System Dynamic example

Ford (1997) presented collection of articles which used system dynamics in energy sector and mainly for electric power industry. He believes that system dynamics practitioners have been successful in this industry by letting the investigators to focus on feedback loops in the energy system. In supporting that, he began by going through history of electric power in United State from 1880s followed by main historical developments which helped this sector to survive the “energy crisis” in 1970s. He presented 33 articles on system dynamics

55 applications to electric power from 1970s to 1990s and one of the articles was listed as Roger Nail ‘s teamwork and well known as System Dynamics Review in 1970s, 1980s and 1990s. This provided an excellent description of the system dynamics model used at the Department of Energy in the United States. In conclusion he stated that his experience with energy industry modeling convinced him that the ability to simulate the information feedback in the system is a truly unique feature of the system dynamics approach (Ford, 1997. P. 21).

Reddi, Li, Wang and Moon (2013) presented the results from comprehensive system dynamic model of hybrid renewable energy system (HERS) and combined heating and power (CHP) generator. The HRES model includes micro-turbine-based CHP system, wind turbines and solar panel modules in which micro-turbine, wind turbine and solar panel generate power while the heat exchanger manages the waste heat. A model was developed with the main goal of evaluating possible options for planning and operating a hybrid energy system to meet energy demand. The model itself has the capability of calculating the economic and environmental impact for every possible combination of energy source. Modelling with system dynamics is useful in choosing the energy system parameters given different constraints such as cost, climate, environmental commitments and more. With applying system dynamics modelling this study indicates that the components of HERS can have conflicting effect on cost and environmental benefits, therefore there is a need for an organization to make trade-off decision.

Decision making plays a significant role in developing policies to promote photovoltaic (PV) energy market. Movilla, Miguel and Blazquez (2013) used system dynamics model to

analyses PV energy market in Spain. System dynamics has proven to be an adequate tool to perform the modelling and simulation to such a problem to help decision makers by giving a better understanding of the behavior of the main variables. In this case with help of system dynamics modelling, dynamic behavior of PV energy market explained to help design optimal policies. The recognized weakness of this sector is dependency of PV energy market on subsidies for it to be profitable.

Securing energy supply is one of the today’s important factors in developed economic with considering threats of carbon dioxide emission and global warming for developed countries. Aslani and Wong (2014) used system dynamics to evaluate different costs of renewable energy utilization during 2010-2030 in the United States. The focus of this study is to analyze

56 the role of renewable portfolio in the United States energy action plan with system dynamics modelling to evaluate different costs of renewable energy utilization. The study indicates that while renewables create a market worth around $10 billion in 2030, the total value of

renewable energy promotion and utilization in the US will be more than $170 billion during 2010 to 2030 which raises the concern about economic viability of this approach.

Management of energy supply is a complex topic due to number of various effective factors. Effective management of energy supply have become important mainly for import-dependent countries. Aslani et al. (2014) proposed system dynamics model approach to evaluate role of renewable energy policies in energy dependency in Finland which helps decision makers to test their scenarios related to renewable energy policies as well as implementing by other countries. Applied system dynamics modelling and causal loop diagram are used to evaluate different Finish scenarios of renewable energy policies by 2020. As presented in the Figure 3- 3, each renewable electricity source presented in stock and flow diagram with relevant policy that effects the number of systems. Then the output of each stock combined in the

“Renewable Electricity” variable which is the total renewable electricity generated in Finland. The analysis has demonstrated that despite 7% increase in energy consumption by 2020 in Finland, dependency on imported sources will decrease depending on the policies introduced.

57 An example of applying system dynamics in energy policy making is reported by Mutingi et al. (2017) and the purpose of their work was to present system dynamic approach for capacity management of energy system with main emphasis on investigating the effects of capacity inadequacy on system behaviour. The study demonstrates the importance of taking a system thinking approach when managing the capacity of complex energy systems. In this research a limit to growth, growth and underinvestment were identified and modelled in casual loops. In addition to that, stock flow analysis models are presented followed by ‘what if’ simulation experiments that illustrated the main effect of limited capacity growth and growth with underinvestment in the presence of time delays. Figure 3-4 illustrates the stock and flow analysis model for the ‘limit to growth’ section of the modelling. Rise in demand was modelled as a function of growth factor, industrial activity and the following investment activities. On the other side there is an unfilled demand which is a product of failure rate and the current demand.

Figure 3-4: Stock and flow analysis model: Limit to growth [Source: Mutigni et al., 2017]

This study shows that capacity inadequacy causes unpredictable system behaviour which can make it challenging for the energy policy makers to realise the actual source of unwanted demand fluctuations in the capacity and its management. Unpredictable system fluctuations can be avoided through proper capacity adjustment decisions.

Gravelsins et al. (2018) presents another example of system dynamic approach for energy policy maker in which the SD was used for modelling of the energy transition towards low carbon energy system (integration of photovoltaics and wind turbines) by combining techno-

58 economic and socio-technical analysis. This study looks at flexibility issues related to

integration of renewable energy sources (RES) by presenting simplified model to illustrate the flexibility and socio-technical aspects can be modelled with system dynamics. In their study, the growth to electricity demand, unit costs of electricity production with fossil fuels are assumed to happen as a result of exogenous factors. It is assumed that economic growth, electrification and energy efficiency measures could change electricity demand. Results from modelling demonstrated that RES increases due to decrease unit costs of production of RES power technologies and increase of flexibility limit is resulting from potential disruption in power system.

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This research uses system dynamics approach to build a simulation for dealing with a complex system with feedback and long-time horizons. The validity of a model is tested by reproducing historical data and different policies and changes that can be expected in future. The process of using system dynamics follows the steps below.

• First critical step of any modelling is to define the problem. Analysing the economic viability of renewable generation for rural communities; i.e. profitability of the renewable generation in community size. Moreover, identifying variables that affect profitability of renewable generation such as, cost of renewable technology, subsidies, electricity prices, cost of land and more. Furthermore, it is essential to analyse

proposed government policies and results of any changes in new policies introduced. • Following problem definition, the hypothesis about the model should be clearly

defined. System evaluation is presented by stock and flow in system dynamics modelling, therefore, it is necessary to identify the key stocks and flows and influences between these elements. After defining the link between main elements, depending on the influences, the formal model is developed through a computing program. The model contains the most significant variables involved in renewable generation. Also, the evaluation of stock and flow links the parameters with appropriate formula.

• _{The next step is to check the validity of the simulation model. In modelling, it’s} critical to ensure that structure of the model is correct, and no important factors are forgotten. Later some part of the formulation might need to be recalculated for the

59 purpose of validation and this process repeats again and again in a loop as it’s an important part of modelling. The loop will end when the model is validated. For simplicity of the validation, the model can be divided into subsystems which eases identifying the different structures or behaviors.

• _{When the model works in an equilibrium point, the simulation is tested under extreme} condition to check the structure’s behavior. Moreover, sensitivity of the key

parameters is analysed to get better understanding in terms of their effect on the system (Movilla, Miguel and Blazquez, 2013).

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The considered wind and solar PV farm used as part of this research is based in Huntly, a town in North-East of Scotland with a limited number of households. The research aim is to find the most economically viable combination of wind turbine and solar PV panels for this particular place using the simulation and optimization method proposed as part of this research. Considering the nature of the problem being discrete, the model requires both a sensitivity analysis and behavioral analysis of the target variables. Both Excel and Vensim software are used for this simulation. Vensim software, is mainly used for a dynamic analysis of the system that is capable of simulating a system, and it can be used to see the causal relationship between the variables. Vensim’s capability and compatibility with Excel led to this selection to ensure successful simulation required for this research.