WIND POWER PROBABILISTIC PREDICTION AND UNCERTAINTY MODELING FOR OPERATION OF LARGE-SCALE POWER SYSTEMS

Over the last decade, large scale renewable energy generation has been integrated into power systems. Wind power generation is known as a widely-used and interesting kind of renewable energy generation around the world. However, the high uncertainty of wind power generation leads to some unavoidable error in wind power prediction process; consequently, it makes the optimal operation and control of power systems very challenging. Since wind power prediction error cannot be entirely removed, providing accurate models for wind power uncertainty can assist power system operators in mitigating its negative effects on decision making conditions. There are efficient ways to show the wind power uncertainty, (i) accurate wind power prediction error probability distribution modeling in the form of probability density functions and (ii) construction of reliable and sharp prediction intervals. Construction of accurate probability density functions and high-quality prediction intervals are difficult because wind power time series is non-stationary. In addition, incorporation of probability density functions and prediction intervals in power systems’ decision-making problems are challenging. In this thesis, the goal is to propose comprehensive frameworks for wind power uncertainty modeling in the form of both probability density functions and prediction intervals and incorporation of each model in power systems’ decision-making problems such as look-ahead economic dispatch. To accurately quantify the uncertainty of wind power generation, different approaches are studied, and a comprehensive framework is then proposed to construct the probability density functions using a mixture of beta kernels. The framework outperforms benchmarks because it can validly capture the actual features of wind power probability density function such as main mass, boundaries, high skewness, and fat tails from the wind power sample moments. Also, using the proposed framework, a generic convex model is proposed for chance-constrained look-ahead economic dispatch problems. It allows power system operators to use piecewise linearization techniques to convert the problem to a mixed-integer linear programming problem. Numerical simulations using IEEE 118-bus test system show that compared with widely used sequential linear programming approaches, the proposed mixed-integer linear programming model leads to less system’s total cost. A framework based on the concept of bandwidth selection for a new and flexible kernel density estimator is proposed for construction of prediction intervals. Unlike previous related works, the proposed framework uses neither a cost function-based optimization problem nor point prediction results; rather, a diffusion-based kernel density estimator is utilized to achieve high-quality prediction intervals for non-stationary wind power time series. The proposed prediction interval construction framework is also founded based on a parallel computing procedure to promote the computational efficiency for practical applications in power systems. Simulation results demonstrate the high performance of the proposed framework compared to well-known conventional benchmarks such as bootstrap extreme learning machine, lower upper bound estimation, quantile regression, auto-regressive integrated moving average, and linear programming-based quantile regression. Finally, a new adjustable robust optimization approach is used to incorporate the constructed prediction intervals with the proposed fuzzy and adaptive diffusion estimator-based prediction interval construction framework. However, to accurately model the correlation and dependence structure of wind farms, especially in high dimensional cases, C-Vine copula models are used for prediction interval construction. The simulation results show that uncertainty modeling using C-Vine copula can lead the system operators to get more realistic sense about the level of overall uncertainty in the system, and consequently more conservative results for energy and reserve scheduling are obtained

Forecasting Models for Integration of Large-Scale Renewable Energy Generation to Electric Power Systems

