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.

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 .

An optimal scheduling problem in distribution networks considering V2G

This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.

Emergency energy resource management in SmartGrids

Energy resources management can play a very relevant role in future power systems in a SmartGrid context, with intensive penetration of distributed generation and storage systems. This paper deals with the importance of resource management in incident situations. The paper presents DemSi, an energy resources management simulator that has been developed by the authors to simulate electrical distribution networks with high distributed generation penetration, storage in network points and customers with demand response contracts. DemSi is used to undertake simulations for an incident scenario, evidencing the advantages of adequately using flexible contracts, storage, and reserve in order to limit incident consequences.

Combining artificial intelligence techniques for the training of power system control centre operators

Control Centre operators are essential to assure a good performance of Power Systems. Operators’ actions are critical in dealing with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in incident analysis and diagnosis, and service restoration of Power Systems, offering context awareness and an easy integration in the working environment.

Caracterização de águas do processo e optimização dos consumos de água nos vários circuitos da Petibol, S.A. – Embalagens de Plástico

A empresa Petibol, S.A. – Embalagens de plástico centra-se na produção de embalagens de plástico a partir da matéria-prima Poliestireno Expandido (EPS) e Polipropileno Expandido (EPP). A empresa possui uma preocupação ao nível da qualidade da água e do aproveitamento energético, tendo desta forma surgido a realização do estudo na unidade industrial, com o objectivo de anular e/ou diminuir as possíveis lacunas existentes na unidade industrial. Numa primeira etapa foi realizada uma caracterização global à qualidade da água e à empresa, actualizando-se os esquemas já existentes, contabilizando-se os custos actuais relativamente aos processos no circuito da água (arrefecimento, aquecimento e pressurização), e por fim, efectuou-se um levantamento in loco do circuito de água, relativamente à pressão, temperatura e caudal. Numa fase posterior, foram propostos equipamentos e processos, tendo em vista a colmatação dos problemas identificados, realizando-se um subsequente estudo relativamente aos custos inerentes a esses novos processos. A caracterização à água foi avaliada em diferentes pontos do circuito industrial, tendo-se determinado na Sala de Bombagem que o filtro de areia não possuía as dimensões mais apropriadas, existindo também um problema a nível mecânico associado ao processo de contra-lavagem. Tais factos podem ser a causa da ocorrência de um aumento do teor de sólidos após a passagem na camada filtrante. Relativamente ao amaciador, este deveria amaciar de forma completa a água para alimentação à caldeira, embora se tenha registado à saída do amaciador uma dureza de 21,3 mg/L, denunciando problemas na troca iónica. No que toca à água de alimentação à caldeira, verifica-se a existência de parâmetros que não se encontram de acordo com os critérios enunciados para uma óptima qualidade, sendo eles o pH (10,14), condutividade (363 μS/cm), teor de ferro (1,21 mg/L) e a dureza (16 mg/L). De salientar que somente o teor de cobre, que se encontra em quantidades vestigiais, apresenta-se de acordo com os valores impostos. No que respeita à água da caldeira, esta apresenta parâmetros incompatíveis com os recomendados, sendo eles a condutividade (7350 μS/cm), teor de sólidos dissolvidos (5248 mg/L) e alcalinidade total (780 mg/L). De referir que o valor de pH (11,8) não se encontra de acordo com a aplicação do tratamento “fosfato-pH coordenado”. Em relação aos parâmetros com valores que se encontram dentro dos limites, estes correspondem à dureza (0 mg/L), ao teor de fosfatos (45 mg/L) e teor de sílica (0 mg/L). A água do circuito de arrefecimento foi sujeita a uma análise microbiológica, que corroborou a presença de um biofilme. Um dos problemas enunciados pela empresa, prendia-se com a impossibilidade de descarga, no colector municipal, dos condensados dos compressores, visto apresentarem uma quantidade de óleo de cerca de 43,3 mg/L, equivalente a quatro vezes o valor limite de emissão, de acordo com a legislação municipal. Por fim, o efluente de descarga industrial apresenta um valor de pH (10,3) acima do intervalo permitido por lei (6,0 – 9,0), sendo que a corrente que mais contribui para este acréscimo de pH corresponde à corrente proveniente da água de purga, visto esta apresentar um valor de pH de 12,22. De maneira a contornar os parâmetros enunciados, é proposto a substituição do filtro de areia da Sala de Bombagem, assim como a inserção de um conjunto de medidas de remoção de ferro e desinfecção, sendo a conjugação de arejamento, coagulação, filtração e desinfecção, por parte do hipoclorito, a proposta apresentada. Aos condensados dos compressores é apresentado um sistema de separação, que possibilita a remoção do óleo da água, e uma consequente descarga da mesma. Actualmente, não existe qualquer filtro de areia no circuito de arrefecimento da água, sendo proposto assim esse equipamento, de forma a minorar o desenvolvimento da população microbiana, bem como a permitir uma maior eficiência na transferência de calor na torre de arrefecimento. Relativamente à descarga industrial, é recomendável a colocação de um sistema de regularização automática de pH. A inserção de uma válvula de três vias permite um aproveitamento energético e de água, a partir da confluência da água oriunda dos furos com a água do tanque de água fria, sendo posteriormente alimentada à central de vácuo. No estudo da recuperação energética, um outro equipamento avaliado correspondeu à serpentina, no entanto, verificou-se que a poupança no consumo de gás natural era de apenas 0,005%, o que não se mostrou uma proposta viável. O orçamento de todos os equipamentos é de 11.720,76 €, possibilitando não só um melhor funcionamento industrial, como um menor impacto a nível ambiental. Os custos futuros de funcionamento aumentam em 3,36%, tendo a pressurização um aumento do seu custo em 3,4% em relação ao custo actual, verificando-se um custo anual de 10.781,21€, em relação aos processos de arejamento, coagulação e desinfecção.

