Document Type

Dissertation - Open Access

Award Date


Degree Name

Doctor of Philosophy (PhD)

Department / School

Electrical Engineering and Computer Science

First Advisor

Timothy M. Hansen


Cost Curves, Data Analysis, Electricity Market, Emissions, Pattern Recognition, Test Cases


The primary aim of this dissertation is to deliver a technique to augment power system test cases with realistic open-source data to represent a deregulated power system. These test cases are intended to be used by power system researchers who require a test case that is capable of performing economic and environmental analysis on a bulk-power level. These test cases are capable of estimating the cost of bulk-energy for economic analysis and harmful greenhouse gas (GHG) and air polluting (AP) emissions for environmental sustainability analysis. These cases are developed for simulations that are intended to be at the transmission level where the independent system operator (ISO) has control. In the second part of this dissertation, an aggregator based demand response (DR) model is studied as-a-service to the bulk-power market, and its economic benefit is estimated using the augmented test cases. The augmentation technique presented in this dissertation has three-layer data over the existing generator information in a test case. The first layer of augmented data replaces the cost functions of the test case generators with functions developed based on the generator offers from a real electricity market. An unsupervised learning technique had to be implemented to classify the market offer data because the identity of the generators is masked to honor a fair market policy. The offer data was converted to cost functions and is sampled statistically such that the test cases represent a similar generator supply curve as the real power system. In addition to the cost functions layer, the test case generator data has an augmented generator fuel-turbine data. This data in a test case will represent the energy sources and generator technology of the system that the test case is intended to emulate. The hourly energy mix of the electricity market is utilized to augment the generator fuel-turbine type of test case generators. Because the number and capacities of test case generators may not represent the real system, assigning one fuel-turbine type to one test case generator will not result in a right energy mix. The augmentation technique creates an additional layer of information for each test case generator which can represent multiple fuel-types. The third layer of augmented data on test cases contains the heat curve and emission information. With all these layers of data, the test case is capable of representing the dynamic cost nature of a deregulated power system and is able to dispatch generators similar to the real power system. PJM interconnection data was chosen to implement the proposed augmentation technique. The marginal cost result from optimal power flow (OPF) is compared with the marginal cost of energy of the PJM interconnection along with the GHG and AP emissions. Smart-grids have opened opportunities for end customers to participate in the power system operation. DR is one of the activities that the end customers can perform to participate in the electricity market. Revenue earned from energy markets has been relatively low compared to DR used for capacity markets and ancillary services. An aggregated DR model participating in the bulk-power market as a service through a pool-based entity called demand response exchange (DRX) is proposed to improve the benefits of DR to the market. The economic benefits to the market entities have been studied using the proposed augmented test cases.
The key contributions of this dissertation are:

  • power systems test case generator data for researchers who do not have access to the real power system data,
  • a technique that utilizes only open-source data to develop augmented data for any test case to represent the dispatch of a real power system in terms of cost, and emissions,
  • a DR model capable of improving the revenue for DR participants in the bulk-energy market.

Library of Congress Subject Headings

Electric power systems -- Testing.
Smart power grids -- Testing.



Number of Pages



South Dakota State University



Rights Statement

In Copyright