Supporting materials
Methodology
A. Economic Loss due to Outage
The analysis estimates the macroeconomic cost of outages in three steps. First, the energy not supplied (ENS) is derived from the System Average Interruption Duration Index (SAIDI):
where SAIDI is measured in hours per year and annual demand in MWh. Second, the ENS is translated into economic loss by scaling with Gross Value Added (GVA) for the industry:
This approach assumes outages occur uniformly across all hours of the year. In practice, however, the economic impact of interruptions is highly sensitive to whether they fall during peak or off-peak periods, as peak outages can impose disproportionately higher costs. Because such temporal detail is not available consistently across ASEAN, the uniform assumption provides an indicative regional estimate but may understate peak-hour risks.
Furthermore, due to disparate reporting, SAIDI data rely on the ACE report based on World Bank data, with 2020 levels projected forward under a “no-improvement” scenario. This simplification ensures comparability but implies that local gains in reliability are not reflected, while deteriorations may also be overlooked.
Hence, results must be interpreted as broad regional indicators of potential economic losses, rather than precise country-level estimates. They highlight the order of magnitude of the reliability challenge, but should not be used as exact measures of current performance or future risk without more granular, time-resolved, and country-specific data.
B. Value of Loss Load Calculation
VOLL expresses the monetary value of each MWh of unserved energy. Several estimation approaches are recognised internationally.
Given ASEAN’s data constraints, the study adopts the macroeconomic approach, where:
This method provides a transparent and replicable regional benchmark. However, because the calculation is based solely on industry GVA, it implicitly assumes that the value of reliability is uniform across all industrial activity. This approach may mask large variations, with energy-intensive or export-oriented sectors likely facing much higher outage costs, while less electricity-dependent industries may be overstated.
Evidence from other studies confirms that macroeconomic estimates of VOLL usually sit at the lower end of the range, while preference-based or sector-specific approaches produce much higher values, especially for industries where interruptions carry severe operational or safety risks.
C. Smart grid investment cost estimation
Smart grid investment needs for ASEAN were estimated using international per-capita benchmarks, given the lack of consistent technology-level cost data across the region. A bottom-up approach—summing costs of metering, automation, or demand-response platforms—would in principle be more precise, but data gaps and differing national priorities make such aggregation unreliable at the regional scale.
Instead, this report applies comparative benchmarks from global peers:
- European Union: Investments of $15–20 per capita, reflecting its advanced digitalisation agenda and strong emphasis on cross-border integration.
- China: Investment is estimated at $10–12 per capita, driven by applying a strategic 19% digitalisation factor to its grid expenditure in 2023. This covers the high cost of advanced digital monitoring necessary for stable Ultra-High Voltage transmission and renewable curtailment reduction.
- India: Investment is estimated at $6–8 per capita, anchored on the country’s national digitalisation program budget and further reinforced by complementary state-level and private initiatives, particularly the large-scale rollout of smart meters to reduce system losses.
Applying these per-capita ranges to ASEAN provides an indicative scale of investment need, rather than a definitive requirement. This underscores the importance of country-level cost–benefit analysis to refine allocations in line with national priorities and system conditions.
D. Job creation estimation
Employment impacts were estimated using an investment–employment multiplier approach. Job coefficients were derived from the Senegal smart grid project, where a $9–10 million investment generated 574 jobs (192 direct; 382 indirect/induced). Applying these coefficients to ASEAN’s projected smart grid investment needs ($4–10.7 billion) yields an estimated 234,000–627,000 jobs.
Acknowledgement
Contributors
Ember: Alnie Demoral, Dinita Setyawati, Aditya Lolla, Shiyao Zhang, Uni Lee, Libby Copsey, Giang Ngoc Huong Vu, Jivan Zhen Thiru, Ardhi Arsala Rahmani
ASEAN Centre for Energy: Silvira Ayu Rosalia
Cover image
Photo credit: Andrey Kulagin / iStock Getty Images Plus
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