A data-driven approach for maximizing available wind energy through a dedicated pricing mechanism for charging residential plug-in electric vehicles
Date
2019
Authors
Eldali, Fathalla, author
Suryanarayanan, Siddharth, advisor
Collins, George J., committee member
Zimmerle, Dan, committee member
Abdel-Ghany, Salah, committee member
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Abstract
Wind energy generation is growing significantly because of its favorable attributes such as cost-effectiveness and environment-friendliness. Electricity is the most perishable commodity as it must be consumed almost instantaneously as it is produced. Because of that, the variable nature of wind power generation and the challenges in forecasting the output power of wind impose problems of curtailment (excess of available wind energy than forecast) and deployment of reserves (deficit of available wind energy than forecast). Energy storage for wind power installations is a potential solution; however, storing large amounts of energy over long time periods is an expensive and inefficient solution. Plug-in electric vehicles (PEVs) are recognized as one of the assets to integrate energy storage on the distribution side of the electricity grid. Thus, PEVs charging presents an alternative solution for managing this excess energy in wind energy-rich grids. An accurate wind power forecasting (WPF) in the day-ahead market leads to a more predictable dispatch and unit-commitment (UC) of generators, thus reducing the need for reserves and storage. Typically, reserves to match the imbalance in supply and demand of electricity are provided by generators that are more expensive than the ones engaged in primary services. Markets in different regions of the world have specific designs, operation policies, and regulations when it comes to variable sources (e.g., wind, and solar). Independent system operators (ISOs), tasked with handling electricity markets in the US, must meet regulating reserve as directed by the North America Electric Reliability Council (NERC). One of these requirements is that the sufficient reserve must be available to cover the generation deficit. This deficit can be due to under-forecasting. There is also a case when ISOs need to curtail wind energy generation because of over-forecasting. In the first part of this dissertation, wind power data from the Electric Reliability Council of Texas (ERCOT) market is used to improve WPF as Texas has the highest installed wind energy capacity in the North American electricity grid. Autoregressive integrated moving average (ARIMA) model is used for WPF improvement. There is also a need to develop a coherent metric to quantify the improvements to WPF because different studies use different metrics. Also, using the statistical representation of the reduction in error does not necessarily reflect the overall benefit, especially the economic benefit, for ISOs. In the second part of this dissertation work, modifications of on risk-adjusted metrics used in investments assessments are developed and applied to the operation cost (OC). OC is the result of running the economic dispatch (ED) on realistic synthetic models of the actual Texas grid to evaluate the impact of the WPF improvement on the cost of operation. The modifications of the above-mentioned risk-adjusted metrics are also applied to deferring the capital investment on the distribution systems. Then, the metrics are used to assess the combination of photovoltaic (PV) and battery energy storage system (BESS) at the residential section of the distribution grid as explained in appendix A. The third part of this dissertation uses a data-driven approach to investigate existing pricing mechanisms for a Texan city (i.e., Austin) located in a wind energy-rich grid such as ERCOT with an increased adoption rate of PEVs. The study performed indicates the need for an alternative dynamic pricing mechanism dedicated to PEVs than the existing choices for maximizing the utility of available energy from wind in the absence of grid-level energy storage. Dynamic pricing produces an opportunity to avoid high costs for the power provider and benefits the consumers if they respond to the change of the price. However, achieving these benefits needs smart rate design and real data. After justifying the need for fair pricing mechanisms to benefit the utility and the customers for the coordination of wind energy and PEVs charging in wind energy-rich grid, this dissertation designs a time-varying pricing mechanism. This dissertation employs a data decomposition technique to design a dedicated pricing mechanism for PEVs. We use real data of a city with high projections of PEVs (Austin, Texas) located in a wind-rich electricity grid (ERCOT) to demonstrate this design of a dynamic pricing method.
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Subject
dynamic pricing
energy storage
wind power forecasting improvement metrics
electric vehicles
data-driven
wind power forecasting