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

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

A framework for the near-real-time optimization of integrated oil & gas midstream processing networks

The oil and gas industry plays a key role in the world’s economy. Vast quantities of crude oil, their by-products and derivatives are produced, processed and distributed every day. Indeed, producing and processing significant volumes of crude oil requires connecting to wells in different fields that are usually spread across large geographical areas. This crude oil is then processed by Gas Oil Separation Plants (GOSPs). These facilities are often grouped into clusters that are within approximate distance from each other and then connected laterally via swing lines which allow shifting part or all of the production from one GOSP to another. Transfer lines also exist to allow processing intermediate products in neighbouring GOSPs, thereby increasing complexity and possible interactions. In return, this provides an opportunity to leverage mathematical optimization to improve network planning and load allocation. Similarly, in major oil producing countries, vast gas processing networks exist to process associated and non-associated gases. These gas plants are often located near major feed sources. Similar to GOSPs, they are also often connected through swing lines, which allow shifting feedstock from some plants to others. GOSPs and gas plants are often grouped as oil and gas midstream plants. These plants are operated on varied time horizons and plant boundaries. While plant operators are concerned with the day-to-day operation of their facility, network operators must ensure that the entire network is operated optimally and that product supply is balanced with demand. They are therefore in charge of allocating load to individual plants, while knowing each plants constraints and processing capabilities. Network planners are also in charge of producing production plans at varied time-scales, which vary from yearly to monthly and near-real time. This work aims to establish a novel framework for optimizing Oil and Gas Midstream plants for near-real time network operation. This topic has not been specifically addressed in the existing literature. It examines problems which involve operating networks of GOSPs and gas plants towards an optimal solution. It examines various modelling approaches which are suited for this specific application. It then focuses at this stage of the research on the GOSP optimization problem where it addresses optimizing the operation of a complex network of GOSPs. The goal is to operate this network such that oil production targets are met at minimum energy consumption, and therefore minimizing OpEx and Greenhouse Gas Emissions. Similarly, it is often required to operate the network such that production is maximized. This thesis proposes a novel methodology to formulate and solve this problem. It describes the level of fidelity used to represent physical process units. A Mixed Integer Non-Linear Programming (MINLP) problem is then formulated and solved to optimize load allocation, swing line flowrates and equipment utilization. The model demonstrates advanced capabilities to systematically prescribe optimal operating points. This was then applied to an existing integrated network of GOSPs and tested at varying crude oil demand levels. The results demonstrate the ability to minimize energy consumption by up to 51% in the 50% throughput case while meeting oil production targets without added capital investment.Open Acces

A system dynamics & emergency logistics model for post-disaster relief operations

Emergency teams’ efficiency in responding to disasters is critical in saving lives, reducing suffering, and for damage control. Quality standards for emergency response systems are based on government policies, resources, training, and team readiness and flexibility. This research investigates these matters in regards to Saudi emergency responses to floods in Jeddah in 2009 and again in 2011. The study is relevant to countries who are building emergency response capacity for their populations: analysing the effects of the disaster, communications and data flows for stakeholders, achieving and securing access, finding and rescuing victims, setting up field triage sites, evacuation, and refuges. The research problem in this case was to develop a dynamic systems model capable of managing real time data to allow a team or a decision-maker to optimise their particular response within a rapidly changing situation. The Emergency Logistics Centre capability model responds to this problem by providing a set of nodes relevant to each responsibility centre (Civil Defence, regional/local authority including rescue teams, police and clean-up teams, Red Crescent). These nodes facilitate information on resource use and replenishment, and barriers such as access and weather can be controlled for in the model. The dynamic systems approach builds model capacity and transparency, allowing emergency response decision-makers access to updated instructions and decisions that may affect their capacities. After the event, coordinators and researchers can review data and actions for policy change, resource control, training and communications. In this way, knowledge from the experiences of members of the network is not lost for future position occupants in the emergency response network. The conclusion for this research is that the Saudi emergency response framework is now sufficiently robust to respond to a large scale crisis, such as may occur during the hajj with its three million pilgrims. Researchers are recommended to test their emergency response systems using the Emergency Logistics Centre model, if only to encourage rethinking and flexibility of perhaps stale or formulaic responses from staff. This may lead to benefits in identification of policy change, training, or more appropriate pathways for response teams

The doctoral research abstracts. Vol:6 2014 / Institute of Graduate Studies, UiTM

