Ongoing risks from climate change have 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, more easily
allowing for the interconnection of nomadic communities, both to each other and
to the main grid. In light of 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) is implemented for
the design and planning problem tackled. 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.
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.

本文探讨了为游牧社区设计和规划移动多能源供应系统的策略，并采用深度强化学习（DRL）作为设计和规划问题的解决方案，结果表明游牧能源系统在技术和经济上都是可行的，特别是在考虑灵活性时，对可用基线值的潜在改进高达 25％，67％和 76％。