Xiao, Jingjie (2013): Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming. Published in: Ph.D. Dissertation (13 August 2013)

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Abstract
A key hurdle for implementing realtime pricing of electricity is a lack of consumers’ responses. Solutions to overcome the hurdle include the energy management system that automatically optimizes household appliance usage such as plugin hybrid electric vehicle charging (and discharging with vehicletogrid) via a twoway communication with the grid. Realtime pricing, combined with household automation devices, has a potential to accommodate an increasing penetration of plugin hybrid electric vehicles. In addition, the intelligent energy controller on the consumerside can help increase the utilization rate of the intermittent renewable resource, as the demand can be managed to match the output proﬁle of renewables, thus making the intermittent resource such as wind and solar more economically competitive in the long run.
One of the main goals of this dissertation is to present how realtime retail pricing, aided by control automation devices, can be integrated into the wholesale electricity market under various uncertainties through approximate dynamic programming. What distinguishes this study from the existing work in the literature is that wholesale electricity prices are endogenously determined as we solve a system operator’s economic dispatch problem on an hourly basis over the entire optimization horizon. This modeling and algorithm framework will allow a feedback loop between electricity prices and electricity consumption to be fully captured. While we are interested in a nearoptimal solution using approximate dynamic programming; deterministic linear programming benchmarks are use to demonstrate the quality of our solutions.The other goal of the dissertation is to use this framework to provide numerical evidence to the debate on whether realtime pricing is superior than the current ﬂat rate structure in terms of both economic and environmental impacts. For this purpose, the modeling and algorithm framework is tested on a largescale test case with hundreds of power plants based on data available for California, making our ﬁndings useful for policy makers, system operators and utility companies to gain a concrete understanding on the scale of the impact with realtime pricing.
Item Type:  MPRA Paper 

Original Title:  Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming 
Language:  English 
Keywords:  Energy Economics, Electricity Markets, Plug in hybrid vehicles, electric vehicles, mathematical modeling 
Subjects:  C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling Q  Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q2  Renewable Resources and Conservation Q  Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4  Energy Q  Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4  Energy > Q42  Alternative Energy Sources Q  Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4  Energy > Q47  Energy Forecasting Q  Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4  Energy > Q48  Government Policy 
Item ID:  58696 
Depositing User:  Dr. Cynthia Van Der Meer 
Date Deposited:  22 Sep 2014 17:07 
Last Modified:  26 Sep 2019 14:52 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/58696 