Integration of renewable energy into Nigerian power systems

Many countries are advancing down the road of electricity privatization, deregulation, and competition as a solution to their growing electricity demand and other challenges posed by the monopolistic nature of the existing structure. Presently, Nigeria has a supply deficit of electricity as a result of the growing demand. This imbalance has negatively affected the economy of the country and the social-economic well-being of the population. Hence, there is an urgent need to reform the power sector for greater efficiency and better performance. The objectives of the reform are to meet the growing power demand by increasing the electric power generation and also by increasing competitiveness through the participation of more private sector entities. The renewable energy integration is one way of increasing the electricity generation in the country in order to cater for the growing demand adequately. Examples of the renewable energy that is available in the country include wind, geothermal, solar and hydro. They are considered to be environmentally friendly, replenishable and do not contribute to the climate change phenomena. The country presently generates the bulk of its electricity from both thermal (85%) and hydroelectric (15%) power plants. While electricity generation from the thermal power stations constitutes the largest share of greenhouse emission, this is mostly from burning coal and natural gas. The effect of this high proportion of greenhouse emission causes climate change which is referred to as a variation in the climate system statistical properties over a long period of time. It has been observed that many of the activities of human beings are contributory factors to the release of these greenhouse gases (GHG). But, as the traditional sources of energy continue to threaten the present and future existence on the planet earth, it is, therefore, imperative to increase the integration of the variable renewable energy sources in a sustainable and eco-friendly manner over a long period of time. The variability and the uncertainties of the renewable energy source's output, present a major challenge in the design of an efficient electricity market in a deregulated environment. The system deregulation and the use of renewable sources for the generation of electricity are major changes presently being experienced in power system. In a deregulated power system, the integration of renewable generation and its penetration affects both the physical and the economic operations. The main focus of this research is on the integration of wind energy into Nigerian power systems. Up till now, research on the availability of the wind energy and its economic impacts has been limited in Nigeria. Generally, the previous study of wind energy availability in Nigeria has been limited in scope. The wind energy assessment study has not been detailed enough to be able to ascertain the wind energy potential of the country. To cope with this shortcoming, a detailed statistical wind modeling and forecasting methodology have been used in this thesis to determine the amount of extractable wind energy in six selected locations in Nigeria using historical wind speed data for 30 years. The accuracy test of the statistical models was also carried using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Chi-Square methods to determine the inherent error margin in the modeling and analysis. It is found that the error margin of the evaluations falls within the expected permissible tolerance range. For a more detailed wind assessment study of the Nigeria weather, the seasonal variation of the weather conditions as it affects the wind speed and availability during the two major seasons of dry and rainy was considered. A Self-Adaptive Differential Evolution (SADE) was used to solve the economic load dispatch problem that considers the valve-point effects and the transmission losses subject to many constraints. The results obtained were compared with those obtained using the "standard" Differential Evolution (DE), Genetic Algorithm (GA), and traditional Gradient Descent method. The results of the SADE obtained when compared with the GA, DE, and Gradient descent show the superiority of SADE over all the other methods. The research work shows that the wind energy is available in commercial quantity for generation of electricity in Nigeria. And, if tapped would help reduce the gap between the demand and supply of electricity in the country. It was also demonstrated that the wind energy integration into the power systems affects the generators total production cost

MalBoT-DRL: Malware botnet detection using deep reinforcement learning in IoT networks

In the dynamic landscape of cyber threats, multi-stage malware botnets have surfaced as significant threats of concern. These sophisticated threats can exploit Internet of Things (IoT) devices to undertake an array of cyberattacks, ranging from basic infections to complex operations such as phishing, cryptojacking, and distributed denial of service (DDoS) attacks. Existing machine learning solutions are often constrained by their limited generalizability across various datasets and their inability to adapt to the mutable patterns of malware attacks in real world environments, a challenge known as model drift. This limitation highlights the pressing need for adaptive Intrusion Detection Systems (IDS), capable of adjusting to evolving threat patterns and new or unseen attacks. This paper introduces MalBoT-DRL, a robust malware botnet detector using deep reinforcement learning. Designed to detect botnets throughout their entire lifecycle, MalBoT-DRL has better generalizability and offers a resilient solution to model drift. This model integrates damped incremental statistics with an attention rewards mechanism, a combination that has not been extensively explored in literature. This integration enables MalBoT-DRL to dynamically adapt to the ever-changing malware patterns within IoT environments. The performance of MalBoT-DRL has been validated via trace-driven experiments using two representative datasets, MedBIoT and N-BaIoT, resulting in exceptional average detection rates of 99.80% and 99.40% in the early and late detection phases, respectively. To the best of our knowledge, this work introduces one of the first studies to investigate the efficacy of reinforcement learning in enhancing the generalizability of IDS

