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BCIT Citations Collection
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- Impact of EV penetration on Volt–VAR Optimization of distribution networks using real-time co-simulation monitoring platform
- This paper aims to investigate the impact of different Electric Vehicle (EV) penetration on quasi real-time Volt–VAR Optimization (VVO) of smart distribution networks. Recent VVO solutions enable capturing data from Advanced Metering Infrastructure (AMI) in quasi real-time to minimize distribution networks loss costs and perform Conservation Voltage Reduction (CVR) to save energy. The emergence of EVs throughout distribution feeder increases grid complexity and uncertainty levels that could affect AMI-based VVO objectives. Hence, this paper primarily introduces an AMI-based VVO engine, able to minimize grid loss and Volt–VAR control assets operating costs while maximizing CVR benefit. It then presents a real-time co-simulation platform comprised of the VVO engine, grid model in a real-time simulator and monitoring platform, communicating with each other through DNP.3 protocol, to test the precision and performance of AMI-based VVO in presence of different EV penetration levels. Accordingly, 33-node distribution feeder is studied through different EV penetration scenarios. The results show significant changes in AMI-based VVO performance especially in CVR sub-part of VVO according to EV model and type. Thus, this study could lead near future VVO solutions to gain higher levels of accuracy and efficiency considering smart microgrid components such as EV in their models., Article, Published. Received 27 November 2015, Revised 8 January 2016, Accepted 22 January 2016, Available online 16 February 2016.
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- Smart grid adaptive energy conservation and optimization engine utilizing Particle Swarm Optimization and Fuzzification
- This paper aims to present a novel smart grid adaptive energy conservation and optimization engine for smart distribution networks. The optimization engine presented in this paper tries to minimize distribution network loss, improve voltage profile of the system and minimize the operating cost of reactive power injection by switchable shunt Capacitor Banks using Advanced Metering Infrastructure data. Moreover, it performs Conservation Voltage Reduction (CVR) and minimizes transformer loss. To accurately weight the optimization engine objective function sub-parts, Fuzzification technique is employed in this paper. Particle Swarm Optimization (PSO) is applied as Volt-VAR Optimization (VVO) algorithm. Substantial benefits of the proposed energy conservation and optimization engine include but not limited to: adequate accuracy and speed, comprehensive objective function, capability of using AMI data as inputs, and ability to determine weighting factors according to the cost of each objective sub-part. To precisely test the applicability of proposed engine, 33-node distribution feeder is used as case study. The result analysis shows that the proposed approach could lead distribution grids to achieve higher levels of optimization and efficiency compared with conventional techniques., Article, Published. Received 27 November 2015, Revised 13 April 2016, Accepted 16 April 2016, Available online 26 April 2016.
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- Smart grid adaptive volt-VAR optimization
- In recent years, smart grid technologies such as Distribution Management Systems (DMS) and Advanced Metering Infrastructure (AMI) have created remarkable opportunities for distribution grids in terms of operation, control and optimization. The advent of AMI has created considerable amount of data that can be used in optimization applications. Other smart grid functionalities could increase the performance of energy conservation and optimization solutions. As such, this paper aims to review the main requirements of two important smart grid adaptive energy conservation and optimization solutions called Volt-VAR Optimization and Conservation Voltage Reduction, in terms of control, measurement, communication and standards for grids., Article, Published. Received 13 May 2016, Revised 13 September 2016, Accepted 22 September 2016, Available online 3 October 2016.