Browsing by Author "Schellenberg, Antony"
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- ItemOpen AccessOn optimal scheduling of residential energy management systems(2014-05-14) Beaudin, Marc; Zareipour, Hamidreza; Schellenberg, AntonyDemand response is an attractive topic due to the opportunities offered by the coming of Smart Grid, and increasing concern of greenhouse gas emissions. To increase the responsiveness of demand, residential energy management systems (REMS) have been proposed to automate and schedule electric energy consumption within a dwelling. This work addresses several challenges and implications associated to REMS in the electricity grid. First, this work presents a framework for modelling residential electric load and tariff structures for creating optimal consumption schedules for electrical devices in a dwelling. Second, this work provides a framework for evaluating a multi-criteria performance of REMS, and compares the performance of REMS under various electricity tariff structures. Third, this work presents two scheduling algorithms that reduces the impact of scheduling errors and provides analysis on the improvement on computational tractability. Numerical simulations based on real-life data are provided. Each of the simulations includes optimizing the energy consumption patterns of several load types, subject to forecasting errors and variable electricity prices. The results allow us to describe the trade-offs between solution quality and various parameters.
- ItemOpen AccessProbabilistic optimal power flow enhancements and applications(2008) Tamtum, Ali; Rosehart, William Daniel; Schellenberg, Antony
- ItemOpen AccessProbablistic and stochastic optimal power flow(2006) Schellenberg, Antony; Rosehart, William Daniel
- ItemOpen AccessResidential demand management in smart grids(2012) Wai, Chon Hou; Zareipour, Hamidreza; Schellenberg, AntonyThe new generation of electricity grid, the Smart Grid, has the promise to bring further efficiency, security and reliability to the power system. With the integration of communication network, smart meters and other intelligent technologies, power companies can develop demand response programs that allow their customers to participate in the power market. Smart meters usually collect electricity consumption data at hourly basis. The data can not reveal the true inter-hour electricity-use dynamics, which is important in demand response programs. The first part of this thesis studies two different metering approaches, i.e. interval metering & threshold metering, with the hope to enlighten the metering configuration for future smart meters. The second part of this thesis directly compares the demand response potential contributed by a pool of cooling devices with two different control mechanisms, e.g. direct compressor control & thermostat set-point control. Finally, this thesis also proposes a new damping technique used to reduce the demand oscillation created in demand response.