Bilevel approach for optimal location and contract pricing of distributed generation in radial distribution systems using mixed-integer linear programming

In this study, a novel approach for the optimal location and contract pricing of distributed generation (DG) is presented. Such an approach is designed for a market environment in which the distribution company (DisCo) can buy energy either from the wholesale energy market or from the DG units within its network. The location and contract pricing of DG is determined by the interaction between the DisCo and the owner of the distributed generators. The DisCo intends to minimise the payments incurred in meeting the expected demand, whereas the owner of the DG intends to maximise the profits obtained from the energy sold to the DisCo. This two-agent relationship is modelled in a bilevel scheme. The upper-level optimisation is for determining the allocation and contract prices of the DG units, whereas the lower-level optimisation is for modelling the reaction of the DisCo. The bilevel programming problem is turned into an equivalent single-level mixed-integer linear optimisation problem using duality properties, which is then solved using commercially available software. Results show the robustness and efficiency of the proposed model compared with other existing models. As regards to contract pricing, the proposed approach allowed to find better solutions than those reported in previous works. © The Institution of Engineering and Technology 2013.

Stocking density of Nile tilapia in cages placed in a hydroelectric reservoir

The Brazilian government has been encouraging fish farming in cages in federal water bodies, including hydroelectric reservoirs. Despite the government support, it is a new activity and the production model still needs some adjustment to reduce the production costs and achieve sustainability. The aims of this study were to determine the appropriate stocking density of Nile tilapia in cages in a hydroelectric reservoir and to evaluate to what extent fish size selection could improve their uniformity. Twelve cages (6m3) were placed at the Fish Farmers' Cooperative of Santa Fé do Sul and Region, Ilha Solteira reservoir, São Paulo, Brazil (20°12'10″S, 50°58'31.15″W). In stage I (initial fish weight, 78g), four stocking densities were tested: D1-800, D2-2000, D3-2500 and D4-3000 fish/cage, with three replicates. At the end of this stage (average fish weight, 255g), the fish were selected into three sizes, except for D1. In stage II, four stocking densities were tested, designed to obtain the following final production: D1-100kg/m3 (800 non-selected fish/cage), D2-80kg/m3 (600 fish/cage), D3-100kg/m3 (800 fish/cage) and D4-120kg/m3 (900 fish/cage). The trial ended when the fish weighed 800g. By reducing the initial stocking density from 2500 to 800 tilapia juveniles per cage, there was no need for selection. The growth performance was higher, the feed conversion rate was better and the time taken to reach harvesting was shorter. Consequently, the production cost reduced and the operating profit increased. Using the lowest initial stocking density, the risk of disease outbreak was also lower, and there was no need to use drugs for disease control since the mortality rate and occurrences of disease and deformity decreased and the dissolved oxygen level inside the cages was higher. © 2013 Elsevier B.V.

Vibration Control Applied in a Semi-Active Suspension using Magneto Rheological Damper and Optimal Linear Control Design

In this paper we study the behavior of a semi-active suspension witch external vibrations. The mathematical model is proposed coupled to a magneto rheological (MR) damper. The goal of this work is stabilize of the external vibration that affect the comfort and durability an vehicle, to control these vibrations we propose the combination of two control strategies, the optimal linear control and the magneto rheological (MR) damper. The optimal linear control is a linear feedback control problem for nonlinear systems, under the optimal control theory viewpoint We also developed the optimal linear control design with the scope in to reducing the external vibrating of the nonlinear systems in a stable point. Here, we discuss the conditions that allow us to the linear optimal control for this kind of non-linear system.

Optimal Reactive Power Planning Using Risk Analysis

In this paper, the optimal reactive power planning problem under risk is presented. The classical mixed-integer nonlinear model for reactive power planning is expanded into two stage stochastic model considering risk. This new model considers uncertainty on the demand load. The risk is quantified by a factor introduced into the objective function and is identified as the variance of the random variables. Finally numerical results illustrate the performance of the proposed model, that is applied to IEEE 30-bus test system to determine optimal amount and location for reactive power expansion.

Positioning and number of nutritional levels in dose-response trials to estimate the optimal-level and the adjustment of the models

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

A mixed-integer quadratically-constrained programming model for the distribution system expansion planning

This paper presents a mixed-integer quadratically-constrained programming (MIQCP) model to solve the distribution system expansion planning (DSEP) problem. The DSEP model considers the construction/reinforcement of substations, the construction/reconductoring of circuits, the allocation of fixed capacitors banks and the radial topology modification. As the DSEP problem is a very complex mixed-integer non-linear programming problem, it is convenient to reformulate it like a MIQCP problem; it is demonstrated that the proposed formulation represents the steady-state operation of a radial distribution system. The proposed MIQCP model is a convex formulation, which allows to find the optimal solution using optimization solvers. Test systems of 23 and 54 nodes and one real distribution system of 136 nodes were used to show the efficiency of the proposed model in comparison with other DSEP models available in the specialized literature. (C) 2014 Elsevier Ltd. All rights reserved.

An Optimal Control Framework for Resources Management in Agriculture

An optimal control framework to support the management and control of resources in a wide range of problems arising in agriculture is discussed. Lessons extracted from past research on the weed control problem and a survey of a vast body of pertinent literature led to the specification of key requirements to be met by a suitable optimization framework. The proposed layered control structure—including planning, coordination, and execution layers—relies on a set of nested optimization processes of which an “infinite horizon” Model Predictive Control scheme plays a key role in planning and coordination. Some challenges and recent results on the Pontryagin Maximum Principle for infinite horizon optimal control are also discussed.

