A detailed description of all model input parameters is available here. These are discussed further here and here.
Update 14 December 2015 (v2.5): correction to net output basis LCOE calculation, to include actual self power demand for wind, PV and batteries in place of "2015 reference" values.
Update 20 November 2015 (v2.4): levelised O&M costs now added for wind & PV, so that complete (less transmission-related investments) LCOE for wind and PV is calculated, for both gross and net output.
Update 18 November 2015 (v2.3: development of capital cost estimates for wind, PV and battery buffering, adding levelised capital cost per unit net output, for comparison with levelised capital cost per unit gross output. Levelised capital cost estimate has been substantially refined, bringing this into line with standard practice for capital recovery calculation. Discount rate is user adjustable.
Default maximum autonomy periods reduced to 48 hours for wind and 72 hours for PV.
Update 22 October 2015 (v2.2): added ramped introduction of wind and PV buffering capacity. Wind and PV buffering ramps from zero to the maximum autonomy period as wind and PV generated electricity increases as a proportion of overall electricity supply. The threshold proportion for maximum autonomy period is user adjustable. Ramping uses interpolation based on an elliptical curve between zero and the threshold proportion, to avoid discontinuities that produce poor response shape in key variables.
Update 23 September 2015 (v2.1): added capital investment calculation and associated LCOE contribution for wind generation plant, PV generation plant and storage batteries.
**This version (v2.0) includes refined energy conversion efficiency estimates, increasing the global mean efficiency, but also reducing the aggressiveness of the self-demand learning curves for all sources. The basis for the conversion efficiencies, including all assumptions relating to specific types of work & heat used by the economy, is provided in this Excel spreadsheet.
Conversion of self power demand to energy services demand for each source is carried out via a reference global mean conversion efficiency, set as a user input using the global mean conversion efficiency calculated in the model at the time of transition commencement (taken to be the time for which all EROI parameter values are defined. A learning curve is applied to this value to account for future improvement in self power demand to services conversion efficiency.**
The original "standard run" version of the model is available here.
This is the original model version (v1.0) with default "standard run" parameter set: see detailed commentary here and here. As of 2 September 2015, ongoing development has now shifted to this version of the model.
The significance of reduced energy return on energy invested (EROI) in the transition from fossil fuel to renewable primary energy sources is often disputed by both renewable energy proponents and mainstream economists. This model illustrates the impact of EROI in large-scale energy transition using a system dynamics approach. The variables of primary interest here are: 1) net energy available to "the rest of the economy" as renewable penetration increases [Total final energy services out to the economy]; and 2) the size of the energy sector as a proportion of overall economic activity, treating energy use as a very rough proxy for size [Energy services ratio]. This model aggregates energy supply in the form of fuels and electricity as a single variable, total final energy services, and treats the global economy as a single closed system. The model includes all major incumbent energy sources, and assumes a transition to wind, PV, hydro and nuclear generated electricity, plus biomass electricity and fuels. Hydro, biomass and nuclear growth rates are built into the model from the outset, and wind and PV emplacement rates respond to the built-in retirement rates for fossil energy sources, by attempting to make up the difference between the historical maximum total energy services out to the global economy, and the current total energy services out. Intermittency of PV and wind are compensated via Li-ion battery storage. Note, however, that seasonal variation of PV is not fully addressed i.e. PV is modeled using annual and global average parameters. For this to have anything close to real world validity, this would require that all PV capacity is located in highly favourable locations in terms of annual average insolation, and that energy is distributed from these regions to points of end use. The necessary distribution infrastructure is not included in the model at this stage. It is possible to explore the effect of seasonal variation with PV assumed to be distributed more widely by de-rating capacity factor and increasing the autonomy period for storage.
This
version of the model takes values for emplaced capacities of
conventional sources (i.e. all energy sources except wind and PV) as
exogenous inputs, based on data generated from earlier
endogenously-generated emplaced capacities (for which emplacement rates as a proportion of existing installed capacity were the primary exogenous input).
The statement that there can be no economic activity
without energy and that fossil fuels are
finite contrasts with the fact that money is not finite and can be created by governments
via their central banks at zero marginal cost whenever needed.
An important fact about COAL, GAS and OIL (especially when produced via fracking) is that their net energy ratios are falling rapidly.
In other words the energy needed to extract a given quantity of fossil fuels is
constantly increasing. The falling ratio 'EROI' (Energy Return on Energy Invested ) provides
yet another warning that we can no longer rely on fossil fuels to power our
economies. In 1940 it took the energy of only one barrel of oil to extract 100. Today the energy of 1 barrel of oil will yield only 15. We cannot wait until the ratio falls to 1/1 before we invest seriously in alternative sources of energy, because by then industrial society as we know it doday will have ceased to exist. An EROI of 1:1 means that it takes the energy of one barrel of oil to extract one barrel of oil - oil production would simply stop!
A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania. Simulation borrowed from the Easter Island simulation.
How the model works.
Trees grow, we cut them down because of demand for Timber amd sell the logs.
