Colombia has the opportunity to implement the Autoswitch, but there are no guarantees of its impact on the market, given its complexity. This model implements two policies: Price Control through Demand Response - RD and Autoswitch. In this model we explore de impact of the AMI cost.
Demand Response Model and Autoswitch - AMI costs - v4 - Imprimir
Buying and storing electricity when it is cheap, and selling it when it is expensive. What are the benefits, both public and private?
Clone of Smart Grid: Electricity storage and variable energy pricing
Buying and storing electricity when it is cheap, and selling it when it is expensive. What are the benefits, both public and private?
Clone of Smart Grid: Electricity storage and variable energy pricing
Climate Sector Boundary Diagram By Guy Lakeman Climate, Weather, Ecology, Economics, Population, Welfare, Energy, Policy, CO2, Carbon Cycle, GHG (green house gasses, combined effects)
As general population is composed of 85% with an education level of a 12 grader or less (a 17 year old), a simple block of components concerning the health of the planet needs to be broken down into simple blocks.
Perhaps this picture will show the basics on which to vote for a sustained healthy future
Democracy is only as good as the ability of the voters to FULLY understand the implications of the policies on which they vote., both context and the various perspectives. National voting of unqualified voters on specific policy issues is the sign of corrupt manipulation.
Clone of Climate Sector Boundary Diagram of Guy Lakeman
Attempt to clarify the differences in the models described in Speakman and Hall's 2021 science article
Clone of Body weight regulation models
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 is a first attempt to illustrate 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 use 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 dealt with via Li-ion battery storage. Note, however, that seasonal variation of PV is not 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.
Clone of Energy transition to lower EROI sources
Combining electromobility and renewable energies since 2014.
http://www.amsterdamvehicle2grid.nl/
Clone of Amsterdam V2G simulation 2.0
Buying and storing electricity when it is cheap, and selling it when it is expensive. What are the benefits, both public and private?
Clone of Smart Grid: Electricity storage and variable energy pricing
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.
Clone of Energy transition to lower EROI sources (v2.5)
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.
Clone of Energy transition to lower EROI sources (v3.0)
Buying and storing electricity when it is cheap, and selling it when it is expensive. What are the benefits, both public and private?
Clone of Smart Grid: Electricity storage and variable energy pricing
Buying and storing electricity when it is cheap, and selling it when it is expensive. What are the benefits, both public and private?
Clone of Smart Grid: Electricity storage and variable energy pricing
Evaluating the effect of Shale oil and gas on the balance of power globally: seeking to understand the potential impact on the Middle East in particular and on major demand centres such as North America, China and Europe
Shale
Two households with PV systems and Electric Vehicles, sharing a battery and connected to the grid. What are the advantages?
Clone of Vehicle to Grid Simulation
Simulate an impact of an asteroid of any Diameter at any given Speed!
Clone of Asteroid impact simulator
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.
Clone of Energy transition to lower EROI sources (v2.5)
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.
Clone of Energy transition to lower EROI sources (v2.6)
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!
Clone of Energy and Economic Activity
This is a simulation of growth rate
New York's Population Growth
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.
Clone of Energy transition to lower EROI sources (v2.5)
Afirmația că nu poate exista activitate economică fără energie și că combustibilii fosili sunt finiți contrastează cu faptul că banii nu sunt finiți și pot fi creați de guverne prin intermediul băncilor lor centrale la costuri marginale zero ori de câte ori este nevoie.
Un fapt important despre cărbunele, gazul și petrolul (mai ales atunci când sunt produse prin fracking) este că raporturile lor energetice nete scad rapid. Cu alte cuvinte, energia necesară pentru a extrage o anumită cantitate de combustibili fosili este în continuă creștere. Raportul în scădere „EROI” (Returul Energiei asupra Energiei Investite) oferă încă un avertisment că nu ne mai putem baza pe combustibilii fosili pentru a ne alimenta economiile. În 1940 a fost nevoie de energia unui singur baril de petrol pentru a extrage 100. Astăzi, energia unui baril de petrol va da doar 15. Nu putem aștepta până când raportul scade la 1/1 înainte de a investi serios în surse alternative de energie, pentru că până atunci societatea industrială așa cum o cunoaștem în prezent va fi încetat să mai existe. Un EROI de 1:1 înseamnă că este nevoie de energia unui baril de petrol pentru a extrage un baril de petrol - producția de petrol s-ar opri pur și simplu!
Clone of Energy and Economic Activity
Two households with PV systems and electric vehicles sharing a battery and connected to the grid. What are the advantages?
This model prototypes the working of an Smart Grid with Electric Vehicles
The objective is testing the theoretical advantages of batteries (also batteries in Electric Vehicles) in combination with renewable energies. The model considers two houses, that store energy both in Electric Vehicles (Vehicle to Grid), and in a communal battery.
Except when specified otherwise, the units of all variables are expressed in W/h.
Press "Story" in the lower bar for a guided tour over the model. Better seen at 50% zoom.
by Carlos Varela (cvarela@gmx.at)
Clone of Vehicle to Grid Simulation