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Proceedings of the XIII Global Optimization Workshop: GOW'16

[Excerpt] Preface: Past Global Optimization Workshop shave been held in Sopron (1985 and 1990), Szeged (WGO, 1995), Florence (GO’99, 1999), Hanmer Springs (Let’s GO, 2001), Santorini (Frontiers in GO, 2003), San José (Go’05, 2005), Mykonos (AGO’07, 2007), Skukuza (SAGO’08, 2008), Toulouse (TOGO’10, 2010), Natal (NAGO’12, 2012) and Málaga (MAGO’14, 2014) with the aim of stimulating discussion between senior and junior researchers on the topic of Global Optimization. In 2016, the XIII Global Optimization Workshop (GOW’16) takes place in Braga and is organized by three researchers from the University of Minho. Two of them belong to the Systems Engineering and Operational Research Group from the Algoritmi Research Centre and the other to the Statistics, Applied Probability and Operational Research Group from the Centre of Mathematics. The event received more than 50 submissions from 15 countries from Europe, South America and North America. We want to express our gratitude to the invited speaker Panos Pardalos for accepting the invitation and sharing his expertise, helping us to meet the workshop objectives. GOW’16 would not have been possible without the valuable contribution from the authors and the International Scientific Committee members. We thank you all. This proceedings book intends to present an overview of the topics that will be addressed in the workshop with the goal of contributing to interesting and fruitful discussions between the authors and participants. After the event, high quality papers can be submitted to a special issue of the Journal of Global Optimization dedicated to the workshop. [...

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

A layout design decision-support framework and concept demonstrator for rural hospitals using mixed methods

