An efficient energy planning model optimizing cost, emission, and social impact with different carbon tax scenarios

This study proposes a modified energy planning model that considers a broad range of future uncertainties. Modifications to hybrid stochastic robust optimization and robust optimization methodology allow for the introduction of multi-objective functions that reflect the various dimensions of energy planning including cost, emission, and social impact. A lexicographical and weighted-sum type multi-objective function are used in formulating the proposed optimization model. Changing the priorities of the objective functions generates different energy policies, which are then compared. Data envelopment analysis is applied to measure the energy efficiency of each optimal energy policy produced by the energy planning model. Energy efficiency is defined as the ability to satisfactorily address five main aspects—cost, emissions, social impact, employment, and security. An updated power development plan for Thailand is used as an illustrative case study. The empirical analysis indicates that a policy prioritizing the environment first, followed by social impact and cost, is the most efficient policy among the five alternatives considered, with an average efficiency score of 0.9988. Sensitivity analysis involving various carbon tax scenarios is used to establish the importance of weight selection in the weighted-sum method. Specifically, the empirical case study reveals that when the weighted-sum method is used, increasing the carbon tax increases the relative policy efficiency score. Results from the case study offer quantitative support for policy makers seeking to devise an efficient energy policy that meets extensive requirements while still dealing with the bounds of uncertain future projections.