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The Smart Grid Network: Pricing, Mar...

Tang, Wenyuan.

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The Smart Grid Network: Pricing, Markets and Incentives.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Smart Grid Network: Pricing, Markets and Incentives./
Author:	Tang, Wenyuan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
Description:	161 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
Contained By:	Dissertations Abstracts International80-06B.
Subject:	Economics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=11012525

The Smart Grid Network: Pricing, Markets and Incentives.
Tang, Wenyuan.

The Smart Grid Network: Pricing, Markets and Incentives. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 161 p.

Source: Dissertations Abstracts International, Volume: 80-06, Section: B.

Thesis (Ph.D.)--University of Southern California, 2015.

This item must not be sold to any third party vendors.

The electric power system is regarded as the largest and most complex machine ever built, in which supply and demand must be balanced on a second-by-second basis. Consequently, the electricity market has to be carefully designed to prevent a recurrence of the California electricity crisis. This task is even more demanding with the popularization of the smart grid. Our work attempts to address the following question: what smart mechanisms should a smart grid be equipped with, for a smart market that can be utilized but not manipulated by smart people? Based on a two-level market model, we focus on the control, optimization and market design of the smart grid, where we incorporate three key elements: the increasing penetration of renewable generation, the increasing participation of demand response, and the fast development and deployment of energy storage systems. On the upper level is the primary market coordinated by the independent system operator. We consider the economic dispatch problem, and develop a game-theoretic framework to investigate the market outcomes with strategic generators under locational marginal pricing. We then consider a dynamic extension, and investigate how the use of storage may affect the market structure and market outcomes. We also study a multistage energy procurement problem, and design incentivizing pricing mechanisms that facilitate efficient participation of the generators. On the lower level, there are two secondary markets. The secondary market I, when it exists, involves an aggregator who buys power from a group of generators and sells it to the primary market. In particular, we study how to design market mechanisms for buying wind power. We first consider the welfare-maximizing objective, and propose the stochastic resource auction paradigm that elicits probability distributions of wind power generation. We then consider the revenue-maximizing objective, and study how to extract the surplus given the correlation among wind power generation. The secondary market II mimics the retail electricity market. We study the distributor's problem how to utilize demand response in an adaptive manner. In a stochastic setting, the optimal pricing scheme should evolve according to the up-to-date information. We develop a distributed algorithm to compute the optimal price process that incentives the agents to choose the socially optimal decisions. Finally, we study a network resource allocation problem in a hierarchical setting. Motivated by the results on hierarchical mechanism design, we discuss the challenges of the integration of the two-level markets. This is an open problem and the direction of our future work.Subjects--Topical Terms:

517137
Economics.

The Smart Grid Network: Pricing, Markets and Incentives.
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500 $a Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
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502 $a Thesis (Ph.D.)--University of Southern California, 2015.
506 $a This item must not be sold to any third party vendors.
520 $a The electric power system is regarded as the largest and most complex machine ever built, in which supply and demand must be balanced on a second-by-second basis. Consequently, the electricity market has to be carefully designed to prevent a recurrence of the California electricity crisis. This task is even more demanding with the popularization of the smart grid. Our work attempts to address the following question: what smart mechanisms should a smart grid be equipped with, for a smart market that can be utilized but not manipulated by smart people? Based on a two-level market model, we focus on the control, optimization and market design of the smart grid, where we incorporate three key elements: the increasing penetration of renewable generation, the increasing participation of demand response, and the fast development and deployment of energy storage systems. On the upper level is the primary market coordinated by the independent system operator. We consider the economic dispatch problem, and develop a game-theoretic framework to investigate the market outcomes with strategic generators under locational marginal pricing. We then consider a dynamic extension, and investigate how the use of storage may affect the market structure and market outcomes. We also study a multistage energy procurement problem, and design incentivizing pricing mechanisms that facilitate efficient participation of the generators. On the lower level, there are two secondary markets. The secondary market I, when it exists, involves an aggregator who buys power from a group of generators and sells it to the primary market. In particular, we study how to design market mechanisms for buying wind power. We first consider the welfare-maximizing objective, and propose the stochastic resource auction paradigm that elicits probability distributions of wind power generation. We then consider the revenue-maximizing objective, and study how to extract the surplus given the correlation among wind power generation. The secondary market II mimics the retail electricity market. We study the distributor's problem how to utilize demand response in an adaptive manner. In a stochastic setting, the optimal pricing scheme should evolve according to the up-to-date information. We develop a distributed algorithm to compute the optimal price process that incentives the agents to choose the socially optimal decisions. Finally, we study a network resource allocation problem in a hierarchical setting. Motivated by the results on hierarchical mechanism design, we discuss the challenges of the integration of the two-level markets. This is an open problem and the direction of our future work.
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