Conceção, seleção e implementação de um sistema de informação: ERP - enterprise resource planning

As organizações são entidades de natureza sistémica, composta, na sua maioria por várias pessoas que interagindo entre si, se propõem atingir objetivos comuns. Têm, frequentemente, de responder a alterações da envolvente externa através de processos de mudança organizacional, sendo fundamentalmente adaptativas, pois, para sobreviver, precisam de se reajustar continuamente às condições mutáveis do meio. O sucesso das organizações depende da sua capacidade de interação com o meio envolvente, ou seja, da sua capacidade de inovar e operar local ou globalmente, criando novas oportunidades de negócio que importa aproveitar. As tecnologias e os sistemas de informação e a forma como são utilizadas são fatores determinantes nesses processos de evolução e mudança. É necessário que a estratégia de TI esteja alinhada com os objetivos de negócio e que a sua utilização contribua para aumentos de produtividade e de eficiência no seu desempenho. Este trabalho descreve a análise, conceção, seleção e implementação de um Sistema de Informação na Portgás, S.A. baseado de um ERP - Enterprise Resource Planning, capaz de suportar a mudança organizacional e melhorar o desempenho global da organização. Promovendo numa primeira fase um crescimento exponencial do negócio e, de seguida, a adaptação da organização ao mercado concorrencial. O caso descreve o trabalho realizado pelo candidato e por equipas internas e externas, de levantamentos de requisitos gerais, técnicos e funcionais, desenvolvimento de um caderno de encargos, seleção, implementação e exploração de um ERP SAP. A apresentação e discussão do caso são enquadradas numa revisão de literatura sobre o papel das TI nos processos de mudança organizativa, alinhamento estratégico e vantagem competitiva das TI, contributo das TI para o aumento da produtividade, processos adoção e difusão das TI, fatores críticos de sucesso e BPM –Business Process Management

O Contributo do sistema SAP R/3 no controlo interno na REFER

Dissertação apresentada ao Instituto Superior de Contabilidade para a obtenção do Grau de Mestre em Auditoria Orientador: Mestre Agostinho Sousa Pinto

Desenvolvimento e aluguer de software de faturação

As aplicações de Gestão ou Faturação são uma presença indispensável hoje em dia. Tendo o seu início nas aplicações “MS-DOS” em modo de texto, estas aplicações acompanharam a evolução dos sistemas operativos adotando um ambiente gráfico de forma natural. Se há poucos anos apenas as empresas com volumes de negócio significativo possuíam software de faturação, este foi sendo adotado por cada vez mais empresas e pequenos negócios. As alterações legislativas introduzidas desde 2011 conduziram a uma adoção generalizada por parte de pequenas e microempresas. O mercado de aplicações de gestão está saturado pelos grandes produtores de software nacionais: Primavera, Sage, etc. Estas aplicações, tendo sido construídas para PMEs (Pequenas e Médias Empresas) e mesmo grandes empresas, são excessivamente complexas e onerosas para muito pequenas e microempresas. O Modelo de negócio destes produtores de software é primordialmente a venda de Licenças e contratos de Manutenção, nalguns casos através de redes de Agentes. Este projeto teve como objetivo o desenvolvimento de uma Aplicação de Faturação, de baixo custo, simples e cross-platform para ser comercializada em regime de aluguer em Pequenas e Micro Empresas.

Adaptation and Localization of Open Source ERPs - The case of Parties in OFBiz

O software tem vindo a tornar-se uma parte importante de qualquer empresa, cobrindo várias áreas funcionais, tais como manufaturação, vendas ou recursos humanos. O facto de uma empresa possuir um software capaz de ligar todas ou a maior parte das suas áreas funcionais e de acomodar as suas regras de negócio permite que estas tenham acesso a dados em tempo real nos quais se podem basear para tomar decisões. Estes tipos de software podem ser categorizados como Enterprise resource planning (ERP). Tendo em conta que estes tipos de software têm um papel importante dentro de uma empresa, a aquisição dos mesmos é algo que deve ser bem estudado. As grandes empresas normalmente optam pela aquisição de soluções comerciais uma vez que estas tendem a ter mais funcionalidades, maior suporte e certificações. Os ERPs comerciais representam, no entanto, um esforço elevado para que a sua compra possa ser feita, o que limita a possibilidade de aquisição dos mesmos por parte de pequenas ou médias empresas. No entanto, tal como acontece com a maior parte dos tipos de software, existem alternativas open-source. Se nos colocássemos na posição de uma pequena empresa, a tentar iniciar o seu negócio em Portugal, que tipo de ERP seria suficiente para os nossos requisitos? Teríamos que optar por comprar uma solução comercial, ou uma solução open-source seria suficiente? E se optássemos por desenvolver uma solução à medida? Esta tese irá responder a estas questões focando-se apenas num dos componentes base de qualquer ERP, a gestão de entidades. O componente de gestão de entidades é responsável por gerir todas as entidades com as quais a empresa interage abrangindo colaboradores, clientes, fornecedores, etc. A nível de funcionalidades será feita uma comparação entre um ERP comercial e um ERP open-source. Como os ERPs tendem a ser soluções muito genéricas é comum que estes não implementem todos os requisitos de um negócio em particular, como tal os ERPs precisam de ser extensíveis e adaptáveis. Para perceber até que ponto a solução open-source é extensível será feita uma análise técnica ao seu código fonte e será feita uma implementação parcial de um gerador de ficheiros de auditoria requerido pela lei Portuguesa, o SAF-T (PT). Ao estudar e adaptar a solução open-source podemos especificar o que teria que ser desenvolvido para podermos criar uma solução à medida de raiz.

