Renewable energy support policy based on contracts for difference and bilateral negotiation

ABSTRACT: The European Union has been one of the major drivers of the development of renewable energy. The energy policies of most European countries have involved subsidized tariffs, such as the feed-in tariff in Portugal, the regulated tariff and the market price plus premium in Spain, and the Renewables Obligation in UK, that came into effect in 2002. Recently, UK has made some reforms and started to consider contracts for difference (CfDs) as a key element of the energy policy. This paper presents a support policy based on CfDs and bilateral negotiation. The first phase consists in a CfD auction and the second phase involves a bilateral negotiation between a Government and each of the selected investors. The paper also presents a case-study to analyze the potential benefits of the support policy. It was performed with the help of the MATREM system. The preliminary results indicate some advantages for the Government (and, in some cases, for the investors as well).info:eu-repo/semantics/publishedVersio

Participation of wind power producers in intra-day markets : a case-study

ABSTRACT: The evolution of renewable energy has increased substantially over the past decade. Wind power producers (WPPs) can submit bids to energy markets, making short-term commitments to produce specific quantities of energy. This article presents a case-study to analyze the benefits of the active participation of wind power producers in energy markets, particularly intra-day markets. The case-study is carried out with the help of the MATREM system. The preliminary results indicate a reduction of the deviations of WPPs, but also a decreasing in their remuneration. Thus, the results highlight to some extent the importance of new market mechanisms to enable the active participation of WPPs in markets, without support policies.info:eu-repo/semantics/publishedVersio

Bilateral negotiation in a multi-agent supply chain system

A supply chain is a set of organizations directly linked by flows of services from suppliers to customers. Supply chain activities range from the ordering and receipt of raw materials to the production and distribution of finished goods. Supply chain management is the integration of key activities across a supply chain for the purposes of building competitive infrastructures, synchronizing supply with demand, and leveraging worldwide logistics. This paper addresses the challenges created by supply chain management towards improving long-term performance of companies. It presents a multi-agent supply chain system composed of multiple software agents, each responsible for one or more supply chain activities, and each interacting with other agents in the execution of their responsibilities. Additionally, this paper presents the key features of a negotiation model for software agents. The model handles bilateral multi-issue negotiation and incorporates an alternating offers protocol, a set of logrolling strategies, and a set of negotiation tactics

CHL Dyons and Statistical Entropy Function from D1-D5 System

We give a proof of the recently proposed formula for the dyon spectrum in CHL string theories by mapping it to a configuration of D1 and D5-branes and Kaluza-Klein monopole. We also give a prescription for computing the degeneracy as a systematic expansion in inverse powers of charges. The computation can be formulated as a problem of extremizing a duality invariant statistical entropy function whose value at the extremum gives the logarithm of the degeneracy. During this analysis we also determine the locations of the zeroes and poles of the Siegel modular forms whose inverse give the dyon partition function in the CHL models.Comment: LaTeX file, 48 pages; v2: typos correcte

New type II Cepheids from VVV data towards the Galactic center

The Galactic center (GC) is the densest region of the Milky Way. Variability surveys towards the GC potentially provide the largest number of variable stars per square degree within the Galaxy. However, high stellar density is also a drawback due to blending. Moreover, the GC is affected by extreme reddening, therefore near infrared observations are needed. We plan to detect new variable stars towards the GC, focusing on type II Cepheids (T2Cs) which have the advantage of being brighter than RR Lyrae stars. We perform parallel Lomb-Scargle and Generalized Lomb-Scargle periodogram analysis of the $K_s$-band time series of the VISTA variables in the Via Lactea survey, to detect periodicities. We employ statistical parameters to clean our sample. We take account of periods, light amplitudes, distances, and proper motions to provide a classification of the candidate variables. We detected 1,019 periodic variable stars, of which 164 are T2Cs, 210 are Miras and 3 are classical Cepheids. We also found the first anomalous Cepheid in this region. We compare their photometric properties with overlapping catalogs and discuss their properties on the color-magnitude and Bailey diagrams. We present the most extensive catalog of T2Cs in the GC region to date. Offsets in E($J-K_s$) and in the reddening law cause very large ($\sim$1-2 kpc) uncertainties on distances in this region. We provide a catalog which will be the starting point for future spectroscopic surveys in the innermost regions of the Galaxy.Comment: A&A, accepte

Spatial patterns of mango malformation in irrigated areas of the brazilian semi-arid.

