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Corrective receding horizon EV charge scheduling using short-term solar forecasting
Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution
Smart Vehicle to Grid Interface Project: Electromobility Management System Architecture and Field Test Results
This paper presents and discusses the electromobility management system
developed in the context of the SMARTV2G project, enabling the automatic
control of plug-in electric vehicles' (PEVs') charging processes. The paper
describes the architecture and the software/hardware components of the
electromobility management system. The focus is put in particular on the
implementation of a centralized demand side management control algorithm, which
allows remote real time control of the charging stations in the field,
according to preferences and constraints expressed by all the actors involved
(in particular the distribution system operator and the PEV users). The results
of the field tests are reported and discussed, highlighting critical issues
raised from the field experience.Comment: To appear in IEEE International Electric Vehicle Conference (IEEE
IEVC 2014
Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems
Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management
Electric Vehicles Charging Control based on Future Internet Generic Enablers
In this paper a rationale for the deployment of Future Internet based
applications in the field of Electric Vehicles (EVs) smart charging is
presented. The focus is on the Connected Device Interface (CDI) Generic Enabler
(GE) and the Network Information and Controller (NetIC) GE, which are
recognized to have a potential impact on the charging control problem and the
configuration of communications networks within reconfigurable clusters of
charging points. The CDI GE can be used for capturing the driver feedback in
terms of Quality of Experience (QoE) in those situations where the charging
power is abruptly limited as a consequence of short term grid needs, like the
shedding action asked by the Transmission System Operator to the Distribution
System Operator aimed at clearing networks contingencies due to the loss of a
transmission line or large wind power fluctuations. The NetIC GE can be used
when a master Electric Vehicle Supply Equipment (EVSE) hosts the Load Area
Controller, responsible for managing simultaneous charging sessions within a
given Load Area (LA); the reconfiguration of distribution grid topology results
in shift of EVSEs among LAs, then reallocation of slave EVSEs is needed.
Involved actors, equipment, communications and processes are identified through
the standardized framework provided by the Smart Grid Architecture Model
(SGAM).Comment: To appear in IEEE International Electric Vehicle Conference (IEEE
IEVC 2014
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
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