A Markovian jump system approach for the estimation and adaptive diagnosis of decreased power generation in wind farms

In this study, a Markovian jump model of the power generation system of a wind turbine is proposed and the authors present a closed-loop model-based observer to estimate the faults related to energy losses. The observer is designed through an H∞-based optimisation problem that optimally fixes the trade-off between the observer fault sensitivity and robustness. The fault estimates are then used in data-based decision mechanisms for achieving fault detection and isolation. The performance of the strategy is then ameliorated in a wind farm (WF) level scheme that uses a bank of the aforementioned observers and decision mechanisms. Finally, the proposed approach is tested using a well-known benchmark in the context of WF fault diagnosis

Design, management and control of energy storage DC nano-grid.

Ph.DDOCTOR OF PHILOSOPH

A methodology for the economic evaluation of power storage technologies in the UK market

In this thesis, we present a methodology for assessing the economic impact of power storage technologies. The methodology is founded on classical approaches to the optimal stopping of stochastic processes. Power storage is regarded as a complement to the intermittent output of renewable energy generators, and is important in contributing to the reduction of carbon intensive power generation. Therefore, the recommendations to study the future economic storage assessment have been increased. Our aim is to present a methodology suitable for use by policy makers that is simple to maintain, adapt to different technologies and is easy to interpret. The thesis start by giving an overview of the UK power market and an introduction to storage technologies in Chapter 2. Chapter 3 summarize the mathematical tools, that the methodology is based on, more precisely the discretionary stopping theory based on dynamic programming techniques. An algorithm to assess the storage is presented in Chapter 4, where the storage problem is formulated as an entry, exit problem, which allow the investigation of different optimal strategies to fill and empty a storage facility. An analysis of power demand, and an approximation of power prices through the merit order curve of the UK power market presented in Chapter 5. Based on a theoretical study, the methodology is applied to a Compressed Air Energy Storage (CAES) in Chapter 6. Chapter 7 present an empirical study that applied the methodology directly on the observed data, this approach is shown to have benefits over current techniques and is able to value, by identifying a viable optimal operational strategy for a CAES operating in the UK market

Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

Energy: A continuing bibliography with indexes

This bibliography lists 1096 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System from April 1, 1979 through June 30, 1979

Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint

Design and optimization of hybrid renewable energy systems for off-grid continuous operations

The mining industry accounts for a significant portion of the energy demand by the industrial sector. The rising demand for metals around the world, coupled with the depletion of readily accessible ore deposits, has led to mining operations moving to more remote locations with no grid supply of energy. As a result, the operations require transport of fuel over large distances, leading to a significant increase in the overall mining cost. Renewable energy is considered to be the most promising solution to the mining industry energy problem. This work investigates the possibility of operating remote mines on local generation from renewables. A survey of recent literature revealed that while a lot of research had been done on hybrid renewable energy systems design and sizing, little thought had been given to accounting for the stochastic nature of renewable resources in the sizing process. Previous works focused on the sizing of PV-wind-battery systems; other potential generation and storage technologies were largely ignored. The challenge of intermittency in the power output of renewable generation systems had also largely been ignored. This thesis extends the state of the art on hybrid systems sizing by developing models and methodologies to address these challenges. A novel hybrid energy system integrating thermal and electrical renewable generation options with multiple large scale energy storage options is considered in this thesis. Models are developed for the different components of the energy system, with dynamic models incorporated for the material and energy balances of the storage alternatives, leading to a system of nonlinear differential algebraic equations (DAEs). The temporal nature of the renewable resources is accounted for by considering multiple stochastic renewable input scenarios generated from probability distribution functions (PDFs) as inputs into the system model. A reliability measure to quantify the impact of weather-based variability, called the modified loss of power supply probability, is developed. A bi-criteria sizing methodology which allows for the stochastic nature of renewable resources to be accounted for is presented. The approach combines the time series approach to reliability evaluation with a stochastic simulation model. Two approaches for mitigating the impact of intermittency in power outputs of renewable generation technologies are also developed. The first approach is based on system redesign, while the second approach is based on the introduction of an instantaneous response storage option. Case studies were presented to demonstrate the various methodologies. The results show that climate-based variability can have a significant impact on the cost and performance of hybrid energy systems and should always be accounted for in the sizing process. Intermittency needs to be accounted for in some form at the design stage as it can have an impact on the choice of technologies. The integration of thermal and electrical power generation and storage options provide a way to reduce hybrid system costs. The methodologies developed in this thesis are applicable to any location and can easily be extended to incorporate other generation and storage alternatives. They provide the decision maker with necessary information for making preliminary sizing decisions

Cumulative index to NASA Tech Briefs, 1986-1990, volumes 10-14

Tech Briefs are short announcements of new technology derived from the R&D activities of the National Aeronautics and Space Administration. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This cumulative index of Tech Briefs contains abstracts and four indexes (subject, personal author, originating center, and Tech Brief number) and covers the period 1986 to 1990. The abstract section is organized by the following subject categories: electronic components and circuits, electronic systems, physical sciences, materials, computer programs, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences