Energy transition to lower EROI sources (v1.0)
Josh Floyd
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).
- 5 years 4 months ago
Energy transition to lower EROI sources (v2.5)
Josh Floyd
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.
- 4 years 10 months ago
2017 Weather & Climate Extreme Loss of Arable Land and Ocean Fertility by Guy Lakeman - The World3+ Model: Forecaster
Guy Lakeman
THE 2017 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 MODEL IS ZONE SPECIFIC AS GLOBAL WEATHER IS NOT HOMOGENEOUS BUT A COLLECTION OF HEAT BUMBPS DEPENDENT ON POPULATION SIZE OF URBAN HEAT ISLANDS AND MASSED CONURBATIONS AND AGGLOMERATIONS
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 ADDEDUse 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
- 1 year 2 months ago
Amsterdam V2G simulation 2.0
Carlos
- 3 years 11 months ago
Vehicle to Grid Simulation
Carlos
Environment Energy Electric Vehicles Renewable Energies Home Storage
- 3 years 11 months ago
Energy transition to lower EROI sources (v2.6)
Josh Floyd
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.
- 4 years 6 months ago
Electric Vehicles in District: Agent Based Modeling
Carlos
- 3 years 10 months ago
2018 OVERPOPULATION LEADS TO POLLUTION based on Weather & Climate Extreme Loss of Arable Land and Ocean Fertility by Guy Lakeman - The World3+ Model: Forecaster
Guy Lakeman
THE 2018 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 ADDEDUse 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
- 1 year 7 months ago
Energy and Economic Activity
Hanns-Jürgen Hodann
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!
- 3 years 1 month ago
Climate Sector Boundary Diagram of Guy Lakeman
Guy Lakeman
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 futureDemocracy 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.
Climate Weather Ecology Economics Population Welfare Energy Policy CO2 Carbon GHG Green House Gas
- 5 years 4 months ago
Smart Grid: Electricity storage and variable energy pricing
Carlos
- 4 years 2 months ago
Ideal Energy System
Maximilian Wild
- 5 years 3 months ago
WORLD2020 to PLANET2020
Guy Lakeman
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 ADDEDUse 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
- 1 year 7 months ago
POPULATION CONTROL
Guy Lakeman
POPULATION CONTROL BASED ON THE 2017 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
This expanded 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 ADDEDUse 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
- 1 year 7 months ago
Energy transition to lower EROI sources (v3.1)
Josh Floyd
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.
- 4 years 10 months ago
Energy Transition Model
Energy Transition Team
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 Electricity Pollution Texas Coal Natural Gas Renewables Solar
- 5 years 4 months ago
Core of HR4286
Steve Wallis
- 6 years 2 months ago
Energy transition to lower EROI sources (v3.0)
Josh Floyd
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.
- 4 years 11 months ago
LIMITS TO ECONOMIC GROWTH AND PROMINENT NEGATIVE FEEDBACK LOOPS
Hanns-Jürgen Hodann
- 4 years 1 month ago
Energy System
GuacamonsterX
- 5 years 3 months ago
Gestao tecnologica de sistema integrado alimento - energia
Pagandai V Pannirselvam
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.
- 1 year 6 months ago
RTG First Cut
Alexander Hoppe
Your omega is too high
- 5 years 5 months ago
INDUSTRIAL AGRICULTURE IMPACT ON POLLUTION AND RESOURCES
Guy Lakeman
INDUSTRIAL AGRICULTURE IMPACT ON POLLUTION AND RESOURCES THE 2017 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 ADDEDUse 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
- 1 year 7 months ago
Smart Grid: Grid balancing
Carlos
- 3 years 8 months ago