System Dynamics and Energy Modeling
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The World Models

System dynamics modeling has been used for strategic energy planning and policy analysis for more than twenty-five years. The story begins with the world modeling projects conducted in the early 1970s by the System Dynamics Group at the Massachusetts Institute of Technology. During these projects the WORLD2 and WORLD3 models were created to examine the "predicament of mankind" -- that is, the long term socioeconomic interactions that cause, and ultimately limit, the exponential growth of the world’s population and industrial output.

One of the central assumptions underlying the world models is that the earth’s natural resources are, at some level, finite and that the exponential growth in their use could ultimately lead to their depletion and hence, to the overshoot and collapse of the world socioeconomic system. Due to the decision to explain the "predicament of mankind" with fairly small system dynamics models, the resource depletion dynamics were represented in the world models with structures that aggregated all of the earth’s natural resources into a single variable.

The decision to represent the earth’s natural resources in aggregate form did not take place in a vacuum. As part of the WORLD3 modeling project, several disaggregated, resource-specific/issue-specific, models were created. The conclusion drawn from these models was that it was appropriate to lump all natural resources into a single variable in the WORLD3 model.

One of the disaggregated, resource-specific, system dynamics analyses that was conducted in support of the world modeling efforts was a natural gas discovery and production model created by MIT Master’s student Roger Naill. Naill based his model on the life cycle theory of oil and gas discovery and production put forth by petroleum geologist M. King Hubbert.

In formulating his theory, Hubbert took the physical structure of the fossil fuel system into account and assumed that the total amount of oil and gas in the United States (i.e., the amount of oil and gas "in place"), and hence the "ultimately recoverable" amount of oil and gas in the United States, is finite. As a result, according to Hubbert, the cumulative production of domestic oil and gas must be less than or equal to the ultimately recoverable amount of oil and gas in the United States.

Figure 1 is a system dynamics stock-flow structure that represents Hubbert’s theory. The most important features of the structure are that (1) there is no inflow to the Ultimately_Recoverable stock (i.e., there is a fixed stock of oil and gas), and (2) the resource is being produced and consumed at an exponential rate.

Figure 1: Stock-Flow Structure Representing Hubbert’s View of Oil and Gas Discovery and Production

A direct implication of Hubbert’s theory is that a time series graph of either oil or gas production (at either the world-wide or domestic levels) must, at a minimum, be "hump" shaped. That is, the area beneath the production curve for oil or gas is the cumulative production of the resource, and the cumulative production of the resource must be a finite number. In fact, Hubbert argued that the life cycle of oil and gas discovery and production yields a bell-shaped production curve, which describes a period of low resource price and exponential growth in production, a peaking of production as the effects of resource depletion cause discoveries per foot of exploratory drilling to drop and resource price to rise, and a long period of rising costs and declining production as the substitution to alternative resources proceeds. Figure 2 shows a graphical representation of Hubbert’s life cycle theory of oil and gas discovery and production.

Figure 2: M. King Hubbert’s Life Cycle Theory of Oil and Gas Discovery and Production

Before proceeding, it is important to note that Hubbert’s view of natural resource discovery and production is not shared by all energy analysts. For example, Morris Adelman, a world-renowned resource economist (emeritus) at the Massachusetts Institute of Technology, believes that there is no fixed stock of oil. Indeed, Adelman’s views vis-à-vis oil price and depletion were summarized in a 1991 column by Boston Globe reporter David Warsh:

Figure 3: Stock-Flow Structure Representing Adelman’s View of Oil and Gas Discovery and Production

Figure 3 is a system dynamics stock-flow structure representing Adelman’s view of oil and gas discovery and production. It is directly comparable to Figure 1. Two things about the figure are important to note. First, the cloud-like icon on the left side of the figure indicates that there is no limit to oil or gas discovery (i.e., there is no fixed stock of oil or gas). Second, two influences are battling each other for control of the Discovery_Rate: technological change and diminishing returns. Historically, technology has always defeated diminishing returns and Adelman, as well as many energy analysts, feels it will continue to do so.

The results of Roger Naill’s Master’s thesis study confirmed Hubbert’s life cycle hypothesis. Indeed, Naill concluded that the production of US domestic natural gas, which peaked in 1973, will continue to decline well below the US natural gas discovery rate until depletion halts all domestic production sometime in the late twentieth or early twenty-first century.

