Diagnosis-Driven Co-planning of Network Reinforcement and BESS for Distribution Grid with High Penetration of Electric Vehicles
For distribution grid operators facing operational challenges from high EV penetration, this framework provides a computationally tractable solution for co-planning infrastructure upgrades and BESS deployment.
The paper proposes a Diagnosis-Driven Co-Planning (DDCP) framework to jointly optimize grid reinforcement and BESS installation for distribution grids with high EV penetration, achieving superior techno-economic performance over alternative approaches.
While the rapid proliferation of electric vehicles (EVs) accelerates net-zero goals, uncoordinated charging activities impose severe operational challenges on distribution grids, including exacerbated peak loads, thermal overloading, and voltage violations. To overcome the computational intractability of jointly optimizing grid infrastructure reinforcements and Battery Energy Storage System (BESS) installations, this paper proposes a novel three-stage Diagnosis-Driven Co-Planning (DDCP) framework. The methodology integrates a Violation Detection and Quantification (VDQ) model to systematically identify system breaches, and a Violation Mitigation-Based Planning (VMBP) model for optimal BESS allocation. Specifically, Stage I of the DDCP framework diagnoses critical bottleneck lines that render standalone BESS solutions infeasible; Stage II executes targeted physical upgrades exclusively on these bottlenecks; and Stage III finalizes the optimal BESS deployment on the updated network topology. Furthermore, this study quantifies the EV hosting capacity thresholds before and after BESS integration across varying EV adoption rates and base voltages. Finally, a comprehensive comparative analysis evaluates four mitigation approaches: the VDQ-driven cable upgrade (VCU) model, the VMBP model, system-wide voltage uprating, and the proposed DDCP framework. The results demonstrate that the DDCP framework not only resolves the complex joint-optimization hurdle but also achieves superior techno-economic performance in addressing high-EV-penetration challenges.