Analysis of short-term operational forecast deviations and controllability of utility-scale photovoltaic plants
Introduction
The falling costs and environmental benefits of utility-scale photovoltaic (PV) plants are driving growing deployment in electricity industries around the world. However, their highly variable and somewhat unpredictable output raises some challenges for electricity industry operation. A particular challenge is short term frequency management. Deviations of PV generation from forecasts will contribute to frequency deviations that then need to be corrected [1]. In many electricity industries such short-term balancing is primarily managed through frequency control ancillary services (FCAS). While arrangements vary across jurisdictions, these are often divided into regulation or load following services to address inherent ongoing load variation, and contingency services to address infrequent major supply-demand balance disturbances such as large generating plant or network failures. Utility PV plants have particular operating characteristics that would seem to create both challenges and opportunities for frequency management. The output of even large utility PV plants which have inherent spatial smoothing, can vary greatly in a matter of seconds under particular cloud patterns, with limited predictability; while these plants have effectively no energy storage to smooth output, unless battery energy storage is deployed. On the other hand, their power inverter interface typically does mean the output of PV plants can be rapidly and finely controlled between zero and their maximum achievable output depending on available solar irradiance.
A range of strategies are now being deployed to improve the integration of variable renewable energy (VRE) generation such as PV into short-term electricity industry operation. Implementation of many of these requires characterising solar resource and PV generation variability (change in output from one time interval to another) and uncertainty (unpredictability of the output) [[2], [3], [4], [5]] characterise PV variability at 1-s and [6] at 1 min with comparison to point-source measurement and plant output. All these studies quantify variability in terms of statistical distribution which is useful for quantifying frequency regulation reserve requirements [7]. A smaller number of studies have assessed PV variability compared to the balancing capabilities in the power system. For example [8], compared PV variability with the ramping capability of a gas turbine unit while [9] modelled the impact of PV siting in Ontario on aggregated PV output variability at hourly resolution and observed its correlation to the system-wide demand. The uncertainty of PV generation has been characterised in the form of deviation from a reference line which could be a forecast target [10] or a line of moving average [11,12] at a timeframe chosen to reflect the frequency control services provided in that system. These studies quantified and compared the aggregated deviation to the variability of load to assess the requirement for additional FCAS services, which are traditionally based on load variability [10]. also investigated different short-term market operational strategies to reduce reserve requirements due to increasing PV penetration. Nonetheless, understanding of the characteristics of short-term PV operation relevant to the power system operator and required to improve integration strategies for VRE is still limited, particularly short-term deviations from the generation forecasts used in generator dispatch. More generally, as discussed in Ref. [4], there is a need to better understand how this variability compares with that of other generation technologies, given that all generators typically exhibit some uncertainty. Furthermore, there is the question of what the impacts of this uncertainty around dispatch targets (or for wind and PV short-term forecasts) are on power system FCAS requirements. The degree of uncertainty around the expected output of PV plants would also be useful to indicate volatile periods [13]. Note that our interest is not specifically in the accuracy of such forecasts although improving forecast accuracy could significantly reduce the need for frequency control reserves [14,15]. Instead, we seek to better understand the short-term variability and uncertainty that might be expected around these forecasts, and its potential impacts on frequency control requirements.
In addition to variability characterisation, there is the question of how power system operational arrangements might be improved, including greater participation by VRE generation. A number of jurisdictions with significant wind generation have for some time been looking to require such plants to be able to provide regulation services [16]. Key barriers to entry to the FCAS markets for VRE generation have been identified in Refs. [17] for the Spanish market [18,19]. developed strategies for VRE to participate in FCAS markets, based on a range of factors including the resource availability, system security constraints and market price. These studies were based on the assumption that PV inverters had a great capability in controlling PV output. However, even though the necessary inverter capabilities to provide advanced control for utility PV plant are well established [20,21], there is limited evidence to date of their actual application in real power system operation.
