Projecting Future Locations for Commercial Wind Energy Development in the Conterminous United States using a Logistic Regression-Cellular Automata Model

Pressures to decarbonize the United States’ electricity production, reduce dependence on foreign energy imports, and the declining levelized cost of renewable electricity is making wind energy an increasingly appealing means of meeting electricity demand in the United States. However, the installation of new commercial wind farms to meet this demand requires knowledge of the most suitable locations for their installation, which depends on a combination of environmental, technical, economic, political, and social characteristics. Wind Farm Site Suitability (WiFSS) models are frequently enlisted to assist in this decision-making process in countries around the world for both onshore and offshore wind farm siting decisions. However, existing WiFSS models serve to assess present-day wind farm siting potential, rather than project specific locations for future wind energy development. Taking cues from Socio-Environmental Systems (SES) models of urban growth, this dissertation presents a Logistic Regression-Cellular Automata (LRCA) model, henceforth referred to as WiFSS-LRCA, conceived to produce maps that identify scenarios of potential future locations and timing of future commercial wind farms across the Conterminous United States (CONUS) between now and the year 2050. Following a review of existing WiFSS modeling approaches, and of common practices by which WiFSS modeling studies select and represent their predictors, the niche that WiFSS-LRCA serves to fill was consequently identified. The majority of WiFSS studies take a Geographic Information Systems-based Multi-Criteria Decision Analysis (GIS-MCDA) approach that combines spatial data layers corresponding to selected predictors to construct a composite suitability surface. Other common approaches include Non-GIS-MCDA models that rank discrete potential wind farm sites to prioritize their order of development, Bayesian Network (BN) models that construct and convey probabilistic relationships between predictors, and Logistic Regression (LR) models that perform either spatial or non-spatial assessment of a wind farm’s suitability of presence based on the log-odds of a linear combination of predictors. The common limitation of these modeling approaches is their lack of a temporal component, meaning that they can assess WiFSS only at a single point in time. WiFSS-LRCA fills this niche by combining an LR equation with the decision rules of Cellular Automata (CA) to iteratively advance the computed probabilities of each grid cell, based on areas constrained from development and neighboring grid cells that already contain wind farms. WiFSS-LRCA enlists a large set of predictors ranging from wind speed to legislation in effect in order for the model to represent the influence that environmental, technical, economic, political, and social predictors have on wind farm siting decisions. Data were aggregated at 20 different grid cell resolutions, collated in four different predictor configurations, and adjustments to the model’s constraint, neighborhood effect, and equation-based scenario transition rules were incorporated into the model’s construction, facilitating WiFSS-LRCA’s sensitivity and scenario analysis of model outputs by end-users. WiFSS-LRCA incorporates both calibration of its LR equation’s predictors and validation of the model’s performance to determine its ability to correctly identify the observed locations of present-day wind farms. Subsequently, the model constructs a WiFSS map whose interpretation and predictive accuracy are informed by the calibration and validation process. Construction of scenarios that modify WiFSS-LRCA’s predictors allow for the model to consider the impacts of changes in these predictors on the locations of future wind energy development (e.g., new transmission line construction, opinions of wind energy improving with time, increasing temperatures due to climate change). The ability of WiFSS-LRCA to produce suitability surfaces with verifiable accuracy is greatest under the following conditions: when running the model over an individual U.S. state rather than the CONUS, when using a smaller grid cell size, when using a more complete (Full configuration) or more refined (Reduced configuration) set of predictors, and when the selected study area contains a larger number of present-day commercial wind farms. Across most study areas, however, WiFSS-LRCA is typically able to correctly identify 75-85% of grid cells that do and do not contain commercial wind farms, with these classifications most often associated with high wind speed, proximity to transmission lines, legislation that supports wind energy development, and large tracts of undeveloped land. CONUS-level model runs indicate five regions as being the most suitable for present wind energy development: Southern California, the Pacific Northwest, the Central Plains, the Great Lakes, and the Northeastern United States. CONUS-level model runs have a tendency to over(under)-estimate grid cell probabilities within (outside) the Central Plains and Great Lakes, which makes state-level model runs useful for revealing smaller-scale differences in the probabilities computed within these five broad regions. Subsequent iterations of WiFSS-LRCA out to the year 2050 show projected wind energy development to remain concentrated within these same regions. Many of the grid cells initially classified as false positive in the model’s first iteration are those that gain wind farms in subsequent iterations, particularly false positive grid cells that were part of high-probability hotspots identified by Getis-Ord statistics. Running WiFSS-LRCA over states outside of these five regions projects wind energy development potential in low-probability areas (as shown in this dissertation for Florida and Kentucky) with projected wind farms in these states concentrated closer to existing infrastructure and away from protected natural areas. The Odds Ratios (ORs) computed during WiFSS-LRCA’s initial calibration provide geographical insight into its projections, with grid cells characterized by high wind speed, undeveloped land, and ambitious Renewable Portfolio Standards (RPS) being the most likely to gain wind farms in future decades. The model’s projections are, however, shown to be sensitive to end-user definitions of parameters, with neighborhood effect and constraint definitions greatly affecting the location and timing of projected wind farm locations. The scenario setup, by contrast, is shown to mostly influence the timing of these projections, with grid cell size moderately affecting both. Multiple limitations exist in the application and interpretation of WiFSS-LRCA. Firstly, the lack of existing LRCA approaches to assessing wind farm siting potential meant few standards existed to guide this model’s development, such as the setting of default constraints and establishing cutoff statistics for refining the model’s enlisted predictors. Secondly, the use of an LR equation to construct suitability surfaces in the model’s first iteration means that both classes of the dependent variable must be filled, requiring a study area to contain at least two commercial wind farms, compromising the model’s reliability in runs over the Southeastern United States. Finally, the lack of spatial stratification during WiFSS-LRCA’s calibration and validation means that the model is trained to recognize predictors associated with wind energy development in regions where many wind farms exist, namely the Central Plains and Great Lakes, hence the greater number of Type 2 errors in CONUS-level model runs outside of these regions. Selecting stratified samples of grid cells that contain wind farms from different parts of the CONUS could be incorporated into WiFSS-LRCA to address this bias. Other directions for future work with WiFSS-LRCA include the following: optimization to assess offshore wind energy development potential by training the model with proposed offshore wind farm sites surrounding the CONUS; adapting WiFSS-LRCA to run over multiple states simultaneously to identify predictors that influence wind farm siting decisions at regional spatial scales; and performing projections of other types decentralized land-use change, such as solar energy development given similarities in the required model predictors