Congratulations to Institute of Graduate Studies on the continuous efforts to publish the 6th issue of the Doctoral Research Abstracts which ranged from the discipline of science and technology, business and administration to social science and humanities. This issue captures the novelty of research from 52 PhD doctorates receiving their scrolls in the UiTM’s 81st Convocation. This convocation is very significant especially for UiTM since we are celebrating the success of 52 PhD graduands – the highest number ever conferred at any one time. To the 52 doctorates, I would like it to be known that you have most certainly done UiTM proud by journeying through the scholastic path with its endless challenges and impediments, and by persevering right till the very end. This convocation should not be regarded as the end of your highest scholarly achievement and contribution to the body of knowledge but rather as the beginning of embarking into more innovative research from knowledge gained during this academic journey, for the community and country. As alumni of UiTM, we hold you dear to our hearts. The relationship that was once between a student and supervisor has now matured into comrades, forging and exploring together beyond the frontier of knowledge. We wish you all the best in your endeavour and may I offer my congratulations to all the graduands. ‘UiTM sentiasa dihati ku’ Tan Sri Dato’ Sri Prof Ir Dr Sahol Hamid Abu Bakar , FASc, PEng Vice Chancellor Universiti Teknologi MAR

SPARC 2017 retrospect & prospects : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2017 SPARC conference. This year we not only celebrate the work of our PGRs but also the 50th anniversary of Salford as a University, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 130 presenters, the conference truly showcases a vibrant PGR community at Salford. These abstracts provide a taster of the research strengths of their works, and provide delegates with a reference point for networking and initiating critical debate. With such wide-ranging topics being showcased, we encourage you to exploit this great opportunity to engage with researchers working in different subject areas to your own. To meet global challenges, high impact research inevitably requires interdisciplinary collaboration. This is recognised by all major research funders. Therefore engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers

Modelling land use using demographic forecasting and local optimisation: A case study of general education provision in Riyadh, Saudi Arabia

Globally accepted guidelines for land use allocation in Riyadh, Saudi Arabia have been based on an outmoded practice that was created over a century ago. This approach is based on a mix of predetermined population densities, walking distances, and per person area ratios. The latter criterion is essentially based on a worldwide average for facility areas and user numbers. The fundamental criticism levelled at such practices is their insensitivity to population trends and limited land resources. In this context, this research is aimed at updating common practice in the light of population growth and residential mobility projections at the city and district levels. The models introduced aim to provide comprehensive and adaptable simulation tools for optimising any type of land use provision standard over a specified time period. The simulation environment makes use of an agent-based framework that adapts and integrates a number of well-known methodologies, including Cohort Component Modelling (CCM) for population projection, Spatial Interaction (SI) modelling for residential mobility, and AutoRegressive Integrated Moving Average (ARIMA) for various ratio extrapolation. Additionally, new hybrid concepts and approaches have been evaluated, including a household based CCM and the use of Neural Network algorithms (NN) to forecast residential mobility. The case study focuses on Saudi populations in Riyadh, Saudi Arabia where the three general education stages at elementary, middle, and secondary levels were optimised for both genders. Moreover, the optimisation time horizon spans 50 years, from 2020 to 2070 while the focus of research at the city level optimises the conventional ratio of area per student based on the present stock of education allocated land and a land consumption ratio defined for every five years. The district level optimisation, on the other hand, balances the demand and supply of education over 50 years by utilising the Ministry of Education's (MOE) predesigned school prototypes. The research findings demonstrate the feasibility of developing a tool for optimising land use guidelines that is capable of producing acceptable outcomes while being sensitive to demographic change and land resource availability

Solar power integration in the Kingdom of Saudi Arabia

Power systems around the globe are undergoing a transition from their traditional form to a modern version, able to host new and different types of generation. This is driven by policies related to renewables and climate change. The electricity system in the Kingdom of Saudi Arabia (KSA) has not been thoroughly discussed in the literature, which makes it difficult to understand the nature of the transition in the biggest network in the Middle East. The focus of this thesis is on providing a better understanding of the challenges in the KSA in deploying renewable generation. In order to deploy renewables in the KSA system, it is important to have a full understanding of the following: (1) the electricity system in the KSA; (2) the resources available; and since this thesis focuses on solar resources, it is further important to analyse (3) the integration of renewables. This thesis presents the historical development of the KSA electricity system in relation to such aspects as geography, social context and climate, while examining grid and load. Data is gathered from related entities in the Kingdom to construct a validated test network for the KSA. This allows for the creation of a credible realistic network with time series demand and generation data, all of which is described by this thesis. This work assesses the available resources, specifically those of solar energy, by considering two different technologies: photovoltaic (PV) and concentrating solar power (CSP). This helps to propose various suitable locations for solar power plants following an in-depth examination using a high-resolution ground-measurement dataset. Time series data of solar power is then employed through multi-objective optimisation to identify candidate solar generation systems considering technical and financial trade-offs. A range of scenarios of these different technologies are used to assess solar power plants’ integration into the KSA electricity system using an economic dispatch model. This approach illustrates the value of renewables integration in terms of potential fuel and operational cost savings