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BCIT Citations Collection
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- Predictive algorithm for Volt/VAR optimization of distribution networks using Neural Networks
- Proceedings of IEEE Canadian Conference on Electrical and Computer Engineering (CCECE2014),May 2014, Toronto, Canada. Smart Grid functions such as Advanced Metering Infrastructure, Pervasive Control and Distribution Management Systems have brought numerous control and optimization opportunities for distribution networks through more accurate and reliable techniques. This paper presents a new predictive approach for Volt/VAr Optimization (VVO) of smart distribution systems using Neural Networks (NN) and Genetic Algorithm (GA). The proposed predictive algorithm is capable of predicting the load profile of target nodes a day ahead by employing the historical metrology data of Smart Meters, It can further perform a comprehensive VVO in order to minimize distribution network loss/operating costs and run Conservation Voltage Reduction (CVR) to conserve more energy. To test the merits of the proposed algorithm, British Columbia Institute of Technology north campus distribution grid is used as research case study., Conference paper, Published.
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- Real-time co-simulated platform for novel Volt-VAR Optimization of smart distribution network using AMI data
- Accepted in IEEE International Conference on Smart Energy Grid Engineering, May 2015. This paper presents a real-time co-simulated platform for novel voltage and reactive power optimization (VVO) of distribution grids through a real-time digital simulator (RTDS) in presence of a reliable communication platform. The proposed VVO engine is able to capture quasi real-time data from local Advanced Metering Infrastructure (AMI) and optimizes the distribution network for each quasi real-time stage (every 5 minutes) based on system main characteristics (i.e. active/reactive power of nodes). At each time stage, the VVO engine tries to minimize losses in the distribution network as well as to improve the voltage profile of the system. In order to test robustness, performance and the applicability of proposed Volt-VAR Optimization engine, a 33 node distribution network has been modeled and studied in a real-time Co-simulated environment by real-time simulator (RTDS) and a real communication platform with DNP.3 protocol. The preliminary results prove well-performance of proposed AMI-based VVO engine and show that the engine enables system to achieve higher level of loss/operating cost reduction through a sophisticated optimization engine compare with conventional approaches., Conference paper, Published.
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- Real-time communication platform for Smart Grid adaptive Volt-VAR Optimization of distribution networks
- Proceeding of IEEE International Conference on Smart Energy Grid Engineering (SEGE), Aug. 2015, Oshawa, ON, Canada. This paper investigates a real-time communication platform for a Smart Grid adaptive Volt-VAR Optimization (VVO) engine. Novel VVO techniques receive inputs from Advanced Metering Infrastructure (AMI) to dynamically optimize distribution networks. As communication platform design and characteristics affect Smart Grid-based VVO performance in terms of control accuracy and response time, VVO ICT studies is essential for grid planners and/or power utilities. Hence, this paper primarily introduces a real-time co-simulated environment comprised of Smart Grid adaptive VVO engine, RTDS model and system communication platform using DNP3 protocol. This platform is built to test and asses the influence of different components included in Smart Grid monitoring and control system; namely the sensors, measurement units, communication infrastructure on the operation and control of VVO. Moreover, this paper uses a real-time platform to check the robustness of the monitoring and control applications for communication network considerations such as delays and packet loss. Next, this paper investigates how such a platform could look into communication issues while taking system requirements into consideration. A 33-node distribution feeder is employed to check system performance through communication parameters such as throughput and response time., Conference paper, Published.