Observed behavior of distributed photovoltaic systems during major voltage disturbances and implications for power system security

Applied Energy 260 (2020) 114283

Contents lists available at ScienceDirect

Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Observed behavior of distributed photovoltaic systems during major voltage disturbances and implications for power system security

T

Naomi Stringera,b, , Navid Haghdadia,b,c, Anna Brucea,b, Jenny. Rieszd, Iain MacGillb,c ⁎

a

School of Photovoltaic and Renewable Energy Engineering, University of New South Wales Sydney, Australia Centre for Energy and Environmental Markets, University of New South Wales Sydney, Australia c School of Electrical and Telecommunications, University of New South Wales Sydney, Australia d Australian Energy Market Operator, Brisbane, Australia b

HIGHLIGHTS

PV power reduction of ~30–40% during voltage disturbance is observed. • Aggregate and duration of response is shown to be diverse across individual PV systems. • Depth from 376 distributed PV systems during major voltage disturbances is analyzed. • Data data analysis techniques are demonstrated including geographical analysis. • Four • Findings indicate that distributed PV may contribute to frequency management needs. ARTICLE INFO

ABSTRACT

Keywords: Distributed PV Power system security Voltage disturbance Operational data Voltage response

As distributed photovoltaics (PV) levels increase around the world, it is becoming apparent that the aggregate behavior of many small-scale PV systems during major power system disturbances may pose a significant system security threat if unmanaged. Alternatively, appropriate coordination of these systems might greatly assist in managing such disturbances. A key issue is PV behavior under extreme voltage events. PV connection standards typically specify aspects of inverter voltage behavior. However unresolved questions remain regarding compliance, ambiguity and transition between versions of these standards. In addition, how major voltage disturbances manifest in the low voltage network is complex, and analysis of operational system data could be particularly useful for establishing the behavior of distributed PV in the field. Our study utilizes 30 s operational PV generation data from 376 sites during two major voltage disturbances in Australia. Australia has one of the highest penetrations of distributed PV worldwide, and as such provides a useful case study. Results show that an aggregate ~30–40% reduction in distributed PV generation occurred during these events, but individual inverter behavior varied markedly. To the authors’ knowledge, this is the first time the aggregate response of distributed small-scale PV to voltage disturbances originating in the transmission system has been demonstrated. Four novel techniques for analyzing events are proposed. Results show a potential increase in system security service requirements as distributed PV penetrations grow. Our findings would seem to have major implications for development of composite load models used by power system operators and for contingency management.

1. Introduction Over recent years there has been unprecedented growth in the deployment of distributed small-scale rooftop PV systems within numerous electricity industries around the world. Whilst this PV uptake has contributed to reducing sector emissions and energy consumer electricity bills, it does raise several technical challenges for safe and secure power system operation. An early concern and hence focus of ⁎

research, has been the technical challenges of distribution network operation and planning with growing distributed PV penetrations, particularly maintaining network voltage within safe limits [1–6]. However, as the penetration of distributed PV continues to climb, system level security implications are emerging. With grid connection through a power electronic ‘inverter’ interface, PV systems have the potential for fast and precise response, according to local grid requirements. Prior work has focused on grid codes

Corresponding author. E-mail address: [email protected] (N. Stringer).

https://doi.org/10.1016/j.apenergy.2019.114283 Received 25 July 2019; Received in revised form 15 November 2019; Accepted 28 November 2019 0306-2619/ © 2019 Elsevier Ltd. All rights reserved.

Applied Energy 260 (2020) 114283

N. Stringer, et al.

for utility scale wind and PV plants [7–9], whilst efforts considering distributed PV has focused on response to frequency disturbances [10,11]. However there is a growing awareness that high penetrations of distributed PV can materially impact the power system following major power system events and that voltage disturbances also pose a serious challenge [12]. Distributed PV presents a particular challenge due to its highly dispersed nature, with limited visibility and typically very limited, if any, control afforded to power system operators. Major grid disturbances such as the loss of a large generator, network element or load can affect both system frequency and voltage. Early attention has focused on PV inverter behavior during frequency disturbances [13], given the generally system wide impact of such events. Specific examples include the German 50.2 Hz challenge resulting in substantial, highly correlated, distributed PV disconnection [10,11]. As well as widespread under-frequency load shedding in Hawaii exacerbated by legacy distributed PV [14] and under frequency PV tripping in the remote Alice Springs grid in Australia [15]. However inverter response to voltage disturbance is also significant, as recognized in the considerable efforts being undertaken internationally to develop fault detection and ride through strategies [16–18], as well as review [10,11,19–24] and test [25,26] inverter connection standards. Along with inverter technology progress and other market developments, revision of grid connection standards has resulted in a diverse fleet of installed inverters, with potentially highly varied responses to major voltage disruptions, in some jurisdictions. As distributed PV penetrations grow, there is a clear need to better understand the behavior of their inverters during possible power system security events. In an effort to address these challenges, recent work [27–29] has developed composite load models incorporating distributed energy resources (DER), predominantly solar PV and battery energy storage systems, to better predict the aggregate response of load following contingency events including voltage disturbances. Dynamic models that accurately capture the behavior of load, and increasingly also DER, during disturbances play a key role in power system operation, including for the determination of constraints and power system limits for security and stability, as well as studies to assure the stable connection of new generation, and the correct allocation of reserves for ancillary services. However, the incorporation of DER into such models is particularly challenging given the limited experience with these technologies to date, their potentially varied operation and the complexities of Low Voltage (LV) system operation during disturbances. As such, there is considerable value in actual DER operational data analysis during disturbances to both inform as well as validate these models. As PV uptake continues worldwide, Australia provides a unique snapshot into the future for many grids due to its high penetration of distributed PV. More than 20% of residential dwellings in the Australian National Electricity Market (NEM) have PV installed [30,31], with over two million systems in total. The NEM regions of Queensland and South Australia (Fig. 1) have some of the highest residential penetrations in the world with systems installed at over 30%

of standalone dwellings [32]. Experiences in the NEM are therefore relevant to power systems worldwide as a window into a possible future with very high distributed PV penetrations. Despite its growing role, direct system-wide SCADA is not available for distributed PV [33], which is therefore currently seen as reduced load, to be met by utility-scale generation. Availability of higher resolution data in the low voltage network is expected to increase as more DERs are installed. The industry’s early focus has been on increased understanding of inverter frequency response set points [34]. Further, whilst there is some publicly available operational data for distributed PV [35], it is typically reported on 5 min or longer intervals and is therefore of limited benefit for assessing response to grid disturbances. Recent work has developed tools and techniques for analyzing distributed PV operational data however these studies have not included techniques for PV performance analysis during major grid disturbances. For instance, recent work has focused on PV impacts on network peak demand [35–37], identifying clear sky days [38], degradation analysis [39] and management of voltage in the low voltage network [40,41]. The analysis set out in this paper utilizes a novel operational data set, reporting individual household and business distributed PV generation, load, and local network voltage at 30sec time intervals, from solar monitoring company Solar Analytics [42]. It examines the behavior of distributed PV systems during two major system voltage disturbances following large, non-credible contingency events in the NEM. The first, in South Australia, was due to a large gas generating plant failure, whilst the second, in Victoria, followed a transmission fault during extreme heat conditions. A gap in the literature exists with regards to distributed PV behavior during such major voltage disturbances. In particular there is very limited understanding of the degree of PV response to such disturbances and of the diverse range of responses that may occur. Our study makes two key contributions. First, it presents four novel techniques for analyzing this operational data set. Two techniques are presented for analyzing individual PV systems with regards to depth and duration of their response to these events. Two further techniques are presented for analyzing aggregate response. These include spatial analysis of response to voltage disturbance, and upscaling observed behaviors from monitored PV systems to the total installed distributed PV capacity. These techniques provide a possible framework for future analysis of large operational data sets examining the behavior of many small DERs and indeed have been adopted by the Australian Energy Market Operator (AEMO) with analysis presented in this study included in its incident reporting [43] and DER Program [44]. Secondly, this analysis highlights that the response of distributed PV during voltage disturbances can be material, with a ~30–40% reduction in aggregate PV power output during the two events studied, and therefore has significant implications for management of power system security. The observed responses are also highly diverse across different inverters in different locations, highlighting the criticality of operational data for security modelling and analysis as distributed PV penetrations increase. This complexity is challenging to capture through deterministic modelling efforts and is complimented by data-driven modelling practices. We particularly consider the implications of these findings for the provision of raise frequency control services. Finally, the observed PV behavior is verified using an independent and publicly available data set. The rest of this paper is structured as follows. Section 2 provides an overview of data sets used in the analysis and Section 3 summarizes the case studies examined and notes expected behavior given inverter connection standards. Section 4 sets out the method, whilst Section 5 sets out the detailed method and findings. The detailed method presents four novel analytical tools; two for assessing the diversity of individual system behaviors (depth and duration of response), and two for assessing the overall power system impact (spatial analysis and upscaling). Section 6 concludes the study.

