Projetos geotérmicos para usos diretos em Portugal Continental: tipologias e viabilidade económica

Os aproveitamentos geotérmicos têm vindo a aumentar significativamente em todo o mundo, sendo os Estados Unidos da América, o maior produtor desta energia proveniente do interior da Terra, com cerca de 3.187 MW de capacidade instalada. Portugal tem capacidade instalada total de 29 MW, no entanto no que se refere ao aproveitamento de “alta entalpia”, isto é, o aproveitamento geotérmico para produção elétrica, apenas se encontra no arquipélago dos Açores, na ilha de S. Miguel, onde estão instaladas e em funcionamento duas centrais geotérmicas com a potência total de 23 MW, com produção de energia de 185 GWh. Em Portugal Continental, não se consegue produzir energia elétrica devido às temperaturas existentes, restringindo esta utilização apenas ao aproveitamento de baixa entalpia (máximo de 76 ºC). Este aproveitamento normalmente é feito em cascata, segundo, predominando o aquecimento de águas sanitárias, climatização, e para termas, usando águas termominerais. Para a exploração deste recurso renovável, é necessário conhecer a hidrogeologia do país, e relacioná-la com a fracturação, e acidentes tectónicos. Portugal Continental, está divido em quatros partes distintas a nível hidrogeológico, o Maciço Antigo, a Orla Ocidental, a Bacia Tejo-Sado e a Orla Meridional. Qualquer aproveitamento geotérmico em Portugal terá de atender a estas características, potenciando também, novas explorações geotérmicas orientadas para as pessoas, respeitando os valores sociais, culturais e ambientais. Neste contexto, existem alguns complexos geotérmicos em funcionamento, outros abandonados, e muitos outros em estudo para uma breve aplicação. Um exemplo de sucesso no aproveitamento do calor geotérmico, é o complexo de Chaves, que foi evoluindo desde 1985, até aos dias de hoje, continuando em exploração e em expansão para um melhor servir da população local. A existência de dois furos, e brevemente dum terceiro, servem para o abastecimento duma piscina, dum hotel, das termas, e da balneoterapia. Devido à riqueza a nível das temperaturas, dos caudais, e ao nível das necessidades energéticas existentes, este complexo apresenta um tempo de retorno de investimento de cerca de 7 anos, o que é geralmente considerado para investimentos para fins públicos, como é o caso. No âmbito das investigações agora realizadas, foi constatado que estes projetos suportam a cobertura de alguma incerteza hidrogeológica, dada a importante procura existente.

Avaliação de Impacto Ambiental, Valorização do Efluente e dos Resíduos Sólidos da Indústria Corticeira

