The grounding system in a substation is used to protect personnel and equipment. When there is fault current injected into the ground, a well-designed grounding system should disperse the fault current into the ground in order to limit the touch potential and the step potential to an acceptable level defined by the IEEE Std 80. On the other hand, from the point of view of economy, it is desirable to design a ground grid that minimizes the cost of labor and material. To design such an optimal ground grid that meets the safety metrics and has the minimum cost, an optimal ground grid application was developed in MATLAB, the OptimaL Ground Grid Application (OLGGA).

In the process of ground grid optimization, the touch potential and the step potential are introduced as nonlinear constraints in a two layer soil model whose parameters are set by the user. To obtain an accurate expression for these nonlinear constraints, the ground grid is discretized by using a ground-conductor (and ground-rod) segmentation method that breaks each conductor into reasonable-size segments. The leakage current on each segment and the ground potential rise (GPR) are calculated by solving a matrix equation involving the mutual resistance matrix. After the leakage current on each segment is obtained, the touch potential and the step potential can be calculated using the superposition principle.

A genetic algorithm is used in the optimization of the ground grid and a pattern search algorithm is used to accelerate the convergence. To verify the accuracy of the application, the touch potential and the step potential calculated by the MATLAB application are compared with those calculated by the commercialized grounding system analysis software, WinIGS.

The user's manual of the optimal ground grid application is also presented in this work.

Traditional deterministic reserve requirements rely on ad-hoc, rule of thumb methods to determine adequate reserve in order to ensure a reliable unit commitment. Since congestion and uncertainties exist in the system, both the quantity and the location of reserves are essential to ensure system reliability and market efficiency. The modeling of operating reserves in the existing deterministic reserve requirements acquire the operating reserves on a zonal basis and do not fully capture the impact of congestion. The purpose of a reserve zone is to ensure that operating reserves are spread across the network. Operating reserves are shared inside each reserve zone, but intra-zonal congestion may block the deliverability of operating reserves within a zone. Thus, improving reserve policies such as reserve zones may improve the location and deliverability of reserve.

As more non-dispatchable renewable resources are integrated into the grid, it will become increasingly difficult to predict the transfer capabilities and the network congestion. At the same time, renewable resources require operators to acquire more operating reserves. With existing deterministic reserve requirements unable to ensure optimal reserve locations, the importance of reserve location and reserve deliverability will increase. While stochastic programming can be used to determine reserve by explicitly modelling uncertainties, there are still scalability as well as pricing issues. Therefore, new methods to improve existing deterministic reserve requirements are desired.

One key barrier of improving existing deterministic reserve requirements is its potential market impacts. A metric, quality of service, is proposed in this thesis to evaluate the price signal and market impacts of proposed hourly reserve zones.

Three main goals of this thesis are: 1) to develop a theoretical and mathematical model to better locate reserve while maintaining the deterministic unit commitment and economic dispatch structure, especially with the consideration of renewables, 2) to develop a market settlement scheme of proposed dynamic reserve policies such that the market efficiency is improved, 3) to evaluate the market impacts and price signal of the proposed dynamic reserve policies.