Integrated pricing framework for optimal power and semi‐dynamic traffic flow problem

Abstract

Large‐scale fast charging of electric vehicles inextricably entwines the operation of power and transportation systems. This study examines the integrated pricing of electricity and roads to maximise the social welfare associated with the coupled systems, i.e. minimising the total travel time and energy generation costs. While electric demand and transportation network conditions vary significantly over the day, most of the work, however, considers only static network flows for a single time period. This study proposes a multi‐period integrated pricing model that can describe the variations of electricity and travel demands. Specifically, the distribution of traffic flows is characterised by a semi‐dynamic traffic assignment model to consider flow propagation between adjacent time periods. To determine the optimal prices of electricity and road tolls, a marginal‐cost pricing scheme based on the first‐best tolls and locational marginal prices is developed. Mathematically, the multi‐period pricing model is formulated as a convex optimisation problem. Moreover, a decentralised collaborative pricing framework is developed for independent power and transportation system operators to attain the optimal pricing strategies. Numerical experiments are conducted to demonstrate the effectiveness of the pricing method.

Collaborative Pricing in a Power-Transportation Coupled Network: A Variational Inequality Approach

“…Note that the DLMP is determined based on the charging demand at the different nodes of the grid, which is affected by EV driving patterns. As proved in [2], [14], efficient DLMPs and congestion prices can be obtained from the dual variables of the model (32). By observing the model formulation, there are three main challenges when attempting to solve it efficiently:…”

“…In practice, regulating traffic and power flow can realize the social optimum of PTCNs, which is usually achieved by price making [14]. Instead of considering prices as static [8]- [10], a number of studies treated the prices for the EV drivers as either the locational marginal price (LMP) [6], [15], [16] or the retail price [17], [18].…”

“…Furthermore, [19], [20] investigated the road pricing mechanism for managing road congestion and routing individual drivers to improve social welfare. By jointly considering the PDN and TN, several studies proposed integrated pricing strategies [2], [14], [21], in which the toll prices of the TNs and the electricity prices of PDNs were determined collaboratively.…”

“…Despite the great progress in the integrated pricing of the PTCNs, there are several research gaps. 1) Most existing research omitted the joint participation of EVs and gasoline vehicles (GVs) in such a pricing problem [2], [9], [14], [21]- [22]. However, this may be improper in the current case where multiple vehicles co-exist in urban cities to satisfy different traffic demands.…”

Optimal semi‐dynamic traffic and power flow assignment of coupled transportation and power distribution systems for electric vehicles

“…The second type of system-level scheduling was typically made up of social operators as the decision-makers. Current literature primarily used electricity prices and road tolls to guide the large-scale traffic flow of EVs [18]. Geng et al [19] proposed a distributed-set retail electricity price model to improve the voltage quality of PDN and reduce the distribution operating costs.…”