Patterns of lipid storage and mobilisation in the female green sea turtle (Chelonia mydas)

Reproductive data from southern Queensland indicate that vitellogenesis in female Chelonia mydas takes approximately 8 months and is followed by a migration to a breeding area. At Heron Island, females lay multiple clutches over approximately 3 months. To investigate how females mobilise and store lipid during the breeding season we collected plasma, yolk, and fat tissue samples from females at a variety of stages during the nesting season. In breeding females, concentrations of plasma triglyceride increased seasonally. They reached peak concentrations during vitellogenesis and courtship, remained high throughout the nesting season, and then declined to a nadir after the last clutch. Plasma protein concentration increased throughout the breeding season, peaking following the last clutch for the season. Yolk lipids were highest during courtship and were similar throughout the nesting season, suggesting that uptake of lipid by ovarian follicles is completed prior to the beginning of the nesting season. Plasma triglyceride decreases in females with prolonged periods of unsuccessful nesting, and total lipid levels in adipose tissue and follicle yolks were significantly lower in atretic females. It appears that: (1) endogenous energy reserves can be reduced by stochastic environmental events (such as those reducing nesting success), and (2) a metabolic shift signalling the end of the nesting season is characterised by a drop in plasma triglycerides and slight increase in total plasma protein.

The peroxisome proliferator-activated receptor beta/delta agonist, GW501516, regulates the expression of genes involved in lipid catabolism and energy uncoupling in skeletal muscle cells

Lipid homeostasis is controlled by the peroxisome proliferator-activated receptors (PPARalpha, -beta/delta, and -gamma) that function as fatty acid-dependent DNA-binding proteins that regulate lipid metabolism. In vitro and in vivo genetic and pharmacological studies have demonstrated PPARalpha regulates lipid catabolism. In contrast, PPARgamma regulates the conflicting process of lipid storage. However, relatively little is known about PPARbeta/delta in the context of target tissues, target genes, lipid homeostasis, and functional overlap with PPARalpha and -gamma. PPARbeta/delta, a very low-density lipoprotein sensor, is abundantly expressed in skeletal muscle, a major mass peripheral tissue that accounts for approximately 40% of total body weight. Skeletal muscle is a metabolically active tissue, and a primary site of glucose metabolism, fatty acid oxidation, and cholesterol efflux. Consequently, it has a significant role in insulin sensitivity, the blood-lipid profile, and lipid homeostasis. Surprisingly, the role of PPARbeta/delta in skeletal muscle has not been investigated. We utilize selective PPARalpha, -beta/delta, -gamma, and liver X receptor agonists in skeletal muscle cells to understand the functional role of PPARbeta/delta, and the complementary and/or contrasting roles of PPARs in this major mass peripheral tissue. Activation of PPARbeta/delta by GW501516 in skeletal muscle cells induces the expression of genes involved in preferential lipid utilization, beta-oxidation, cholesterol efflux, and energy uncoupling. Furthermore, we show that treatment of muscle cells with GW501516 increases apolipoprotein-A1 specific efflux of intracellular cholesterol, thus identifying this tissue as an important target of PPARbeta/delta agonists. Interestingly, fenofibrate induces genes involved in fructose uptake, and glycogen formation. In contrast, rosiglitazone-mediated activation of PPARgamma induces gene expression associated with glucose uptake, fatty acid synthesis, and lipid storage. Furthermore, we show that the PPAR-dependent reporter in the muscle carnitine palmitoyltransferase-1 promoter is directly regulated by PPARbeta/delta, and not PPARalpha in skeletal muscle cells in a PPARgamma coactivator-1-dependent manner. This study demonstrates that PPARs have distinct roles in skeletal muscle cells with respect to the regulation of lipid, carbohydrate, and energy homeostasis. Moreover, we surmise that PPARgamma/delta agonists would increase fatty acid catabolism, cholesterol efflux, and energy expenditure in muscle, and speculate selective activators of PPARbeta/delta may have therapeutic utility in the treatment of hyperlipidemia, atherosclerosis, and obesity.

Influence of foliar fertilization with manganese on germination, vigor and storage time of RR soybean seeds

ABSTRACTThis study aimed to evaluate the influence of foliar fertilizer doses containing Mn of phenological stages of suggested application in RR soybeans, to recover management damages with glyphosate at postemergence application on seed vigor in post-harvest and post six months storage. The seeds originated from a field experiment conducted , which included two applications of glyphosate, concomitant with foliar fertilizer in growth stages V4 and V6, with 0.00, 113.50 and 227.00 mg ha-1doses of Mn2+. Germination, GSI (Germination Speed Index), electrical conductivity tests and the first count of seeds were conducted. The application of Mn did not affect the physiological quality of RR soy in postharvest. However, in post-storage, higher doses of Mn had a negative effect on tests of abnormal seedlings, GSI and electrical conductivity. The applications of Mn, regardless of the developmental stage, did not interfere in the germination and first count tests, with and without storage. The electrical conductivity test showed a higher correlation with the seed germination test in the post-harvest treatment.

