A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

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.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Optimization model for medium term natural gas commitment

In this paper we study the optimal natural gas commitment for a known demand scenario. This study implies the best location of GSUs to supply all demands and the optimal allocation from sources to gas loads, through an appropriate transportation mode, in order to minimize total system costs. Our emphasis is on the formulation and use of a suitable optimization model, reflecting real-world operations and the constraints of natural gas systems. The mathematical model is based on a Lagrangean heuristic, using the Lagrangean relaxation, an efficient approach to solve the problem. Computational results are presented for Iberian and American natural gas systems, geographically organized in 65 and 88 load nodes, respectively. The location model results, supported by the computational application GasView, show the optimal location and allocation solution, system total costs and suggest a suitable gas transportation mode, presented in both numerical and graphic supports.

Natural gas system operation: lagrangean optimization techniques

To comply with natural gas demand growth patterns and Europe´s import dependency, the gas industry needs to organize an efficient upstream infrastructure. The best location of Gas Supply Units – GSUs and the alternative transportation mode – by phisical or virtual pipelines, are the key of a successful industry. In this work we study the optimal location of GSUs, as well as determining the most efficient allocation from gas loads to sources, selecting the best transportation mode, observing specific technical restrictions and minimizing system total costs. For the location of GSUs on system we use the P-median problem, for assigning gas demands nodes to source facilities we use the classical transportation problem. The developed model is an optimisation-based approach, based on a Lagrangean heuristic, using Lagrangean relaxation for P-median problems – Simple Lagrangean Heuristic. The solution of this heuristic can be improved by adding a local search procedure - the Lagrangean Reallocation Heuristic. These two heuristics, Simple Lagrangean and Lagrangean Reallocation, were tested on a realistic network - the primary Iberian natural gas network, organized with 65 nodes, connected by physical and virtual pipelines. Computational results are presented for both approaches, showing the location gas sources and allocation loads arrangement, system total costs and gas transportation mode.

Particle swarm optimization applied to integrated demand response resources scheduling

The concept of demand response has a growing importance in the context of the future power systems. Demand response can be seen as a resource like distributed generation, storage, electric vehicles, etc. All these resources require the existence of an infrastructure able to give players the means to operate and use them in an efficient way. This infrastructure implements in practice the smart grid concept, and should accommodate a large number of diverse types of players in the context of a competitive business environment. In this paper, demand response is optimally scheduled jointly with other resources such as distributed generation units and the energy provided by the electricity market, minimizing the operation costs from the point of view of a virtual power player, who manages these resources and supplies the aggregated consumers. The optimal schedule is obtained using two approaches based on particle swarm optimization (with and without mutation) which are compared with a deterministic approach that is used as a reference methodology. A case study with two scenarios implemented in DemSi, a demand Response simulator developed by the authors, evidences the advantages of the use of the proposed particle swarm approaches.

Technical and economic resources management in smart grids using heuristic optimization methods

Intensive use of Distributed Generation (DG) represents a change in the paradigm of power systems operation making small-scale energy generation and storage decision making relevant for the whole system. This paradigm led to the concept of smart grid for which an efficient management, both in technical and economic terms, should be assured. This paper presents a new approach to solve the economic dispatch in smart grids. The proposed methodology for resource management involves two stages. The first one considers fuzzy set theory to define the natural resources range forecast as well as the load forecast. The second stage uses heuristic optimization to determine the economic dispatch considering the generation forecast, storage management and demand response

Collective intelligence on dynamic manufacturing scheduling optimization

Swarm Intelligence (SI) is a growing research field of Artificial Intelligence (AI). SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviours of insects and of other animals. This paper presents hybridization and combination of different AI approaches, like Bio-Inspired Techniques (BIT), Multi-Agent systems (MAS) and Machine Learning Techniques (ML T). The resulting system is applied to the problem of jobs scheduling to machines on dynamic manufacturing environments.

Numerical optimization experiments using the hyperbolic smoothing strategy to solve MPCC

In this work we solve Mathematical Programs with Complementarity Constraints using the hyperbolic smoothing strategy. Under this approach, the complementarity condition is relaxed through the use of the hyperbolic smoothing function, involving a positive parameter that can be decreased to zero. An iterative algorithm is implemented in MATLAB language and a set of AMPL problems from MacMPEC database were tested.

The development and optimization of a modified single-drop microextraction method for organochlorine pesticides determination by gas chromatography-tandem mass spectrometry

We have developed a new method for single-drop microextraction (SDME) for the preconcentration of organochlorine pesticides (OCP) from complex matrices. It is based on the use of a silicone ring at the tip of the syringe. A 5 μL drop of n-hexane is applied to an aqueous extract containing the OCP and found to be adequate to preconcentrate the OCPs prior to analysis by GC in combination with tandem mass spectrometry. Fourteen OCP were determined using this technique in combination with programmable temperature vaporization. It is shown to have many advantages over traditional split/splitless injection. The effects of kind of organic solvent, exposure time, agitation and organic drop volume were optimized. Relative recoveries range from 59 to 117 %, with repeatabilities of <15 % (coefficient of variation) were achieved. The limits of detection range from 0.002 to 0.150 μg kg−1. The method was applied to the preconcentration of OCPs in fresh strawberry, strawberry jam, and soil.

