Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Stochastic Short-term Incentive-based Demand Response Scheduling of Load-serving Entities

In competitive electricity markets it is necessary for a profit-seeking load-serving entity (LSE) to optimally adjust the financial incentives offering the end users that buy electricity at regulated rates to reduce the consumption during high market prices. The LSE in this model manages the demand response (DR) by offering financial incentives to retail customers, in order to maximize its expected profit and reduce the risk of market power experience. The stochastic formulation is implemented into a test system where a number of loads are supplied through LSEs.

Epidemiology and antimicrobial resistance of B. fragilis group organisms isolated from clinical specimen and human intestinal microbiota

Epidemiological aspects and the antimicrobial susceptibility profile of the Bacteroides fragilis group isolated from clinical and human intestinal specimens were examined in this study. B. fragilis group strains were isolated from 46 (37%) of 124 clinical specimens and the source of the samples was: Blood culture (3), intraabdominal infection (27), brain abscess (2), soft tissue infection (17), respiratory sinus (3), pleural aspirate (9), breast abscess (3), surgical infected wound (22), pelvic inflammatory disease (22), chronic otitis media (9) and miscellaneous (7). Intraabdominal and soft tissue infections were responsible for more than half of the clinical isolates. Susceptibility to penicillin, cefoxitin, tetracycline, metronidazole, chloramphenicol and clindamycin was examined. All isolates were susceptible to metronidazole and chloramphenicol. For clindamycin and cefoxitin the resistance rates observed were 21.7% and 10.9% respectively. Susceptibility profiles varied among the different species tested. A total of 37 species of B. fragilis group isolated from intestinal microbiota of individuals who had no antimicrobial therapy for at least 1 month before the sampling was also examined. All strains were also susceptible to chloramphenicol and motronidazole and the resistance rates to clindamycin and cefoxitin were 19.4% and 5.4% respectively. A few institutions, in Brazil, have monitored the antimicrobial susceptibility of B. fragilis group strains isolated from anaerobic infections. The resistance rates to cefoxitin and clindamycin and the variation in susceptibility patterns among the species isolated in this study emphasize the need for monitoring of susceptibility patterns of B. fragilis group organisms isolated, especially at our University Hospitals.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Constrained Consumption Shifting Management in the Distributed Energy Resources Scheduling Considering Demand Response

Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods.

Distributed energy resources scheduling considering real-time resources forecast

Energy resource scheduling is becoming increasingly important, such as the use of more distributed generators and electric vehicles connected to the distribution network. This paper proposes a methodology to be used by Virtual Power Players (VPPs), regarding the energy resource scheduling in smart grids and considering day-ahead, hour-ahead and realtime time horizons. This method considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. In this paper, distribution function errors are used to simulate variations between time horizons, and to measure the performance of the proposed methodology. A 33-bus distribution network with large number of distributed resources is used.

Real-time Energy Resource Scheduling considering a Real Portuguese Scenario

The development in power systems and the introduction of decentralized generation and Electric Vehicles (EVs), both connected to distribution networks, represents a major challenge in the planning and operation issues. This new paradigm requires a new energy resources management approach which considers not only the generation, but also the management of loads through demand response programs, energy storage units, EVs and other players in a liberalized electricity markets environment. This paper proposes a methodology to be used by Virtual Power Players (VPPs), concerning the energy resource scheduling in smart grids, considering day-ahead, hour-ahead and real-time scheduling. The case study considers a 33-bus distribution network with high penetration of distributed energy resources. The wind generation profile is based on a real Portuguese wind farm. Four scenarios are presented taking into account 0, 1, 2 and 5 periods (hours or minutes) ahead of the scheduling period in the hour-ahead and realtime scheduling.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

A New Heuristic Providing an Effective Initial Solution for a Simulated Annealing approach to Energy Resource Scheduling in Smart Grids

An intensive use of dispersed energy resources is expected for future power systems, including distributed generation, especially based on renewable sources, and electric vehicles. The system operation methods and tool must be adapted to the increased complexity, especially the optimal resource scheduling problem. Therefore, the use of metaheuristics is required to obtain good solutions in a reasonable amount of time. This paper proposes two new heuristics, called naive electric vehicles charge and discharge allocation and generation tournament based on cost, developed to obtain an initial solution to be used in the energy resource scheduling methodology based on simulated annealing previously developed by the authors. The case study considers two scenarios with 1000 and 2000 electric vehicles connected in a distribution network. The proposed heuristics are compared with a deterministic approach and presenting a very small error concerning the objective function with a low execution time for the scenario with 2000 vehicles.

