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.

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.

Nanosized iron based permeable reactive barriers for nitrate removal - Systematic review

It is unquestionable that an effective decision concerning the usage of a certain environmental clean-up technology should be conveniently supported. Significant amount of scientific work focussing on the reduction of nitrate concentration in drinking water by both metallic iron and nanomaterials and their usage in permeable reactive barriers has been worldwide published over the last two decades. This work aims to present in a systematic review of the most relevant research done on the removal of nitrate from groundwater using nanosized iron based permeable reactive barriers. The research was based on scientific papers published between 2004 and June 2014. It was performed using 16 combinations of keywords in 34 databases, according to PRISMA statement guidelines. Independent reviewers validated the selection criteria. From the 4161 records filtered, 45 met the selection criteria and were selected to be included in this review. This study's outcomes show that the permeable reactive barriers are, indeed, a suitable technology for denitrification and with good performance record but the long-term impact of the use of nanosized zero valent iron in this remediation process, in both on the environment and on the human health, is far to be conveniently known. As a consequence, further work is required on this matter, so that nanosized iron based permeable reactive barriers for the removal of nitrate from drinking water can be genuinely considered an eco-efficient technology.

Determinação das forças de impacto num quebra-mar misto utilizando o modelo numérico Smoothed Particle Hydrodynamics

Dissertação para obtenção do Grau de Mestre em Engenharia Mecânica

Comportamento estrutural de ligações em painéis de cement bonded particle boards

Dissertação para obtenção do Grau de Mestre em Engenharia Civil, Perfil de Estruturas

Measurement of charged-particle spectra in Pb+Pb collisions at √sNN = 2.76TeV with the ATLAS detector at the LHC

Charged-particle spectra obtained in 0.15nb−1 of Pb+Pb interactions at sNN−−−√=2.76TeV and 4.2pb−1 of pp interactions at s√=2.76TeV with the ATLAS detector at the LHC are presented in a wide transverse momentum (0.5

Search for a Heavy Neutral Particle Decaying to eμ, eτ, or μτ in pp Collisions at s√=8 TeV with the ATLAS Detector

This Letter presents a search for a heavy neutral particle decaying into an opposite-sign different-flavor dilepton pair, e±μ∓, e±τ∓, or μ±τ∓ using 20.3 fb−1 of pp collision data at s√=8 TeV collected by the ATLAS detector at the LHC. The numbers of observed candidate events are compatible with the Standard Model expectations. Limits are set on the cross section of new phenomena in two scenarios: the production of ν~τ in R-parity-violating supersymmetric models and the production of a lepton-flavor-violating Z′ vector boson.

Two-particle Bose–Einstein correlations in pp collisions at s√= 0.9 and 7 TeV measured with the ATLAS detector

The paper presents studies of Bose--Einstein Correlations (BEC) for pairs of like-sign charged particles measured in the kinematic range pT> 100 MeV and |η|< 2.5 in proton--proton collisions at centre-of-mass energies of 0.9 and 7 TeV with the ATLAS detector at the CERN Large Hadron Collider. The integrated luminosities are approximately 7 μb−1, 190 μb−1 and 12.4 nb−1 for 0.9 TeV, 7 TeV minimum-bias and 7 TeV high-multiplicity data samples, respectively. The multiplicity dependence of the BEC parameters characterizing the correlation strength and the correlation source size are investigated for charged-particle multiplicities of up to 240. A saturation effect in the multiplicity dependence of the correlation source size is observed using the high-multiplicity 7 TeV data sample. The dependence of the BEC parameters on the average transverse momentum of the particle pair is also investigated.

Particle transport in fouling caused by kaolin-water suspensions on copper tubes

Particulate fouling tests were carried out using kaolin-water suspensions flowing through an annular heat exchanger with a copper inner tube. The flow rate was changed from test to test, but the fluid temperature and pH, as well as the particle concentration, were maintained constant. In the lower range of fluid velocities (<0.5 m/s), the deposition process seemed to be controlled by mass transfer. The corresponding experimental transport fluxes were compared to the predictions obtained with several models, showing that diffusion governed particle transport. The absolute values of the mass transfer fluxes and their dependences on the Reynolds number were satisfactorily predicted by some of the models.