Thesis (MEng)--Stellenbosch University, 2017.ENGLISH ABSTRACT: Layout design is an ever-present problem that has a significant effect on the operations of an organisation, especially in the context of healthcare which deals with the lives of patients. It is a complex problem that has long-term consequences and oftentimes competing objectives. Literature has focused almost exclusively on using either quantitative or qualitative layout design methods for designing layouts. This study develops a generic framework using both quantitative and qualitative layout design methods that will guide the user to design a near optimal layout for a rural hospital while taking into consideration the relevant laws and standards as well as the health outcomes of the surrounding rural community. Rural and urban lifestyles, health, and illnesses differ in many ways. General hospital design methods are therefore not necessarily appropriate for hospitals in these areas. There is thus a need for a framework to be tailored for a rural community. Following a mixed methods methodology, a systematic literature review of quantitative and qualitative layout design methods along with hospital design considerations were conducted in order to determine the most adequate methods for designing a hospital layout at the block diagram level of detail. Furthermore, the commonalities and differences between rural and urban hospitals were investigated including laws and standards relevant to hospital layouts. The qualitative layout design methods involved different layout procedures and Muther’s Systematic Layout Planning Procedure was found to be most adequate. Furthermore, hospital design considerations such as patient-centeredness, efficiency, flexibility and expandability, sustainability, and therapeutic environment were identified and linked with the quantitative layout methods. It was also found that rural communities have different needs to urban ones with regard to access to medical care, prominent illnesses, and attitudes towards health. The healthcare personnel shortages are particularly problematic for rural communities. The quantitative layout design methods involved layout models, solution methods (exact methods, metaheuristics, and hybrid metaheuristics), and layout software. Using criteria of objectives, assumptions, inputs, outputs, and hospital design considerations, the Quadratic Set Covering Problem was determined to be the most appropriate model for designing a rural hospital block diagram layout. It was deemed possible to integrate the quantitative and qualitative methods by embedding the qualitative data into this quantitative model. The rural hospital design framework was developed using Excel VBA and RStudio. The framework was validated via two routes. Firstly, semi-structured interviews were conducted with experts in the field, i.e. expert analyses. Secondly a case study of the Semonkong Hospital Project was employed wherein the framework was applied successfully. The framework was deemed valid according to both the expert analyses and the case study.AFRIKAANSE OPSOMMING: Die uitleg van ‘n gebou het ‘n belangrike impak op die bedrywighede van ‘n organisasie – veral in die konteks van gesondheidsorg waar daar met pasiënte se lewens gewerk word. Dit is ‘n ingewikkelde probleem wat oor langtermyneffekte beskik en dikwels teenstrydige doelwitte. Die literatuur vir uitleg ontwerpsmetodes het meestal gefokus op óf kwantitatiewe óf kwalitatiewe uitleg ontwerpsmetodes. Hierdie studie ontwikkel ‘n generiese raamwerk wat beide van hierdie metodes gebruik om ‘n gebruiker te lei om die uitleg van ‘n plattelandse hospital te ontwerp wat die gepaste wette en standaarde en die gesondheid van die omliggende gemeenskap in ag neem. Landelike- en stedelike gemeenskappe verskil in terme van hul lewenstyl, gesondheid en tipe siektes. Algemene uitleg ontwerpsmetodes is dus nie noodwendig geskik vir ‘n plattelandse hospitaal nie. Daar is dus ‘n behoefte om ‘n raamwerk te ontwikkel wat spesifiek is vir die uitleg van ‘n plattelandse hospitaal. Hierdie studie volg ‘n gemengde metode benadering en ‘n sistematiese literatuurstudie is gevolglik afsonderlik gedoen op kwantitatiewe- en kwalitatiewe uitleg ontwerpsmetodes met die doel om die mees geskikte ontwerpsmetodes vir ‘n hospitaal uitleg te bepaal. Die verskille en ooreenkomste tussen landelike- en stedelike hospitale was ook ondersoek. Hierdie sluit in wette en standaarde wat van toepassing is op hospitaal uitlegte. Die kwalitatiewe uitleg ontwerpsmetodes het verskillende uitleg prosedures ondersoek en dit is gevind dat Muther se Sistematiese Uitleg Prosedure die mees geskik is vir die probleem van hierdie studie. Daar is gevind dat die hoof ontwerpsoorwegings vir die uitleg van ‘n hospitaal pasiënt-gesentreerdheid, doeltreffendheid, aanpasbaarheid, volhoubaarheid en terapeutiese omgewing is. Daar is gevind dat landelike- en stedelike gemeenskappe verskil in terme van hul toegang tot mediese sorg, prominente siektes, en hul houdings teenoor gesondheid. Een van die grootste probleme in landelike hospitale was hul tekort aan personeel. Die kwantitatiewe uitleg ontwerpsmetodes sluit uitleg modelle, oplossingsmetodes (presiese metodes, metaheuristieke en hibriede metaheuristieke) en uitleg sagteware in. ‘n Kriteria van doelwitte, aannames, insette, uitsette en hospitaal ontwerpsoorwegings was gebruik om die mees geskikte uitleg model te kies naamlik: die ‘Quadratic Set Covering Problem’. Dit is gevind dat die kwantitatiewe- en kwalitatiewe uitleg ontwerpsmetodes deur middel van ‘embedding’ geïntegreer kan word. Die uitleg ontwerp raamwerk vir plattelandse hospitale was ontwikkel met behulp van Excel VBA en RStudio. Die raamwerk is bekragtig deur twee roetes. Eerstens, semi-gestruktureerde onderhoude was gevoer met kundiges in die velde van gesondheidsorg, plattelandse gemeenskappe en uitleg ontwerp. Tweedens, die raamwerk is toegepas op ‘n gevallestudie van die Semonkong Hospitaal Projek. Albei roetes dui daarop dat die raamwerk geldig is

