Demand Response (DR) is an approach that allows electricity users to actively participate in keeping supply-demand balance in power systems, or its future version, smart grid, in order to increase the system efficiency, lower consumers' electricity bills, and thus improve social welfare. To encourage users' participation in DR, a time-varying pricing, also known as dynamic pricing (DP), was proposed over the past years. However, in terms of an appropriate design of DP and its performance in DR market, many aspects are still unclear. In this work we focus on four aspects of DP, first we study and prove the existence of a market clearing dynamic price in a general equilibrium market environment as electricity consumers becomes prosumers (meaning they are allowed to net sell). Second, we build a general sensitivity analysis approach under DP environment that can quantify each prosumers' contribution potential on social welfare as additional resource capacity/flexibility is introduced, which can be used to allocate potentially limited market resources in a more impactful way. Third, to fully explore the potential of DP, we introduce price discrimination in it and compare it with DP schemes without price discrimination. Last but not least, a massive data analytics is provided based on the real-world data and machine learning techniques, which can help us learn DR consumers' preference, especially their price responsiveness that forms one of the most important and necessary factors in DP implementation.

Creator

• Xiang, Ding

DOI

• https://doi.org/10.21985/n2-0x1t-b764

Subject

• Electrical engineering
• Operations research

Language

• en

Alternate Identifier

• etdadmin_upload_719699
• http://dissertations.umi.com/northwestern:15016

Keyword

• Price Discrimination
• Smart Grid
• Demand Response
• Price Responsiveness
• Sensitivity Analysis
• Dynamic Pricing

Date created

• 2020-01-01

Resource type

• Dissertation

Rights statement

• In Copyright