Real-time Energy Resource Scheduling considering a Real Portuguese Scenario

The development in power systems and the introduction of decentralized generation and Electric Vehicles (EVs), both connected to distribution networks, represents a major challenge in the planning and operation issues. This new paradigm requires a new energy resources management approach which considers not only the generation, but also the management of loads through demand response programs, energy storage units, EVs and other players in a liberalized electricity markets environment. This paper proposes a methodology to be used by Virtual Power Players (VPPs), concerning the energy resource scheduling in smart grids, considering day-ahead, hour-ahead and real-time scheduling. The case study considers a 33-bus distribution network with high penetration of distributed energy resources. The wind generation profile is based on a real Portuguese wind farm. Four scenarios are presented taking into account 0, 1, 2 and 5 periods (hours or minutes) ahead of the scheduling period in the hour-ahead and realtime scheduling.

Energy resource scheduling in a real distribution network managed by several virtual power players

Smart Grids (SGs) appeared as the new paradigm for power system management and operation, being designed to integrate large amounts of distributed energy resources. This new paradigm requires a more efficient Energy Resource Management (ERM) and, simultaneously, makes this a more complex problem, due to the intensive use of distributed energy resources (DER), such as distributed generation, active consumers with demand response contracts, and storage units. This paper presents a methodology to address the energy resource scheduling, considering an intensive use of distributed generation and demand response contracts. A case study of a 30 kV real distribution network, including a substation with 6 feeders and 937 buses, is used to demonstrate the effectiveness of the proposed methodology. This network is managed by six virtual power players (VPP) with capability to manage the DER and the distribution network.

Energy resource scheduling considering reactive power management

The reactive power management is an important task in future power systems. The control of reactive power allows the increase of distributed energy resources penetration as well as the optimal operation of distribution networks. Currently, the control of reactive power is only controlled in large power units and in high and very high voltage substations. In this paper a reactive power control in smart grids paradigm is proposed, considering the management of distributed energy resources and of the distribution network by an aggregator namely Virtual Power Player (VPP).

Short-term scheduling considering five-minute and hour-ahead energy resource management

The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Intelligent electric vehicle heuristic for energy resource management using simulated annealing

The smart grid concept appears as a suitable solution to guarantee the power system operation in the new electricity paradigm with electricity markets and integration of large amounts of Distributed Energy Resources (DERs). Virtual Power Player (VPP) will have a significant importance in the management of a smart grid. In the context of this new paradigm, Electric Vehicles (EVs) rise as a good available resource to be used as a DER by a VPP. This paper presents the application of the Simulated Annealing (SA) technique to solve the Energy Resource Management (ERM) of a VPP. It is also presented a new heuristic approach to intelligently handle the charge and discharge of the EVs. This heuristic process is incorporated in the SA technique, in order to improve the results of the ERM. The case study shows the results of the ERM for a 33-bus distribution network with three different EVs penetration levels, i. e., with 1000, 2000 and 3000 EVs. The results of the proposed adaptation of the SA technique are compared with a previous SA version and a deterministic technique.

Virtual power players dealing with excessive wind power situations using real time pricing

The increasing importance given by environmental policies to the dissemination and use of wind power has led to its fast and large integration in power systems. In most cases, this integration has been done in an intensive way, causing several impacts and challenges in current and future power systems operation and planning. One of these challenges is dealing with the system conditions in which the available wind power is higher than the system demand. This is one of the possible applications of demand response, which is a very promising resource in the context of competitive environments that integrates even more amounts of distributed energy resources, as well as new players. The methodology proposed aims the maximization of the social welfare in a smart grid operated by a virtual power player that manages the available energy resources. When facing excessive wind power generation availability, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. The proposed method is especially useful when actual and day-ahead wind forecast differ significantly. The proposed method has been computationally implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20310 consumers and 548 distributed generators, some of them with must take contracts.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

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.