Stochastic short-term maintenance scheduling of GENCOs in an oligopolistic electricity market

In the proposed model, the independent system operator (ISO) provides the opportunity for maintenance outage rescheduling of generating units before each short-term (ST) time interval. Long-term (LT) scheduling for 1 or 2 years in advance is essential for the ISO and the generation companies (GENCOs) to decide their LT strategies; however, it is not possible to be exactly followed and requires slight adjustments. The Cournot-Nash equilibrium is used to characterize the decision-making procedure of an individual GENCO for ST intervals considering the effective coordination with LT plans. Random inputs, such as parameters of the demand function of loads, hourly demand during the following ST time interval and the expected generation pattern of the rivals, are included as scenarios in the stochastic mixed integer program defined to model the payoff-maximizing objective of a GENCO. Scenario reduction algorithms are used to deal with the computational burden. Two reliability test systems were chosen to illustrate the effectiveness of the proposed model for the ST decision-making process for future planned outages from the point of view of a GENCO.

Distribution system operation supported by contextual energy resource management based on intelligent SCADA

Future distribution systems will have to deal with an intensive penetration of distributed energy resources ensuring reliable and secure operation according to the smart grid paradigm. SCADA (Supervisory Control and Data Acquisition) is an essential infrastructure for this evolution. This paper proposes a new conceptual design of an intelligent SCADA with a decentralized, flexible, and intelligent approach, adaptive to the context (context awareness). This SCADA model is used to support the energy resource management undertaken by a distribution network operator (DNO). Resource management considers all the involved costs, power flows, and electricity prices, allowing the use of network reconfiguration and load curtailment. Locational Marginal Prices (LMP) are evaluated and used in specific situations to apply Demand Response (DR) programs on a global or a local basis. The paper includes a case study using a 114 bus distribution network and load demand based on real data.

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 he 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.

Quality-of-service in wireless sensor networks: state-of-the-art and future directions

Wireless sensor networks (WSNs) are one of today’s most prominent instantiations of the ubiquituous computing paradigm. In order to achieve high levels of integration, WSNs need to be conceived considering requirements beyond the mere system’s functionality. While Quality-of-Service (QoS) is traditionally associated with bit/data rate, network throughput, message delay and bit/packet error rate, we believe that this concept is too strict, in the sense that these properties alone do not reflect the overall quality-ofservice provided to the user/application. Other non-functional properties such as scalability, security or energy sustainability must also be considered in the system design. This paper identifies the most important non-functional properties that affect the overall quality of the service provided to the users, outlining their relevance, state-of-the-art and future research directions.

Modified particle swarm optimization for day-ahead distributed energy resources scheduling including vehicle-to-grid

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Towards high-available and energy-efficient virtual computing environments in the cloud

Empowered by virtualisation technology, cloud infrastructures enable the construction of flexi- ble and elastic computing environments, providing an opportunity for energy and resource cost optimisation while enhancing system availability and achieving high performance. A crucial re- quirement for effective consolidation is the ability to efficiently utilise system resources for high- availability computing and energy-efficiency optimisation to reduce operational costs and carbon footprints in the environment. Additionally, failures in highly networked computing systems can negatively impact system performance substantially, prohibiting the system from achieving its initial objectives. In this paper, we propose algorithms to dynamically construct and readjust vir- tual clusters to enable the execution of users’ jobs. Allied with an energy optimising mechanism to detect and mitigate energy inefficiencies, our decision-making algorithms leverage virtuali- sation tools to provide proactive fault-tolerance and energy-efficiency to virtual clusters. We conducted simulations by injecting random synthetic jobs and jobs using the latest version of the Google cloud tracelogs. The results indicate that our strategy improves the work per Joule ratio by approximately 12.9% and the working efficiency by almost 15.9% compared with other state-of-the-art algorithms.

A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles

The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.

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.

Integration in MASCEM of the Joint Dispatch of Energy and Reserves Provided by Generation and Demand Resources

The provision of reserves in power systems is of great importance in what concerns keeping an adequate and acceptable level of security and reliability. This need for reserves and the way they are defined and dispatched gain increasing importance in the present and future context of smart grids and electricity markets due to their inherent competitive environment. This paper concerns a methodology proposed by the authors, which aims to jointly and optimally dispatch both generation and demand response resources to provide the amounts of reserve required for the system operation. Virtual Power Players are especially important for the aggregation of small size demand response and generation resources. The proposed methodology has been implemented in MASCEM, a multi agent system also developed at the authors’ research center for the simulation of electricity markets.

Simulated Annealing Approach Applied to the Energy Resource Management Considering Demand Response for Electric Vehicle

The aggregation and management of Distributed Energy Resources (DERs) by an Virtual Power Players (VPP) is an important task in a smart grid context. The Energy Resource Management (ERM) of theses DERs can become a hard and complex optimization problem. The large integration of several DERs, including Electric Vehicles (EVs), may lead to a scenario in which the VPP needs several hours to have a solution for the ERM problem. This is the reason why it is necessary to use metaheuristic methodologies to come up with a good solution with a reasonable amount of time. The presented paper proposes a Simulated Annealing (SA) approach to determine the ERM considering an intensive use of DERs, mainly EVs. In this paper, the possibility to apply Demand Response (DR) programs to the EVs is considered. Moreover, a trip reduce DR program is implemented. The SA methodology is tested on a 32-bus distribution network with 2000 EVs, and the SA results are compared with a deterministic technique and particle swarm optimization results.

