Energy trading among electric vehicles based on Stackelberg approaches: A review

This paper analyses user preferences related to electric vehicle (EV) charging decisions. The analysis includes both a qualitative as well as a quantitative assessment. The qualitative assessment consists of a literature review of existing studies about EV charging behaviour and an analysis of semi-structured interviews with Danish EV users. This assessment identifies the most relevant factors for charging. In addition, it highlights that the time horizon of the charging decision is an essential factor. We use this to design two stated-choice (SC) experiments explicitly taking these findings into account. The first SC experiment looks into long-term decisions related to regular charging practices. The second SC experiment captures decisions related to occasional charging needs on longer trips. The SC data consist of three responses from 558 EV owners for each of the two experiments. We model the data using mixed logit models to allow for unobserved heterogeneity and the repeated nature of the SC data. The main findings indicate a high preference for all-inclusive/flat fee pricing structures. Moreover, they show that respondents value interoperability between charging networks, both nationally and internationally. Related to occasional charging, we find willingness-to-pay measures describing the detour respondents are willing to drive to obtain lower costs, a higher likelihood of available chargers, and facilities at the charging location. Overall the analyses highlight many factors that influence the charging decisions of consumers that are relevant for policymakers and charging operators. In addition, they highlight the barriers in a charging context, such as network roaming, lack of charging etiquette, and congestion at chargers. 

User preferences for EV charging, pricing schemes, and charging infrastructure

In this paper, we analyze the influence of forward trading on the volatility of spot power prices, in models where forward contracts are strategic tools used by energy producers to obtain profit security. We define volatility as the variance of the percentage change in spot power prices over a given time interval. As shown in Sapio (2008), volatility is related to stochastic fluctuations in preference and technology fundamentals, and is tuned by the price-elasticity of demand and supply, evaluated at equilibrium. We study two cases. First, we analyze the volatility implications of a model wherein the amount of forward trading is fixed, and producers compete a la Cournot. Fixed forward trading increases spot volatility, because forwards lower the spot price level, corresponding to a less elastic region of a linear demand function. However, if the amount of forward trading is endogenous, as in the two-stage model of Allaz (1992), producers can anticipate the spot market impact of stochastic shocks on fundamentals and 'sterilize' them. As a result, spot price volatility is closer to the value implied by an efficient market. Our theoretical results are illustrated by means of a simple simulation study.

The impact of forward trading on the spot power price volatility with Cournot competition

Electric vehicles (EVs) have the promising potential to be effective in mitigating greenhouse gas emissions in the transportation sector. Hence, the penetration of EVs in some countries experiences steady growth. However, the proliferation of EVs can cause negative impacts on the power distribution network (PDN) and the transportation network (TN). Thus, in this paper, a dynamic pricing strategy for electric vehicle charging stations (EVCSs) is proposed to facilitate EVCSs to earn higher profits while alleviating the potential negative impacts on both PDN and TN. First, a pricing strategy, considering the competition effect established based on the TN, is formulated to facilitate the EVCS to attract the defined competitive charging demand. Second, a two-step approach is proposed to mathematically formulate the responsiveness of demand towards the charging price. Third, EV user behaviors are incorporated based on both an admission control scheme and a queueing model to further adjust the charging demand. We have conducted simulations to verify the effectiveness of the pricing strategy in a PDN and TN coupled system, which contains approximately 2000 EVs. Results show that the proposed pricing strategy can facilitate the EVCS to gain 14.2% more net charging profit on average compared with the other three cases. Moreover, the operational stability of the EVCS can be ensured because the proposed pricing strategy can result in the least queueing length volatility with moderate profit variance compared with the other three cases. Furthermore, the proposed pricing strategy can achieve spatial load shifting by incentivizing EVs to alter their station-selection decisions to avoid possible power congestion in the electricity network.

Pricing for Electric Vehicle Charging Stations Based on the Responsiveness of Demand

The rapid growth of the electrical vehicle (EV) market over the last decade has rendered the existence and accuracy of the business models of the EV charging market a critical factor for a company’s success. To address this issue, this paper presents a quantification framework for the business models of the core participants of the EV charging market, defining the factors that directly influence their revenues and costs and providing two sets of earnings before interest and taxes (EBIT) formulas: explicit and implicit. The explicit formulas would be useful for business analytics of the current participants of the EV charging market, while the implicit could be applied by the new entrants, to make reliable predictions based on the benchmark data. These formulas include factors that have not been previously addressed in the literature such as different prices per type of charger, the annual consumed amount of energy per charger and their utilization rate among others. Finally, this research applies the defined framework on an EBIT scenario of an archetypical charge point operator, based on real-life data.

Business model quantification framework for the core participants of the EV charging market

In this article, we present the optimal strategy for public plug-in electric vehicle (PEV) charging services with multiple charging options: fast charging service and slow charging service. The PEV user make decisions by firstly considering the remaining energy, travel distance to charging station, then about the charging prices, charging rates and estimated charging time. To obtain insights of such a highly coupled system, we consider both monopoly and duopoly markets, where the two services are operated by a single service provider (SP) and two different SPs, respectively. For the monopoly market, we propose a three-stage Stackelberg game model. For the duopoly market, we propose a two-stage Bertrand competition and Cournot competition. We provided the SP’s optimal pricing strategies and decisions in the monopoly market, and the optimal pricing contract and quantity contract for SPs in the duopoly market with Bertrand competition and Cournot competition. Both the analytical and simulation results show that, in the monopoly market, offering two charging options can potentially improve the SP’s profit compared with offering one option only in the monopoly market; while in the duopoly market, it is optimal for SPs to offer the quantity contract rather than the price contract to maximize profits.

Changkun Jiang

Papers

Plug-in Electric Vehicle Charging With Multiple Charging Options: A Systematic Analysis of Service Providers’ Pricing Strategies