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Reliability Risk Assessment and Modeling

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State & Local

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Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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The contract titled Reliability Risk Assessment and Modeling requires the performance of comprehensive reliability risk assessments to evaluate vulnerabilities within the electrical grid. This involves modeling potential failure scenarios and quantifying risks using historical operational data alongside environmental factors to determine likelihood and impact of disruptions. The work is focused on identifying systemic weaknesses and providing data-driven insights to enhance grid resilience and operational continuity. All tasks must be completed in alignment with accepted engineering and risk analysis methodologies to ensure robust and actionable outcomes. This is a subcontract issued under NAICS code 541330, managed by the Georgia Environmental Finance Authority through the Georgia state agency. The solicitation was posted on July 16, 2026, with a firm deadline for response set for August 10, 2026, at 7:00 PM Eastern Time. The place of performance and point of contact details are unspecified, but the work is expected to support state-level infrastructure planning efforts in Georgia. Interested parties must submit proposals through the provided online portal before the deadline to be considered.

General Info

Reliability risk assessment for Georgia’s electrical grid due August 10, 2026, using data-driven modeling to enhance resilience.

Agency

Georgia Environmental Finance AuthorityView Agency

NAICS

541330 - Engineering ServicesView NAICS

Place of Performance

GA, USA

Set-Aside

NONE

Documents

(0)

No documents available

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Organization & Contact Information

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AgencyGeorgia Environmental Finance Authority
ContactsNo contacts available
OfficeN/A
Organization / Agency
Georgia Environmental Finance Authority
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Office AddressN/A
ContactsNo contact information available

Full Description

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Conduct reliability risk assessments to identify grid vulnerabilities, model failure scenarios, and quantify risks using historical and environmental data.

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