Candidate: Omar Alarfaj
Title: Energy Management and Demand Response of Industrial Systems
Date: November 13, 2018
Time: 9:30 am
Place: EIT 3142
Supervisor(s): Bhattacharya, Kankar
Abstract:
Energy management is an important concept that has come to the forefront in recent years under the smart grid paradigm. Energy conservation and management can help defer some capacity addition requirements in the long-term, which is very significant in the context of continuously growing demand for energy. It can also alleviate the adverse environmental impacts of commissioning new generation plants. Therefore, there is a continuous need for the development of appropriate tools to ensure efficient energy usage by existing and new loads and the efficient integration of distributed energy resources (DER).
There is a need for energy conservation in the industrial sector as it accounts for the largest share of energy consumption among all customer sectors. Also considering their high energy density, industrial facilities have significant potential for participating in demand side management (DSM) programs and help in reducing the system peak demand by reducing or shifting their load in response to energy price signals. However industrial demand response (DR) is typically constrained by the operational requirements such as process interdependencies and material flow management.
As EMS is introduced in industrial facilities, the diversity of load types would require good load modeling techniques to capture the characteristics of these loads and how their operations and conditions affect energy consumption. The monitoring capability of EMS, capturing real-time measurements of power system quantities, encourages the application of measurement based load fitting techniques with different load model structures; such as polynomial and neural network (NN) models. As the models improve in precision and accuracy, operational and economic efficiency can be achieved by the optimization model within the EMS. Another factor affecting the performance of the EMS is the uncertainty in forecasted variables, which renders the expected benefits of EMS decisions to be uncertain. To this effect, uncertainty management techniques such as model predictive control (MPC) can be applied in EMS to reduce the impact of uncertainty on optimization results.
An EMS framework is proposed in this thesis for optimal load management of industrial loads which includes improved load estimation technique and uncertainty mitigation using MPC. The framework has been applied to a water pumping system (WPS) where an equipment level load modeling is implemented using a NN-based model. Another EMS framework is proposed for an oil refinery process. The refinery EMS is developed based on power demand modeling of the oil refinery process, considering an on-site cogeneration facility. A joint electrical-thermal model is proposed for the cogeneration units to account for the electricity and steam production costs.
In addition to load management, DR for industrial loads is investigated as another energy management application. However since DR requires interaction between the energy supplier and the customer, this thesis considers DR from both the local distribution company's (LDC) and industrial customer's perspectives. From the LDC’s perspective, the objective is to reduce the network operational costs by minimizing peak demand and flattening the load profile for better utilization of system resources. From the industrial customer's perspective, the objective is to minimize the energy cost using both load management decisions and DR signals sent by the LDC.