There are over 18.000 ha of irrigated mango in the São Francisco River Valley, and 90% of the fruit produced in this semi-arid region is exported to the european and american markets. With the expansion of the mango areas in the last decade, the intensity of malformation has been increasing, and there are many unresolved questions on the dynamics of the disease in the region, such as the role of the mango bud mite (Aceria mangiferae) or mechanical transmission in the dissemination of the disease. This study aimed to characterize the spatial patterns of mango malformation in commercial orchards, of the brazilian semi-arid

Discovering trends in brand interest through topic models

Topic Modeling is a well-known unsupervised learning technique used when dealing with text data. It is used to discover latent patterns, called topics, in a collection of documents (corpus). This technique provides a convenient way to retrieve information from unclassified and unstructured text. Topic Modeling tasks have been performed for tracking events/topics/trends in different domains such as academic, public health, marketing, news, and so on. In this paper, we propose a framework for extracting topics from a large dataset of short messages, for brand interest tracking purposes. The framework consists training LDA topic models for each brand using time intervals, and then applying the model on aggregated documents. Additionally, we present a set of preprocessing tasks that helped to improve the topic models and the corresponding outputs. The experiments demonstrate that topic modeling can successfully track people’s discussions on Social Networks even in massive datasets, and ca pture those topics spiked by real-life events.info:eu-repo/semantics/acceptedVersio

Hybrid Model For Word Prediction Using Naive Bayes and Latent Information

Historically, the Natural Language Processing area has been given too much attention by many researchers. One of the main motivation beyond this interest is related to the word prediction problem, which states that given a set words in a sentence, one can recommend the next word. In literature, this problem is solved by methods based on syntactic or semantic analysis. Solely, each of these analysis cannot achieve practical results for end-user applications. For instance, the Latent Semantic Analysis can handle semantic features of text, but cannot suggest words considering syntactical rules. On the other hand, there are models that treat both methods together and achieve state-of-the-art results, e.g. Deep Learning. These models can demand high computational effort, which can make the model infeasible for certain types of applications. With the advance of the technology and mathematical models, it is possible to develop faster systems with more accuracy. This work proposes a hybrid word suggestion model, based on Naive Bayes and Latent Semantic Analysis, considering neighbouring words around unfilled gaps. Results show that this model could achieve 44.2% of accuracy in the MSR Sentence Completion Challenge

Web of Science citation gaps: An automatic approach to detect indexed but missing citations

The number of citations a research paper receives is a crucial metric for both researchers and institutions. However, since citation databases have their own source lists, finding all the citations of a given paper can be a challenge. As a result, there may be missing citations that are not counted towards a paper’s total citation count. To address this issue, we present an automated approach to find missing citations leveraging the use of multiple indexing databases. In this research, Web of Science (WoS) serves as a case study and OpenAlex is used as a reference point for comparison. For a given paper, we identify all citing papers found in both research databases. Then, for each citing paper we check if it is indexed in WoS, but not referred in WoS as a citing paper, in order to determine if it is a missing citation. In our experiments, from a set of 1539 papers indexed by WoS, we found 696 missing citations. This outcome proves the success of our approach, and reveals that WoS does not always consider the full list of citing papers of a given publication, even when these citing papers are indexed by WoS. We also found that WoS has a higher chance of missing information for more recent publications. These findings provide relevant insights about this indexing research database, and provide enough motivation for considering other research databases in our study, such as Scopus and Google Scholar, in order to improve the matching and querying algorithms, and to reduce false positives, towards providing a more comprehensive and accurate view of the citations of a paper.info:eu-repo/semantics/publishedVersio

Spatio-temporal analysis of brand interest using social networks

Social Networks have become part of many people's lives, and applications like Facebook and Twitter are used on a daily basis by millions of users. In such applications, users share their feelings, opinions, and experiences. Twitter in particular, is used to talk about diverse topics, including brands and their products and services. In this paper, we analyze how brand interest is reflected on Twitter and how this platform can be used to monitor mentions of specific brands, as an indicator of brand interest. Our methodology is based on time, location, and the number of brand-related tweets to perform a spatio-temporal analysis. This type of analysis can provide relevant insights into how brand interest evolves over the time and how it might differ from one country to another. We have collected four years' worth of data and report trends, differences, and similarities in terms of brand interest for each brand and for each country.info:eu-repo/semantics/acceptedVersio