Modernizing Grid Resilience Planning: From Spreadsheets to Strategy
According to the U.S. Department of Energy (DOE) Electric Emergency Incident and Disturbance Reports, weather-related outages increased 86% between 2003 and 2023. Utilities are responding with billions in grid-hardening investments while regulators increasingly require submission of wildfire mitigation, resilience, and/or reliability plans that quantify risk reduction.
Risk-spend efficiency (RSE) has emerged as a leading framework for meeting this demand. By comparing dollars invested with the amount of risk reduction achieved, RSE provides a structured, auditable way to prioritize investments across a portfolio of mitigations. These methods sit at the intersection of grid resilience strategies and utility risk management, providing a framework that is both transparent and defensible.
Yet many utilities still base these decisions on static spreadsheets and stale data. Wire-level models can overwhelm planners with data that is difficult to use. Combining near-real-time data, advanced analytics, and intuitive visualization transforms RSE from a compliance exercise into a decision engine, enabling timely, defensible infrastructure decisions.
When Grid Resilience Planning Hits Data Roadblocks
Many utilities struggle to make confident investment decisions, not for lack of data but due to poor data management, modeling, and presentation.
Data Volume Overwhelms Decision-Makers
Wire- and pole-level analyses generate massive datasets that slow planning cycles. In states like California, these reports are more than technical exercises; they inform regulatory funding allocations. Reports can take months to prepare, face multiple reviews, and may be returned for revision. The combination of overwhelming data volumes and regulatory scrutiny makes it challenging for utilities to deliver filings that clearly demonstrate risk reduction and grid resilience improvements.
Customer Context is Missing
Traditional models focus on asset condition and failure probability but often omit analysis of who is affected when assets fail. Without integrating AMI and customer data, utilities risk overlooking impacts to hospitals, emergency services, and medically vulnerable populations.
Abstract Scores Block Consensus
Even when the data is sound, poor formatting makes it difficult to translate results into actionable decisions. Risk expressed as abstract scores can be challenging to interpret and explain to executives, regulators, and community stakeholders, leading to delays in decision-making.
Turning Data into Decisions
Utilities can overcome these challenges by transforming their approach to gathering, processing, and communicating information. The path forward combines more timely data, advanced analytics, and visualization techniques that make risk planning clear, defensible, and actionable.
Timely Data Keeps Models Current
Near-real-time AMI data and updated asset records allow risk models to reflect today’s grid—not last year’s.
Predictive Optimization Guides Mitigation Mix
Machine learning can simulate thousands of portfolios and rank them by cost effectiveness, helping planners select the most impactful mix within budget.
Dollarizing Risk Makes Results Relatable
Replacing abstract scores with financial equivalents translates probability and consequence into cost. This “dollarized” view makes it easier for executives, regulators, and community leaders to compare options, justify spending, and align around a shared definition of value.
Visualization Turns Complexity into Clarity
Interactive dashboards convert massive outputs into intuitive views. Planners can compare portfolios side by side, view grid evolution over a multi-year horizon, and drill down to wires or poles to understand risk drivers. This visual approach speeds up alignment, shortens planning cycles, and reinforces grid resilience frameworks and utility risk management.
Customer Impact Layer Adds Equity to Planning
Overlaying location data on hospitals, fire stations, schools, and medically vulnerable populations lets utilities target investments to protect critical services.
A Real-World Example: From Static Spreadsheets to Daily Scenario Testing
A major West Coast utility migrated its wildfire mitigation model from spreadsheets to a cloud-based platform connected to a central data lake, enabling near-real-time updates, daily scenario testing, and interactive dashboards.
During a severe fire season, the platform processed millions of data points per day, generated composite risk scores for every supervisory control and data acquisition (SCADA) device—field sensors and controllers that monitor grid operations—in high-threat districts, and supported 500+ hours of continuous emergency operations monitoring. These capabilities created a shared understanding of risk across more than 20 business units—including electrical engineering, asset management, wildfire mitigation, and emergency operations—strengthening both resilience planning and risk management.
