Evaluation of the Electric Vehicle Impact in the Power Demand Curve in a Smart Grid Environment

Smart grids with an intensive penetration of distributed energy resources will play an important role in future power system scenarios. The intermittent nature of renewable energy sources brings new challenges, requiring an efficient management of those sources. Additional storage resources can be beneficially used to address this problem; the massive use of electric vehicles, particularly of vehicle-to-grid (usually referred as gridable vehicles or V2G), becomes a very relevant issue. This paper addresses the impact of Electric Vehicles (EVs) in system operation costs and in power demand curve for a distribution network with large penetration of Distributed Generation (DG) units. An efficient management methodology for EVs charging and discharging is proposed, considering a multi-objective optimization problem. The main goals of the proposed methodology are: to minimize the system operation costs and to minimize the difference between the minimum and maximum system demand (leveling the power demand curve). The proposed methodology perform the day-ahead scheduling of distributed energy resources in a distribution network with high penetration of DG and a large number of electric vehicles. It is used a 32-bus distribution network in the case study section considering different scenarios of EVs penetration to analyze their impact in the network and in the other energy resources management.

Multi-agent system for distributed manufacturing scheduling with genetic algorithms and tabu search

Computerized scheduling methods and computerized scheduling systems according to exemplary embodiments. A computerized scheduling method may be stored in a memory and executed on one or more processors. The method may include defining a main multi-machine scheduling problem as a plurality of single machine scheduling problems; independently solving the plurality of single machine scheduling problems thereby calculating a plurality of near optimal single machine scheduling problem solutions; integrating the plurality of near optimal single machine scheduling problem solutions into a main multi-machine scheduling problem solution; and outputting the main multi-machine scheduling problem solution.

Electrochemical response of 70Co-30Ni highly branched 3D-dendritic structures for charge storage electrodes

A 70Co-30Ni dendritic alloy was produced on stainless steel by pulse electrodeposition in the cathodic domain, and oxidized by potential cycling. X-ray diffraction (XRD) identified the presence of two phases and scanning electron microscopy (SEM) evidenced an open 3D highly branched dendritic morphology. After potential cycling in 1 M KOH, SEM and X-ray photoelectron spectroscopy (XPS) revealed, respectively, the presence of thin nanoplates, composed of Co and Ni oxi-hydroxides and hydroxides over the original dendritic film. Cyclic voltammetry tests showd the presence of redox peaks assigned to the oxidation and reduction of Ni and Co centres in the surface film. Charge/discharge measurements revealed capacity values of 121 mAh g(1) at 1 mA cm(2). The capacity retention under 8000 cycles was above 70%, stating the good reversibility of these redox materials and its suitability to be used as charge storage electrodes. Electrochemical impedance spectroscopy (EIS) spectra, taken under different applied bias, showed that the capacitance increased when the electrode was fully oxidized and decreased when the electrode was reduced, reflecting different states-of-charge of the electrode. (C) 2015 Elsevier Ltd. All rights reserved.

Supporting On-Line Distributed Monitoring and Debugging

Monitoring systems have traditionally been developed with rigid objectives and functionalities, and tied to specific languages, libraries and run-time environments. There is a need for more flexible monitoring systems which can be easily adapted to distinct requirements. On-line monitoring has been considered as increasingly important for observation and control of a distributed application. In this paper we discuss monitoring interfaces and architectures which support more extensible monitoring and control services. We describe our work on the development of a distributed monitoring infrastructure, and illustrate how it eases the implementation of a complex distributed debugging architecture. We also discuss several issues concerning support for tool interoperability and illustrate how the cooperation among multiple concurrent tools can ease the task of distributed debugging.

Cloud-assisted Wireless Body Area Networks

Wireless Body Area Networks (WBANs) have emerged as a promising technology for medical and non-medical applications. WBANs consist of a number of miniaturized, portable, and autonomous sensor nodes that are used for long-term health monitoring of patients. These sensor nodes continuously collect information of patients, which are used for ubiquitous health monitoring. In addition, WBANs may be used for managing catastrophic events and increasing the effectiveness and performance of rescue forces. The huge amount of data collected by WBAN nodes demands scalable, on-demand, powerful, and secure storage and processing infrastructure. Cloud computing is expected to play a significant role in achieving the aforementioned objectives. The cloud computing environment links different devices ranging from miniaturized sensor nodes to high-performance supercomputers for delivering people-centric and context-centric services to the individuals and industries. The possible integration of WBANs with cloud computing (WBAN-cloud) will introduce viable and hybrid platform that must be able to process the huge amount of data collected from multiple WBANs. This WBAN-cloud will enable users (including physicians and nurses) to globally access the processing and storage infrastructure at competitive costs. Because WBANs forward useful and life-critical information to the cloud – which may operate in distributed and hostile environments, novel security mechanisms are required to prevent malicious interactions to the storage infrastructure. Both the cloud providers and the users must take strong security measures to protect the storage infrastructure.

