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.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

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.

Incentive-based demand response programs designed by asset-light retailers for day-ahead market

Following the deregulation experience of retail electricity markets in most countries, the majority of the new entrants of the liberalized retail market were pure REP (retail electricity providers). These entities were subject to financial risks because of the unexpected price variations, price spikes, volatile loads and the potential for market power exertion by GENCO (generation companies). A REP can manage the market risks by employing the DR (demand response) programs and using its' generation and storage assets at the distribution network to serve the customers. The proposed model suggests how a REP with light physical assets, such as DG (distributed generation) units and ESS (energy storage systems), can survive in a competitive retail market. The paper discusses the effective risk management strategies for the REPs to deal with the uncertainties of the DAM (day-ahead market) and how to hedge the financial losses in the market. A two-stage stochastic programming problem is formulated. It aims to establish the financial incentive-based DR programs and the optimal dispatch of the DG units and ESSs. The uncertainty of the forecasted day-ahead load demand and electricity price is also taken into account with a scenario-based approach. The principal advantage of this model for REPs is reducing the risk of financial losses in DAMs, and the main benefit for the whole system is market power mitigation by virtually increasing the price elasticity of demand and reducing the peak demand.

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.

Bases de Dados NoSQL

Com o advento da invenção do modelo relacional em 1970 por E.F.Codd, a forma como a informação era gerida numa base de dados foi totalmente revolucionada. Migrou‐se de sistemas hierárquicos baseados em ficheiros para uma base de dados relacional com tabelas relações e registos que simplificou em muito a gestão da informação e levou muitas empresas a adotarem este modelo. O que E.F.Codd não previu foi o facto de que cada vez mais a informação que uma base de dados teria de armazenar fosse de proporções gigantescas, nem que as solicitações às bases de dados fossem da mesma ordem. Tudo isto veio a acontecer com a difusão da internet que veio ligar todas as pessoas de qualquer parte do mundo que tivessem um computador. Com o número de adesões à internet a crescer, o número de sites que nela eram criados também cresceu (e ainda cresce exponencialmente). Os motores de busca que antigamente indexavam alguns sites por dia, atualmente indexam uns milhões de sites por segundo e, mais recentemente as redes sociais também estão a lidar com quantidades gigantescas de informação. Tanto os motores de busca como as redes sociais chegaram à conclusão que uma base de dados relacional não chega para gerir a enorme quantidade de informação que ambos produzem e como tal, foi necessário encontrar uma solução. Essa solução é NoSQL e é o assunto que esta tese vai tratar. O presente documento visa definir e apresentar o problema que as bases de dados relacionais têm quando lidam com grandes volumes de dados, introduzir os limites do modelo relacional que só até há bem pouco tempo começaram a ser evidenciados com o surgimento de movimentos, como o BigData, com o crescente número de sites que surgem por dia e com o elevado número de utilizadores das redes sociais. Será também ilustrada a solução adotada até ao momento pelos grandes consumidores de dados de elevado volume, como o Google e o Facebook, enunciando as suas características vantagens, desvantagens e os demais conceitos ligados ao modelo NoSQL. A presente tese tenciona ainda demonstrar que o modelo NoSQL é uma realidade usada em algumas empresas e quais as principias mudanças a nível programático e as boas práticas delas resultantes que o modelo NoSQL traz. Por fim esta tese termina com a explicação de que NoSQL é uma forma de implementar a persistência de uma aplicação que se inclui no novo modelo de persistência da informação.

New approach for vitamin E extraction in rainbow trout flesh: Application in fish fed commercial and red seaweed-supplemented diets

A vitamin E extraction method for rainbow trout flesh was optimized, validated, and applied in fish fed commercial and Gracilaria vermiculophylla-supplemented diets. Five extraction methods were compared. Vitamers were analyzed by HPLC/DAD/fluorescence. A solid-liquid extraction with n-hexane, which showed the best performance, was optimized and validated. Among the eight vitamers, only α- and γ-tocopherol were detected in muscle samples. The final method showed good linearity (>0.999), intra- (<3.1%) and inter-day precision (<2.6%), and recoveries (>96%). Detection and quantification limits were 39.9 and 121.0 ng/g of muscle, for α-tocopherol, and 111.4 ng/g and 337.6 ng/g, for γ-tocopherol, respectively. Compared to the control group, the dietary inclusion of 5% G. vermiculophylla resulted in a slight reduction of lipids in muscle and, consequently, of α- and γ-tocopherol. Nevertheless, vitamin E profile in lipids was maintained. In general, the results may be explained by the lower vitamin E level in seaweed-containing diet. Practical Applications: Based on the validation results and the low solvent consumption, the developed method can be used to analyze vitamin E in rainbow trout. The results of this work are also a valuable information source for fish feed industries and aquaculture producers, which can focus on improving seaweed inclusion in feeds as a source of vitamin E in fish muscle and, therefore, take full advantage of all bioactive components with an important role in fish health and flesh quality.

Step based physical activity guidelines for preschool-aged children

Objective Public health organizations recommend that preschool-aged children accumulate at least 3 h of physical activity (PA) daily. Objective monitoring using pedometers offers an opportunity to measure preschooler's PA and assess compliance with this recommendation. The purpose of this study was to derive step-based recommendations consistent with the 3 h PA recommendation for preschool-aged children. Method The study sample comprised 916 preschool-aged children, aged 3 to 6 years (mean age = 5.0 ± 0.8 years). Children were recruited from kindergartens located in Portugal, between 2009 and 2013. Children wore an ActiGraph GT1M accelerometer that measured PA intensity and steps per day simultaneously over a 7-day monitoring period. Receiver operating characteristic (ROC) curve analysis was used to identify the daily step count threshold associated with meeting the daily 3 hour PA recommendation. Results A significant correlation was observed between minutes of total PA and steps per day (r = 0.76, p < 0.001). The optimal step count for ≥ 3 h of total PA was 9099 steps per day (sensitivity (90%) and specificity (66%)) with area under the ROC curve = 0.86 (95% CI: 0.84 to 0.88). Conclusion Preschool-aged children who accumulate less than 9000 steps per day may be considered Insufficiently Active.