An official website of the United States government
With the increasing integration of electric vehicles (EVs), there is a growing recognition of the contribution of EVs to the flexibility of distribution networks. However, quantitative methods for precisely assessing the flexibility that EVs can provide to the upstream power grid have not been fully explored. To fill the research gap, this article proposes an online hierarchical aggregate flexibility characterization method, which considers the distinct roles of the distribution system operator (DSO) and charging station operators (CSOs). Under this framework, each CSO first characterizes the aggregate EV power flexibility of its charging station as a time-decoupled flexibility region. We prove that any trajectory within the region can be disaggregated into feasible individual EV charging strategies. Upon receiving the aggregate EV power flexibility regions submitted by all CSOs, the DSO solves an optimization problem to derive the aggregate power flexibility region of the distribution network, and we also prove that any power level within this region can be disaggregated into a feasible solution satisfying the network constraints. Furthermore, to account for the impact of the latest dispatch order on future EV power flexibility, real-time feedback is designed and integrated into the hierarchical framework. Numerical experiments on a multiphase distribution network with 123 buses demonstrate the effectiveness and advantages of the proposed method. It is worth noting that the proposed online method outperforms the offline benchmark with perfect information, due to the consideration of dispatch signals.
more »
« less
A Reliability-aware Distributed Framework to Schedule Residential Charging of Electric Vehicles
Residential consumers have become active participants in the power distribution network after being equipped with residential EV charging provisions. This creates a challenge for the network operator tasked with dispatching electric power to the residential consumers through the existing distribution network infrastructure in a reliable manner. In this paper, we address the problem of scheduling residential EV charging for multiple consumers while maintaining network reliability. An additional challenge is the restricted exchange of information: where the consumers do not have access to network information and the network operator does not have access to consumer load parameters. We propose a distributed framework which generates an optimal EV charging schedule for individual residential consumers based on their preferences and iteratively updates it until the network reliability constraints set by the operator are satisfied. We validate the proposed approach for different EV adoption levels in a synthetically created digital twin of an actual power distribution network. The results demonstrate that the new approach can achieve a higher level of network reliability compared to the case where residential consumers charge EVs based solely on their individual preferences, thus providing a solution for the existing grid to keep up with increased adoption rates without significant investments in increasing grid capacity.
more »
« less
- PAR ID:
- 10385314
- Date Published:
- Journal Name:
- Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
- Page Range / eLocation ID:
- 5115 to 5121
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
In this study, we raise the concern that current understandings of user perceptions and decision-making processes may jeopardize the sustainable development of charging infrastructure and wider EV adoption. This study addresses three main concerns: (1) most research focuses solely on battery electric vehicle users, neglecting plug-in hybrid (PHEV) and non-EV owners, thus failing to identify common preferences or transitional perceptions that could guide an inclusive development plan; (2) potential factors influencing charging station selection, such as the availability of nearby amenities and the role of information from social circles and user reviews, are often overlooked; and (3) used methods cannot reveal individual items' importance or uncover patterns between them as they often combine or transform the original items. To address these gaps, we conducted a survey experiment among 402 non-EV, PHEV and EV users and applied network analysis to capture their charging station selection decision-making processes. Our findings reveal that non-EV and PHEV users prioritize accessibility, whereas EV owners focus on the number of chargers. Furthermore, certain technical features, such as vehicle-to-grid capabilities, are commonly disregarded, while EV users place significant importance on engaging in amenities while charging. We also report an evolution of preferences, with users shifting their priorities on different types of information as they transition from non-EV and PHEV to EV ownership. Our results highlight the necessity for adaptive infrastructure strategies that consider the evolving preferences of different user groups to foster sustainable and equitable charging infrastructure development and broader adoption of EVs.more » « less
