Long-Term Economic Model for Allocation of FACTS Devices in Restructured Power System Integrated Wind Generation

This study proposes an approach to optimally allocate multiple types of flexible AC transmission system (FACTS) devices in market-based power systems with wind generation. The main objective is to maximise profit by minimising device investment cost, and the system's operating cost considering both normal conditions and possible contingencies. The proposed method accurately evaluates the long-term costs and benefits gained by FACTS devices (FDs) installation to solve a large-scale optimisation problem. The objective implies maximising social welfare as well as minimising compensations paid for generation re-scheduling and load shedding. Many technical operation constraints and uncertainties are included in problem formulation. The overall problem is solved using both particle swarm optimisations for attaining optimal FDs allocation as main problem and optimal power flow as sub-optimisation problem. The effectiveness of the proposed approach is demonstrated on modified IEEE 14-bus test system and IEEE 118-bus test system.

Analysis of the Second Harmonic Effect on Power Amplifier Intermodulation Products

A theoretical analysis is reported in this paper to investigate the effect that a second harmonic signal which might be present at an amplifier’s input has on generating additional intermodulation products, particularly the third-order intermodulation (IM3) products. The analysis shows that the amplitude of an extra generated IM3 component is equal to the product of the fundamental amplitude, the second harmonic amplitude, and the second order Taylor series coefficient. The effect of the second order harmonic on the IM3 is examined through a simulated example of a 2.22-GHz 10-W Class-EF amplifier whereby the IM3 levels have been reduced by 2-3 dB after employing a second harmonic termination stub at the input.

Distribution and visual impact of postoperative refractive error after cataract surgery in rural China: study of cataract outcomes and up-take of services report 4.

PURPOSE: To model the possible impact of using average-power intraocular lenses (IOLs) and evaluate the postoperative refractive error in patients having cataract surgery in rural China.SETTING: Rural Guangdong, China.METHODS: Patients having cataract surgery by local surgeons were examined and visual function was assessed 10 to 14 months after surgery. Subjective refraction at near and distance was performed bilaterally by an ophthalmologist. Patients had a target refraction of -0.50 diopter (D) based on ocular biometry.RESULTS: Of the 313 eligible patients, 242 (77%) could be contacted and 176 (74% of contacted patients, 56% overall) were examined. Examined patients had a mean age of 69.4 +/- 10.5 years. Of the 211 operated eyes, 73.2% were within +/-1.0 D of the target refraction after surgery. The best presenting distance vision was in patients within +/-1.0 D of plano and the best presenting near vision, in those with mild myopia (<-1.0 D to > or =2.0 D) (P= .005). However, patients with hyperopia (>+1.0 D) reported significantly better adjusted visual function than those with emmetropia or myopia (<-1.0 D). When the predicted use of an average-power IOL (median +21.5 D) was modeled, predicted visual acuity was significantly reduced (P= .001); however, predicted visual function was not significantly altered (P>.3).CONCLUSIONS: Accurate selection of postoperative refractive error was achieved by local surgeons in this rural area. Based on visual function results, aiming for mild postoperative myopia may not be suitable in this setting. Implanting average-power IOLs significantly reduced postoperative presenting vision, but not visual function.

Parallel Time Series Analysis and Income Inequality: A New Approach to Comparative Case Studies

This case study deals with the role of time series analysis in sociology, and its relationship with the wider literature and methodology of comparative case study research. Time series analysis is now well-represented in top-ranked sociology journals, often in the form of ‘pooled time series’ research designs. These studies typically pool multiple countries together into a pooled time series cross-section panel, in order to provide a larger sample for more robust and comprehensive analysis. This approach is well suited to exploring trans-national phenomena, and for elaborating useful macro-level theories specific to social structures, national policies, and long-term historical processes. It is less suited however, to understanding how these global social processes work in different countries. As such, the complexities of individual countries - which often display very different or contradictory dynamics than those suggested in pooled studies – are subsumed. Meanwhile, a robust literature on comparative case-based methods exists in the social sciences, where researchers focus on differences between cases, and the complex ways in which they co-evolve or diverge over time. A good example of this is the inequality literature, where although panel studies suggest a general trend of rising inequality driven by the weakening power of labour, marketisation of welfare, and the rising power of capital, some countries have still managed to remain resilient. This case study takes a closer look at what can be learned by applying the insights of case-based comparative research to the method of time series analysis. Taking international income inequality as its point of departure, it argues that we have much to learn about the viability of different combinations of policy options by examining how they work in different countries over time. By taking representative cases from different welfare systems (liberal, social democratic, corporatist, or antipodean), we can better sharpen our theories of how policies can be more specifically engineered to offset rising inequality. This involves a fundamental realignment of the strategy of time series analysis, grounding it instead in a qualitative appreciation of the historical context of cases, as a basis for comparing effects between different countries.

