Graduation Semester and Year
2019
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Industrial Engineering
Department
Industrial and Manufacturing Systems Engineering
First Advisor
Jay M Rosenberger
Second Advisor
Victoria Chen
Abstract
This research describes a real-time optimization model for multi-agent demand response (DR) from a Load Serving Entity (LSE) perspective. We formulate two infinite horizon stochastic optimization models; specifically, an LSE model and a dynamic pricing customer model. The objective of these models is to minimize long-term cost and discomfort penalty of the LSE and dynamic pricing customers. We solve a deterministic finite horizon linear program as an approximation of the suggested stochastic model and provide computational experiments. In stochastic programming (SP), a wait-and-see solution is at least as good as an optimal policy. On the other hand, a policy that uses the expected value problem is never as good as an optimal policy. This is well established in SP when there is a single agent. A question arises whether bounds exist when we have two agents. The present study develops a research methodology to answer this question. Our experiments show that if we have two separate agents, and both agents get perfect information, this can be worse compared to both agents doing the mean value problem. Nevertheless, we have found that there are bounds when the first stage follows the same set of actions. A two-agent demand response problem has been used as a case study to show this claim.
Keywords
Linear programming, Multi-agent demand response, Demand side management, Dynamic pricing customers, Stochastic bounds, Smart grid
Disciplines
Engineering | Operations Research, Systems Engineering and Industrial Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Fallahi, Alireza, "A MULTI-AGENT DEMAND RESPONSE PLANNING AND OPERATIONAL OPTIMIZATION FRAMEWORK" (2019). Industrial, Manufacturing, and Systems Engineering Dissertations. 138.
https://mavmatrix.uta.edu/industrialmanusys_dissertations/138
Comments
Degree granted by The University of Texas at Arlington