Amid growing concerns about climate change and non-renewable energy sources deple¬tion, vari¬able renewable energy sources (VRESs) are considered as a feasible substitute for conventional environment-polluting fossil fuel-based power plants. Furthermore, the transition towards clean power systems requires additional transmission capacity. Dynamic thermal line rating (DTLR) is being considered as a potential solution to enhance the current transmission line capacity and omit/postpone transmission system expansion planning, while DTLR is highly dependent on weather variations. With increasing the accommodation of VRESs and application of DTLR, fluctuations and variations thereof impose severe and unprecedented challenges on power systems operation. Therefore, short-term forecasting of large-scale VERSs and DTLR play a crucial role in the electric power system op¬eration problems. To this end, this thesis devotes on developing forecasting models for two large-scale VRESs types (i.e., wind and tidal) and DTLR. Deterministic prediction can be employed for a variety of power system operation problems solved by deterministic optimization. Also, the outcomes of deterministic prediction can be employed for conditional probabilistic prediction, which can be used for modeling uncertainty, used in power system operation problems with robust optimization, chance-constrained optimization, etc. By virtue of the importance of deterministic prediction, deterministic prediction models are developed. Prevalently, time-frequency decomposition approaches are adapted to decompose the wind power time series (TS) into several less non-stationary and non-linear components, which can be predicted more precisely. However, in addition to non-stationarity and nonlinearity, wind power TS demonstrates chaotic characteristics, which reduces the predictability of the wind power TS. In this regard, a wind power generation prediction model based on considering the chaosity of the wind power generation TS is addressed. The model consists of a novel TS decomposition approach, named multi-scale singular spectrum analysis (MSSSA), and least squares support vector machines (LSSVMs). Furthermore, deterministic tidal TS prediction model is developed. In the proposed prediction model, a variant of empirical mode decomposition (EMD), which alleviates the issues associated with EMD. To further improve the prediction accuracy, the impact of different components of wind power TS with different frequencies (scales) in the spatiotemporal modeling of the wind farm is assessed. Consequently, a multiscale spatiotemporal wind power prediction is developed, using information theory-based feature selection, wavelet decomposition, and LSSVM. Power system operation problems with robust optimization and interval optimization require prediction intervals (PIs) to model the uncertainty of renewables. The advanced PI models are mainly based on non-differentiable and non-convex cost functions, which make the use of heuristic optimization for tuning a large number of unknown parameters of the prediction models inevitable. However, heuristic optimization suffers from several issues (e.g., being trapped in local optima, irreproducibility, etc.). To this end, a new wind power PI (WPPI) model, based on a bi-level optimization structure, is put forward. In the proposed WPPI, the main unknown parameters of the prediction model are globally tuned based on optimizing a convex and differentiable cost function. In line with solving the non-differentiability and non-convexity of PI formulation, an asymmetrically adaptive quantile regression (AAQR) which benefits from a linear formulation is proposed for tidal uncertainty modeling. In the prevalent QR-based PI models, for a specified reliability level, the probabilities of the quantiles are selected symmetrically with respect the median probability. However, it is found that asymmetrical and adaptive selection of quantiles with respect to median can provide more efficient PIs. To make the formulation of AAQR linear, extreme learning machine (ELM) is adapted as the prediction engine. Prevalently, the parameters of activation functions in ELM are selected randomly; while different sets of random values might result in dissimilar prediction accuracy. To this end, a heuristic optimization is devised to tune the parameters of the activation functions. Also, to enhance the accuracy of probabilistic DTLR, consideration of latent variables in DTLR prediction is assessed. It is observed that convective cooling rate can provide informative features for DTLR prediction. Also, to address the high dimensional feature space in DTLR, a DTR prediction based on deep learning and consideration of latent variables is put forward. Numerical results of this thesis are provided based on realistic data. The simulations confirm the superiority of the proposed models in comparison to traditional benchmark models, as well as the state-of-the-art models

