Optimizing Resilience: A Model-Based Approach to Establishing Energy Hubs in Puerto Rico (Part 3)

 

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In our previous articles, we explored the challenges and barriers to Puerto Rico’s renewable energy transition, highlighting the roles of political corruption and fossil fuel interests. In this third installment, we delve into a practical solution aimed at enhancing energy resilience amidst potential transportation and power system failures. This solution leverages an optimization-based model to strategically locate and configure electric power-generating resilience hubs, crucial for maintaining energy access during disasters.

The Model: Maximizing Accessibility and Energy Satisfaction

The proposed model addresses the critical need for resilience hubs by focusing on two main objectives: maximizing transportation accessibility to the hubs and ensuring the primary energy needs of communities are met through hub-generated power. This dual approach considers the limitations of energy generation capacity relative to community demands and the transportation network distances from communities to hubs, all within a defined budget constraint.

Key Features of the Model

1. Budget Constraints: The model operates within a set budget, optimizing the number and configuration of resilience hubs that can be established without exceeding financial limits.
2. Energy Generation and Demand: It accounts for the capacity of each hub to generate energy and the specific energy needs of nearby communities, ensuring that hubs can effectively meet these demands.
3. Transportation Network: The model considers the accessibility of hubs based on the transportation network, factoring in the distances that communities need to travel to reach these hubs.

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Heuristics for Solution Generation

To solve this complex optimization problem, three heuristics are employed:

1. Genetic Algorithm (GA): This heuristic uses problem-specific solution generation procedures to explore various configurations and identify optimal solutions. It was found to outperform the other heuristics in numerical experiments.
2. Greedy Reduction Heuristic: This approach starts with the most expensive configurations and progressively reduces them to meet the budget constraint while maintaining feasibility.
3. Greedy Increase Heuristic: Conversely, this heuristic begins with no active hubs and incrementally adds configurations until all constraints are satisfied.

Numerical Experiments and Findings

Using data from rural Puerto Rico, numerical experiments were conducted to evaluate the performance of the proposed model and heuristics. Key findings include:

• The Genetic Algorithm heuristic consistently found better solutions than the greedy heuristics, demonstrating its effectiveness in optimizing the hub locations and configurations.
• Solutions featuring spatially dispersed hubs with lower energy generation capacities were more effective than those with spatially concentrated high-capacity hubs. This dispersion reduces travel distances for communities and enhances overall accessibility.
• Across various demand scenarios, a limited number of candidate hub locations consistently ranked as the best options, highlighting the strategic importance of certain areas for hub placement.

Practical Implications

The model's ability to identify optimal hub locations and configurations is crucial for enhancing Puerto Rico’s energy resilience. By ensuring that hubs are accessible and capable of meeting energy demands, this approach can mitigate the impacts of natural disasters on vulnerable populations. The findings also underscore the importance of strategic planning in establishing a resilient energy infrastructure that can adapt to varying disaster scenarios.

Conclusion

Optimizing the location and configuration of energy resilience hubs is a vital step toward achieving energy security and resilience in Puerto Rico. This model-based approach offers a practical framework for decision-makers to allocate resources effectively, ensuring that communities can maintain access to critical energy services during disasters. By leveraging advanced heuristics, such as the Genetic Algorithm, this model provides robust solutions that enhance both accessibility and energy satisfaction.

Works Cited

• Rodriguez-Roman, D., Carlo, H. J., Sperling, J., Duvall, A., Leoncio-Cabán, R. E., & López del Puerto, C. (2024). Optimizing the location and configuration of disaster resilience hubs under transportation and electric power network failures. Transportation Research Interdisciplinary Perspectives, 24, 101079. doi:10.1016/j.trip.2024.101079.
• People's World. "As Fossil Fuel Plants Face Retirement, a Puerto Rico Community Pushes for Rooftop Solar." People's World, 2024. Link.
• U.S. Department of Energy. "PR100 Study." U.S. Department of Energy, 2023.