The results of Naill’s work brought to the forefront the following question among the system dynamicists who were working on energy modeling problems under the umbrella of the world modeling programs: Will US economic growth be impeded by an energy limit similar to those suggested in the Limits to Growth? To begin answering this question, in 1972 the Resource Policy Group at Dartmouth College received a three year contract from the National Science Foundation to study the United States’ "energy transition problem."

The US Energy Transition Problem

The "energy transition problem" refers to the set of disruptions that the US economy must go through as it reduces its dependence on domestic gas and oil (due to depletion) and increases its reliance on new sources of energy. Historically, the US economy has gone through two energy transitions: (1) from wood to coal during the late 1800s, and (2) from coal to oil and gas during the early 1900s. But, these transitions were motivated by the availability of abundant new energy sources that were cheaper and more productive than the existing sources. The energy transition that the US is currently facing, however, is being forced by depletion and rising production costs, and not by a cheaper and more productive energy source.

The implications of the energy transition problem for the United States are quite significant. The continued growth in US energy demand, coupled with the depletion of domestic oil and gas resources and long delays in the development of alternative domestic energy sources is causing a widening domestic energy gap (domestic energy demand - domestic energy supply). This gap can only be filled, in the near term at least, through increased dependence on foreign imports of natural gas and oil. In addition, as long as abundant oil and gas imports are available at prices that are low relative to the marginal cost of developing new domestic supplies (i.e., as long as it’s easier to import oil and gas than it is to develop new domestic energy sources), US oil and gas depletion will continue, if not accelerate.

The COAL1, COAL2 and FOSSIL1 Models

Roger Naill’s natural gas model represented the US gas system at a very aggregate level. The model was not broken down by region, technology, or type of gas. It did not allow for the substitution of fuels nor for endogenous technological change. Thus, although it helped to motivate the study of the US energy transition problem, it was inadequate for the study itself. A new, expanded, model was required.

For his Ph.D. dissertation at Dartmouth, again under the supervision of Dennis Meadows, Naill expanded the boundary of his natural gas model to include all major US energy sources (energy supply), as well as US energy consumption (energy demand). He called his dissertation model COAL1, because his analysis showed that the best fuel for the US to rely on during the energy transition was coal.

After he had completed his Ph.D., Naill worked with the Dartmouth Resource Policy Group to improve and extend COAL1 as part of the Group’s National Science Foundation grant activities. The improved and extended version of the model was called COAL2. In 1975, the Energy Research and Development Administration (which later became the US Department of Energy) provided support to further improve and extend COAL2 for use in government energy planning. This improved and extended model was called FOSSIL1, since it looked at the transition of an economy that is powered by fossil fuels (i.e., by oil, gas, and coal) to one that is powered by alternative energy sources.

The FOSSIL1 model (as were its predecessors) was thus based on Hubbert’s theory of resource abundance, depletion, and substitution, and used to analyze and design new legislation that would enable the US economy to pass through the energy transition smoothly. It consisted of four main sectors: (1) energy demand, (2) oil and gas, (3) coal, and (4) electricity, and addressed, among others things, the following questions:

• Is energy independence for the US feasible and, if so, when?
• Should a national energy strategy emphasize conservation or increased supply?
• Which transition energy source should be accelerated?

The results from using FOSSIL1 to analyze the energy transition questions were that:

• Due to the momentum of past energy policies and the inherent delays before new policies become effective, in the short term the US energy problem cannot be solved.
• Neither supply side nor demand side policies alone will ameliorate the transition problem sufficiently.
• Smoothly passing through the energy transition requires policies that both stabilize energy demand and increase alternative energy supplies.

The FOSSIL2 and IDEAS Models

In response to the United States’ first energy crisis in 1977, the Carter Administration created the first National Energy Plan. Shortly thereafter, the US House of Representatives asked the Dartmouth Resource Policy Group to evaluate the Plan using the FOSSIL1 model. After the evaluation of the Plan was completed, Roger Naill left the Resource Policy Group to head the Office of Analytical Services at the Department of Energy and, among other things, prepare energy projections in support of future National Energy Plans.

To prepare the energy projections for future National Energy Plans, Naill implemented FOSSIL1 in-house at the Department of Energy and supervised a team that extensively modified it so that national energy policy issues could be analyzed. The modified version of FOSSIL1 was called FOSSIL2.

