Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming

A key hurdle for implementing real-time pricing of electricity is a lack of con-sumers’ responses. Solutions to overcome the hurdle include the energy management system that automatically optimizes household appliance usage such as plug-in hybrid electric vehicle charging (and discharging with vehicle-to-grid) via a two-way com-munication with the grid. Real-time pricing, combined with household automation devices, has a potential to accommodate an increasing penetration of plug-in hybrid electric vehicles. In addition, the intelligent energy controller on the consumer-side 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 real-time 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 whole-sale 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 near-optimal 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 ev-idence to the debate on whether real-time pricing is superior than the current flat rate structure in terms of both economic and environmental impacts. For this pur-pose, the modeling and algorithm framework is tested on a large-scale 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 real-time pricing.