Fig 3.1 from Jorgen Randers  book  2052 a Global Forecast for the Next Forty Years

Fig 3.1 from Jorgen Randers book 2052 a Global Forecast for the Next Forty Years

10 6 months ago
Assignment 1-  Part 2 Energy Economics and Fossil Fuels
Assignment 1- Part 2 Energy Economics and Fossil Fuels
While investment in smart grid technology can have many positive benefits for distribution utility companies and their customers, there are consequences that could have negative impacts on both parties. Utility companies could see their business model crumble, while customers who are unable to go of
While investment in smart grid technology can have many positive benefits for distribution utility companies and their customers, there are consequences that could have negative impacts on both parties. Utility companies could see their business model crumble, while customers who are unable to go off the grid could see progressively higher energy delivery prices.
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 e
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.

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).
     <Documentation Link>          SUST1001U Sustainability Fundamentals   Dr. Bob Bailey  Group 4:  Amandeep Saroa 	(100836651) Matt Baird	 	(1008406500)  Nami Zuha 		(100821467)  Zachary Wayne 	(100814747)    
The purpose of the InsightMaker model is to model how Waste-to-Energy (WtE) techno


SUST1001U Sustainability Fundamentals
Dr. Bob Bailey

Group 4:

Amandeep Saroa (100836651)
Matt Baird (1008406500)

Nami Zuha (100821467)

Zachary Wayne (100814747)

The purpose of the InsightMaker model is to model how Waste-to-Energy (WtE) technology impacts waste management efficiency, energy output and greenhouse gas emissions for the scale of ten years (assuming the WtE technology integration in an urban setting). This will determine if WtE has the capability of minimizing the reliance on waste landfills as well as assisting reaching renewable energy targets. This model will shine light on Waste-to-Energy sustainability opportunities and challenges.


Orange variables are associated with calculating waste volume, green variables are associated with calculating energy generation.


References:


https://www.durham.ca/en/living-here/resources/Documents/GarbageandRecycling/Annual-Reports/2022-Waste-Management-Annual-Report-Acces.pdf


https://energyforgrowth.org/article/waste-to-energy-one-solution-for-two-problems/


https://www.nature.com/articles/s41598-024-69321-7

4 5 months ago
 This forecasting model can be used to predict global data center electricity needs, based on understanding usage growth. Please note that the corresponding problem description, model developments, and results are discussed in the following paper:     Koot, M., & Wijnhoven, F. (2021). Usage impa
This forecasting model can be used to predict global data center electricity needs, based on understanding usage growth. Please note that the corresponding problem description, model developments, and results are discussed in the following paper:

Koot, M., & Wijnhoven, F. (2021). Usage impact on data center electricity needs: A system dynamic forecasting model. Applied Energy, 291, 116798. DOI: https://doi.org/10.1016/j.apenergy.2021.116798.
This insight maker depicts the correlations between energy supply and water use in desalination potential in South Africa.  Pink: economics and quality of life.  Yellow: energy supply  Orange: variable links  Blue: water and its relationships   Green: household unit of population measurement
This insight maker depicts the correlations between energy supply and water use in desalination potential in South Africa. 
Pink: economics and quality of life.
Yellow: energy supply
Orange: variable links
Blue: water and its relationships 
Green: household unit of population measurement