Access Type

Open Access Embargo

Date of Award

January 2020

Degree Type


Degree Name



Electrical and Computer Engineering

First Advisor

Caisheng Wang

Second Advisor

Masoud H. Nazari


In the U.S., several electricity markets have been formed throughout the country to efficiently and economically manage large power grids for their safe and reliable operations. In the electricity markets, the load management techniques have become important tools in improving voltage profile, system efficiency and stability. In this thesis, several algorithms and methods for optimal load and energy storage managements are proposed and studied. As the accurate prediction of market information is critical for demand side management and power generation scheduling, the algorithms based on the autoregressive integrated moving average (ARIMA) models are developed in this thesis to improve the predictions of electricity price and fuel cost distributions.

To address the challenge of climate change, reducing emission due to the electric power generation and consumption has received increasing attention worldwide. The load management can help decrease emissions and costs, especially in future smart grids where customers will have more flexibility in controlling their electricity usages. Several new load management methods, such as the temporal and spatial load management, are proposed for the market operator. The results show the proposed load management methods are capable of reducing the cost and emission, and mitigating the price spikes.

While the developed load management methods are proven to be beneficial to the market operator, the benefits for market participants to respond to the regulation signal and manage their demands are also worthy of study. In modern power systems, the battery energy storage system (BESS), as an important market participant, can provide a variety of functions, such as energy arbitrage and ancillary services. However, a poor selection of installation location for a BESS in the power market can make the BESS project struggle to compete with other market participants. Therefore, a comprehensive revenue analysis of BESSs is carried out in this thesis. Based on the results of revenue analysis, an optimal placement algorithm is proposed for finding the profitable sites to install BESSs in the system. The effectiveness of proposed algorithm is validated with real electricity market data.

Furthermore, the BESS can also be formed by aggregating a fleet of electric vehicles (EVs) that have the vehicle to grid (V2G) capabilities. One of the key implementations is to aggregate the EVs in the workplace parking lots, which can raise more regulation resources for power system operations. However, there are still highly challenging issues, such as the uncertainties of EV owners' behaviors and market conditions, which can derail the aggregator's performance. To address these challenges, a two-stage optimal bidding algorithm is proposed for the incentive-based EV aggregator to participate in the electricity markets. The simulation results show that the proposed bidding algorithm performs well on handling the uncertainty of market data, and the EV aggregator can have a stable revenue when participating in the electricity market at different locations.

Available for download on Tuesday, January 25, 2022