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Abstract
Forecasting energy demand and supply is the most crucial concern for energy policymakers. However, forecasting may introduce uncertainty in the energy model, and an energy policy based on an uncertain model could be misleading. Without certainty in energy data, investors cannot quantify risk and trade-offs, which are compulsory for investments in energy projects. In this work, the energy policies of Pakistan are taken as a case study, and flaws in its energy policymaking are identified. A novel probabilistic model integrated with curve fitting methods was proposed and was applied to 17 different energy demand and supply variables. Monte Carlo simulation (MCS) was performed to develop probabilistic energy profiles for each year from 2017 to 2050. Results show that the forecasted energy supply of Pakistan in the years 2025 and 2050 would be 70.69 MTOE and 131.65 MTOE, respectively. The probabilistic analysis showed that there is 14% and 6% uncertainty in achieving these targets. The research shows the expected energy consumption of 70.33 MTOE and 189.48 MTOE in 2025 and 2050, respectively, indicating uncertainties of 65% and 31%. Based on the results, eight energy policy guidelines and recommendations are provided for sustainable energy resource management. This study recommends developing a robust and sustainable energy policy for Pakistan with the help of transparent governance.
Highlights
Coordinated energy planning and formulation of energy policy can help a country to overcome an energy crisis
An energy forecast that fails to account for the uncertainties in the model or the energy system can lead to the failure of integrated energy planning (IEP) and could be a setback to developing a robust energy policy
This study aims to develop a methodological framework that can address the issue of uncertainty in energy policymaking
Summary
Coordinated energy planning and formulation of energy policy can help a country to overcome an energy crisis. Such a milestone is only plausible when using robust energy models that can predict future energy demand and supply in a reliable manner [1]. The analytical mechanism to achieve such a goal is developing an integrated energy planning (IEP) and policy formulation for the energy sector [2]. The IEP helps to integrate energy plans and policies of the energy sector through rigorous coordination among various energy subsectors. An energy forecast that fails to account for the uncertainties in the model or the energy system can lead to the failure of IEPs and could be a setback to developing a robust energy policy. Energy policymakers can make better decisions by considering uncertainties in energy data measurement methods and tools
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