Pavan Balram

Effects of plug-in electric vehicle charge scheduling on the day-ahead electricity market price

With the advent of plug-in electric vehicles (EV), it becomes increasingly important to study the impact of charge scheduling of a large number of EVs on the day-ahead electricity market price. In this paper, two scheduling models are proposed-joint scheduling model and aggregator scheduling model. They are used to study the effects of scheduling of EV charging on the day-ahead market price of electricity at various penetration levels of the EVs. Results from studies on an IEEE 30-bus test system show that, at lower penetration of EVs, a simple scheduling method using fixed-period charging during low demand periods can be used, without a large increase in market price. But at higher penetration levels, the opposite holds true, requiring advanced scheduling methods such as proposed in this paper. Between the two, joint scheduling model results in a lower increase in electricity price and is further tested on a simplified Nordic day-ahead market model. An overall conclusion is that the Nordic day-ahead market can accommodate large penetration of EVs without a significant increase in market price.

Predictive voltage control of batteries and tap changers in distribution system with photovoltaics

This paper proposes a model predictive control approach for coordinated secondary voltage control of on-load tap changing transformers and battery energy storage systems in a distribution system to maintain the bus voltage levels in the presence of photovoltaic generation. Optimal control actions are obtained based on a quadratic objective function with linear constraints. The control actions are implemented in a case study using a modified CIGRÉ European low voltage distribution network with corresponding models of the constituent devices and their local controllers. The results indicate that battery energy storage system could aid the system voltages and reduce the number of transformer tap operations if the control is performed in a coordinated manner enabled by the model predictive control framework.

Centralized charging control of plug-in electric vehicles and effects on day-ahead electricity market price

Global policy targets to reduce greenhouse gas emissions have led to increased interest in plug-in electric vehicles (PEV) and their integration into the electricity network. Existing electricity markets, however, are not well suited to encourage direct participation of flexible demand from small consumers such as PEV owners. The introduction of an aggregator agent with the functions of gathering, aggregating, controlling and representing the energy needs of PEV owners in the electricity market could prove useful in this regard. In this chapter, a mathematical model of PEV aggregator for participation in the day-ahead electricity market is described. The modeling is done by treating each of the individual vehicle batteries as a single large battery. The centralized charging and discharging of this battery is then scheduled based on the traveling needs of the PEV owners determined by an aggregated driving profile and the cumulative electrical energy needs of vehicles over the optimization horizon. Two methods for scheduling PEV demand named as joint scheduling method (JSM) and aggregator scheduling method (ASM) are presented. The two methods are subsequently used to observe the effects of introducing flexible scheduling of PEVs on the day-ahead market price in an IEEE test system and a Nordic test system. Results from the IEEE test system case studies will indicate that the scheduling of PEV energy through direct centralized control at high PEV penetration levels of 50 % or greater could lead to potential lowering of day-ahead market prices as compared to an indirect control method such as the use of fixed period charging. Results from the Nordic test system case study shows that controlled scheduling of PEV demand could lead to only a small increase in day-ahead market price of electricity.