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

Wind generation forecasting methods and proliferation of artificial neural network:A review of five years research trend

To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Analytical approach based generation planning with wind energy integration

A large power grid consists of generation, transmission, and distribution. Power system planning is to develop new and upgrade existing power grids to satisfy the future load demand. Reliability evaluation has a great importance in power system planning and is viewed from two aspects, adequacy and system security. This thesis focuses on adequacy, which concerns the existence of enough power generation in the system to satisfy load demand. The output power of a wind turbine depends on wind speed which is highly uncertain and random. Hence, the first step in generation adequacy evaluation is modeling wind speed. In this research, the wind speed was predicted using the ARMA model and artificial neural network (ANN). After this step, hourly power output of wind energy was determined. This was done by the power curve characteristics of the wind turbine. Fuzzy C-Means (FCM) was then used to reduce the number of states in the wind turbine generator model. The main objective of this thesis is to evaluate the influence of wind energy to the overall reliability of the system. In addition, megawatt (MW) capacity of wind energy system required for replacing conventional generators while maintaining the same risk criteria was investigated. In this thesis, the Roy Billinton Test System (RBTS) was adopted for generation adequacy evaluation. The St. John’s International Airport was selected as the wind speed measurement site. The Vestas V90-2MW (IEC IIIA) was selected as the wind turbine for the case study. The main contributions of this thesis include modeling of generation adequacy evaluation of wind energy systems using an analytical approach; wind speed prediction by ARMA and Neural Networks; Fuzzy C means algorithm to reduce the number of wind turbine states; standalone renewable energy system design; and a procedure and guideline development for generation planning with wind power integration using the analytical approach

