Natural gas demand response (GDR) is an emerging technology that has garnered significant attention in recent years due to its potential as a tool for grid management. Despite the growing interest in GDR, a careful examination of the literature suggests that there is still much to learn about the full potential of this technology, particularly with regards to demand-side management which is the response from the customer or user side. In this thesis, we aim to address this critical research gap by proposing various actions and strategies to optimize the utilization of natural gas during Gas Demand Response events, with the goal of encouraging customers to participate in these programs.This study focuses specifically on the use of multi-source residential heating, with natural gas serving as the primary source. To calculate consumption during periods of high demand, we utilized a simulation model using Open Studio and Energy Plus software, which is funded by the Department of Energy and managed by the National Renewable Energy Laboratory. The simulation model enabled the determination of the heating energy requirements for a typical residential apartment over the course of an entire year, with the ability to break down the demand for each hour based on location and weather conditions. The generated energy requirement was utilized by an optimization tool to decide which source of energy the customer should use based on different incentive and pricing programs. Both the model and the optimization tools allowed us to test different possible solutions and modifications to quantify the energy savings, particularly during Gas Demand Response events. Through this research, we seek to contribute to the growing body of literature on GDR and advance the understanding of demand response from the demand side. By proposing various strategies to optimize natural gas utilization during Gas Demand Response events, we hope to encourage greater participation in demand response programs and promote the adoption of GDR as a reliable and sustainable solution to grid management challenges. The simulation model represents a valuable tool for testing proposed solutions and modifications, and our findings can inform the development of effective demand response strategies that enhance the overall potential of GDR as a tool for grid management.

This thesis examines the application of model-free multi-agent reinforcement learning for autonomous model-free control and constrained optimization of process networks comprised of multiple interconnected processes. Initially, different types of multi-agent reinforcement learning algorithms are outlined, and the challenges faced in their implementation for process control are identified. Reinforcement learning is then combined with optimal control, where the reward function is designed based on quadratic penalty functions to improve computational time and achieve improved performance compared to sparse rewards proposed in previous research studies. Several multi-agent reinforcement learning strategies, including centralized, decentralized, and mixed-type (centralized action and decentralized execution) are considered and compared. An assessment of the benefits and drawbacks of each control strategy is presented. Finally, an evaluation of the robustness of the control system against parametric uncertainties and sensor noise which are of practical significance is given. Throughout the thesis, a model system comprising of two interconnected non-isothermal chemical reactors with a recycle stream and multiple reactions is used to illustrate the design and implementation of the reinforcement learning agents. The control objective is to regulate the reactors' temperatures and concentrations at desired set-points. The proposed framework can be applied to more general process networks.

Modern industrial plants have become increasingly dependent on networked control system architectures in which dedicated sensor‐controller, controller‐actuator, and controller-controller links are replaced by real-time shared (wired or wireless) digital communication networks that operate over specialized industrial networks and protocols. While networked control systems offer a multitude of economic and operational benefits, the increased reliance on shared communication networks comes with a host of fundamental challenges that need to be addressed. For example, challenges such as network resource constraints, data losses, communication delays and real-time scheduling constraints are tied to the inherent limitations on the transmission and processing capabilities of the communication medium, and if left unaddressed can cause operational instabilities or closed-loop performance deterioration. These challenges have motivated a significant and growing body of research work on the analysis and design of networked control systems. An approach that has been proposed to address a number of these challenges is the use of model-based control, however, a central problem that has yet to be addressed is the robustness of the control system to plant-model mismatches that could arise due to things like fouling in heat-exchanger systems or deactivation of catalysts in catalytic reactors.

Motivated by these considerations, this dissertation aims to develop an identification-integrated model-based control framework that enhances the performance and security of networked control systems, and address framework implementation issues such as communication resource constraints, lack of full state measurements, distributed systems, communication strategies, nonlinearities, and measurement errors. Finally, the implementation and effectiveness of the developed framework through simulated chemical process examples.

With the increasing penetration of intermittent energy resources such as solar and wind power, the issue of instantaneous mismatch between power generation and power demand has become increasingly severe and difficult to deal with. Adapting to such an energy paradigm, electricity markets have become more and more volatile, jeopardizing the stability of the power grid and potentially causing significant damage to the network. One solution to address such a challenge is to adopt Demand Response (DR) policies, where the consumers of electricity will modify or reduce their electricity usage from their normal consumption patterns responding to the changes in the electricity price or incentives offered. As one of the largest electricity consumers, the process industry could potentially benefit from demand response operation, such as lowering the production capacity when the electricity price is high and raising it when the electricity price is lower. However, such flexible process operation usually depends on two main factors: fast response to the price or incentive signals and large capacity allowing flexible operation, both of which will depend on the underlying process design. While process operation under a volatile electricity market has attracted a lot of attention from researchers, the design of DR-enabled chemical processes has received limited scrutiny.

Motivated by above considerations, this dissertation focuses on developing a process design and analysis framework to integrate process design and process operation, with the consideration of DR. The first part of the dissertation will focus on developing the framework to design process networks, allowing varied operating levels, as well as enabling optimal process flow sheet reconfiguration under different DR scenarios. The second part of the dissertation will focus on developing the framework and proposing new metrics to evaluate and quantify the ability and potential of the process to participate in DR programs (i.e., DR-particibility).