Optimal Operation of a Northwest Grid of Saudi Arabia Including Renewable Resources

The use of fossil fuel, which has been one of the major sources of energy of the modern world, has led to environmental concerns. One solution to these issues is the application of renewable energy, which can also address the fluctuation in fuel prices. The Kingdom of Saudi Arabia (KSA) faces a demand of energy expected to exceed 120 GW by 2032. The government is taking appropriate actions, introducing sustainable renewable energy not only to meet the demand with clean energy sources but also to reduce the Kingdom’s consumption of fuel and gas. KSA, which has a high irradiation rate especially in the northwest area, Tabuk Region, plans to invest 41 GW maximum of solar power. In light of this decision, this research will present a comprehensive study of PV penetration up to peak output of 40 MW with battery storage to the isolated northwest grid, Tabuk Grid, as a first stage development. However, the increase of grid-connected photovoltaic (PV) in the presence of nonlinear loads, and the growth of power electronic applications produce harmonics in the power system. These harmonics may distort the current and voltage waveforms which impact the power quality and affect the operation of all electric devices. Renewable energy systems nowadays are sufficiently developed to be widely used for environmental and economic dispatch (ED) concerns. However, renewable energy that are not geographically distributed present a considerable challenge with respect to variability and availability. One of the solutions for addressing the challenge of solar variability is to use battery storage, which has been found to be effective when working in parallel with PV in peak load shaving. Time shifting renewable energy generation through the use of Battery Energy Storage Systems (BESS) can reduce the operating cost. Many studies have been focused on optimal operation with PV and battery storage. However, while achieving this optimal operation for the generators is necessary, it does not ensure secure operation of power systems. Therefore, validating secure operation with optimal generation scheduling is important. Furthermore, disregarding the battery life in optimal power scheduling creates an unrealistic scenario since replacing the battery is costly. In this research, a comprehensive study of a 40 MW PV penetration with battery storage to the Tabuk Grid is presented. The study includes complete simulation and analysis of the PV integration with storage. Moreover, a power quality study for the PV farm is conducted, one that included nonlinear loads to enhance the analysis regarding harmonics penetration. In addition, this research presents an optimal generation scheduling considering renewable energy sources, the BESS, battery life and short term outages. This will enables the system to respond and resolve outages quickly without affecting the optimal operation. The feasibility of the proposed approach is demonstrated on Tabuk system – an isolated northwest grid in Saudi Arabia

Design of Smart Energy Generation and Demand Response System in Saudi Arabia

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThough the promising benefits of renewable sources have already pushed many countries into implementing RE units, Saudi Arabia is still highly dependent on fossil fuel. However, the decrease in value of oil reserves has enforced Saudi Arabia to prioritize renewable energy sources in the next decades. Such energy sources are highly dependable on accurate forecasts, due to their intermittence and operability. The present research has the objective to develop models that can accurately forecast energy load for implementation in a decision-making system. The case investigated is the western region of Saudi Arabia. Two modelling approaches were evaluated, linear regression (LR) and artificial neural network (ANN). This last one was chosen because it is a mathematical model able to deal with non-linear relationship among input(s) and output(s) in the data. Time series (past load data) and multivariate data from 2010 until 2016 were investigated A hybrid model structure (combiner) was implemented to analyse the effects of combining outputs of two models in a single one. This hybrid model consisted of a regular average and weighted average of the time series and multivariate model, with calibration through Fuzzy and Particle Swarm Optimisation. These two were selected because, while Particle Swarm Optimization is an optimization algorithm, Fuzzy consists in a complete structured model. The forecasted load and the available input were used in the last chapter for power generation planning and decision-making support. The software used for the modelling and simulation is ETAP®. Different scenarios for replacement of fossil fuel power plants by renewable units were tested considering the network of western Saudi Arabia. The results show that Artificial Neural Network with time series input and 15 neurons in hidden layer shows superior performance (MSE 3.7*105 and R2 equals 99%) compared to other neural networks and linear regression. Though the application of combiner models did not significantly improve model performance, the Fuzzy Combiner shows the best one (MSE 5.8*105 and R2 equals 93%) since it incorporates information from time series and multivariate data. It is important to mention that all the modelling approaches evaluated have some limitations, such as the necessity of accurate input data and they are limited in capability of extrapolating over the training range. In the last section, it was observed that renewable energy sources can be integrated in the grid network without excessive risk regarding demand. This occurs because the current energy management policy of western Saudi Arabia enables the use of energy units with fast compensation (using gas units) in the case of demand increase or decrease in solar or wind power