Topics in high dimensional energy forecasting

The forecasting of future energy consumption and generation is now an essential part of power system operation. In networks with high renewable power penetration, forecasts are used to help maintain security of supply and to operate the system efficiently. Historically, uncertainties have always been present in the demand side of the network, they are now also present in the generation side with the growth of weather dependent renewables. Here, we focus on forecasting for wind energy applications at the day(s)- ahead scale. Most of the work developed is for power forecasting, although we also identify an emerging opportunity in access forecasting for offshore operations. Power forecasts are used by traders, power system operators, and asset owners to optimise decision making based on future generation. Several novel methodologies are presented based on post–processing Numerical Weather Predictions (NWP) with measured data, using modern statistical learning techniques; they are linked with the increasingly relevant challenge of dealing with high-dimensional data. The term ‘high-dimensional’ means different things to different people, depending on their background. To statisticians high dimensionaility occurs when the dimensions of the problem are greater than the number of observations, i.e. the classic p >> n problem, an example of which can be found in Chapter 7. In this work we take the more general view that a high dimensional dataset is one with a high number of attributes or features. In wind energy forecasting applications, this can occur in the input and/or output variable space. For example, multivariate forecasting of spatially distributed wind farms can be a potentially very-high dimensional problem, but so is feature engineering using ultra-high resolution NWP in this framework. Most of the work in this thesis is based on various forms of probabilistic forecasting Probabilistic forecasts are essential for risk-management, but also to risk-neutral participants in asymmetrically penalised electricity markets. Uncertainty is always present, it is merely hidden in deterministic, i.e. point, forecasts. This aspect of forecasting has been the subject of a concerted research effort over the last few years in the energy forecasting literature. However, we identify and address gaps in the literature related to dealing with high dimensional data in both the input and output side of the modelling chain. It is not necessarily given that increasing the resolution of the weather forecast increases the skill, and therefore reduces errors associated with the forecast. In fact and when regarding typical average scoring rules, they often perform worse than smoother forecasts from lower-resolution models due to spatial and/or temporal displacement errors. Here, we evaluate the potential of using ultra high resolution weather models for offshore power forecasting, using feature engineering and modern statistical learning techniques. Two methods for creating improved probabilistic wind power forecasts through the use of turbine-level data are proposed. Although standard resolution NWP data is used, high dimensionality is now present in the output variable space; the two methods scale by the number of turbines present in the wind farm, although to a different extent. A methodology for regime-switching multivariate wind power forecasting is also elaborated, with a case study demonstrated on 92 wind balancing mechanism units connected to the GB network. Finally, we look at an emerging topic in energy forecasting: offshore access forecasting. Improving access is a priority in the offshore wind sector, driven by the opportunity to increase revenues, reduce costs, and improve safety at operational wind farms. We describe a novel methodology for producing probabilistic forecasts of access conditions during crew transfers.The forecasting of future energy consumption and generation is now an essential part of power system operation. In networks with high renewable power penetration, forecasts are used to help maintain security of supply and to operate the system efficiently. Historically, uncertainties have always been present in the demand side of the network, they are now also present in the generation side with the growth of weather dependent renewables. Here, we focus on forecasting for wind energy applications at the day(s)- ahead scale. Most of the work developed is for power forecasting, although we also identify an emerging opportunity in access forecasting for offshore operations. Power forecasts are used by traders, power system operators, and asset owners to optimise decision making based on future generation. Several novel methodologies are presented based on post–processing Numerical Weather Predictions (NWP) with measured data, using modern statistical learning techniques; they are linked with the increasingly relevant challenge of dealing with high-dimensional data. The term ‘high-dimensional’ means different things to different people, depending on their background. To statisticians high dimensionaility occurs when the dimensions of the problem are greater than the number of observations, i.e. the classic p >> n problem, an example of which can be found in Chapter 7. In this work we take the more general view that a high dimensional dataset is one with a high number of attributes or features. In wind energy forecasting applications, this can occur in the input and/or output variable space. For example, multivariate forecasting of spatially distributed wind farms can be a potentially very-high dimensional problem, but so is feature engineering using ultra-high resolution NWP in this framework. Most of the work in this thesis is based on various forms of probabilistic forecasting Probabilistic forecasts are essential for risk-management, but also to risk-neutral participants in asymmetrically penalised electricity markets. Uncertainty is always present, it is merely hidden in deterministic, i.e. point, forecasts. This aspect of forecasting has been the subject of a concerted research effort over the last few years in the energy forecasting literature. However, we identify and address gaps in the literature related to dealing with high dimensional data in both the input and output side of the modelling chain. It is not necessarily given that increasing the resolution of the weather forecast increases the skill, and therefore reduces errors associated with the forecast. In fact and when regarding typical average scoring rules, they often perform worse than smoother forecasts from lower-resolution models due to spatial and/or temporal displacement errors. Here, we evaluate the potential of using ultra high resolution weather models for offshore power forecasting, using feature engineering and modern statistical learning techniques. Two methods for creating improved probabilistic wind power forecasts through the use of turbine-level data are proposed. Although standard resolution NWP data is used, high dimensionality is now present in the output variable space; the two methods scale by the number of turbines present in the wind farm, although to a different extent. A methodology for regime-switching multivariate wind power forecasting is also elaborated, with a case study demonstrated on 92 wind balancing mechanism units connected to the GB network. Finally, we look at an emerging topic in energy forecasting: offshore access forecasting. Improving access is a priority in the offshore wind sector, driven by the opportunity to increase revenues, reduce costs, and improve safety at operational wind farms. We describe a novel methodology for producing probabilistic forecasts of access conditions during crew transfers

