESULTS

Cases

Overvoltage

1.6 miles

1.148 miles

Mid-Feeder

1.579 miles

Voltage deviations

Feeder End 1

Capacitors PV system

Voltage unbalance

Substation and meter

Figure 3. One-line diagram of the selected distribution feeder. Number of Days (Total Number of days =315 )

50 40 Bin Resolution = 320 kW

30 20 10 0 1

2

3

4 5 6 7 8 Minimum Daytime Load Demand (MW)

9

10

11

Figure 4. Obtaining a statistically representative minimum load condition.

IV.

RESULTS AND DISCUSSIONS

This section summarizes the PV hosting results for the selected distribution feeder. The steady-state PV impact analysis is simulated for both a maximum and a minimum load condition. For each case, the additional PVs are assumed to be generating at their peak capacity. A three-phase load flow solution is simulated for each PV deployment case and PV hosting capacity is obtained corresponding to each selected voltage quality impact criteria. A. PV Hosting Results The PV hosting capacity is calculated for each impact criteria: overvoltage, voltage deviations, and voltage unbalance, and the results are summarized in Table 3. For each impact criterion, hosting capacities corresponding to first violation, 50% violation, and all violation are calculated at both maximum and minimum loading conditions. It is observed from the table that for overvoltage violation, PV integration affects the circuit most during the minimum load

Additional PV Size (kW) Max Load

Min Load

1st violation

9,442

5,454

50% scenarios with violation

9,578

5,536

All scenarios with violation

9,659

5,722

1st violation

1,756

2,776

50% scenarios with violation

1,834

2,970

All scenarios with violation

1,909

3,088

1st violation

0

5,760

50% scenarios with violation

0

6,101

All scenarios with violation

0

6,291

B. Discussion on Overvoltage Results The PV system impacts on system overvoltages are discussed here in detail. First, the worst-case loading condition for overvoltage violation is identified, followed by characterizing the PV size and location resulting in the largest impacts. The numbers and locations of buses observing an overvoltage violation are also identified. 1) Worst-Case Loading Condition For overvoltage violations, lower PV hosting capacities are observed during the minimum load condition, implying that PVs affect the distribution voltages most when the circuit is lightly loaded. The first violation hosting capacity for the minimum load condition is approximately 4 MW lower than the corresponding hosting capacity recorded during the maximum load. 2) PV Hosting Capacities during Minimum Load Condition The results for primary bus voltages corresponding to all 100 PV deployment scenarios simulated for 50 customer penetration levels are shown in Fig. 5. Note that each point in the figure corresponds to the maximum primary bus voltage recorded for a particular PV scenario. The circuit records the first violation on adding 5.454 MW of additional PV, while an all violation hosting capacity comes out to be 5.722 MW. In between the first violation and all violation hosting capacities, some scenarios record an overvoltage violation while others do not. A 50% hosting capacity is observed on adding 5.536 MW of additional PV.

increased, the number of primary buses reporting an overvoltage violation increases rapidly. In fact, for all violation case, around 300 primary buses observe an overvoltage condition. Therefore, increasing PV penetration not only increases the maximum bus voltage, but also the number of customers observing a violation.

Figure 5. Maximum voltages recorded during the minimum load condition.

3) PV Locations and Sizes for the First Violation Scenario Increasing the PV penetration increases the likelihood of overvoltage violation, but there are additional factors as well. Even after the first violation scenario, we observe several scenarios with a higher PV penetration but not reporting an overvoltage. The objective of this section is to observe the PV deployment scenario corresponding to the first violation and to identify the factors potentially resulting in an overvoltage. Fig. 6 shows the locations and sizes of PV systems corresponding to the first violation scenario. The first violation is observed for a customer penetration of 52%. From the figure, it can be observed that for this scenario, large PV systems were located farther away from the substation. These nodes generally have low short-circuit capacities and therefore, installing a large PV system may result in an overvoltage violation. Thus, locating large PVs farther away from the substation is more likely to result in an overvoltage violation.

