Building and investigating generators' bidding strategies in an electricity market

In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings

Enhancement of bees algorithm for global optimisation

This research focuses on the improvement of the Bees Algorithm, a swarm-based nature-inspired optimisation algorithm that mimics the foraging behaviour of honeybees. The algorithm consists of exploitation and exploration, the two key elements of optimisation techniques that help to find the global optimum in optimisation problems. This thesis presents three new approaches to the Bees Algorithm in a pursuit to improve its convergence speed and accuracy. The first proposed algorithm focuses on intensifying the local search area by incorporating Hooke and Jeeves’ method in its exploitation mechanism. This direct search method contains a pattern move that works well in the new variant named “Bees Algorithm with Hooke and Jeeves” (BA-HJ). The second proposed algorithm replaces the randomly generated recruited bees deployment method with chaotic sequences using a well-known logistic map. This new variant called “Bees Algorithm with Chaos” (ChaosBA) was intended to use the characteristic of chaotic sequences to escape from local optima and at the same time maintain the diversity of the population. The third improvement uses the information of the current best solutions to create new candidate solutions probabilistically using the Estimation Distribution Algorithm (EDA) approach. This new version is called Bees Algorithm with Estimation Distribution (BAED). Simulation results show that these proposed algorithms perform better than the standard BA, SPSO2011 and qABC in terms of convergence for the majority of the tested benchmark functions. The BA-HJ outperformed the standard BA in thirteen out of fifteen benchmark functions and is more effective in eleven out of fifteen benchmark functions when compared to SPSO2011 and qABC. In the case of the ChaosBA, the algorithm outperformed the standard BA in twelve out of fifteen benchmark functions and significantly better in eleven out of fifteen test functions compared to qABC and SPSO2011. BAED discovered the optimal solution with the least number of evaluations in fourteen out of fifteen cases compared to the standard BA, and eleven out of fifteen functions compared to SPSO2011 and qABC. Furthermore, the results on a set of constrained mechanical design problems also show that the performance of the proposed algorithms is comparable to those of the standard BA and other swarm-based algorithms from the literature

Frequency deviations stabilizations in restructured power systems using coordinative controllers

Modern restructured power system faces excessive frequency aberrations due to the intermittent renewable generations and persistently changing load demands. An efficient and robust control strategy is obligatory to minimise deviations in the system frequency and tie-line to avoid any possible blackout. Hence, in this research, to achieve this target, automatic generation control (AGC) is utilized as a secondary controller to alleviate the changes in interconnected restructured systems at uncertainties. The objective of AGC is to quickly stabilize the deviations in frequency and tie-line power following load fluctuations. This thesis addresses the performance of AGC in two-area restructured power systems with many sophisticated control strategies in the presence of renewable and traditional power plants. As per literature of research work, there are quite a few research studies on AGC of a restructured system using optimized coordinative controllers. Besides, investigations on advanced optimized-based coordinative controller approaches are also rare to find in the literature. So, various combinations of two degrees of freedom (2DOF) controllers are utilized as supplementary controllers to diminish the frequency deviations. Nevertheless, the interconnected tie-lines are typically congested in areas with huge penetration of renewable sources, which may reduce the tie -line capability. Therefore, distinct FACTS controllers and ultra-capacitor (UC) are integrated into two-area restructured systems for strengthening the tie-line power and frequency. Further, new optimization techniques such as cuckoo search (CS), bat algorithm (BA), moth-flame optimization (MFO) are utilized in this work for investigating the suggested 2DOF controllers and compared their performance in all contracts of restructured systems. As per the simulation outcomes, the amalgamation of DPFC and UC with MFObased 2DOF PID-FOPDN shows low fluctuation rate in frequency and tie-line power. Besides, the settling times (ST) of two areas are 9.5 S for ΔF1, 8.2 S for ΔF2, and 10.15 S for ΔPtie. The robustness of the suggested controller has been verified by ±25% variations in system parameters and loading conditions

Advances in Supercapacitor Technology and Applications

Energy storage is a key topic for research, industry, and business, which is gaining increasing interest. Any available energy-storage technology (batteries, fuel cells, flywheels, and so on) can cover a limited part of the power-energy plane and is characterized by some inherent drawback. Supercapacitors (also known as ultracapacitors, electrochemical capacitors, pseudocapacitors, or double-layer capacitors) feature exceptional capacitance values, creating new scenarios and opportunities in both research and industrial applications, partly because the related market is relatively recent. In practice, supercapacitors can offer a trade-off between the high specific energy of batteries and the high specific power of traditional capacitors. Developments in supercapacitor technology and supporting electronics, combined with reductions in costs, may revolutionize everything from large power systems to consumer electronics. The potential benefits of supercapacitors move from the progresses in the technological processes but can be effective by the availability of the proper tools for testing, modeling, diagnosis, sizing, management and technical-economic analyses. This book collects some of the latest developments in the field of supercapacitors, ranging from new materials to practical applications, such as energy storage, uninterruptible power supplies, smart grids, electrical vehicles, advanced transportation and renewable sources

