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 (even
wh
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 (even 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. This ratio (Energy Invested on Energy Returned - EIOER) provides yet another warning that we can no longer rely on fossil fuels to power our economies. 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. 

PS: A link between growth in energy consumption and GDP growth is clearly illustrated on slide 13 of Gail Tverberg's presentaion entitled ''Oops! The world economy depends on an energy-related bubble''. In fact, the slide shows that growth in energy consumption usually precedes GDP growth.

https://gailtheactuary.files.wordpress.com/2015/10/oops-debt-bubble-10_30_15.pdf

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 (even
wh
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 (even 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. This ratio (Energy Invested on Energy Returned - EIOER) provides yet another warning that we can no longer rely on fossil fuels to power our economies. 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. 

PS: A link between growth in energy consumption and GDP growth is clearly illustrated on slide 13 of Gail Tverberg's presentaion entitled ''Oops! The world economy depends on an energy-related bubble''. In fact, the slide shows that growth in energy consumption usually precedes GDP growth.

https://gailtheactuary.files.wordpress.com/2015/10/oops-debt-bubble-10_30_15.pdf

This simulation examines the linkages between cultural, material, spatial demographic, and hierarchical dynamics.
This simulation examines the linkages between cultural, material, spatial demographic, and hierarchical dynamics.
 Análise comparativa e econômica de diferentes tipos de biomassa e óleos vegetais brasileiros para produção  de  alimento, ração animal e  de biocombustível e Agroenergia com o estudo de sua Rota Química.    

Análise comparativa e econômica de diferentes tipos de biomassa e óleos vegetais brasileiros para produção  de  alimento, ração animal e  de biocombustível e Agroenergia com o estudo de sua Rota Química. 

 

This simulation examines the linkages between cultural, material, spatial demographic, and hierarchical dynamics.
This simulation examines the linkages between cultural, material, spatial demographic, and hierarchical dynamics.
The Bioresource Model is circular in its nature, and can be divided into two halves, or arcs, within the circle: 1) Primary bioresources = the food and fiber system, and  2) Secondary bioresources = organic waste that is manufactured into secondary bioproducts, e.g. soil amendments (like compost), a
The Bioresource Model is circular in its nature, and can be divided into two halves, or arcs, within the circle:
1) Primary bioresources = the food and fiber system, and
2) Secondary bioresources = organic waste that is manufactured into secondary bioproducts, e.g. soil amendments (like compost), animal feed, materials & chemicals, and energy (fuels, electricity, and CHP)
Mapping the Chinese job sector during energy transition i.e. shutting down coal
Mapping the Chinese job sector during energy transition i.e. shutting down coal
A detailed description of all model input parameters is available  here . These are discussed further  here  and  here .   Update 6 August 2018 (v2.8): Updated historical wind and PV deployment
 data for 2016-2017, adding projected PV deployment for 2018. Data via 
https://en.wikipedia.org/wiki/Grow
A detailed description of all model input parameters is available here. These are discussed further here and here.

Update 6 August 2018 (v2.8): Updated historical wind and PV deployment data for 2016-2017, adding projected PV deployment for 2018. Data via https://en.wikipedia.org/wiki/Growth_of_photovoltaics and https://en.wikipedia.org/wiki/Wind_power_by_country.

Update 26 October 2017 (v2.7): Updated historical wind and PV deployment data for 2015-2016, adding projected PV deployment for 2017. Data via https://en.wikipedia.org/wiki/Growth_of_photovoltaics and https://en.wikipedia.org/wiki/Wind_power_by_country.

Update 18 December 2016 (v2.7): Added feature to calculate a global EROI index for all energy sources plus intermittency buffering (currently batteries only, but this could be diversified). The index is calculated specifically in terms of energy services in the form of work and heat. That is, it takes the aggregated energy services made available by all sources as the energy output term, and the energy services required to provided the buffered output as the energy input term.

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 an adjustment 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.
Trying to show the drop in Barrels of Oil per day as the number of Electric Vehicles on the road increases.
Trying to show the drop in Barrels of Oil per day as the number of Electric Vehicles on the road increases.
Buying and storing electricity when it is cheap, and selling it when it is expensive. What are the benefits, both public and private?
Buying and storing electricity when it is cheap, and selling it when it is expensive. What are the benefits, both public and private?

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  The current electricity portfolio of Texas  is heavily reliant on high-emission sources of fossil fuel (i.e. Coal). Texas has a range of energy options at its disposal and has the opportunity to make choices that grow renewables (e.g. solar and wind) while encouraging the production of le

The current electricity portfolio of Texas is heavily reliant on high-emission sources of fossil fuel (i.e. Coal). Texas has a range of energy options at its disposal and has the opportunity to make choices that grow renewables (e.g. solar and wind) while encouraging the production of less carbon-intensive fossil fuels (e.g. natural gas).

As boundaries to our problem, we will be using 35 years as our time frame. We will also limit our model to the State of Texas as our spatial extent. Over the past decade, Texas is becoming a major natural gas consumer; the electricity portfolio has been gradually changing. However, around 40% of electricity is still generated from burning coal, and only a very minor portion of electricity is from renewables. Texas is betting better in adopting solar and wind energy, however generally speaking the state is still falling behind in renewable energy.

The two main goals are to lower the overall emission of greenhouse gases for the electricity grid and to encourage growth of cleaner, renewable energy resources.

Our objectives include maximizing the economic benefits of exploring unconventional oil and natural gas resources, diversifying the energy portfolio of Texas, encouraging the production and exportation of unconventional hydrocarbon resources, and reallocating the added revenue to the transition to renewables, like wind and solar

Shows the payout sub-model for the energy savings the consultants provide
Shows the payout sub-model for the energy savings the consultants provide