Figure 7. Heat plot for primary bus voltages corresponding to the first overvoltage violation scenario.

Figure 8. Number of primary buses observing overvoltage during the minimum load condition for each PV deployment scenario.

4) Bus Locations observing Overvoltages Corresponding to the first violation case, the bus locations reporting overvoltages are identified (see Fig. 7). This analysis helps in understanding which buses are the first to observe an overvoltage violation due to PV integration. In Fig. 7, a heatchart is plotted representing all buses with voltages more than 1.05 pu in red and the rest in orange. From the figure, it is observed that all buses observing overvoltages are farther away from the substation. Next, for each PV deployment scenario, the number of primary buses recording an overvoltage violation are identified (see Fig. 8). From the figure, it is observed that for the PV hosting corresponding to the first violation, only three primary buses report an overvoltage violation. As the PV penetration is

10000 9000 8000 7000 6000 5000 4000 3000 2000 1000 0

PV Hosting Capacity (kW)

Figure 6. PV locations and sizes corresponding to PV deployment scenario for the first overvoltage violation.

C. Effects of Minimum Load on PV Hosting Capacity The objective of this section is to understand the impact of the minimum load on the circuit’s PV hosting capacity. For this study, the minimum load for the circuit is increased from 4 MW to 12 MW in a step of 1 MW, and the stochastic steady-state PV analysis is simulated at each loading condition. The corresponding first hosting capacity is calculated at each loading condition and is shown in Fig. 9. It is observed that on increasing the minimum load, the PV hosting capacity of the circuit increases. In fact, for every 1 MW increase in the minimum load, the circuit can accommodate approximately 543 kW of additional PV capacity.

0

2

4

6 8 Minimum Load (MW)

10

Figure 9. Minimum feeder load vs. PV hosting capacity.

12

V.

EFFECTS OF PV LOCATIONS ON HOSTING CAPACITY

In the previous section, we observed that a few PV deployment scenarios may have a higher impact on system voltages, based on the relative PV sizes and locations. It may not be possible to schedule the locations of customer-owned PVs, but utility-owned PVs may be installed at those locations which potentially have the least impact on the circuit voltages. For this purpose, an additional analysis typically for the utilityowned PV system is conducted. For this study, four locations for PV deployment are selected, at substation, mid-feeder, and at two feeder ends (see Fig. 3). A 500-kW three-phase PV system is selected for the analysis. The number of PV systems is increased from one to twenty, thus adding a total of 10 MW of additional PV at each location. For each location and each number of PV systems, the highest primary wire voltage is recorded (see Fig. 10). The hosting capacity is calculated for each location by identifying the additional PV capacity leading to an overvoltage violation. From Fig. 10, no violation is recorded when PV systems were placed at the substation. The hosting capacity is lowest at the feeder ends. To further understand the locational impacts, the distance of the PV deployment location from the substation is increased in an approximate step of 0.25 miles. As the distance from the substation increases the PV hosting capacity of the circuit decreases, as shown in Fig. 11. Maximum feeder voltage (pu)

1.14 1.12 1.1

At substation At mid-feeder At feeder-end 1 At feeder-end 2

1.08 1.06 1.04 1.02 1

2

3

4

5 6 Added PV(MW)

7

8

9

10

Figure 10. PV hosting results for each selected PV deployment locations. 12

Added PV (MW)

9

6

3

First Violation

Expon. (First Violation)

0 0

0.2

0.4

0.6

0.8

1

Distance (mi)

1.2

1.4

1.6

1.8

Figure 11. Impact of PV deployment location on PV hosting capacity.

VI.

