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Jaboi geothermal project experiences a long delay since its concession winning bidder declared in 2008. Based on the latest study, Jaboi geothermal resource can produce up to 40 MW electricity. However, resource location in an isolated grid island that has limited electricity demand is the constraint for its development. In the current study, we propose a conceptual design of a geothermal power plant adapted the load profile in an isolated electricity grid such as in Sabang City, Weh Island, in Indonesia. The electricity generated in the island is not able to be transmitted to the main island of Sumatra. Therefore, to be viable the power plant must be designed to follow the demand existed on the island. To design a demandbased power plant, we perform a thermodynamic analysis of a geothermal binary cycle by using Engineering Equation Solver (EES) software. The optimal flow rate of geothermal fluid required to meet the demand is determined. The simulation results reveal that to produce a 3 MW net power output geothermal binary power plant, it requires a total of 95 kg/s geothermal fluid mass flow rate. Theoretically, this mass flow rate can be supplied from one production well. Therefore, a welldoublet system consisting of a production and re-injection wells can be deployed as the most economic option. [ABSTRACT FROM AUTHOR]

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Macro Demand Spatial Approach (MDSA) is an approach introduced in long time electricity demand forecasting considering location. It will be used at transmission planning and policy decision on electricity infrastructure development in a region. In the model, MDSA combined with qualitative approach and quantitative approach as mixed method. QA used to determine main development area and supporting area in region. This method is used to prove the hypothesis that the observed transmission service area are not homogeneous. Main development area is an area with industrial domination as a driver of economic growth. Whilst, supporting area is an area with residential domination that supporting economic growth at main development area. Hence, the electricity demand driver variables are different for type of electricity consumer at different spatial characteristics. The variables have no significant effect can be reduced by using PCA. The results of PCA should be validated with the results of QA. Generated models formed from the variables generated by PCA. The generated models tested to assess whether it fit with the actual data. Range of interval confidence level used as fit criteria. At the case study, generated model for main development areas and supporting area in Southern Sumatra Regionas a part of Sumatra System is still in the range of confidence level. Thus, MDSA can be proposed as alternative approach on demand forecasting at transmission planning that considering location. [ABSTRACT FROM AUTHOR]