Technical and economic resources management in smart grids using heuristic optimization methods

Intensive use of Distributed Generation (DG) represents a change in the paradigm of power systems operation making small-scale energy generation and storage decision making relevant for the whole system. This paradigm led to the concept of smart grid for which an efficient management, both in technical and economic terms, should be assured. This paper presents a new approach to solve the economic dispatch in smart grids. The proposed methodology for resource management involves two stages. The first one considers fuzzy set theory to define the natural resources range forecast as well as the load forecast. The second stage uses heuristic optimization to determine the economic dispatch considering the generation forecast, storage management and demand response

Distributed energy resources management with cyber-physical SCADA in the context of future smart grids

In the energy management of a small power system, the scheduling of the generation units is a crucial problem for which adequate methodologies can maximize the performance of the energy supply. This paper proposes an innovative methodology for distributed energy resources management. The optimal operation of distributed generation, demand response and storage resources is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The paper deals with a vision for the grids of the future, focusing on conceptual and operational aspects of electrical grids characterized by an intensive penetration of DG, in the scope of competitive environments and using artificial intelligence methodologies to attain the envisaged goals. These concepts are implemented in a computational framework which includes both grid and market simulation.

Distributed, Agent-Based Intelligent System for Demand Response Program Simulation in Smart Grids

A distributed, agent-based intelligent system models and simulates a smart grid using physical players and computationally simulated agents. The proposed system can assess the impact of demand response programs.

Using Baseline Methods to Identify Non-technical Losses in the Context of Smart Grids

Demand response has gained increasing importance in the context of competitive electricity markets and smart grid environments. In addition to the importance that has been given to the development of business models for integrating demand response, several methods have been developed to evaluate the consumers’ performance after the participation in a demand response event. The present paper uses those performance evaluation methods, namely customer baseline load calculation methods, to determine the expected consumption in each period of the consumer historic data. In the cases in which there is a certain difference between the actual consumption and the estimated consumption, the consumer is identified as a potential cause of non-technical losses. A case study demonstrates the application of the proposed method to real consumption data.

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.

Reactive power management strategies in future smart grids

The reactive power management in distribution network with large penetration of distributed energy resources is an important task in future power systems. The control of reactive power allows the inclusion of more distributed recourses and a more efficient operation of distributed network. Currently, the reactive power is only controlled in large power plants and in high and very high voltage substations. In this paper, several reactive power control strategies considering a smart grids paradigm are proposed. In this context, the management of distributed energy resources and of the distribution network by an aggregator, namely Virtual Power Player (VPP), is proposed and implemented in a MAS simulation tool. The proposed methods have been computationally implemented and tested using a 32-bus distribution network with intensive use of distributed resources, mainly the distributed generation based on renewable resources. Results concerning the evaluation of the reactive power management algorithms are also presented and compared.

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.

A New Heuristic Providing an Effective Initial Solution for a Simulated Annealing approach to Energy Resource Scheduling in Smart Grids

An intensive use of dispersed energy resources is expected for future power systems, including distributed generation, especially based on renewable sources, and electric vehicles. The system operation methods and tool must be adapted to the increased complexity, especially the optimal resource scheduling problem. Therefore, the use of metaheuristics is required to obtain good solutions in a reasonable amount of time. This paper proposes two new heuristics, called naive electric vehicles charge and discharge allocation and generation tournament based on cost, developed to obtain an initial solution to be used in the energy resource scheduling methodology based on simulated annealing previously developed by the authors. The case study considers two scenarios with 1000 and 2000 electric vehicles connected in a distribution network. The proposed heuristics are compared with a deterministic approach and presenting a very small error concerning the objective function with a low execution time for the scenario with 2000 vehicles.

Impacto da Introdução de Baterias de armazenamento de Energia em Smart Grids

De forma a não comprometer o conforto ou a qualidade de vida, nos dias de hoje, é obrigatório que a energia elétrica esteja presente. Sendo indispensável, torna-se necessário assegurar que a sua distribuição seja feita da forma mais qualitativa possível. Uma resposta rápida e eficaz a possíveis falhas que ocorram na rede, irá garantir a tal qualidade de serviço desejada. Para isso, a automatização dos processos é uma grande evolução e objetivo de concretização do setor elétrico. Neste contexto surge o conceito de Smart Grid, que tem como principal objetivo a combinação entre o setor elétrico e a evolução da tecnologia. A par desta característica, estes tipos de redes vêm também trazer evoluções no âmbito ambiental, pois a produção de energia elétrica é feita, maioritariamente, por fontes de energia renovável. Este projeto incide na análise das vantagens técnicas e económicas da inclusão de equipamentos que detêm capacidades de armazenamento de energia, as Baterias de Armazenamento de Energia (BAE), neste tipo de redes. Para tal, procedeu-se à utilização do método do Despacho Económico, que tem como principal objetivo a determinação dos níveis de produção de todas as unidades geradoras do sistema, satisfazendo a carga, ao mais baixo custo de produção. Com este método, foram criados vários cenários de estudo com vista a validar todo o propósito deste projeto. Nesta dissertação, é também realizado um estudo de viabilidade económica destes equipamentos de armazenamento de energia.

Cattle with Hair Whorl Patterns above the Eyes are More Behaviorally Agitated During Restraint

The article focuses on the temperament of cattle depending on the position of hair whorl. The article was published in the journal Applied Animal Behaviour Science.

Serpentine Cattle Chute Gives Handling Advantages

The article discusses the efficiency of a curved chute as opposed to a straight chute when leading cattle into a conveyor restrainer. The article was published in The National Provisioner in 1984.

Bos Indicus-Cross Feedlot Cattle with Excitable Temperaments have Tougher Meat and a Higher Incidence of Borderline Dark Cutters

The article was published in the journal Meat Science, Vol. 46, No.4. The focus is data collected for cattle temperament and the quality of meat produced.

Selected Articles from Beef Cattle Science Handbook Volume 19

The articles within the handbook are: "Livestock Psychology and Handling-Facility Design", "Design of Corrals, Squeeze Chutes, and Dip Vats", "Handling Feedlot Cattle", and "Reducing Transportation Stresses".

The Feasibility of Using Vocalization Scoring as an Indicator of Poor Welfare During Cattle Slaughter

The article discusses the vocalization of cattle in six slaughter plants and the results indicate that "vocalization scoring could be used as a simple method for detecting welfare problems that need to be corrected".

Welfare of Cattle during Slaughter and the Prevention of Nonambulatory (downer) cattle

The article focuses on keeping cattle calm and removing distractions that would scare cattle back from the chute.