The GRIP domain is a specific targeting sequence for a population of trans-Golgi network derived tubulo-vesicular carriers

Vesicular carriers for intracellular transport associate with unique sets of accessory molecules that dictate budding and docking on specific membrane domains. Although many of these accessory molecules are peripheral membrane proteins, in most cases the targeting sequences responsible for their membrane recruitment have yet to be identified. We have previously defined a novel Golgi targeting domain (GRIP) shared by a family of coiled-coil peripheral membrane Golgi proteins implicated in membrane trafficking. We show here that the docking site for the GRIP motif of p230 is a specific domain of Golgi. membranes. By immunoelectron microscopy of HeLa cells stably expressing a green fluorescent protein (GFP)-p230(GRIP) fusion protein, we show binding specifically to a subset of membranes of the trans-Golgi network (TGN). Real-time imaging of live HeLa cells revealed that the GFP-p230(GRIP) was associated with highly dynamic tubular extensions of the TGN, which have the appearance and behaviour of transport carriers. To further define the nature of the GRIP membrane binding site, in vitro budding assays were performed using purified rat liver Golgi membranes and cytosol from GFP-p230(GRIP) transfected cells. Analysis of Golgi-derived vesicles by sucrose gradient fractionation demonstrated that GFP-p230(GRIP) binds to a specific population of vesicles distinct from those labelled for beta -COP or gamma -adaptin. The GFP-p230(GRIP) fusion protein is recruited to the same vesicle population as full-length p230, demonstrating that the GRIP domain is solely proficient as a targeting signal for membrane binding of the native molecule. Therefore, p230 GRIP is a targeting signal for recruitment to a highly selective membrane attachment site on a specific population of trans-Golgi network tubulovesicular carriers.

Intracellular sorting and transport of proteins

The secretory and endocytic pathways of eukaryotic organelles consist of multiple compartments, each with a unique set of proteins and lipids. Specific transport mechanisms are required to direct molecules to defined locations and to ensure that the identity, and hence function, of individual compartments are maintained. The localisation of proteins to specific membranes is complex and involves multiple interactions. The recent dramatic advances in understanding the molecular mechanisms of membrane transport has been due to the application of a multi-disciplinary approach, intergrating membrane biology, genetics, imaging, protein and lipid biochemistry and structural biology. The aim of this review is to summarise the general principles of protein sorting in the secretory and endocytic pathways and to highlight the dynamic nature of these processes. The molecular mechanisms involved in this transport along the secretory and endocytic pathways are discussed along with the signals responsible for targeting proteins to different intracellular locations. (C) 2003 Elsevier Science Ltd. All rights reserved.

GLUT4 recycles via a trans-Golgi network (TGN) subdomain enriched in Syntaxins 6 and 16 but not TGN38: Involvement of an acidic targeting motif

Insulin stimulates glucose transport in fat and muscle cells by triggering exocytosis of the glucose transporter GLUT4. To define the intracellular trafficking of GLUT4, we have studied the internalization of an epitope-tagged version of GLUT4 from the cell surface. GLUT4 rapidly traversed the endosomal system en route to a perinuclear location. This perinuclear GLUT4 compartment did not colocalize with endosomal markers (endosomal antigen I protein, transferrin) or TGN38, but showed significant overlap with the TGN target (t)-soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) Syntaxins 6 and 16. These results were confirmed by vesicle immunoisolation. Consistent with a role for Syntaxins 6 and 16 in GLUT4 trafficking we found that their expression was up-regulated significantly during adipocyte differentiation and insulin stimulated their movement to the cell surface. GLUT4 trafficking between endosomes and trans-Golgi network was regulated via an acidic targeting motif in the carboxy terminus of GLUT4, because a mutant lacking this motif was retained in endosomes. We conclude that GLUT4 is rapidly transported from the cell surface to a subdomain of the trans-Golgi network that is enriched in the t-SNAREs Syntaxins 6 and 16 and that an acidic targeting motif in the C-terminal tail of GLUT4 plays an important role in this process.

GRIP domain-mediated targeting of two new coiled-coil proteins, GCC88 and GCC185, to subcompartments of the trans-Golgi network

The GRIP domain is a targeting sequence found in a family of coiled-coil peripheral Golgi proteins. Previously we demonstrated that the GRIP domain of p230/golgin245 is specifically recruited to tubulovesicular structures of the traps-Golgi network (TGN). Here we have characterized two novel Golgi proteins with functional GRIP domains, designated GCC88 and GCC185. GCC88 cDNA encodes a protein of 88 kDa, and GCC185 cDNA encodes a protein of 185 kDa. Both molecules are brefeldin A-sensitive peripheral membrane proteins and are predicted to have extensive coiled-coil regions with the GRIP domain at the C terminus. By immunofluorescence and immunoelectron microscopy GCC88 and GCC185, and the GRIP protein golgin97, are all localized to the TGN of Hela cells. Overexpression of full-length GCC88 leads to the formation of large electron dense structures that extend from the traps-Golgi. These de novo structures contain GCC88 and co-stain for the TGN markers syntaxin 6 and TGN38 but not for alpha2,6-sialyltransferase, beta-COP, or cis-Golgi GM130. The formation of these abnormal structures requires the N-terminal domain of GCC88. TGN38, which recycles between the TGN and plasma membrane, was transported into and out of the GCC88 decorated structures. These data introduce two new GRIP domain proteins and implicate a role for GCC88 in the organization of a specific TGN subcompartment involved with membrane transport.

