Open Source Energy Model for the Electricity Sector of Sri Lanka
Sammanfattning: A long term generation expansion model for the electricity sector of Sri Lanka was developed in this thesis. The model provides the least cost development pathways to cater the future electricity demand within the user defined constraints that need to be adhered. Starting from the present electricity system of the south Asian island nation, the model spans for the period from 2018 to 2050. Open Source Energy Modelling System (OSeMOSYS) was used to create the model. It utilises linear optimization and minimize the net present value of the modelled system in the entire period. Four electricity end user sectors were modelled namely, residential, industry, services and transport. Final electricity demand at present is around 13 TWh and it is projected to grow at a rate of 5.6% per annum for the next ten years to be around 24 TWh in year 2028 and to rise at a rate of 4.3% per annum there onwards to exceed 61 TWh in year 2050. Twelve fuel options were used by the existing and candidate technologies for electricity generation in the model, namely biomass, coal, diesel, furnace oil, hydro, liquefied natural gas, naphtha, natural gas, nuclear, residual oil, solar and wind. Electricity production in different levels such as transmission, distribution and end user locations were modelled in the system. Capital cost, fixed and variable operation and maintenance cost and salvage value of technologies were considered for the cost optimisation. Environmental emissions were included in the model and CO 2 emission limit of 20% for the modelling period was included in the Base Scenario to represent the expected development pathway of the country in the future. Scenario analysis was conducted to examine the sensitivity of input variables such as electricity demand and hydro condition, and the impact of user defined constraints to the least cost solution. Renewable energy integration in to the system was studied and the impact of higher shares of renewable energy was examined. Capacity mix, energy mix, CO2 emission and LCOE of different scenarios were compared in the analysis.
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