From the late 1970s to the early 1990s, the FOSSIL2 model was used at the Department of Energy to analyze, among other things:

• the net effect of supply side initiatives (including price deregulation) on US oil imports.
• the US vulnerability to oil supply disruptions due to political unrest in the Middle East or the doubling of oil prices.
• policies aimed at stimulating US synfuel production.
• the effects of tax initiatives (carbon, BTU, gasoline, oil import fees) on the US energy system.
• the effects of the Cooper-Synar CO2 Offsets Bill on the US energy system.

In 1989, the Congress directed DOE to conduct a study of energy technology and policy options aimed at mitigating greenhouse gas emissions. FOSSIL2 was used for this purpose. Some preliminary conclusions from the study were that:

• Reforestation is a promising alternative to taxes or standards.
• Effectively promoting cost-effective conservation measures would be worthwhile.
• There needs to be a significant long-term switch from coal to advanced nuclear power and renewables (environmentally benign) in the US electric power sector.
• Due to compensating feedbacks in the US energy system, a combination of policies, rather than any single policy, is going to be necessary to successfully combat the global warming problem.
• Policy makers should not aim policy changes at a single sector of the US energy system because this approach ignores the ramifications of the policy changes in other sectors of the US energy system.

In recent years, extensive improvements have been made to FOSSIL2’s transportation and electric utilities sectors. The improved version of FOSSIL2 has been renamed IDEAS, which stands for Integrated Dynamic Energy Analysis Simulation. The IDEAS model is now maintained for the DOE by Applied Energy Services of Arlington, Virginia.

Sterman's Model of Energy-Economy Interactions

During the late 1970s John Sterman, an MIT Ph.D. student and former Dartmouth College undergraduate, was hired by Roger Naill to work with a team to modify and extend the FOSSIL1 model into the FOSSIL2 model. During this work, Sterman came to realize that the FOSSIL2 model ignored important feedbacks and interactions between the energy sector of the economy and the economy itself. For his Ph.D. dissertation, Sterman built a system dynamics energy model that captured, for the first time, significant energy-economy interactions.

To be more precise, Sterman noticed that in the COAL-FOSSIL-IDEAS family of models, the energy sector is modeled in isolation from the rest of the economy. That is:

• GDP is exogenous to the model. It is not affected by the price or availability of energy.
• Costs of unconventional energy technologies are exogenous to the model.
• Investment in energy is unconstrained by the investment needs of other sectors of the economy.
• Interest rates are exogenous to the model.
• Inflation is unaffected by domestic energy prices, production, or policies.
• World oil prices are unaffected by domestic energy prices, production, or policies.

Sterman addressed these deficiencies through his modeling and found that:

• The economic consequences of depletion are much more severe during the transition period (extending to approximately 2030) than during the long run or equilibrium state.
• The magnitude of the economic effects are substantial in absolute terms and include reductions in economic growth; increased unemployment;inflationary stress; higher real interest rates;reduced consumption per capita.
• Energy price increases (sudden or gradual) alone cannot produce sustained inflation. An accompanying increase in the money supply, relative to real economic activity, is also required (or an increase in the velocity of money).
• The model’s major behavior modes are remarkably robust -- i.e., insensitive to parameter variations (uncertainties).
• In the model, a large exise tax on energy coupled with offsetting income tax reductions caused economic performance to improve; energy prices to fall; OPEC revenues to fall; short term inflationary pressures to worsen; income taxes to be reduced only during the transition.

Fiddaman's Model of Economy-Climate Interactions

Building on the work of his teachers, in 1997 Tom Fiddaman submitted his Ph.D. dissertation on economy-climate interactions to the Sloan School of Management at MIT. The dissertation included a critique of existing (non system dynamics) climate-economy models and a new climate-economy system dynamics model called FREE (Feedback-Rich Energy Economy model). The FREE model explicitly incorporates the dynamics of oil and gas depletion as a "source constraint" on the energy-economy system (as do all of its system dynamics predecessors), as well as the dynamics of a "sink constraint" (i.e., climate change) on the energy-economy system. The FREE model is the first energy-economy model of any kind to explicitly examine the impact of a source constraint on energy-economy interactions.

