Optimizing Urban Microgrids for Resilience and Equity

In a recent study in Nature Sustainability, researchers explored how urban microgrid planning can enhance city resilience, well-being, and equity against diverse threats such as natural disasters and cyber-attacks. They proposed an integrated approach that uses social and technical indicators to design optimal microgrid districts, balancing economic feasibility, renewable energy potential, and fair democratic participation.

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Background

Microgrids are small and local energy systems that produce, store, and distribute energy independently or alongside the primary grid. They offer solutions for mitigating the impacts of natural hazards, cyber-attacks, and climate change on urban infrastructure and populations.

Microgrids can support innovative energy democracy models and local energy communities, enabling citizen participation in energy-related decisions. However, current microgrid planning often neglects the long-term effects on urban resilience, equity, and well-being, with most research focusing primarily on technical and economic aspects while overlooking social and environmental dimensions.

About the Research

This paper addressed how urban microgrid design can enhance resilience and well-being while ensuring fair and democratic decision-making processes. They developed a framework integrating social and technical indicators for evaluating and optimizing microgrid districts to answer this.

The social indicators included the social vulnerability index, the criticality of basic services, energy technology locations within microgrids, and the representation of socially vulnerable groups. The technical indicators covered infrastructure criticality and peak load, availability and distribution of renewable energy sources, microgrid implementation costs and feasibility, and the resilience and well-being impacts of power outages or microgrid failures.

The researchers applied this framework to a case study in New Hanover County, North Carolina, where Hurricane Florence in 2018 highlighted the need for resilient energy systems. They utilized spatial and infrastructural data, stakeholder surveys, focus groups, and an evolutionary algorithm to identify optimal microgrid solutions for the county.

They also assessed different microgrid districting scenarios using data on existing grid infrastructure, rooftop photovoltaic potential, building permits, critical infrastructure, and the social vulnerability index, considering future threats, such as natural disasters, cyber-attacks, and microgrid failures.

Research Findings

The outcomes revealed that the optimal microgrid districting solution for New Hanover County, comprising six microgrids, effectively balanced economic feasibility, urban resilience, well-being, and equity. This approach also promoted sustainable development. The solution prevented the concentration of high-criticality and high-peak load infrastructure in single microgrids. It ensured that each microgrid had access to relief, health, and security services.

It also offered similar potentials for integrating neighborhood energy storage and rooftop photovoltaics. Finally, it established a well-balanced distribution of socially vulnerable groups, mitigating the risk of energy gerrymandering.

The authors demonstrated that their solution was cost-efficient and minimized well-being losses in baseline scenarios. It also supported several Sustainable Development Goals (SDGs), including clean energy access, income growth, access to basic services, disaster resilience, climate change mitigation and adaptation, and participatory urban planning.

Applications

This research can be applied to any urban context, considering each city's specific characteristics and needs. It suggests that microgrid planning should be integrated with sustainable urban development projects, such as enhancing critical services, improving housing conditions, and increasing mobility options.

It also highlights the importance of involving local governments, city planners, critical service providers, and communities in collaborative and inclusive microgrid districting and management decision-making. The results emphasize the need for further research and adaptation in characterizing and measuring social vulnerability, well-being, and energy literacy, as well as refining the optimization methods and data sources for microgrid planning.

Conclusion

Microgrids enhance urban resilience, well-being, and equity against future hazards. The study demonstrated that the proposed novel technique using social and technical indicators could optimize microgrid districting and planning. The benefits were illustrated through a case study in a US county, showing how microgrid solutions contributed to multiple SDGs.

The researchers suggested that addressing individual SDG targets in isolation might hinder progress toward others and highlighted that some urban energy system designs could simultaneously advance multiple SDG targets. Additionally, they identified several limitations and challenges in microgrid planning, such as data availability, stakeholder participation, and scalability.

They also proposed future research directions and policy considerations for sustainable urban transformation, emphasizing the need to address emerging hazards and develop integrated policies to enhance urban resilience and well-being.

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Source:

Ottenburger, S.S., Cox, R., Chowdhury, B.H. et al. Sustainable urban transformations based on integrated microgrid designs. Nat Sustain (2024). DOI: 10.1038/s41893-024-01395-7, https://www.nature.com/articles/s41893-024-01395-7

Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.

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