Capacity Value of Wind Power

Power systems are planned such that they have adequate generation capacity to meet the load, according to a defined reliability target. The increase in the penetration of wind generation in recent years has led to a number of challenges for the planning and operation of power systems. A key metric for generation system adequacy is the capacity value of generation. The capacity value of a generator is the contribution that a given generator makes to generation system adequacy. The variable and stochastic nature of wind sets it apart from conventional energy sources. As a result, the modeling of wind generation in the same manner as conventional generation for capacity value calculations is inappropriate. In this paper a preferred method for calculation of the capacity value of wind is described and a discussion of the pertinent issues surrounding it is given. Approximate methods for the calculation are also described with their limitations highlighted. The outcome of recent wind capacity value analyses in Europe and North America, along with some new analysis, are highlighted with a discussion of relevant issues also given

Concepts for design of an energy management system incorporating dispersed storage and generation

New forms of generation based on renewable resources must be managed as part of existing power systems in order to be utilized with maximum effectiveness. Many of these generators are by their very nature dispersed or small, so that they will be connected to the distribution part of the power system. This situation poses new questions of control and protection, and the intermittent nature of some of the energy sources poses problems of scheduling and dispatch. Under the assumption that the general objectives of energy management will remain unchanged, the impact of dispersed storage and generation on some of the specific functions of power system control and its hardware are discussed

Fundamentals in selecting input and output variables for composting process automatic controllers

El títol del pre-print és Review of fundamentals in selecting input and output variables for composting process automatic controllersThis paper provides a critical analysis of the fundamental principles involved in the selection of input and output variables for automatic controllers of composting processes. Research results and technological advancements make available a number of parameters which may be used by a composting process controller. Parameters based on ventilation have been identified as the most appropriate controller output variables. On-line monitoring of odour generation potential and pathogen destruction has not become feasible, although recent advances indicate potential for electronic noses and biosensors. On-line measurement of reaction rate heavily depends on the suitability of microbial respirometric methods. Water content of the material being composted may be useful in on-line evaluation of reaction rate if relationships between water loss rate and respiration rate are adequately described. Developments in artificial intelligence offer, however, new avenues regarding real-time estimation of reaction rates. In general, the first experiences from application of artificial intelligence on composting process control indicate potential for substantial utility

Fluidics research, including vortex and jet pipe valves

The research at the Systems and Control Laboratory is reported. Topics discussed include: response characteristics of laminar fluidic amplifiers, power amplification with a vortex valve, pulse-supply-mode fluidics, speed control system employing a jet pipe valve, and the fluidics reference center

Generation and transmission adequacy evaluation of power systems with wind generation

In response to the challenge of proposed reductions to greenhouse gas emissions outlined in international agreements such as the Kyoto Protocol, countries are considering supplying a significant share of their future energy requirements from renewable energy sources. Wind power, both on and offshore, is the principal commercially available and scaleable renewable energy technology. It is expected to remain the dominant technology in the medium-term future by delivering the majority of the required growth in renewable energy. The unique characteristics of wind power generation raise issues for its integration into the existing power systems. This thesis explores three specific issues, namely, wind generation’s limited capacity value, its remoteness from demand centres and the appropriateness of the regulatory framework governing its integration. The first issue was addressed by examining how the presence of flexible generation sources like hydro power affects the capacity value of wind in an assessment of overall system generation capacity. Wind capacity credit is interpreted from a planning perspective, and also as a component of the economic value of wind. The results illustrate that hydro power can compensate the variability of wind generation thereby augmenting its capacity value. The second issue required the development of a transmission planning methodology to evaluate the sufficiency of transmission network capacity to accommodate wind generation and to manage security of supply. The methodology was used to assess, over the long term investment horizon, the requirement for additional transmission network capacity driven by wind generation. The assessment found that wind generation drives less transmission network capacity than conventional generation and that wind and conventional generation should share the same transmission network capacity. Finally, the thesis looked into the establishment of regulatory framework that could recognise the realistic contribution of wind generation characteristics to transmission security and capture this contribution within the network pricing structure. The current 4 transmission security standards were reviewed to evaluate whether they are capable of recognising the different operation characteristics and output of wind generation. Standards for assessing transmission adequacy were found to lead to under-investment in capacity for importing areas and over-investment in exporting areas. Consequently, a set of ‘contribution factors’ capturing the interaction between wind and system characteristics were derived to augment the standards. At the same time, a modification of the present TNUoS charging mechanism in order to discriminate between generation technology types and to devise cost-reflective pricing regimes is proposed. This is particularly important when transmission investment is driven by reliability, as in exporting areas the cost reflective charges for wind were uniformly found to be lower than the charges for conventional generators

Investigation of Dynamic and Steady State Calculation Methodologies for Determination of Building Energy Performance in the Context of the EPBD

