BCIT Citations Collection | BCIT Institutional Repository

BCIT Citations Collection

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.
A novel Volt-VAR Optimization engine for smart distribution networks utilizing Vehicle to Grid dispatch
In recent years, Smart Grid technologies such as Advanced Metering, Pervasive Control, Automation and Distribution Management have created numerous control and optimization opportunities and challenges for smart distribution networks. Availability of Co-Gen loads and/or Electric Vehicles (EVs) enable these technologies to inject reactive power into the grid by changing their inverter’s operating mode without considerable impact on their active power operation. This feature has created considerable opportunity for distribution network planners to explore if EVs could be used in the distribution network as reliable VAR suppliers. It may be possible for network operators to employ some EVs as VAR suppliers for future distribution grids. This paper proposes an innovative Smart Grid-based Volt-VAR Optimization (VVO) engine, capable of minimizing system power loss cost as well as the operating cost of switched Capacitor Banks, while optimizing the system voltage using an improved Genetic Algorithm (GA) with two levels of mutation and two levels of crossover. The paper studies the impact of EVs with different charging and penetration levels on VVO in different operating scenarios. Furthermore, the paper demonstrates how a typical VVO engine could benefit from V2G’s reactive power support. In order to assess V2G impacts on VVO and test the applicability of the proposed VVO, revised IEEE-123 Node Test Feeder in presence of various load types is used as case study., Article, Published. Received 24 May 2014, Revised 23 July 2015, Accepted 29 July 2015, Available online 8 August 2015.
Quasi real-time ZIP load modeling for Conservation Voltage Reduction of smart distribution networks using disaggregated AMI data
This paper aims to investigate quasi real-time ZIP load models for new Smart Grid-based Volt-VAR Optimization (VVO) techniques. As recent VVO solutions are able to perform in quasi real-time using Advanced Metering Infrastructure (AMI) data, more accurate load modeling could give distribution network operators and/or planners more precise Conservation Voltage Regulation (CVR) and energy saving values at each operating time stage. Furthermore, more accurate load modeling of each quasi real-time stage could improve VVO efficiency. As type, amount and operating time of each residential appliance varies throughout a day, this paper aims to discover ZIP load model of each quasi real-time stage separately through disaggregated data (i.e. decomposing residential load consumption into home appliance consumptions). This paper shows that the energy conservation achieved by CVR operation through presented quasi real-time ZIP load modeling could lead AMI-based VVO solutions to higher level of accuracy and data resolution compared with conventional techniques. Therefore, this paper primarily introduces a new quasi real-time AMI-based VVO engine. Then, it investigates ZIP load model of each quasi real-time stage through statistical data to conserve energy consumption. To check the authenticity and the applicability of presented model in a whole system, 33-node distribution feeder is employed., Published. Received 16 March 2015, Revised 14 May 2015, Accepted 3 June 2015, Available online 2 July 2015.
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.
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.