A preocupação com o meio ambiente, nomeadamente na descarga de águas residuais, consumo de água excessivo e produção de resíduos industriais, está cada vez mais presente no quotidiano. Devido a estas problemáticas, efetuou-se a avaliação de impacte ambiental (AIA) do processo produtivo das rolhas de cortiça naturais, tratamento das águas de cozedura da cortiça (estudo da possível reutilização do efluente tratado) e valorização de subprodutos – resíduo sólido (raspa de cortiça), sendo estes os objetivos propostos para a realização da presente dissertação. Na AIA, efetuada no decorrer das fases da Análise do Ciclo de Vida (ACV), foram selecionadas 8 categorias de impacte – aquecimento global, acidificação, dessecação, toxicidade e ecotoxicidade, eutrofização, consumo de recursos não renováveis e oxidação foto-química. A água de cozedura caracterizou-se por uma elevada carga poluente, apresentando elevada concentração de cor, Carência Química de Oxigénio (CQO), taninos e lenhina e Sólidos Suspensos Totais (SST). O processo de tratamento proposto consistiu num pré-tratamento por ultrafiltração (UF), com membranas de 30.000 e 20.000 MWCO, seguido de adsorção por carvão ativado (comercial e produzido a partir de raspa de cortiça). No tratamento por UF, utilizando uma membrana de 30.000 MWCO, foram obtidas percentagens de remoção para a primeira amostra de água de cozedura de 74,8 % para a cor, 33,1 % para a CQO e para a segunda amostra de 85,2 % para a cor e 41,8 % para a CQO. Posteriormente, apenas para a segunda amostra de água de cozedura e com uma membrana de 20.000 MWCO, as percentagens de remoção obtidas foram superiores, de 93% para a cor, 68,9 % para a CQO, 88,4 % para taninos e lenhina e 43,0 % para azoto total. No tratamento por adsorção com carvão ativado estudou-se o tempo de equilíbrio do carvão ativado comercial e do carvão ativado produzido a partir de aparas de cortiça, seguindo-se o estudo das isotérmicas de adsorção, no qual foram analisados os parâmetros da cor e CQO para cada solução. Os ajustes dos modelos teóricos aos pontos experimentais demonstraram que ambos os modelos (Langmuir e Freundlich) poderiam ser considerados, uma vez que apresentaram ajustes idênticos. Relativamente ao tratamento de adsorção em contínuo do permeado, obtido por UF com membrana de 20.000 MWCO, constatou-se que ambos os carvões ativados (comercial e produzido) não ficaram saturados, tendo em consideração os tempos de saturação estimados pela capacidade máxima de adsorção (determinada para a isotérmica de Langmuir) e as representações gráficas dos valores experimentais obtidos para cada ensaio. No ensaio de adsorção com carvão ativado comercial verificou-se que o efluente tratado poderia ser descarregado no meio hídrico ou reutilizado no processo industrial (considerando os parâmetros analisados), uma vez que até aos 11 minutos de ensaio a concentração da solução à saída foi de 111,50 mg/L O2, para a CQO, e incolor, numa diluição de 1:20. Em relação à adsorção em contínuo com carvão ativado produzido verificou-se no ensaio 4 que o efluente resultante apresentou uma concentração de CQO de 134,5 mg/L O2 e cor não visível, numa diluição de 1:20, ao fim de 1h22 min de ensaio. Assim, concluiu-se que os valores obtidos são inferiores aos valores limite de emissão (VLE) presentes no Decreto-Lei n.º 236/98 de 1 de Agosto. O carvão ativado produzido apresentou elevada área superficial específica, com 870 m2/g, comparativamente ao carvão comercial que foi de 661 m2/g. O processo de extração da suberina a partir de raspa de cortiça isenta de extraíveis, efetuado através da metanólise alcalina, apresentou percentagens de extração superiores aos restantes métodos. No processo efetuado em scale-up, por hidrólise alcalina, obteve-se uma extração de 3,76 % de suberina. A aplicação da suberina no couro demonstrou que esta cera apresenta enormes potencialidades, uma vez que a sua aplicação confere ao couro um aspeto sedoso, com mais brilho e um efeito de “pull-up”.

Optimal methodology for renewable energy dispatching in islanded operation

The management of energy resources for islanded operation is of crucial importance for the successful use of renewable energy sources. A Virtual Power Producer (VPP) can optimally operate the resources taking into account the maintenance, operation and load control considering all the involved cost. This paper presents the methodology approach to formulate and solve the problem of determining the optimal resource allocation applied to a real case study in Budapest Tech’s. The problem is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The problem has also been solved by Evolutionary Particle Swarm Optimization (EPSO). The obtained results are presented and compared.

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.

A New Heuristic Providing an Effective Initial Solution for a Simulated Annealing approach to Energy Resource Scheduling in Smart Grids

An intensive use of dispersed energy resources is expected for future power systems, including distributed generation, especially based on renewable sources, and electric vehicles. The system operation methods and tool must be adapted to the increased complexity, especially the optimal resource scheduling problem. Therefore, the use of metaheuristics is required to obtain good solutions in a reasonable amount of time. This paper proposes two new heuristics, called naive electric vehicles charge and discharge allocation and generation tournament based on cost, developed to obtain an initial solution to be used in the energy resource scheduling methodology based on simulated annealing previously developed by the authors. The case study considers two scenarios with 1000 and 2000 electric vehicles connected in a distribution network. The proposed heuristics are compared with a deterministic approach and presenting a very small error concerning the objective function with a low execution time for the scenario with 2000 vehicles.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

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.

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.

Intelligent energy resources management in the context of smart grids

Smart grids are envisaged as infrastructures able to accommodate all centralized and distributed energy resources (DER), including intensive use of renewable and distributed generation (DG), storage, demand response (DR), and also electric vehicles (EV), from which plug-in vehicles, i.e. gridable vehicles, are especially relevant. Moreover, smart grids must accommodate a large number of diverse types or players in the context of a competitive business environment. Smart grids should also provide the required means to efficiently manage all these resources what is especially important in order to make the better possible use of renewable based power generation, namely to minimize wind curtailment. An integrated approach, considering all the available energy resources, including demand response and storage, is crucial to attain these goals. This paper proposes a methodology for energy resource management that considers several Virtual Power Players (VPPs) managing a network with high penetration of distributed generation, demand response, storage units and network reconfiguration. The resources are controlled through a flexible SCADA (Supervisory Control And Data Acquisition) system that can be accessed by the evolved entities (VPPs) under contracted use conditions. A case study evidences the advantages of the proposed methodology to support a Virtual Power Player (VPP) managing the energy resources that it can access in an incident situation.