Data Driven Synthetic Load Modeling for Smart City Energy Management Studies

The primary aim of this dissertation is to provide synthetic residential load models with granular level information on the customers having information about the appliances that constitute each individual residential customer through time. The synthetic load model is capable of being widely utilized by the power system research community since only publicly available data is utilized for its generation. This gives researcher’s access to how the synthetic load was made and also how accurate the model is in representing real power system regions. As the title of the dissertation suggests, the synthetic residential load models are intended for smart city energy management studies. Smart city energy management studies have the ability to control tens of thousands of electricity customers in a coordinated manner to enact system-wide electric load changes. Such load changes have the potential to reduce congestion (i.e. stress on power system components) and peak demand (i.e. the need for peaking generation), among other benefits. For smart city energy management studies to have the capability of evaluating how their strategies would impact the actual power system, datasets that accurately characterize the system load are required that also contain individual loads of all buildings in a given area. Currently, such data is publicly unavailable due to privacy concerns. This dissertation’s synthetic residential load model combines a top down and bottom up approach for modeling individual residential customers and their individual electric assets, each possessing their own characteristics, using time-varying queueing models. The aggregation of all customer loads created by the queueing models represents a known city-sized load curve to be used in smart city energy management studies. The dissertation presents three queueing residential load models that make use of only publicly available data to alleviate privacy concerns. The proposed approach is mainly driven by the aggregated distribution companies load. An open-source Python tool to allow researchers to generate residential load data for their studies is also provided. The simulation results comparing the three queueing synthetic load models consider the ComEd region (utility company from Chicago, IL) to demonstrate the model’s characteristics, impact of the choice of model parameters, and scalability performance of the Python tool. The developed residential synthetic queueing load models are utilized to create the Midwest 240-Node distribution test case system, which generates appliance-level synthetic residential load for 1,120 homes for the Iowa State distribution system test case with 193 load nodes over three feeders. The Midwest 240-Node is a real distribution system from the Midwest region of the U.S. with real one-year smart meter data at the hourly aggregated node level resolution for 2017 available in an OpenDSS model. The synthetic residential queueing load model generated for the Midwest 240-Node one-year date has a mean absolute percentage error of 2.5828% in relation to the real smart meter data. The Midwest 240-Node distribution system OpenDSS model was converted to GridLAB-D to enable smart grid and transactive energy studies. The percentage of maximum error observed on voltage magnitude from the OpenDSS to GridLAB-D model is below 0.0009%. The GridLAB-D model and the generated synthetic residential load is made publicly available. The Midwest 240-Node real distribution system with the synthetic residential load that follows the real data from smart meters is intended to be a distributed energy active consumer test system network. The focus of the developed synthetic residential load models is smart city energy management studies; however, they can be utilized in many power systems studies to evaluate economic and technical impacts of distributed energy resources. For example, this dissertation also presents the utilization of the synthetic models for a PV rich low voltage network. The main component of the smart grid is demand response. Demand response, or energy management, utilizes commonly passive load in to active power system resources. Residential demand response, when aggregated, is capable of performing system-wide changes that enable its participation in the power system markets. This dissertation developed residential synthetic models to enable the standardization of approaches and allow different approaches to be compared under the same environment

Sistema multiagente para proteção adaptativa de retaguarda de linha de distribuição aplicado em ambiente de microrredes