The FREE model also explores a number of feedback processes (e.g., endogenous technological change and bounded rational decision making with perception delays and biases) that have not been previously explored in a climate change context. In addition, it is constructed so that a particular parameterization will yield the results found in neoclassical (traditional) climate-economy models.

Estimating the Amount of Oil In-Place

In the early 1980s, system dynamicist George Richardson met a British petroleum analyst who claimed that the "amount of oil in the world is increasing." Richardson replied that, although world oil reserves may be increasing, or that the estimate of the amount of oil in the world may be increasing, the actual amount of oil in the world (the amount of oil in-place) is decreasing.

Richardson was unable to persuade the British petroleum analyst to change his mind, so he decided to build a system dynamics model that could demonstrate his point. He enlisted the assistance of John Sterman, who had recently finished his dissertation on energy-economy interactions and, as a starting point, turned to M. King Hubbert’s research and Roger Naill’s natural gas model.

Richardson and Sterman produced an oil exploration, discovery, and production model that was similar in spirit to Naill’s natural gas model, but that also had important extensions and improvements. More precisely, their model allowed for endogenous technological change and the substitution of synfuels for oil.

Richardson and Sterman first used their model to run a synthetic data experiment that addressed the following question: Which method of forecasting the world’s ultimately recoverable supply of oil is more accurate, M. King Hubbert’s life cycle method or the geological analogy method? Since the world’s ultimately recoverable supply of oil is currently not known, and cannot be known until all of the world’s oil has been depleted, a synthetic data experiment was required to answer the question.

The logic of Richardson and Sterman’s synthetic data experiment was quite simple. First build a system dynamics model that accurately replicates the exploration, discovery, and production behavior of the world oil system and assume that it is the "real world." Second, formally code and add the Hubbert and geologic analogy methods to the model so that they "watch" the "real world oil system" and create forecasts of the ultimately recoverable amount of oil in the "world." The results of the synthetic data experiment were that:

• The model replicated the behavior of the actual world oil system very well.
• The model replicated the actual forecasts produced by the Hubbert and geologic analogy methods very well.
• Hubbert’s method was clearly the most accurate.
• over simulated time, the geologic analogy method rose to, and then overestimated, the ultimately recoverable amount of oil in the world.

The implications of the geologic analogy method significantly overestimating the ultimately recoverable amount of oil in the world include:

• a possible reduction in oil conservation efforts.
• the probable overestimation of the amount of time available to develop substitutes for oil and the technologies, institutions and values needed to create a sustainable energy system.

Sterman and Richardson, with the assistance of system dynamicist Pål Davidsen, went on to apply their model and synthetic data technique to the question of the amount of ultimately recoverable oil in the United States. As in the case of world oil, Hubbert’s method was judged to be clearly superior and the model was able to replicate US oil discovery and production data extremely well. Of course, unlike the case of world oil production which has not yet peaked, US domestic oil production (in the lower 48 states) peaked in 1970. Since Hubbert had forecast in 1956 that US oil production (in the lower 48 states) would peak between the years 1966 and 1971, his forecast is one of the most accurate and remarkable in the history of energy forecasting. In light of this, Sterman, Richardson and Davidsen’s synthetic data experiment for the United States is perhaps best interpreted as supporting the argument that Hubbert’s method is the most accurate.

Other System Dynamics Modeling in the Oil and Gas Industry

System dynamics modeling has been used by numerous researchers, outside of the Naill-to-IDEAS lineage, to examine firm-level and industry-level issues in the oil and gas industry. Table 1 lists some of the work that has been done. Inspection of the table reveals that topics such as the behavior of OPEC and world oil markets, business process re-engineering in an oil and gas producing firm, international relations stemming from world oil supply and demand relationships, and oil firms as learning organizations, have been addressed with system dynamics. The Energy 2020 model was developed by George Backus and Jeff Amlin to provide individual energy firms and state agencies with a multi-fuel energy model. It is similar in design to the DOE’s IDEAS model.

Table 1: Some Well-Known System Dynamics Studies in the Oil and Gas Industry

The efforts of oil companies to become "learning organizations" through the use of "management flight simulators" is a particularly noteworthy use of system dynamics in energy modeling. In 1990, system dynamicist Peter Senge wrote a book that outlined a way for organizations to become "learning organizations" through the use of system dynamics and other tools. A learning organization is composed of employees who possess a shared, holistic, and systemic vision, and have the commitment and capacity to continually learn, rather than simply executing a "plan" put forth by the "grand strategist" at the top of the organization.

