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IEEE: The expertise to make smart grid a reality

Plug-in Hybrid Electric Vehicle
New applications in power electronics for highly integrated high-speed magnetoresistive current sensors
The usage of magnetic sensors is increasing steadily in the field of electric drives. Above all magnetoresistive (MR) sensors are experiencing a significant increase in applications in the industrial, automotive and aerospace fields. MR sensors are not only used for measuring rotational and linear motion, but also for non-contact switching applications and furthermore for highly dynamic current measurement. This is largely the result of increasingly complex demands on the sensors for high performance electrical drives. Sensors must not only be accurate and dynamic, but must also be robust under difficult operating conditions and exhibit very high reliability. Recent developments, such as the trend to electromobility, in the form of electric road vehicles and ??more electric aircraft??, are generating additional demands, with respect to compact dimensions and energy efficient operation. This combination of demands is leading to the more intensive use of magnetoresistive current sensors, compared to more traditional solutions, such as hall-effect based current sensors or shunts.
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Responsive End-User-Based Demand Side Management in Multimicrogrid Environment
This paper presents an agent-based demand side management framework (ADSMF) as an intelligent solution to shorten the supply-demand gap in microgrids by forming virtual market environments that allow neighboring microgrids to trade with each other. The framework does two essential tasks: 1) routing power from surplus locations to deficit locations by effectively coordinating with distributed energy resources (DERs); and 2) enabling customers' participation in electricity enterprise through demand response (DR). The low preference loads of the end-users are grouped into two categories, viz. shiftable and curtailable loads, according to the level of flexibility involved in their operation. Also, three smart DR options are identified to allow the customers to choose as per their requirement. The customer's participation is encouraged through a priority-based incentive mechanism. The proposed incentive mechanism uses frequency of participation of customers in DR and their contribution to benefit gained by the overall system to cater the incentives. The feasibility of the framework is demonstrated by applying on a case study network with two grid-tied microgrids simulated using Java Agent DEvelopment framework (JADE). The extensive simulation studies on the test network have proved the applicability and effectiveness of the proposed agent-based framework in mitigating the supply-demand gap in multimicrogrid environment.
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Optimal placement of vehicle-to-grid charging station in distribution system using Particle Swarm Optimization with time varying acceleration coefficient
This paper proposes optimal placement of vehicle to grid (V2G) charging station in a distribution system by using Particle Swarm Optimization with time varying coefficient (PSO-TVAC). While Electric Vehicles (EVs) will be additional load to the distribution system, utilities can use V2G to maximize total benefit including peak power providing, reliability improvement, and power loss reduction within system operating constraints. Charging stations are simulated as loads when they are charging EVs and as distributed generation when they are discharging to the grid. The optimal placement of V2G charging stations and sizes are determined at peak period. Test results on the nine bus test system render a higher total benefit than GA, Basic PSO, and PSO-TVIW.
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