Multiarea optimal power flow using multiobjective evolutionary algorithm

In this work the multiarea optimal power flow (OPF) problem is decoupled into areas creating a set of regional OPF subproblems. The objective is to solve the optimal dispatch of active and reactive power for a determined area, without interfering in the neighboring areas. The regional OPF subproblems are modeled as a large-scale nonlinear constrained optimization problem, with both continuous and discrete variables. Constraints violated are handled as objective functions of the problem. In this way the original problem is converted to a multiobjective optimization problem, and a specifically-designed multiobjective evolutionary algorithm is proposed for solving the regional OPF subproblems. The proposed approach has been examined and tested on the RTS-96 and IEEE 354-bus test systems. Good quality suboptimal solutions were obtained, proving the effectiveness and robustness of the proposed approach. ©2009 IEEE.

Optimal allocation of distributed generators providing reactive power support ancillary service

Problems as voltage increase at the end of a feeder, demand supply unbalance in a fault condition, power quality decline, increase of power losses, and reduction of reliability levels may occur if Distributed Generators (DGs) are not properly allocated. For this reason, researchers have been employed several solution techniques to solve the problem of optimal allocation of DGs. This work is focused on the ancillary service of reactive power support provided by DGs. The main objective is to price this service by determining the costs in which a DG incurs when it loses sales opportunity of active power, i.e, by determining the Loss of Opportunity Costs (LOC). The LOC will be determined for different allocation alternatives of DGs as a result of a multi-objective optimization process, aiming the minimization of losses in the lines of the system and costs of active power generation from DGs, and the maximization of the static voltage stability margin of the system. The effectiveness of the proposed methodology in improving the goals outlined was demonstrated using the IEEE 34 bus distribution test feeder with two DGs cosidered to be allocated. © 2011 IEEE.

An unequivocal normalization-based paradigm to solve dynamic economic and emission active-reactive OPF (optimal power flow)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Interior-exterior point method with global convergence strategy for solving the reactive optimal power flow problem

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Differential evolution based total transfer capability assessment with optimal dispatch

In this paper, a new differential evolution (DE) based power system optimal available transfer capability (ATC) assessment is presented. Power system total transfer capability (TTC) is traditionally solved by the repeated power flow (RPF) method and the continuation power flow (CPF) method. These methods are based on the assumption that the productions of the source area generators are increased in identical proportion to balance the load increment in the sink area. A new approach based on DE algorithm to generate optimal dispatch both in source area generators and sink area loads is proposed in this paper. This new method can compute ATC between two areas with significant improvement in accuracy compared with the traditional RPF and CPF based methods. A case study using a 30 bus system is given to verify the efficiency and effectiveness of this new DE based ATC optimization approach.

Optimal dispatch of spinning reserve in a competitive electricity market using genetic algorithms

Ancillary service plays a key role in maintaining operation security of the power system in a competitive electricity market. The spinning reserve is one of the most important ancillary services that should be provided effectively. This paper presents the design of an integrated market for energy and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the cost of service while maintaining system security. Genetic algorithms (GA) are used for finding the global optimal solutions for this dispatch problem. Case studies and corresponding analyses have been carried out to demonstrate and discuss the efficiency and usefulness of the proposed method.

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).

Simulated annealing metaheuristic to solve the optimal power flow

The optimal power flow problem has been widely studied in order to improve power systems operation and planning. For real power systems, the problem is formulated as a non-linear and as a large combinatorial problem. The first approaches used to solve this problem were based on mathematical methods which required huge computational efforts. Lately, artificial intelligence techniques, such as metaheuristics based on biological processes, were adopted. Metaheuristics require lower computational resources, which is a clear advantage for addressing the problem in large power systems. This paper proposes a methodology to solve optimal power flow on economic dispatch context using a Simulated Annealing algorithm inspired on the cooling temperature process seen in metallurgy. The main contribution of the proposed method is the specific neighborhood generation according to the optimal power flow problem characteristics. The proposed methodology has been tested with IEEE 6 bus and 30 bus networks. The obtained results are compared with other wellknown methodologies presented in the literature, showing the effectiveness of the proposed method.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context

In the traditional paradigm, the large power plants supply the reactive power required at a transmission level and the capacitors and transformer tap changer were also used at a distribution level. However, in a near future will be necessary to schedule both active and reactive power at a distribution level, due to the high number of resources connected in distribution levels. This paper proposes a new multi-objective methodology to deal with the optimal resource scheduling considering the distributed generation, electric vehicles and capacitor banks for the joint active and reactive power scheduling. The proposed methodology considers the minimization of the cost (economic perspective) of all distributed resources, and the minimization of the voltage magnitude difference (technical perspective) in all buses. The Pareto front is determined and a fuzzy-based mechanism is applied to present the best compromise solution. The proposed methodology has been tested in the 33-bus distribution network. The case study shows the results of three different scenarios for the economic, technical, and multi-objective perspectives, and the results demonstrated the importance of incorporating the reactive scheduling in the distribution network using the multi-objective perspective to obtain the best compromise solution for the economic and technical perspectives.