Despacho ótimo de potências ativa e reativa de sistema elétricos multi-áreas considerando restrições físicas, econômicas e ambientais =: Envronmentally constrained active-reactive optimal power flow-a compromising strategy for economic-emission dispatch and a multi-area paradigm

Pós-graduação em Engenharia Elétrica - FEIS

Reading model to estimate optimum economic intakes of amino acids for poultry

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Optimal control strategy for fed-batch enzymatic hydrolysis of lignocellulosic biomass based on epidemic modeling

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

An MILP model for the plug-in electric vehicle charging coordination problem in electrical distribution systems

A new mixed-integer linear programming (MILP) model is proposed to represent the plug-in electric vehicles (PEVs) charging coordination problem in electrical distribution systems. The proposed model defines the optimal charging schedule for each division of the considered period of time that minimizes the total energy costs. Moreover, priority charging criteria is taken into account. The steady-state operation of the electrical distribution system, as well as the PEV batteries charging is mathematically represented; furthermore, constraints related to limits of voltage, current and power generation are included. The proposed mathematical model was applied in an electrical distribution system used in the specialized literature and the results show that the model can be used in the solution of the PEVs charging problem.

A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.

Predicting Growth of Panicum maximum: An Adaptation of the CROPGRO-Perennial Forage Model

Warm-season grasses are economically important for cattle production in tropical regions and tools to aid in management and research on these forages would be highly beneficial both in research and the industry. This research was conducted to adapt the CROPGRO-Perennial Forage model to simulate growth of the tropical species guineagrass (Panicum maximum Jacq. cv. 'Tanzania') and to describe model adaptation for this species. To develop the CROPGRO parameters for this species, we began with values and relationships reported in the literature. Some parameters and relationships were calibrated by comparison with observed growth, development, dry matter accumulation, and partitioning during a 17-mo experiment with Tanzania guineagrass in Piracicaba, SP, Brazil. Compared with starting parameters for palisadegrass [Brachiaria brizantha (A. Rich.) Stapf. cv. 'Xaraes'], dormancy effects of the perennial forage model had to be minimized, partitioning to storage tissue or root decreased, and partitioning to leaf and stem increased to provide for more leaf and stem growth and less root. Parameters affecting specific leaf area and senescence of plant tissues were improved. After these changes were made to the model, biomass accumulation was better simulated, mean predicted herbage yield was 6576 kg ha(-1), averaged across 11 regrowth cycles of 35 (summer) or 63 d (winter), with a RMSE of 494 kg ha(-1) (Willmott's index of agreement d = 0.985, simulated/observed ratio = 1.014). The model also gave good predictions against an independent data set, with similar RMSE, ratio, and d. The results of the adaptation suggest that the CROPGRO model is an efficient tool to integrate physiological aspects of guineagrass and can be used to simulate growth.

Multiple Intravenous Administrations of Human Umbilical Cord Blood Cells Benefit in a Mouse Model of ALS

Background: A promising therapeutic strategy for amyotrophic lateral sclerosis (ALS) is the use of cell-based therapies that can protect motor neurons and thereby retard disease progression. We recently showed that a single large dose (25x10(6) cells) of mononuclear cells from human umbilical cord blood (MNC hUCB) administered intravenously to pre-symptomatic G93A SOD1 mice is optimal in delaying disease progression and increasing lifespan. However, this single high cell dose is impractical for clinical use. The aim of the present pre-clinical translation study was therefore to evaluate the effects of multiple low dose systemic injections of MNC hUCB cell into G93A SOD1 mice at different disease stages. Methodology/Principal Findings: Mice received weekly intravenous injections of MNC hUCB or media. Symptomatic mice received 10(6) or 2.5x10(6) cells from 13 weeks of age. A third, pre-symptomatic, group received 10(6) cells from 9 weeks of age. Control groups were media-injected G93A and mice carrying the normal hSOD1 gene. Motor function tests and various assays determined cell effects. Administered cell distribution, motor neuron counts, and glial cell densities were analyzed in mouse spinal cords. Results showed that mice receiving 10(6) cells pre-symptomatically or 2.5x10(6) cells symptomatically significantly delayed functional deterioration, increased lifespan and had higher motor neuron counts than media mice. Astrocytes and microglia were significantly reduced in all cell-treated groups. Conclusions/Significance: These results demonstrate that multiple injections of MNC hUCB cells, even beginning at the symptomatic disease stage, could benefit disease outcomes by protecting motor neurons from inflammatory effectors. This multiple cell infusion approach may promote future clinical studies.

Experimental model of tooth movement in mice: A standardized protocol for studying bone remodeling under compression and tensile strains

During orthodontic tooth movement (OTM), alveolar bone is resorbed by osteoclasts in compression sites (CS) and is deposited by osteoblasts in tension sites (TS). The aim of this study was to develop a standardized OTM protocol in mice and to investigate the expression of bone resorption and deposition markers in CS and TS. An orthodontic appliance was placed in C57BL6/J mice. To define the ideal orthodontic force, the molars of the mice were subjected to forces of 0.1 N, 0.25 N, 0.35 N and 0.5 N. The expression of mediators that are involved in bone remodeling at CS and TS was analyzed using a Real-Time PCR. The data revealed that a force of 0.35 N promoted optimal OTM and osteoclast recruitment without root resorption. The levels of TNF-alpha, RANKL, MMP13 and OPG were all altered in CS and TS. Whereas TNF-a and Cathepsin K exhibited elevated levels in CS. RUNX2 and OCN levels were higher in TS. Our results suggest that 0.35 N is the ideal force for OTM in mice and has no side effects. Moreover, the expression of bone remodeling markers differed between the compression and the tension areas, potentially explaining the distinct cellular migration and differentiation patterns in each of these sites. (C) 2012 Elsevier Ltd. All rights reserved.