Candidate: Fadi Elghitani
Title: Large-Scale Demand Response in Smart Grid
Date: March 29, 2018
Time: 1:00 PM
Place: EIT 3142
Supervisor(s): El-Saadany, Ehab
Abstract:
In order to support demand response (DR) that requires the scheduling of numerous loads, this research is to develop efficient load aggregation models for three different DR applications which have significant economic and environmental impact. The aggregation of residential appliances is the concern of the first two applications, where the first aims to minimize the energy consumption cost, while the second aims to reduce the magnitude of fluctuations in net demand, as a result of a large-scale integration of renewable energy sources (RESs). Existing models for residential demand aggregation suffer from two limitations: first, demand models ignore inter-temporal demand dependence that is induced by scheduling deferrable appliances; Second, aggregated demand models for thermostatically-controlled loads are computationally inefficient to be used in DR problems that require optimization over multiple time intervals. The first part of our research is concerned with mitigating these two limitations by developing a new framework suitable for scheduling a large number of residential appliances, where the only source of randomness is the consumers' demand. For the second DR application, an additional source of randomness is considered, which is the power fluctuations of the RESs.
The last part of our research focuses on minimizing the expected service time needed for charging electric vehicles (EVs). This target is achieved by coordinating two types of decisions, the assignment of EVs to charging stations and the charging of EVs' batteries. While there exist aggregation models for batteries' charging, aggregation models for EVs' assignment are almost non-existent. In addition, aggregation models for batteries' charging assume that information about EVs' arrival times, departure times and their required charging energies are given in advance. Such assumption is non-realistic for a charging station, where vehicles arrive randomly. Hence, the third part of our research is concerned with developing an aggregation model for EVs' assignment and charging, while considering the stochastic nature of EVs' arrivals.
This research is expected to have important contribution from both research and application perspectives. From the research side, it will provide a tool for managing a large, diverse number of electric appliances by classifying them according to how much they can benefit the utility. From the application side, our work will help to include residential consumers in demand response (while current DR programs focus on the industrial sector only). It will also facilitate RESs and EVs in a large scale to help address environmental concerns.