Energy resource management in smart grids considering an Intensive use of electric vehicles

The introduction of electricity markets and integration of Distributed Generation (DG) have been influencing the power system’s structure change. Recently, the smart grid concept has been introduced, to guarantee a more efficient operation of the power system using the advantages of this new paradigm. Basically, a smart grid is a structure that integrates different players, considering constant communication between them to improve power system operation and management. One of the players revealing a big importance in this context is the Virtual Power Player (VPP). In the transportation sector the Electric Vehicle (EV) is arising as an alternative to conventional vehicles propel by fossil fuels. The power system can benefit from this massive introduction of EVs, taking advantage on EVs’ ability to connect to the electric network to charge, and on the future expectation of EVs ability to discharge to the network using the Vehicle-to-Grid (V2G) capacity. This thesis proposes alternative strategies to control these two EV modes with the objective of enhancing the management of the power system. Moreover, power system must ensure the trips of EVs that will be connected to the electric network. The EV user specifies a certain amount of energy that will be necessary to charge, in order to ensure the distance to travel. The introduction of EVs in the power system turns the Energy Resource Management (ERM) under a smart grid environment, into a complex problem that can take several minutes or hours to reach the optimal solution. Adequate optimization techniques are required to accommodate this kind of complexity while solving the ERM problem in a reasonable execution time. This thesis presents a tool that solves the ERM considering the intensive use of EVs in the smart grid context. The objective is to obtain the minimum cost of ERM considering: the operation cost of DG, the cost of the energy acquired to external suppliers, the EV users payments and remuneration and penalty costs. This tool is directed to VPPs that manage specific network areas, where a high penetration level of EVs is expected to be connected in these areas. The ERM is solved using two methodologies: the adaptation of a deterministic technique proposed in a previous work, and the adaptation of the Simulated Annealing (SA) technique. With the purpose of improving the SA performance for this case, three heuristics are additionally proposed, taking advantage on the particularities and specificities of an ERM with these characteristics. A set of case studies are presented in this thesis, considering a 32 bus distribution network and up to 3000 EVs. The first case study solves the scheduling without considering EVs, to be used as a reference case for comparisons with the proposed approaches. The second case study evaluates the complexity of the ERM with the integration of EVs. The third case study evaluates the performance of scheduling with different control modes for EVs. These control modes, combined with the proposed SA approach and with the developed heuristics, aim at improving the quality of the ERM, while reducing drastically its execution time. The proposed control modes are: uncoordinated charging, smart charging and V2G capability. The fourth and final case study presents the ERM approach applied to consecutive days.

Melhoria do processo produtivo da Yazaki Porto Technical Centre

No actual contexto macroeconómico, a melhoria dos processos e o aproveitamento de todas as sinergias, são factores que se tornaram ainda mais importantes e nalguns casos, condição sine qua non para a sobrevivência das próprias empresas. Cada vez mais as empresas são obrigadas a produzir mais com menos recursos, com a qualidade desejada pelos clientes e a preços competitivos. A qualidade do produto final não deve ser afectada com a desculpa da implementação de uma política da redução de custos. Pelo contrário, deve existir a preocupação de eliminar da cadeia de valor tudo o que não contribui com valor acrescentado, melhorando nalguns casos a própria qualidade do produto final. A realização deste projecto tem como objectivo, analisar e implementar, através de ferramentas relacionadas com a metodologia Lean, melhorias na produção de aplicadores de cravação numa empresa multinacional ligada ao ramo automóvel. Pretende-se um aumento da taxa de produção diária em 50%, obtida essencialmente através do balanceamento dos recursos humanos e no desenvolvimento de um sistema kanban incorporado no sector produtivo. A parte inicial do trabalho incidiu no estudo e análise do produto e respectivo processo produtivo. Posteriormente e por várias fases efectuaram-se análises aos tempos de fabrico e ao sequenciamento das operações, com vista ao conhecimento de todo o processo de montagem de modo a identificar os aspectos de melhoria. Após o registo dos pontos a eliminar e/ou a melhorar, procedeu-se a uma análise criteriosa dos dados recolhidos, efectuando-se o balanceamento dos recursos de modo a tornar eficaz a implementação do sistema kanban. Este sistema é a base da melhoria proposta para este tema de dissertação. Após implementação do sistema kanban, foi avaliado o seu desempenho e foram registadas melhorias na produção diária dos aplicadores bem como no lead time despendido no processamento dos mesmos.

Distribution system operation supported by contextual energy resource management based on intelligent SCADA

Future distribution systems will have to deal with an intensive penetration of distributed energy resources ensuring reliable and secure operation according to the smart grid paradigm. SCADA (Supervisory Control and Data Acquisition) is an essential infrastructure for this evolution. This paper proposes a new conceptual design of an intelligent SCADA with a decentralized, flexible, and intelligent approach, adaptive to the context (context awareness). This SCADA model is used to support the energy resource management undertaken by a distribution network operator (DNO). Resource management considers all the involved costs, power flows, and electricity prices, allowing the use of network reconfiguration and load curtailment. Locational Marginal Prices (LMP) are evaluated and used in specific situations to apply Demand Response (DR) programs on a global or a local basis. The paper includes a case study using a 114 bus distribution network and load demand based on real data.