State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

An overview of distributed microgrid state estimation and control for smart grids

© 2015 by the authors; licensee MDPI, Basel, Switzerland. Given the significant concerns regarding carbon emission from the fossil fuels, global warming and energy crisis, the renewable distributed energy resources (DERs) are going to be integrated in the smart grid. This grid can spread the intelligence of the energy distribution and control system from the central unit to the long-distance remote areas, thus enabling accurate state estimation (SE) and wide-area real-time monitoring of these intermittent energy sources. In contrast to the traditional methods of SE, this paper proposes a novel accuracy dependent Kalman filter (KF) based microgrid SE for the smart grid that uses typical communication systems. Then this article proposes a discrete-time linear quadratic regulation to control the state deviations of the microgrid incorporating multiple DERs. Therefore, integrating these two approaches with application to the smart grid forms a novel contributions in green energy and control research communities. Finally, the simulation results show that the proposed KF based microgrid SE and control algorithm provides an accurate SE and control compared with the existing method

Modelling the interaction of tidal range power systems for renewable energy conversion

Tidal range power plants (TPPs) have the potential to provide a reliable and long-term source of renewable power. The inherent predictability and operation flexibility of TPPs presents opportunities regarding the phasing of energy supply. However, TPPs can notably impact the surrounding coastal system. These benefits and challenges could be magnified in scenarios where a national energy system incorporates multiple schemes. This thesis investigates interactions between TPPs that are sited within the same geographical region, with regards to their combined energy supply phasing and impacts. Depth-averaged coastal models are applied in characterising the ambient tidal resource. Techniques for numerically parameterising the presence, operation and power output of TPPs are established. Particular focus is granted to methods for optimising operation characteristics. A model of Ramsey Sound is employed in investigating common numerical model configuration choices. An extended model domain, to include the entire west coast of Great Britain, is then configured. Benefits and challenges associated with how multiple TPPs interact are explored: - A system of TPPs sited across the Bristol Channel and Irish Sea regions is implemented in the model, with their designs based on existing proposals. Operation control schedules targeting continuous power generation are optimised. The notable tidal phase difference between the two regions permits cumulative continuous supply for approximately half of the year during periods around spring tides. Financial incentives associated with reliable, baseload supply are proposed. - Combinations of seven consistently designed TPPs in the Bristol Channel and Irish Sea are investigated regarding their hydro-environmental and energy resource impacts. Scheme design consistency provides a basis to focus solely on impacts associated with development sites, by minimising differences in impacts that occur from TPP design variations. Results indicate that the more constrained geometry of the Bristol Channel contributes to higher individual and cumulative impacts than TPP developments in the Irish Sea.Open Acces

Multi-objective Optimization Based on Improved Differential Evolution Algorithm

On the basis of the fundamental differential evolution (DE), this paper puts forward several improved DE algorithms to find a balance between global and local search and get optimal solutions through rapid convergence. Meanwhile, a random mutation mechanism is adopted to process individuals that show stagnation behaviour. After that, a series of frequently-used benchmark test functions are used to test the performance of the fundamental and improved DE algorithms. After a comparative analysis of several algorithms, the paper realizes its desired effects by applying them to the calculation of single and multiple objective functions

Short Term Unit Commitment as a Planning Problem

‘Unit Commitment’, setting online schedules for generating units in a power system to ensure supply meets demand, is integral to the secure, efficient, and economic daily operation of a power system. Conflicting desires for security of supply at minimum cost complicate this. Sustained research has produced methodologies within a guaranteed bound of optimality, given sufficient computing time. Regulatory requirements to reduce emissions in modern power systems have necessitated increased renewable generation, whose output cannot be directly controlled, increasing complex uncertainties. Traditional methods are thus less efficient, generating more costly schedules or requiring impractical increases in solution time. Meta-Heuristic approaches are studied to identify why this large body of work has had little industrial impact despite continued academic interest over many years. A discussion of lessons learned is given, and should be of interest to researchers presenting new Unit Commitment approaches, such as a Planning implementation. Automated Planning is a sub-field of Artificial Intelligence, where a timestamped sequence of predefined actions manipulating a system towards a goal configuration is sought. This differs from previous Unit Commitment formulations found in the literature. There are fewer times when a unit’s online status switches, representing a Planning action, than free variables in a traditional formulation. Efficient reasoning about these actions could reduce solution time, enabling Planning to tackle Unit Commitment problems with high levels of renewable generation. Existing Planning formulations for Unit Commitment have not been found. A successful formulation enumerating open challenges would constitute a good benchmark problem for the field. Thus, two models are presented. The first demonstrates the approach’s strength in temporal reasoning over numeric optimisation. The second balances this but current algorithms cannot handle it. Extensions to an existing algorithm are proposed alongside a discussion of immediate challenges and possible solutions. This is intended to form a base from which a successful methodology can be developed

Design tools for the optimal exploitation of tidal-stream renewable energy

Tidal stream power generation is attractive for a number of reasons. However, this will only be deployed on a commercial scale, in arrays of tens to hundreds of turbines, if these arrays can be shown to be viable from economic, engineering and environmental perspectives. With limited experience from real arrays and constraints on the size of lab-based experiments, advanced numerical tools are needed to both predict and maximise power yield. These tools can be used to prove viability of new sites and aid array design in this fledgling industry. Holistic economic models are needed to aid the industry's move from demonstrator arrays to commercially sized arrays that can compete at a lower subsidy level. This thesis investigates economic models for evaluating the performance of arrays and different cost reduction methods, which may help to bring the cost of tidal energy in line with other sustainable energy sources. A methodology to optimise array design with respect to complex economic models is presented. This method builds an emulator of the trade-off curve between total yield and number of turbines, generated from a computationally expensive set of optimisation loops. It enables far more robust analysis of the implications of changes to the economic models than is possible through direct optimisation alone. A tool is created to investigate further cost reductions that could be obtained through the assessment of a range of different turbine rotor sizes and rated capacities, as well as other array design specifications. The tool is used to make preliminary assessments of array design choices, while adhering to practical constraints such as sea bed depth and steepness, along with legal constraints such as consents on the number of turbines and spacing between them. The tool developed can be applied to early-stage assessments and narrowing down the scope of array design specifications.Open Acces

