Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Abdullah Algarni
Ph.D. Preliminary
Mar 30, 2020, 2:00 pm - 4:00 pm
ECE Conference Room
Abstract: Smart Grid Initiative started after realizing the urge for changes in conventional electric power grids. These changes should be made in response to a number of emerging issues in electricity industry. The increasing involvement of renewable energy technologies either as large-scale generators or as small-rated distributed generators (DGs) poses a challenge for the grid. The renewable energy generators being intermittent and uncontrollable brings worrying uncertainty at the supply side of the grid. This uncertainty makes the grid’s operators anxious about balancing generation with load, which is a must for the reliability of the system. Demand side management should be the solution for the uncontrollability of renewable energy. Residential customers, though new entities called demand response (DR) aggregators, can bring demand response services for covering renewable production intermittency.
A cost minimization framework is proposed for power supply-demand adjustment with the involvement of variable resources (i.e., renewable energy generators). The resources in the power supply-demand adjustment problem are demand reduction through aggregators, power flow exchange between areas, and balancing generators’ services. The method is simulated in IEEJ east 30-machine test system after dividing it into 4 areas. The results of the proposed method show lower cost than the traditional method of using only balancing generators’ services.
DR aggregators also use a load shifting technique to shift part of the residential load from peak to off-peak times. The effects of integration multiple aggregators into transmission level power grid is studied and simulated in Roy Billinton test system (RBTS) after dividing it into 2 areas. The results show peak demand reductions, electricity prices reduction and lower peak to average ratio (PAR). In line with integrating DR aggregators, a proposed carbon tax function is applied to the fossil fueled generators in the system. The proposed tax demonstrates less dispatch of coal and natural gas-based generators. As a result, emissions reduction and tax revenues are achieved and calculated using proposed math models.
Finally, a test bed is designed for experimental studies to find a relationship between the aggregator’ performance and utilities pricing mechanisms. The experiment aims to find how the utility pricing mechanisms affect the profitability and peak load shifting. These pricing mechanisms include fixed tariff, time-of-use pricing, and real-time pricing. The results are not considered to be conclusive but the test bed for this experiment is designed. For future work, I plan to find detailed conclusions about the relationship between the pricing mechanisms and the aggregators performance.
Adviser: Prof. Siddharth Suryanarayanan
Co-Adviser: N/A
Non-ECE Member: Prof. Michael Bell, Atmospheric Science
Member 3: Prof. Tony Maciejewski, ECE
Addional Members: Prof. Howard J Siegel, and Prof. George J Collins
A. Algarni and T. Namerikawa, "Integrating Demand Response Aggregators with Negawatt Trading Mechanisms in Electricity Markets," 2019 North American Power Symposium (NAPS), Wichita, KS, USA, 2019, pp. 1-5.
Program of Study:
ECE 508
ECE 565
ECE 622
ECE 666
ENGR 510
GRAD 544
ENGR 530