IEOM Society International

IEOM Society Internationa

Advancing Carbon Sequestration through Smart Proxy Modeling: Leveraging Domain Expertise and Machine Learning for Efficient Reservoir Simulation

Geological carbon sequestration (GCS) offers a promising solution to effectively manage extra carbon, mitigating the impact of climate change. This doctoral research introduces a cutting-edge Smart Proxy Modeling-based framework, integrating artificial neural networks (ANNs) and domain expertise, to re-engineer and empower numerical reservoir simulation for efficient modeling of CO2 sequestration and demonstrate predictive conformance and replicative capabilities of smart proxy modeling. Creating well-performing proxy models requires extensive human intervention and trial-and-error processes. Additionally, a large training database is essential to ANN model for complex tasks such as deep saline aquifer CO2 sequestration since it is used as the neural network\u27s input and output data. One major limitation in CCS programs is the lack of real field data due to a lack of field applications and issues with confidentiality. Considering these drawbacks, and due to high-dimensional nonlinearity, heterogeneity, and coupling of multiple physical processes associated with numerical reservoir simulation, novel research to handle these complexities as it allows for the creation of possible CO2 sequestration scenarios that may be used as a training set. This study addresses several types of static and dynamic realistic and practical field-base data augmentation techniques ranging from spatial complexity, spatio-temporal complexity, and heterogeneity of reservoir characteristics. By incorporating domain-expertise-based feature generation, this framework honors precise representation of reservoir overcoming computational challenges associated with numerical reservoir tools. The developed ANN accurately replicated fluid flow behavior, resulting in significant computational savings compared to traditional numerical simulation models. The results showed that all the ML models achieved very good accuracies and high efficiency. The findings revealed that the quality of the path between the focal cell and injection wells emerged as the most crucial factor in both CO2 saturation and pressure estimation models. These insights significantly contribute to our understanding of CO2 plume monitoring, paving the way for breakthroughs in investigating reservoir behavior at a minimal computational cost. The study\u27s commitment to replicating numerical reservoir simulation results underscores the model\u27s potential to contribute valuable insights into the behavior and performance of CO2 sequestration systems, as a complimentary tool to numerical reservoir simulation when there is no measured data available from the field. The transformative nature of this research has vast implications for advancing carbon storage modeling technologies. By addressing the computational limitations of traditional numerical reservoir models and harnessing the synergy between machine learning and domain expertise, this work provides a practical workflow for efficient decision-making in sequestration projects

Econometric framework for electricity infrastructure modernization in Saudi Arabia, An