Fig. 1. Australian National Electricity Market (NEM), transmission network indicated in purple. 2

Applied Energy 260 (2020) 114283

N. Stringer, et al.

2. Data sets

2.3. Spatial data sets

2.1. Solar Analytics data set

Australian Bureau of Statistics Australian postcodes [47], and boundaries of Greater Capital City statistical areas [48] are used as the basis for spatial analysis and upscaling. This data is also publicly available.

Data including PV generation, local network voltage and frequency from 376 individual sites at 30 s time intervals and 428 individual sites at 5 min intervals was provided by solar monitoring company Solar Analytics [42]. The data set has commercial value and is therefore not publicly available, however it can be purchased on commercial terms. A growing number of industry stakeholders are acquiring these types of data sets, and hence are seeking analysis approaches and tools such as those presented in this paper. Systems with erroneous data, including zero generation, non-zero generation outside sunshine hours, periods of negative generation, constant 50 Hz frequency measurements (i.e. no deviations or ‘stuck’ values), and missing data during the period immediately following the event were removed from the analysis. This data cleaning should serve to increase accuracy of results, for instance through removing any very unusual (e.g. negative) metered generation profiles which have almost certainly been mislabeled as PV. One vexed issue is the treatment of zero generation sites. Their removal may result in an inflated estimate of PV generation during upscaling, since experience suggests that some proportion of sites in the PV fleet are likely to be operating at zero generation over extended periods due to, for example, inverter or breaker trips that haven’t been reset. However removal of zero generation sites can also increase the accuracy of results given the upscaling methods applied, and is a more transparent and clear approach so the decision was made to remove these sites from the analysis. Missing data was forward filled, however it is important to reiterate that sites with missing data immediately following the event were removed and therefore this practice should not impact the accuracy of results. Forward filling is particularly relevant for the Victorian data set due to apparent monitoring and communication issues with some of the Solar Analytics metering systems on the day of the event which might otherwise adversely impact the clarity of the voltage disturbance event. Voltage measurements from the Solar Analytics devices are Vrms recorded over the final 100 ms in each measurement interval. Note, therefore they provide a snapshot rather than continuous visibility of local network conditions. PV generation was converted from energy to average power per time interval. Table 1 summarizes the number of sites in the Solar Analytics data set (after filtering).

2.4. System operational data sets The state-wide load in South Australia and Victoria has been estimated using 4 s SCADA data publicly available through AEMO’s NEMWeb portal [49], accessed using the NEMOSIS open source tool [50]. This data set contains 4 s data for all registered generators and interconnectors in the NEM. Historical Frequency Control Ancillary Services (FCAS) data was also obtained through AEMO’s NEMWeb portal [49], accessed using the NEMOSIS open source tool [50]. The data set obtained contains FCAS enablement for each NEM region over the period November 2017 to November 2018. 3. NEM event case studies and specified inverter response The low population density in Australia has resulted in a long grid with relatively low levels of transmission interconnection between states. The first case study region of South Australia has two points of interconnection with the neighboring region (Victoria), and is a relatively small power system, serving a typical peak demand of 3GW. The South Australian generation fleet contains significant wind capacity, along with a large gas thermal plant, a CCGT, gas cogeneration and some peaking gas (and diesel) plants. The state has an extremely high penetration of distributed PV, which can occasionally supply more than 40% of demand across South Australia for short periods of time [51,52]. Significantly, AEMO forecasts PV growth will continue to the extent that periods of negative demand will be observed in South Australia as early as 2025–26 [53]. Victoria, by contrast, is a comparatively large grid (around 10GW peak demand), with interconnections to the neighboring regions of South Australia, New South Wales and via an undersea HVDC cable, Basslink, Tasmania. Generation capacity is primarily coal, and whilst distributed PV uptake has grown consistently in terms of absolute installed capacity [54], it has lagged South Australia in terms of penetration (Table 2). Again, however, strong uptake of distributed PV is considered feasible for the region over the next two decades [54]. Table 3 summarizes the situation at the time of the non-credible contingency events being studied here.

2.2. Public PV data sets A publicly available database of historical PV generation from the PVOutput.org website [45] was accessed via the Australian Photovoltaic Institute (APVI) live map database [32,36]. This database contained 400 sites in Victoria and 268 in South Australia with sites either reporting on 5 min, 10 min or 15 min basis. The missing intervals and likely invalid data (due to monitoring issues) were not considered in the analysis. This dataset was primarily used for validation of the Solar Analytics data. The Australian Clean Energy Regulator collects information regarding distributed PV installations which are installed under the Small Scale Renewable Energy Target [46]. Data collected includes PV capacity, installation postcode and installation date, hence applicable inverter connection standard. This dataset was used for the upscaling described in Section 5.8.

Table 2 Case study region characteristics [55–58].

1

Number of customers Transmission network length1 Wind generation (% statewide rated generation capacity)2 Thermal generation (% statewide rated generation capacity)2 Peak load2 Distributed PV capacity2 Distributed PV penetration2,3 Forecast distributed PV 2036–371

Table 1 Solar Analytics data summary: number of sites.

30 s data set 5 min data set

South Australia

Victoria

214 260

162 168

1

South Australia

Victoria

858,647 5524 km 36%

2,797,458 6559 km 13%

51%

64%

3.4 GW 746 MW 29% 2.1 GW

10.5 GW 1221 MW 15% 4.3 GW

2017. 2018. 3 Penetration means the proportion of ‘free standing and semi-detached’ dwellings with PV installed 2

3

Applied Energy 260 (2020) 114283

N. Stringer, et al.

similar to those existing for utility scale generators, such as frequency, voltage and phase angle jump (or vector shift) ride through requirements. Despite these efforts and ongoing development of power system models, the small and distributed nature of devices makes it infeasible to audit all connections in the manner used for utility plant. Therefore, there is limited evidence to support the efficacy of these standards, and as penetrations increase, this necessitates analysis of operational data to support development of power system models. In Australia the most widely adopted inverter connection standard is AS/NZS 4777 ‘Grid connection of energy systems via inverters’ [65,66]. Australian standards are not mandatory unless specified in legislation, however compliance with AS4777 is typically required by distribution system operators for connection to their grids. This standard has gone through a number of revisions over the past 15 years. As result, the Australian PV fleet is comprised of systems installed under the current standard (‘post 2016′) and a substantial legacy fleet of inverters installed under the superseded standard (‘pre 2015′) as well as during a 12 month transition period in which either standard could be applied. Notably, anti-islanding requirements feature in both the superseded and current standard (Appendix A). The superseded standard lists an allowable range for anti-islanding set points, where Pre 2015 systems over and under voltage set points can fall between 0.87 and 1pu and 1–1.17pu respectively. In comparison, the current standard states specific over and under voltage set points at 0.87pu and 1.13pu respectively. Post 2016 systems are also required to ramp at no greater than 16.67% of rated capacity per minute after reconnection (6 min to rated capacity). Without knowledge of local voltage conditions it is not possible to assess compliance with anti-islanding requirements. This would require detailed modelling of the LV network and is outside the scope of this study. However, given the severity of the voltage disturbances studied (with 0.1pu observed at Torrens Island in the South Australian case and 0.45pu on the 200 kV network in the Victorian case), local under voltage is likely and therefore the anti-islanding requirements are a plausible cause of PV response. More PV disconnections are expected close to the source of the disturbance, consistent with the voltage dip being deepest at the site of the disturbance. However, given the widespread loss of load following each event localized over voltages may also have occurred and it is unclear whether anti-islanding due to over voltage may also have caused at least some of the PV response. In addition to the anti-islanding requirements set out in Appendix A, the current standard also includes requirements regarding active antiislanding protection, limits for sustained operation and volt-watt response, as well as specifications for volt-var response, voltage balance mode, fixed power factor and reactive power mode. It is possible that these response modes may have impacted some inverter behaviors in the operational data set. Notably, the present standard is silent on voltage phase angle jump which may also have been a cause of PV response during the voltage disturbances studied.

