This is a simulation of monetary flows for a business that uses Circular Money.

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.

A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania.  Simulation borrowed from the Easter Island simulation.

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).

国連が公表している人口の将来推計とOECDが公表している各種経済統計を参考にして、2100年までの人口・経済見通しを作成するためのダイナミクスモデル。

A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania.  Simulation borrowed from the Easter Island simulation.

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! 

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.

This model explains the difference between Mountain bikes riding compared to logging in the Tasmanian forests.

A simple model of economic growth where a government taxes the economy, and spends it on capital and revenue goods.

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

国連が公表している人口の将来推計とOECDが公表している各種経済統計を参考にして、2000年から2100年までの人口・経済見通しを作成するためのダイナミクスモデル。

This model describes the influences different taxes have on the behaviour in our society

Overview

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.

Model based on the Sustainable Money System.

Neoliberalism uses a deceptive narrative to declare that money the government spends into the economy in excesses of the taxes it collects creates a ‘government debt’. In fact, the money the government spends into the economy in excess of the taxes is an income, a benefit for the private sector. When the government issues bonds, the money the private sector uses to buy them via banks comes from a residual cushion of dollars that the government already spent into the economy but has not yet taxed back.  If this were not the case, if the government had taxed back all the money it spent into the economy, then the economy could not function. There would be no dollars in the economy, since the government is the sole supplier of U.S. dollars! In the doted rectangle in the graph you can see that the dollars paid to the government for bonds sits in a dollar asset account. When the government issues bonds it simply provides the public and institutions with a desirable money substitute that pays interest i.e. Treasury bonds. It is a swap of one kind of financial asset for another. To register this swap the government debits the dollar asset account and credits the bond account.  When the time comes to redeem (take back) the bonds, all the government does is revers the swap, and that’s all!  When you look at the total amount of finacial assets in the private sector,  these remain constant at $ 25 BN  after the payment of $ 5 BN taxes. This implies that  no lending of financial assets of the private sector to the government has taken place during the swap operation. The money was always there. The debt mountain is an illusion!

A detailed description of all model input parameters is available here. These are discussed further here and here.

Update 29 June 2016 (v2.6): Added historical emplacement for wind and PV capacity. The maximum historical emplacement rates are then maintained from year 114/115 until the end of the model period. This acts as a base emplacement rate that is then augmented with the contribution made via the feedback control mechanism. Note that battery buffering commences only once the additional emplacement via the feedback controller kicks in. This means that there is a base capacity for both wind and PV for which no buffering is provided, slightly reducing the energy services required for wind and PV supplies, as well as associated costs. Contributions from biomass and nuclear have also been increased slightly, in line with the earlier intention that these should approximately double during the transition period. This leads to a modest reduction in the contributions required from wind and PV.

Added calculation of global mean conversion efficiency energy to services on primary energy basis. This involves making a compensation to the gross energy outputs for all thermal electricity generation sources. The reason for this is that standard EROI analysis methodology involves inclusion of energy inputs on a primary energy equivalent basis. In order to convert correctly between energy inputs and energy service inputs, the reference conversion efficiency must therefore be defined on a primary energy basis. Previously, this conversion was made on the basis of the mean conversion efficiency from final energy to energy services.

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.

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.