One of the principal tools used by learning organizations is the "management flight simulator." Management flight simulators are computerized learning environments that invite decision makers to train in a simulator just like a pilot does. The flight simulator runs an underlying system dynamics model for a number of periods, pauses, and waits for the decision maker to make a policy change. After the policy change has been entered, the flight simulator again simulates the model forward in time, pauses, and waits for the next policy change. After a decision maker has finished a session in the simulator, he or she is invited to determine why the system behaved as it did. Once the decision maker ascertains this, he or she is invited to play again. Of course, after a number of plays the decision maker’s understanding of the system should improve and, hopefully, he or she will apply the lessons learned to an actual organization.

System Dynamics Modeling in the Coal Industry

Applications of system dynamics outside of the Naill-to-IDEAS lineage also exist in the coal industry. As shown in Table 2, system dynamics has been used to study industry-level problems, mining systems, the dynamics of small surface coal operations, international mining ownership, and the representation of discrete events in system dynamics models.

Table 2: Some Well-Known System Dynamics Studies in the Coal Industry

System Dynamics Modeling in the Electric Power Industry

One of the studies that followed the world modeling projects, was conducted by the Dartmouth Resource Policy Group and undertaken in a fashion parallel to Roger Naill’s COAL1 study, was Andrew Ford’s system dynamics analysis of the future of the US electric power industry. For his Ph.D. dissertation, Ford produced the ELECTRIC1 model, which was the first in a series of system dynamics electric utility models known as the EPPAM models. Modified versions of Ford’s model and its descendants were also used to build the electricity sectors of the COAL2, FOSSIL1 and FOSSIL2 models.

Since Ford’s pathbreaking work, system dynamics has been used extensively by utility managers for strategic planning. Table 1 lists some well-known system dynamics studies that have addressed problems in the electric power industry, including: the effects of regulatory policy on utility performance, the "spiral of impossibility," the effects of external agents on utility performance, the financial performance of utilities, the effects of energy conservation practices on utility performance, regional strategic electricity/energy planning, national strategic electricity/energy planning, electric vehicles, deregulation in the UK electric power industry, deregulation in the US electric power industry, and river use and its impact on hydroelectric power.

The system dynamics work on river use and its impact on hydroelectric power is particularly noteworthy as it involves the use of a management flight simulator in a public policy context. More precisely, the management flight simulator is designed to allow ordinary citizens, as well as utility managers and other stakeholders, to test policies aimed at moving hydroelectric systems in desired directions, while taking multiple criteria into account.

Table 3: Some Well-Known System Dynamics Studies in the Electric Power Industry

Summary: Intellectual Lineage of System Dynamics Energy Modeling

Figure 4 presents a diagram of the intellectual lineage of system dynamics energy modeling. The lineage begins with the first book on system dynamics modeling -- Industrial Dynamics by Jay W. Forrester. Forrester’s original work spawned various firm and industry-level system dynamics energy models and inspired Peter Senge to write the Fifth Discipline. Senge’s book has led to the creation of energy-related management flight simulators and several attempts at turning energy companies into learning organizations.

Forrester was also responsible for creating the WORLD2 model and initiating the world modeling projects at MIT. The world models, along with M. King Hubbert’s work on oil and gas discovery and production, stimulated the creation of Roger Naill’s natural gas model, his COAL1 model, and the improvements to COAL1 that have culminated in the IDEAS model and its offshoots (FOSSIL79 and DEMAND81). Naill and Hubbert’s work formed the basis for Sterman, Richardson and Davidsen’s synthetic data experiments on analyzing techniques for forecasting the ultimately recoverable amount of oil in the world and in the United States, while knowledge of the weaknesses in the FOSSIL2 model caused Sterman to investigate the dynamics of energy-economy interactions during the energy transition. Fiddaman’s recognition that, although the source constraints on the energy-economy system had been investigated by energy modelers, sink constraints had not, lead to the creation of the FREE model. The world modeling projects also stimulated the study of the US electric power industry by Andrew Ford and the subsequent EPPAM models and their offshoots.