The Australian National Electricity Market (NEM) presents an interesting opportunity for investigating issues of PV variability characterisation, and both its implications for managing frequency under existing FCAS arrangements as well as opportunities to contribute such services, because the NEM includes utility-scale PV as a market participant and the market operator publishes high resolution SCADA data from every participating plant. The NEM has had four utility PV plants operating for over four years with an aggregate capacity of 231 MW in the region of New South Wales. At the end of 2019, the NEM-wide installed capacity of utility-scale PV plants was 2.8 GW and many more are now coming on-line [22]. Utility wind and PV generators are required to participate in wholesale market arrangements as “semi-scheduled” units that must follow “dispatch” targets when required but can otherwise generate up to their maximum level. They are required to contribute to the cost of FCAS regulation (through “Causer Pays” arrangements) according to how their variability within 5 min dispatch periods contributes to short-term supply-demand imbalance, while also being allowed to provide FCAS services (although to date, the only participation of this type has been through trials where wind farms have participated in both regulation and contingency, raise and lower, FCAS markets [23]). The NEM is facing growing challenges in frequency control with questions of whether growing renewable penetrations or declining primary frequency control from scheduled plants might be contributing [24].
Of particular importance to the study reported in this paper, the Causer Pays arrangements for regulation FCAS are based around the ability, or failure, of generators to follow their target trajectories within 5-min dispatch periods. Hence, 4-s SCADA data of the generation of every scheduled and semi-scheduled generator in the NEM is collected and made publicly available, as well as all details of wholesale market dispatch targets and FCAS “enabled” market participants. This high-resolution operational data permits analysis in this paper of the generation variability of a number of geographically dispersed utility PV plants with 4-s sampling. As such, the impacts of PV generators on short-term supply-demand balance and FCAS market operation, and the potential implications for these generators can be assessed. Also, the requirement for these plants to meet “upper bound” dispatch targets in some circumstances provides some evidence of the potential controllability of utility PV generation. Importantly, the high data transparency of the NEM also allows us to compare the potential controllability of utility PV against other NEM generation technologies including utility wind as well as gas and coal-fired units and hydro generation.
The novelty of this paper is summarised as follows. Most existing studies have analysed PV variability independently of electricity market operation, or in the absence of high-resolution generation data, have relied on modelled PV generation. Our paper is the first as far as we are aware to analyse real-world utility PV plant deviation around short-term forecast dispatch targets compared to the deviations of all other participating generators, the potential implications of this for frequency control, and the potential controllability of these plants to assist in managing their deviations. Uncertainty analysis across all generator types contributes to the understanding of the scale of PV variability compared to dispatched generation uncertainty in large power systems, and its influence on frequency deviation and therefore frequency control requirements. Observation of PV behaviour under constraint provides a greater understanding of the current controllability of PV plants in the existing grid operation and therefore their potential to provide frequency control services. A 4-s timeframe is commonly used for automatic generation control (AGC) in power system operation. The results from this analysis therefore have direct implications for frequency regulation and short-term balancing within the electricity market dispatch timeframes of a range of power systems as PV penetrations increase. There is very limited work to date internationally that has used such detailed operational and market data. While individual plant variability and deviation observed will be context specific, the insights of this analysis, therefore, have broader electricity industry relevance for other jurisdictions.
The structure of the rest of the paper is as follows. NEM generator classification and generation capacity are outlined in Section 2. The data used for this study and the methods for analysis of uncertainty of generators in meeting their dispatch target trajectories and their controllability when under instruction are explained in Section 3. Section 4 presents an overview of SCADA output profiles from the main generation technologies exhibited in the NEM. Section 5 analyses forecast deviations of wind and PV plants while Section 6 and Section 7 carry out the analysis of MW deviations of all market participants and their relationship to frequency deviation. Section 8 investigates the controllability of the PV plants. Lastly, Section 9 presents the implications of the observed short-term operation of PV for electricity market design.
Section snippets
Australian NEM context
The Australian Energy Market Operator (AEMO) categorises generators in the NEM according to Fig. 1. Scheduled generators include thermal power plants, hydro and battery energy storage while semi-scheduled generators are wind and solar farms. Both of these participant types are allowed to provide frequency regulation services.
