Energy transition plans frequently depend on timely completion of large generation projects, yet construction delays and supply-chain disruptions can jeopardize grid adequacy and force utilities to deploy costly temporary solutions. A new analysis of Indonesia's Jawa–Bali power system quantifies how project delays translate into operational challenges and financial exposure during the critical 2025–2034 planning window.
The research employs a scenario-based stochastic model to evaluate system impacts across three pathways: on-time delivery of planned capacity, moderate delays, and severe delays. The optimization framework co-evaluates flexible gas engines, photovoltaic battery systems, and standalone battery storage while respecting reserve margins, capacity-credit calculations, hourly operational constraints, and unit-commitment requirements.
Under the base on-time scenario, the system maintains adequate reserves without substantial new mitigation investments. However, moderate-delay cases necessitate 4.0 GW of additional gas engine capacity and 3.0 GW of photovoltaic-battery systems, adding USD 3.80 billion in expected costs. Severe-delay cases require 5.70 GW of gas engines paired with the same 3.0 GW of photovoltaic storage, increasing total cost exposure to USD 5.25 billion.
These findings underscore a critical planning gap in energy-transition strategies. Most long-term adequacy assessments assume committed projects arrive as scheduled, masking latent financial and reliability risks. By explicitly modeling delay scenarios, utilities can better calibrate mitigation investments, optimize technology portfolios, and communicate realistic cost ranges to stakeholders.
For the power industry, the study highlights the value of flexible, rapid-deployment resources—particularly battery storage and gas generation—in hedging against project delays. As countries worldwide accelerate renewable transitions, incorporating delay-risk analysis into resource-adequacy planning will become essential for maintaining grid stability while controlling system costs. The methodology is transferable to other regions facing similar infrastructure-development uncertainties.