Table 3 Event conditions [32,46,59–61].

Event date and time Event day maximum temperature Mean global solar irradiance over 1 min (at event time) Load at time of event

South Australia

Victoria

15:03:46 AEST Fri 3 Mar 2017 28 °C 645 W/m2

15:18:54 AEST Thurs 18 Jan 2018 40 °C 643 W/m2

2.0 GW

8.7 GW

3.1. South Australian event 3 March 2017 The first case study considers a major voltage disturbance in South Australia on 3 March 2017 following the explosive failure of a capacity voltage transformer in the switchyard of Torrens Island (the largest gas generator in the state). This triggered a number of faults starting at 15:03:46 Australian Eastern Standard Time (AEST). An initial loss of 410 MW thermal generation occurred within 1.5 s, with a further 200 MW lost shortly afterwards. The voltage levels fell to around 0.1pu on one phase at Torrens Island and flow across the Heywood Interconnector from Victoria increased [62]. Notably, conditions caused by the contingency event shared some similarities to those which caused a system black event in South Australia on 28 September 2016 [62]. Although the Under Frequency Load Shedding scheme did not operate, demand was observed to reduce by 400 MW, believed to be caused by ‘shake off’ of load in response to under voltage conditions [62]. This was almost immediately followed by a 150 MW increase in state demand, suggested by AEMO to have possibly been caused by distributed PV systems “shutting off in response to the voltage disturbance” [62]. The analysis presented in this paper provides evidence that the 150 MW increase was indeed likely due to PV response during the event, although we are unable confirm whether the PV inverters responded to voltage conditions directly, or perhaps other aspects of the disturbance. However, it is important to note that whilst frequency during the event did reduce to 49.77 Hz (outside the NEM normal operating frequency band of ± 0.15 Hz) this is not sufficiently low to expect to cause PV disconnection [34]. 3.2. Victorian event 18 January 2018 At 15:18:54 AEST on 18 January 2018, during extreme high temperature conditions, a series of transmission faults occurred at Rowville terminal station (a major substation) and nearby sections of the transmission network on the outskirts of Melbourne [63]. These faults followed the failure of a 500 kV single phase current transformer. As result, voltage levels fell as low as 0.51pu in the 500 kV network and 0.45pu in the 200 kV network. Although load shedding was not instructed and indeed no single point of load loss was observed, there was an aggregate ~550 MW reduction in demand observed across Victoria, followed shortly afterwards by an increase in load of approximately ~110 MW. A modest frequency rise was observed across the NEM following this event due to load loss with a maximum of 50.22 Hz occurring [64]. Similarly to the South Australian case study, the observed frequency, whilst outside the NEM normal operating frequency band, is not expected to have caused PV disconnection. Again it was suggested that distributed PV may have contributed to this apparent load increase, however prior to our study, there was no evidence to support this hypothesis.

4. Methods The analysis approach aims to provide insight into the diversity of individual PV system behaviors, as well as characterizing aggregate trends. Custom algorithms were developed and implemented in Python and QGIS to analyze the operational data set, with data cleaning completed as set out in Section 2. Four novel techniques are presented. Following the analysis presented in this paper AEMO has also accessed the Solar Analytics data set in order to repeat the analysis and an open source tool with a user interface has been developed in R-Shiny, available at [67]. The first two techniques characterize individual system responses with regards to depth and duration of response. This provides insight into the variability of power output response across the sample and highlights the importance of developing tools to accurately estimate the operation of the broader PV fleet.

3.3. Inverter connection standards for voltage excursion Internationally, there has been considerable effort to update connection standards for small inverters to incorporate requirements 4

Applied Energy 260 (2020) 114283

N. Stringer, et al.

Section 5.2 sets out the detailed method for characterizing depth of response, whilst Section 5.3 presents findings from the South Australian and Victorian events. Section 5.4 sets out the detailed method for characterizing duration of response and Section 5.5 presents findings. The third and fourth techniques consider aggregate PV response. A spatial analysis method is developed to analyze geographical trends in PV response. Outcomes of spatial analysis is then used as the basis to upscaling (the fourth analysis technique). This upscaling technique models the behavior of the entire installed distributed PV capacity. The upscaled PV loss estimates are shown to present substantial implications for managing power system security. Section 5.6 provides the detailed method for spatial analysis, with Section 5.7 presenting findings. Section 5.8 then sets out the detailed upscaling method and Section 5.9 presents findings and considers power system security implications. 5. Detailed methods and event findings 5.1. Initial event characterization Fig. 3. South Australia event: (a) Aggregate average power (top) and average voltage RMS (bottom) over the day (b) aggregate average power during the event, measured at Solar Analytics sites, 30 s data.

In the first instance, the aggregate PV behavior observed in both the South Australian and Victorian datasets was examined, and the 4 s state-wide load profile was estimated using AEMO SCADA data for all generators and interconnectors. 5.1.1. South Australia Fig. 2 shows approximate state-wide load in South Australia at the time of the event, after an initial reduction in load of around 400 MW, load increased by around 150 MW. The Solar Analytics operational PV data set used for this study shows a clear PV response to the disturbance with a 42% reduction in aggregate generation observed (Fig. 3 (a) and (b)). As stated in 2.1, the voltage data provides a ‘snapshot’ of voltage conditions and therefore the average voltage presented in Fig. 3(a) does not capture the true local voltage variation.

Fig. 4. Victoria: approximate statewide demand during event, 4 s data.

5.1.2. Victoria Fig. 4 shows the approximate Victorian state-wide load at the time of the Victorian case study event with an initial reduction of ~550 MW followed by an increase of ~110 MW. Similarly to the South Australian case there was no evidence that the increase in load was due to reduced distributed PV generation prior to this study. However the data set examined indicates substantial PV response, with an aggregate reduction of 28% observed (Fig. 5 (a) and (b)). 5.2. Depth of response analysis The diversity of response across individual PV systems was initially assessed in terms of fractional power loss to nadir (PL%), where seven categories were developed to characterize response. These categories are summarized in Table 4 where PL% is calculated as per (1) for individual premises. Pn (t 0 ) is the operational power immediately prior to curtailment and Pn (tnadir ) is the minimum operational power after the Fig. 5. Victorian event: (a) Aggregate average power (top) and average voltage RMS (bottom) over the day (b) aggregate average power during the event, measured at Solar Analytics sites, 30 s data.

event interval for system n :

PL%n =

Pn (t 0 )

Pn (tnadir ) Pn (t 0 )

(1)

The first category ‘Ride through’ indicates sites which are assessed as having been relatively unaffected by the event. Whereas the seventh

Fig. 2. South Australia: approximate statewide demand during event, 4 s data. 5

Applied Energy 260 (2020) 114283

N. Stringer, et al.

Table 4 Definition of categories for diversity analysis. Category

Fractional power loss PL%

Additional criteria

1 – Ride through

≤4%

Not applicable

Partial responses 2 – Slight dip 3 – Mild 4 – Medium 5 – Significant 6 – Severe

≤10% > 10% and ≤25% > 25% and ≤50% > 50%, and ≤75% > 75%

Cat 1 is false Not applicable Not applicable Not applicable Cat 7 is false

7 - Disconnect

Not applicable

Pn (tnadir ) < 0.1 kW

Fig. 7. South Australia event: Proportion of sites in each response category, 30 s data.

category ‘Disconnect’ indicates sites for which generation was reduced to less than 0.1 kW for at least one measurement interval immediately following the event. Categories 2 to 6 exhibit a range of partial responses and have been selected in order to provide an understanding of the spread of responses. Category 2 was included due to a significant number of sites observed to exhibit this behavior during visual checking. Without more detailed time resolution data it is not possible to assess precisely what is actually occurring at sites exhibiting partial responses. For instance, it is feasible that power output at these sites reduces to zero without triggering actual disconnection, and therefore the inverter does not wait 60 s until reconnection (see Appendix A). Given the 30 s data resolution, cases where PV systems reduce generation to zero briefly may be seen to exhibit modest curtailment. Ideally these categories would be further developed in collaboration with inverter bench testing efforts [68].