Resource Scheduling in Residential Microgrids Considering Energy Selling to External Players

The operation of distribution networks has been facing changes with the implementation of smart grids and microgrids, and the increasing use of distributed generation. The specific case of distribution networks that accommodate residential buildings, small commerce, and distributed generation as the case of storage and PV generation lead to the concept of microgrids, in the cases that the network is able to operate in islanding mode. The microgrid operator in this context is able to manage the consumption and generation resources, also including demand response programs, obtaining profits from selling electricity to the main network. The present paper proposes a methodology for the energy resource scheduling considering power flow issues and the energy buying and selling from/to the main network in each bus of the microgrid. The case study uses a real distribution network with 25 bus, residential and commercial consumers, PV generation, and storage.

FATAL GROUP A STREPTOCOCCAL TOXIC SHOCK-LIKE SYNDROME IN A CHILD WITH VARICELLA: REPORT OF THE FIRST WELL DOCUMENTED CASE WITH DETECTION OF THE GENETIC SEQUENCES THAT CODE FOR EXOTOXINS SPE A AND B, IN SÃO PAULO, BRAZIL

A previously healthy seven-year-old boy was admitted to the intensive care unit because of toxaemia associated with varicella. He rapidly developed shock and multisystem organ failure associated with the appearance of a deep-seated soft tissue infection and, despite aggressive treatment, died on hospital day 4. An M-non-typable, spe A and spe B positive Group A Streptococcus was cultured from a deep soft tissue aspirate. The criteria for defining Streptococcal toxic shock-like syndrome were fulfilled. The authors discuss the clinical and pathophysiological aspects of this disease as well as some unusual clinical findings related to this case.

Susceptibility of clinical isolates of Bacteroides fragilis group strains to cefoxitin, cefoperazone and ticarcillin/clavulanate

A total of 40 strains of the B. fragilis group was isolated from clinical specimens in two hospital centers in Fortaleza from 1993 to 1997. The most frequently isolated species was Bacteroides fragilis (19 strains) and most isolates came from intra-abdominal and wound infections. The susceptibility profile was traced for cefoxitin, cefoperazone and ticarcillin-clavulanate by using the agar dilution reference method. All isolates were susceptible to ticarcillin-clavulanate (128/2mug/ml). Resistance rates of 15 and 70% were detected to cefoxitin (64mug/ml) and cefoperazone (64mug/ml), respectively. Such regional results permit a better orientation in choosing this group of antibiotics for prophylaxis and therapy especially in relation to cefoxitin, which is frequently used in the hospital centers studied.

Group B streptococcal neonatal infections in Rio Grande do Sul, Brazil

Group B Streptococcus is the most common pathogen found in neonatal sepsis in North America. OBJECTIVES: We describe 15 cases of neonatal infections by Group B Streptococcus (Streptococcus agalactiae) at a Neonatal Intensive Care Unit of a public and teaching hospital. METHODS: We conducted a study at Hospital de Clínicas de Porto Alegre, from January 1st, 1996 to June 30, 1999. Diagnosis of neonatal infection was established according to the findings of Group B Streptococcus in blood culture associated with alterations resembling sepsis on the basis of clinical picture and laboratory findings. RESULTS: Fifteen cases of neonatal infections by Group B Streptococcus were detected. Eleven cases consisted of early-onset sepsis, 2 cases of occult bacteremia and 2 cases of late-onset sepsis. Eight cases had septic shock (53%), 8 cases had pneumonia (53%), and 4 cases had meningitis (27%). Fourteen cases were diagnosed from a positive blood culture, and 1 case from evidence of these bacteria in pulmonary anatomopathological examination. Thirteen cases (87%) were diagnosed before 72 hours of life. We had 3 deaths (20%), and 3 cases of meningitis developing neurological deficits. CONCLUSIONS: Streptococcus Group B is one of the most important pathogens in the etiology of early-onset neonatal sepsis at our hospital, with high mortality and morbidity. However, we do not know the incidence of GBS neonatal infections at other hospitals. More data are needed to establish a basis for trials of different strategies to reduce these infections.

Evidence of spotted fever group rickettsiae in state of Rio de Janeiro, Brazil

Ticks were obtained from dogs from February to September of 1999 at weekly intervals, in the County of Piraí, State of Rio de Janeiro. Four hundred seventy four ixodids were taxonomically identified, 103 Amblyomma cajennense, seven Amblyomma ovale, 209 Rhipicephalus sanguineus, and 155 Amblyomma sp. An hemolymph test associated with Giemsa's stain revealed two specimens in 163 ticks tested (R. sanguineus and Amblyomma sp), containing rickettsia-like organisms. Direct immunofluorescence verified the presence of spotted fever group rickettsia in one specimen of R. sanguineus. Considering the limited information on rickettsiosis in Brazil, principally in relation to the vectors involved in perpetuating it in foci, these preliminary results give us an idea on the importance of infection in ticks, allowing to expand our knowledge on this zoonosis.