With mountain bkie visits. This depends on past experience and recommendations. Past experience and recommendations depends on Scenery number of trees compared to visitor and Adventure number of trees and users. Park capacity limits the number of users.
Interesting insights
It seems that high logging does not deter mountain biking. By reducing park capacity, visitor experience and numbers are improved. A major problem is that any success with the mountain bike park leads to an explosion in visitor numbers. Also a high price of timber is needed to balance popularity of the park. It seems also that only a narrow corridor is needed for mountain biking
Simulation of the effect of a basic income on rental prices based on the assumption people are only willing to spend a certain percentage of their income on rent.
Economic growth cannot go on forever, although politicians and most economist
seem to think so. The activity involved in economic growth necessarily generates entropy (disorder and environmental degradation). Entorpy in turn generates powerful negative feedback loops which will, as
a response from nature, ensure that economic activity will eventually grind to
a complete halt. In these circumstances organised
society cannot persist and will collapse. The negative
feedback loops shown in this graph have already started to operate. The longer economic growth continues unabated, the more powerful these negative feedback loops will become. How long
can economic growth continue before it is overwhelmed? It may not be very far in the future.
A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania. Simulation borrowed from the Easter Island simulation.
How the model works.
Trees grow, we cut them down because of demand for Timber amd sell the logs.
With mountain bkie visits. This depends on past experience and recommendations. Past experience and recommendations depends on Scenery number of trees compared to visitor and Adventure number of trees and users. Park capacity limits the number of users.
Interesting insights
It seems that high logging does not deter mountain biking. By reducing park capacity, visitor experience and numbers are improved. A major problem is that any success with the mountain bike park leads to an explosion in visitor numbers. Also a high price of timber is needed to balance popularity of the park. It seems also that only a narrow corridor is needed for mountain biking
This model explains the difference between Mountain bikes riding compared to logging in the Tasmanian forests.
Logging allows the activity in the forest with a negative demand for timber providing an income (with the price variable). The deforestation variable shows us that over time, the forest will run out if the logging keeps going on this way.
Alternatively, mountain biking allows a demand of visitors who want to see the scenary. They increase the regional tourism which is good for the community as it involves other businesses around too. The charges paid by visitors and tourists allow an income for the activity which makes it productive over time and great for TAS.
As we stimulate the model, we can see that it is better to have more visitors and more tourists rather than more logging as it will be better over time.
The model shows the industry connection and conflict between Forestry and Mountain Tourism in Derby, Tasmania. The objective of this simulation is to find out the balance point for co-exist.
How Does the Model Work?
Both industries can provide economic contribution to Tasmania. Firstly, selling timbers through logging would generate income. Also, spendings from mountain bike riders would generate incomes. However, low tree regrowth rate can not cover up logging, which influences the beautiful vistas and riders' experiences. While satisfaction and expectation depend on vistas and experience, the demand of mountain biking would be influenced through repeat visits and world of mouth as well.
Interesting Insights
Although forestry can provide a great amount of economic contribution to Tasmania, over logging goes against ESG framework as well as creating conflict with mountain tourism. As long as the number of rider visits is stable, tourism can always provide a greater economic contribution compared to forestry. Therefore, the government should consider the balance point between two industries.
Simulates total money mass over time. An aggregate of all demurrage free buffers is used to calculate demurrage fees. The total amount of demurrage free money in the system can never exceed the number of users multiplied with the size of the demurrage free buffer.
Model showing the effect of bank lending of deposited money as a multiplier in the creation of new money. Multiplier effect is shown as related to the bank reserve requirement on deposited funds.
A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy. Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover
Assumptions
Govt policy reduces infection and economic growth in the same way.
Govt policy is trigger when reported COVID-19 case are 10 or less.
A greater number of COVID-19 cases has a negative effect on the economy. This is due to economic signalling that all is not well.
Interesting insights
Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases. The impact on the economy though of higher detected cases though is negative.
It is a model simulating
logging and adventure tourism (mountain bike riding) competition in Derby,
Tasmania. It is a chance for northeast Tasmania to become an exciting, new, world-class
product for the mountain bike tourism industry, which drives local economic
development.
Simulation borrowed from the
Easter Island simulation.
How the
model works
l Trees grow; we cut them down because of demand for Timber and sell the
logs.
l The mountain bike visits depend on previous experience and suggestions.
l Previous experience and suggestions depend on the number of trees
compared to visitors and adventure number of trees and users. Park capacity
limits the number of mountain bike trail users.
l The employment opportunity depends on the mountain bike demand and demand
for Timber.
Interesting
Insights
Mountain biking appears to be
unaffected by heavy logging. The visitor experience and numbers are improved by
reducing park capacity. The main issue is that any success with the mountain
bike park increases visitor numbers. A high timber price is also required to
balance the park's popularity. Mountain biking appears to require only a narrow
corridor; that is, single-track mountain bike trails are enough. The employment is a measure of the economic acting, a recession or growth trends.