Investigation on electricity market designs enabling demand response and wind generation

Demand Response (DR) comprises some reactions taken by the end-use customers to decrease or shift the electricity consumption in response to a change in the price of electricity or a specified incentive payment over time. Wind energy is one of the renewable energies which has been increasingly used throughout the world. The intermittency and volatility of renewable energies, wind energy in particular, pose several challenges to Independent System Operators (ISOs), paving the way to an increasing interest on Demand Response Programs (DRPs) to cope with those challenges. Hence, this thesis addresses various electricity market designs enabling DR and Renewable Energy Systems (RESs) simultaneously. Various types of DRPs are developed in this thesis in a market environment, including Incentive-Based DR Programs (IBDRPs), Time-Based Rate DR Programs (TBRDRPs) and combinational DR programs on wind power integration. The uncertainties of wind power generation are considered through a two-stage Stochastic Programming (SP) model. DRPs are prioritized according to the ISO’s economic, technical, and environmental needs by means of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The impacts of DRPs on price elasticity and customer benefit function are addressed, including the sensitivities of both DR parameters and wind power scenarios. Finally, a two-stage stochastic model is applied to solve the problem in a mixed-integer linear programming (MILP) approach. The proposed model is applied to a modified IEEE test system to demonstrate the effect of DR in the reduction of operation cost.A Resposta Dinâmica dos Consumidores (DR) compreende algumas reações tomadas por estes para reduzir ou adiar o consumo de eletricidade, em resposta a uma mudança no preço da eletricidade, ou a um pagamento/incentivo específico. A energia eólica é uma das energias renováveis que tem sido cada vez mais utilizada em todo o mundo. A intermitência e a volatilidade das energias renováveis, em particular da energia eólica, acarretam vários desafios para os Operadores de Sistema (ISOs), abrindo caminho para um interesse crescente nos Programas de Resposta Dinâmica dos Consumidores (DRPs) para lidar com esses desafios. Assim, esta tese aborda os mercados de eletricidade com DR e sistemas de energia renovável (RES) simultaneamente. Vários tipos de DRPs são desenvolvidos nesta tese em ambiente de mercado, incluindo Programas de DR baseados em incentivos (IBDRPs), taxas baseadas no tempo (TBRDRPs) e programas combinados (TBRDRPs) na integração de energia eólica. As incertezas associadas à geração eólica são consideradas através de um modelo de programação estocástica (SP) de dois estágios. Os DRPs são priorizados de acordo com as necessidades económicas, técnicas e ambientais do ISO por meio da técnica para ordem de preferência por similaridade com a solução ideal (TOPSIS). Os impactes dos DRPs na elasticidade do preço e na função de benefício ao cliente são abordados, incluindo as sensibilidades dos parâmetros de DR e dos cenários de potência eólica. Finalmente, um modelo estocástico de dois estágios é aplicado para resolver o problema numa abordagem de programação linear inteira mista (MILP). O modelo proposto é testado num sistema IEEE modificado para demonstrar o efeito da DR na redução do custo de operação

Quantification and mitigation of the impacts of extreme weather on power system resilience and reliability

