Design and planning of flexible mobile Micro-Grids using Deep Reinforcement Learning
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Published version
Author(s)
Type
Journal Article
Abstract
Ongoing risks from climate change have significantly impacted the livelihood of global nomadic communities and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced “Plug and Play” control strategies have been recently developed with such a decentralized framework in mind, allowing easier interconnection of nomadic communities, both to each other and to the main grid. Considering the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) Flexibility Analysis is implemented for the design and planning problem. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Additionally, the DRL based policies lead to the development of dynamic evolution and adaptability strategies, which can be used by the targeted communities under a very wide range of potential scenarios. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important implications for both nomadic communities and policymakers focused on enabling their energy access.
Date Issued
2023-04-01
Date Acceptance
2023-01-14
Citation
Applied Energy, 2023, 335
ISSN
0306-2619
Publisher
Elsevier
Journal / Book Title
Applied Energy
Volume
335
Copyright Statement
© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000963995200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
ALGORITHMS
Deep Reinforcement Learning
Energy & Fuels
Energy Systems Design
Engineering
Engineering, Chemical
Flexibility in Design
GENERATION
MANAGEMENT
Mobile Micro-Grids Systems
OPTIMIZATION
Real Options Analysis
REAL OPTIONS APPROACH
REANALYSIS
RURAL ELECTRIFICATION
Science & Technology
SOLAR PV
SYSTEMS
Technology
ULAANBAATAR
Uncertainty and Risk Analysis
Publication Status
Published
Article Number
120707
Date Publish Online
2023-02-06