Evaluation of the utilization of electric vehicles for building energy management in hotels

Governments are working in new policies to slow down total energy consumption and greenhouse gases (GHG) emissions, promoting the deployment of electric vehicles (EVs) in all countries. In order to facilitate this deployment and help to reduce the final costs of their batteries, additional utilization of EVs when those are parked has been proposed. EVs can be used to minimize the total electricity cost of buildings (named vehicle to building applications, V2B). In this paper an economic evaluation of EVs in the Building Energy Management System is shown. The optimal storage capacity and its equivalent number of EVs are determined. This value is then used for determining the optimal charging schedule to be applied to the batteries. From this schedule, the total expected profit is derived for the case of a real hotel in Spain

Modeling and coordinated control for integrating electric vehicles into the power grid

This paper introduces a framework for the integration of renewable energy generation units and electric vehicle into smart grid, which takes into account the setting up of the PHEV recharging infrastructure and modern power system. The impact of recharging a large amount of PHEVs on the existing power system is estimated considering the PHEV characteristics and the driving pattern of the vehicle owners. Three scenarios for uncontrolled and controlled charging are derived to investigate the impact in terms of power quality. The simulation results show the necessity to coordinate the PHEV recharging with the power network condition. Therefore an optimal algorithm is also designed to minimize the power losses based on the hierarchical structure of the proposed framework. The aggregation of PHEVs is expected to act as a controllable load or resource. Both of the battery charging and discharging are comprised in the optimal algorithm to achieve better performance in the V2G operation. © 2011 IEEE.published_or_final_versionThe 2011 International Conference on Electrical Machines and Systems (ICEMS 2011), Beijing, China, 20-23 August 2011. In Proceedings of ICEMS, 2011, p. 1-

Energy Management Algorithm for Resilient Controlled Delivery Grids

Resilience of the power grid is most challenged at power blackouts since the issues that led to it may not be fully resolved by the time the power is back. In this paper, a Real-Time Energy Management Algorithm (RTEMA) has been developed to increase the resilience of power systems based on the controlled delivery grid (CDG) concept. In a CDG, loads communicate with a central controller, periodically sending requests for power. The central controller runs an algorithm, based on which it may decide whether to grant the requested energy fully or partially. Therefore, the CDG limits loads discretionary access to electric energy until all problems are resolved. The developed algorithm aims at granting most or all of the requested loads, while maintaining the health of the power system (i.e. the voltage at each bus, and the line loading are within acceptable limits), and minimizing the overall losses. An IEEE 30-bus standard Test Case, encountering a blackout condition, with high penetration of microgrids, has been used to test the developed algorithm. Results proved that the developed algorithm with the CDG have the potential to substantially increase the resilience of power systems

A Complex Network Approach for the Estimation of the Energy Demand of Electric Mobility

We study how renewable energy impacts regional infrastructures considering the full deployment of electric mobility at that scale. We use the Sardinia Island in Italy as a paradigmatic case study of a semi-closed system both by energy and mobility point of view. Human mobility patterns are estimated by means of census data listing the mobility dynamics of about 700,000 vehicles, the energy demand is estimated by modeling the charging behavior of electric vehicle owners. Here we show that current renewable energy production of Sardinia is able to sustain the commuter mobility even in the theoretical case of a full switch from internal combustion vehicles to electric ones. Centrality measures from network theory on the reconstructed network of commuter trips allows to identify the most important areas (hubs) involved in regional mobility. The analysis of the expected energy flows reveals long-range effects on infrastructures outside metropolitan areas and points out that the most relevant unbalances are caused by spatial segregation between production and consumption areas. Finally, results suggest the adoption of planning actions supporting the installation of renewable energy plants in areas mostly involved by the commuting mobility, avoiding spatial segregation between consumption and generation areas

Electric vehicles on-board battery charger for the future smart grids

The recent and massive investments in Electric Vehicles (EVs) reveal a change of paradigm in the transports sector and the proliferation of EVs will contribute to an effective reduction in the emissions of greenhouse gases. Nevertheless, for the electrical power grids EVs will be extra loads, which will require the demand energy to charge their batteries. With the advent of the Smart Grids, besides the usual battery charging mode (Grid-to-Vehicle G2V), where the batteries receives energy from the power grid, arises a new concept for the users of EVs and for the power grid market, denominated as Vehicle-to-Grid (V2G). In the V2G operation mode, EVs return to the power grid part of the energy stored in their batteries. The V2G concept requires the use of battery chargers for the EVs with bidirectional power flow capability and bidirectional communication with the Smart Grids through Information and Communication Technology (ICT) applications. It is important to highlight that the proliferation of EVs and the impact of their battery chargers on the power grid quality is a matter of concern, since conventional chargers present current harmonics and power factor problems. In this paper it is presented the preliminary studies resulting from a PhD work about a bidirectional battery charger for EVs, which was designed to operate in collaboration with the power grid as G2V and V2G through an ICT application. In this way, it is expectable to contribute to the technological innovation of the electric mobility in Smart Grids. To assess the behavior of the proposed battery charger under different scenarios of operation, a prototype has been developed, and some simulation and experimental results of the battery charger are presented. Fundação para a Ciência e a Tecnologia (FCT); FEDER Funds, through the Operational Programme for Competitiveness Factors COMPETE, and by National Funds through FCT Foun- dation for Science and Technology of Portugal, under the projects: FCOMP-01-0124- FEDER-022674 an d PTDC/EEA-EEL/104569/2008 Document type: Part of book or chapter of boo

Optimal Decentralized Protocols for Electric Vehicle Charging

We propose decentralized algorithms for optimally scheduling electric vehicle charging. The algorithms exploit the elasticity and controllability of electric vehicle related loads in order to fill the valleys in electric demand profile. We formulate a global optimization problem whose objective is to impose a generalized notion of valley-filling, study properties of the optimal charging profiles, and give decentralized offline and online algorithms to solve the problem. In each iteration of the proposed algorithms, electric vehicles choose their own charging profiles for the rest horizon according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. The offline algorithms are guaranteed to converge to optimal charging profiles irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles at the expense of a restrictive assumption that all electric vehicles are available for negotiation at the beginning of the planning horizon. The online algorithms relax this assumption by using a scalar prediction of future total charging demand at each time instance and yield near optimal charging profiles. The proposed algorithms need no coordination among the electric vehicles, hence their implementation requires low communication and computation capability. Simulation results are provided to support these results