Optimal integrated energy systems design incorporating variable renewable energy sources

The effect of variability in renewable input sources on the optimal design and reliability of an integrated energy system designed for off-grid mining operation is investigated via a two-stage approach. Firstly, possible energy system designs are generated by solving a deterministic non-linear programming (NLP) optimization problem to minimize the capital cost for a number of input scenarios. Two measures of reliability, the loss of power supply probability (LPSP) and energy index of reliability (EIR), are then evaluated for each design based on the minimization of the external energy required to satisfy load demands under a variety of input conditions. Two case studies of mining operations located in regions with different degrees of variability are presented. The results show that the degree of variability has an impact on the design configuration, cost and performance, and highlights the limitations associated with deterministic decision making for high variability systems

Heat Exchanger Network Cleaning Scheduling: From Optimal Control to Mixed-Integer Decision Making

An approach for optimising the cleaning schedule in heat exchanger networks (HENs) subject to fouling is presented. This work focuses on HEN applications in crude oil preheat trains located in refineries. Previous approaches have focused on using mixed-integer nonlinear programming (MINLP) methods involving binary decision variables describing when and which unit to clean in a multi-period formulation. This work is based on the discovery that the HEN cleaning scheduling problem is in actuality a multistage optimal control problem (OCP), and further that cleaning actions are the controls which appear linearly in the system equations. The key feature is that these problems exhibit bang-bang behaviour, obviating the need for combinatorial optimisation methods. Several case studies are considered; ranging from a single unit up to 25 units. Results show that the feasible path approach adopted is stable and efficient in comparison to classical methods which sometimes suffer from failure in convergence.Support of this research by the Ministry of Higher Education in the Sultanate of Oman and Petroleum Development Oman (PDO) is gratefully acknowledged

Optimisation of heat exchanger network maintenance scheduling problems

This thesis focuses on the challenges that arise from the scheduling of heat exchanger network maintenance problems which undergo fouling and run continuously over time. The original contributions of the current research consist of the development of novel optimisation methodologies for the scheduling of cleaning actions in heat exchanger network problems, the application of the novel solution methodology developed to other general maintenance scheduling problems, the development of a stochastic programming formulation using this optimisation technique and its application to these scheduling problems with parametric uncertainty. The work presented in this thesis can be divided into three areas. To efficiently solve this non-convex heat exchanger network maintenance scheduling problem, new optimisation strategies are developed. The resulting contributions are outlined below. In the first area, a novel methodology is developed for the solution of the heat exchanger network maintenance scheduling problems, which is attributed towards a key discovery in which it is observed that these problems exhibit bang-bang behaviour. This indicates that when integrality on the binary decision variables is relaxed, the solution will tend to either the lower or the upper bound specified, obviating the need for integer programming solution techniques. Therefore, these problems are in ac- tuality optimal control problems. To suitably solve these problems, a feasible path sequential mixed integer optimal control approach is proposed. This methodology is coupled with a simple heuristic approach and applied to a range of heat exchanger network case studies from crude oil refinery preheat trains. The demonstrated meth- odology is shown to be robust, reliable and efficient. In the second area of this thesis, the aforementioned novel technique is applied to the scheduling of the regeneration of membranes in reverse osmosis networks which undergo fouling and are located in desalination plants. The results show that the developed solution methodology can be generalised to other maintenance scheduling problems with decaying performance characteristics. In the third and final area of this thesis, a stochastic programming version of the feasible path mixed integer optimal control problem technique is established. This is based upon a multiple scenario approach and is applied to two heat exchanger network case studies of varying size and complexity. Results show that this methodology runs automatically with ease without any failures in convergence. More importantly due to the significant impact on economics, it is vital that uncertainty in data is taken into account in the heat exchanger network maintenance scheduling problem, as well as other general maintenance scheduling problems when there is a level of uncertainty in parameter values

Developing alternative SCDDP implementations for hydro-thermal scheduling in New Zealand.

In a hydro-dominated system, such as New Zealand, the continual improvement and development of effective optimization and simulation software to inform decision making is necessary for effective resource management. Stochastic Constructive Dual Dynamic Programming (SCDDP) is a technique which has been effectively applied to the New Zealand system for optimization and simulation. This variant of Dynamic Programming (DP) allows optimization to occur in the dual space reducing the computational complexity and allows solutions from a single run to be formed as price signal surfaces and trajectories. However, any application of this method suffers from issues with computational tractability for higher reservoir numbers. Furthermore, New Zealand specific applications currently provide limited information on the system as they all use the same two-reservoir approximation of the New Zealand system. This limitation is of increasing importance with the decentralization of the New Zealand electricity sector. In this thesis we develop this theory with respect to two key goals: • To advance the theory surrounding SCDDP to be generalizable to higher reservoir numbers through the application of the point-wise algorithm explored in R. A. Read, Dye, S. & Read, E.G. (2012) to the stochastic case. • To develop at least two new and distinct two-reservoir SCDDP representations of the New Zealand system to provide a theoretical basis for greater flexibility in simulation and optimization of hydro-thermal scheduling in the New Zealand context