Cournot model with investments to change the market size

We present a new deterministic dynamical model on the market size of Cournot competitions, based on Nash equilibria of R&D investment strategies to increase the size of the market of the firms at every period of the game. We compute the unique Nash equilibrium for the second subgame and the profit functions for both firms. Adding uncertainty to the R&D investment strategies, we get a new stochastic dynamical model and we analyse the importance of the uncertainty to reverse the initial advantage of one firm with respect to the other.

Estudo da norma contabilística e de relato financeiro nº 13 – interesses em empreendimentos conjuntos e investimentos em associadas e suas implicações fiscais e de auditoria

Dissertação apresentada ao Instituto Superior de Contabilidade e Administração do Porto para a obtenção do Grau de Mestre em Auditoria Orientada pelo Dr. José da Silva Fernandes

Expectativas dos stakeholders para as fusões e aquisições: um estudo de caso: aquisição do Finibanco pelo Montepio

Dissertação apresentada ao ISCAP para a obtenção do Grau de Mestre em Auditoria Orientada por: Prof. Doutora Alcina Dias

Sistema de garantia mútua: estudo de caso da “SPGM - Sociedade de Investimento, S.A.”

Dissertação de Mestrado em Contabilidade e Finanças Orientador: Mestre António Pinto Marques

Responsabilidade ambiental: análise das divulgações ambientais das empresas do PSI-20 comparativamente com empresas não cotadas

Orientador: Mestre, António Pinto Marques

Investment optimization in distribution network based on fuzzy outage parameters

This paper presents a methodology that aims to increase the probability of delivering power to any load point of the electrical distribution system by identifying new investments in distribution components. The methodology is based on statistical failure and repair data of the distribution power system components and it uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A mixed integer non-linear optimization technique is developed to identify adequate investments in distribution networks components that allow increasing the availability level for any customer in the distribution system at minimum cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a real distribution network.

A simulation tool for demand response programs implementation

The design and development of simulation models and tools for Demand Response (DR) programs are becoming more and more important for adequately taking the maximum advantages of DR programs use. Moreover, a more active consumers’ participation in DR programs can help improving the system reliability and decrease or defer the required investments. DemSi, a DR simulator, designed and implemented by the authors of this paper, allows studying DR actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. DemSi considers the players involved in DR actions, and the results can be analyzed from each specific player point of view.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Advances in smart grids - benefits on sharing background experiences from Portugal, Central Europe and Brazil

This paper presents ELECON - Electricity Consumption Analysis to Promote Energy Efficiency Considering Demand Response and Non-technical Losses, an international research project that involves European and Brazilian partners. ELECON focuses on energy efficiency increasing through consumer´s active participation which is a key area for Europe and Brazil cooperation. The project aims at significantly contributing towards the successful implementation of smart grids, focussing on the use of new methods that allow the efficient use of distributed energy resources, namely distributed generation, storage and demand response. ELECON puts together researchers from seven European and Brazilian partners, with consolidated research background and evidencing complementary competences. ELECON involves institutions of 3 European countries (Portugal, Germany, and France) and 4 Brazilian institutions. The complementary background and experience of the European and Brazilian partners is of main relevance to ensure the capacities required to achieve the proposed goals. In fact, the European Union (EU) and Brazil have very different resources and approaches in what concerns this area. Having huge hydro and fossil resources, Brazil has not been putting emphasis on distributed renewable based electricity generation. On the contrary, EU has been doing huge investments in this area, taking into account environmental concerns and also the economic EU external dependence dictated by huge requirements of energy related products imports. Sharing these different backgrounds allows the project team to propose new methodologies able to efficiently address the new challenges of smart grids.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

Optimal location of gas supply units in natural gas system network

Natural gas industry has been confronted with big challenges: great growth in demand, investments on new GSUs – gas supply units, and efficient technical system management. The right number of GSUs, their best location on networks and the optimal allocation to loads is a decision problem that can be formulated as a combinatorial programming problem, with the objective of minimizing system expenses. Our emphasis is on the formulation, interpretation and development of a solution algorithm that will analyze the trade-off between infrastructure investment expenditure and operating system costs. The location model was applied to a 12 node natural gas network, and its effectiveness was tested in five different operating scenarios.

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.

Monitorização integrada para sistemas de alimentação distribuídos

Mestrado em Engenharia Electrotécnica e de Computadores

Demand response in future power systems management – a conceptual framework and simulation tool

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Melhoria do fluxo produtivo de uma linha de fabrico

Mestrado em Engenharia Electrotécnica e de Computadores