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.

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.

Realistic Traffic Scenarios Using a Census Methodology: Vila Real Case Study

This paper presents the first phase of the redevelopment of the Electric Vehicle Scenario Simulator (EVeSSi) tool. A new methodology to generate traffic demand scenarios for the Simulation of Urban MObility (SUMO) tool for urban traffic simulation is described. This methodology is based on a Portugal census database to generate a synthetic population for a given area under study. A realistic case study of a Portuguese city, Vila Real, is assessed. For this area the road network was created along with a synthetic population and public transport. The traffic results were obtained and an electric buses fleet was evaluated assuming that the actual fleet would be replaced in a near future. The energy requirements to charge the electric fleet overnight were estimated in order to evaluate the impacts that it would cause in the local electricity network.

Energy Harvesting for Autonomous Underwater Vehicles

In this study, energy production for autonomous underwater vehicles is investigated. This project is part of a bigger project called TURTLE. The autonomous vehicles perform oceanic researches at seabed for which they are intended to be kept operational underwater for several months. In order to ful l a long-term underwater condition, powerful batteries are combined with \micro- scale" energy production on the spot. This work tends to develop a system that generates power up to a maximum of 30 W. Latter energy harvesting structure consists basically of a turbine combined with a generator and low-power electronics to adjust the achieved voltage to a required battery charger voltage. Every component is examined separately hence an optimum can be de ned for all, and subsequently also an overall optimum. Di erent design parameters as e.g. number of blades, solidity ratio and cross-section area are compared for di erent turbines, in order to see what is the most feasible type. Further, a generator is chosen by studying how ux distributions might be adjusted to low velocities, and how cogging torque can be excluded by adapted designs. Low-power electronics are con gured in order to convert and stabilize heavily varying three-phase voltages to a constant, recti ed voltage which is usable for battery storage. Clearly, di erent component parameters as maximum power and torque are matched here to increase the overall power generation. Furthermore an overall maximum power is set up for achieving a maximum power ow at load side. Due to among others typical low velocities of about 0.1 to 0.5 m/s, and constructing limits of the prototype, the vast range of components is restricted to only a few that could be used. Hence, a helical turbine is combined in a direct drive mode to a coreless-stator axial- ux permanent-magnet generator, from which the output voltage is adjusted subsequently by a recti er, impedance matching unit, upconverter circuit and an overall control unit to regulate di erent component parameters. All these electronics are combined in a closed-loop design to involve positive feedback signals. Furthermore a theoretical con guration for the TURTLE vehicle is described in this work and a solution is proposed that might be implemented, for which several design tests are performable in a future study.

Gestão de Energia Renovável para Sistemas Insulares - Projeto para Ilha de Santiago – Cabo Verde

A opção pela elaboração do presente projeto decorre da importância que as energias renováveis já têm, e poderão vir a assumir, no contexto do desenvolvimento económico das ilhas de Cabo Verde. O projeto foi elaborado com base nas necessidades reais do país, identificadas por estudos realizados por entidades especializadas na área, e visa desbravar vias para um possível e desejável alargamento da utilização de energias renováveis no país. O projeto é direccionado para a produção de energia visando complementar o abastecimento através da rede pública, que é gerida por uma empresa estatal, a ELECTRA. Pressupõe que toda a energia produzida é adquirida por esta empresa como, aliás, legalmente está estabelecido. Na elaboração do projeto teve-se em conta o impacto ambiental do mesmo, incluindo os seus efeitos no plano financeiro. O projeto assenta num diagnóstico relativamente aprofundado do setor de produção de energia elétrica e caracteriza o setor tal como se apresenta actualmente. Foi direccionado essencialmente para a ilha de Santiago, a maior do país, que congrega cerca de 56 % da população. Procedeu-se, em particular, a uma análise detalhada dos parques eólicos e solares existentes no país. O projeto avalia com relativa profundidade a evolução recente da procura de energia elétrica, e o potencial das energias renováveis, com ênfase nas energias eólica e solar, as mais relevantes para o país, pelo menos no futuro próximo. O sistema tarifário foi, igualmente, objecto de discussão no decurso da elaboração do projeto. Finalmente, a elaboração do projeto conduziu-nos ao estudo das orientações estratégicas, objetivos e políticas governamentais para a área da produção de energia elétrica. No plano financeiro, foram considerados três cenários baseados no grau de utilização da radiação solar (fotovoltaica) ou aproveitamento dos ventos (eólica). Para cada cenário foram avaliadas alternativas, que consistem basicamente na utilização de diferentes preços de venda, num leque que se situa em níveis comparáveis aos que actualmente são praticados.