Data-Driven Optimization in Power Systems Operations

Today's power systems are large scale systems consisting of multiple generating stations, load zones (distributors or utilities) and very complex interconnected power transmission networks. One of the major issues for power systems operators is that they face with several sources of uncertainty such as equipment failure uncertainty and demand uncertainty in the power systems operations. Moreover, the growing trend in renewable energy capacity installments has added a higher level of uncertainty to power systems operations. Therefore, uncertainty management has recently become one of the most challenging issues in power system operations and control.However, in many cases, partial information of the uncertain parameters are available. Distributionally robust optimization is a newly emerged optimization approach to address optimization problems under uncertainty with partial information. In this study, we develop efficient distributionally robust optimization models to address several challenging problems arising in power systems operations.First, we propose a data-driven approach to solve the stochastic transmission expansion planning problem under demand uncertainty. Then, we develop a data-driven approach to deal with the stochastic transmission system hardening planning problem in the presence of wind generation uncertainty and multiple simultaneous disruptive events. Afterward, we propose two reliability analysis schemes for the power transmission system hardening under distributional uncertainty of random contingencies. Finally, we present a data-driven chance-constrained stochastic unit commitment (power generation scheduling) under wind power uncertainty, in which the chance constraint controls and limits the level of energy imbalance. In all cases, we reformulate the original problems to two-stage stochastic mixed integer programs. Then, we deploy decomposition approaches to solve the developed models.Industrial Engineering and Managemen

A multi-disaster-scenario distributionally robust planning model for enhancing the resilience of distribution systems

Resilience oriented network planning provides an effective solution to protect the distribution system from natural disasters by the pre-planned line hardening and backup generator allocation. In this paper, a multi-disaster-scenario based distributionally robust planning model (MDS-DRM) is proposed to hedge against two types of natural disaster-related uncertainties: random offensive resources (ORs) of various natural disasters, and random probability distribution of line outages (PDLO) that are incurred by a certain natural disaster. The OR uncertainty is represented by the defined probability-weighted scenarios with stochastic programming, and the PDLO uncertainty is modeled as the moment based ambiguity sets. Moreover, the disaster recovery strategies of network reconfiguration and microgrid formation are integrated into the pre-disaster network planning for resilience enhancement in both planning and operation stages. Then, a novel primal cut based decomposition solution method is proposed to improve the computational efficiency of the proposed model. In particular, the equivalent reformulation of the original MDS-DRM is first derived to eliminate the PDLO-related variables. Then, the reformulation problem is solved by the proposed primal cut based decomposition method and linearization techniques. Finally, Simulation results are demonstrated for IEEE 13-node, 33-node and 135-node distribution systems to validate the effectiveness of the proposed method in enhancing the disaster-induced network resilience

Optimization under uncertainty models in power systems operations

Uncertainty is a critical issue in many power system problems. While distributed energy resources (DERs) like solar panels and wind turbines are exciting energy sources in meeting the nation's increasing energy demand, backing up the electricity grid in the event of outages, and peak shaving in the case of high demand charges, they also introduce new difficulties to the operation of power systems. One of the primary hurdles is the stochasticity of renewable energy generation caused by variations in day-to-day weather. If not properly addressed, this can lead to rolling blackouts and other detrimental outcomes in the grid. In addition, modern energy infrastructure is highly vulnerable to increasingly severe weather conditions. Because of inherently unpredictable weather conditions and intricacy of power systems, evaluating and mitigating the underlying risk of power system interruption are highly demanding for the system operators. This introduces new degrees of uncertainty that must be accounted for by power production facilities and system operators.This study explores reformulations as well as approximation approaches to derive innovative decision-making under uncertainty models in the power system management. In particular, using techniques in Stochastic Programming, Robust Optimization, and Distributionally Robust Optimization, different uncertainty management schema are developed for protecting power grids from adversarial environments and accommodating renewable energy resources in the optimization of power system operations to provide resilient, reliable and cost-effective daily power generation scheduling.More specifically, we begin with developing an incentive-based coordination mechanism between a wind energy supplier and a conventional energy supplier to hedge against the risks of electricity market price and wind power generation. Then, we address the energy management problem of a portfolio of DERs, a virtual power plant (VPP), to characterize and evaluate the standard attributes/parameters in the VPP's bid submitted to the energy market. Finally, we propose a data-driven model to assist the system operators to reduce the impacts of random component failures. In particular, a distributionally robust model is devised for designing a distribution power system to withstand the risk of disruptions imposed by natural disasters

Microgrids for power system resilience enhancement

Power system resilience is defined as the ability of power grids to anticipate, withstand, adapt and recover from high-impact low-probability (HILP) events. There are both long-term and short-term measures that system operators can employ for resilience reinforcement. Longer-term measures include infrastructure hardening and resilient planning, while short-term operational measures are applied in the pre-event, during-event and post-event phases. Microgrids (MGs) can effectively enhance resilience for both transmission and distribution systems, due to their ability to operate in a controlled, coordinated way, when connected to the main power grid and in islanded mode. In this paper, MG-based strategies for resilience enhancement are presented, including MG-based resilient planning and MG-based operational measures, consisting of preventive MG scheduling and emergency measures and MG-based system restoration. Classification of literature is made by considering whether the transmission system, distribution system or individual MG resilience is targeted. The way uncertainties are handled by various methods is also outlined. Finally, challenges and future research requirements for improving MG-based power system resilience are highlighted

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

Persuasion, Political Warfare, and Deterrence: Behavioral and Behaviorally Robust Models

This dissertation examines game theory models in the context of persuasion and competition wherein decision-makers are not completely rational by considering two complementary threads of research. The first thread of research pertains to offensive and preemptively defensive behavioral models. Research in this thread makes three notable contributions. First, an offensive modeling framework is created to identify how an entity optimally influences a populace to take a desired course of action. Second, a defensive modeling framework is defined wherein a regulating entity takes action to bound the behavior of multiple adversaries simultaneously attempting to persuade a group of decision-makers. Third, an offensive influence modeling framework under conditions of ambiguity is developed in accordance with historical information limitations, and we demonstrate how it can be used to select a robust course of action on a specific, data-driven use case. The second thread of research pertains to behavioral and behaviorally robust approaches to deterrence. Research in this thread makes two notable contributions. First, we demonstrate the alternative insights behavioral game theory generates for the analysis of classic deterrence games, and explicate the rich analysis generated from its combined use with standard equilibrium models. Second, we define behaviorally robust models for an agent to use in a normal form game under varying forms of uncertainty in order to inform deterrence policy decisions