The photovoltaic (PV) energy conversion chain: irradiation to grid impact

The research presented in this thesis aims to enhance understanding of the influence of the inherent variability of solar irradiance on nationwide photovoltaic (PV) system performance. The spatial and temporal consistency of the solar resource is investigated. The case study area is the UK and the body of work presents nine publications written over four years with this objective in mind.The key research theme is to produce national solar resource maps from ground-based measurements of solar radiation. Geographical Information System (GIS) techniques are utilised to build a UK map of irradiation from geographically sparse data, requiring development of new tools to both generate and verify the map data. With an augmented understanding of the solar resource, PV system dispersal is then investigated, allowing analysis and prediction of the impact on the electrical grid. The papers describe: (1) determination of the most appropriate algorithm for interpolating ground-based irradiation measurements in the UK to countrywide coverage; (2) selection of solar irradiance component separation and translation models to obtain plane-of-array irradiation from the weather station global horizontal records; (3) justification of weather stations data as a fundamental model input; (4) statistical analysis of LiDAR data and application of GIS models to LiDAR data to obtain PV system tilts and azimuths as model inputs for (2); (5) conversion of solar irradiation to electrical output; (6) shading effects; (7) study of geographic divergence of generation; (8) aggregate grid variability; and (9) future installation scenarios.There has been no previous study which commences with obtaining irradiation values for PV and proceeds through the entire modelling chain to assess cumulative impacts on grid transformers. This study may be adapted as a guide when undertaking equivalent research in other countries. Specifically, the work presented here is more extensively validated than that of previous authors. A nationwide analysis of spatial and temporal variation of PV output is delivered and current and future impacts on the National Grid are taken into consideration.</div

Bayesian network development and validation for siting selection

In this study, increasing electricity demand requires considerable attention to increasing the diversity of power generation. Alternative energy can produce heating and power systems and thermal storage. Our objective and every organization’s objectives are to minimize its energy consumption cost under electricity demand uncertainty. In rural areas, heat and power availability and stability are also crucial. Combined heat and power have proven their effectiveness as a subsequent to Electricity. This paper identified four criteria and eleven sub-criteria to determine the most appropriate structure location for combined heat and power in the rural community. The Bayesian Network technology has been applied to analyze these criteria comprehensively. A case study including multiple sites across the Mississippi state was used to validate the proposed approach, and propagation and sensitivity analysis were used to evaluate performance. Results showed the summarized eleven criteria proposed Bayesian Network approach could aid location selection for Combined heat and power location in the rural area. Supplementary, the created model can support decision-makers to select the best alternatives under different electricity demand variability levels

GIS and Remote Sensing for Renewable Energy Assessment and Maps

This book aims at providing the state-of-the-art on all of the aforementioned tools in different energy applications and at different scales, i.e., urban, regional, national, and even continental for renewable scenarios planning and policy making

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice

Feasibility Evaluation of a Vibration-Based Leak Detection Technique for Sustainable Water Distribution Pipeline System Monitoring

Conventional water pipeline leak-detection surveys employ labor-intensive acoustic techniques, which are usually expensive and less useful for continuous monitoring of distribution pipelines. Based on a comprehensive review of literature and available commercial products, it has been recognized that despite previous studies and products attempting to address the limitations of the conventional surveys by proposing and evaluating a myriad of leak-detection techniques (LDTs), they lacked extensive validation on complex looped systems. Additionally, they offer limited compatibility with some pipe materials such as those made of plastic and may even fail to distinguish leaks from other system disturbances. A novel LDT that addresses some of these limitations is developed and evaluated in the current study using an experimental set-up that is representative of a real-world pipeline system and made of Polyvinyl Chloride (PVC) pipe. The studied LDT requires continuous monitoring of the change in the cross spectral density of surface vibration measured at discrete locations along the pipeline. This vibration-based LDT was hypothesized to be capable of not only detecting the onset of leakage, but also determining its relative severity in complex pipeline systems. Findings based on a two-phase, controlled experimental testing revealed that the proposed LDT is capable of detecting leakages and estimating their relative severities in a real-size, multi-looped pipeline system that is comprised of multiple joints, bends and pipes of multiple sizes. Furthermore, the sustainability merits of the proposed LDT for a representative application scenario are estimated. Specifically, life cycle costs and energy consumption for monitoring the large diameter pipelines in the water distribution system of the Charleston peninsula region in South Carolina are estimated by developing conceptual prototypes of the sensing, communication and computation schemes for practically employing the proposed LDT. The prototype designs are informed by the knowledge derived from the two-phase experimental testing campaign. Overall, the proposed study contributes to the body of knowledge on water pipeline leak detection, specifically to non-intrusive vibration-based monitoring, applications on plastic pipelines, and smart and sustainable network-wide continuous monitoring schemes

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

The development of object oriented Bayesian networks to evaluate the social, economic and environmental impacts of solar PV

Domestic and community low carbon technologies are widely heralded as valuable means for delivering sustainability outcomes in the form of social, economic and environmental (SEE) policy objectives. To accelerate their diffusion they have benefited from a significant number and variety of subsidies worldwide. Considerable aleatory and epistemic uncertainties exist, however, both with regard to their net energy contribution and their SEE impacts. Furthermore the socio-economic contexts themselves exhibit enormous variability, and commensurate uncertainties in their parameterisation. This represents a significant risk for policy makers and technology adopters. This work describes an approach to these problems using Bayesian Network models. These are utilised to integrate extant knowledge from a variety of disciplines to quantify SEE impacts and endogenise uncertainties. A large-scale Object Oriented Bayesian network has been developed to model the specific case of solar photovoltaics (PV) installed on UK domestic roofs. Three specific model components have been developed. The PV component characterises the yield of UK systems, the building energy component characterises the energy consumption of the dwellings and their occupants and a third component characterises the building stock in four English urban communities. Three representative SEE indicators, fuel affordability, carbon emission reduction and discounted cash flow are integrated and used to test the model s ability to yield meaningful outputs in response to varying inputs. The variability in the percentage of the three indicators is highly responsive to the dwellings built form, age and orientation, but is not just due to building and solar physics but also to socio-economic factors. The model can accept observations or evidence in order to create scenarios which facilitate deliberative decision making. The BN methodology contributes to the synthesis of new knowledge from extant knowledge located between disciplines . As well as insights into the impacts of high PV penetration, an epistemic contribution has been made to transdisciplinary building energy modelling which can be replicated with a variety of low carbon interventions