Document Type

Thesis - Open Access

Award Date

2026

Degree Name

Master of Science (MS)

Department / School

Electrical Engineering and Computer Science

First Advisor

Robert Fourney

Abstract

The primary objective of this thesis is to create a framework for modeling, forecasting Electric Vehicle load demand and optimizing the EV charging network operation for Charging network operators. As EV’s popularity grows accurate forecasting of EV charging demand and its effect on the distribution network is critical for the efficient planning and operation of charging infrastructure, as well as for enabling Charging Network Operators to participate effectively in wholesale electricity markets. The first part of the work develops Mt /M/1 & Mt /M/c queuing models calibrated using real-world EV charging data to characterize station-level operations under time-varying arrival rates. Key performance metrics including station utilization, system size, and vehicle wait times are evaluated, demonstrating that queuing theory, when parameterized with realistic inputs, provides an effective and tractable framework for assessing the performance and reliability of EV charging infrastructure. A dynamic pricing framework is introduced alongside the operational model, linking predicted user demand with prevailing electricity market conditions through a linear demand-price relationship applied to both Day-Ahead and Real-Time wholesale market prices, offering a practical strategy for setting time-varying charging prices that reflect actual market signals. The second part extends the single-station framework to a geographically distributed network of charging stations. Through the incorporation of empirical traffic data, spatially distributed stations, and probabilistic arrival processes, the model captures how EVs interact with both the transportation network and the broader charging ecosystem throughout the day. The spatial outputs support practical infrastructure planning decisions, including the identification of optimal sites for new charging stations by highlighting high-traffic corridors and persistently congested locations, as well as quantifying congestion risk across the network. The third part introduces a HELICS-based co-simulation framework that couples the EV charging demand model with an OpenDSS distribution network simulator to assess the grid-level impact of citywide EV charging demand. The synchronization between the EV federate and the distribution federate is validated through close agreement in the demand signals exchanged across the co-simulation interface. Voltage profile analysis across representative feeders confirms that all node voltages remain within the ANSI C84.1 regulatory bounds of 0.95–1.05 p.u. under the modeled EV penetration scenario, while revealing feeder-level heterogeneity in voltage sensitivity that underscores the importance of circuit-specific assessment in large-scale EV integration planning. The fourth part proposes a preliminary methodology for extending the reinforcement learning framework for joint day-ahead energy procurement and dynamic pricing toward a fully integrated, market-spatial co-simulation architecture. By incorporating station-level queue lengths, charger utilization, and congestion-based price multipliers directly into the RL state space, and extending the reward function to jointly optimize financial performance and network service quality, this framework establishes a pathway toward closed-loop, spatially resolved EV charging network management. This thesis contributes to the EV infrastructure research community by providing an integrated and extensible modeling framework that connects data-driven operational analysis, market-aware pricing strategies, grid-level impact assessment, and reinforcement learning-based optimization. The methods proposed can be leveraged by CNOs and distribution system planners for effective infrastructure planning, energy procurement, and real-time operational decision-making across both single and spatially distributed charging networks.

Publisher

South Dakota State University

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Rights Statement

In Copyright