Fig. 8. Victoria event: Proportion of sites in each response category, 30 s data.

contingency event (68%) whilst around one fifth of systems disconnected (21%), as shown in Fig. 8. The higher proportion of ride through sites is consistent with the higher level of system strength in Victoria compared with South Australia, as well as the less extreme voltage disturbance observed. Similarly to the South Australian case, sites that exhibited a moderate response (did not disconnect or ride through) tended to occur in Category 2 or 3 with very few examples of curtailment beyond Category 3 without disconnection. In contrast to the South Australia event, Fig. 11 shows that the sites located outside Greater Melbourne and which disconnected (Category 7) were comparatively close to the original disturbance.

5.3. Depth of response findings Initial diversity analysis was based on depth of response, where Fig. 6 (indicating normalized average profiles for each category in South Australia) serves to confirm the categorization process as per Table 4.

5.4. Duration of response analysis

5.3.1. South Australia Examination of Fig. 7 shows that the majority of individual systems across South Australia exhibited disconnect (38%) or ride through (41%) behaviors, with relatively few partial responses. The majority of sites which disconnected were located in the Adelaide region, which is to be expected given Adelaide’s proximity to Torrens Island Power Station and thus, the source of the original transmission level voltage disturbance. Fig. 10 maps the number of sites in each category per postcode region and also indicates the approximate fault location. It shows that whilst the majority occurred within Greater Adelaide, disconnection responses were observed across the entirety of the state. Given South Australia’s relatively low level of system strength [69], this is unsurprising, however does emphasize the criticality of effectively managing PV response to major disturbances under such circumstances.

Next, diversity of response across individual system was assessed in terms of ‘total response time’ (tresponse, n ), or the period between the curtailment interval (t 0 ) and the interval in which generation returns to within 10% of the pre-event level, as indicated in Fig. 9 and expressed in (2) and (3). The time at which generation returns to within 10% of the pre-event level, treturn for each system n , is calculated as follows:

treturn, n = minimum{{t : Pn (t )

(90%Pn (t 0))}

{t : t > t 0}}

(2)

The total response time is then:

tresponse, n = treturn, n

t0

(3)

5.5. Duration of response findings

5.3.2. Victoria By contrast, the majority of sites across Victoria rode through the

Substantial diversity is observed in the duration of individual systems’ response times across the 30 s measurement intervals. To manage

Fig. 6. Average PV output power by category, normalized to pre event power, 30 s data.

Fig. 9. Total response time illustrative example. 6

Applied Energy 260 (2020) 114283

N. Stringer, et al.

Fig. 10. Mapping the South Australian event: count of PV sites by category and postcode. Insets show sites located in the Greater Adelaide region only, filtered for (a) Category 7, (b) Categories 2–6 and (c) Category 1. Pink lines indicate transmission network. Red triangle indicates approximate fault location.

the frequency during contingency events, AEMO procures Frequency Control Ancillary Services (FCAS) on a 6 s, 60 s and 5 min basis for contingency raise and lower services [70]. As penetrations increase, loss of distributed PV during disturbances may need to be considered when procuring FCAS. 5.5.1. South Australia Fig. 12 shows that the most commonly occurring total response time was 150 s (2.5 min), which was observed at 28% of the 106 sites for which a response occurred (that is, a reduction greater than 10%). Fig. 12 also shows that typically sites that disconnected (Category 7)

Fig. 12. South Australia event: distribution of total response times, 106 sites, 30 s data.

Fig. 11. Mapping the Victorian event: count of sites by category and postcode. Insets show sites located in the Greater Melbourne region only, filtered for (a) Category 7, (b) Categories 2–6 and (c) Category 1. Pink lines indicate transmission network. Red triangle indicates approximate fault location. 7

Applied Energy 260 (2020) 114283

N. Stringer, et al.

took longer to return. It indicates that additional 60 s and 5 min FCAS raise may be required in future given that 37% of sites had a total response time above 5 min, whilst 53% of sites exhibited a response time between 60 s and 5 min (considered further in Section 5.10). To assess the need for additional 6 s FCAS raise services would necessitate a higher resolution data set. Examining only sites that disconnected, Fig. 13 shows the distribution of total response times by AS4777 version1. Fig. 13 suggests that systems installed post 2016 make up the majority of response times greater than or equal to 300 s. This is to be expected, given the current standard requires a 6 min ramp to rated capacity (Table 4). It suggests that the overall fleet response profile will change as the proportion of systems installed under the current standard increases. Further, Fig. 13 shows that a significant proportion of post 2016 inverters exhibited a total response time of less than 360 s, which may indicate non-compliance with the standard’s ramp rate requirements. However, the total response time alone cannot be used to assess compliance with the standard ramp rate, since the ramp rate is a function of system capacity, whilst total response time is determined using preevent operating level, and systems are unlikely to be operating at full capacity. This is an area for further investigation.

Fig. 13. South Australia event: distribution of total response times for disconnect sites, indicating AS4777 version, 82 sites, 30 s data.

Fig. 14. Victoria event: distribution of total response times, 45 sites, 30 s data.

5.5.2. Victoria The distribution of total response times in Victoria is shown in Fig. 14 for sites that curtailed by 10% or more. Similarly to the South Australia event, generally those sites that disconnected (Category 7) took longer to recover compared with the other response categories. Notably, 71% of sites had a total response time of between 60 s and 5 min, whilst 22% had a total response time of greater than 5 min. As noted for South Australian case, this may result in increased FCAS requirements for both 60 s and 5 min contingency raise in the future, this is considered further in Section 5.10. It is important to note that there are fewer Victorian sites in Categories 3–7 compared with South Australia, since the majority of Victorian sites rode through the event. Examining only sites that disconnected, Fig. 15 shows that inverters installed post 2016 typically returned more slowly compared with pre 2015 or transition period systems. Similarly to the South Australian case, this is consistent with the new 6 min ramp rate requirement in the current standard, and assessing compliance is an area for further investigation.

Fig. 15. Victorian event: distribution of total response times for disconnect sites, indicating AS4777 version, 35 sites, 30 s data.

power output reduction). 5.6.1. Setting zone boundaries The zones should ideally reflect graduating disturbance severity and are therefore expected to depend heavily on electrical distance. Without extensive power system modelling and access to a detailed LV network model (outside the scope of this study), it is not possible to determine such spatial boundaries accurately. Instead, boundaries are set using concentric circles as a proxy for electrical distance. Three criteria are applied in this process: (1) a minimum number of sites per zone, (2) a minimum distance between zones given the high density of sites in some districts2, and (3) consideration of large generators online at the time of the event. The weighting of each criteria is considered on a case by case basis, given the limitations of available data.

5.6. Spatial analysis method Inverter response is dictated by the inverter settings and the voltage conditions experienced. Local voltage conditions are affected by how the disturbance manifests across the network and therefore depends upon a wide range of factors including system strength at the time of the event. Both voltage disturbance and phase angle jump (or vector shift) will be deepest at the site of the disturbance and then decrease for sites that are more electrically remote from the disturbance. The purpose of this study’s spatial analysis is threefold; firstly, to establish whether PV response exhibits a spatial pattern thereby indicating likely response to voltage disturbance or vector shift. Secondly, spatial analysis can be used to assess the severity of PV response for the whole power system (as an input to upscaling). Thirdly, if local voltage conditions can be estimated, the degree of response across different regions may indicate the degree of compliance with AS4777 voltage set points (outside the scope of this study). Spatial analysis consists of two steps: initially, zone boundaries are determined and PV systems are categorized using system location. Then, the degree of PV system response is determined, with two metrics for the degree of response assessed (proportion disconnecting and 1

5.6.2. Analyzing the degree of response The degree of response is assessed using two metrics: the proportion of sites that disconnected and reduction in generation within each zone. 5.6.2.1. Proportion disconnect. The proportion of sites that disconnected in zone z is expressed in (4) whereD (z ) is the number of sites in zone z for which Category 7 Disconnect (Table 4) is true, and N (z ) is the total number of sites in zone z :

%disconnect (z ) =

D (z ) N (z )

(4)

2 This can otherwise result in concentric circles with small differences in radii, and subsequently, systems being classified in different regions despite likely being a similar electrical distance from the disturbance.

The AS4777 version is determined using installation date. 8

Applied Energy 260 (2020) 114283

N. Stringer, et al.

5.6.2.2. Generation reduction. The generation reduction in zone z is calculated using the average PV capacity factor observed in each zone, in order to avoid large systems distorting results. The capacity factor profile over time for individual system, n is calculated as follows:

CFn (t ) =

Pn (t ) PVcap, n, DC

(5)

where Pn (t ) is the average power generation profile in interval t for system n , and where PVcap, n, DC is the DC capacity of system n . The average capacity factor profile over time is then calculated for zone z :

CFave (z , t ) =

N n=1

CFn (t )

N

, whereN = {sites in z }

(6)

Finally, the generation loss in zone z is calculated as per (7), that is, the change in average capacity factor for zone z at the time of the event. Where taggnadir is the time at which the aggregate nadir occurs and noting that taggnadir is based on the total aggregate profile and is consistent across zones.