C-reactive protein in early risk assessment in acute pancreatitis

ABSTRACT:C-reactive protein (CRP) has been widely used in the early risk assessment of patients with acute pancreatitis (AP), but unclear aspects about its prognostic accuracy in this setting persist. This project evaluated first CRP prognostic accuracy for severity, pancreatic necrosis (PNec), and in-hospital mortality (IM) in AP in terms of the best timing for CRP measurement and the optimal CRP cutoff points. Secondly it was evaluated the CRP measured at approximately 24 hours after hospital admission (CRP24) prognostic accuracy for IM in AP individually and in a combined model with a recent developed tool for the early risk assessment of patients with AP, the Bedside Index for Severity in AP (BISAP). Two single-centre retrospective cohort studies were held. The first study included 379 patients and the second study included 134 patients. Statistical methods such as the Hosmer-Lemeshow goodness-of-fit test, the area under the receiver-operating characteristic curve, the net reclassification improvement, and the integrated discrimination improvement were used. It was found that CRP measured at approximately 48 hours after hospital admission (CRP48) had a prognostic accuracy for severity, PNec, and IM in AP better than CRP measured at any other timing. It was observed that the optimal CRP48 cutoff points for severity, PNec, and IM in AP varied from 170mg/l to 190mg/l, values greater than the one most often recommended in the literature – 150mg/l. It was found that CRP24 had a good prognostic accuracy for IM in AP and that the cutoff point of 60mg/l had a negative predictive value of 100%. Finally it was observed that the prognostic accuracy of a combined model including BISAP and CRP24 for IM in AP could perform better than the BISAP alone model. These results might have a direct impact on the early risk assessment of patients with AP in the daily clinical practice.--------- RESUMO: A proteina c-reactiva (CRP) tem sido largamente usada na avaliação precoce do risco em doentes com pancreatite aguda (AP), mas aspectos duvidosos acerca do seu valor prognóstico neste contexto persistem. Este projecto avaliou primeiro o valor prognóstico da CRP para a gravidade, a necrose pancreática (PNec) e a mortalidade intra-hospitalar (IM) na AP em termos do melhor momento para efectuar a sua medição e dos seus pontos-de-corte óptimos. Em segundo lugar foi avaliado o valor prognóstico da proteína c-reactiva medida aproximadamente às 24 horas após a admissão hospitalar (CRP24) para a IM na AP isoladamente e num modelo combinado, que incluiu uma ferramenta de avaliação precoce do risco em doentes com AP recentemente desenvolvida, o Bedside Index for Severity in Acute Pancreatitis (BISAP). Dois estudos unicêntricos de coorte retrospectivo foram realizados. O primeiro estudo incluiu 379 doentes e o segundo estudo incluiu 134 doentes. Metodologias estatísticas como o teste de Hosmer-Lemeshow goodness-of-fit, a area under the receiver-operating characteristic curve, o net reclassification improvement e o integrated discrimination improvement foram usadas. Verificou-se que a CRP medida às 48 horas após a admissão hospitalar (CRP48) teve um valor prognóstico para a gravidade, a PNec e a IM na AP melhor do que a CRP medida em qualquer outro momento. Observou-se que os pontos de corte óptimos da CRP48 para a gravidade, a PNec e a IM na AP variaram entre 170mg/l e 190mg/l, valores acima do valor mais frequentemente recomendado na literatura – 150mg/l. Verificou-se que a CRP medida aproximadamente às 24 horas após a admissão hospitalar (CRP24) teve um bom valor prognóstico para a IM na AP e que o ponto de corte 60mg/l teve um valor preditivo negativo de 100%. Finalmente observou-se que o valor prognóstico de um modelo combinado incluindo o BISAP e a CRP24 para a IM na AP pode ter um desempenho melhor do que o do BISAP isoladamente. Estes resultados podem ter um impacto directo na avaliação precoce do risco em doentes com AP na prática clínica diária.

Evaluation of C-reactive protein, procalcitonin, tumor necrosis factor alpha, interleukin-6, and interleukin-8 as diagnostic parameters in sepsis-related fatalities.

