Mathematical foundations for energy networks: buffering, storage and transmission

Lead Research Organisation: University of Cambridge
Department Name: Pure Maths and Mathematical Statistics

Abstract

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Description The increasing penetration of electric vehicles over the coming decades, taken together with the high cost to upgrade local distribution networks and consumer demand for home charging, suggest that managing congestion on low voltage networks will be a crucial component of the electric vehicle revolution and the move away from fossil fuels in transportation. We have modelled protocols for the control of congestion caused by a fleet of vehicles charging on two real-world distribution networks. We show that the system undergoes a phase transition to a congested state as a function of the rate of vehicles plugging to the network to charge. We show that charging times are considerably more equitable in proportional fairness than in max-flow.
Exploitation Route Several questions are raised, and this is a rich field.
Sectors Energy

Transport
URL http://iopscience.iop.org/article/10.1088/1367-2630/17/9/095001
 
Description The urgent need to reduce our carbon dioxide emissions has provided much of the motivation for this project. Renewable energy, from for example solar or wind sources, is becoming more plentiful; but renewable sources are highly variable and often unpredictable even on relatively short time scales. The project has helped develop some of the mathematical approaches that are needed to model the combination of statistical fluctuations, capacity limited networks and multiple interacting subsystems. The findings have contributed to ongoing discussions with government agencies and system operators on the impact of renewable sources of energy on the management of electricity grids - see e.g. "Analytic Research Foundations for the Next-Generation Electric Grid" - https://www.nap.edu/catalog/21919/analytic-research-foundations-for-the-next-generation-electric-grid The most profound long term technology transfer may be the movement of people: e.g. Andrei Bejan to become the Commercial Optimisation Lead at Origami Energy.
First Year Of Impact 2017
Sector Energy
Impact Types Economic

Policy & public services
 
Title Large Scale Fast Response Energy Storage Model 
Description The model represents a stochastic model for large scale fast response energy storage and slow-to-moderate ramping generators with high wind penetration. The model defines a strategy for operating the storage facility and allows one to investigate the system-wide long-term effects of fast response energy storage in reducing the amount of conventional power used. In particular, trade-offs between various system performance quantities, including wind spill and the loss of load probability can be analysed using this model. 
Type Of Material Computer model/algorithm 
Year Produced 2012 
Provided To Others? Yes  
Impact We studied statistical properties of the above novel stochastic model applied to a large scale electricity system with aggregated storage (e.g. water pumped storage) and integrated renewables (wind power) when conventional power have to be scheduled some time in advance. This work is important as it looks at the interplay of renewables and energy storage as a trade-off between various system characteristics, in particular, the ability to curtail wind and energy lost to the system. This model has been further extended and its optimality studied and improved upon by the research community starting with the Sigmetrics 2012 paper 'Optimal Storage Policies with Wind Forecast Uncertainties' by Gast, N., Tomozei, D., and Le Boudec, J.-Y. The novelty of the introduced model was also emphasised in this latter paper. 
URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6310769