Optimal Energy Transition Planning: Navigating the Trade-Offs between Short-Term and Long-Term Decision-Making

Abstract

Transforming emission-intensive energy systems into low-carbon configurations requires strategic planning that balances efficiency, costs, and emissions reductions. While many capacity expansion models support long-term investment planning, only a few integrate multiple energy vectors, spatial distribution optimization, and foresight approaches to identify realistic pathways. This work presents a multivector, multinodal optimization model for least-cost capacity expansion and retirement planning, formulated as a mixed-integer linear programming model. The model incorporates different foresight approaches to examine their influences on technology choices, investment timing, technology deployment sites, and system-wide costs and emissions. Applied to a case study in Sarawak, Malaysia, results show that perfect foresight supports smoother transitions with lower costs, whereas myopic foresight risks overshooting emissions and missing long-term targets. Importantly, incorporating intermediate emissions targets under myopic foresight helps align near-term actions with long-term objectives, which is essential when short-term uncertainties make myopic decision-making unavoidable. Although myopic planning raises the capital expenditure by 18.1%, incorporating intermediate emissions targets provides structured transition pathways and prevents capacity overexpansion.

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