Renewable Energies for Sustainable Development

In the current scenario in which climate change dominates our lives and in which we all need to combat and drastically reduce the emission of greenhouse gases, renewable energies play key roles as present and future energy sources. Renewable energies vary across a wide range, and therefore, there are related studies for each type of energy. This Special Issue is composed of studies integrating the latest research innovations and knowledge focused on all types of renewable energy: onshore and offshore wind, photovoltaic, solar, biomass, geothermal, waves, tides, hydro, etc. Authors were invited submit review and research papers focused on energy resource estimation, all types of TRL converters, civil infrastructure, electrical connection, environmental studies, licensing and development of facilities, construction, operation and maintenance, mechanical and structural analysis, new materials for these facilities, etc. Analyses of a combination of several renewable energies as well as storage systems to progress the development of these sustainable energies were welcomed

Exploration and evaluation of offshore repurposing concepts

Økende etterspørsel etter ressurser, en global miljøkrise og politiske uroligheter krever nye måter å drive forretning på, og løsningen kan være å skape et fungerende forhold mellom økonomisk utvikling og økologiske systemer ved å innføre sirkulære strategier. Ettersom et økende antall offshore ressurser går inn i de senere livssyklusstadiene, kan norsk olje- og gassindustri utforske alternativer for å spare nedstengningskostnader for installasjoner og redusere karbonfotavtrykk. En mulig, ny ide som er verdt å studere, er alternativ bruk av strukturer og topsides for nye næringer som et kostnadseffektivt alternativ til den tradisjonelle dekommisjonerings- og resirkuleringsmetoden. Derfor er hensikten med denne oppgaven å utforske og evaluere mulige, norske konsept for alternativ bruk av offshore ressurser som i dag brukes innen olje- og gassindustrien. Det første målet er å utforske gjenbrukskonsepter og identifisere beslutningskriterier ved tematisk analyse av en litteraturgjennomgang og kvalitativ forskning. Det andre målet er å evaluere hvert gjenbrukskonsept ved å konstruere en beslutningsmatrise basert på identifiserte beslutningskriterier. Flere konsepter har blitt utforsket og evaluert, som for eksempel offshore oppdrettsanlegg, transformatorstasjoner, hydrogenproduksjon, hoteller, CCS, rigger-til-skjær. De mest fremtredende, identifiserte beslutningskriteriene var type platform, bevegelighet, tilgjengelig teknologi og ekspertise, markedspotensial og miljørisiko. En beslutningsmatrise med tekniske, økonomiske og miljømessige beslutningskriterier bekrefter at oppdrettsanlegg og transformatorstasjoner for offshore vindparker var foretrukne gjenbrukskonsepter. Denne oppgavens resultater kan veilede videre introduksjon og utvikling av sirkulære strategier i en tradisjonell industri, og potensielt gi veiledning i å finne innovative løsninger på aktuelle utfordringer.Increasing demand for resources, a global environmental crisis and political disruption demand new ways of doing business, and the solution may be to create a workable relationship between economic development and ecological systems by the introduction of circular strategies. As a growing number of offshore assets are entering the later lifecycle stages, the Norwegian oil and gas industry may explore options for saving decommissioning costs of installations and reducing carbon footprints. A viable, unexplored option worth studying is the repurposing of structures and topsides for new industries as a cost-efficient alternative to the traditional decommissioning and recycling approach. Thus, the purpose of this thesis is to explore and evaluate Norwegian repurposing concepts for offshore assets currently within the oil and gas industry. The first objective is to explore repurposing concepts and extract decision criteria by thematic analysis of a literature review and qualitative research. The second objective is to evaluate each repurposing concept by constructing a decision-making matrix based on identified decision criteria. Several concepts have been explored and evaluated, such as offshore fish farms, substations, hydrogen production, hotels, CCS, rigs-to-reefs, and more. The most prominent decision criteria during this study were asset type, movability, available technology and expertise, market potential and environmental risk. A decision matrix with technical, economic, and environmental decision criteria confirms that fish farms and substations for offshore wind parks were preferred repurposing concepts. This thesis results may guide further introduction and development of circular strategies in a traditional industry, and potentially provide guidance in finding innovative solutions to current challenges