Cost Allocation Model for Distribution Networks Considering High Penetration of Distributed Energy Resources

The high penetration of distributed energy resources (DER) in distribution networks and the competitiveenvironment of electricity markets impose the use of new approaches in several domains. The networkcost allocation, traditionally used in transmission networks, should be adapted and used in the distribu-tion networks considering the specifications of the connected resources. The main goal is to develop afairer methodology trying to distribute the distribution network use costs to all players which are usingthe network in each period. In this paper, a model considering different type of costs (fixed, losses, andcongestion costs) is proposed comprising the use of a large set of DER, namely distributed generation(DG), demand response (DR) of direct load control type, energy storage systems (ESS), and electric vehi-cles with capability of discharging energy to the network, which is known as vehicle-to-grid (V2G). Theproposed model includes three distinct phases of operation. The first phase of the model consists in aneconomic dispatch based on an AC optimal power flow (AC-OPF); in the second phase Kirschen’s andBialek’s tracing algorithms are used and compared to evaluate the impact of each resource in the net-work. Finally, the MW-mile method is used in the third phase of the proposed model. A distributionnetwork of 33 buses with large penetration of DER is used to illustrate the application of the proposedmodel.

Maximum penetration of microgeneration photovoltaic panels in distribution network

In smart grids context, the distributed generation units based in renewable resources, play an important rule. The photovoltaic solar units are a technology in evolution and their prices decrease significantly in recent years due to the high penetration of this technology in the low voltage and medium voltage networks supported by governmental policies and incentives. This paper proposes a methodology to determine the maximum penetration of photovoltaic units in a distribution network. The paper presents a case study, with four different scenarios, that considers a 32-bus medium voltage distribution network and the inclusion storage units.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

Intelligent energy resources management in the context of smart grids

Smart grids are envisaged as infrastructures able to accommodate all centralized and distributed energy resources (DER), including intensive use of renewable and distributed generation (DG), storage, demand response (DR), and also electric vehicles (EV), from which plug-in vehicles, i.e. gridable vehicles, are especially relevant. Moreover, smart grids must accommodate a large number of diverse types or players in the context of a competitive business environment. Smart grids should also provide the required means to efficiently manage all these resources what is especially important in order to make the better possible use of renewable based power generation, namely to minimize wind curtailment. An integrated approach, considering all the available energy resources, including demand response and storage, is crucial to attain these goals. This paper proposes a methodology for energy resource management that considers several Virtual Power Players (VPPs) managing a network with high penetration of distributed generation, demand response, storage units and network reconfiguration. The resources are controlled through a flexible SCADA (Supervisory Control And Data Acquisition) system that can be accessed by the evolved entities (VPPs) under contracted use conditions. A case study evidences the advantages of the proposed methodology to support a Virtual Power Player (VPP) managing the energy resources that it can access in an incident situation.

A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

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.

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.

Smart Grid Market Using Joint Energy and Ancillary Services Bids

The power systems operation in the smart grid context increases significantly the complexity of their management. New approaches for ancillary services procurement are essential to ensure the operation of electric power systems with appropriate levels of stability, safety, quality, equity and competitiveness. These approaches should include market mechanisms which allow the participation of small and medium distributed energy resources players in a competitive market environment. In this paper, an energy and ancillary services joint market model used by an aggregator is proposed, considering bids of several types of distributed energy resources. In order to improve economic efficiency in the market, ancillary services cascading market mechanism is also considered in the model. The proposed model is included in MASCEM – a multi-agent system electricity market simulator. A case study considering a distribution network with high penetration of distributed energy resources is presented.

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.

Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the whole system. The work presented in this paper comprises a methodology able to define the cost allocation in distribution networks considering large integration of DG and DR resources. The proposed methodology is divided into three phases and it is based on an AC Optimal Power Flow (OPF) including the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity.

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.

Projeto de Sistema Fotovoltaico Para as Naves Industriais da Zona Econômica Especial de Luanda-Bengo

