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.

Domestic Consumption Simulation and Management Using a Continuous Consumption Management and Optimization Algorithm

The recent changes concerning the consumers’ active participation in the efficient management of load devices for one’s own interest and for the interest of the network operator, namely in the context of demand response, leads to the need for improved algorithms and tools. A continuous consumption optimization algorithm has been improved in order to better manage the shifted demand. It has been done in a simulation and user-interaction tool capable of being integrated in a multi-agent smart grid simulator already developed, and also capable of integrating several optimization algorithms to manage real and simulated loads. The case study of this paper enhances the advantages of the proposed algorithm and the benefits of using the developed simulation and user interaction tool.

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.

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.

An Exercise Program Improves Health-Related Quality of Life of Workers

Low back problems are associated with decreased quality of life. Specific exercises can improve quality of life, resulting in better professional performance and functionality. The purpose of this study was to evaluate the effect of following a 21-month exercise program on the quality of life of warehouse workers. The population included 557 male warehouse workers from a food distribution company in Oporto, Portugal. Upon application of the selection criteria, 249 workers were deemed eligible, which were randomized into two groups (125 in the intervention group and 124 in the control group). Then, subjects were asked to volunteer for the study, the sample being formed by 229 workers (112 in the intervention group and 117 in the control group). All subjects completed the SF-36 questionnaire prior to beginning the program and on the 11th and 21st months following it. The exercises were executed in the company facilities once a day for 8 min. Data were analyzed using SPSS® 17.0 for Windows®. After 11 months of following the exercise program, there was an increase in all scores for the experimental group, with statistically significant differences in the dimensions physical functioning (0.019), bodily pain (0.010), general health (0.004), and rolephysical (0.037). The results obtained at the end of the study (21 months) showed significant improvements in the dimensions physical functioning (p = 0.002), rolephysical (p = 0.007), bodily pain (p = 0.001), social functioning (p = 0.015), role-emotional (p = 0.011), and mental health (p = 0.001). In the control group all dimensions showed a decrease in mean scores. It can be concluded that the implementation of a low back specific exercise program has changed positively the quality of life of warehouse workers.

Extremum estimators and stochastic optimization methods

Submitted in partial fulfillment for the Requirements for the Degree of PhD in Mathematics, in the Speciality of Statistics in the Faculdade de Ciências e Tecnologia

Invited review: Models for the optimization of regional wastewater treatment systems

European Journal of Operational Research, nº 73 (1994)

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.

Probabilistic maintenance and optimization strategies for deteriorating civil infrastructures

In developed countries, civil infrastructures are one of the most significant investments of governments, corporations, and individuals. Among these, transportation infrastructures, including highways, bridges, airports, and ports, are of huge importance, both economical and social. Most developed countries have built a fairly complete network of highways to fit their needs. As a result, the required investment in building new highways has diminished during the last decade, and should be further reduced in the following years. On the other hand, significant structural deteriorations have been detected in transportation networks, and a huge investment is necessary to keep these infrastructures safe and serviceable. Due to the significant importance of bridges in the serviceability of highway networks, maintenance of these structures plays a major role. In this paper, recent progress in probabilistic maintenance and optimization strategies for deteriorating civil infrastructures with emphasis on bridges is summarized. A novel model including interaction between structural safety analysis,through the safety index, and visual inspections and non destructive tests, through the condition index, is presented. Single objective optimization techniques leading to maintenance strategies associated with minimum expected cumulative cost and acceptable levels of condition and safety are presented. Furthermore, multi-objective optimization is used to simultaneously consider several performance indicators such as safety, condition, and cumulative cost. Realistic examples of the application of some of these techniques and strategies are also presented.

