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.

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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.

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The electrification of transportation is seen as one of the solutions to challenges such as global warming, sustainability, and geopolitical concerns on the availability of oil. From the perspective of power systems, an introduction of plug‐in electric vehicles presents many challenges but also opportunities to the operation and planning of power systems. On the one hand, if vehicles are considered regular loads without flexibility, uncontrolled charging can lead to problems at different network levels endangering secure operation of installed assets. However, with direct or indirect control approaches the charging of vehicles can be managed in a desirable way, e.g., shifted to low‐load hours. Furthermore, vehicles can be used as distributed storage resources to contribute to ancillary services for the system, such as frequency regulation and peak‐shaving power or help integrate fluctuating renewable resources. All these modes of operation need appropriate regulatory frameworks and market design if the flexibility of the vehicles is to be capitalized. In most of the proposed approaches, a so‐called aggregator could be in charge of directly or indirectly controlling the charging of vehicles and serve as an interface with other entities such as the transmission system operator or energy service providers. However, fully decentralized schemes without an aggregator are also conceivable, for instance, to provide primary frequency control. Communication also plays a key role, as in most of the control schemes a significant amount of information needs to be transmitted between vehicles and control entities. The management of electric vehicles as distributed resources fits well in the paradigm of smart grids, where an advanced use of communication technologies and metering infrastructure, increased controllability and load flexibility, and a larger share of fluctuating and distributed resources are foreseen. This article is categorized under: Energy Infrastructure > Economics and Policy Energy Systems Economics > Systems and Infrastructure Energy Systems Analysis > Systems and Infrastructure

The strive for sustainable energy systems will most likely lead to an increased use of renewable energy sources which to a large extent are fluctuating. Therefore, a larger demand for balancing capacity will be required to maintain system security. Plug-in Hybrid Electric Vehicles (PHEV) represent a potential storage which could offer additional balancing power. Here, the applicability of PHEVs for this purpose is investigated. The study is performed in a system including a wind farm. The PHEV storage management approaches are based on a heuristic-and on a model predictive control scheme. They are compared in case studies. It is shown that the heuristic scheme is scalable while both schemes are able to balance the forecast error of renewable sources to an acceptable level.Index Terms-PHEV, V2G, balancing energy, MPC, wind power forecast, energy hub, heuristic