Behavioural Change in Green Transportation: Micro-Economics Perspectives and Optimization Strategies

Abstract

The increasing demand for Electric Vehicle (EV) charging is putting pressure on the power grids and capacities of charging stations. This work focuses on how to use indirect control through price signals to level out the load curve in order to avoid the power consumption from exceeding these capacities. We propose mathematical programming models for the indirect control of EV charging that aim at finding an optimal set of price signals to be sent to the drivers based on price elasticities. The objective is to satisfy the demand for a given price structure, or minimize the curtailment of loads, when there is a shortage of capacity. The key contribution is the use of elasticity matrices through which it is possible to estimate the EV drivers’ reactions to the price signals. As real-world data on relating the elasticity values to the EV driver’s behaviour are currently non-existent, we concentrate on sensitivity analysis to test how different assumptions on elasticities affect the optimal price structure. In particular, we study how market segments of drivers with different elasticities may affect the ability of the operator to both handle a capacity problem and properly satisfy the charging needs.

Pedagogical Perspectives of Interdisciplinary Teaching and Research: An Energy System Modelling Outlook in Relation to Energy Informatics

“…Starting with broad high-level models of the entire European Energy system [44], for example, it is possible to progress to local models for smart microgrids design [45] or thermal network design [46], and then to more detailed optimal power flow models for network restructuring and reconfiguration [47,48] or for virtual power plants [49]. The level of detail can then be increased by investigating more detailed models for smaller systems such as charging stations [50] and demand response [51], or buildings [52], and then moving down to detailed and specialized models of single generating units [53] or specific storage technologies [54]. Nuclear energy [55] can also be included in large energy system models to evaluate its long-term impact under different future scenarios.…”