Electric Power Grid Restoration Considering Disaster Economics

This paper presents a cost-effective system-level restoration scheme to improve power grids resilience by efficient response to the damages due to natural or manmade disasters. A post-disaster decision making model is developed to find the optimal repair schedule, unit commitment solution, and system configuration in restoration of the damaged power grid. The physical constraints of the power grid, associated with the unit commitment and restoration, are considered in the proposed model. The value of lost load is used as a viable measure to represent the criticality of each load in the power grid. The model is formulated as a mixed-integer program and, then, is decomposed into an integer master problem and a dual linear subproblem to be solved using Benders decomposition algorithm. Different scenarios are developed to analyze the proposed model on the standard IEEE 118-bus test system. This paper provides a prototype and a proof of concept for utility companies to consider economics of disaster and include unit commitment model into the post-disaster restoration process

Electric Power Grid Restoration Considering Disaster Economics

This paper presents a cost-effective system-level restoration scheme to improve power grids resilience by efficient response to the damages due to natural or manmade disasters. A post-disaster decision making model is developed to find the optimal repair schedule, unit commitment solution, and system configuration in restoration of the damaged power grid. The physical constraints of the power grid, associated with the unit commitment and restoration, are considered in the proposed model. The value of lost load is used as a viable measure to represent the criticality of each load in the power grid. The model is formulated as a mixed-integer program and, then, is decomposed into an integer master problem and a dual linear subproblem to be solved using Benders decomposition algorithm. Different scenarios are developed to analyze the proposed model on the standard IEEE 118-bus test system. This paper provides a prototype and a proof of concept for utility companies to consider economics of disaster and include unit commitment model into the post-disaster restoration process

Case-Based Reasoning for Cash Flow Forecasting using Fuzzy Retrieval

Case-Based Reasoning (CBR) simulates human way of solving problems as it solves a new problem using a successful past experience applied to a similar problem. In this paper we describe a CBR system that performs forecasts for cash flow accounts. Forecasting cash flows to a certain degree of accuracy, is an important aspect of a Working Capital decision support system. Working Capital (WC) management decisions reflect a choice among different options on how to arrange the cash flow. The decision establishes an actual event in the cash flow and that means that one needs to envision the consequences of such a decision. Hence, forecasting cash flows accurately can minimize losses caused by usually unpredictable events. Cash flows are usually forecasted by a combination of different techniques enhanced by human experts' feelings about the future, which are grounded in past experience. That is what makes the use of the CBR paradigm the proper choice. Advantages of a CBR system over other A..

Proactive Recovery of Electric Power Assets for Resiliency Enhancement

This paper presents a significant change in current electric power grid response and recovery schemes by developing a framework for proactive recovery of electric power assets with the primary objective of resiliency enhancement. Within the proposed framework, which can potentially present the next generation decision-making tool for proactive recovery, several coordinated models will be developed including: 1) the outage models to indicate the impact of hurricanes on power system components; 2) a stochastic pre-hurricane crew mobilization model for managing resources before the event; and 3) a deterministic post-hurricane recovery model for managing resources after the event. Proposed models will be extended to ensure applicability to a variety of electric power grids with different technologies and regulatory issues. The theoretical and practical implications of the developed models will push the research frontier of proactive response and recovery schemes in electric power grids, while its flexibility will support application to a variety of infrastructures, in response to a wide range of extreme weather events and natural disasters

System Hardening and Condition-Based Maintenance for Electric Power Infrastructure Under Hurricane Effects

The devastating impact of hurricanes on electric power systems calls for development of sophisticated asset management strategies for utility infrastructure. El Niño/La Niña are shown to have seasonal effects on hurricane arrivals in the long-term climatological horizon. The periodic effects of such natural phenomena need to be analyzed and incorporated into decision making processes for strategic asset management of power systems. In this paper, an integrated infrastructure hardening and condition-based maintenance scheduling model for critical components of the power systems is proposed. Partially observable Markov decision processes are used to formulate the problem. The survival function against hurricanes is derived as a dynamic stress-strength model, and is incorporated in the proposed framework. A generalized formulation for two hardening strategies, i.e., with temporary and permanent hardening effects, is proposed. Case studies are conducted to analyze the model and further illustrate the effectiveness of the proposed strategy

Stochastic Pre-hurricane Restoration Planning for Electric Power Systems Infrastructure

Proactive preparedness to cope with emergencies, especially those of nature origins, significantly improves the resilience and minimizes the restoration cost of electric power systems. In this paper, a proactive resource allocation model for repair and restoration of potential damages to the power system infrastructure located on the path of an upcoming hurricane is proposed. The objective is to develop an efficient framework for system operators to minimize potential damages to power system components in a cost-effective manner. The problem is modeled as a stochastic integer program with complete recourse. The large-scale mixed-integer equivalence of the original model is solved by the Benders\u27 decomposition method to handle computation burden. The standard IEEE 118-bus system is employed to demonstrate the effectiveness of the proposed model and further discuss its merits

with emphasis in Production Engineering.

Esta tese foi julgada adequada para a obtenção título de “Doutor em Engenharia”, especialidade Engenharia de Produção e aprovada em sua forma final pelo Programa de Pós-graduação