This paper presents a method for tracking a secondary frequency control (Load Frequency Control) signal by groups of plug-in hybrid electric vehicles (PHEVs), controllable thermal household appliances under a duty-cycle coordination scheme, and a decentralized combined-heat-and-power generation unit. The distribution of the control action on the participating units is performed by an aggregator utilizing a Model Predictive Control strategy which allows the inclusion of unit and grid constraints. In addition to the individual dynamic behavior, the varying availability of the units during the day is taken into account. The proposed methodology, easily extendable to larger networks, is evaluated on a four-bus system corresponding to a medium-voltage distribution grid and illustrates a possible operation mode of an aggregator in the power system.Index Terms-Aggregators, cogeneration, electric appliances, Load Frequency Control (LFC), load management, plug-in hybrid electric vehicles (PHEVs), smart grids, vehicle to grid (V2G), virtual power plants.

Deployment of PHEV will initiate an integration of transportation and power systems. Intuitively, the PHEVs will constitute an additional demand to the electricity grid, potentially violating converter or line capacities when recharging. Smart management schemes can alleviate possible congestions in power systems, intelligently distributing available energy. As PHEV are inherently independent entities, an agent based approach is expedient. Nonlinear pricing will be adapted to model and manage recharging behavior of large numbers of autonomous PHEV agents connecting in one urban area modelled as an energy hub. The scheme will incorporate price dependability. An aggregation entity, with no private information about its customers, will manage the PHEV agents whose individual parameters will be based on technical constraints and individual objectives. Analysis of the management scheme will give implications for PHEV modelling and integration schemes as well as tentative ideas of possible repercussions on power systems.

No abstract

Introduction of Plug-in Hybrid Electric Vehicles (PHEVs) could potentially trigger a stepwise electrification of the whole transportation sector. But the impact on the electric grid by electrical vehicle charging is still not fully known. This paper investigates several PHEV charging schemes, including smart charging, using a novel iterative approach. An agent based traffic demand model is used for modeling the electrical demand of PHEVs over the day. For modeling the different parts of the electric grid, an approach based on interconnected multiple energy carrier systems is used. For a given charging scheme the power system simulation gives back a price signal indicating whether grid constraints, such as maximum power output at hub transformators, have been violated. This leads to a corrective step in the iterative process, until a charging pattern is found, which does not violate grid constraints. The proposed system allows to investigate existing electric grids, whether they are capable of meeting increased electricity demand by certain future PHEV penetration. Furthermore, in the future, different types of smart charging schemes can be added into the system for comparison.

No abstract