Managing uncertainty for DSOS
With 80% of new renewable energy sources expected to connect at the DSO level, long-term grid planning faces significant uncertainty. Complex permitting and regulatory barriers slow grid expansion, while debates over integrating large-scale renewable projects, such as ffshore networks, further complicate future scenarios. To manage this uncertainty over a 10+ year horizon, DSOs must broaden the toolbox of available technologies for network development. Many Innovative Grid Technologies (IGTs) are commercially available but remain underutilized due to lengthy type-testing and a lack of standardization. Collaborative testing frameworks and reliance on peer-reviewed evaluations could accelerate their adoption. IGTs can also serve as flexible, cost-effective solutions to fill gaps where traditional grid expansion is delayed, reducing the need for extensive future build-outs. Additionally, leveraging consumer-side flexibility can complement these efforts. This paper introduces a cost-benefit analysis framework tailored to the evolving needs of DSOs, emphasizing the value of IGTs in managing uncertainty. The analysis highlights how faster deployment of innovative solutions can address short-term challenges while ensuring long-term grid resilience. The paper concludes with actionable recommendations for industry stakeholders, regulators, and the European Commission on integrating IGTs into DSO planning to enhance flexibility, efficiency, and reliability.
Authors
- Peter Hughes (ORCID: https://orcid.org/0009-0008-6656-3355)
- Mark Norton (ORCID: https://orcid.org/0000-0003-2649-5307)
- Pablo Arboleyá
- Lucie Richard (ORCID: https://orcid.org/0000-0001-5969-4252)
- E Ramsay
Institutions
- Economic and Social Research Institute (IE)
- Universidad de Oviedo (ES)
- Université de technologie de belfort-montbéliard (FR)
- McGaw Technology (United States) (US)
- Epsilon Systems (United States) (US)
Publication Details
- Journal
- IET conference proceedings.
- Published
- 2025-10-14
- DOI
- https://doi.org/10.1049/icp.2025.1829
- Primary Topic
- Business Process Modeling and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00