Um modelo bi-nível para o investimento multi-etapa em capacidade de transmissão e geração de energia elétrica em mercados competitivos

Pós-graduação em Engenharia Elétrica - FEIS

Otimização combinatória para alocação de fontes renováveis em microrredes de corrente contínua

Pós-graduação em Engenharia Elétrica - FEIS

Programação de horários usando um algoritmo genético especializado

Pós-graduação em Engenharia Elétrica - FEIS

A bi-objective genetic approach for the selection of sugarcane varieties to comply with environmental and economic requirements

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

Multistage Transmission Expansion Planning Considering Fixed Series Compensation Allocation

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.

Aplicação de técnicas metaheurísticas para resolver o problema de coordenação de carregamento de veículos elétricos em sistemas de distribuição

Pós-graduação em Engenharia Elétrica - FEIS

Reconfiguração de sistemas de distribuição operando em vários níveis de demanda através de uma meta-heurística de busca em vizinhança variável

Pós-graduação em Engenharia Elétrica - FEIS

A hybrid method for the probabilistic maximal covering location-allocation problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Mutagenicity profile of atmospheric particulate matter in a small urban center subjected to airborne emission from vehicle traffic and sugar cane burning

Atmospheric particulate matter (PM) is genotoxic and recently was classified as carcinogenic to humans by the International Agency for Research on Cancer. PM chemical composition varies depending on source and atmospheric conditions. The Salmonella/microsome assay is the most used mutagenicity test and can identify the major chemical classes responsible for observed mutagenicity. The objective of this work was to characterize the mutagenicity of PM samples from a countryside city, Limeira, Brazil, which is influenced by heavy traffic and sugar cane biomass burning. Six samples of total PM were collected. Air mass backward trajectories were calculated. Organic extracts were assayed using the Salmonella/microsome microsuspension mutagenicity assay using TA98, YG1041, and TA1538, with and without metabolic activation (S9). YG1041 was the most sensitive strain and mutagenicity reached 9,700 revertants per m(3) without metabolic activation. Potency for TA1538 was higher than TA98, indicating that this strain should be considered in air mutagenicity studies. The increased response to YG1041 relative to TA98, and the decreased response with S9, suggests that nitroaromatics are the major contributors. Limeira is among the most mutagenic cities in the world. High mutagenicity in Limeira seems to occur when the air mass from the area of sugarcane production is mixed with air from the region impacted by anthropogenic activities such as traffic. An increase in the formation of nitro-polycyclic aromatic hydrocarbons may result from longer contact time between the aromatic compounds and the atmosphere with high NOx and ozone concentration, although more studies are required to confirm this hypothesis. Environ. Mol. Mutagen., 2015. © 2015 Wiley Periodicals, Inc.

Routing and Wavelength Assignment (RWA) with Power Considerations In All- Optical Wavelength-Routed Networks

Routing and wavelength assignment (RWA) is an important problem that arises in wavelength division multiplexed (WDM) optical networks. Previous studies have solved many variations of this problem under the assumption of perfect conditions regarding the power of a signal. In this paper, we investigate this problem while allowing for degradation of routed signals by components such as taps, multiplexers, and fiber links. We assume that optical amplifiers are preplaced. We investigate the problem of routing the maximum number of connections while maintaining proper power levels. The problem is formulated as a mixed-integer nonlinear program and two-phase hybrid solution approaches employing two different heuristics are developed

Minimizing the Number of Optical Amplifiers Needed to Support a Multi-Wavelength Optical LAN/MAN

Optical networks based on passive star couplers and employing wavelength-division multiplexing (WDhf) have been proposed for deployment in local and metropolitan areas. Amplifiers are required in such networks to compensate for the power losses due to splitting and attenuation. However, an optical amplifier has constraints on the maximum gain and the maximum output power it can supply; thus optical amplifier placement becomes a challenging problem. The general problem of minimizing the total amplifier count, subject to the device constraints, is a mixed-integer non-linear problem. Previous studies have attacked the amplifier placement problem by adding the “artificial” constraint that all wavelengths, which are present at a particular point in a fiber, be at the same power level. In this paper, we present a method to solve the minimum amplifier- placement problem while avoiding the equally powered- wavelength constraint. We demonstrate that, by allowing signals to operate at different power levels, our method can reduce the number of amplifiers required in several small to medium-sized networks.

