Computational intelligence applications for future power systems

Power system planning, control and operation require an adequate use of existing resources as to increase system efficiency. The use of optimal solutions in power systems allows huge savings stressing the need of adequate optimization and control methods. These must be able to solve the envisaged optimization problems in time scales compatible with operational requirements. Power systems are complex, uncertain and changing environments that make the use of traditional optimization methodologies impracticable in most real situations. Computational intelligence methods present good characteristics to address this kind of problems and have already proved to be efficient for very diverse power system optimization problems. Evolutionary computation, fuzzy systems, swarm intelligence, artificial immune systems, neural networks, and hybrid approaches are presently seen as the most adequate methodologies to address several planning, control and operation problems in power systems. Future power systems, with intensive use of distributed generation and electricity market liberalization increase power systems complexity and bring huge challenges to the forefront of the power industry. Decentralized intelligence and decision making requires more effective optimization and control techniques techniques so that the involved players can make the most adequate use of existing resources in the new context. The application of computational intelligence methods to deal with several problems of future power systems is presented in this chapter. Four different applications are presented to illustrate the promises of computational intelligence, and illustrate their potentials.

Computational analysis of quinoxalines N,N-Dioxide and ITS Derivates

A quinoxalina e seus derivativos são uma importante classe de compostos heterocíclicos, onde os elementos N, S e O substituem átomos de carbono no anel. A fórmula molecular da quinoxalina é C8H6N2, formada por dois anéis aromáticos, benzeno e pirazina. É rara em estado natural, mas a sua síntese é de fácil execução. Modificações na estrutura da quinoxalina proporcionam uma grande variedade de compostos e actividades, tais como actividades antimicrobiana, antiparasitária, antidiabética, antiproliferativa, anti-inflamatória, anticancerígena, antiglaucoma, antidepressiva apresentando antagonismo do receptor AMPA. Estes compostos também são importantes no campo industrial devido, por exemplo, ao seu poder na inibição da corrosão do metal. A química computacional, ramo natural da química teórica é um método bem desenvolvido, utilizado para representar estruturas moleculares, simulando o seu comportamento com as equações da física quântica e clássica. Existe no mercado uma grande variedade de ferramentas informaticas utilizadas na química computacional, que permitem o cálculo de energias, geometrias, frequências vibracionais, estados de transição, vias de reação, estados excitados e uma variedade de propriedades baseadas em várias funções de onda não correlacionadas e correlacionadas. Nesta medida, a sua aplicação ao estudo das quinoxalinas é importante para a determinação das suas características químicas, permitindo uma análise mais completa, em menos tempo, e com menos custos.

Application of computational intelligence to engineering

Computational Intelligence (CI) includes four main areas: Evolutionary Computation (genetic algorithms and genetic programming), Swarm Intelligence, Fuzzy Systems and Neural Networks. This article shows how CI techniques overpass the strict limits of Artificial Intelligence field and can help solving real problems from distinct engineering areas: Mechanical, Computer Science and Electrical Engineering.

Lanczos algorithm on the grassmann manifold

5th. European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2008) 8th. World Congress on Computational Mechanics (WCCM8)

Biochemical and structural characterization of β-lactamases tem-180 and tem-201

With the constant development of new antibiotics, selective pressure is a force to reckon when investigating antibiotic resistance. Although advantageous for medical treatments, it leads to increasing resistance. It is essential to use more potent and toxic antibiotics. Enzymes capable of hydrolyzing antibiotics are among the most common ways of resistance and TEM variants have been detected in several resistant isolates. Due to the rapid evolution of these variants, complex phenotypes have emerged and the need to understand their biological activity becomes crucial. To investigate the biochemical properties of TEM-180 and TEM-201 several computational methodologies have been used, allowing the comprehension of their structure and catalytic activity, which translates into their biological phenotype. In this work we intent to characterize the interface between these proteins and the several antibiotics used as ligands. We performed explicit solvent molecular dynamics (MD) simulations of these complexes and studied a variety of structural and energetic features. The interfacial residues show a distinct behavior when in complex with different antibiotics. Nevertheless, it was possible to identify some common Hot Spots among several complexes – Lys73, Tyr105 and Glu166. The structural changes that occur during the Molecular Dynamic (MD) simulation lead to the conclusion that these variants have an inherent capacity of adapting to the various antibiotics. This capability might be the reason why they can hydrolyze antibiotics that have not been described until now to be degraded by TEM variants. The results obtained with computational and experimental methodologies for the complex with Imipenem have shown that in order to this type of enzymes be able to acylate the antibiotics, they need to be capable to protect the ligand from water molecules.

Towards an efficient and robust foot classification from pedobarographic images

O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).

A novel framework for modeling value for the customer, an essay on negotiation

This paper proposes a novel framework for modelling the Value for the Customer, the so-called the Conceptual Model for Decomposing Value for the Customer (CMDVC). This conceptual model is first validated through an exploratory case study where the authors validate both the proposed constructs of the model and their relations. In a second step the authors propose a mathematical formulation for the CMDVC as well as a computational method. This has enabled the final quantitative discussion of how the CMDVC can be applied and used in the enterprise environment, and the final validation by the people in the enterprise. Along this research, we were able to confirm that the results of this novel quantitative approach to model the Value for the Customer is consistent with the company's empirical experience. The paper further discusses the merits and limitations of this approach, proposing that the model is likely to bring value to support not only the contract preparation at an Ex-Ante Negotiation Phase, as demonstrated, but also along the actual negotiation process, as finally confirmed by an enterprise testimonial.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Meta-heuristics self-configuration for scheduling

Scheduling resolution requires the intervention of highly skilled human problemsolvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. This paper addresses the resolution of complex scheduling problems using cooperative negotiation. A Multi-Agent Autonomic and Meta-heuristics based framework with self-configuring capabilities is proposed.

A hybrid intelligent system for distributed dynamic scheduling

This chapter addresses the resolution of dynamic scheduling by means of meta-heuristic and multi-agent systems. Scheduling is an important aspect of automation in manufacturing systems. Several contributions have been proposed, but the problem is far from being solved satisfactorily, especially if scheduling concerns real world applications. The proposed multi-agent scheduling system assumes the existence of several resource agents (which are decision-making entities based on meta-heuristics) distributed inside the manufacturing system that interact with other agents in order to obtain optimal or near-optimal global performances.

Decision support system with incomplete and domain incoherent information management

Many of the most common human functions such as temporal and non-monotonic reasoning have not yet been fully mapped in developed systems, even though some theoretical breakthroughs have already been accomplished. This is mainly due to the inherent computational complexity of the theoretical approaches. In the particular area of fault diagnosis in power systems however, some systems which tried to solve the problem, have been deployed using methodologies such as production rule based expert systems, neural networks, recognition of chronicles, fuzzy expert systems, etc. SPARSE (from the Portuguese acronym, which means expert system for incident analysis and restoration support) was one of the developed systems and, in the sequence of its development, came the need to cope with incomplete and/or incorrect information as well as the traditional problems for power systems fault diagnosis based on SCADA (supervisory control and data acquisition) information retrieval, namely real-time operation, huge amounts of information, etc. This paper presents an architecture for a decision support system, which can solve the presented problems, using a symbiosis of the event calculus and the default reasoning rule based system paradigms, insuring soft real-time operation with incomplete, incorrect or domain incoherent information handling ability. A prototype implementation of this system is already at work in the control centre of the Portuguese Transmission Network.

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.

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.

LMP based bid formation for virtual power players operating in smart grids

Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.