% gen reduction (z ) =

CFave (z, t0)

Fig. 16. South Australia zone boundaries, pink indicates transmission network.

CFave (z , taggnadir ) (7)

CFave (z, t 0 )

5.7. Spatial analysis results 5.7.1. South Australia 5.7.1.1. Setting zone boundaries. Given the limited number of synchronous generators operating at the time of the event and limited number of sites available in the data set, the zone boundaries were set to ensure a reasonable spread of sites per zone, and to ensure zones are set a reasonable distance apart. Characteristics of the zones used in South Australia are summarized in Table 5 and mapped in Fig. 16.

Fig. 17. South Australia spatial response of distributed PV systems. Table 6 Victorian zone boundaries: data set summary.

5.7.1.2. Spatial response. Fig. 17 shows a clear spatial trend in terms of the proportion of sites that disconnected and the reduction in generation with the most severe response close to the disturbance in zone 1. Zone 1 experienced an extremely high rate of disconnections with just under 50% of systems disconnecting.

Number of sites

Capacity (kWDC)

Radius (km)

Zone 1 Zone 2 Zone 3

63 48 51

821 997 1238

38 127 506

Total

162

3056

–

5.7.2. Victoria 5.7.2.1. Setting zone boundaries. In the Victorian case, zone boundaries were set primarily based on the location of large synchronous generating units operating at the time of the disturbance, with consideration also given to the number of sites per zone. Characteristics of the zones applied in Victoria are summarized in Table 6 and mapped in Fig. 18. 5.7.2.2. Spatial response. Similarly to the South Australia case, there is a clear spatial trend (Fig. 19) in the percentage of generation reduction and proportion of PV system disconnections immediately following the disturbance. Notably, the response is not as severe as that in South Australia, where 49% of zone 1 systems disconnected, compared with 30% of sites in the Victorian zone 1 region.

Fig. 18. Victoria zone boundaries, pink indicates transmission network, diamonds indicate generators operating at > 400 MW (approximate operating level at time of disturbance is shown).

5.8. Upscaling method Upscaling is an important tool for projecting observed behavior

from a sample to an entire installed fleet of PV systems. It is similarly applied for cases where only a sample of distributed PV data is available in [32,35–37]. Clearly, there are a number of variables that could factor in the upscaling technique applied; namely inverter connection standard version, response ‘zones’ (Section 5.6) and system capacity, with potential for inverter manufacturer and model to also feature. The most significant factor for response observed is distance from the event origin, therefore the spatial analysis has been utilized here as the basis to upscaling. Future iterations, particularly those concerned with the

Table 5 South Australian zone boundaries: data set summary. Number of sites

Capacity (kWDC)

Radius (km)

Zone 1 Zone 2 Zone 3

120 56 38

577 259 222

34 182 942

Total

214

1058

–

9

Applied Energy 260 (2020) 114283

N. Stringer, et al.

Fig. 21. South Australia upscaled PV generation estimate, time of event.

Fig. 19. Victorian spatial response of distributed PV systems.

Table 7 South Australia upscaling results.

recovery profile, should take into account standard version, given the total response time results set out in 5.5. Upscaling involves two steps; first, the upscaled profile in zone z is calculated using the average capacity factor profile (5.6.2.2), and the fleet installed capacity PVcap, DC, (z ):

UP (z , t ) = CFave (z , t ) × PVcap, DC , (z )

(8)

The state-wide upscaled profile UP (t ) is then calculated as the sum across all spatial zones:

UP (t ) =

Z z=1

UP (z , t ), whereZ = {spatial zones}

PV Fleet capacity (MW)

Upscaled power loss (MW)

Zone 1 Zone 2 Zone 3

430 196 118

149 44 12

Total

744

205

% of load*

% of load in 2037**

10%

29%

* Load prior to the event was ~2.0 GW. ** Forecast PV capacity of 2.1 GW.

(9)

event with a proportional response, the PV generation loss estimate of ~580 MW would be equivalent to 29% of demand, posing clear challenges for the system operator to avoid system black.

5.9. Upscaling results 5.9.1. South Australia The upscaling results for South Australia show an estimated 45% or 205 MW loss in distributed PV generation (Fig. 20, Fig. 21). This reduction is slightly greater than the 42% observed in the raw data, perhaps due to the sample containing a smaller proportion of sites (by capacity) in the most affected region of zone 1 compared with the overall fleet. The upscaled reduction in generation in the three zones is summarized in Table 7. The upscaled total PV generation loss estimate of 205 MW is 36% greater than the observed 150 MW increase in statewide load. However it is important to note that state-wide load is impacted by load behavior as well as PV behavior and so this comparison is indicative rather than a method for calculating upscaling error. There are several possible explanations for this discrepancy including (a) that the loss of load which also occurred following the event effectively ‘masked’ total PV response, (b) that filtering out zero generation sites during data cleaning results in an increased PV generation estimate at the time of the event since some sites in the fleet will likely be operating at zero, and (c) that the sample is not sufficiently representative, particularly regarding differences due to inverter connection standard version and system capacity (larger systems may be subject to additional requirements compared with smaller systems). The development of statistical methods for assessing upscaling error bounds given the sample size remain an important area for further work. The upscaled power loss estimate is approximately 10% of the load observed immediately prior to the event, and it is important to stress that this was a near miss event, with similar conditions to those resulting in a system black event in South Australia in 2016. If the forecast 2037 PV capacity of 2.1GW had been installed at the time of this

5.9.2. Victoria Upscaling in Victoria resulted in an estimated 25% or 177 MW reduction in PV generation (Fig. 22, Fig. 23). This is comparable with the 29% reduction observed in the raw data set.Fig. 24. The upscaled reduction in each zone is summarized in Table 8. Similarly to the South Australia case study, the upscaling estimation of PV response is approximately 61% greater than the observed 110 MW increase in state load, noting that this comparison is indicative only and does not provide an estimate of the upscaling error. Possible causes of the discrepancy are consistent with those set out in 5.9.1. The PV response constituted only 2% of state load at the time of event. If the same event occurred in 2037 where the forecast uptake of PV had been realized, the upscaled impact would be around 600 MW, which would still only constitute around 7% of total demand, however this may still have implications for FCAS procurement. 5.10. Operational implications The analysis in this paper suggests that distributed PV could contribute to contingency FCAS requirements in several ways: 1. A deep fault near a metropolitan center could cause a significant proportion of region-wide distributed PV to disconnect. With ongoing growth in distributed PV, the loss of the quantities of PV indicated here could start to approach credible contingency sizes due to loss of a unit. 2. A loss of a large unit close to a metropolitan center could be

Fig. 20. South Australia upscaled PV generation estimate.

Fig. 22. Victoria upscaled PV generation estimate. 10

Applied Energy 260 (2020) 114283

N. Stringer, et al.

Table 9 South Australian estimated generation deficit due to PV behavior during disturbances, at time of event and forecast.

Table 8 Victoria upscaling results.

Zone 1 Zone 2 Zone 3

449 425 362

96 59 22

Total

1237

177

1 2

% of load1

2%

2020

2025

2030

2035

60 s after event 5 min after event

205 40

241 47

344 67

461 90

536 105

Table 9 indicates the largest PV generation deficit may be approaching the typical largest contingency size in the NEM (600–750 MW), especially under low load conditions where larger units may not be operating at high levels. This analysis only considers a similar fault in the Adelaide metropolitan area; similar events could occur near other metropolitan centers, and may produce similar results. This should also be explored in future work. These results indicate that it may already be appropriate to consider distributed PV when setting FCAS requirements during some periods, which is not current practice in the NEM. As shown in Fig. 2 and Fig. 4, considerable loss of load occurred along with the reduction in PV generation. Understanding the balance between load ‘shake off’ and reduction in aggregate PV will be important for setting appropriate FCAS requirements, considering both depth as well as duration of response.

Fig. 24. Historical FCAS requirement duration curve (1 Nov 2017 – 30 Nov 2018) for all hours and during sunlight hours (07:00 – 17:00 AEST).

