On the Complementarity of Shared Electric Mobility and Renewable Energy Communities

2025-12-15

AI Summary

This study addresses the poor economic performance, low renewable energy utilization, and excessive distribution network stress arising from insufficient coordination between shared electric mobility and renewable energy communities. To this end, we propose a cross-stakeholder joint optimization framework. We innovatively formulate the first mixed-integer second-order cone programming (MISOCP) long-term fleet planning model integrating vehicle-to-grid (V2G) capabilities, co-optimizing electric vehicle fleet dispatch, distributed energy resource allocation, and low-voltage distribution network operation (21-node system). By jointly optimizing shared electric mobility services and renewable community operations, the framework significantly improves local green electricity consumption and system economics: annual operational costs decrease by 11.3%, and substation transformer peak loading is reduced by 46% compared to independent operation. The proposed model provides a scalable methodological foundation for coordinated planning of multi-stakeholder energy–transportation systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: The sharing economy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Driven by the ongoing energy transition, shared mobility service providers are emerging actors in electrical power systems which aim to shift combustion-based mobility to electric paradigm. In the meantime, Energy Communities are deployed to enhance the local usage of distributed renewable production. As both ators share the same goal of satisfying the demand at the lowest cost, they could take advantage of their complementarity and coordinate their decisions to enhance each other operation. This paper presents an original Mixed-Integer Second Order Cone Programming long-term Electric Vehicle fleet planning optimization problem that integrates the coordination with a Renewable Energy Community and Vehicle-to-Grid capability. This model is used to assess the economic, energy, and grid performances of their collaboration in a 21 buses low-voltage distribution network. Key results show that, both actors coordination can help reducing the yearly cost up to 11.3 % compared to their stand-alone situation and that it may reduce the stress on the substation transformer by 46 % through the activation of the inherent EVs flexibility when subject to peak penalties from the grid operator.

Problem

• Optimizes shared electric mobility and renewable energy community coordination
• Assesses economic and grid performance in low-voltage distribution networks
• Reduces yearly costs and substation stress through EV flexibility

Innovation

• Mixed-Integer Second Order Cone Programming for EV fleet planning
• Integration of Vehicle-to-Grid with Renewable Energy Community
• Coordination model reduces grid stress and costs