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Final Presentation _ Ahmed

bench marking of energy modelling tools

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Final Presentation _ Ahmed

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  1. Benchmarking Study of Energy Modelling Tools in a Multi-Nodal Approach Case Study: Sudan Electric Power Sector Prof. Emanuela Colombo Eng. Giacomo Crevani Ahmed Mubarak Ibrahim Abdalla

  2. Agenda Energy Modelling tools. Sudanese electric power sector: current situation. Objectives. Models set-up. Demand Evolution. Resource Assessment. Techno-economic data. 8. Transmission lines modelling. 9. Results and Key Findings. 10. Limitations and key findings.

  3. Energy Modelling Tools • Energy Modelling Tools: Mathematical models used to balance out energy supply/demand with the objective of simulating and or optimizing the energy sector. • Brief History: • 1970’s oil crisis. • Post world war II wake of scenario planning. • Goals: • Energy sector expansion strategies in a certain geographical region under constraints and pre-defined scenarios. • Integration of new technologies. • Limiting negative externalities.

  4. Energy modelling tools Energy Modelling Tools Analytical approach Time Horizon Mathematical approach General Purpose Geographical Coverage Bottom-Up Models: Short Term Linear Programming Project Forecast MI Programming Multi-Agent models Local Medium Term Scenario Analysis Back casting Dynamic Programming Simulation models Long Term National Regional Optimization model Top-Down models Global

  5. Sudan electricity supply Supply: Source: World Bank review report 2018

  6. Sudan electricity sector: current situation Sudan electricity supply Source: World Bank review report 2018

  7. Sudan electricity demand • Demand: Source: own illustration (IEA data) Source: own illustration (IEA data)

  8. Sudan electricity sector: current situation • Policies: • Sudan national development strategy. (2007-2031): • Goal 3 Sustainable development: • loss reduction. • Interconnection with east Nile basin countries. • Energy efficiency. • Data management. • Capacity building. • Power sector development framework. (2015-2020): • Development of thermal generation. • Strengthening distribution system. • Address fuel storage issue. • Universal access to electricity by 2031. • Private sector involvement in power generation.

  9. Objectives Quantitatively and qualitatively compare: Hypatia Universal electricity acess

  10. Models setup: Regional division, Time Representation Regional division of Sudan • Time Representation OSEMOSYS and Hypatia: Time horizon: 2022-2030 Time resolution: 4 Seasons 2 brackets • Calliope: Time resolution: 1 Hour.

  11. Demand Evolution World Bank Sectoral divided current demand Regional Distribution Demand evolution • Demand Sectors: • Residential. • Services. • Agriculture. • Industrial. • Connected households. • Services access. • Agricultural areas. • Industrial areas. • Residential & Services: • population growth rate. • Access % growth rate. • Agriculture & industrial: • Historical growth rate.

  12. Demand Forecast: Demand Evolution • Electricity Demand Sectors: • 1- Residential. • 2- Services. • 3- Agriculture. • 4- industrial. Source: World Bank review report 2018.

  13. Demand Forecast: Regional distribution Demand Evolution Source: World Bank review report 2018.

  14. Demand Forecast: Regional distribution Demand Evolution

  15. Demand Evolution • Small industries: 80 % in Khatroum 20% in Central region • Large industries:

  16. Demand Evolution Residential consumption per household 2017= 3825 kWh/year (World bank review report 2018) Source: World energy council. Source: World bank.

  17. Demand Evolution Services: same as residential. Industrial and agricultural: historical growth rate. Note: The demand profile is assumed to be the same.

  18. Resource Assessment: PV and Wind • Considerations: • Exclusion of agricultural areas. • Exclusion of protected areas. • Exclusion of water bodies. • The maximum distance between the installed system and the transmission grid is 25 km.

  19. Resource Assessment: CSP & geothermal CSP capacity factor = 0.45 Geothermal resource = 400 MW Source: World Bank Group. Source: National electric corporation of Sudan

  20. Resource Assessment: Hydroelectric Hydroelectric power resource: 4860 MW. Capacity Factor: Source: PLEXOS-World 2015 model dataset. Source: IRENA planning and prospects EA 2021

  21. Resource Assessment: Fossil Fuel Reserve & biomass Resource Assessment: oil and gas • Proven Oil Reserve: 5 billion barell. • Proven Gas Reserve: 3 trillion m3. • Rain-fed sugar cane and baggase resource: 2066 MW Source: Sudan ministry of petroleum and electricity

  22. Reference Energy System (RES) Reference energy system

  23. Reference Energy System (RES) Reference energy system

  24. Reference energy system

  25. Techno-economic data Sources: 1-capital cost , Fixed O&M Cost, Variable O&M cost: IRENA planning and prospects EA 2021. 2- efficiency , lifetime: starter kit for modelling of Sudan energy sector.

  26. Techno-economic data

  27. Techno-economic data Techno-economic data • HFO: 0.8, LFO: 1.33 • (Assumption) • Import: 10% higher • (Assumption) • Biomass fuel cost: 1.6 M$/PJ

  28. Transmission lines modelling OSeMOSYS Calliope & Hypatia • Free trade of electricity. Transmission line capacity: 250 MW Transmission efficiency: 0.95 Capital cost: 800 $/kW Distribution efficiency: 0.85 Assumption Source: World Bank review report 2018 Source: IRENA report 2021

  29. Scenarios definition 3 Scenarios Business As Usual (BAU) Limited Trade Free Trade Ethiopian side cap: 4 GW Reference scenario Domestic Production Egyptian side cap: 1 GW Electricity Imports: Egypt & Ethiopia Cost: 0.05 & 0.025 USD/kWh

  30. Results and key findingsBAU Scenario Outcomes and Justifications OSeMOSYS tendency to curtail capacity in certain regions Lack of transmission lines modelling in OSeMOSYS 24 GW Total installed capacity 23 GW Total installed capacity Hourly time representation in Calliope Calliope Results shows higher capacity for wind and large hydropower 44 GW Snapshot logic followed by calliope Total installed capacity Total installed capacity in calliope is twice the capacity of other models

  31. Results and key findingsFree Trade Scenario Outcomes and Justifications OSeMOSYS Imports are the least costly alternative Favors the less variable imports Calliope High Contribution of Solar and wind due to their falling costs Hypatia

  32. Results and Key FindingsFree trade scenario

  33. Results and key findingsLimited Trade Scenario Conclusions: Seasonal time representation results in overestimation of VRE’s capacity Sudan untapped solar and wind potential can be of high importance to expand electricity supply Transmission lines modelling is of core importance for nodal analysis of electricity systems. natural gas reserve can be utilized for electricity supply instead of gas flaring at wells electricity imports specially from Ethiopia can reduce the cost of achieving universal electricity access at the compromise of energy security Capacity expansion logic provides a pathway unlike the snapshot logic that might results in over-estimated capacity

  34. Limitations and future development Natural gas infrastructure costs CO2 as a decision-making parameter Limitations Future developments Grid expansion cost Solvers Demand forecasting

  35. https://github.com/AhmedAhmed101/Sudan-Electricity-sector-modelling.githttps://github.com/AhmedAhmed101/Sudan-Electricity-sector-modelling.git Personal info: : +971561064960 : U00043229@gmail.com

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