The aim of this thesis was to investigate the ability of a dynamic and a quasi-steady state calculation methodology to capture the heating and cooling aspects of a buildings energy performance in the context of the requirements of the EU Energy Performance of Buildings Directive (EPBD). Chapters 1 and 2 provide a general background review and description of the implementation of the directive’s requirements in Ireland. Chapter 3 established the usefulness and relevance of building energy benchmarks, traditional approaches to building energy performance calculation methodologies. Chapter 4 established the ability of a sample of simplified and dynamic calculation tools to deal with the requirements set out in the directive and the extent the requirements are dealt with. This investigation observed that the underlying calculations and assumptions vary across different calculation tools; resulting in a variety of energy performance solutions. Chapter 5 investigated the ability of a dynamic methodology (IES) and simplified quasi-steady state methodology (SBEM/ prEN 13790) to capture the effects of variation of key parameters of a building design in order to generate an improvement in energy performance. The investigation analysed the sensitivity of both methodologies to the variation of design parameters and their effect in terms of the annual energy performance calculation. In addition, the calculation algorithms of both IES and SBEM were summarised and analysed to account for the difference in results obtained. This investigation established that a dynamic methodology rewards design improvements with greater magnitude than a quasi-steady state methodology

Load forecast on a Micro Grid level through Machine Learning algorithms

As Micro Redes constituem um sector em crescimento da indústria energética, representando uma mudança de paradigma, desde as remotas centrais de geração até à produção mais localizada e distribuída. A capacidade de isolamento das principais redes elétricas e atuar de forma independente tornam as Micro Redes em sistemas resilientes, capazes de conduzir operações flexíveis em paralelo com a prestação de serviços que tornam a rede mais competitiva. Como tal, as Micro Redes fornecem energia limpa eficiente de baixo custo, aprimoram a coordenação dos ativos e melhoram a operação e estabilidade da rede regional de eletricidade, através da capacidade de resposta dinâmica aos recursos energéticos. Para isso, necessitam de uma coordenação de gestão inteligente que equilibre todas as tecnologias ao seu dispor. Daqui surge a necessidade de recorrer a modelos de previsão de carga e de produção robustos e de confiança, que interligam a alocação dos recursos da rede perante as necessidades emergentes. Sendo assim, foi desenvolvida a metodologia HALOFMI, que tem como principal objetivo a criação de um modelo de previsão de carga para 24 horas. A metodologia desenvolvida é constituída, numa primeira fase, por uma abordagem híbrida de multinível para a criação e escolha de atributos, que alimenta uma rede neuronal (Multi-Layer Perceptron) sujeita a um ajuste de híper-parâmetros. Posto isto, numa segunda fase são testados dois modos de aplicação e gestão de dados para a Micro Rede. A metodologia desenvolvida é aplicada em dois casos de estudo: o primeiro é composto por perfis de carga agregados correspondentes a dados de clientes em Baixa Tensão Normal e de Unidades de Produção e Autoconsumo (UPAC). Este caso de estudo apresenta-se como um perfil de carga elétrica regular e com contornos muito suaves. O segundo caso de estudo diz respeito a uma ilha turística e representa um perfil irregular de carga, com variações bruscas e difíceis de prever e apresenta um desafio maior em termos de previsão a 24-horas A partir dos resultados obtidos, é avaliado o impacto da integração de uma seleção recursiva inteligente de atributos, seguido por uma viabilização do processo de redução da dimensão de dados para o operador da Micro Rede, e por fim uma comparação de estimadores usados no modelo de previsão, através de medidores de erros na performance do algoritmo.Micro Grids constitute a growing sector of the energetic industry, representing a paradigm shift from the central power generation plans to a more distributed generation. The capacity to work isolated from the main electric grid make the MG resilient system, capable of conducting flexible operations while providing services that make the network more competitive. Additionally, Micro Grids supply clean and efficient low-cost energy, enhance the flexible assets coordination and improve the operation and stability of the of the local electric grid, through the capability of providing a dynamic response to the energetic resources. For that, it is required an intelligent coordination which balances all the available technologies. With this, rises the need to integrate accurate and robust load and production forecasting models into the MG management platform, thus allowing a more precise coordination of the flexible resource according to the emerging demand needs. For these reasons, the HALOFMI methodology was developed, which focus on the creation of a precise 24-hour load forecast model. This methodology includes firstly, a hybrid multi-level approach for the creation and selection of features. Then, these inputs are fed to a Neural Network (Multi-Layer Perceptron) with hyper-parameters tuning. In a second phase, two ways of data operation are compared and assessed, which results in the viability of the network operating with a reduced number of training days without compromising the model's performance. Such process is attained through a sliding window application. Furthermore, the developed methodology is applied in two case studies, both with 15-minute timesteps: the first one is composed by aggregated load profiles of Standard Low Voltage clients, including production and self-consumption units. This case study presents regular and very smooth load profile curves. The second case study concerns a touristic island and represents an irregular load curve with high granularity with abrupt variations. From the attained results, it is evaluated the impact of integrating a recursive intelligent feature selection routine, followed by an assessment on the sliding window application and at last, a comparison on the errors coming from different estimators for the model, through several well-defined performance metrics