2026-10-13T21:02:20Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33988942 2026-09-24T17:44:17Z category_21 category_811 portal_5 item_type_3 month_year_09_2026

<p>Conditional damage model of each cause type.</p> Yongju Gu (25105303) Zhiwei Zhang (12077) Liyun Zhang (280172) Biotechnology Mathematical Sciences not elsewhere classified standardized regression coefficient restoration technology distinguishes monte carlo sampling limit extreme losses joint parameter uncertainty expected unserved energy dominant economic driver channels carry cause cause mix governs aware scenario reduction aware reduction keeps averse plan attains 200 retained scenarios reserve fund scale appropriate reserve scale tractable representative set energization channel supplied emergency response capability emergency resource allocation 95 </ sub xlink "> 0 type blackout scenarios stage reserve fund scale blackout risk force power utilities div >< p power utilities financial reserve unified set resource pre resource portfolio repair channel stage risk stage post validated prescription sub xmlns stylized 118 shaped decomposition risk measure results characterize random subsampling preparedness trade positioning together paper develops neutral scheme <div><p>Large-scale blackouts increasingly arise from heterogeneous causes such as natural disasters, equipment failures and cyber attacks, which force power utilities to decide, before any event is known, both how large a financial reserve to hold and how to convert it into emergency response capability. This paper develops a coordinated optimization of reserve fund scale and emergency resource allocation under multi-type large-scale blackout risk. A unified set of multi-type blackout scenarios is constructed by Monte Carlo sampling and condensed by a tail-aware scenario reduction into a tractable representative set. The restoration technology distinguishes a repair channel, in which crews and materials enter as complementary inputs, from a re-energization channel supplied by mobile equipment, and both channels carry cause-dependent productivity so that the response mechanism differs across disasters, failures and cyber attacks. A two-stage risk-averse stochastic program co-optimizes the first-stage reserve fund and resource pre-positioning together with the second-stage post-event response, embeds a conditional-value-at-risk measure to limit extreme losses, and is solved by a sample-average-approximation scheme with an L-shaped decomposition. Case studies on a stylized 118-node planning instance show that the coordinated risk-averse plan attains an out-of-sample risk-adjusted cost of 352.1 $M against 558.2 $M for a decoupled scheme and 604.7 $M for a risk-neutral scheme, while holding CVaR_0.95 to 154.3 $M and expected unserved energy to 1478 MWh. The tail-aware reduction keeps the CVaR_0.95 error at 1.08% of its raw-sample value with 200 retained scenarios, against 11.91% for random subsampling. The cause mix governs the resource portfolio, and the value of lost load is the dominant economic driver of the appropriate reserve scale, with a standardized regression coefficient of 0.66 under joint parameter uncertainty. The study is presented as a methodological proof of concept: the instance is synthetic and network operability is not modelled, so the results characterize the structure of the preparedness trade-off rather than a validated prescription for a specific utility.</p></div> 2026-09-24T17:44:02Z Dataset Dataset 10.1371/journal.pone.0352511.t003 https://figshare.com/articles/dataset/_p_Conditional_damage_model_of_each_cause_type_p_/33988942 CC BY 4.0