The installed NEM generation capacity by type of registration and generation technology (as of 2019) are listed in Table 1 and Table 2, respectively. The main providers for
SCADA generation data
The instantaneous power output at 4-s intervals of all 229 scheduled and semi-scheduled generators (as of 2016 to consider a full year of data) in the NEM was extracted from the AEMO Ancillary Services Market Causer Pays Data which is publicly available on the AEMO website [26]. For each generator, for each 5-min dispatch interval, the dataset contains 4-s SCADA output, 5-min dispatch target, measured system frequency deviation from the nominal value (usually 50 Hz), and assigned 4-s MW for
Overview of SCADA outputs and sample analysis
Fig. 7 shows the generation profile for selected NEM plants compared to their dispatch targets for a 24-min window on October 18, 2016, a period where both minor under and over-frequency deviations were present. The output of scheduled generators which were not providing regulation services can be seen to track their dispatch targets reasonably well. Gas and hydro plants during this period demonstrated steady output while some slightly larger fluctuations around the targets could be seen in
Forecast deviations of PV and wind
Research has highlighted that the very short-term variability of PV is considerably greater than wind [30]. Both wind and PV are classified under the same semi-scheduled participation status by AEMO but their characteristics are different. Fig. 8 shows the 4-s MW deviations for a wind and PV plant of roughly equivalent MW size against both the current forecast target and our proposed timeshift of these forecasts, classified according to the output of the plant, and using a year of data. The
Deviations of all NEM generators
To put the deviations of PV and wind plants into the wider NEM context, the short-term deviations of all NEM scheduled and semi-scheduled generators from their targets/forecasts are analysed. This study categorises 229 generators into 12 technology types and by their participant status (Fig. 1).
Fig. 9 illustrates the mean of deviations from the dispatch target and the standard deviation of these values for each generator. For non-wind and solar plant, deviations can be caused by the ramping
Aggregated power deviation and its relationship with frequency excursions
Throughout the year, the aggregated MW deviation from target trajectory of all the NEM generators is within the range of 500 MW which is around 1% of total generation capacity (Fig. 10). Some large generation trips are also visible in the figure. The negative trend of the deviation is shown by fitting a linear line. This shows a negative correlation between frequency deviations and MW deviations, which implies that in aggregate, generation deviations from targets are generally working to return
Variability during peak generation
A frequency distribution of the variability of each of the utility PV plants during peak generation curtailment periods is shown in Fig. 14. Most of the fluctuations of all three PV plants during the peak generation curtailment periods are within ±0.2% of the plant capacity which is around ±0.1–0.2 MW. Fig. 14(a) shows the results for Nyngan. A zero deviation occurs 85% of the time showing that the plant can produce extremely smooth output. For the two other plants, output generally sits a
Discussion
By explicitly analysing the deviation around dispatch target of individual generators, our analysis shows that PV has by far the highest mean deviation of all generation technologies. This result is as expected as PV is known to have higher variability and uncertainty than wind [34]. However, we have been able to quantify the magnitude of this difference for the Australian NEM the mean deviation of PV generation (7.6%) is around five times greater than the average of the mean deviations of
Conclusion
Our findings highlight some key aspects of utility PV variability, deviation from forecasts and ability to follow constraints compared to the behaviour of other generating units. These results have implications for short-term frequency control and the potential controllability of these units if and when required.
The uncertainty analysis demonstrates that the NEM’s linear ramp calculation procedure does not account for the fast characteristics of PV generation, resulting in consistent bias
Data availability
Datasets related to this paper can be found at https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Data/Ancillary-Services/Ancillary-Services-Market-Causer-Pays-Data and http://www.nemweb.com.au/Data_Archive/Wholesale_Electricity/MMSDM/, public online data sources hosted at Australian Energy Market Operator [26,29]. The detail of data cleaning and processing is listed in supplementary material.
CRediT authorship contribution statement
Kanyawee Keeratimahat: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing - original draft, Visualization. Anna Bruce: Conceptualization, Supervision, Writing - review & editing. Iain MacGill: Conceptualization, Supervision, Writing - review & editing.
Declaration of competing interest
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.
Acknowledgements
This research is supported by University of New South Wales International Postgraduate Award and Australian Government Research Training Program Scholarship.
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