Distributed controllers modeling through Petri nets with multi-asynchronous-channels

This paper proposes the concept of multi-asynchronous-channel for Petri nets. Petri nets extended with multi-asynchronous-channels and time-domains support the specification of distributed controllers, where each controller has a synchronous execution but the global system is asynchronous (globally-asynchronous locally-synchronous systems). Each multi-asynchronous-channel specify the interaction between two or more distributed controllers. These channels, together with the time-domain concept, ensure the creation of network-independent models to support implementations using heterogeneous communication networks. The created models support not only the systems documentation but also their validation and implementation through simulation tools, verification tools, and automatic code generators. An application example illustrates the use of a Petri net class extended with the proposed channels. © 2015 IEEE.

Fabrication of electrochemically reduced graphene oxide/cobalt oxide composite for charge storage electrodes

Electrochemically-reduced graphene oxide (Er-GO) and cobalt oxides (CoOx) were co-electrodeposited by cyclic voltammetry, from an electrolyte containing graphene oxide and cobalt nitrate, directly onto a stainless steel substrate to produce composite electrodes presenting high charge storage capacity. The electrochemical response of the composite films was optimized by studying the parameters applied during the electrodeposition process, namely the number of cycles, scan rate and ratio between GO/Co(NO3)(2) concentrations in the electrolyte. It is shown that, if the appropriate conditions are selected, it is possible to produced binder-free composite electrodes with improved electrochemical properties using a low-cost, facile and scalable technique. The optimized Er-GO/CoOx developed in this work exhibits a specific capacitance of 608 F g(-1) at a current density of 1 A g(-1) and increased reversibility when compared to single CoOx. (C) 2015 Elsevier B.V. All rights reserved.

Petri net model decomposition - a model based approach supporting distributed execution

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica, Especialidade de Sistemas Digitais, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Time-domain optimization of amplifiers based on distributed genetic algorithms

Thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Electrical and Computer Engineering

A lightweight distributed super peer election algorithm for unstructured dynamic P2P systems

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Energy resources management in three distinct time horizons considering a large variation in wind power

The intensive use of distributed generation based on renewable resources increases the complexity of power systems management, particularly the short-term scheduling. Demand response, storage units and electric and plug-in hybrid vehicles also pose new challenges to the short-term scheduling. However, these distributed energy resources can contribute significantly to turn the shortterm scheduling more efficient and effective improving the power system reliability. This paper proposes a short-term scheduling methodology based on two distinct time horizons: hour-ahead scheduling, and real-time scheduling considering the point of view of one aggregator agent. In each scheduling process, it is necessary to update the generation and consumption operation, and the storage and electric vehicles status. Besides the new operation condition, more accurate forecast values of wind generation and consumption are available, for the resulting of short-term and very short-term methods. In this paper, the aggregator has the main goal of maximizing his profits while, fulfilling the established contracts with the aggregated and external players.

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.

Intelligent Remuneration and Tariffs for Virtual Power Players

Power systems have been through deep changes in recent years, namely due to the operation of competitive electricity markets in the scope the increasingly intensive use of renewable energy sources and distributed generation. This requires new business models able to cope with the new opportunities that have emerged. Virtual Power Players (VPPs) are a new type of player that allows aggregating a diversity of players (Distributed Generation (DG), Storage Agents (SA), Electrical Vehicles (V2G) and consumers) to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players’ benefits. A major task of VPPs is the remuneration of generation and services (maintenance, market operation costs and energy reserves), as well as charging energy consumption. This paper proposes a model to implement fair and strategic remuneration and tariff methodologies, able to allow efficient VPP operation and VPP goals accomplishment in the scope of electricity markets.

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.

Demand Response Design and Use Based on Network Locational Marginal Prices

Power systems have been experiencing huge changes mainly due to the substantial increase of distributed generation (DG) and the operation in competitive environments. Virtual Power Players (VPP) can aggregate several players, namely a diversity of energy resources, including distributed generation (DG) based on several technologies, electric storage systems (ESS) and demand response (DR). Energy resources management gains an increasing relevance in this competitive context. This makes the DR use more interesting and flexible, giving place to a wide range of new opportunities. This paper proposes a methodology to support VPPs in the DR programs’ management, considering all the existing energy resources (generation and storage units) and the distribution network. The proposed method is based on locational marginal prices (LMP) values. The evaluation of the impact of using DR specific programs in the LMP values supports the manager decision concerning the DR use. The proposed method has been computationally implemented and its application is illustrated in this paper using a 33-bus network with intensive use of DG.