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

From Warren C. Sanderson in Population - Development - Environment, Wolfgang Lutz (Ed.), 1994, Springer.

This is a reconstruction of the SIMM model presented in Chapter 2 of Feedback Economics (Contemporary Systems Thinking)

ISCI 360 - Project Finale

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

From Warren C. Sanderson in Population - Development - Environment, Wolfgang Lutz (Ed.), 1994, Springer.

This is a reconstruction of the SIMM model presented in Chapter 2 of Feedback Economics (Contemporary Systems Thinking)

Overview

A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania. 

How the model works:

Trees grow, and we cut them down because of the demand for Timber and sell the logs. Mountain bikers and holiday visitors will come to the park and this depends on experience and recommendations.  Past experience and recommendations depend on the Scenery, number of trees compared to the visitor and Adventure number of trees and users.  Park capacity limits the number of users.  To utilize highest park capacity, they need to promote to the holiday visitor segment as well. Again, the visit depends on the scenery. So, both mountain biking and forestry (logging) businesses need to contribute a significant amount of revenue to CSR for faster regrowth of trees.

Interesting insights

It looks like a lot of logging doesn't stop people from mountain biking. 

Faster replantation of the tree will balance out the impact created by logging which will give the visitor a positive experience and the number of visitors is both improved. 

To keep the park's popularity in check, the price of wood needs to be high. 

Also, it looks like mountain biking only needs a narrow path.

CSR contribution to nature can be a crucial factor. 

Update 24 Feburary 2016 (v3.1): This version has biomass, hydro and nuclear continuing at pre-transition maxima, rather than increasing. The combined emplacement rate cap for wind and PV is set at a default value of 5000 GW/year.

Major update 12 December 2015 (v3.0): This new version of the model overhauls the way that incumbent energy source (fossil sources plus biomass, hydro electricity and nuclear electricity) supply capacity is implemented. This is now based on direct (exogenous) input of historical data, with the future supply curve also set directly (but using a separate input array to the historical data). For coal and natural gas fired electricity, this also requires that the simple, direct-input EROI method be used (i.e. same as for coal and NG heating, and petroleum transport fuels).

Note that this new version of the model no longer provides a historical view of the emplacement rates for energy supply sources other than wind and PV, and therefore no longer allows comparison of required emplacement rates for wind and PV with incumbent energy sources. Output data relating to this is available in model version v2.5 (see link below), for the specific transition duration built into that version of the model.

The previous version of the model (version 2.5) is available here.

The original "standard run" version of the model (v1.0) is available here.

C8 Article through the Economy lense

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

Fig 2 to 14 Land Use added to 4 Quadrant Model SD Model from Eskanasi 2014  thesis 

This is a reconstruction of the SIMM model presented in Chapter 2 of Feedback Economics (Contemporary Systems Thinking)

This is a reconstruction of the SIMM model presented in Chapter 2 of Feedback Economics (Contemporary Systems Thinking)