A new approach for modeling and control of nonlinear systems via norm-bounded linear differential inclusions

A systematic approach to model nonlinear systems using norm-bounded linear differential inclusions (NLDIs) is proposed in this paper. The resulting NLDI model is suitable for the application of linear control design techniques and, therefore, it is possible to fulfill certain specifications for the underlying nonlinear system, within an operating region of interest in the state-space, using a linear controller designed for this NLDI model. Hence, a procedure to design a dynamic output feedback controller for the NLDI model is also proposed in this paper. One of the main contributions of the proposed modeling and control approach is the use of the mean-value theorem to represent the nonlinear system by a linear parameter-varying model, which is then mapped into a polytopic linear differential inclusion (PLDI) within the region of interest. To avoid the combinatorial problem that is inherent of polytopic models for medium- and large-sized systems, the PLDI is transformed into an NLDI, and the whole process is carried out ensuring that all trajectories of the underlying nonlinear system are also trajectories of the resulting NLDI within the operating region of interest. Furthermore, it is also possible to choose a particular structure for the NLDI parameters to reduce the conservatism in the representation of the nonlinear system by the NLDI model, and this feature is also one important contribution of this paper. Once the NLDI representation of the nonlinear system is obtained, the paper proposes the application of a linear control design method to this representation. The design is based on quadratic Lyapunov functions and formulated as search problem over a set of bilinear matrix inequalities (BMIs), which is solved using a two-step separation procedure that maps the BMIs into a set of corresponding linear matrix inequalities. Two numerical examples are given to demonstrate the effectiveness of the proposed approach.

Algorithms for network design and routing problems

In this thesis we study three combinatorial optimization problems belonging to the classes of Network Design and Vehicle Routing problems that are strongly linked in the context of the design and management of transportation networks: the Non-Bifurcated Capacitated Network Design Problem (NBP), the Period Vehicle Routing Problem (PVRP) and the Pickup and Delivery Problem with Time Windows (PDPTW). These problems are NP-hard and contain as special cases some well known difficult problems such as the Traveling Salesman Problem and the Steiner Tree Problem. Moreover, they model the core structure of many practical problems arising in logistics and telecommunications. The NBP is the problem of designing the optimum network to satisfy a given set of traffic demands. Given a set of nodes, a set of potential links and a set of point-to-point demands called commodities, the objective is to select the links to install and dimension their capacities so that all the demands can be routed between their respective endpoints, and the sum of link fixed costs and commodity routing costs is minimized. The problem is called non- bifurcated because the solution network must allow each demand to follow a single path, i.e., the flow of each demand cannot be splitted. Although this is the case in many real applications, the NBP has received significantly less attention in the literature than other capacitated network design problems that allow bifurcation. We describe an exact algorithm for the NBP that is based on solving by an integer programming solver a formulation of the problem strengthened by simple valid inequalities and four new heuristic algorithms. One of these heuristics is an adaptive memory metaheuristic, based on partial enumeration, that could be applied to a wider class of structured combinatorial optimization problems. In the PVRP a fleet of vehicles of identical capacity must be used to service a set of customers over a planning period of several days. Each customer specifies a service frequency, a set of allowable day-combinations and a quantity of product that the customer must receive every time he is visited. For example, a customer may require to be visited twice during a 5-day period imposing that these visits take place on Monday-Thursday or Monday-Friday or Tuesday-Friday. The problem consists in simultaneously assigning a day- combination to each customer and in designing the vehicle routes for each day so that each customer is visited the required number of times, the number of routes on each day does not exceed the number of vehicles available, and the total cost of the routes over the period is minimized. We also consider a tactical variant of this problem, called Tactical Planning Vehicle Routing Problem, where customers require to be visited on a specific day of the period but a penalty cost, called service cost, can be paid to postpone the visit to a later day than that required. At our knowledge all the algorithms proposed in the literature for the PVRP are heuristics. In this thesis we present for the first time an exact algorithm for the PVRP that is based on different relaxations of a set partitioning-like formulation. The effectiveness of the proposed algorithm is tested on a set of instances from the literature and on a new set of instances. Finally, the PDPTW is to service a set of transportation requests using a fleet of identical vehicles of limited capacity located at a central depot. Each request specifies a pickup location and a delivery location and requires that a given quantity of load is transported from the pickup location to the delivery location. Moreover, each location can be visited only within an associated time window. Each vehicle can perform at most one route and the problem is to satisfy all the requests using the available vehicles so that each request is serviced by a single vehicle, the load on each vehicle does not exceed the capacity, and all locations are visited according to their time window. We formulate the PDPTW as a set partitioning-like problem with additional cuts and we propose an exact algorithm based on different relaxations of the mathematical formulation and a branch-and-cut-and-price algorithm. The new algorithm is tested on two classes of problems from the literature and compared with a recent branch-and-cut-and-price algorithm from the literature.

Model Predictive Control for Vehicle Stabilization at the Limits of Handling

Recent developments in vehicle steering systems offer new opportunities to measure the steering torque and reliably estimate the vehicle sideslip and the tire-road friction coefficient. This paper presents an approach to vehicle stabilization that leverages these estimates to define state boundaries that exclude unstable vehicle dynamics and utilizes a model predictive envelope controller to bound the vehicle motion within this stable region of the state space. This approach provides a large operating region accessible by the driver and smooth interventions at the stability boundaries. Experimental results obtained with a steer-by-wire vehicle and a proof of envelope invariance demonstrate the efficacy of the envelope controller in controlling the vehicle at the limits of handling.