Real-time Control and Optimization of Water Supply and Distribution infrastructure

Across North America, water supply and distribution systems (WSDs) are controlled manually by operational staff - who place a heavy reliance on their experience and judgement when rendering operational decisions. These decisions range from scheduling the operation of pumps, valves and chemical dosing in the system. However, due to the uncertainty of demand, stringent water quality regulatory constraints, external forcing (cold/drought climates, fires, bursts) from the environment, and the non-stationarity of climate change, operators have the tendency to control their systems conservatively and reactively. WSDs that are operated in such fashion are said to be 'reactive' because: (i) the operators manually react to changes in the system behaviour, as measured by Supervisory Control and Data Acquisition (SCADA) systems; and (ii) are not always aware of any anomalies in the system until they are reported by consumers and authorities. The net result is that the overall operations of WSDs are suboptimal with respect to energy consumption, water losses, infrastructure damage and water quality. In this research, an intelligent platform, namely the Real-time Dynamically Dimensioned Scheduler (RT-DDS), is developed and quantitatively assessed for the proactive control and optimization of WSD operations. The RT-DDS platform was configured to solve a dynamic control problem at every timestep (hour) of the day. The control problem involved the minimization of energy costs (over the 24-hour period) by recommending 'near-optimal' pump schedules, while satisfying hydraulic reliability constraints. These constraints were predefined by operational staff and regulatory limits and define a tolerance band for pressure and storage levels across the WSD system. The RT-DDS platform includes three essential modules. The first module produces high-resolution forecasts of water demand via ensemble machine learning techniques. A water demand profile for the next 24-hours is predicted based on historical demand, ambient conditions (i.e. temperature, precipitation) and current calendar information. The predicted profile is then fed into the second module, which involves a simulation model of the WSD. The model is used to determine the hydraulic impacts of particular control settings. The results of the simulation model are used to guide the search strategy of the final module - a stochastic single solution optimization algorithm. The optimizer is parallelized for computational efficiency, such that the reporting frequency of the platform is within 15 minutes of execution time. The fidelity of the prediction engine of the RT-DDS platform was evaluated with an Advanced Metering Infrastructure (AMI) driven case study, whereby the short-term water consumption of the residential units in the city were predicted. A Multi-Layer Perceptron (MLP) model alongside ensemble-driven learning techniques (Random forests, Bagging trees and Boosted trees) were built, trained and validated as part of this research. A three-stage validation process was adopted to assess the replicative, predictive and structural validity of the models. Further, the models were assessed in their predictive capacity at two different spatial resolutions: at a single meter and at the city-level. While the models proved to have strong generalization capability, via good performance in the cross-validation testing, the models displayed slight biases when aiming to predict extreme peak events in the single meter dataset. It was concluded that the models performed far better with a lower spatial resolution (at the city or district level) whereby peak events are far more normalized. In general, the models demonstrated the capacity of using machine learning techniques in the context of short term water demand forecasting - particularly for real-time control and optimization. In determining the optimal representation of pump schedules for real-time optimization, multiple control variable formulations were assessed. These included binary control statuses and time-controlled triggers, whereby the pump schedule was represented as a sequence of on/off binary variables and active/idle discrete time periods, respectively. While the time controlled trigger representation systematically outperformed the binary representation in terms of computational efficiency, it was found that both formulations led to conditions whereby the system would violate the predefined maximum number of pump switches per calendar day. This occurred because at each timestep the control variable formulation was unaware of the previously elapsed pump switches in the subsequent hours. Violations in the maximum pump switch limits lead to transient instabilities and thus create hydraulically undesirable conditions. As such, a novel feedback architecture was proposed, such that at every timestep, the number of switches that had elapsed in the previous hours was explicitly encoded into the formulation. In this manner, the maximum number of switches per calendar day was never violated since the optimizer was aware of the current trajectory of the system. Using this novel formulation, daily energy cost savings of up to 25\% were achievable on an average day, leading to cost savings of over 2.3 million dollars over a ten-year period. Moreover, stable hydraulic conditions were produced in the system, thereby changing very little when compared to baseline operations in terms of quality of service and overall condition of assets

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems