A risk-averse optimization model for trading wind energy in a market environment under uncertainty

In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where each scenario represents a plausible realization of the uncertain parameters with an associated occurrence probability. Also, an appropriate risk measurement is considered. The proposed approach is applied on a realistic case study, based on a wind farm in Portugal. Finally, conclusions are duly drawn. (C) 2011 Elsevier Ltd. All rights reserved.

Biochemical and structural characterization of β-lactamases tem-180 and tem-201

With the constant development of new antibiotics, selective pressure is a force to reckon when investigating antibiotic resistance. Although advantageous for medical treatments, it leads to increasing resistance. It is essential to use more potent and toxic antibiotics. Enzymes capable of hydrolyzing antibiotics are among the most common ways of resistance and TEM variants have been detected in several resistant isolates. Due to the rapid evolution of these variants, complex phenotypes have emerged and the need to understand their biological activity becomes crucial. To investigate the biochemical properties of TEM-180 and TEM-201 several computational methodologies have been used, allowing the comprehension of their structure and catalytic activity, which translates into their biological phenotype. In this work we intent to characterize the interface between these proteins and the several antibiotics used as ligands. We performed explicit solvent molecular dynamics (MD) simulations of these complexes and studied a variety of structural and energetic features. The interfacial residues show a distinct behavior when in complex with different antibiotics. Nevertheless, it was possible to identify some common Hot Spots among several complexes – Lys73, Tyr105 and Glu166. The structural changes that occur during the Molecular Dynamic (MD) simulation lead to the conclusion that these variants have an inherent capacity of adapting to the various antibiotics. This capability might be the reason why they can hydrolyze antibiotics that have not been described until now to be degraded by TEM variants. The results obtained with computational and experimental methodologies for the complex with Imipenem have shown that in order to this type of enzymes be able to acylate the antibiotics, they need to be capable to protect the ligand from water molecules.

Coordination Chemistry of the (eta(6)-p-Cymene)ruthenium(II) Fragment with Bis-, Tris-, andTetrakis(pyrazol-1-yl)borate Ligands: Synthesis, Structural, Electrochemical, and CatalyticDiastereoselective Nitroaldol Reaction Studies

Novel [Ru(eta(6)-p-cymene)(kappa(2)-L)X] and [Ru(eta(6)-p-cymene)(kappa(3)-L)]X center dot nH(2)O complexes (L = bis-, tris-, or tetrakis-pyrazolylborate; X = Cl, N-3, PF6, or CF3SO3) are prepared by treatment of [Ru(eta(6)-p-cymene)Cl-2](2) with poly-(pyrazolyl)borate derivatives [M(L)] (L in general; in detail L = Ph(2)Bp = diphenylbis-(pyrazol-1-yl)borate; L = Tp = hydrotris(pyrazol-1-yl)borate; L = pzTp = tetrakis(pyrazol-1-yl)borate; L = Tp(4Bo) = hydrotris(indazol-1-yl)borate, L = T-p4Bo,T-5Me = (5-methylindazol-1-yl)borate; L = Tp(Bn,4Ph) = hydrotris(3-benzyl-4-phenylpyrazol-1-yl)borate; M = Na, K, or TI) and characterized by analytical and spectral data (IR, ESIMS, H-1 and C-13 NMR). The structures of [Ru(eta(6)-p-cymene)(Ph(2)Bp)Cl] (1) and [Ru(eta(6)-p-cymene)(Tp)Cl] (3) have been established by single-crystal X-ray diffraction analysis. Electrochemical studies allowed comparing the electron-donor characters of Tp and related ligands and estimating the corresponding values of the Lever E-L ligand parameter. The complexes [Ru(eta(6)-p-cymene)-(kappa(2)-L)X] and [Ru(eta(6)-p-cymene)(kappa(3)-L)]X center dot nH(2)O act as catalyst precursors for the diastereoselective nitroaldol reaction of benzaldehyde and nitroethane to the corresponding beta-nitroalkanol (up to 82% yield, at room temperature) with diastereoselectivity toward the formation of the threo isomer.

Direct multisearch for multiobjective optimization

In practical applications of optimization it is common to have several conflicting objective functions to optimize. Frequently, these functions are subject to noise or can be of black-box type, preventing the use of derivative-based techniques. We propose a novel multiobjective derivative-free methodology, calling it direct multisearch (DMS), which does not aggregate any of the objective functions. Our framework is inspired by the search/poll paradigm of direct-search methods of directional type and uses the concept of Pareto dominance to maintain a list of nondominated points (from which the new iterates or poll centers are chosen). The aim of our method is to generate as many points in the Pareto front as possible from the polling procedure itself, while keeping the whole framework general enough to accommodate other disseminating strategies, in particular, when using the (here also) optional search step. DMS generalizes to multiobjective optimization (MOO) all direct-search methods of directional type. We prove under the common assumptions used in direct search for single objective optimization that at least one limit point of the sequence of iterates generated by DMS lies in (a stationary form of) the Pareto front. However, extensive computational experience has shown that our methodology has an impressive capability of generating the whole Pareto front, even without using a search step. Two by-products of this paper are (i) the development of a collection of test problems for MOO and (ii) the extension of performance and data profiles to MOO, allowing a comparison of several solvers on a large set of test problems, in terms of their efficiency and robustness to determine Pareto fronts.

