A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Imagem da loja: estudo empírico de uma loja de ótica

Dissertação Apresentada ao Instituto Politécnico do Porto para obtenção do Grau de Mestre em Gestão das Organizações, Ramo Gestão de Empresas Orientada pelo Professor Doutor José de Freitas Santos

A satisfação profissional dos enfermeiros especialistas em enfermagem de reabilitação

Em termos profissionais, a satisfação no trabalho é sem dúvida um tema bastante debatido e atual. Neste sentido, este estudo teve como objetivo analisar a satisfação profissional dos enfermeiros especialistas em enfermagem de reabilitação e também averiguar se o local de trabalho ou o exercício de cuidados de especialidade influenciam a satisfação profissional deste grupo de enfermeiros. A satisfação profissional foi avaliada através da aplicação da “Escala de Avaliação da Satisfação no Trabalho dos Enfermeiros”, construída e validada por Frederico & Loureiro (2009) a 306 profissionais de Enfermagem, especialistas em enfermagem de reabilitação. O Alpha de Cronbach foi de 0,85, refletindo um bom nível de consistência interna. Foram analisadas seis dimensões da satisfação: satisfação com as chefias; satisfação com benefícios e recompensas; satisfação com a natureza do trabalho; satisfação com a comunicação; satisfação com a equipa e satisfação com os requisitos do trabalho. Foi realizado um estudo transversal analítico. Na análise estatística dos dados, recorreu-se à análise fatorial, ao coeficiente de correlação de Spearman, a testes paramétricos t-student para amostras independentes e One-Way ANOVA. Os resultados mostram que os enfermeiros especialistas em enfermagem de reabilitação encontram-se ligeiramente insatisfeitos. Os fatores de insatisfação estão relacionados com benefícios e recompensas, requisitos do trabalho e com a comunicação. A natureza do trabalho e o relacionamento com a equipa são fatores com os quais obtêm maior satisfação. Em relação às chefias existe uma aproximação à satisfação. Relativamente à avaliação do grau de satisfação profissional global, concluiu-se que os enfermeiros que exercem funções nos Cuidados de Saúde Primários encontram-se mais insatisfeitos profissionalmente, do que os que exercem a sua atividade profissional ao nível hospitalar. Os enfermeiros que exercem cuidados gerais apresentam-se mais insatisfeitos do que os que exercem cuidados de especialidade. Também a idade e a remuneração pelo cargo desempenhado são fatores determinantes. Os enfermeiros mais jovens são os que se apresentam mais insatisfeitos. Os enfermeiros que não são remunerados pelo cargo desempenhado demonstram maior insatisfação profissional.

Consumers Performance Evaluation of the Participation in Demand Response Programs Using Baseline Methods

Demand response has gain increasing importance in the context of competitive electricity markets environment. The use of demand resources is also advantageous in the context of smart grid operation. In addition to the need of new business models for integrating demand response, adequate methods are necessary for an accurate determination of the consumers’ performance evaluation after the participation in a demand response event. The present paper makes a comparison between some of the existing baseline methods related to the consumers’ performance evaluation, comparing the results obtained with these methods and also with a method proposed by the authors of the paper. A case study demonstrates the application of the referred methods to real consumption data belonging to a consumer connected to a distribution network.

Analysis of Consumption Data to Detect Commercial Losses Using Performance Evaluation Methods in a Smart Grid

Electric power networks, namely distribution networks, have been suffering several changes during the last years due to changes in the power systems operation, towards the implementation of smart grids. Several approaches to the operation of the resources have been introduced, as the case of demand response, making use of the new capabilities of the smart grids. In the initial levels of the smart grids implementation reduced amounts of data are generated, namely consumption data. The methodology proposed in the present paper makes use of demand response consumers’ performance evaluation methods to determine the expected consumption for a given consumer. Then, potential commercial losses are identified using monthly historic consumption data. Real consumption data is used in the case study to demonstrate the application of the proposed method.

Performance of state space and ARIMA models for consumer retail sales forecasting

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.