Zhaomiao Guo

The long-distance evacuation of electric vehicles (EVs) presents significant challenges for disaster management owing to their limited driving range and constrained charging infrastructure. EVs with long charging times can create negative externalities for all other EVs waiting in queues, especially during evacuation scenarios. This study investigates the use of express charging lanes to reduce overall evacuation delays. We propose optimization models to optimize the allocation of charging plugs for express and regular charging, considering both user-equilibrium and system-optimal scenarios. To account for heterogeneities and uncertainties, such as stochastic EV arrival patterns and variable charging demands, we further develop numerical simulation models to quantify the delay distribution. We found that separating EVs with lower charging demand had the potential to minimize total system delay. The proposed models identified the optimal level of express charging plug allocation to minimize the total charging delay without centralized enforcement of traffic distribution. In addition, the models could enable government agencies to estimate the required charging resources to fulfill an evacuation within a given time window. The insights generated by the proposed theoretical models were validated using agent-based simulation, in which uncertainties could be flexibly represented.

Large-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.

A large-scale electric vehicles (EV) penetration will consume significantly more electricity, which may overload power distribution systems and charging stations (CSs). We propose a multiagent optimization approach to study the multistage decentralized distributed generation (DG) and CS planning process considering the decision makings of the DG investors, CS aggregator, and distribution system operator (DSO). Furthermore, we develop an exact convex reformulation of the proposed multiagent optimization problem to significantly improve the computational efficiency. Through numerical examples, we found that with the increase of EV adoption, CS investors expand the CS capacities significantly towards the later years of the planning horizon, whereas the DG owners expand their generation capacities in response to both load and charging demand (CD). The model is able to describe the close coupling between stakeholders and efficiently identify the equilibrium investment patterns of DGs and CSs over space and time to satisfy the growing EV, CD, and other existing power loads. In addition, the modeling framework can allow DSO and government agencies to evaluate viable system evolvement paths and better understand the system needs of energy generation and charging infrastructure in order to achieve a higher level of EV penetration.

This paper presents a network-based multi-agent optimization model for the strategic planning of service facilities in a stochastic and competitive market. We focus on the type of service facilities that are of intermediate nature, i.e., users may need to deviate from the shortest path to receive/provide services in between the users' planned origins and destinations. This problem has many applications in emerging transportation mobility, including dynamic ride-sharing hub design and competitive facility location and allocation problems for alternative fuel vehicle refueling stations. The main contribution of this paper is establishing a new multi-agent optimization framework considering decentralized decision makings of facility investors and users over a transportation network and providing rigorous analyses of its mathematical properties, such as uniqueness and existence of system equilibrium. In addition, we develop an exact convex reformulation of the original multi-agent optimization problems to overcome computational challenges brought by non-convexity. Extensive analysis on case studies showed how the proposed model can capture the complex interaction between different stakeholders in an uncertain environment. Additionally, our model allowed quantifying the value of stochastic modeling and information availability by exploring stochastic metrics, including value of stochastic solution (VSS) and expected value of perfect information (EVPI), in a multi-agent framework.