Matching Items (3)
Description
The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation.
ContributorsHaghnevis, Moeed (Author) / Askin, Ronald G. (Thesis advisor) / Armbruster, Dieter (Thesis advisor) / Mirchandani, Pitu (Committee member) / Wu, Tong (Committee member) / Hedman, Kory (Committee member) / Arizona State University (Publisher)
Created2013
Description
The grand transition of electric grids from conventional fossil fuel resources to intermittent bulk renewable resources and distributed energy resources (DERs) has initiated a paradigm shift in power system operation. Distributed energy resources (i.e. rooftop solar photovoltaic, battery storage, electric vehicles, and demand response), communication infrastructures, and smart measurement devices provide the opportunity for electric utility customers to play an active role in power system operation and even benefit financially from this opportunity. However, new operational challenges have been introduced due to the intrinsic characteristics of DERs such as intermittency of renewable resources, distributed nature of these resources, variety of DERs technologies and human-in-the-loop effect. Demand response (DR) is one of DERs and is highly influenced by human-in-the-loop effect. A data-driven based analysis is implemented to analyze and reveal the customers price responsiveness, and human-in-the-loop effect. The results confirm the critical impact of demographic characteristics of customers on their interaction with smart grid and their quality of service (QoS). The proposed framework is also applicable to other types of DERs. A chance-constraint based second-order-cone programming AC optimal power flow (SOCP-ACOPF) is utilized to dispatch DERs in distribution grid with knowing customers price responsiveness and energy output distribution. The simulation shows that the reliability of distribution gird can be improved by using chance-constraint.
ContributorsHe, Mingyue (Author) / Khorsand, Mojdeh (Thesis advisor) / Vittal, Vijay (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2019
Description
With demand for increased efficiency and smaller carbon footprint, power system operators are striving to improve their modeling, down to the individual consumer device, paving the way for higher production and consumption efficiencies and increased renewable generation without sacrificing system reliability. This dissertation explores two lines of research. The first part looks at stochastic continuous-time power system scheduling, where the goal is to better capture system ramping characteristics to address increased variability and uncertainty. The second part of the dissertation starts by developing aggregate population models for residential Demand Response (DR), focusing on storage devices, Electric Vehicles (EVs), Deferrable Appliances (DAs) and Thermostatically Controlled Loads (TCLs). Further, the characteristics of such a population aggregate are explored, such as the resemblance to energy storage devices, and particular attentions is given to how such aggregate models can be considered approximately convex even if the individual resource model is not. Armed with an approximately convex aggregate model for DR, how to interface it with present day energy markets is explored, looking at directions the market could go towards to better accommodate such devices for the benefit of not only the prosumer itself but the system as a whole.
ContributorsHreinsson, Kári (Author) / Scaglione, Anna (Thesis advisor) / Hedman, Kory (Committee member) / Zhang, Junshan (Committee member) / Alizadeh, Mahnoosh (Committee member) / Arizona State University (Publisher)
Created2020