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2016.Este trabalho propõe um esquema de proteção de retaguarda de sobrecorrente direcional adaptativo para as linhas de uma microrrede baseado na utilização de Sistemas Multiagentes. Esta função de proteção foi escolhida devido a necessidade de proteção de retaguarda para as linhas da microrrede. A proteção primária da microrrede utiliza uma arquitetura multiagente hierárquica, que já havia sido proposta em trabalhos anteriores, e depende do sistema de comunicação para sua operação. Os geradores síncronos da microrrede possuem diversas proteções de retaguarda, que também já haviam sido propostas. A proteção adaptativa de retaguarda de linha, foco do trabalho, utiliza um agente extra, o qual é responsável pelo planejamento da coordenação das proteções de sobrecorrente e, caso necessário, o mesmo calcula novos ajustes de proteção. Todas as estratégias de proteção foram programadas em linguagem JAVA, utilizando os recursos oferecidos pelo framework JADE (Java Agent Development Framework). A validação do sistema de proteção multiagente adaptativo de retaguarda proposto é realizado através de simulações. Para a realização das simulações uma microrrede baseada em uma rede de distribuição real, que foi adaptada para apresentar possibilidade de operação, tanto ilhada quanto conectada à concessionária, foi escolhida como sistema teste. A microrrede do caso teste foi modelada no software PSCAD/EMTDC. No mesmo software diversas situações de falta foram realizadas para a validação do sistema de proteção proposto. Os resultados apresentados comprovam a eficácia do sistema multiagente de proteção proposto.Abstract : This dissertation proposes an adaptive directional overcurrent backup protection scheme for the lines of a microgrid, based on the use of Multi-Agent Systems. This protection function was chosen in order to supply the need for microgrid lines backup protection. The primary protection of the microgrid makes use of a hierarchical multi-agent architecture that had been proposed in previous works and its operation depends on the communication system. The synchronous generators of the microrgrid have several backup protections functions, who have also been previously proposed. An adaptive line backup protection is the focus of the present work. It utilizes an additional agent, which is responsible for planning the coordination of overcurrent protections functions, and if necessary, it calculates new protection settings. All these protection strategies have been programmed in Java language, using the resources offered by the JADE framework (Java Agent Development Framework). The validation of the proposed backup adaptive multi-agent protection system is carried out by simulations. It was chosen a microgrid test system based on a real distribution network, which has been adapted to present possibility of operating both as islanded mode and connected to de main grid mode, to perform the simulations. The microrrede test case was modeled in PSCAD / EMTDC software. In the same software various faults situations were performed to validate the proposed protection system. The presented results demonstrate the effectiveness of the proposed multi-agent protection scheme

Physiological, biochemical, and ultrastructural characterization of selenium toxicity in cowpea plants

Selenium (Se) is considered a beneficial element for plants; however, in high concentrations, it causes negative effects on plant physiology and development. This study reports the first physiological, nutritional, and ultrastructural description of Se toxicity in cowpea growing under field conditions. Selenium was supplied as a foliar application of sodium selenite at varying concentrations (0, 50, 100, 200, 400, 800, 1200, and 1600 g ha−1). An increased yield was observed with the application of 50 g ha−1 Se. Application of concentrations higher than 50 g ha−1 caused leaf toxicity. Increased lipid peroxidation and hydrogen peroxide concentration and reduced total sugars, sucrose, and carotenoid concentration were observed at highest doses tested (1200 and 1600 g ha−1). Applications of more than 50 g ha−1 Se reduced the phloem diameter, caused chlorosis of the leaf blade with a coalescence of lesions, and caused pink salt deposits to appear. Lesions were observed mainly near the trichomes on the adaxial surface of the leaf blade. An analysis of the element distribution with microprobe X-ray fluorescence spectrometry (μ-XRF) revealed accumulation of Se, calcium (Ca), potassium (K), copper (Cu), and manganese (Mn) near the primary vein and in the necrotic brown areas of the leaf lesions. In contrast, Na was homogeneously distributed in the leaf tissue

Multi-agent dual strategy based adaptive protection for microgrids

This paper presents an adaptive protection dual strategy based on Relay Agents distributed along strategic locations of a microgrid. The architecture is based on the application of Relay Agents and three additional agents named Selectivity, Configurator, and Coordinator, executed in a microgrid central controller. The multi-agent strategy is divided in two complementary approaches, named online and offline. The online approach operates on fast protection, centralizing key information and selecting actions based on expert systems, thus requiring a communication infrastructure to eventually operate circuit breakers. The offline approach works as a backup protection in case of communication failures and delays, operating by adjusting relay settings in real time. The dual multi-agent protection strategy has been simulated to verify the performance of microgrid protection both in connected and isolated modes

A Risk-Based Framework for Power System Modeling to Improve Resilience to Extreme Events

The extent of the damage to Puerto Rico from Hurricane Maria in September 2017 led to outages in electricity service that persisted for months. Power system operators attempting to restore critical facilities faced challenges on almost every front, from supply chain interruptions to the inaccessibility of key assets. After a disaster of this magnitude, it is critical, but challenging, to prioritize how limited resources are directed toward rebuilding and fortifying the electric power system. To inform these decisions, the U.S. Department of Energy funded efforts investigating methodologies to identify critical vulnerabilities to the Puerto Rican power system, and to provide data-driven recommendations on how to harden and operate the system for greater resilience. This work presents the Risk-based Contingency Analysis Tool (RCAT), a framework developed as a part of that resilience initiative. The framework can qualitatively and quantitatively describe the most critical system vulnerabilities with an understanding of both likelihood of occurrence and impact. It evaluates the effectiveness of candidate remediation strategies in reducing overall risk to the system from future hurricane events. This paper will describe RCAT, with an emphasis on how different modeling capabilities have been integrated along with probabilistic methods and analytical metrics to better describe risk