A DC-DC Step-Up mu-Power Converter for Energy Harvesting Applications, Using Maximum Power PointTracking, Based on Fractional Open Circuit Voltage

A DC-DC step-up micro power converter for solar energy harvesting applications is presented. The circuit is based on a switched-capacitorvoltage tripler architecture with MOSFET capacitors, which results in an, area approximately eight times smaller than using MiM capacitors for the 0.131mu m CMOS technology. In order to compensate for the loss of efficiency, due to the larger parasitic capacitances, a charge reutilization scheme is employed. The circuit is self-clocked, using a phase controller designed specifically to work with an amorphous silicon solar cell, in order to obtain themaximum available power from the cell. This will be done by tracking its maximum power point (MPPT) using the fractional open circuit voltage method. Electrical simulations of the circuit, together with an equivalent electrical model of an amorphous silicon solar cell, show that the circuit can deliver apower of 1132 mu W to the load, corresponding to a maximum efficiency of 66.81%.

Real-time energy resources scheduling considering intensive wind penetration

The use of distributed energy resources, based on natural intermittent power sources, like wind generation, in power systems imposes the development of new adequate operation management and control methodologies. A short-term Energy Resource Management (ERM) methodology performed in two phases is proposed in this paper. The first one addresses the day-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. The ERM scheduling is a complex optimization problem due to the high quantity of variables and constraints. In this paper the main goal is to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixedinteger non-linear programming approach. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units and 1000 electric vehicles has been implemented in a simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Energy resource scheduling in a real distribution network managed by several virtual power players

Smart Grids (SGs) appeared as the new paradigm for power system management and operation, being designed to integrate large amounts of distributed energy resources. This new paradigm requires a more efficient Energy Resource Management (ERM) and, simultaneously, makes this a more complex problem, due to the intensive use of distributed energy resources (DER), such as distributed generation, active consumers with demand response contracts, and storage units. This paper presents a methodology to address the energy resource scheduling, considering an intensive use of distributed generation and demand response contracts. A case study of a 30 kV real distribution network, including a substation with 6 feeders and 937 buses, is used to demonstrate the effectiveness of the proposed methodology. This network is managed by six virtual power players (VPP) with capability to manage the DER and the distribution network.

Mobi_System: A Personal Travel Assistance for Electrical Vehicles in Smart Cities

In this work it is proposed the design of a mobile system to assist car drivers in a smart city environment oriented to the upcoming reality of Electric Vehicles (EV). Taking into account the new reality of smart cites, EV introduction, Smart Grids (SG), Electrical Markets (EM), with deregulation of electricity production and use, drivers will need more information for decision and mobility purposes. A mobile application to recommend useful related information will help drivers to deal with this new reality, giving guidance towards traffic, batteries charging process, and city mobility infrastructures (e. g. public transportation information, parking places availability and car & bike sharing systems). Since this is an upcoming reality with possible process changes, development must be based on agile process approaches (Web services).

Short-term scheduling considering five-minute and hour-ahead energy resource management

The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Remuneration and tariffs in the context of virtual power players

Power systems have been through deep changes in recent years, namely with the operation of competitive electricity markets in the scope and the increasingly intensive use of renewable energy sources and distributed generation. This requires new business models able to cope with the new opportunities that have emerged. Virtual Power Players (VPPs) are a new player type which allows aggregating a diversity of players (Distributed Generation (DG), Storage Agents (SA), Electrical Vehicles, (V2G) and consumers), to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players` benefits. A major task of VPPs is the remuneration of generation and services (maintenance, market operation costs and energy reserves), as well as charging energy consumption. This paper proposes a model to implement fair and strategic remuneration and tariff methodologies, able to allow efficient VPP operation and VPP goals accomplishment in the scope of electricity markets.

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

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.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

Adaptable control for electrical generation at irregural wind speeds

The main aims of this work are the development and the validation of one generic algorithm to provide the optimal control of small power wind generators. That means up to 40 kW and blades with fixed pitch angle. This algorithm allows the development of controllers to fetch the wind generators at the desired operational point in variable operating conditions. The problems posed by the variable wind intensity are solved using the proposed algorithm. This is done with no explicit measure of the wind velocity, and so no special equipment or anemometer is required to compute or measure the wind velocity.

An optimal scheduling problem in distribution networks considering V2G

This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.

Intelligent energy resources management in the context of smart grids

Smart grids are envisaged as infrastructures able to accommodate all centralized and distributed energy resources (DER), including intensive use of renewable and distributed generation (DG), storage, demand response (DR), and also electric vehicles (EV), from which plug-in vehicles, i.e. gridable vehicles, are especially relevant. Moreover, smart grids must accommodate a large number of diverse types or players in the context of a competitive business environment. Smart grids should also provide the required means to efficiently manage all these resources what is especially important in order to make the better possible use of renewable based power generation, namely to minimize wind curtailment. An integrated approach, considering all the available energy resources, including demand response and storage, is crucial to attain these goals. This paper proposes a methodology for energy resource management that considers several Virtual Power Players (VPPs) managing a network with high penetration of distributed generation, demand response, storage units and network reconfiguration. The resources are controlled through a flexible SCADA (Supervisory Control And Data Acquisition) system that can be accessed by the evolved entities (VPPs) under contracted use conditions. A case study evidences the advantages of the proposed methodology to support a Virtual Power Player (VPP) managing the energy resources that it can access in an incident situation.