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ROBUST OPTIMIZATION OF UNIT COMMITMENT PROBLEM WITH RENEWABLE RESOURCES AND ELECTRICAL ENERGY STORAGE
Title
ROBUST OPTIMIZATION OF UNIT COMMITMENT PROBLEM WITH RENEWABLE RESOURCES AND ELECTRICAL ENERGY STORAGE
Creator
Kashyap, Prakash
Date
2016, 2016-12
Description
The Chinese proverb |\To be uncertain is to be uncomfortable, but to be cer- tain is to be ridiculous" |mention in the preface of the book...
Show moreThe Chinese proverb |\To be uncertain is to be uncomfortable, but to be cer- tain is to be ridiculous" |mention in the preface of the book Robust Optimization by Aharon Ben-Tal, Laurent El Ghaoui and Arkadi Nemirovski, truly capture the cer- tainty of uncertainty in every walk of life. However, it is human endeavor to manage uncertainty by properly engineered system. Power system is no exception. Uncer- tainty with load forecasting and contingencies such as generator and/or transmission line outages impose reliability and security issue with power system operation. In wholesale market, tools like spinning reserve and non-spinning reserve are used by ISO/RTO to mitigate severe consequences of such uncertainties. With increased share of highly volatile renewable energy resources such as wind and solar power, price-based demand response and electric vehicle charging station, uncertainty in power system operation is going to be further aggravated. A scenario based stochastic approach instead of system reserve based deterministic approach is considered another solution to the problem. However, scenario based stochastic solution may miss some critical scenarios. Secondly, we may need a large number of scenarios to get a su ciently reliable solution. In recent years, robust optimization based security constrained economic dis- patch (SCED) and security constrained unit commitment (SCUC) have been explored by several researchers. This thesis explores implementation of robust optimization for secure and economical operation of power system in presence of renewable energy resources (RES) and electrical energy storages (EES).
M.S. in Electrical Engineering, December 2016
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LOAD ANALYSIS BASED ON MACHINE LEARNING IN POWER SYSTEMS
Title
LOAD ANALYSIS BASED ON MACHINE LEARNING IN POWER SYSTEMS
Creator
Lu, Dan
Date
2017, 2017-05
Description
The dissertation is composed by four parts, first, load sampling for SCUC based on Principal Component Analysis (PCA) and Kernel Density...
Show moreThe dissertation is composed by four parts, first, load sampling for SCUC based on Principal Component Analysis (PCA) and Kernel Density Estimation (KDE); second, load forecasting based on PCA and Bayesian ridge regression; third, anomalies detection based on Machine Learning methodology; fourth the long-term planning of Battery-based Energy Storage Transportation (BEST) in power system. Mathematical models are constructed to fulfill the research of the three targets, and numerical examples are used to test the models. The first three parts are based on PCA, which reduced the load dimensions. In the first part, a robust power system Unit Commitment (UC) is the aim to fulfil the possible load. In the second part, a novel short-term nodal load forecasting is raised to give better prediction of the next day load to improve the next data UC scheduling. In the third part, anomalies are detected in the reduced power flow space based on the pattern identified in the lower dimensional space. The purpose of the fourth part is to find ways of better utilizing the existing resources from integrating the frontier technology, the mobility of more compact and higher capacity batteries. Mix-integer programming (MIP) is used in the formulation.
Ph.D. in Electrical Engineering, May 2017
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