2017 Summer.Includes bibliographical references.The electricity infrastructure in Saudi Arabia is facing several challenges represented by demand growth, high peak demand, high level of government subsidies, and system losses. This dissertation aims at addressing these challenges and proposing a multi-dimensional framework to modernize the electricity infrastructure in Saudi Arabia. The framework proposes four different scenarios—identified by two dimensions—for the future electric grid. The first and second dimensions are characterized by electricity market deregulation and Smart Grid technologies (SGTs) penetration, respectively. The framework analysis estimates global welfare (GW) and economic feasibility of the two dimensions. The first dimension quantifies the impact of deregulating the electricity market in Saudi Arabia. A non-linear programming (NLP) algorithm optimizes consumers surplus, producers surplus, and GW. The model indicates that deregulating the electricity market in Saudi Arabia will improve market efficiency. The second dimension proposes that allowing the penetration of SGTs in the Saudi electricity infrastructure is expected to mitigate the technical challenges faced by the grid. The dissertation examines the priorities of technologies for penetration by considering some key performance indicators (KPIs) identified by the Saudi National Transformation Program, and Saudi Vision 2030. A multi-criteria decision making (MCDM) algorithm—using the fuzzy Analytic Hierarchy Process (AHP)—evaluates the prioritization of SGTs to the Saudi grid. The algorithm demonstrates the use of triangular fuzzy numbers to model uncertainty in planning decisions. The results show that advanced metering infrastructure (AMI) technologies are the top priority for modernizing the Saudi electricity infrastructure; this is followed by advanced assets management (AAM) technologies, advanced transmission operations (ATO) technologies, and advanced distribution operations (ADO) technologies. SGTs prioritization is followed by a detailed cost benefit analysis (CBA) conducted for each technology. The framework analysis aims at computing the economic feasibility of SGTs and estimating their outcomes and impacts in monetary values. The framework maps Smart Grid assets to their functions and benefits to estimate the feasibility of each Smart Grid technology and infrastructure. Discounted cash flow (DCF) and net present value (NPV) models, benefit/cost ratio, and minimum total cost are included in the analysis. The results show that AAM technologies are the most profitable technologies of Smart Grid to the Saudi electricity infrastructure, followed by ADO technologies, ATO technologies, and AMI technologies. Considering the weights resulting from the fuzzy AHP and the economic analysis models for each infrastructure, the overall ranking places AAM technologies as the top priority of SGTs to the Saudi electricity infrastructure, followed by AMI technologies, ADO technologies, and ATO technologies. This dissertation has contributed to the existing body of knowledge in the following areas: • Proposing an econometric framework for electricity infrastructure modernization. The framework takes into account technical, economic, environmental, societal, and policy factors. • Building an NLP algorithm to optimize a counterfactual deregulation of a regulated electricity market. The algorithm comprises short run price elasticity of electricity demand (ε), level of technical efficiency improvement, and discount rate (r). • Proposing an MCDM model using AHP and fuzzy set theory to prioritize SGTs to electricity infrastructures. • Adapting a Smart Grid asset-function-benefit linkage model that maps SGTs to their respected benefits. • Conducting a detailed CBA to estimate the economic feasibility of SGTs to the Saudi electricity infrastructure, This work opens avenues for more analysis on electricity infrastructure modernization. Measuring risk impact and likelihood is one area for future research. In fact, risk assessment is an important factor in determining the economic feasibility of the modernization. Probabilistic economic analysis can be applied to assess the risk associated with the implantation of the previously mentioned dimensions. The parameters used for the economic analysis, such as economic life of a project, and the discount rate, are usually deterministic. However, a probabilistic method can be applied to capture the uncertainty of the parameters. Another area for future research is the integration of both dimensions into one model in which GW resulted from market deregulation and SGTs insertion are summed

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Construction management abstracts : cumulative abstracts and indexes of journals in construction management, 1983-2000

The purpose of this document is to provide a single source of reference for every paper published in the journals directly related to research in Construction Management. It is indexed by author and keyword and contains the titles, authors, abstracts and keywords of every article from the following journals: • Building Research and Information (BRI) • Construction Management and Economics (CME) • Engineering, Construction and Architectural Management (ECAM) • Journal of Construction Procurement (JCP) • Journal of Construction Research (JCR) • Journal of Financial Management in Property and Construction (JFM) • RICS Research Papers (RICS) The index entries give short forms of the bibliographical citations, rather than page numbers, to enable annual updates to the abstracts. Each annual update will carry cumulative indexes, so that only one index needs to be consulted

Proceedings of the 9th Arab Society for Computer Aided Architectural Design (ASCAAD) international conference 2021 (ASCAAD 2021): architecture in the age of disruptive technologies: transformation and challenges.

The ASCAAD 2021 conference theme is Architecture in the age of disruptive technologies: transformation and challenges. The theme addresses the gradual shift in computational design from prototypical morphogenetic-centered associations in the architectural discourse. This imminent shift of focus is increasingly stirring a debate in the architectural community and is provoking a much needed critical questioning of the role of computation in architecture as a sole embodiment and enactment of technical dimensions, into one that rather deliberately pursues and embraces the humanities as an ultimate aspiration