Upscaled power loss (MW)

2017 (event)

Note: estimates based on upscaled profile. For instance, 60 s estimate shows the difference between pre-event upscaled PV generation and PV generation 60 s later. Forecasts scaled based on projected PV capacity.

Fig. 23. Victoria upscaled PV generation estimate, time of event.

PV Fleet capacity (MW)

Generation deficit (MW)

% of load in 20,372

5.10.2. Victorian event Table 10 summarizes the current and projected generation deficit estimates, given the upscaled PV profile for the Victorian event (Fig. 23). Considering the projected PV uptake in Victoria, it is estimated that a similar event occurring in around 2034 could result in ~535 MW of PV reduction over 60 s. As for the South Australian case, this is approaching the realm of typical contingency sizes (600–750 MW), particularly if larger units are not operating, or operating at lower levels during low load periods.

7%

Load prior to the event was ~8.7 GW. Forecast PV capacity of 4.3 GW.

associated with a voltage dip, causing additional disconnection of distributed PV. This would increase the size of the original contingency, summing the loss of the unit, with the loss of distributed PV. This may mean that the largest credible contingency is no longer defined simply by the size of the largest unit, but could be defined by a slightly smaller unit, if it happens to be close to a metropolitan center.

5.11. Verification using independent data set An independent and publicly available data set was accessed from PVOutput.org and compared against the Solar Analytics data. The PVOutput.org data set is only available at 5 min or greater time increments, and as result the PV response following the disturbance is muted compared with that of the 30 s data analyzed in this paper. PV system capacity factor (ratio of generation to DC capacity) is used as the basis for comparison. Fig. 25 and Fig. 27 show the aggregate performance observed in both data sets in South Australia and Victoria respectively. Whilst Fig. 26 (a) and Fig. 28(a) show the event period for both regions. The correlation analysis shown in Fig. 26(b) and Fig. 28(b) indicates a close match between the PVOutput.org and Solar Analytics data set as indicated by large r-squared values. The residual (Fig. 28(b)) show that the there is a consistent difference between the data sets for both the South Australian and Victorian event in the second time interval

In typical periods, the largest credible contingency (loss of a single unit) in the NEM is in the range 600–750 MW. Examination of historical FCAS data shows the global maximum FCAS contingency enablement at around 750–900 MW (enabled under unusual circumstances). However, the lowest level of 60 s contingency FCAS enabled was around 200 MW. The FCAS enablement level is smaller than the anticipated largest credible contingency, because AEMO assumes load relief of 1.5% of system demand [71]. It is important to note that estimates of PV contingency sizes are not directly comparable with FCAS raise enablement, given complexity around several factors including load relief. Note also that the two events considered in this study are classified as non-credible contingencies. AEMO only enables FCAS based upon contingencies classified as credible, under the National Electricity Rules.

Table 10 Victorian estimated contingency sizes due to PV behavior during disturbances, current and forecast.

5.10.1. South Australian event Examining the upscaled PV profile in Fig. 21, it is possible to estimate PV generation reduction from pre-event levels at 60 s and 5 min after the event. It is useful to consider the potential PV contingency size in context of contingency requirements. An estimate of 60 s and 5 min generation deficit due to PV behavior is presented in Table 9, along with an estimate for the future based on projected PV uptake in South Australia.

Generation deficit (MW)

2018 (event)

2020

2025

2030

2035

60 s after event 5 min after event

177 66

236 88

372 139

479 180

550 206

Note: estimates based on upscaled profile. For instance, 60 s estimate shows the difference between pre-event upscaled PV generation and PV generation 60 s later. Forecasts scale based on projected PV capacity. 11

Applied Energy 260 (2020) 114283

N. Stringer, et al.

South Australian event showed distributed PV generation reduced by 45% following a major voltage disturbance, constituting approximately 10% of regional demand at the time of the event. If a similar event occurred in 2035 and the projected level of distributed PV installation were to be realized, then the PV response could represent approximately 29% of the total demand or ~536 MW, with larger fractions possible if the event occurred at midday or during a period of low demand. It is of course possible to envisage more severe voltage disturbances that would involve even greater distributed PV power loss. This poses a significant security challenge and it is critical that power system planning and operation takes into account distributed PV in regions of high penetration. This is reflected in the inclusion of some of the analysis from this study in AEMO reporting [43,44]. Critically, this study suggests that it may be appropriate to consider distributed PV response to disturbances when setting FCAS requirements. Given that distributed PV is not currently considered when settings FCAS contingency requirements in the NEM, this represents a significant operational change for operators. This study emphasizes the need for analysis of actual operational data in order to capture legacy issues (for instance systems installed under superseded connection standards), the diversity of installed inverter models, and the complexity of events within the low voltage network. For example, both case studies presented here consider voltage disturbances in which severe undervoltage was observed in the transmission network. However, both events resulted in substantial loss of load and as result, the observed local voltages show both under and over voltage, suggesting localized over voltage due to load ‘shake off’. Modelling such interactions is challenging, and in this context, operational PV data is likely to provide an important opportunity to test and verify composite load models with a DER component. Such models are currently under development in Australia and internationally. Four novel techniques for analyzing operational distributed PV data are presented and an open source tool has been developed to enable analysis of future events and adaptation of techniques [67]. Two techniques are presented for analyzing individual responses and it is shown that there is considerable diversity in both depth and duration of response to the events. Two further techniques are presented for analyzing aggregate response. Spatial analysis shows clear trends in severity of response with systems closer to the source of disturbance more likely to be affected. The upscaling technique is a powerful tool for assessing fleet behavior, however further work is required to develop methods for estimating upscaling error. Increase in state-wide load during the event provides an indicative comparison however there is the potential for load ‘shake off’ to mask PV response. Importantly, it is not possible to establish whether the observed PV disconnections were caused by over voltage, under voltage, vector shift or other inverter responses by examining this data set and further, it is not possible to directly assess compliance with the inverter connection standards. To better understand the cause of inverter behaviors and assess compliance, in lab testing is required. Operational data analysis is a useful complimentary tool and powerful further insights may be possible through coupling operational data, system modelling and lab testing.

Fig. 25. South Australia comparison of PVOutput.org data set (268 sites) and Solar Analytics data set (260 sites), time is AEST.

Fig. 26. (a) South Australia comparison during event period of PVOutput.org data set (268 sites) and Solar Analytics data set (260 sites), time is AEST, (b) correlation and residuals.

Fig. 27. Victoria comparison of PVOutput.org data set (400 sites) and Solar Analytics data set (168 sites), time is AEST.

Fig. 28. (a) Victoria comparison during event period of PVOutput.org data set (400 sites) and Solar Analytics data set (168 sites), time is AEST, (b) correlation and residuals.

immediately following the event. This is likely due to the higher proportion of pre 2015 systems in the PVOutput.org data set, compared with the Solar Analytics data set and new ramp rate requirements for post 2016 systems (Appendix A). 6. Conclusions

CRediT authorship contribution statement

As distributed PV penetrations continue to grow, power system operators world wide will need to manage the very real system security challenges posed by mass response of distributed inverters to disturbances originating in the transmission system. Whilst early work focused on frequency disturbances, there is a growing awareness that large voltage disturbances also pose a significant risk to secure power system operation. This study presents analysis of two case studies examining major power system voltage disturbances. Upscaling for the more extreme

Naomi Stringer: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration. Navid Haghdadi: Software, Formal analysis, Data curation, Writing - review & editing, Supervision. Anna Bruce: Conceptualization, Writing - review & editing, Supervision, Resources. Jenny Riesz: Conceptualization, Methodology, Writing - review & editing, Supervision. Iain MacGill: Conceptualization, Writing - review & editing, Supervision, Resources. 12

Applied Energy 260 (2020) 114283

N. Stringer, et al.

Declaration of Competing Interest

search by an Australian Government Research Training Program Scholarship, the Faculty of Engineering at UNSW, CRC LCL project RP1023u1 and ARENA project ‘UNSW, Addressing Barriers to Efficient Renewable Integration’. The authors gratefully acknowledge the contributions of R. Bunder, R. Egan and J. Dore of Solar Analytics for the provision of data. The authors also gratefully acknowledge the contributions of N. Gorman for support accessing data, T. Barton for data cleaning, and A. Millican for discussion throughout analysis.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement The authors gratefully acknowledge support provided for this reAppendix A (See Table 11)

Table 11 AS4777 Passive Anti-islanding requirement summary, current and legacy standards, nominal voltage is 230 V [65,66]. Protective function

Protective function limit

Trip delay time

Maximum disconnection time

Standard

Minimum time before reconnection1

Reconnection ramp rate1

Under voltage

200–230 V

Not specified

2s

AS4777.3-2005 (superseded)

60 s

Not specified

Not specified

2s

60 s

1s

2s

1s

2s

–

0.2 s

AS4777.3-2005 (superseded) AS4777.2-2015 (current) AS4777.2-2015 (current) AS4777.2-2015 (current)

Over voltage Under voltage Over voltage 1 Over voltage 2 1

0.87–1p.u. 230–270 V 1–1.17p.u. 180 V 0.78p.u. 260 V 1.13p.u. 265 V 1.15p.u.

