Graduate Exam Abstract

Turki Alaqeel

Ph.D. Final

May 5, 2017, 11:00 am - 1:00 pm

Engineering- C101B ECE Conference Room


Abstract: The electricity infrastructure in Saudi Arabia is facing several challenges represented by demand growth, high peak demand, high level of government subsidies, and system losses. This dissertation aims at addressing these challenges and proposing a multi-dimensional framework to modernize the electricity infrastructure in Saudi Arabia. The framework proposes four different scenarios—identified by two dimensions—for the future electric grid. The first and second dimensions are characterized by electricity market deregulation and Smart Grid technologies (SGTs) penetration, respectively. The framework analysis estimates global welfare (GW) and economic feasibility for each dimension. The first dimension quantifies the impact of deregulating the electricity market in Saudi Arabia. A non-linear programming (NLP) algorithm optimizes consumers surplus, producers surplus, and GW. The model indicates that deregulating the electricity market in Saudi Arabia will improve market efficiency. The second dimension proposes that allowing the penetration of SGTs in the Saudi electricity infrastructure is expected to mitigate the technical challenges faced by the grid. The dissertation examines the priorities of technologies for penetration by considering some key performance indicators (KPIs) identified by the Saudi National Transformation Program, and Saudi 2030 Vision. A multi- criteria decision making (MCDM) algorithm—using the fuzzy Analytic Hierarchy Process (AHP)—evaluates the prioritization of SGTs to the Saudi grid. The algorithm demonstrates the use of triangular fuzzy numbers to model uncertainty in planning decisions. The results show that advanced metering infrastructure (AMI) technologies are the top priority for modernizing the Saudi electricity infrastructure; this is followed by advanced assets management (AAM) technologies, advanced transmission operations (ATO) technologies, and advanced distribution operations (ADO) technologies. SGTs prioritization is followed by a detailed cost benefit analysis (CBA) conducted for each technology. The framework analysis aims at computing the economic feasibility of SGTs and estimating their outcomes and impacts in monetary values. The framework maps Smart Grid assets to their functions and benefits to estimate the feasibility of each Smart Grid technology and infrastructure. Discounted cash flow (DCF) and net present value (NPV) models, benefit/cost ratio, and minimum total cost are included in the analysis. The results show that AAM technologies are the most profitable technologies of Smart Grid to the Saudi electricity infrastructure, followed by ADO technologies, ATO technologies, and AMI technologies. Considering the weights resulting from the fuzzy AHP and the economic analysis models for each infrastructure, the overall ranking places AAM technologies as the top priority of SGTs to the Saudi electricity infrastructure, followed by AMI technologies, ADO technologies, and ATO technologies. This dissertation has contributed to the existing body of knowledge in the following areas: • Proposing an econometric framework for electricity infrastructure modernization. The framework takes into account technical, economic, environmental, societal, and policy factors. • Building an NLP algorithm to optimize a counterfactual deregulation of a regulated electricity market. The algorithm comprises short run price elasticity of electricity demand (), level of technical efficiency improvement, and discount rate (r). • Proposing an MCDM model using AHP and fuzzy set theory to prioritize SGTs to electricity infrastructures. • Adapting a Smart Grid asset-function-benefit linkage model that maps SGTs to their respected benefits. • Conducting detailed CBA to estimate the economic feasibility of SGTs to the Saudi electricity infrastructure, This work opens avenues for more analysis on electricity infrastructure modernization. Measuring risk impact and likelihood is one area for future research. In fact, risk assessment is an important factor in determining the economic feasibility of the modernization. Another area for future research is the integration of both dimensions into one model in which GW resulted from market deregulation and SGTs insertion are summed.

Adviser: Prof. Siddharth Suryanarayanan
Co-Adviser: N/A
Non-ECE Member: Prof. Jennifer Coats, Department of Finance and Real Estate
Member 3: Prof. George J. Collins, Electrical and Computer Engineering Department
Addional Members: Prof. Anthony A. Maciejewski, Electrical and Computer Engineering Department

[1] T. Alaqeel and S. Suryanarayanan, " A Comprehensive Cost-benefit Economic Analysis of the Penetration of Smart Grid Technologies in the Saudi Arabian Electricity Infrastructure," IEEE Transactions on Engineering Management, (Under Review)
[2] T. Alaqeel and S. Suryanarayanan, "A Fuzzy Analytic Hierarchy Process Algorithm to Prioritize Smart Grid Technologies for the Saudi Electricity Infrastructure," Journal of Sustainable Energy, Grids and Networks, (Under Review)
[3] T. Alaqeel, S. Almohaimeed, and S. Suryanarayanan, " A review on the impact of air conditioning motor stalling on voltage recovery in the Saudi electric grid," in North American Power Symposium (NAPS), Morgantown, WV, 2017, (Under Review)
[4] T. Alaqeel and S. Suryanarayanan, "A decision analysis framework using analytic hierarchy process for the modernization of the Saudi electricity infrastructure," presented at the Energy Policy Research Conference (EPRC), Santa Fe, NM, 2016.

[5] T. Alaqeel and S. Suryanarayanan, "Ex ante cost-benefit analysis for optimal deregulation of electricity markets," in IEEE Power & Energy Society General Meeting, Boston, MA, 2016.

[6] T. Alaqeel and S. Suryanarayanan, "Examining some prospect scenarios for the electricity grid infrastructure modernization in Saudi Arabia," in North American Power Symposium (NAPS), Pullman, WA, 2014, pp. 1-6.

Program of Study:
ECE 501
ECE 508
ECE 509
ECE 461
ECE 565
ECE 566
ECE 623
PSY 792A