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.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

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 environment. 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. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

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.

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

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.

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)

Epidemiologia da malária em Cabo Verde - Factores genéticos humanos e estrutura populacional do mosquito vector

A malária, doença parasitária complexa que resulta da interacção entre parasita, hospedeiros humano e vector, constitui um dos principais problemas de saúde a nível mundial. À semelhança de outras doenças parasitárias e infecciosas a malária tem um papel importante na evolução, tendo já sido demonstrado o papel da variação genética humana na resistência à infecção. Após quase meio século de controlo, a malária persiste na ilha de Santiago onde, apesar da baixa endemicidade, os indivíduos apresentam geralmente manifestações moderadas, são diagnosticadas infecções abaixo do nível detectável pela microscopia e o vector se encontra muito próximo da população supostamente susceptível, desconhecendo-se a frequência dos principais polimorfismos genéticos humanos mais relacionados com a doença e a estrutura populacional do mosquito vector. Os objectivos gerais de trabalho desta tese assentam 1) no estudo dos dois clássicos factores genéticos do hospedeiro humano relacionados com a malária, nomeadamente os afectos à anemia das células falciformes, à deficiência em G6PD e a análise dum provável envolvimento da PK e 2) na análise genética das populações do mosquito vector, tentando contribuir para a compreensão da epidemiologia da doença na Ilha, e para a escolha de medidas de controlo apropriadas. Os trabalhos incidiram na detecção do alelo responsável pela hemoglobina S, de polimorfismos no gene da G6PD e da PK em indivíduos não aparentados (Infectados e não Infectados) com análise da sua provável associação com a infecção e, ainda, na genotipagem de loci microssatélites de Anopheles arabiensis com recurso a técnicas baseadas na PCR. Relativamente à anemia falciforme, a frequência dos portadores do traço (indivíduos HbAS) e do alelo HbS foi 6% e 5%, respectivamente, e para as variantes da G6PD, 0,8% para G6PDA- e 0,0% para a G6PDMed, não tendo sido encontrado associação entre os genótipos desses dois factores e a presença de infecção. No que concerne ao gene PKLR não foi encontrada uma associação clara entre os polimorfismos analisados e o estado de infecção, mas foi detectado um acentuado desequilíbrio de linkage entre os loci, apenas nos Não Infectados, o que pode significar que essa região do gene, aparentemente conservada, tenha sido seleccionada por fornecer protecção contra a infecção e/ou doença. A diversidade genética das populações de A. arabiensis em onze loci microssatélites foi moderada com valores médio de He, variando de 0,481 a 0,522 e a Rs de 4 a 5. O valor da diferenciação genética baseado em 7 loci polimórficos foi baixo (FST=0,012; p<0,001) mas significativo, variando entre 0,001 e 0,023 entre os pares de populações. Não foram detectados os alelos de resistência associados ao gene Kdr. A baixa frequência dos alelos associados à G6PD (A- e Med) tem implicações importantes nas estratégias de controlo definidas pelo Programa Nacional de Luta contra o Paludismo (PNLP), uma vez que a primaquina pode continuar a ser administrada como complemento aos regimes terapêuticos, em caso de necessidade. A população de A. arabiensis em Santiago revelou-se relativamente homogénea e com uma estrutura reduzida o que pode, por um lado, representar uma desvantagem por permitir uma provável dispersão dos genes de resistência. Por outro lado, essa relativa homogeneidade poderá representar uma vantagem para a introdução de um programa de controlo baseado na libertação de mosquitos transgénicos.

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.