Effects of a home-based exercise program in body composition, abdominal fat and lipid profile in patients with coronary artery disease

Introduction: Coronary artery disease and aging seems to be associated with a sedentary lifestyle, contributing to increased abdominal fat and consequently metabolic complications. The exercise can break this cycle by stimulating lipolysis and the use of fatty acids. In Europe there is still a lack of cardiac rehabilitation programmes in hospitals, therefore, this study aims to demonstrate the advantages of implementing home-based exercise programmes, as well as, their effects on cardiovascular prevention. This study analyzed the effects of a home-based exercise programme, in patients with coronary artery disease (myocardial infarction for 1 year), in body composition, abdominal fat, lipid profile. Methods: An ongoing randomized controlled trial with a sample of 20 participants were randomly allocated to intervention (n = 10) and control groups (n = 10). Intervention group performed a specific exercise programme during 8 weeks, consisting of ten home based exercises taking into account flexibility, muscle endurance and strength as well as cardiovascular endurance. Skinfolds thickness were measure to calculate the percentage of total fat: Skinfolds used were suprailiac, abdominal horizontal and vertical. Body mass index calculation and blood tests for lipidic profile were performed. Results: After eight weeks the intervention group decreased significantly the percentage of total fat (p < 0.05), the suprailiac skinfold (p < 0.05), the abdominal horizontal and vertical skinfold (p < 0.05) when compared with control group. In the intervention group it was observed after 8 weeks a significant decrease in body mass index, LDL-cholesterol and triglycerides. Conclusions: Home-based exercise programme influenced body composition, abdominal fat and lipid profile. These results highlight the importance of implementing home based exercises that are easy and cheap to implement in cardiac patients, in order to promote health and reduce cardiovascular risk factors.

Effects of microcurrents and physical exercise on the abdominal fat: a randomized controlled trial in patients with coronary artery disease

Objectives: Coronary artery disease are associated with decreased levels of physical activity, contributing to increases in abdominal fat and consequently the metabolic risk. The use of microcurrents is an innovative and effective method to increase lipolytic rate of abdominal adipocytes. This study aims to investigate the effects of microcurrents with a homebased exercise program on total, subcutaneous and visceral abdominal adipose tissue in subjects with coronary artery disease. Methods: This controlled trial included 44 subjects with myocardial infarction, randomly divided into Intervention Group 1 (IG1; n = 16), Intervention Group 2 (IG2; n = 12) and Control Group (CG; n = 16). IG1 performed a specific exercise program at home during 8 weeks, and IG2 additionally used microcurrents on the abdominal region before the exercise program. All groups were subjected to health education sessions. Computed Tomography was used to evaluate abdominal, subcutaneous and visceral fat, accelerometers to measure habitual physical activity and the semiquantitative Food Frequency Questionnaire for dietary intake. Results: After 8 weeks, IG2 showed a significantly decreased in subcutaneous fat (p ≤ 0.05) when compared to CG. Concerning visceral fat, both intervention groups showed a significant decrease in comparison to the CG (p ≤ 0.05). No significant changes were found between groups on dietary intake and habitual physical activity, except for sedentary activity that decreased significantly in IG2 in comparison with CG (p ≤ 0.05). Conclusions: This specific exercise program showed improvements in visceral fat in individuals with coronary artery disease. Microcurrent therapy associated with a home-based exercise program suggested a decreased in subcutaneous abdominal fat.

Factors that Influence Adherence to Exercise among Patients Attending a Cardiac Rehabilitation Program

Objective: The aim of this study was to compare the factors of adherence to physical activity in subjects attending a cardiac rehabilitation program, and subjects who have withdrawal this same program using the Transtheoretical Model of behavior change. Methods: We conducted an observational, cross sectional type study, with a sample of 33 individuals (15 currently participating in the Cardiac Rehabilitation Program and 18 who no more attended the same program), with the questionnaires being personally delivered or sent by mail. For data analysis, we used the computer program SPSS® version 16.0. The significance level was set at 0.05. Results: There were no significant differences in the states of Change, Self-efficacy, Decisional Balance and Change Processes in both groups. We obtained a high Spearman correlation between States of Change and Self-efficacy (r2 = 0.778) and the Pros (r2 = 0.764) and Againsts (r2 = -0.744) in Decisional Balance. However, there were no significant evidence to affirm that States of Change and experiential processes of change (p = 0.465) andbehavioral (p = 0.300) had a correlation. A relationship was found, in terms of proportions between physical activity incorporated or not in a Cardiac Rehabilitation Program and age (p = 0.003), occupation (p = 0.010) and the entity paying the costs of program (p = 0.027). Conclusion: It was concluded that perceived self-efficacy and Pros and Againsts of the Decisional Balance are related to adherence to physical activity. Results also point out that age, profession and the entity paying the costs of the program influences the dropout of Cardiac Rehabilitation Programs.