System Dynamics and Electricity Systems

At the same time, SD modelling has been used for strategic energy planning and related policy analysis for more than thirty-five years and can efficiently provide a basis for a well-documented, understandable, and concise representation of complex electricity systems (Dyner, 1996; Ford, 2008; Steel, 2008). By definition, SD is a whole-systems approach, based on theories of non-linear dynamics and feedback control, which are used to represent, and understand, the structure and dynamics of complex systems (Sterman, 2000). It was developed in the 1950s by Jay Forrester (Forrester, 1961) and has been used extensively from the early 1970s up until today. Many pioneering models such as Roger Naill’s FOSSIL2 were used to simulate and inform oil and natural gas policies in the United States during the 1970s and 1980s (Naill, 1992). In a parallel fashion, Andrew Ford developed the ELECTRIC1 model which was used to analyse the future of the US electric power industry (Ford, 1975). This work was the first in a series of SD electric utility models known as the EPPAM models, adaptations of which were useful in formulating the COAL2 and FOSSIL2 models, along with IDEAS and its evolved Energy2020 (Systematic Solutions Inc, 2014) counterpart. Similar to these models are two currently available open web-based SD based models, C-ROADS used for global climate policy analysis (Climate Interactive, 2014a). And its extension En-ROADS is useful for linking global energy, to economic and public policy, and climate policy analysis (Climate Interactive, 2014b). These models are important in managing energy and environmental

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resources and continue to be influential in shaping the decisions of many policy makers worldwide (Dyner, 1996; Ford, 2010; Jordan, 2013; POLES, 2016).

It terms of other aspects, it is seen in the literature that SD studies have addressed many problems in the electricity industry. Numerous such models can be found in the literature each highlighting different aspects of energy policy or electric power grid systems (Arango et al., 2002; Ford, 2010;

Caravajal, Arango and Arango, 2011). The effects of external agents on utility performance, the financial performance of utilities, the effects of energy conservation practices on utility performance and deregulation in both the UK and US electric power industries are also highlighted (Radzicki and Taylor, 1997). It is claimed that these insights can be attributed to the ability of SD for representing rapidly changing, deregulated utility markets with high uncertainty and risk (Dyner and Larsen, 2001). Additionally, the elicitation of the impact of market structures, power, and competition, uncertainties on capacity investments, technology mix and cost to consumers are all efficiently captured (Sanchez et al., 2007; Jordan, 2013). These models are all targeted to assess macro-level policy analysis by capturing multiple feedbacks, delays, and the behaviours of stakeholders such as utilities/power companies, consumers, and governments (Jordan, 2013).

In the area of markets and transportation, two concise detailed review of SD modelling over the past few decades is provided in Teufel, Miller and Genoese (2013) and Shepherd (2014). These articles show the appropriateness of using SD for these types of electricity system models based on the fact that qualitative aspects and salient features can be easily incorporated to reflect more realistic behaviours and system structures. Shepherd (2014) gave a comprehensive review of SD models applied in the field of transportation. According to Teufel, Miller and Genoese (2013), three trends for the use of SD were highlighted. Ranging from SD being used on its own to being complemented in conjunction with other methodologies such as decision trees, game theoretic approaches, and real options theory. Examples of these combinations include the use of embedded game theory to simulate generation expansion in the context of security of supply mechanisms based on long-term auctions (Rodilla et al., 2011). There is also the successful combination of SD

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with optimisation methods for simulating power plant construction in the Western Electricity Coordinating Council while capturing detailed power grid transmission operation (Dimitrovski, Ford and Tomsovic, 2007).

In a paper by Arango et al. (2002), a model was developed for the estimation of cash flows and other financial indicators of capacity expansion within the Colombian energy market. The authors modelled the costing of endogenous electricity market prices without considering the relative installed capacity investments existing within the system. Deregulated electricity markets as a catalyst for capacity expansion were also explored by others (Vogstad, 2004; Jaeger, Schmidt and Karl, 2009). In addition, more highly detailed and new market design models are emerging based on green electricity certificate markets, distributed integration of renewable energy sources, carbon policy incentives and taxation, and the use of newer types of energy storage mechanisms (Ford, Vogstad and Flynn, 2007; Ford, 2008, 2010; Rooney, Kazantzis and Nuttall, 2013; Robalino-Lopez, Mena-Nieto and Garcia-Ramos, 2014). In Bildik et al. (2015), the SD approach was applied to understand the diffusion of a new technology, namely wind power. The authors showed the extent to which SD captures the underlying mechanisms of the diffusion process and applied this as a comparative study for the large interconnected energy systems of California and the Netherlands.

Furthermore, stochastic variable distributions and related methods such as applying Markov Chain Monte Carlo (MCMC) simulations within SD modelling for calibration and sensitivity testing were observed in some studies (Sterman, 2000; Chyong Chi, Nuttall and Reiner, 2009; Pierson and Sterman, 2013). In addition, SD combination with scenario planning Lindgren and Bandhold (2009) can be seen in Connors et al. (2002) and other similar works. These works have helped reinforced the concept that there is significant relevance for the use of SD as a methodology for the evolving low-carbon electricity system analysis.

With a focus on transitioning low-carbon electricity systems, the work of Black (2005) was highlighted as an initial detailed and insightful SD model of these evolving systems. The author

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studied the US power grid deregulated market focusing on demand response technology adoption.

Black (2005) formulated his model with endogenous demand load and accounted for its impact on the demand side management of the electricity system, a useful formulation that is applied in this thesis. Collins et al. (2013) produced an SD-aided study using an approach of disaggregated demand and supply for their analysis of electricity system planning. The authors incorporated load seasonality and technology operations into the generation capacity expansion problem using exogenous energy demand and supply. Other studies including Steel (2008), Jordan (2013) and like with Black (2005) suggest that endogenous demand dynamics cannot be ignored, being intrinsically tied to the structure of the system. Jordan (2013) focused on the aggregated endogenous demand dynamics to study electricity capacity expansion. Previously, Steel (2008) looked at aggregated endogenous demand dynamics and the effect of consumer decisions on electricity grid reliability, energy resources depletion and electricity tariffs. Both the modelling efforts of Jordan (2013) and Steel (2008) were targeted to large-scale electricity grid networks in developing countries. Our focus is on a small-scale grid within a developed, isolated territory.

This collection of literature points to the benefits provided by SD as a basis for understanding the emerging challenges and opportunities of an evolving low-carbon electricity system. The literature reveals the mature and rich history of SD for modelling electricity systems. The combinational use of SD with other methodologies was also highlighted. In addition, the importance of endogenous electricity demand in electricity systems became apparent in this section. The following section provides an overview of the major strides worldwide towards testing and studying evolving low-carbon electricity systems. Additionally, the particular role that SD can play is highlighted.