Investigating evolutionary computation with smart mutation for three types of Economic Load Dispatch optimisation problem

The Economic Load Dispatch (ELD) problem is an optimisation task concerned with how electricity generating stations can meet their customers’ demands while minimising under/over-generation, and minimising the operational costs of running the generating units. In the conventional or Static Economic Load Dispatch (SELD), an optimal solution is sought in terms of how much power to produce from each of the individual generating units at the power station, while meeting (predicted) customers’ load demands. With the inclusion of a more realistic dynamic view of demand over time and associated constraints, the Dynamic Economic Load Dispatch (DELD) problem is an extension of the SELD, and aims at determining the optimal power generation schedule on a regular basis, revising the power system configuration (subject to constraints) at intervals during the day as demand patterns change. Both the SELD and DELD have been investigated in the recent literature with modern heuristic optimisation approaches providing excellent results in comparison with classical techniques. However, these problems are defined under the assumption of a regulated electricity market, where utilities tend to share their generating resources so as to minimise the total cost of supplying the demanded load. Currently, the electricity distribution scene is progressing towards a restructured, liberalised and competitive market. In this market the utility companies are privatised, and naturally compete with each other to increase their profits, while they also engage in bidding transactions with their customers. This formulation is referred to as: Bid-Based Dynamic Economic Load Dispatch (BBDELD). This thesis proposes a Smart Evolutionary Algorithm (SEA), which combines a standard evolutionary algorithm with a “smart mutation” approach. The so-called ‘smart’ mutation operator focuses mutation on genes contributing most to costs and penalty violations, while obeying operational constraints. We develop specialised versions of SEA for each of the SELD, DELD and BBDELD problems, and show that this approach is superior to previously published approaches in each case. The thesis also applies the approach to a new case study relevant to Nigerian electricity deregulation. Results on this case study indicate that our SEA is able to deal with larger scale energy optimisation tasks

ECOS 2012

The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology

Emerging Technologies

This monograph investigates a multitude of emerging technologies including 3D printing, 5G, blockchain, and many more to assess their potential for use to further humanity’s shared goal of sustainable development. Through case studies detailing how these technologies are already being used at companies worldwide, author Sinan Küfeoğlu explores how emerging technologies can be used to enhance progress toward each of the seventeen United Nations Sustainable Development Goals and to guarantee economic growth even in the face of challenges such as climate change. To assemble this book, the author explored the business models of 650 companies in order to demonstrate how innovations can be converted into value to support sustainable development. To ensure practical application, only technologies currently on the market and in use actual companies were investigated. This volume will be of great use to academics, policymakers, innovators at the forefront of green business, and anyone else who is interested in novel and innovative business models and how they could help to achieve the Sustainable Development Goals. This is an open access book

Aeronautical Engineering. A continuing bibliography with indexes, supplement 135, May 1981

This bibliography lists 536 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1981

Improvements of task scheduling and load balancing in cloud environment by swarm intelligence metaheuristics