Natural organic matter fractionation along the treatment of water for human consumption

The main objective of this study was to characterize the organic matter present in raw water and along the treatment process, as well as its seasonal variation. A natural organic matter fractionation approach has been applied to Lever water treatment plant located in Douro River, in Oporto (Portugal). The process used was based on the sorption of dissolved organic matter in different types of ion exchange resins, DAX-8, DAX-4 and IRA-958, allowing its separation into four fractions: very hydrophobic acids (VHA), slightly hydrophobic acids (SHA), charged hydrophilic (CHA) and hydrophilic neutral (NEU). The dissolved organic carbon (DOC) determination was used to quantify dissolved organic matter. Samples were collected monthly, during approximately one year, from raw water captured at the surface and under the bed of the river, and after each step of the treatment: pre-filtration in sand/anthracite filters, ozonation, coagulation/flocculation, counter current dissolved air flotation and filtration (CoCoDAFF) and chlorination. The NEU fraction showed a seasonal variation, with maximum values in autumn for the sampling points corresponding to raw water captured at the surface and under the bed of the river. It was usually the predominating fraction and did not show a significant decrease throughout the treatment. Nevertheless their low concentration, the same occurred for the CHA and VHA fractions. There was an overall decrease in the SHA fraction throughout the water treatment (especially after CoCoDAFF and ozonation) as well as in the DOC. The TSUVA254 values obtained for raw water generally varied between 2.0 and 4.0 L mgC-1 m-1 and between 0.75 and 1.78 L mgC-1 m-1 for treated water. It was observed a decrease of TSUVA values along the treatment, especially after ozonation. These results may contribute to a further optimization in the process of treating water for human consumption.

Análise e optimização dos consumos de utilidades (vapor e energia eléctrica) na Petibol – Embalagens de Plásticos, S.A.

Mestrado em Engenharia Química. Ramo optimização energética na indústria química.

Indústria de curtumes: energia e desenvolvimento

Este trabalho, numa fase inicial, teve como pilar fundamental, a optimização energética de uma indústria de curtumes, mais concretamente da empresa Dias Ruivo. Pretendia-se avaliar todos os gastos de energia apresentados pela empresa, com o intuito de verificar se haveria possibilidade de diminuir a factura da electricidade e os custos associados, mantendo a qualidade dos produtos finais. Ainda se pretendia averiguar a viabilidade de reutilização de alguns desperdícios permitindo diminuir a quantidade de energia necessária para o aquecimento de algumas correntes de água. Por último, após os dados recolhidos, sugeriram-se medidas de melhoria, que possibilitariam que a empresa utilizasse os seus recursos de forma optimizada, não apresentando assim gastos desnecessários. Começando pela análise da factura de electricidade, verificou-se que nos anos de 2010 e 2011, a empresa apresentou um consumo de energia de 120 e 128 tep/ano, respectivamente. Determinaram-se igualmente os respectivos indicadores constatando-se que o valor médio para a intensidade carbónica foi de 900 e 1148 kg CO2/tep e para o consumo específico obteve-se 0,131 e 0,152 kgep/ft2, respectivamente. Numa segunda fase, tendo em conta a constante aposta da empresa na inovação de artigos em couro e o facto de estar envolvida num projecto mobilizador de ciência e tecnologia com esse fim, o trabalho incidiu no desenvolvimento de um produto inovador designado por floater que deve ser macio e mais leve que os produtos normais. Com base na aplicação de proteases apropriadas, desenvolveu-se um produto que, ainda na fase de ensaios de bancada e piloto, satisfaz no que respeita à macieza e leveza, sendo que se conseguiu um valor de 67 g/ft2 contra um valor de 75g/ft2 do padrão.

Avaliação de compostos halogenados em água e ar de piscinas

A procura de piscinas para a prática de atividades desportivas, recreativas e/ou terapêuticas tem sofrido um aumento gradual ao longo do tempo. No entanto, nas piscinas existem vários perigos associados à sua utilização. Relativamente aos perigos químicos, a utilização de desinfetantes à base de cloro, bromo ou compostos derivados vai, por um lado, inativar microrganismos patogénicos mas, por outro, dar origem a subprodutos ao reagir com compostos orgânicos presentes na água. Os trihalometanos são um exemplo de subprodutos que se podem formar e, entre os compostos principais, estão o clorofórmio (TCM), bromodiclorometano (BDCM), clorodibromometano (CDBM) e bromofórmio (TBM). Este trabalho teve como objetivo o desenvolvimento de uma metodologia analítica para a determinação de trihalometanos em água e ar de piscinas e a sua aplicação a um conjunto de amostras. Para a análise dos compostos, foi utilizada a microextração em fase sólida no espaço de cabeça (HS-SPME) com posterior quantificação dos compostos por cromatografia gasosa com detetor de captura eletrónica (GC-ECD). Foi realizada uma otimização das condições de extração dos compostos em estudo em amostras de água, através da realização de dois planeamentos experimentais. As condições ótimas são assim obtidas para uma temperatura de extração de 45ºC, um tempo de extração de 25 min e um tempo de dessorção de 5 min. Foram analisadas amostras de águas de piscina cedidas pelo Centro de Estudos de Águas, sendo avaliada a aplicação da técnica HS-SPME e o efeito de matriz. O modo como se manuseiam as soluções que contêm os compostos em estudo influencia os resultados devido ao facto destes serem bastante voláteis. Concluiu-se também que existe efeito de matriz, logo a concentração das amostras deverá ser determinada através do método de adição de padrão. A caraterização da água de piscinas interiores permitiu conhecer a concentração de trihalometanos (THMs). Foram obtidas concentrações de TCM entre 4,5 e 406,5 μg/L sendo que apenas 4 das 27 amostras analisadas ultrapassam o valor limite imposto pelo Decreto-Lei nº306/2007 (100 μg/L) no que diz respeito a águas de consumo humano e que é normalmente utilizado como valor indicativo para a qualidade das águas de piscina. Relativamente à concentração obtida no ar de uma piscina interior, foi detetada uma concentração média de 224 μg/m3 de TCM, valor muito abaixo dos 10000 μg/m3 impostos pelo Decreto-lei nº24/2012, como valor limite para exposição profissional a agentes químicos.

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.