The most significant contributions of this thesis to addressing the research gap outlined in Section 1.2 include:
This thesis consists of 7 chapters and 4 appendices.
-Chapter 2 provides relevant context on power systems and power system operation, and an overview of the literature on the challenges with and the desirable outcomes of designing operational practices in electricity markets with growing penetrations of VRE.
+Chapter 2 provides relevant context on power systems and power system operation, and an overview of the literature on the desirable outcomes of and challenges associated with designing operational practices in electricity markets with growing penetrations of VRE.
Chapter 3 outlines the motivating research question, the research objectives and research methods of this thesis in detail.
Chapter 4 considers the question of how frequency control arrangements should be designed with growing penetrations of VRE. In this chapter, I first provide an overview of typical frequency control arrangements, with a focus on restructured electricity industries in North America and Europe, and the main challenges faced in their design. I then describe the NEM’s frequency control arrangements and the specific challenges posed by increasing penetrations of VRE. Based on an analysis of the performance of the NEM’s frequency control arrangements in responding to these challenges, I conclude this chapter by offering four key insights to policy-makers.
Chapter 5 focuses on understanding balancing flexibility capabilities available in scheduling timeframes as VRE and storage become a larger part of system resource mixes. In this chapter, I first provide an overview of how balancing flexibility is enabled and procured in the NEM before describing a methodology to quantify available reserves and footroom across deployment horizons for various resource types. I then quantify the available reserves and footroom in two regions of the NEM for existing resource mixes in 2020 and potential resources mixes in 2025, with two scenarios for the latter. From the findings of this case study, I explore the role of reserve products in securing balancing flexibility. Appendix 8 outlines the sources for key input data and assumptions, and provides further details regarding how these data were used in the analysis.
-Chapter 6 explores how future pricing information and market participant operational strategies affect the deployability of balancing flexibility from energy storage resources. In this chapter, I first summarise market information, participation and clearing processes in the NEM in addition to providing context on grid-scale energy storage resource deployment, operation and market participation to date. Then, in a case study of the NEM, I examine errors in the NEM’s centralised price forecasts, propose a hypothesis to explain increasing divergence and the occurrence of price swings in these forecasts, and subsequently use the same centralised price forecasts to schedule a variety of battery energy storage systems for wholesale energy market arbitrage to assess the impact of imperfect foresight on arbitrage revenues. I conclude by discussing changes to market participant scheduling and market design that could maximise the balancing value of resources such as battery energy storage systems. Appendix 9 presents the mixed-integer linear program formulations used in the storage modelling in Chapter 6, and Appendix 10 describes the methodology used to model a storage scheduler discounting price forecasts (one of the formulations used in the storage modelling in Chapter 6 and described in Appendix 9).
+Chapter 6 explores how future pricing information and market participant operational strategies affect the deployability of balancing flexibility from energy storage resources. In this chapter, I first summarise market information, participation and clearing processes in the NEM in addition to providing context on grid-scale energy storage resource deployment, operation and market participation to date. Then, in a case study of the NEM, I examine errors in the NEM’s centralised price forecasts, propose a hypothesis to explain increasing divergence and the occurrence of price swings in these forecasts, and subsequently use these same forecasts to schedule a variety of battery energy storage systems for wholesale energy market arbitrage to assess the impact of imperfect foresight on arbitrage revenues. I conclude by discussing changes to market participant scheduling and market design that could maximise the balancing value of resources such as battery energy storage systems. Appendix 9 presents the mixed-integer linear program formulations used in the storage modelling in Chapter 6, and Appendix 10 describes the methodology used to model a storage scheduler discounting price forecasts (one of the formulations used in the storage modelling in Chapter 6 and described in Appendix 9).
Chapter 7 concludes the thesis. In this chapter, I summarise the contributions of this thesis and highlight avenues for further work.
Appendix 11 is the Journal of Open Source Software article for NEMSEER, a Python package developed as a part of this thesis to download and handle historical NEM forecast data produced by the Australian Energy Market Operator.
Figure 2 presents a high-level overview of power system phenomena in operational timeframes and common operational practices (i.e. processes, services and markets in operational timeframes). The processes, services and markets discussed in detail within this thesis are highlighted in bold red text. Though processes, services and markets in investment and planning timeframes are not within the scope of this thesis, it is important to acknowledge that they can influence the manner in which a power system is operated.
A knowledge of physical power system phenomena and the timescales in which they occur is an important prerequisite to achieving good outcomes when designing operational practices for power systems. As shown in Figure 2, power system operations is concerned with three phenomena that dominate on timescales ranging from a few milliseconds to several months (Hatziargyriou et al., 2021; Machowski et al., 2020):
@@ -490,9 +490,9 @@Power system variability refers to expected or forecasted changes to active power supply and/or demand. Sources of variability include fluctuations in load, oscillatory active power output from synchronous generators and VRE generation ramping (i.e. a sustained increase or decrease in active power output). VRE ramping includes changes in solar PV generation during sunrise or sunset and in wind generation with wind speed variations (Australian Energy Market Operator, 2020d; Bloom et al., 2017; Ela et al., 2011).