Optimal alarms systems and its application to financial time series

This thesis focuses on the application of optimal alarm systems to non linear time series models. The most common classes of models in the analysis of real-valued and integer-valued time series are described. The construction of optimal alarm systems is covered and its applications explored. Considering models with conditional heteroscedasticity, particular attention is given to the Fractionally Integrated Asymmetric Power ARCH, FIAPARCH(p; d; q) model and an optimal alarm system is implemented, following both classical and Bayesian methodologies. Taking into consideration the particular characteristics of the APARCH(p; q) representation for financial time series, the introduction of a possible counterpart for modelling time series of counts is proposed: the INteger-valued Asymmetric Power ARCH, INAPARCH(p; q). The probabilistic properties of the INAPARCH(1; 1) model are comprehensively studied, the conditional maximum likelihood (ML) estimation method is applied and the asymptotic properties of the conditional ML estimator are obtained. The final part of the work consists on the implementation of an optimal alarm system to the INAPARCH(1; 1) model. An application is presented to real data series.

Integer-valued APARCH processes in the analysis of time series of counts

The Asymmetric Power Arch representation for the volatility was introduced by Ding et al.(1993) in order to account for asymmetric responses in the volatility in the analysis of continuous-valued financial time series like, for instance, the log-return series of foreign exchange rates, stock indices or share prices. As reported by Brannas and Quoreshi (2010), asymmetric responses in volatility are also observed in time series of counts such as the number of intra-day transactions in stocks. In this work, an asymmetric power autoregressive conditional Poisson model is introduced for the analysis of time series of counts exhibiting asymmetric overdispersion. Basic probabilistic and statistical properties are summarized and parameter estimation is discussed. A simulation study is presented to illustrate the proposed model. Finally, an empirical application to a set of data concerning the daily number of stock transactions is also presented to attest for its practical applicability in data analysis.

Analysis of annuli in otoliths, age distribution and growth rates of the Namibian hake (Merluccius capensis)

Dissertação mest., Gestão da Água e da Costa, Universidade do Algarve, 2007

Structure, tissue distribution and estrogen regulation of splice variants of the sea bream estrogen receptor alpha gene

Estrogen actions are mainly mediated by specific nuclear estrogen receptors (ERs), for which different genes and a diversity of transcript variants have been identified, mainly in mammals. In this study, we investigated the presence of ER splice variants in the teleost fish gilthead sea bream (Sparus auratus), by comparison with the genomic organization of the related species Takifugu rubripes. Two exon2-deleted ERα transcript variants were isolated from liver cDNA of estradiol-treated fish. The ΔE2 variant lacks ERα exon 2, generating a premature termination codon and a putative C-terminal truncated receptor, while the ΔE2,3* variant contains an in-frame deletion of exon 2 and part of exon 3 and codes for a putative ERα protein variant lacking most of the DNA-binding domain. Both variants were expressed at very low levels in several female and male sea bream tissues, and their expression was highly inducible in liver by estradiol-17β treatment with a strong positive correlation with the typical wild-type (wt) ERα response in this tissue. These findings identify novel estrogen responsive splice variants of fish ERα, and provide the basis for future studies to investigate possible modulation of wt-ER actions by splice variants.