Modelling the impact of extreme weather on power systems is a computationally expensive, challenging area of study due to the diversity of threats, complicatedness of modelling, and data and simulation requirements to perform the relevant studies. The impacts of extreme weather – specifically wind – are considered. Factors such as the distribution of outage probability on lines and the potential correlation with wind power generation during storms are investigated; so too is sensitivity of security assessments involving extreme wind to the relationships used between failures and the natural hazard being studied, specifically wind speed. A large scale simulation ensemble is developed and demonstrated to investigate what are deemed the most significant features of power system simulation during extreme weather events. The challenges associated with modelling high impact low probability (HILP) events are studied and demonstrate that the results of security assessments are significantly affected by the granularity of incident weather data being used and the corrections or interpolation being applied to the source data. A generalizable simulation framework is formulated and deployed to investigate the significance of the relationship between incident natural hazards, in this case wind, and its corresponding impact on system resilience. Based on this, a large-scale simulation model is developed and demonstrated to take consideration of a wide variety of factors which can affect power systems during extreme weather events including, but not limited to, under frequency load shedding, line overloads, and high wind speed shutdown and its impact on wind generation. A methodology for quantifying and visualising distributed overhead line failure risk is also demonstrated in tandem with straightforward methods for making wind power projections over transmission systems for security studies. The potential correlation between overhead line risk and wind power generation risk is illustrated visually on representations of GB power networks based on real world data.Open Acces

State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems

The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon

Biopsychosocial Assessment and Ergonomics Intervention for Sustainable Living: A Case Study on Flats

This study proposes an ergonomics-based approach for those who are living in small housings (known as flats) in Indonesia. With regard to human capability and limitation, this research shows how the basic needs of human beings are captured and analyzed, followed by proposed designs of facilities and standard living in small housings. Ninety samples were involved during the study through in- depth interview and face-to-face questionnaire. The results show that there were some proposed of modification of critical facilities (such as multifunction ironing work station, bed furniture, and clothesline) and validated through usability testing. Overall, it is hoped that the proposed designs will support biopsychosocial needs and sustainability