Nonlinear optimization method for short-term hydro scheduling considering head-dependency

This paper is on the problem of short-term hydro scheduling, particularly concerning head-dependent reservoirs under competitive environment. We propose a new nonlinear optimization method to consider hydroelectric power generation as a function of water discharge and also of the head. Head-dependency is considered on short-term hydro scheduling in order to obtain more realistic and feasible results. The proposed method has been applied successfully to solve a case study based on one of the main Portuguese cascaded hydro systems, providing a higher profit at a negligible additional computation time in comparison with a linear optimization method that ignores head-dependency.

An heuristic approach for optimal location of gas supply units in transportation system

The best places to locate the Gas Supply Units (GSUs) on a natural gas systems and their optimal allocation to loads are the key factors to organize an efficient upstream gas infrastructure. The number of GSUs and their optimal location in a gas network is a decision problem that can be formulated as a linear programming problem. Our emphasis is on the formulation and use of a suitable location model, reflecting real-world operations and constraints of a natural gas system. This paper presents a heuristic model, based on lagrangean approach, developed for finding the optimal GSUs location on a natural gas network, minimizing expenses and maximizing throughput and security of supply.The location model is applied to the Iberian high pressure natural gas network, a system modelised with 65 demand nodes. These nodes are linked by physical and virtual pipelines – road trucks with gas in liquefied form. The location model result shows the best places to locate, with the optimal demand allocation and the most economical gas transport mode: by pipeline or by road truck.

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Optimal location of gas supply units in natural gas system network

Natural gas industry has been confronted with big challenges: great growth in demand, investments on new GSUs – gas supply units, and efficient technical system management. The right number of GSUs, their best location on networks and the optimal allocation to loads is a decision problem that can be formulated as a combinatorial programming problem, with the objective of minimizing system expenses. Our emphasis is on the formulation, interpretation and development of a solution algorithm that will analyze the trade-off between infrastructure investment expenditure and operating system costs. The location model was applied to a 12 node natural gas network, and its effectiveness was tested in five different operating scenarios.

Natural gas system operation: lagrangean optimization techniques

To comply with natural gas demand growth patterns and Europe´s import dependency, the gas industry needs to organize an efficient upstream infrastructure. The best location of Gas Supply Units – GSUs and the alternative transportation mode – by phisical or virtual pipelines, are the key of a successful industry. In this work we study the optimal location of GSUs, as well as determining the most efficient allocation from gas loads to sources, selecting the best transportation mode, observing specific technical restrictions and minimizing system total costs. For the location of GSUs on system we use the P-median problem, for assigning gas demands nodes to source facilities we use the classical transportation problem. The developed model is an optimisation-based approach, based on a Lagrangean heuristic, using Lagrangean relaxation for P-median problems – Simple Lagrangean Heuristic. The solution of this heuristic can be improved by adding a local search procedure - the Lagrangean Reallocation Heuristic. These two heuristics, Simple Lagrangean and Lagrangean Reallocation, were tested on a realistic network - the primary Iberian natural gas network, organized with 65 nodes, connected by physical and virtual pipelines. Computational results are presented for both approaches, showing the location gas sources and allocation loads arrangement, system total costs and gas transportation mode.

Distribution network short term scheduling in Smart Grid context

The large increase of Distributed Generation (DG) in Power Systems (PS) and specially in distribution networks makes the management of distribution generation resources an increasingly important issue. Beyond DG, other resources such as storage systems and demand response must be managed in order to obtain more efficient and “green” operation of PS. More players, such as aggregators or Virtual Power Players (VPP), that operate these kinds of resources will be appearing. This paper proposes a new methodology to solve the distribution network short term scheduling problem in the Smart Grid context. This methodology is based on a Genetic Algorithms (GA) approach for energy resource scheduling optimization and on PSCAD software to obtain realistic results for power system simulation. The paper includes a case study with 99 distributed generators, 208 loads and 27 storage units. The GA results for the determination of the economic dispatch considering the generation forecast, storage management and load curtailment in each period (one hour) are compared with the ones obtained with a Mixed Integer Non-Linear Programming (MINLP) approach.

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Web-based application programming interface to solve nonlinear optimization problems

Nonlinear Optimization Problems are usual in many engineering fields. Due to its characteristics the objective function of some problems might not be differentiable or its derivatives have complex expressions. There are even cases where an analytical expression of the objective function might not be possible to determine either due to its complexity or its cost (monetary, computational, time, ...). In these cases Nonlinear Optimization methods must be used. An API, including several methods and algorithms to solve constrained and unconstrained optimization problems was implemented. This API can be accessed not only as traditionally, by installing it on the developer and/or user computer, but it can also be accessed remotely using Web Services. As long as there is a network connection to the server where the API is installed, applications always access to the latest API version. Also an Web-based application, using the proposed API, was developed. This application is to be used by users that do not want to integrate methods in applications, and simply want to have a tool to solve Nonlinear Optimization Problems.

Modified particle swarm optimization for day-ahead distributed energy resources scheduling including vehicle-to-grid

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Industry energy optimization: A case study in a biodiesel production site

This paper presents a case study of heat exchanger network (HEN) retrofit with the objective to reduce the utilities consumption in a biodiesel production process. Pinch analysis studies allow determining the minimum duty utilities as well the maximum of heat recovery. The existence of heat exchangers for heat recovery already running in the process causes a serious restriction for the implementation of grassroot HEN design based on pinch studies. Maintaining the existing HEN, a set of alternatives with additional heat exchangers was created and analysed using some industrial advice and selection criteria. The final proposed solution allows to increase the actual 18 % of recovery heat of the all heating needs of the process to 23 %, with an estimated annual saving in hot utility of 35 k(sic)/y.