Energy transition to lower EROI sources (v2.7)
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.
Energy Transition Model - Team 3
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
Energy, Population, Urban Dependency black and white
SEEV4-City Prototype model
Solar - Energy Consultant Saving Payout
Stock and Flow Model
Flexibility of the energy system
Hubbert's Life Cycle
Example copied from _Introduction to Systems Dynamics_ by Michael J. Radzicki an Robert A. Taylor.
Dystopia-2: simple energy system "clean transition" model
New York's Cropland Carrying Capacity
Dystopia: simple energy system model
Green village simulation
New York's Population Growth
Clone of Electric Vehicles in District: Agent Based Modeling
Clone of WORLD2020 to PLANET2020
THE 2020 MODEL (BY GUY LAKEMAN) EMPHASIZES THE PEAK IN POLLUTION BEING CREATED BY OVERPOPULATION.
WITH THE CARRYING CAPACITY OF ARABLE LAND NOW BEING 1.5 TIMES OVER A SUSTAINABLE FUTURE (PASSED IN 1990) AND NOW INCREASING IN LOSS OF HUMAN SUSTAINABILITY DUE TO SEA RISE AND EXTREME GLOBAL WATER RELOCATION IN WEATHER CHANGES IN FLOODS AND DROUGHTS AND EXTENDED TROPICAL AND HORSE LATTITUDE CYCLONE ACTIVITY AROUND HADLEY CELLS
The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors.THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST WEATHER EXTREMES AND LOSS OF ARABLE LAND BY THE ALBEDO EFECT MELTING THE POLAR CAPS TOGETHER WITH NORTHERN JETSTREAM SHIFT NORTHWARDS, AND A NECESSITY TO ACT BEFORE THERE IS HUGE SUFFERING.BY SETTING THE NEW ECOLOGICAL POLICIES TO 2015 WE CAN SEE THAT SOME POPULATIONS CAN BE SAVED BUT CITIES WILL SUFFER MOST. CURRENT MARKET SATURATION PLATEAU OF SOLID PRODUCTS AND BEHAVIORAL SINK FACTORS ARE ALSO ADDED
Use the sliders to experiment with the initial amount of non-renewable resources to see how these affect the simulation. Does increasing the amount of non-renewable resources (which could occur through the development of better exploration technologies) improve our future? Also, experiment with the start date of a low birth-rate, environmentally focused policy.
Environment Demographics Population Growth Population Weather Climate Failure Death Mortality Science Technology Engineering Strategy Economics Politics Fertility Health Services Resources Land Jobs Labor Urban Industrial Rural Lifetime Pollution Regeneration Yield Ocean Sea Fish Plants Animals Flood Drought Loss Hurricane Typhoon Tornado Cyclone Agriculture Food Energy Nuclear Solar Resource Graphene Silicene Transport