Degree

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering

Document Type

Dissertation

Abstract

A typical power system consists of three major sectors: generation, transmission, and distribution. Due to ever increasing electricity consumption and aging of the existing components, generation, transmission, and distribution systems and equipment must be analyzed frequently and if needed be replaced and/or expanded timely. By definition, the process of power system expansion planning aims to decide on new as well as upgrading existing system components in order to adequately satisfy the load for a foreseen future.

In this dissertation, multiple economically optimal and computationally efficient methods are proposed for expanding power generation, transmission, and distribution systems. First, a computationally efficient model is proposed for transmission expansion planning (TEP). While the existing TEP models use bus voltage angles, the proposed TEP takes advantages of linear sensitivity factors to omit voltage angles from the formulation and replace all nodal power balance constraints by one equivalent constraint. Simulation results show that the proposed model provides the same results as the classical angle-based model while being much faster. Second, a distributed collaborative TEP algorithm for interconnected multi-regional power systems is proposed. The information privacy is respected as each local planner shares limited information related to cross-border tie-lines with its neighboring planners. To coordinate the local planners, a two-level distributed optimization algorithm is proposed based on the concept of analytical target cascading for multidisciplinary design optimization. Third, a security-constrained generation and transmission expansion planning (G&TEP) model with respect to the risk of possible N-1 contingencies is proposed. Using the concept of risk, non-identical probability and severity of individual contingencies are modeled in the proposed G&TEP model. Finally, a mixed-integer linear programming model is proposed for resilient feeder routing in power distribution systems. Geographical information system (GIS) data is used in the proposed model. As it is proven, having GIS facilities will lead to a more cost-efficient and resilient feeder routing scheme than the scheme obtained using electrical points. The proposed model and solution algorithm are comprehensive from several practical aspects such as economic objectives (installation cost, power losses, resiliency), technical constraints (voltage drops, radially constraint, reliability), and geographical constraints (obstacles, right-of-ways).

Committee Chair

Kargarian, Amin

Available for download on Saturday, April 30, 2022

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