-
Abstract Accurately forecasting electric vehicle (EV) charging demand is critical for managing peak loads and ensuring grid stability in regions with increasing EV adoption. Residential household peak energy usage and EV charging patterns vary significantly across areas, influenced by geographic accessibility, sociodemographic factors, charging preferences, and EV attributes. Averaging data across regions can overlook these differences, leading to an underestimation of charging demand disparities and risking grid overload during peak periods. This study introduces a spatiotemporal trip chain-based EV charging schedule simulation method to address these challenges. The methodology integrates sociodemographic and geographic data with the large language model to generate trip chains, which serve as the basis for simulating EV charging schedules and aggregating regional energy loads to forecast peak demand. A case study of Pescadero, CA employs synthetic profiles, derived from Census statistics, to model local households as EV owners and validate the practical applicability of this approach. The results emphasize the representativeness of the trip chain generation model and the effectiveness of the EV charging schedule simulation model in accurately forecasting energy consumption patterns and assessing peak load impacts. By combining sociodemographic and geographic insights, this study provides a robust tool for evaluating the peak load impacts of EV charging.more » « less
-
The limited availability of affordable and rapid charging infrastructure slows the adoption of electric vehicles (EVs). The modular multilevel converter (MMC) based approach can provide a viable solution for future extreme-fast charging (XFC) infrastructure. Additionally, distributed energy resources (DER) can be integrated to minimize grid dependency and increase availability and reliability. We propose a DER-integrated-MMC for extreme fast charger (MMC-DER-XFC) that incorporates a dual-active bridge (DAB) for EV charging and DER within the sub-module (SM). While some researchers have explored related studies, the absence of access to the SM capacitor terminals limits the integration with DAB and DER. To address this challenge, this paper proposes an innovative and optimal design of MMC-DER-XFC and implements and validates it using an OpalRT real-time simulator-based HIL testbed. This model can act as a testbed for various studies and control developments. This paper simulates a testbed with 60 EVs and DERs, but it can support up to 300 EVs and DERs. Additionally, any number of EVs and DERs can be activated or deactivated for a specified duration. Furthermore, distinct EV charging/discharging and DER generation profiles can be independently defined for each unit. The developed testbed has been evaluated using a standard MMC controller. This paper examines the internal dynamics of the MMC-DER-XFC under different scenarios: (1) only DERs are operational, (2) only EVs are functioning in either grid-to-vehicle (G2V) or vehicle-to-grid (V2G) mode, and (3) both DERs and EVs (G2V and V2G) are active. The testbed demonstrates harmonic distortion at low power levels, but these distortions are eliminated under full load conditions.more » « less
-
Private Electric Vehicles for Emergency Load Pickup: A Multi-Network Stochastic Equilibrium ApproachLarge-scale electric vehicle (EV) adoption has the potential to offer flexible and distributed energy resources to support the power distribution system (DS) during emergent incidents. We developed a network-based multiagent stochastic optimization model with equilibrium constraints (N-MSOPEC) to investigate the potential value of EVs on the emergency load pickup in DSs, considering the uncertainties of line outage and EV participation. Decentralized stakeholders from both transportation and DSs, including the distribution system operator, distributed generator owners, charging station aggregator, and EV drivers, were modeled to reflect realistic decentralized decision making. EV participation was incentivized in a market equilibrium framework for DS support. An exact convex reformulation technique was additionally developed for the proposed N-MSOPEC model, which significantly improved the computational efficiency required to solve high-dimensional complementarity problems. Simulation results on coupled transportation and distribution test systems showed how EV participation could reduce load loss under different line outages and EV adoption scenarios, and demonstrated the effectiveness of our model in capturing the interdependencies of the coupled systems. The DS characteristics and load pickup needs influenced the incentives provided to EV drivers, resulting in different charging station selections and load pickup patterns. We also investigated the value of stochastic modeling in the multiagent framework by calculating metrics such as the value of stochastic solution and the expected value of perfect information for the stakeholders involved.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- https://doi.org/10.24963/ijcai.2022/710
