Consumers Performance Evaluation of the Participation in Demand Response Programs Using Baseline Methods

Demand response has gain increasing importance in the context of competitive electricity markets environment. The use of demand resources is also advantageous in the context of smart grid operation. In addition to the need of new business models for integrating demand response, adequate methods are necessary for an accurate determination of the consumers’ performance evaluation after the participation in a demand response event. The present paper makes a comparison between some of the existing baseline methods related to the consumers’ performance evaluation, comparing the results obtained with these methods and also with a method proposed by the authors of the paper. A case study demonstrates the application of the referred methods to real consumption data belonging to a consumer connected to a distribution network.

Integration in MASCEM of the Joint Dispatch of Energy and Reserves Provided by Generation and Demand Resources

The provision of reserves in power systems is of great importance in what concerns keeping an adequate and acceptable level of security and reliability. This need for reserves and the way they are defined and dispatched gain increasing importance in the present and future context of smart grids and electricity markets due to their inherent competitive environment. This paper concerns a methodology proposed by the authors, which aims to jointly and optimally dispatch both generation and demand response resources to provide the amounts of reserve required for the system operation. Virtual Power Players are especially important for the aggregation of small size demand response and generation resources. The proposed methodology has been implemented in MASCEM, a multi agent system also developed at the authors’ research center for the simulation of electricity markets.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

MASCEM Restructuring: Ontologies For Scenarios Generation in Power Systems Simulators

The electricity market restructuring, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in an rising complexity in power systems operation. Various power system simulators have been introduced in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex environment. This paper focuses on the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The restructuring of MASCEM (Multi-Agent System for Competitive Electricity Markets), and this system’s integration with MASGriP (Multi-Agent Smart Grid Platform), and ALBidS (Adaptive Learning Strategic Bidding System) provide the means for the exemplification of the usefulness of this ontology. A practical example is presented, showing how common simulation scenarios for different simulators, directed to very distinct environments, can be created departing from the proposed ontology.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the whole system. The work presented in this paper comprises a methodology able to define the cost allocation in distribution networks considering large integration of DG and DR resources. The proposed methodology is divided into three phases and it is based on an AC Optimal Power Flow (OPF) including the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity.

Remuneration Structure Definition for Distributed Generation Units and Demand Response Participants Aggregation

The implementation of competitive electricity markets has changed the consumers’ and distributed generation position power systems operation. The use of distributed generation and the participation in demand response programs, namely in smart grids, bring several advantages for consumers, aggregators, and system operators. The present paper proposes a remuneration structure for aggregated distributed generation and demand response resources. A virtual power player aggregates all the resources. The resources are aggregated in a certain number of clusters, each one corresponding to a distinct tariff group, according to the economic impact of the resulting remuneration tariff. The determined tariffs are intended to be used for several months. The aggregator can define the periodicity of the tariffs definition. The case study in this paper includes 218 consumers, and 66 distributed generation units.

Optimization of Generation and Aggregated Consumption Shifting for Demand Response Programs Definition

The concept of demand response has drawing attention to the active participation in the economic operation of power systems, namely in the context of recent electricity markets and smart grid models and implementations. In these competitive contexts, aggregators are necessary in order to make possible the participation of small size consumers and generation units. The methodology proposed in the present paper aims to address the demand shifting between periods, considering multi-period demand response events. The focus is given to the impact in the subsequent periods. A Virtual Power Player operates the network, aggregating the available resources, and minimizing the operation costs. The illustrative case study included is based on a scenario of 218 consumers including generation sources.

Voltage Analysis and Load Curtailment Minimization in Sub-transmission Networks with Distributed Generation

Most of distributed generation and smart grid research works are dedicated to network operation parameters studies, reliability, etc. However, many of these works normally uses traditional test systems, for instance, IEEE test systems. This paper proposes voltage magnitude and reliability studies in presence of fault conditions, considering realistic conditions found in countries like Brazil. The methodology considers a hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models and a remedial action algorithm which is based on optimal power flow. To illustrate the application of the proposed method, the paper includes a case study that considers a real 12-bus sub-transmission network.

EPIDEMIOLOGICAL SURVEY ON CANINE POPULATION WITH THE USE OF IMMUNOLEISH SKIN TEST IN ENDEMIC AREAS OF HUMAN AMERICAN CUTANEOUS LEISHMANIASIS IN THE STATE OF RIO DE JANEIRO, BRAZIL

A survey for canine tegumentary leishmaniasis (CTL) has been carried out between 1986 and 1993 in seven endemic localities for American cutaneous leishmaniasis in the State of Rio de Janeiro. 270 dogs have been examined for their clinical aspects, the development of delayed hypersensitivity (DHS) with Immunoleish antigen and with immunofluorescent antibody research of IgG (IF). 28.2% of them had ulcer lesions and 3.3% had scars. The lesions consisted of single (39.5%) and mucocutaneous lesions (31.6%), multiple cutaneous (25.0%) and mucocutaneous lesions associated with cutaneous ulcers (4.0%). Twelve (15.8%) isolates from biopsies were analyzed by zimodeme and schizodeme and identified as L. (V.) braziliensis. The overall prevalence of canine infection that was evaluated with the skin test was of 40.5% and with IF it was of 25.5%. Both tests showed a high positive rate with relation to the animals with mucosal lesions, as in the case of human mucocutaneous leishmaniasis. The comparison of the two tests showed the skin test to have a better performance although there was no statistical difference (p>0.05) between them. The proportional sensitivity and specificity was of 84.0% and 74.0%, respectively. The Immunoleish skin test and IF are useful tools to be employed in CTL field epidemiological surveys.