Integrated modelling of water security in data-sparse regions under uncertainty

Freshwater scarcity and sustainability is one of the most complicated and difficult issues the world is currently facing, and it has been identified as a global concern. According to expert studies, 80% of the world’s population is projected to live in freshwater threats due to a plethora of factors viz., rapid population growth, urbanization, global climate change resulting from spatial and temporal changes in magnitude, frequencies and intensity of precipitation and temperature which leads to the transformation of the hydrologic cycle. Recent initiatives, including sustainable development goals, have been made to address these problems and offer solutions. However, the quantity and quality of freshwater systems and resources must be objectively and comprehensively understood and assessed at the scale of river basins to provide sufficient mitigation and resilience planning. Hydrologic modelling has been one the most suitable and efficient strategies for basin-scale assessment of freshwater dynamics to current and projected climate change and the focus has been on the application of traditional modelling framework which is tenable where data requirements are sufficient to couple hydrologic models with atmospheric data to account for climate change. The aforementioned strategy is a challenge in regions with inadequate ground-based observations necessary for climate and hydrologic modelling. The rarely available data in such regions may have repetitive gaps of missing data points with negative consequences including biased statistical representation of basin climatic features, ineffective model calibration and unreliable timing of peak flows which may amplify the uncertainties of the hydrologic dynamics leading to flawed depictions of watershed responses. Recently, integrated strategies are evolving that couple hydrologic models with climate data in water resource studies to account for uncertainties through the use of alternative data sources of many spatial climate data products from climate research centres to overcome the identified challenges. This research developed and applied a multi-criteria approach to examine the efficacy of gridded climate products using different performance metrics, a machine learning-based approach, Boruta random forest (BRF) to assess multiple GCM datasets required for hydro climatic studies and an integrated BRF-SWAT technique to define the relationship between the hydrologic variables and improve rainfall-runoff modelling in a data-sparse and climate sensitive watersheds. The developed model was applied to assess the projected green and blue water dynamics and sustainability in the Yobe-Komadugu basin of the greater Lake Chad, a watershed that is prone to extreme events (SPEI of flood and drought hazards). The results demonstrate that though the performance of the gridded data varies in space and time, multi-criteria assessment enhances the choice of a product with reduced uncertainty for climate modelling. The incorporation of the BRF approach in GCM evaluation indicates a consistent spatial and temporal representation of the climatological features with suitable mean correlation (R2 = 0.95), reduced mean annual precipitation bias of 0.69 mm/year and enhanced statistical trend and magnitude of the SPEI drought and flood hazards relative to identified and tested approaches from the literature. The integrated framework of the rainfall-runoff modelling strategy indicated that the hydrologic fluxes can be simulated fairly accurately with varying degrees of acceptability, irrespective of the watershed morphological properties, although there are significant trade-offs in model parameter sensitivity. The availability of satellite-based measurements of hydrologic fluxes and states, coupled with a machine learning feature selection and data refinement process has made integrated water balance modelling widely seen as a viable alternative for improving watershed hydrologic processes in data-sparse regions within acceptable uncertainty limits. Furthermore, the sub-watershed assessment of the projected changes in spatial and temporal green and blue water sustainability status has shown that the sub-basins will be ecologically fragile, and the identified freshwater geographic hotspots may be beyond restoration without adequate long-term river basin water resources plans. The modelling framework developed is, however, independent of the model and data type and can be applied to watersheds with similar modelling challenges. This study has provided a pathway or methods for managing and securing water resources information as a decision support tool to guarantee ongoing watershed monitoring and assessment of water security even in the face of increasingly unpredictable future circumstances in data-sparse watersheds that take into account uncertainty and chat a course for prospective risk assessment or the possibility and understanding that a certain effect brought on by climate-induced hazards would prevail in watershed freshwater sustainability. Therefore, it is essential to comprehend the constraints associated with forecasting changes in the water cycle to improve the climate and hydrologic modelling process, which is required to create effective strategies for adapting to climate change-related water resource hazards. Even in the face of severe uncertainty about the future, this will be essential in addressing concerns related to water security and management and promoting the climatic resilience of ecosystems and society.Freshwater scarcity and sustainability is one of the most complicated and difficult issues the world is currently facing, and it has been identified as a global concern. According to expert studies, 80% of the world’s population is projected to live in freshwater threats due to a plethora of factors viz., rapid population growth, urbanization, global climate change resulting from spatial and temporal changes in magnitude, frequencies and intensity of precipitation and temperature which leads to the transformation of the hydrologic cycle. Recent initiatives, including sustainable development goals, have been made to address these problems and offer solutions. However, the quantity and quality of freshwater systems and resources must be objectively and comprehensively understood and assessed at the scale of river basins to provide sufficient mitigation and resilience planning. Hydrologic modelling has been one the most suitable and efficient strategies for basin-scale assessment of freshwater dynamics to current and projected climate change and the focus has been on the application of traditional modelling framework which is tenable where data requirements are sufficient to couple hydrologic models with atmospheric data to account for climate change. The aforementioned strategy is a challenge in regions with inadequate ground-based observations necessary for climate and hydrologic modelling. The rarely available data in such regions may have repetitive gaps of missing data points with negative consequences including biased statistical representation of basin climatic features, ineffective model calibration and unreliable timing of peak flows which may amplify the uncertainties of the hydrologic dynamics leading to flawed depictions of watershed responses. Recently, integrated strategies are evolving that couple hydrologic models with climate data in water resource studies to account for uncertainties through the use of alternative data sources of many spatial climate data products from climate research centres to overcome the identified challenges. This research developed and applied a multi-criteria approach to examine the efficacy of gridded climate products using different performance metrics, a machine learning-based approach, Boruta random forest (BRF) to assess multiple GCM datasets required for hydro climatic studies and an integrated BRF-SWAT technique to define the relationship between the hydrologic variables and improve rainfall-runoff modelling in a data-sparse and climate sensitive watersheds. The developed model was applied to assess the projected green and blue water dynamics and sustainability in the Yobe-Komadugu basin of the greater Lake Chad, a watershed that is prone to extreme events (SPEI of flood and drought hazards). The results demonstrate that though the performance of the gridded data varies in space and time, multi-criteria assessment enhances the choice of a product with reduced uncertainty for climate modelling. The incorporation of the BRF approach in GCM evaluation indicates a consistent spatial and temporal representation of the climatological features with suitable mean correlation (R2 = 0.95), reduced mean annual precipitation bias of 0.69 mm/year and enhanced statistical trend and magnitude of the SPEI drought and flood hazards relative to identified and tested approaches from the literature. The integrated framework of the rainfall-runoff modelling strategy indicated that the hydrologic fluxes can be simulated fairly accurately with varying degrees of acceptability, irrespective of the watershed morphological properties, although there are significant trade-offs in model parameter sensitivity. The availability of satellite-based measurements of hydrologic fluxes and states, coupled with a machine learning feature selection and data refinement process has made integrated water balance modelling widely seen as a viable alternative for improving watershed hydrologic processes in data-sparse regions within acceptable uncertainty limits. Furthermore, the sub-watershed assessment of the projected changes in spatial and temporal green and blue water sustainability status has shown that the sub-basins will be ecologically fragile, and the identified freshwater geographic hotspots may be beyond restoration without adequate long-term river basin water resources plans. The modelling framework developed is, however, independent of the model and data type and can be applied to watersheds with similar modelling challenges. This study has provided a pathway or methods for managing and securing water resources information as a decision support tool to guarantee ongoing watershed monitoring and assessment of water security even in the face of increasingly unpredictable future circumstances in data-sparse watersheds that take into account uncertainty and chat a course for prospective risk assessment or the possibility and understanding that a certain effect brought on by climate-induced hazards would prevail in watershed freshwater sustainability. Therefore, it is essential to comprehend the constraints associated with forecasting changes in the water cycle to improve the climate and hydrologic modelling process, which is required to create effective strategies for adapting to climate change-related water resource hazards. Even in the face of severe uncertainty about the future, this will be essential in addressing concerns related to water security and management and promoting the climatic resilience of ecosystems and society

