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 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 inevitable xmas on-slaught has prompted renewed thought about how better to control weight... too much food intake [for cell metabolism - anabolism to build and maintain cells or catabolism to extract energy for activity] will increase weight through fat production and storage this is the ene](https://insightmakercloud-files.storage.googleapis.com/ceb3a16c-bb06-4176-8365-c2da9d30c965/images/cover.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=insightmakercloud-file-access%40insightmakercloud.iam.gserviceaccount.com%2F20251218%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20251218T094023Z&X-Goog-Expires=3600&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
