Risk/investment-driven transmission expansion planning with multiple scenarios

This study presents a new methodology based on risk/investment to solve transmission network expansion planning (TNEP) problem with multiple future scenarios. Three mathematical models related to TNEP problems considering multiple future generation and load scenarios are also presented. These models will provide planners with a meaningful risk assessment that enable them to determine the necessary funding for transmission lines at a permissible risk level. The results using test and real systems show that the proposed method presents better solutions compared with scenario analysis method. ©The Institution of Engineering and Technology 2013.

Pequenas centrais hidrelétricas face à reestruturação do setor elétrico brasileiro: uma questão de políticas públicas

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Aplicação de modelagem hidrológica associada à análise geoespacial, para caracterização da vazão na micro Central Hidroelétrica Ilha Anchieta - SP

Pós-graduação em Engenharia Mecânica - FEG

A Multi-Stage Stochastic Non-Linear Model for Reactive Power Planning Under Contingencies

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

Transmission network expansion planning for the Colombian electrical system: Connecting the Ituango hydroelectric power plant

The hydroelectric power plant Hidroltuango represents a major expansion for the Colombian electrical system (with a total capacity of 2400 MW). This paper analyzes the possible interconnections and investments involved in connecting Hidroltuango, in order to strengthen the Colombian national transmission system. A Mixed Binary Linear Programming (MBLP) model was used to solve the Multistage Transmission Network Expansion Planning (MTEP) problem of the Colombian electrical system, taking the N-1 safety criterion into account. The N-1 safety criterion indicates that the transmission system must be expanded so that the system will continue to operate properly if an outage in a system element (within a pre-defined set of contingencies) occurs. The use of a MBLP model guaranteed the convergence with existing classical optimization methods and the optimal solution for the MTEP using commercial solvers. Multiple scenarios for generation and demand were used to consider uncertainties within these parameters. The model was implemented using the algebraic modeling language AMPL and solved using the commercial solver CPLEX. The proposed model was then applied to the Colombian electrical system using the planning horizon of 2018-2025. (C) 2014 Elsevier B.V. All rights reserved.

Multiobjective multistage distribution system planning using tabu search

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

Transmission Expansion Planning Via a Constructive Heuristic Algorithm in Restructured Electricity Industry

The transmission expansion planning problem in modern power systems is a large-scale, mixed-integer, nonlinear and non-convex problem. this paper presents a new mathematical model and a constructive heuristic algorithm (CHA) for solving transmission expansion planning problem under new environment of electricity restructuring. CHA finds an acceptable solution in an iterative process, where in each step a circuit is chosen using a sensitivity index and added to the system. The proposed model consider multiple generation scenarios therefore the methodology finds high quality solution in which it allows the power system operate adequacy in an environment with multiple generators scenarios. Case studies and simulation results using test systems show possibility of using Constructive heuristic algorithm in an open access system.

Short-term expansion planning of radial electrical distribution systems using mixed-integer linear programming

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Reactive power planning under conditional-value-at-risk assessment using chance-constrained optimisation

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

HLA-E coding and 3' untranslated region variability determined by next-generation sequencing in two West-African population samples

HLA-E is a non-classical Human Leucocyte Antigen class I gene with immunomodulatory properties. Whereas HLA-E expression usually occurs at low levels, it is widely distributed amongst human tissues, has the ability to bind self and non-self antigens and to interact with NK cells and T lymphocytes, being important for immunosurveillance and also for fighting against infections. HLA-E is usually the most conserved locus among all class I genes. However, most of the previous studies evaluating HLA-E variability sequenced only a few exons or genotyped known polymorphisms. Here we report a strategy to evaluate HLA-E variability by next-generation sequencing (NGS) that might be used to other HLA loci and present the HLA-E haplotype diversity considering the segment encoding the entire HLA-E mRNA (including 5'UTR, introns and the 3'UTR) in two African population samples, Susu from Guinea-Conakry and Lobi from Burkina Faso. Our results indicate that (a) the HLA-E gene is indeed conserved, encoding mainly two different protein molecules; (b) Africans do present several unknown HLA-E alleles presenting synonymous mutations; (c) the HLA-E 3'UTR is quite polymorphic and (d) haplotypes in the HLA-E 3'UTR are in close association with HLA-E coding alleles. NGS has proved to be an important tool on data generation for future studies evaluating variability in non-classical MHC genes.

GIS-based analytical tools for transport planning: spatial regression models for transportation demand forecast

Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included). This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.