Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks (1511.03611v3)

Abstract: We study the system-level effects of the introduction of large populations of Electric Vehicles on the power and transportation networks. We assume that each EV owner solves a decision problem to pick a cost-minimizing charge and travel plan. This individual decision takes into account traffic congestion in the transportation network, affecting travel times, as well as as congestion in the power grid, resulting in spatial variations in electricity prices for battery charging. We show that this decision problem is equivalent to finding the shortest path on an "extended" transportation graph, with virtual arcs that represent charging options. Using this extended graph, we study the collective effects of a large number of EV owners individually solving this path planning problem. We propose a scheme in which independent power and transportation system operators can collaborate to manage each network towards a socially optimum operating point while keeping the operational data of each system private. We further study the optimal reserve capacity requirements for pricing in the absence of such collaboration. We showcase numerically that a lack of attention to interdependencies between the two infrastructures can have adverse operational effects.

• The paper proposes a collaborative scheme involving power and transportation operators, using dual decomposition to impose optimal pricing for managing EV interactions.
• Analytical results show that neglecting network coupling leads to inefficiencies, while the proposed pricing scheme achieves equilibrium approximating the social optimum.
• The research provides a roadmap for integrated EV management in urban infrastructure and highlights the need for optimal reserve capacity to ensure system security with dynamic pricing.

Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks: An Analytical Overview

The proliferation of Electric Vehicles (EVs) has introduced a significant paradigm shift in the management and interaction of power and transportation networks. The paper "Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks" explores the intricate interplay between these two infrastructure systems and proposes a solution framework for achieving a socially optimal operation through optimal pricing strategies.

Overview

The paper addresses the unique challenges posed by EVs, which couple the power and transportation networks more intimately than traditional vehicles. The central thesis is that the independent management of these networks without considering their interdependencies can lead to inefficiencies, such as congestion in transportation and power distribution, instability in electricity pricing, and suboptimal resource allocation.

Methodology

The researchers propose a system model where individual EV owners solve a decision problem akin to a shortest path problem on an extended transportation graph. This extended graph incorporates virtual arcs representing charging options, thus integrating both travel routing and charging decisions. The decision-making process of individual EVs minimizes cost based on traffic congestion and spatial variations in electricity prices due to grid congestion.

The paper constructs a collaborative scheme involving the Independent Power System Operator (IPSO) and Independent Transportation System Operator (ITSO). The framework facilitates cooperation aimed at achieving a socially optimal operational point while retaining data privacy. This is partly achieved through a dual decomposition technique that helps impose optimal pricing on both electricity and road usage.

Numerical and Analytical Results

The paper presents robust analytical results showing that failing to consider interactions between power and transportation networks can lead to adverse operational impacts. For instance, a disjoint approach, where EV power demand is assumed static, could cause oscillations in electricity prices and inefficient traffic patterns due to non-adaptive tolling strategies. The collaborative pricing scheme, by enabling iterative adjustments based on observed demand, leads to equilibrium that approximates the social optimum.

Moreover, the researchers propose the procurement of optimal reserve capacity to mitigate potential infeasibilities resulting from adjusting electricity prices in response to dynamic EV demands. The reserve capacity considerations highlight the complexities of ensuring system security when relying on market-driven pricing adjustments.

Implications

The implications of this research are multifaceted. Practically, the model offers a roadmap for city planners and network operators to integrate EVs effectively into urban infrastructure. Theoretically, it advances the understanding of cyber-physical system interactions, especially how decentralized decision-making can be harmonized to improve social welfare.

Looking ahead, the integration of more sophisticated models that incorporate real-time dynamics and stochastic demand patterns could further refine the strategies proposed. Additionally, extending the framework to consider privately-owned and competitive market dynamics in charging stations represents a significant area for future work.

Conclusion

Overall, the paper provides a comprehensive methodological approach to managing the dual-network pressures introduced by widespread EV adoption. It underscores the critical need for integrated infrastructure management and suggests practical strategies for optimizing the use of shared public resources through effective pricing mechanisms. As EVs become more prevalent, the insights derived from this paper will likely become increasingly pivotal for policy makers and infrastructure operators worldwide.