The Payoff: Faster Decisions and Stronger Grid Resilience
Combining near-real-time data, predictive optimization, and visualization delivers benefits across the planning and execution spectrum.
Planning Cycles Accelerate
Planning cycles can now run continuously. Planners can test scenarios daily, respond quickly to emerging risks, and move from analysis to decision without delay.
Capital Is Allocated More Effectively
Optimization models help decision-makers select mitigation portfolios that deliver the greatest risk reduction per dollar invested. This approach improves return on investment and creates a transparent, auditable trail showing which projects were selected and why.
Budgets and Approvals Become Easier to Defend
Dollarizing risk translates risk reduction into financial terms, giving utilities a clear means of justifying proposed budgets to regulators and executives. This transparency lowers the risk of regulatory disallowances and provides a defensible narrative if outages occur, helping the utility manage public perception and strengthen its overall risk management strategy.
Stakeholder Confidence Grows
Dashboards that display risk in both geographic and financial terms make decisions easier to explain to regulators, executives, and community leaders, accelerating alignment across teams and fostering more constructive engagement.
Operations and Planning Stay in Sync
Using the same data for planning and real-time decisions keeps teams aligned and prevents conflicting priorities.
A Culture of Data-Driven Action Emerges
Clear evidence of risk reduction enables a proactive approach. Over time, compliance reporting becomes part of a broader culture focused on measurable risk reduction and community protection.
Lessons Learned and Best Practices
Effective grid resilience planning and utility risk management require robust architecture, stakeholder engagement, and transparent analytics.
Build for Reliability and Iteration
Choose an architecture that supports monitoring, alerting, updates, easy data integration, frequent model refreshes, and seamless scaling.
Design for Every Audience
Executives and regulators need high-level summaries, while analysts and engineers require detailed drill-downs. Build dashboards that can switch between these views so every stakeholder gets the right level of detail.
Co-Develop with Stakeholders
Engage end users early and gather feedback continuously. Iterating with planners, operators, and compliance teams keeps the solution relevant and focused on answering real questions.
Ensure Transparency in Modeling and Data Lineage
When using machine learning or other AI to recommend portfolios, ensure results can be explained and traced back to their inputs. Document the model’s purpose, inputs, and key drivers, and provide visualizations that show why one project ranks above another. Transparency ensures recommendations can be reviewed, explained, and defended in regulatory and executive settings.
From Compliance to Competitive Advantage
Utilities are under pressure to deliver more than mitigation plans; they must show that every dollar invested drives measurable risk reduction. Near-real-time data, predictive modeling, and intuitive visualization have moved RSE into the realm of strategic decision-making.
The next opportunity is to treat planning systems as living engines, continuously ingesting new data, running scenarios, and aligning stakeholders in near-real time. This shift enables leaders to act proactively and balance cost efficiency with community impact.
Recent wildfire events underscore the stakes, highlighting the devastating consequences of aging infrastructure and delayed response. For utilities, proactive measures—including PSPS programs where appropriate—remain essential tools to prevent tragedies and protect communities.
AI-enabled risk management is on the horizon. As these systems mature, utilities will adjust plans dynamically, anticipate emerging threats, and direct crews where needed while maintaining regulatory defensibility. Utilities that make the leap will anticipate risk, prioritize with confidence, and set the standard for modern grid resilience planning and risk management.
This article was written with the assistance of artificial intelligence.
References
U.S. Department of Energy. (2003, 2023). DOE-417 Electric Emergency Incident and Disturbance Report. Archives. https://doe417.pnnl.gov/.
About the Author
Tim Holmes is a lead developer in Logic20/20’s AI & Analytics practice. He leads the design and development of data models, infrastructure, and applications that support wildfire mitigation efforts and operational decision-making. With a background in physics, computational mathematics, and software engineering, Tim brings a deep technical skillset to solving complex challenges in the utility sector. He has played a key role in migrating critical systems to the cloud, improving scalability and reliability, and continues to drive innovation across modeling and full-stack development initiatives. Tim is passionate about building high-impact solutions, supporting teams through collaboration, and mentoring talent to deliver long-term value.