Uncertainty Modeling of Wind Power Generation for Power System Planning and Stability Study

To reduce greenhouse gas emissions, higher penetration levels of renewable energy resources are added to existing power grids. Among them, wind energy resources are becoming a major source of electricity generation. However, wind energy production has a critical downside: intermittency. The intermittent nature of wind energy in combination with the load demand uncertainties, make it difficult to maintain power system stability and reliability. In addition, the uncertainty and variability of wind power generation (WPG) forces power utilities to retain higher levels of spinning reserves (SRs) to maintain power balance in the system. While necessary to ensure grid reliability, the utilization of those reserves often leads to an increase in operating costs of the power system. To ensure the continuous operation of reliable and economically efficient power systems, system operators and planners need to study the impact of WPGs on bulk power systems and determine the best ways to manage their variability. Such studies require efficient and effective probabilistic models characterizing the variable nature of wind power. Therefore, this dissertation develops new methodologies for modeling the uncertainty and variability of WPG. The developed methods are combined with stability indices to form analytical tools for analyzing the impact of increased penetration of wind energy on power system steady-state stability. The case study results show that the developed methods simulate real-world wind power scenarios, which lead to an accurate assessment of the impact of wind generation uncertainty on power systems. With large-scale adoption of renewable energy, a significant amount of conventional generation units could be replaced with wind energy resources. The best way to use the variable WPG and the remaining conventional generation resources, for continuous balance between load and generation, remains to be determined. Within this context, this dissertation investigates the problem of optimal substitution of conventional generation units by wind-powered generators, while considering the variability of WPG and the uncertainties of energy demand. The goal is to ensure that during unplanned wind power unavailability, the system has the ability to meet the load demand, and maintain steady acceptable voltage levels in the grid. A two-stage solution methodology is proposed to the problem in consideration. The first stage determines the best candidates, among conventional generator (CG) resources, for retirement and replacement by WPG resources. The best candidates for wind replacement are selected such that the adverse impacts of wind power intermittency on system stability and reliability are minimized. In the second stage, the expected amount of wind generation to be added at each retired CG bus is determined. The simulation results show that the developed method facilitates the integration of high wind energy with a reduced need for additional spinning reserves in the system