Five gonadotrophin-releasing hormone receptors in a teleost fish: isolation, tissue distribution and phylogenetic relationships

Gonadotrophin-releasing hormone (GnRH) is the main neurohormone controlling gonadotrophin release in all vertebrates, and in teleost fish also of growth hormone and possibly of other adenohypophyseal hormones. Over 20 GnRHs have been identified in vertebrates and protochoordates and shown to bind cognate G-protein couple receptors (GnRHR). We have searched the puffer fish, Fugu rubripes, genome sequencing database, identified five GnRHR genes and proceeded to isolate the corresponding complementary DNAs in European sea bass, Dicentrachus labrax. Phylogenetic analysis clusters the European sea bass, puffer fish and all other vertebrate receptors into two main lineages corresponding to the mammalian type I and II receptors. The fish receptors could be subdivided in two GnRHR1 (A and B) and three GnRHR2 (A, B and C) subtypes. Amino acid sequence identity within receptor subtypes varies between 70 and 90% but only 50–55% among the two main lineages in fish. All European sea bass receptor mRNAs are expressed in the anterior and mid brain, and all but one are expressed in the pituitary gland. There is differential expression of the receptors in peripheral tissues related to reproduction (gonads), chemical senses (eye and olfactory epithelium) and osmoregulation (kidney and gill). This is the first report showing five GnRH receptors in a vertebrate species and the gene expression patterns support the concept that GnRH and GnRHRs play highly diverse functional roles in the regulation of cellular functions, besides the ‘‘classical’’ role of pituitary function regulation.

Discrete Gaussian Sampling for Low-Power Devices

Thesis (Master's)--University of Washington, 2015

The relation between content providers and distributors: Lessons from the regulation of television distribution in the United Kingdom

Using the United Kingdom (UK) as a case study, this article analyses the growing commercial and regulatory significance of broadcaster-distributor relations within the contemporary television industry. The first part of the article argues that despite important changes in broadcast delivery technology, more recently shaped by the growth of the Internet, and the associated growth of options of receiving television content, the traditional delivery platforms (digital terrestrial, satellite and cable) remain by far the preferred choice for viewers in Britain. At the same time, public service broadcasters continue to be the biggest investors in domestic original non-sport content and account for over half of all television viewing. The strength of PSBs in content and their growing reliance on commercial proprietary subscription platforms (cable and satellite) and gradually on the Internet presents challenges in the nexus between broadcasters and distributors. The article focuses on the debate over retransmission fees between PSBs and Sky, and on the question of whether Sky should be required to offer some of its premium content to rival pay-TV platforms. These two examples highlight the impact regulatory intervention can have on the balance of power between broadcasters and distributors. The article concludes that such debates concerning the commercial relations between content providers and distributors will remain pivotal and become more heated given that similar issues are raised in the Internet environment.

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.

Study of distribution network demand response events in the Portuguese system

In competitive electricity markets with deep concerns for the efficiency level, demand response programs gain considerable significance. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities. Grid operators and utilities are taking new initiatives, recognizing the value of demand response for grid reliability and for the enhancement of organized spot markets’ efficiency. This paper proposes a methodology for the selection of the consumers that participate in an event, which is the responsibility of the Portuguese transmission network operator. The proposed method is intended to be applied in the interruptibility service implemented in Portugal, in convergence with Spain, in the context of the Iberian electricity market. This method is based on the calculation of locational marginal prices (LMP) which are used to support the decision concerning the consumers to be schedule for participation. The proposed method has been computationally implemented and its application is illustrated in this paper using a 937 bus distribution network with more than 20,000 consumers.

Virtual power players dealing with excessive wind power situations using real time pricing

The increasing importance given by environmental policies to the dissemination and use of wind power has led to its fast and large integration in power systems. In most cases, this integration has been done in an intensive way, causing several impacts and challenges in current and future power systems operation and planning. One of these challenges is dealing with the system conditions in which the available wind power is higher than the system demand. This is one of the possible applications of demand response, which is a very promising resource in the context of competitive environments that integrates even more amounts of distributed energy resources, as well as new players. The methodology proposed aims the maximization of the social welfare in a smart grid operated by a virtual power player that manages the available energy resources. When facing excessive wind power generation availability, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. The proposed method is especially useful when actual and day-ahead wind forecast differ significantly. The proposed method has been computationally implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20310 consumers and 548 distributed generators, some of them with must take contracts.