Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach 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.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

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.

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.

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.

Electricity Markets Portfolio Optimization using a Particle Swarm Approach

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 simulates 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 the market context. However, it is still necessary to adequately optimize the player’s 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 the different markets the player is acting on in each moment, and depending on different contexts of negotiation, such as the peak and offpeak periods of the day, and the type of day (business day, weekend, holiday, etc.). The proposed approach is tested and validated using real electricity markets data from the Iberian operator – OMIE.

Dot-ELISA for the detection of anti-Cysticercus cellulosae antibodies in cerebrospinal fluid using a new solid phase (resin-treated polyester fabric) and Cysticercus longicollis antigens

A dot-ELISA was developed for the detection of antibodies in CSF in the immunologic diagnosis of human neurocysticercosis, using antigen extracts of the membrane and scolex of Cysticercus cellulosae (M+S-Cc) and, alternately, membrane (M) and vesicular fluid (VF) of Cysticercus longicollis (Cl) covalently bound to a new solid phase consisting of polyester fabric treated with N-methylol-acrylamide resin (dot-RT). The test was performed at room temperature, with reduced incubation times and with no need for special care in the manipulation of the support. The sensitivity rates obtained were 95.1% for antigen Cc and 97.6% for antigen Cl. Specificity was 90.6% when Cc was used, and 96.9% and 100% when M-Cl and VF-Cl were used, respectively. No significant differences in titer were observed between tests carried out with homologous and heterologous antigens. The low cost and easy execution of the dot-RT test using antigen extracts of Cysticercus longicollis indicate the test for use in the immunodiagnosis of human neurocysticercosis.

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.

Cerebrospinal fluid profiles in acquired immunodeficiency syndrome with and without neurocryptococcosis

Cryptococcosis is one of the most common fungal infections of the central nervous system (CNS) in AIDS patients and meningoencephalitis or meningitis is a frequently observed manifestation. However, systematic studies of cerebrospinal fluid (CSF) composition from AIDS patients with CNS cryptococcosis have been few. CSF samples from 114 HIV seropositive patients whose clinical complaint suggested CNS involvement, were analyzed; 32 samples from patients diagnosed as having neurocryptococcosis (Group 1) and 82 samples from patients with no identified neurological disfunction (Group 2). Based on cytological and biochemical results, two distinct profiles were observed: Normal (Group 1 = 31%, Group 2 = 39%); Abnormal (Group 1 = 69%, Group 2 = 61%). Lymphocytes were the most frequent cells in both groups. Our CSF cytological and biochemical findings showed that in AIDS patients liquoric abnormalities are quite frequent, non-specific and difficult to interpret. In these circumstances a systematic search to identify the etiologic agent using microbiological and/or immunological assays must be routinely performed

SOLAP+: extending the interaction model

Thesis submitted to Faculdade de Ciências e Tecnologia of the Universidade Nova de Lisboa, in partial fulfillment of the requirements for the degree of Master in Computer Science

Detection of Toxoplasma gondii soluble antigen, SAG-1(p30), antibody and immune complex in the cerebrospinal fluid of HIV positive or negative individuals

Active infection by T. gondii was evaluated by immunoassay for soluble SAG-1 (p30), the major surface antigen from T. gondii, specific antibodies and immune complexes in human cerebrospinal fluid (CSF) samples. A total of 263 samples of CSF were collected from hospitalized patients presenting neurological disorders and analyzed for antibodies to HIV. Patients were divided into two groups: HIV positive (n = 96) or HIV negative (n =167). The results of the assays showed that 45% of all samples were positive for soluble SAG-1. Toxoplasma Ag/Ab immune complexes were detected in 19% of the CSF samples and 62% were positive for T. gondii- specific IgG. A combination of these assays in the presence of clinical findings consistent with active Toxoplasma infection may predict the presence of toxoplasmic encephalitis. Moreover, detection of soluble SAG-1 in the CSF of these individuals appears consistent with active infection.

Total IgE detection in paired cerebrospinal fluid and serum samples from patients with neurocysticercosis

Neurocysticercosis (NC), the presence of Taenia solium metacestodes in tissues, is the most frequent and severe parasitic infection of the central nervous system. We investigated the presence of total IgE by an automated chemiluminescence assay in 53 paired cerebrospinal fluid (CSF) and serum samples from patients with NC (P) and in 40 CSF samples from individuals with other neurological disorders as the control group (C). Total IgE concentration ranged from 1.2 to 6.6 IU/ml (mean = 1.4 IU/ml, standard deviation-sd = 1.1 IU/ml) in 28.3% of CSF samples from the P group, a value significantly higher than for the C group (£1.0 IU/ml). The serum samples from the P group showed concentrations ranging from 1.0 to 2330.0 IU/ml (mean = 224.1 IU/ml, sd = 452.1 IU/ml), which were higher than the normal value cited by the manufacturer (<100.0 IU/ml) in 32.1% of the samples. A significant difference was observed in CSF samples from the P and C groups (p = 0.005) and in serum samples from the P group compared to the normal value (p = 0.005), with sera showing more frequent abnormal results.

Characterization and optimization of bovine Echinococcus granulosus cyst fluid to be used in immunodiagnosis of hydatid disease by ELISA

The aim of this work was to assess the influence in the diagnostic value for human hydatid disease of the composition of bovine hydatid cyst fluid (BHCF) obtained from fertile (FC) and non-fertile cysts (NFC). Eight batches from FC and 5 from NFC were prepared and analysed with respect to chemical composition: total protein, host-derived protein, carbohydrate and lipid contents. No differences were observed in the first two parameters but carbohydrate and lipid contents were shown to be higher in batches from FC than in those from NFC. Bands of 38 and 116 kD in SDS-PAGE profiles were observed to be present in BHCF from FC only. Two pools were prepared from BHCF batches obtained from FC (PFC) and NFC (PNFC), respectively. Antigen recognition patterns were analysed by immunoblot. Physicochemical conditions for adsorption of antigens to the polystyrene surface (ELISA plates) were optimized. The diagnostic value of both types of BHCF as well as the diagnostic relevance of oxidation of their carbohydrate moieties with periodate were assessed by ELISA using 42 serum samples from hydatid patients, 41 from patients with other disorders, and 15 from healthy donors. Reactivity of all sera against native antigen were tested with and without free phosphorylcholine. The best diagnostic efficiency was observed using BHCF from periodate-treated PFC using glycine buffer with strong ionic strength to coat ELISA plates.

A rapid latex agglutination test for the detection of anti-cysticercus antibodies in cerebrospinal fluid (CSF)

Simple and rapid latex-based diagnostic tests have been used for detecting specific antigens or antibodies in several diseases. In this article, we present the preliminary results obtained with a latex agglutination test (LAT) for diagnosing neurocysticercosis by detection of antibodies in CSF. A total of 43 CSF samples were assayed by the LAT: 19 CSF samples from patients with neurocysticercosis and 24 CSF samples from patients with other neurologic disorders (neurosyphilis, n = 8; neurotoxoplasmosis, n = 3; viral meningitis, n = 4, chronic headache, n = 9). The LAT exhibited 89.5% sensitivity and 75% specificity. The use of LAT seems to be an additional approach for the screening of neurocysticercosis with advantage of simplicity and rapidity. Further studies could be performed using purified antigens and serum samples.