Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks

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.Comment: Submitted to IEEE Transactions on Control of Network Systems on June 1st 201

Network-constrained models of liberalized electricity markets: the devil is in the details

Numerical models for electricity markets are frequently used to inform and support decisions. How robust are the results? Three research groups used the same, realistic data set for generators, demand and transmission network as input for their numerical models. The results coincide when predicting competitive market results. In the strategic case in which large generators can exercise market power, the predicted prices differed significantly. The results are highly sensitive to assumptions about market design, timing of the market and assumptions about constraints on the rationality of generators. Given the same assumptions the results coincide. We provide a checklist for users to understand the implications of different modelling assumptions

Electricity transmission: an overview of the current debate

Electricity transmission has emerged as critical for successfully liberalising powermarkets. This paper surveys the issues currently under discussion and provides a framework for the remaining papers in this issue. We conclude that signalling the efficient location of generation investment might require even a competitive LMP system to be complemented with deep connection charges. Although a Europe-wide LMP system is desirable, it appears politically problematic, so an integrated system of market coupling, possibly evolving by voluntary participation, should have high priority. Merchant investors may be able to increase interconnector capacity, although this is not unproblematic and raises new regulatory issues. A key issue that needs further research is how to better incentivize TSOs, especially with respect to cross-border issues

Multi-objective road pricing: a cooperative and competitive bilevel optimization approach

Costs associated with traffic externalities such as congestion, air pollution, noise, safety, etcetera are becoming unbearable. The Braess paradox shows that combating congestion by adding infrastructure may not improve traffic conditions, and geographical and/or financial constraints may not allow infrastructure expansion. Road pricing presents an alternative to combat traffic externalities. The traditional way of road pricing, namely congestion charging, may create negative benefits for society. In this effect, we develop a flexible pricing scheme internalizing costs arising from all externalities. Using a game theoretical approach, we extend the single authority road pricing scheme to a pricing scheme with multiple authorities/regions (with likely contradicting objectives)

Market-based Investment in Electricity Transmission Networks: Controllable Flow

This paper discusses unregulated market-based electricity transmissio

The Green Choice: Learning and Influencing Human Decisions on Shared Roads

Autonomous vehicles have the potential to increase the capacity of roads via platooning, even when human drivers and autonomous vehicles share roads. However, when users of a road network choose their routes selfishly, the resulting traffic configuration may be very inefficient. Because of this, we consider how to influence human decisions so as to decrease congestion on these roads. We consider a network of parallel roads with two modes of transportation: (i) human drivers who will choose the quickest route available to them, and (ii) ride hailing service which provides an array of autonomous vehicle ride options, each with different prices, to users. In this work, we seek to design these prices so that when autonomous service users choose from these options and human drivers selfishly choose their resulting routes, road usage is maximized and transit delay is minimized. To do so, we formalize a model of how autonomous service users make choices between routes with different price/delay values. Developing a preference-based algorithm to learn the preferences of the users, and using a vehicle flow model related to the Fundamental Diagram of Traffic, we formulate a planning optimization to maximize a social objective and demonstrate the benefit of the proposed routing and learning scheme.Comment: Submitted to CDC 201

Effects of decision-making on the transport costs across complex networks

We analyze the effects of agents' decisions on the creation of congestion on a centralized network with ring-and-hub topology. We show that there are two classes of agents each displaying a distinct set of behaviours. The dynamics of the system are driven by an interplay between the formation of, and transition between, unique stable states that arise as the network is varied. We show how the flow of objects across the network can be understood in terms of the ordering and allocation of strategies. Our results show that the existence of congestion in a network is a dynamic process that is as much dependent on the agents' decisions as it is on the structure of the network itself.Comment: Special Issue on Complex Networks, edited by Dirk Helbin

From Packet to Power Switching: Digital Direct Load Scheduling

At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.Comment: Accepted by the IEEE journal of Selected Areas in Communications (JSAC): Smart Grid Communications series, to appea