Developing Energy Forecasting Tools in Power Systems: Application to Microgrids

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atmire.migration.oldid6203
dc.contributor.advisorZareipour, Hamidreza
dc.contributor.advisorWood, David
dc.contributor.authorChitsaz, Hamed
dc.contributor.committeememberKnight, Andrew
dc.contributor.committeememberYanushkevich, Svetlana
dc.contributor.committeememberMessier, Geoffrey
dc.contributor.committeememberFaried, Sherif
dc.date.accessioned2017-11-30T23:20:35Z
dc.date.available2017-11-30T23:20:35Z
dc.date.issued2017
dc.date.submitted2017en
dc.description.abstractIn recent years, distributed energy resources and microgirds have attracted a great deal of interests in the power industry. The development of microgrids has required engineers and operators to enhance the efficiency and the energy management of such small-scale power systems. To do so, energy forecasting plays a key role in their optimal operation. In particular, Short-termWind Forecasting (STWF), Short-term Load Forecasting (STLF) and Short-term Price Forecasting (STPF) are important tools for reliable operation scheduling of grid-connected microgrids with renewable energy sources (e.g., wind). The generated energy forecasts are used in an optimization platform to ensure the most economical operation for microgrids as the end goal of this thesis. The main focus of this thesis is the development of forecasting models that are tailored for the application to grid-connected microgrids. An STWF model is developed based on artificial intelligence and an evolutionary algorithm to provide wind forecasts. This model can be applied to generate wind predictions at the power system, wind farm and/or wind turbine levels. By statistically analyzing the behavior of electricity consumption at campus/building levels, an STLF methodology is developed based on neural networks to provide satisfactory forecasts for volatile electricity loads in microgrids. Further, an STPF is designed to improve the economics of grid-connected microgrids by taking advantage of energy arbitrage opportunities with the grid. It is noted that the microgrid is assumed to be large enough to have transactions based on market prices. Numerical results in Chapters 2, 3, 4 and 5 of this thesis are provided based on Alberta, Ontario, British Columbia, California, Texas and NewYork power systems, and two campuses. The simulations show the effectiveness of the proposed neural networks for STWF and STLF. The statistical and economic evaluations show the satisfactory performance of the developed STPF model in scheduling a storage system in a microgrid. Moreover, deterministic and probabilistic optimization platforms are developed for the optimal operation of microgrids, which can help the operator apply the most effective approach under different scenarios of generation and market conditions.en_US
dc.identifier.citationChitsaz, H. (2017). Developing Energy Forecasting Tools in Power Systems: Application to Microgrids (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25623en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/25623
dc.identifier.urihttp://hdl.handle.net/11023/4259
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectArtificial Intelligence
dc.subjectEnergy
dc.subjectEngineering
dc.subjectEngineering--Electronics and Electrical
dc.subjectEngineering--Environmental
dc.subject.otherPower Systems
dc.subject.otherMicrogrid
dc.subject.otherRenewable Energy
dc.subject.otherArtificial Neural Networks
dc.subject.otherMachine Learning
dc.titleDeveloping Energy Forecasting Tools in Power Systems: Application to Microgrids
dc.typedoctoral thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
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