Com os preços do barril de petróleo a atingir níveis nunca antes atingidos, cada vez mais há uma maior sensibilização para a importância das fontes renováveis de energia, não só pelo seu baixo custo de exploração, mas também pela ausência de poluição ambiental. A integração de sistemas fotovoltaicos nas edificações, começa a ter uma expressão significativa especialmente por ser uma forma de produção renovável. Pelo seu carácter renovável, vai ao encontro de objetivos ambientais, e é também desejável pelo seu carácter distribuído, produção próxima do consumo, evitando perdas de transporte e utilizando o recurso disponível no consumidor. No presente projeto é feita uma breve descrição do atual sistema elétrico angolano, nomeadamente o seu potencial, capacidade instalada, e perspetivas futuras de desenvolvimento do mesmo. Com uma perspetiva introdutória são abordadas as energias renováveis especialmente a energia fotovoltaica, terminando com as diferentes formas de produção e tecnologias existentes. São apresentados diferentes equipamentos, que, com as inúmeras combinações poderão vir a constituir um sistema técnico e financeiramente viável. Devido aos vários cenários possíveis (combinações entre equipamentos), foram usadas como instrumentos de apoio ferramentas informáticas que permitem o dimensionamento de sistemas fotovoltaicos, análise de compatibilidades, e simulação do seu funcionamento. Foram dimensionadas quatro opções de sistemas fotovoltaicos, a instalar nas naves industriais da Zona Económica Especial Luanda-Bengo, para uma mesma área de cobertura, seguido de um estudo económico, onde é feito uma comparação custo/benefício dos vários sistemas.

Estudo sobre viabilidade de produção descentralizada de energia à custa do efeito chaminé

A procura por alternativas ao atual paradigma energético, que se caracteriza por uma predominância indiscutível das fontes combustíveis fósseis, é o motivo primário desta investigação. A energia emitida pelo Sol que chega à Terra diariamente ultrapassa em várias ordens de grandeza a energia que a nossa sociedade atual necessita. O efeito chaminé é uma das formas de aproveitar essa energia. Este efeito tem origem no diferencial de temperaturas existente entre o interior e o exterior de uma chaminé, que provoca um gradiente nas massas volúmicas do fluido entre o interior e o exterior da chaminé, induzindo assim um fluxo de ar. Esta diferença de temperaturas radica na exposição da face exterior da chaminé à radiação solar. No sistema que nos propomos estudar, o ar entra na chaminé por pequenos orifícios situados na sua base, e, ao tomar contacto com as paredes internas da chaminé, aquece desde a temperatura ambiente, Ta, até à temperatura interna, Ti . Este aumento de temperatura torna o ar dentro da chaminé mais “leve” em comparação com o ar mais frio do exterior levando-o a ascender ao longo do interior da chaminé. Este escoamento contém energia cinética que pode, por exemplo, ser transformada em energia elétrica por intermédio de turbinas. A eficiência de conversão da energia será tanto maior quanto menor for a velocidade do ar a jusante da turbina. Esta tecnologia poderá ser instalada de forma descentralizada, como acontece com as atuais centrais concentradoras solares térmicas e fotovoltaicas localizadas na periferia de grandes cidades ou, alternativamente, poderá ser inserida no próprio tecido urbanístico. A investigação demonstra que as dimensões da chaminé, a irradiação e a temperatura do ar são os fatores com maior impacto na potência hidráulica gerada.

Modelling real solar cell using PSCAD/MATLAB

This paper presents the development of a solar photovoltaic (PV) model based on PSCAD/EMTDC - Power System Computer Aided Design – including a mathematical model study. An additional algorithm has been implemented in MATLAB software in order to calculate several parameters required by the PSCAD developed model. All the simulation study has been performed in PSCAD/MATLAB software simulation tool. A real data base concerning irradiance, cell temperature and PV power generation was used in order to support the evaluation of the implemented PV model.

Structural modification of TiO2 nanorod films with an influence on the photovoltaic efficiency of a dye-sensitized solar cell (DSSC).

TiO2 nanorod films have been deposited on ITO substrates by dc reactive magnetron sputtering technique. The structures of these nanorod films were modified by the variation of the oxygen pressure during the sputtering process. Although all these TiO2 nanorod films deposited at different oxygen pressures show an anatase structure, the orientation of the nanorod films varies with the oxygen pressure. Only a very weak (101) diffraction peak can be observed for the TiO2 nanorod film prepared at low oxygen pressure. However, as the oxygen pressure is increased, the (220) diffraction peak appears and the intensity of this diffraction peak is increased with the oxygen pressure. The results of the SEM show that these TiO2 nanorods are perpendicular to the ITO substrate. At low oxygen pressure, these sputtered TiO2 nanorods stick together and have a dense structure. As the oxygen pressure is increased, these sputtered TiO2 nanorods get separated gradually and have a porous structure. The optical transmittance of these TiO2 nanorod films has been measured and then fitted by OJL model. The porosities of the TiO2 nanorod films have been calculated. The TiO2 nanorod film prepared at high oxygen pressure shows a high porosity. The dye-sensitized solar cells (DSSCs) have been assembled using these TiO2 nanorod films prepared at different oxygen pressures as photoelectrode. The optimum performance was achieved for the DSSC using the TiO2 nanorod film with the highest (220) diffraction peak and the highest porosity.