Power system uncertainty refers to unexpected changes to active power supply and/or demand. Source of uncertainty include demand and VRE generation forecast errors, and singular or widespread outage events triggered by the weather or unexpected system responses and interactions (Australian Energy Market Operator, 2020d; Eggleston et al., 2021; Ela et al., 2011).
+Power system uncertainty refers to unexpected changes to active power supply and/or demand. Sources of uncertainty include demand and VRE generation forecast errors, and singular or widespread outage events triggered by the weather or unexpected system responses and interactions (Australian Energy Market Operator, 2020d; Eggleston et al., 2021; Ela et al., 2011).
Though governance and operational arrangements vary from jurisdiction to jurisdiction, the powers, responsibilities and degree of ring-fencing imposed upon the SO are largely dictated by the operational paradigm of the control area (Chawla and Pollitt, 2013). Below, I discuss the two possible operational paradigms: where the SO is a vertically-integrated utility, and where the SO is, at the very least, responsible for operating a transmission system that forms the physical basis of a wholesale electricity market. In both cases, it is the SO that is ultimately responsible for ensuring that the transmission network in their control area is operated in a secure and reliable manner (Roques, 2008).
+Though governance and operational arrangements vary from jurisdiction to jurisdiction, the powers, responsibilities and degree of ring-fencing imposed upon the SO are largely dictated by one of two possible operational paradigms for a control area (Chawla and Pollitt, 2013): where the SO is a vertically-integrated utility, and where the SO is, at the very least, responsible for operating a transmission system that forms the physical basis of a wholesale electricity market. In both cases, it is the SO that is ultimately responsible for ensuring that the transmission network in their control area is operated in a secure and reliable manner (Roques, 2008).
Under this paradigm, a single company (either state-owned or privately-owned but regulated) owns, operates and invests in generation, transmission and distribution infrastructure, as well as being responsible for the retail of electricity to the end-user. This was the sole operational paradigm for much of the 20th century. Having a single owner and operator of power system resources reduces complexity and transaction costs, and enables economies of scale in both asset investment (particularly generation infrastructure) and operation (Sioshansi, 2006). The benefits from economies of scale are material where industrialisation and/or electrification are driving sustained load growth. This was the case in advanced economies in the 20th century and is still the case in many emerging economies (Hogan, 2008; Roques and Finon, 2017).
Over time, inefficiencies in regulation FCAS procurement and cost-allocation have also become apparent. Regulation FCAS procurement in the NEM is dynamic beyond a minimum volume, but the dynamic component is based on the system time error (Australian Energy Market Operator, 2015a). Time error control is largely unnecessary as modern clocks no longer rely on power system frequency to keep the time (Y. Rebours et al., 2007). Furthermore, whilst AEMO is required to control the NEM within certain time error limits, these have been relaxed in recent years (Australian Energy Market Commission Reliability Panel, 2017). Given that time error is no longer prioritised as a control objective, dynamic regulation FCAS procurement based on better measures of sustained frequency deviation (e.g. mean absolute error as suggested by Riesz et al. (2015)) and/or a modelled distribution of potential intra-dispatch ramp uncertainty may be more suitable.
@@ -1327,7 +1327,7 @@With growing deployments of flexible yet potentially energy-constrained VRE and ESRs and increasingly active demand-side resources, policy-makers worldwide are looking towards granular, faster and more flexible electricity markets to effectively and efficiently operate decarbonised power systems. However, achieving good operational outcomes is contingent upon scheduling coordination delivered through sound MP practices, appropriate market participation rules and purpose-fit knowledge process configurations.
Our work highlights that the increasing frequency and severity of price forecast errors in the NEM’s centralised knowledge processes can lead to sub-optimal scheduling outcomes for BESS ESRs (from ~15–20% reduction in potential annual arbitrage revenue for a 4 hour BESS to 60+% for a 15 minute BESS) and other scheduled resources that participate in the NEM’s real-time market. Whilst MPs can mitigate the impact of imperfect information by increasing scheduling lookaheads, modifying scheduling algorithms and/or producing robust price forecasts themselves, these changes alone cannot deliver centralised knowledge processes that guarantee “information adequacy” to a diverse range of MPs. This feature is particularly important for market designs and system architectures that aim to enable the participation of consumer-owned energy resources in electricity markets.
While some of the market design options we discussed may prove effective in improving resource scheduling outcomes, they predominantly consist of larger market design or structural changes that would be particularly challenging to implement in the NEM. Instead, we focus on how schedule and price convergence could be improved. One low-regret option is to increase the frequency at which centralised knowledge processes are run. However, just as continuous trading can overwhelm exchanges and induce an inefficient “arms race for speed” (Ahlqvist et al., 2022; Budish et al., 2015; Silva-Rodriguez et al., 2022), our analysis suggests that bidding, when continuous and unrestricted, may have deleterious impacts on system schedule convergence and thus system balancing. As such, we recommend that policy-makers in the NEM consider market participation restrictions that might better incentivise truthful or, at the very least, structured MP bidding strategies that are less likely to contribute to price forecast divergence and extreme and sudden price forecast swings. Furthermore, our analysis serves as a reminder to policy-makers elsewhere to exercise caution when making the short-term electricity markets in their jurisdictions faster and more flexible.
@@ -2937,9 +2937,9 @@