CONCLUSION

In this paper, the voltage quality impacts of including residential PV systems on the distribution circuit are evaluated. The voltage quality impacts are characterized with respect to overvoltage, voltage deviation, and voltage unbalance. The PV impacts are assessed by calculating the PV hosting capacity of the circuit, which is defined as the maximum PV that the circuit can accommodate without violating the defined thresholds for

a given impact criteria. Various circuit and loading parameters affecting the PV impacts are also identified. Additionally, the voltage quality impacts of PV deployment locations are evaluated as well. Based on the analysis, the following conclusions are drawn: 1. The voltage quality impact of PV system varies with the loading condition. 2. Different impact criteria observe the worst-case PV impacts during different loading conditions. 3. For the same customer penetration, the PV system impact varies with the PV deployment scenario, depending on the relative PV locations and sizes. 4. PV deployment scenarios with larger PVs at farthest load nodes result in higher impacts on the voltage quality. 5. Primary buses farther away from the substation are most likely to observe overvoltages. 6. On increasing the system’s minimum load, the PV hosting capacity increases. 7. As the distance of PV system location with respect to substation increases, the PV hosting capacity decreases. REFERENCES [1]. Solar Market Insight Report 2014, GTM Research/SEIA: U.S. Solar Market Insight. [2]. E. Liu and J. Bebic, “Distribution system voltage performance analysis for high-penetration photovoltaics,” GE Global Res., Niskayuna, NY, Rep. NREL/SR-581-42298, 2008. [3]. M. Thomson and D. G. Infield, “Impact of widespread photovoltaics generation on distribution systems,” IET Renew. Power Generation, vol. 1, pp. 33–40, 2007. [4]. J. W. Smith, R. Dugan, M. Rylander, and T. Key, "Advanced distribution planning tools for high penetration PV deployment ," in Power and Energy Society General Meeting, 2012 IEEE, 2012, pp. 1-7. [5]. "Stochastic Analysis to Determine Feeder Hosting Capacity for Distributed Solar PV," EPRI, Technical Report 1026640, 2012. [6]. M. Rylander, and J. Smith, “Comprehensive Approach for Determining Distribution Network Hosting Capacity for Solar PV”, 2nd International Workshop on Integration of Solar Power Into Power Systems, Lisbon, Portugal, Nov 2012. [7]. M. Rylander, J. Smith, D. Lewis, and S. Steffel, "Voltage impacts from distributed photovoltaics on two distribution feeders," IEEE Power and Energy Society General Meeting (PES) 2013, pp.1-5, 21-25 July 2013. [8]. R. J. Broderick, J. E. Quiroz, M. J. Reno, A. Ellis, J. Smith, and R. Dugan, "Time Series Power Flow Analysis for Distribution Connected PV Generation," Sandia National Laboratories SAND2013-0537, 2013. [9]. K. Coogan, M. J. Reno, S. Grijalva, and R. J. Broderick, "Locational dependence of PV hosting capacity correlated with feeder load," IEEE PES T&D Conference and Exposition, 2014, pp.1-5, 14-17 April 2014. [10]. American National Standard, ANSI C84.1-2011, For Electric Power Systems and Equipment -Voltage Ratings (60 Hertz). [11]. A. Hoke, R. Butler, J. Hambrick, and B. Kroposki, “Steady-State Analysis of Maximum Photovoltaic Penetration Levels on Typical Distribution Feeders,” IEEE Transactions on Sustainable Energy, 2012. [12]. R. A. Shayani, and M. A. G. de Oliviera, “Photovoltaic generation penetration limits in radial distribution systems,” IEEE Trans. on Power Systems, vol. 26, issue 3, pp. 1625 – 1631, Aug 2011. [13]. C. Debruyne, J. Desmet, J. Vanalme J, B. Verhelst, G. Vanalme, and L. Vandevelde, “Maximum power injection acceptance in a residential area” in Proceedings of the International Conference on Renewable Energies and Power Quality, 2010. [14]. L. M. Cipcigan and P. C. Taylor, “Investigation of the reverse power flow requirements of high penetrations of small-scale embedded generation,” IET Renew. Power Gener., vol. 1, pp. 160–166, Sep. 2007. [15]. California Solar Statistics, http://www.californiasolarstatistics.org. [16]. http://www.sunrisesunset.com/.