The aims of this study were to investigate the usefulness of serum C-reactive protein, procalcitonin, tumor necrosis factor alpha, interleukin-6, and interleukin-8 as postmortem markers of sepsis and to compare C-reactive protein and procalcitonin values in serum, vitreous humor, and cerebrospinal fluid in a series of sepsis cases and control subjects, in order to determine whether these measurements may be employed for the postmortem diagnosis of sepsis. Two study groups were formed, a sepsis group (eight subjects coming from the intensive care unit of two university hospitals, with a clinical diagnosis of sepsis in vivo) and control group (ten autopsy cases admitted to two university medicolegal centers, deceased from natural and unnatural causes, without elements to presume an underlying sepsis as the cause of death). Serum C-reactive protein and procalcitonin concentrations were significantly different between sepsis cases and control cases, whereas serum tumor necrosis factor alpha, interleukin-6, and interleukin-8 values were not significantly different between the two groups, suggesting that measurement of interleukin-6, interleukin-8, and tumor necrosis factor alpha is non-optimal for postmortem discrimination of cases with sepsis. In the sepsis group, vitreous procalcitonin was detectable in seven out of eight cases. In the control group, vitreous procalcitonin was clearly detectable only in one case, which also showed an increase of all markers in serum and for which the cause of death was myocardial infarction associated with multi-organic failure. According to the results of this study, the determination of vitreous procalcitonin may be an alternative to the serum procalcitonin for the postmortem diagnosis of sepsis.

Enhanced generation of specific tumor-reactive CTL in vitro by selected Melan-A/MART-1 immunodominant peptide analogues.

The Melan-A/MART-1 gene, which is expressed by normal melanocytes as well as by most fresh melanoma samples and melanoma cell lines, codes for Ags recognized by tumor-reactive CTL. HLA-A*0201-restricted Melan-A-specific CTL recognize primarily the Melan-A(27-35) (AAGIGILTV) and the Melan-A(26-35) (EAAGIGILTV) peptides. The sequences of these two peptides are not necessarily optimal as far as binding to HLA-A*0201 is concerned, since both lack one of the dominant anchor amino acid residues (leucine or methionine) at position 2. In this study we introduced single amino acid substitutions in either one of the two natural peptide sequences with the aim of improving peptide binding to HLA-A*0201 and/or recognition by specific CTL. Surprisingly, analogues of the Melan-A(27-35) peptide, which bound more efficiently than the natural nonapeptide to HLA-A*0201, were poorly recognized by tumor-reactive CTL. In contrast, among the Melan-A(26-35) peptide analogues tested, the peptide ELAGIGILTV was not only able to display stable binding to HLA-A2.1 but was also recognized more efficiently than the natural peptide by two short-term cultured tumor-infiltrated lymph node cell cultures as well as by five of five tumor-reactive CTL clones. Moreover, in vitro generation of tumor-reactive CTL by stimulation of PBMC from HLA-A*0201 melanoma patients with this particular peptide analogue was much more efficient than that observed with either one of the two natural peptides. These results suggest that the Melan-A(26-35) peptide analogue ELAGIGILTV may be more immunogenic than the natural peptides in HLA-A*0201 melanoma patients and should thus be considered as a candidate for future peptide-based vaccine trials.

Toward synthetic combinatorial peptide libraries in positional scanning format (PS-SCL)-based identification of CD8+ Tumor-reactive T-Cell Ligands: a comparative analysis of PS-SCL recognition by a single tumor-reactive CD8+ cytolytic T-lymphocyte clone.

The use of synthetic combinatorial peptide libraries in positional scanning format (PS-SCL) has emerged recently as an alternative approach for the identification of peptides recognized by T lymphocytes. The choice of both the PS-SCL used for screening experiments and the method used for data analysis are crucial for implementing this approach. With this aim, we tested the recognition of different PS-SCL by a tyrosinase 368-376-specific CTL clone and analyzed the data obtained with a recently developed biometric data analysis based on a model of independent and additive contribution of individual amino acids to peptide antigen recognition. Mixtures defined with amino acids present at the corresponding positions in the native sequence were among the most active for all of the libraries. Somewhat surprisingly, a higher number of native amino acids were identifiable by using amidated COOH-terminal rather than free COOH-terminal PS-SCL. Also, our data clearly indicate that when using PS-SCL longer than optimal, frame shifts occur frequently and should be taken into account. Biometric analysis of the data obtained with the amidated COOH-terminal nonapeptide library allowed the identification of the native ligand as the sequence with the highest score in a public human protein database. However, the adequacy of the PS-SCL data for the identification for the peptide ligand varied depending on the PS-SCL used. Altogether these results provide insight into the potential of PS-SCL for the identification of CTL-defined tumor-derived antigenic sequences and may significantly implement our ability to interpret the results of these analyses.