Resilience Enhancement Strategies for Modern Power Systems

The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, and power plants) destructions. Also, the massive integration of information and communication technology to power systems has evolved the power systems into what is known as cyber-physical power systems (CPPSs). Although advanced technologies in the cyber layer improve the operation and control of power systems, they introduce additional vulnerabilities to power system performance. This has motivated studying power system resilience evaluation and enhancements methods. Power system resilience can be defined as ``The ability of a system to prepare for, absorb, adapt to, and recover from disruptive events''. Assessing resilience enhancement strategies requires further and deeper investigation because of several reasons. First, enhancing the operational and planning resilience is a mathematically involved problem accompanied with many challenges related to modeling and computation methods. The complexities of the problem increases in CPPSs due to the large number and diverse behavior of system components. Second, a few studies have given attention to the stochastic behavior of extreme events and their accompanied impacts on the system resilience level yielding less realistic modeling and higher resilience level. Also, the correlation between both cyber and physical layers within the context of resilience enhancement require leveraging sophisticated modeling approaches which is still under investigation. Besides, the role of distributed energy resources in planning-based and operational-based resilience enhancements require further investigation. This calls for developing enhancement strategies to improve resilience of power grids against extreme events. This dissertation is divided into four parts as follows. Part I: Proactive strategies: utilizing the available system assets to prepare the power system prior to the occurrence of an extreme event to maintain an acceptable resilience level during a severe event. Various system generation and transmission constraints as well as the spatiotemporal behavior of extreme events should be properly modeled for a feasible proactive enhancement plan. In this part, two proactive strategies are proposed against weather-related extreme events and cyber-induced failure events. First, a generation redispatch strategy is formulated to reduce the amount of load curtailments in transmission systems against hurricanes and wildfires. Also, a defensive islanding strategy is studied to isolate vulnerable system components to cyber failures in distribution systems. Part II: Corrective strategies: remedial actions during an extreme event for improved performance. The negative impacts of extreme weather events can be mitigated, reduced, or even eliminated through corrective strategies. However, the high stochastic nature of resilience-based problem induces further complexities in modeling and providing feasible solutions. In this part, reinforcement learning approaches are leveraged to develop a control-based environment for improved resilience. Three corrective strategies are studied including distribution network reconfiguration, allocating and sizing of distributed energy resources, and dispatching reactive shunt compensators. Part III: Restorative strategies: retain the power service to curtailed loads in a fast and efficient means after a diverse event. In this part, a resilience enhancement strategy is formulated based on dispatching distributed generators for minimal load curtailments and improved restorative behavior. Part IV: Uncertainty quantification: Impacts of uncertainties on modeling and solution accuracy. Though there exist several sources of stochasticity in power systems, this part focuses on random behavior of extreme weather events and the associated impacts on system component failures. First, an assessment framework is studied to evaluate the impacts of ice storms on transmission systems and an evaluation method is developed to quantify the hurricane uncertainties for improved resilience. Additionally, the role of unavailable renewable energy resources on improved system resilience during extreme hurricane events is studied. The methodologies and results provided in this dissertation can be useful for system operators, utilities, and regulators towards enhancing resilience of CPPSs against weather-related and cyber-related extreme events. The work presented in this dissertation also provides potential pathways to leverage existing system assets and resources integrated with recent advanced computational technologies to achieve resilient CPPSs