Investment optimization in distribution network based on fuzzy outage parameters

This paper presents a methodology that aims to increase the probability of delivering power to any load point of the electrical distribution system by identifying new investments in distribution components. The methodology is based on statistical failure and repair data of the distribution power system components and it uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A mixed integer non-linear optimization technique is developed to identify adequate investments in distribution networks components that allow increasing the availability level for any customer in the distribution system at minimum cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a real distribution network.

Ruthenium(II) Arene Complexes Bearing Tris(pyrazolyl)methanesulfonate Capping Ligands.Electrochemistry, Spectroscopic, and X-ray Structural Characterization

Novel [Ru(L)(Tpms)]Cl and [Ru(L)(Tpms(Ph))]Cl complexes (L = p-cymene, benzene, or hexamethylbenzene, Tpms = tris(pyrazolyl)-methanesulfonate, Tpms(Ph) = tris(3-phenylpyrazoly)methanesulfonate) have been prepared by reaction of [Ru(L)(mu-Cl)(2)](2) with Li[Tpms] and Li[Tpms(Ph)], respectively. [Ru(p-cymene)(Tpms)]BF4 has been synthesized through a metathetic reaction of [Ru(p-cymene)(Tpms)]Cl with AgBF4. [RuCl(cod)(Tpms)] (cod = 1,5-cyclooctadiene) and [RuCl(cod)(Tpms(Ph))] are also reported, being obtained by reaction of [RuCl2(cod)(MeCN)(2)] with Li[Tpms] and Li[Tpms(Ph)], respectively. The structures of the complexes and the coordination modes of the ligands have been established by IR, NMR, and single-crystal X-ray diffraction (for [RuL(Tpms)]X (L = p-cymene or HMB, X = Cl; L = p-cymene, X = BF4)) studies. Electrochemical studies showed that each complex undergoes a single-electron R-II -> R-III oxidation at a potential measured by cyclic voltammetry, allowing to compare the electron-donor characters of the tris(pyrazolyl)methanesulfonate and arene ligands, and to estimate, for the first time, the values of the Lever E-L ligand parameter for Tmps(Ph), HMB, and cod.

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.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Self-optimization for dynamic scheduling in manufacturing systems

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).

Combined heat and power and consumption optimization in a SCADA-based system

The operation of power systems in a Smart Grid (SG) context brings new opportunities to consumers as active players, in order to fully reach the SG advantages. In this context, concepts as smart homes or smart buildings are promising approaches to perform the optimization of the consumption, while reducing the electricity costs. This paper proposes an intelligent methodology to support the consumption optimization of an industrial consumer, which has a Combined Heat and Power (CHP) facility. A SCADA (Supervisory Control and Data Acquisition) system developed by the authors is used to support the implementation of the proposed methodology. An optimization algorithm implemented in the system in order to perform the determination of the optimal consumption and CHP levels in each instant, according to the Demand Response (DR) opportunities. The paper includes a case study with several scenarios of consumption and heat demand in the context of a DR event which specifies a maximum demand level for the consumer.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

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.

Synthesis and structural characterization of iron complexes with 2,2,2-tris(1-pyrazolyl)ethanol ligands: Application in the peroxidative oxidation of cyclohexane under mild conditions

The reactions of FeCl2 center dot 2H(2)O and 2,2,2-tris(1-pyrazolyl) ethanol HOCH2C(pz)(3) (1) (pz = pyrazolyl) afford [Fe{HOCH2C(pz)(3)}(2)][FeCl4]Cl (2), [Fe{HOCH2C(pz)(3)}(2)](2)[Fe2OCl6](Cl)(2)center dot 4H(2)O (3 center dot 4H(2)O), [Fe{HOCH2C(pz)(3)}(2)] [FeCl{HOCH2C(pz)(3)}(H2O)(2)](2)(Cl)(4) (4) or [Fe{HOCH2C(pz)(3)}(2)]Cl-2 (5), depending on the experimental conditions. Compounds 1-5 were isolated as air-stable crystalline solids and fully characterized, including (1-4) by single-crystal X-ray diffraction analyses. The latter technique revealed strong intermolecular H-bonds involving the OH group of the scorpionate 2 and 3 giving rise to 1D chains which, in 3, are further expanded to a 2D network with intercalated infinite and almost plane chains of H-interacting water molecules. In 4, intermolecular pi center dot center dot center dot pi interactions involving the pyrazolyl rings are relevant. Complexes 2-5 display a high solubility in water (S-25 degrees C ca. 10-12 mg mL(-1)), a favourable feature towards their application as catalysts (or catalyst precursors) for the peroxidative oxidation of cyclo-hexane to cyclohexanol and cyclohexanone, with aqueous H2O2/MeCN, at room temperature (TON values up to ca. 385). (C) 2011 Elsevier B. V. All rights reserved.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Optimization model for medium term natural gas commitment

In this paper we study the optimal natural gas commitment for a known demand scenario. This study implies the best location of GSUs to supply all demands and the optimal allocation from sources to gas loads, through an appropriate transportation mode, in order to minimize total system costs. Our emphasis is on the formulation and use of a suitable optimization model, reflecting real-world operations and the constraints of natural gas systems. The mathematical model is based on a Lagrangean heuristic, using the Lagrangean relaxation, an efficient approach to solve the problem. Computational results are presented for Iberian and American natural gas systems, geographically organized in 65 and 88 load nodes, respectively. The location model results, supported by the computational application GasView, show the optimal location and allocation solution, system total costs and suggest a suitable gas transportation mode, presented in both numerical and graphic supports.