Browsing by Author "Mosaddegh, Abolfazl"

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Distributed Computing Architecture for Optimal Control of Distribution Feeders With Smart Loads
(Institute of Electrical and Electronics Engineers (IEEE), 2016-09-28) Mosaddegh, Abolfazl; Canizares, Claudio A.; Bhattacharya, Kankar; Fan, Hongbing
This paper presents a distributed computing architecture for solving a distribution optimal power flow (DOPF) model based on a smart grid communication middleware (SGCM) system. The system is modeled as an unbalanced three-phase distribution system, which includes different kind of loads and various components of distribution systems. In this paper, fixed loads are modeled as constant impedance, current and power loads, and neural network models of controllable smart loads are integrated into the DOPF model. A genetic algorithm is used to determine the optimal solutions for controllable devices, in particular load tap changers, switched capacitors, and smart loads in the context of an energy management system for practical feeders, accounting for the fact that smart loads consumption should not be significantly affected by network constraints. Since the number of control variables in a realistic distribution power system is large, solving the DOPF for real-time applications is computationally expensive. Hence, to reduce computational times, a decentralized system with parallel computing nodes based on an SGCM system is proposed. Using a “MapReduce” model, the SGCM system runs the DOPF model, communicates between master and worker computing nodes, and sends/receives data among different parts of parallel computing system. Compared to a centralized approach, the proposed architecture is shown to yield better optimal solutions in terms of reducing energy losses and/or energy drawn from the substation within adequate practical run-times for a realistic test feeder.
Optimal Demand Response for Distribution Feeders With Existing Smart Loads
(Institute of Electrical and Electronics Engineers (IEEE), 2017-03-23) Mosaddegh, Abolfazl; Canizares, Claudio A.; Bhattacharya, Kankar
Load characteristics play an important role in distribution systems, which are traditionally designed to supply peak load; hence, decreasing this peak can considerably reduce overall grid costs. Basic components of smart grids such as smart meters allow two-way communication between the utilities and customers; in this context, controllable smart loads are being introduced, which allow developing and implementing energy management systems for customers and distribution feeders. Therefore, this paper studies the impact of existing smart loads, in particular Peaksaver PLUS (PS+) loads in ON, Canada, to reduce summer peak loads for distribution feeders. A neural network model of controllable loads is developed and integrated into an unbalanced distribution optimal power flow (DOPF) model to optimally control tap changers and switched capacitors, as well as sent signals to programmable thermostats of air conditioners in residential buildings, in particular those associated with the PS+ program. The developed integrated DOPF is tested and validated using a practical system, demonstrating the benefits of using existing controllable loads to optimally operate distribution feeders.
Smart Residential Load Simulator for Energy Management in Smart Grids
(Institute of Electrical and Electronics Engineers (IEEE), 2018-03-22) Gonzalez Lopez, Juan Miguel; Pouresmaeil, Edris; Canizares, Claudio A.; Bhattacharya, Kankar; Mosaddegh, Abolfazl; Solanki, Bharatkumar V.
This paper describes the development of a freeware smart residential load simulator to facilitate the study of residential energy management systems in smart grids. The proposed tool is based on MATLAB-Simulink-GUIDE toolboxes and provides a complete set of user-friendly graphical interfaces to properly model and study smart thermostats, air conditioners, furnaces, water heaters, stoves, dishwashers, cloth washers, dryers, lights, pool pumps, and refrigerators, whose models are validated with actual measurements. Wind and solar power generation as well as battery sources are also modeled, and the impact of different variables, such as ambient temperature and household activity levels, which considerably contribute to energy consumption, are considered. The proposed simulator allows modeling of appliances to obtain their power demand profiles, thus helping to determine their contribution to peak demand, and allowing the calculation of their individual and total energy consumption and costs. In addition, the value and impact of generated power by residential sources can be determined for a 24-h horizon. This freeware platform is a useful tool for researchers and educators to validate and demonstrate models for energy management and optimization, and can also be used by residential customers to model and understand energy consumption profiles in households. Some simulation results are presented to demonstrate the performance and application of the proposed simulator.