A spatiotemporal clustering method for mobile energy storage routing and vehicle-to-grid

Mobile Energy Storage (MES) has proven effective in integrating renewable energy and alleviating grid congestion due to its flexible deployment. However, in MES routing and Vehicle-to-Grid applications (such as energy arbitrage), the large-scale routing problem involving multiple vehicles and nodes...

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Bibliographic Details
Published in:eTransportation (Amsterdam) Vol. 26; p. 100478
Main Authors: Chen, Xinjiang, Yao, Jiayang, He, Guannan
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2025
Subjects:
Energy-aware routing
Mobile energy storage
Spatiotemporal clustering
Time–space network
Vehicle-to-grid
Vehicle-to-grid
Spatiotemporal clustering
Energy-aware routing
Mobile energy storage
Time–space network
ISSN:2590-1168, 2590-1168
Online Access:Get full text
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Summary:Mobile Energy Storage (MES) has proven effective in integrating renewable energy and alleviating grid congestion due to its flexible deployment. However, in MES routing and Vehicle-to-Grid applications (such as energy arbitrage), the large-scale routing problem involving multiple vehicles and nodes encompasses high-dimensional spatiotemporal decision variables, making it challenging for general commercial solvers to solve efficiently. To address this challenge, we develop an improved time–space network-based model that uses feasible spatiotemporal arcs to represent the routing schemes for MES throughout the entire scheduling period. Furthermore, we propose an adaptive spatiotemporal clustering algorithm based on time–space network aggregation-split to solve the model quickly. In the aggregation phase, given the lower bound of cluster quantities, nodes with closely related spatiotemporal distances are clustered into one representative node. During the split phase, we design a spatiotemporal envelope method to identify nodes with potential arbitrage opportunities in each cluster and classify them into a separate cluster. We apply the proposed model and algorithm to the energy arbitrage of MES within the California power grid. The results reveal that, compared to the commercial solver, the proposed algorithm significantly reduces the average time overhead by 92.7%, while only sacrificing 0.9% in optimality in more than 300 daily scheduling cases. •A MES routing model based on an improved time–space network is developed.•A spatiotemporal clustering method based on grid aggregation-splitting is proposed.•The average optimality gap of spatiotemporal clustering algorithm is 0.9%.•The average time saving of spatiotemporal clustering algorithm is 92.7%.
ISSN:2590-1168
2590-1168
DOI:10.1016/j.etran.2025.100478