Klaud racunarstvo pripada grupi novijih racunarskih paradigmi, koja se poput paradigme mrežnog racunarstva, bazira na grupisanju resursa i na korišcenju mrežnih i Internet tehnologija. U opštem smislu, klaud racunarstvo se odnosi na novi nacin isporuke racunarskih resursa u vidu usluge, gde se pod resursima podrazumeva gotovo sve, od podataka i softvera, do hardverskih komponenti, kao što su procesirajuci elementi, memorija i skladišta. Klaud racunarstvo je aktuelna i važna multidisciplinarna oblast, o cemu svedoci veliki broj objavljenih radova u vrhunskim me unarodnim casopisima i prikazanih na najznacajnijim svetskim skupovima. Na osnovu naucnih rezultata prikupljenih u objavljenim radovima iz ovog domena, može da se zakljuci da u klaud okruženju postoji veliki broj izazova i problema, za cije rešavanje mogu da se prona u bolje metode, tehnike i algoritmi. Jedan od najvažnijih izazova savremenog klaud okruženja je raspore ivanje zahteva krajnjih korisnika za izvršavanje na ogranicenom skupu raspoloživih resursa (virtuelnih mašina). Problem raspore ivanja na klaudu odnosi se na definisanje rasporeda izvršavanja zadataka na ogranicenom skupu raspoloživih resursa uzimajuci pritom u obzir potencijalna ogranicenja i funkciju cilja koju je potrebno optimizovati. Raspore ivanje poslova vrše algoritmi raspore ivanja, koji mogu da se podele na staticke i dinamicke. U slucaju statickog raspore ivanja, gde se poslovi ne mogu dinamicki prebacivati sa preopterecnih na manje opterecene virtuelne mašine, zadaci se raspore uju za izvršavanje na raspoložive virtuelne mašine pre pocetka izvršavanja. S druge strane, primenom metoda dinamickog raspore ivanja, koje je u literaturi poznato pod nazivom balansiranje opterecenja, vrši se preraspodela poslova izme u aktivnih virtuelnih mašina tokom samog izvršavanja programa raspore ivanja. Preraspodela se vrši tako što se zadaci sa virtuelnih mašina koje imaju vece opterecenje dinamicki prebacuju za izvršavanje na virtuelnim mašinama koje imaju manje opterecenje. Za potrebe dinamickog raspore ivanja koriste se uglavnom heuristicke i metaheuristicke optimizacione metode i algoritmi, koji postižu dobre rezultate. Problemi raspore ivanja poslova i balansiranja opterecenja na klaudu pripadaju grupi NP teških kombinatornih i/ili globalnih problema sa ili bez ogranicenja. Na osnovu publikovanih rezultata u relevantnim literaturnim izvorima, vidi se da su metaheuristike inteligencije rojeva, koje spadaju u grupu prirodom-inspirisanih algoritama, uspešno testirane na bencmark problemima i primenjivane na prakticnim NP teškim optimizacionim problemima (globalnim i kombinatornim) i da mogu da postignu bolje rezultate u smislu brzine konvergencije i kvaliteta rešenja, od drugih metoda, tehnika i algoritama. Polazeci od navedenog, u ovom radu je ispitivano da li je moguce dalje unaprediti rešavanja problema raspore ivanja poslova i balansiranja opterecenja na klaudu primenom metaheuristika inteligencije rojeva. Tokom sprovedenog istraživanja, unapre eno je i adaptirano više metaheuristika inteligencije rojeva za rešavanje problema raspore ivanja poslova i balansiranja opterecenja u klaud okruženju. U disertaciji su detaljno prikazane implementacije dva unapre ena algoritma rojeva - algoritma optimizacije monarh leptirovima i algoritma optimizacije jatom kitova. Za potrebe testiranja, rešavana su dva modela raspore ivanja poslova na klaudu. Prvi model, koji pripada grupi jednokriterijumske optimizacije, uzima u obzir minimizaciju vremena izvršavanja svih zadataka na klaudu, dok drugi, višekriterijumski model uzima u obzir minimizaciju vremena izvršavanja svih zadataka na klaudu i budžeta, tj. troškova za izvršavanje svih zahteva krajnjih korisnika. Simulacije su vršene u robusnom okruženju CloudSim simulatora i oba algoritma su testirana sa skupom veštackih podataka, generisanih u okviru CloudSim platforme, i realnih podataka, koji su preuzeti iz globalno dostupne bencmark baze. Osim testiranja za praktican izazov na klaudu, da bi se preciznije utvrdila unapre- enja modifikovanih metaheuristika u odnosu na osnovne verzije, obe metaheuristike su verifikovane i testiranjima na standardnim skupovima bencmark funkcija za globalnu optimizaciju bez ogranicenja. Upore ivanjem generisanih rezultata (kvalitet rešenja i brzina konvergencije) sa rezultatima najboljih poznatih metaheuristika i heuristika iz literature, koje su primenjivane na iste instance problema (na praktican problem raspore ivanja na klaudu i bencmark testove), dokazan je kvalitet implementiranih algoritama, cime je potvr ena i osnovna hipoteza ovog rada da se rešavanje izazova raspore ivanja poslova i balansiranja opterecenja u klaud okruženju mogu dalje unaprediti primenom metaheuristika inteligencije rojeva

QUT Research Graduates Yearbook, 2021

HDR research activity remains at the heart of QUT’s real world research ambitions and our HDR cohort is one of the key the engine rooms of our innovation pipeline. In 2021, our HDR cohort showed great resilence in a world that continues to be challenged by a pandemic, complex international relations and an increasingly fractured world. Our HDR students find answers to questions that touch the very fundamentals of the human existence.and its embedding into our societies, explore ways towards innovative health solutions, understand the complexities of nature from the very small to planetary and extra-terrestrial dimensions on a fundamental level and engineer the innovations that support a sustainable environment. QUT will continue to place great importance on the HDR journey including both the research and career development components. Our graduates continue to take up leadership position ranging from industry to government agencies to academia in Australia and abroad, enabling positive change. We are continuously striving to enhance our HDR experience and research leaders across our schools, centers, faculties and divisions continue to work in partnership with our HDR cohort to maximize their impact. The year ahead will continue to challenge us as a society and a global community on several fronts, thus resilience building remains a key learning outcome for our HDR students, enabling them to thrive in pushing the boundaries of knowledge forward. I hope you enjoy the read. (Professor Christopher Barner-Kowollik Deputy Vice-Chancellor and Vice-President (Research