60 s 60 s

16.67% of rated power per minute (nominal ramp time of 6 min)

60 s

When limits are exceeded the disconnection device operates, with a minimum 1 min before reconnecting, with ramp rate at reconnection as specified.

[12] NERC. 900 MW Fault Induced Solar Photovoltaic Resource Interruption Disturbance Report; February 2018. [13] Yan R, Saha TK, Modi N, Masood N-A, Mosadeghy M. The combined effects of high penetration of wind and PV on power system frequency response. Appl Energy 2015;145:320–30. https://doi.org/10.1016/j.apenergy.2015.02.044. 2015/ 05/01/. [14] Ching C. Adapting to change: Hawai’i’s response to alternative energy sources [In My View]. IEEE Power Energ Mag 2017;15(2):93–6. https://doi.org/10.1109/MPE. 2016.2640440. [15] Hancock M. et al. Alice springs: a case study of increasing levels of PV penetration in an electricity supply system; 2011. [16] Khoshnami A, Sadeghkhani I. Two-stage power–based fault detection scheme for photovoltaic systems. Sol Energy 2018;176:10–21. https://doi.org/10.1016/j. solener.2018.10.014. 2018/12/01/. [17] Al-Shetwi AQ, Sujod MZ, Blaabjerg F. Low voltage ride-through capability control for single-stage inverter-based grid-connected photovoltaic power plant. Sol Energy 2018;159:665–81. https://doi.org/10.1016/j.solener.2017.11.027. 2018/01/01/. [18] Perpinias II, Papanikolaou NP, Tatakis EC. Fault ride through concept in low voltage distributed photovoltaic generators for various dispersion and penetration scenarios. Sustainable Energy Technol Assess 2015;12. https://doi.org/10.1016/j. seta.2015.08.004. pp. 15–25, 2015/12/01/. [19] Boemer Jens C, Huque Mohammad, Seal Brian, Key Tom, Brooks Daniel, Vartanian Charlie. Status of Revision of IEEE Std 1547 and 1547.1. presented at the power & energy society general meeting, 2017 IEEE, Chicago, IL, USA, 16-20 July. 2017. [20] Schäfer N, Arnold G, Heckmann W. Grid code compliance testing of renewables – New requirements and testing experiences. presented at the 1st International Conference on Large-Scale Grid Integration of Renewable Energy in India, New Delhi, 6–8 September. 2017. [21] IEEE standard for interconnection and interoperability of distributed energy resources with associated electric power systems interfaces. IEEE Std 1547-2018 (Revision of IEEE Std 1547-2003); 2018. p. 1–138. [22] Yang Y, Enjeti P, Blaabjerg F, Wang H. Suggested grid code modifications to ensure wide-scale adoption of photovoltaic energy in distributed power generation systems. 2013 IEEE industry applications society annual meeting. 2013. p. 1–8. [23] Hoke A, Giraldez J, Palmintier B, Ifuku E, Asano M, Ueda R, et al. Setting the smart solar standard: collaborations between Hawaiian electric and the national renewable energy laboratory. IEEE Power Energ Mag 2018;16(6):18–29. https://doi.org/ 10.1109/MPE.2018.2864226. [24] Ghatikar G, Mashayekh S, Stadler M, Yin R, Liu Z. Distributed energy systems integration and demand optimization for autonomous operations and electric grid transactions. Appl Energy 2016;167:432–48. https://doi.org/10.1016/j.apenergy. 2015.10.117. 2016/04/01/. [25] Nelson A, Prabakar K, Nagarajan A, Nepal S, Hoke A, Asano M, et al. Power hardware-in-the-loop evaluation of PV inverter grid support on Hawaiin electric feeders. presented at the Power & Energy Society Innovative Smart Grid

References [1] Passey R, Spooner T, MacGill I, Watt M, Syngellakis K. The potential impacts of gridconnected distributed generation and how to address them: a review of technical and non-technical factors. Energy Policy October 2011;39(10):6280–90. https:// doi.org/10.1016/j.enpol.2011.07.027. [2] International Energy Agency. Do It Locally: Local Voltage Support by Distributed Generation – A Management Summary. Management summary of IEA task 14 subtask 2 – recommendations based on research and field experience. 2017. [3] Ji H, Wang C, Li P, Zhao J, Song G, Ding F, et al. A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks. Appl Energy 2018;228. https://doi.org/10.1016/j. apenergy.2018.07.065. 2024–2036, 2018/10/15/. [4] Ji H, Wang C, Li P, Zhao J, Song G, Ding F, et al. An enhanced SOCP-based method for feeder load balancing using the multi-terminal soft open point in active distribution networks. Appl Energy 2017;208:986–95. https://doi.org/10.1016/j. apenergy.2017.09.051. 2017/12/15/. [5] Zhang P, Li W, Li S, Wang Y, Xiao W. Reliability assessment of photovoltaic power systems: Review of current status and future perspectives. Appl Energy 2013;104:822–33. https://doi.org/10.1016/j.apenergy.2012.12.010. 2013/ 04/01/. [6] Kabir MN, Mishra Y, Ledwich G, Xu Z, Bansal RC. Improving voltage profile of residential distribution systems using rooftop PVs and Battery Energy Storage systems. Appl Energy 2014;134:290–300. https://doi.org/10.1016/j.apenergy.2014. 08.042. 2014/12/01/. [7] Susanto J, Shahnia F, Ludwig D. A framework to technically evaluate integration of utility-scale photovoltaic plants to weak power distribution systems. Appl Energy 2018;231:207–21. https://doi.org/10.1016/j.apenergy.2018.09.130. 2018/ 12/01/. [8] Cabrera-Tobar A, Bullich-Massagué E, Aragüés-Peñalba M, Gomis-Bellmunt O. Review of advanced grid requirements for the integration of large scale photovoltaic power plants in the transmission system. Renew Sustain Energy Rev 2016;62:971–87. https://doi.org/10.1016/j.rser.2016.05.044. 2016/09/01/. [9] Purvins A, Zubaryeva A, Llorente M, Tzimas E, Mercier A. Challenges and options for a large wind power uptake by the European electricity system. Appl Energy 2011/05/01/ 2011.;88(5):1461–9. https://doi.org/10.1016/j.apenergy.2010.12. 017. [10] CIGRE C2.16. Challenge in the Control Centre (EMS) due to Distributed Generation and Renewables. September 2017, Available: https://e-cigre.org/publication/700challenges-in-the-control-center-ems-due-to-distributed-generation-andrenewables. [11] Lew D, Asano M, Boemer J, Ching C, Focken U, Hydzik R, et al. The Power of small: the effects of distributed energy resources on system reliability. IEEE Power Energ Mag 2017;15(6):50–60. https://doi.org/10.1109/MPE.2017.2729104.

13

Applied Energy 260 (2020) 114283

N. Stringer, et al.

Distributed-Energy-Resources-Report. [45] PVoutput.org. Live photovoltaic data; 2018. Available: PVoutput.org. [46] Clean Energy Regulator. Postcode data for small-scale installation - all data,“ ed; 2018. [47] Australian Bureau of Statistics. Postal Areas ASGS Ed 2016 Digital Boundaries in ESRI Shapefile Format (zip file). In: 1270.0.55.003 - Australian Statistical Geography Standard (ASGS): Volume 3 - Non ABS Structures, July 2016, ABS, Ed., ed; 2016. [48] Australian Bureau of Statistics. Greater Capital City Statistical Area (GCCSA) ASGS Ed 2011 Digital Boundaries in ESRI Shapefile Format. ed; 2011. [49] AEMO, “NEMweb,” ed; 2018. [50] Gorman N, Haghdadi N, Bruce A, MacGill I. NEMOSIS – NEM Open source information service; open-source access to australian national electricity market data. presented at the Asia Pacific Solar Research Conference 2018, Sydney, Australia, 46 December. APVI; 2018. http://apvi.org.au/solar-research-conference/wpcontent/uploads/2018/12/194_D-I_Gorman_N_2018.pdf. [51] Parkinson G. Rooftop solar provides 48% of South Australia power, pushing grid demand to record low. In: RenewEconomy, ed; 2017. [52] Energy Transition Hub. (2018, 13 February 2018). OpenNEM South Australia. Available: http://opennem.org.au/#/regions/sa. [53] AEMO. Electricity Forecasting Insights – March 2018 update. In: Electricity Minimum Demand Operational; 29 March 2018, Available: http://forecasting. aemo.com.au/Electricity/MinimumDemand/Operational. [54] Walter Gerardi and Damien O’Connor. Projections of uptake of small-scale systems; Jacobs9 June 2017. [55] AER. State of the Energy Market; 30 May 2017. [56] AER and AEMO. Seasonal peak demand (region),“ D11/2285335[V2] ed; 2018. [57] AER and AEMO. Registered capacity in regions by fuel source,“ D11/2285335[V2] ed; 2018. [58] AEMO. Rooftop PV and battery storage; 2017, Available: https://www.aemo.com. au/Electricity/National-Electricity-Market-NEM/Planning-and-forecasting/ Electricity-Forecasting-Insights/Key-component-consumption-forecasts/PV-andstorage. [59] Bureau of Meteorology. Daily maximum temperature; 2018. [60] O'Neil A. Was South Australia lucky the lights stayed on last Friday?. In: WattClarity, ed: GLOBAL-ROAM; 2017. [61] Bureau of Meteorology. One Minute Solar Data. ed; 2019. [62] AEMO. Fault at Torrens Island Switchyard and loss of multiple generating units on 3 March 2017. In: Reviewable Operating Incident Report for the National Electricity Market; 10 March 2017, Available: https://www.aemo.com.au/-/media/Files/ Electricity/NEM/Market_Notices_and_Events/Power_System_Incident_Reports/ 2017/Report-SA-on-3-March-2017.pdf. [63] (2018). Update - Non-credible contingency event - VIC region - Thursday, 18 January 2018. [64] AEMO. NEMWeb FCAS Causer Pays data. ed; 2019. [65] AS/NZS 4777.2:2015 Grid connection of energy systems via inverters; 2015. [66] AS/NZS 4777.3-2005 : Grid connection of energy systems via inverters - Grid protection requirements; 2005. [67] Author. (2019). DER disturbance analysis. Available: https://github.com/TaruAEMO/DER_disturbance_analysis/tree/old_master. [68] ARENA. UNSW Addressing barriers to efficient renewable integration; 2019. Available: https://arena.gov.au/projects/addressing-barriers-efficient-renewableintegration/. [69] AEMO. National Transmission Network Development Plan; 12 December 2016, Available: https://www.aemo.com.au/-/media/Files/Electricity/NEM/Planning_ and_Forecasting/NTNDP/2016/Dec/2016-NATIONAL-TRANSMISSIONNETWORK-DEVELOPMENT-PLAN.pdf. [70] Riesz Jenny, Gilmore Joel, MacGill Iain. Frequency control ancillary service market design: insights from the australian national electricity market. Electr J 2015;28(3):86–99. https://doi.org/10.1016/j.tej.2015.03.006. [71] AEMO. Constraint Formulation Guidelines; 5 December 2013, Available: https:// www.aemo.com.au/-/media/Files/Electricity/NEM/Security_and_Reliability/ Congestion-Information/2016/Constraint_Formulation_Guidelines_v10_1.pdf.

Technologies Conference, 2017 IEEE, Washington, DC, USA, 30 October. 2017. [26] Huka GB, Li W, Chao P, Peng S. A comprehensive LVRT strategy of two-stage photovoltaic systems under balanced and unbalanced faults. Int J Electr Power Energy Syst 2018;103. https://doi.org/10.1016/j.ijepes.2018.06.014. pp. 288–301, 2018/12/01/. [27] North American Electric Reliability Corporation (NERC). Technical reference document dynamic load modelling. December 2016, Available: https://www.nerc. com/comm/PC/LoadModelingTaskForceDL/Dynamic%20Load%20Modeling %20Tech%20Ref%202016-11-14%20-%20FINAL.PDF#search=Load%20modeling %20technical%20reference. [28] Maitra A, Gaikwad A, Pourbeik P, Brooks D. Load model parameter derivation using an automated algorithm and measured data. presented at the 2008 IEEE power and energy society general meeting - conversion and delivery of electrical energy in the 21st century, Pittsburgh, PA, USA, 20-24 July. 2008. [29] Pourbeik P, Clark K, Boemer J, Favela R, Ramasubramanian D, Gaikwad A, et al. Proposal for DER_A Model. 2018, Available: https://www.wecc.biz/ Administrative/DER_A_Final_021518.pdf. [30] International Energy Agency. Status of Power System Transformation 2017 System integration and local grids; 2017, Available: https://www.iea.org/publications/ freepublications/publication/status-of-power-system-transformation-2017.html. [31] APVI. (2019, 26 April). Australian PV market since April 2001. Available: http:// pv-map.apvi.org.au/analyses. [32] APVI. Australian PV Institute (APVI) Solar Map, funded by the Australian Renewable Energy Agency; 2018. Available: http://pv-map.apvi.org.au/. [33] AEMO. Visibility of Distributed Energy Resources. In: Future Power System Security Program; January 2017, Available: https://www.aemo.com.au/-/media/Files/ Electricity/NEM/Security_and_Reliability/Reports/2016/AEMO-FPSS-program—— Visibility-of-DER.pdf. [34] AEMO. Response of existing PV inverters to frequency disturbances. 27; April 2016, Available: https://www.aemo.com.au/Media-Centre/Response-of-Existing-PVInverters-to-Frequency-Disturbances. [35] Haghdadi N, Dennis J, Bruce A, MacGill I. Real time generation mapping of distributed PV for network planning and operations. In: presented at the IEEE PES Asia-Pacific power and energy engineering conference, Brisbane, Australia; 2015. doi: https://doi.org/10.1109/APPEEC.2015.7381030. [36] Haghdadi N, Bruce A, MacGill I, Passey R. Impact of distributed photovoltaic systems on zone substation peak demand. IEEE Trans Sustainable Energy April 2018;9(2):621–9. https://doi.org/10.1109/TSTE.2017.2751647. [37] Haghdadi N, Bruce A, MacGill I. Assessing the representativeness of “Live” distributed PV data for upscaled PV generation estimates. In: presented at the IEEE PES Asia-Pacific Power and Energy Engineering Conference, Brisbane, Australia; 2015. doi: https://doi.org/10.1109/APPEEC.2015.7380908. [38] Meyers B, Tabone M, Kara EC. Statistical Clear Sky Fitting Algorithm. presented at the 45th IEEE photovoltaic specialists conference, Waikoloa. 2019. [39] Meyers B, Deceglie M, Deline C, Jordan D. Signal processing on PV time-series data: robust degradation analysis without physical models. presented at the 46th IEEE Photovoltaic Specialists Conference, Chicago. 2019. [40] Howlader AM, Sadoyama S, Roose LR, Sepasi S. Distributed voltage regulation using Volt-Var controls of a smart PV inverter in a smart grid: an experimental study. Renew Energy 2018;127:145–57. https://doi.org/10.1016/j.renene.2018. 04.058. 2018/11/01/. [41] Sanseverino ER, Tran QTT, Roose LR, Sadoyama ST, Tran T, Doan BV, et al. Optimal placements of SVC devices in low voltage grids with high penetration of PV systems. In: 2018 9th IEEE international symposium on power electronics for distributed generation systems (PEDG); 2018. p. 1–6. doi: https://doi.org/10.1109/PEDG. 2018.8447619. [42] Solar Analytics. Solar Analytics; 2018. Available: https://www.solaranalytics. com/au/. [43] AEMO. Final Report – Queensland and South Australia system separation on 25 August 2018; 10 January 2019, Available: https://www.aemo.com.au/-/media/ Files/Electricity/NEM/Market_Notices_and_Events/Power_System_Incident_ Reports/2018/Qld—SA-Separation-25-August-2018-Incident-Report.pdf. [44] AEMO. Technical Integration of Distributed Energy Resources; April 2019, Available: https://www.aemo.com.au/Media-Centre/Technical-Integration-of-

14