An Optimized CIP: Balancing Climate Change, Funding Dollars and More

Presentation Relevance It is estimated that over $360 billion in operation and maintenance (O&M) costs are needed for the wastewater sector to fund climate adaptation and resilience needs (NACWA 2023). Utility managers are often faced with difficult decisions while developing their capital improvement plans (CIPs). New requirements for emerging contaminants, emergencies and regular annual spending all compete for limited resources. Decision makers at utilities need better tools that help them prioritize spending based on current and long-term needs. This presentation discusses a case study where a flood mitigation CIP schedule is developed using multi-objective optimization. This framework was used to meet the long-term capital planning goals of Hampton Roads Sanitation District (HRSD), a utility interested in implementing climate change resilience projects for their facilities. Since it is not realistic for HRSD to invest in mitigation projects for all their facilities at once, they sought to implement their projects over time, prioritizing high-risk facilities first. The outcome has helped HRSD develop a realistic road map for climate change readiness, build a strong business case for spending rate payer dollars and develop funding goals that may inform future grant applications. HRSD is in Virginia and serves 1.9 million people in 20 towns, cities, and counties. The service area is routinely impacted by flooding, prompting the utility to build a plan that would protect their critical infrastructure from future flooding from sea level rise, precipitation events, and storm surges. The plan included a long-term flood risk evaluation of 60 wastewater facilities (treatment plants and pumping stations) between the years 2030 and 2100. The results were used to develop a mitigation schedule with projected project start and end dates for all facilities. The project timelines align with HRSD's need for incremental spending toward future flood risk management. Methods An effective flood mitigation plan first requires an understanding of where flood risk exists and how that risk changes over time due to climate change. The risk assessment approach conducted for HRSD used current and future flood exposure data and facility-specific asset data to quantify flood risk as annualized losses. A detailed explanation of annualized losses is provided in Gnan et al. (2022). This approach enabled HRSD to meaningfully compare risk across time, at facilities of different sizes, and at facilities that experience nuisance flooding or flooding from a single significant event. Having quantified flood risk, HRSD identified a potential mitigation action at each facility, estimated its cost, and its benefits in reducing flood risk. These results provided over 60 different mitigation actions that could be undertaken over 70 years and serve as the input for the optimization approach. A utility wide mitigation schedule was developed for HRSD using multi-objective optimization. In this approach, annualized losses and mitigation costs were inputs used to develop thousands of mitigation schedules. The optimization ultimately output a suite of 180 schedules that have unique tradeoffs between three key HRSD objectives: 1.Flood Risk Reduction: An optimal schedule would have low annualized loss values (in dollars) after mitigation to represent low flood risk. 2.Smaller Investments over Time: An optimal schedule would call for incremental investments over time so HRSD can allocate resources toward climate change planning annually while continuing to fund other capital projects. The optimal schedule would have a low value for cost variance (in dollars). 3.Maximum Annual Project Count: An optimal schedule would have a low annual project count to meet HRSD's goal for incremental spending and minimize construction disturbance to the community. The optimization, which uses a genetic algorithm from the open source jMetalPy Python library (Bení­tez-Hidalgo et al. 2019), is fully automated and applicable to utilities and projects of any size. Results Figure 1 shows the optimization output as a 'Pareto curve', which depicts the tradeoffs between the three objectives. Each point in the curve corresponds to one of 180 potential mitigation schedules, each with a projected project start and end date for the 60 facilities. A favorable schedule is shown in Figure 2, where most spending occurs between 2030 and 2060. Under this plan, HRSD would first implement mitigation projects for pumping stations that are at high risk for flooding during 2030-2040. Higher cost mitigation projects would not be implemented at treatment plants until mid-century, which is when they will experience the most significant risk. Applicability While HRSD's case study applies to climate change mitigation, an optimization approach could be useful for any CIP if the objectives can be quantified. For example, a utility that was interested in incorporating equity goals in its flood mitigation CIP could develop an optimal schedule to prioritize flood prevention projects in lower income neighborhoods. Optimization can provide the necessary guidance for operations and leaders when making tough decisions about capital planning. After finalizing a plan, utility managers can compare it to the optimal results derived from the Pareto curve and have confidence in knowing their objectives were met.