Influence of commercially available polyimide and formation conditions on the performance and structure of asymmetric polyimide organic solvent nanofiltration membranes

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Química e Bioquímica

Efficiency of indirect immunofluorescence assay as a confirmatory test for the diagnosis of human retrovirus infection (HIV-1 and HTLV-I/II) in different at risk populations

We compared the indirect immunofluorescence assay (IFA) with Western blot (Wb) as a confirmatory method to detect antibodies anti retrovirus (HIV-1 and HTLV-I/II). Positive and negative HIV-1 and HTLV-I/II serum samples from different risk populations were studied. Sensitivity, specificity, positive, negative predictive and kappa index values were assayed, to assess the IFA efficiency versus Wb. The following cell lines were used as a source of viral antigens: H9 ( HTLV-III b); MT-2 and MT-4 (persistently infected with HTLV-I) and MO-T (persistently infected with HTLV-II). Sensitivity and specificity rates for HIV-1 were 96.80% and 98.60% respectively, while predictive positive and negative values were 99.50% and 92.00% respectively. No differences were found in HIV IFA performance between the various populations studied. As for IFA HTLV system, the sensitivity and specificity values were 97.91% and 100% respectively with positive and negative predictive values of 100% and 97.92%. Moreover, the sensitivity of the IFA for HTLV-I/II proved to be higher when the samples were tested simultaneously against both antigens (HTLV-I-MT-2 and HTLV-II-MO-T). The overall IFA efficiency for HIV-1 and HTLV-I/II-MT-2 antibody detection probed to be very satisfactory with an excellent correlation with Wb (Kappa indexes 0.93 and 0.98 respectively). These results confirmed that the IFA is a sensitive and specific alternative method for the confirmatory diagnosis of HIV-1 and HTLV-I/II infection in populations at different levels of risk to acquire the infection and suggest that IFA could be included in the serologic diagnostic algorithm.

ELISA test for the diagnosis of cysticercosis in pigs using antigens of Taenia solium and Taenia crassiceps cysticerci

In the present study ELISA was standardized for the diagnosis of swine cysticercosis based on necropsy parameters and confirmed positive and negative control sera. Serum samples from pigs with other infections were also assayed to determine possible cross-reactions. Four antigens were assayed: from Taenia crassiceps vesicular fluid (VF-Tcra) and crude larvae extract (T-Tcra), and from Taenia solium extracts of scolex (S-Ts) and of larvae (T-Ts). A checkerboard evaluation of antigen, serum and conjugate dilutions, as well as the use of Tween-20 and skim cow milk in wash and blocking solution had a marked effect on improving ELISA performance. All the antigens showed a good performance, but VF-Tcra was the best, with 96.0% and 80.0% sensitivities for cut-offs respectively at 2sd and 3sd, and corresponding specificities of 97.5% and 100.0%. Cross-reactivity was observed only with hydatidosis and ascaridiosis. In view of the high performance observed, the ELISA test should be recommended for the diagnosis of cysticercosis in suspected swine in slaughterhouses and for the screening of cysticercosis in swine production. These results will support integrated measures of cysticercosis control throughout the chain of swine production, effectively contributing to public health.

Advantages of the rapid HIV-1 test in occupational accidents with potentially contaminated material among health workers

In occupational accidents involving health professionals handling potentially contaminated material, the decision to start or to continue prophylactic medication against infection by Human Immunodeficiency Virus (HIV) has been based on the ELISA test applied to a blood sample from the source patient. In order to rationalize the prophylactic use of antiretroviral agents, a rapid serologic diagnostic test of HIV infection was tested by the enzymatic immunoabsorption method (SUDS HIV 1+2, MUREX®) and compared to conventional ELISA (Abbott HIV-1/ HIV-2 3rd Generation plus EIA®). A total of 592 cases of occupational accidents were recorded at the University Hospital of Ribeirão Preto from July 1998 to April 1999. Of these, 109 were simultaneously evaluated by the rapid test and by ELISA HIV. The rapid test was positive in three cases and was confirmed by ELISA and in one the result was inconclusive and later found to be negative by ELISA. In the 106 accidents in which the rapid test was negative no prophylactic medication was instituted, with an estimated reduction in costs of US$ 2,889.35. In addition to this advantage, the good correlation of the rapid test with ELISA, the shorter duration of stress and the absence of exposure of the health worker to the adverse effects of antiretroviral agents suggest the adoption of this test in Programs of Attention to Accidents with Potentially Contaminated Material.