A multi-objective evolutionary approach to simulation-based optimisation of real-world problems.

This thesis presents a novel evolutionary optimisation algorithm that can improve the quality of solutions in simulation-based optimisation. Simulation-based optimisation is the process of finding optimal parameter settings without explicitly examining each possible configuration of settings. An optimisation algorithm generates potential configurations and sends these to the simulation, which acts as an evaluation function. The evaluation results are used to refine the optimisation such that it eventually returns a high-quality solution. The algorithm described in this thesis integrates multi-objective optimisation, parallelism, surrogate usage, and noise handling in a unique way for dealing with simulation-based optimisation problems incurred by these characteristics. In order to handle multiple, conflicting optimisation objectives, the algorithm uses a Pareto approach in which the set of best trade-off solutions is searched for and presented to the user. The algorithm supports a high degree of parallelism by adopting an asynchronous master-slave parallelisation model in combination with an incremental population refinement strategy. A surrogate evaluation function is adopted in the algorithm to quickly identify promising candidate solutions and filter out poor ones. A novel technique based on inheritance is used to compensate for the uncertainties associated with the approximative surrogate evaluations. Furthermore, a novel technique for multi-objective problems that effectively reduces noise by adopting a dynamic procedure in resampling solutions is used to tackle the problem of real-world unpredictability (noise). The proposed algorithm is evaluated on benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of a production cell at Volvo Aero, while the second one concerns the optimisation of a camshaft machining line at Volvo Cars Engine. The results from the optimisations show that the algorithm finds better solutions for all the problems considered than existing, similar algorithms. The new techniques for dealing with surrogate imprecision and noise used in the algorithm are identified as key reasons for the good performance.University of Skövde Knowledge Foundation Swede

Planning and operating energy storage for maximum technical and financial benefits in electricity distribution networks

PhD ThesisThe transmission and distribution networks are facing changes in the way they will be planned, operated and maintained as a result of the rise in the deployment of Low Carbon Technologies (LCTs) on the power grid. These LCTs provide the benefits of a decarbonised grid and reduce reliance on fossil fuels and large centralised generation. As LCTs are close to the demand centres, a significant amount will be deployed in distribution networks. The distribution networks face challenges in enabling a wide deployment of LCTs because they were traditionally built for centralised generation and most are operated passively as demand patterns are well understood and power flows are unidirectional to load centres. The opposite will be the case for distribution networks with LCTs. Utilities that own and operate distribution networks such as the DNOs in the UK will face a host of problems, such as voltage and thermal excursions and power quality issues on their networks. Traditional reinforcement methods will be expensive for DNOs, so they are considering innovative solutions that provide multiple benefits; this is where Energy Storage Systems (ESS) could play a role to provide multiple technical and economic benefits across the grid from voltage and power flow management to upgrade deferral of network assets. This is due to the multifunctional nature of ESS allowing it to act as generation, transmission, demand or demand response based on requirements at any specific time based on the requirements of the stakeholder involved with the asset. ESS is technically capable of providing benefits to DNOs and other stakeholders on the electricity grid but the business case is not proven. Unless multiple benefits are aggregated, investment in ESS is challenging as they have a substantial capital cost and some components will require more frequent replacement than traditional network assets which typically last between 20 – 40+ years. As a result there is a reluctance to include them in future distribution network planning arrangements. IV Furthermore, the electricity regulatory and market design, which was set up in the time of traditional centralised generation and networks, limits investment in ESS by regulated bodies such as DNOs. The regulations and market structures also affects revenue streams and the resulting business case for ESS. This thesis investigates the feasibility of ESS in distribution networks by first studying the effect of current electricity regulatory and market practices on ESS deployment, investigating how ESS can be used under the present rules, and establishing whether there are limitations that can be reduced or removed. Secondly, short and medium term planning is carried out on model Medium Voltage distribution networks (6.6 kV) provided by the IEEE and Electricity North West Limited to establish the technical and financial viability of investing in ESS over conventional reinforcement methods by: Assessing the impact of the proliferation of LCTs in distribution networks using both deterministic and stochastic methods under different scenarios based on current developments and government policies in the UK. This stochastic evaluation considers both spatial and temporal aspects of LCTs in distribution networks with datasets obtained from real distribution network customers; Developing and applying ESS voltage and power flow management, and market control algorithms to resolve distribution network issues resulting from growing LCTs and allowing ESS to participate in the electricity spot market over a planning period up to the year 2030; Providing a framework for assessing the business case of ESS under a DNO or third-party ownership structure where technical and commercial benefits from network asset upgrade deferral, energy arbitrage, balancing market and ancillary services (frequency response and short term operating reserves), distribution and transmission system use of system benefits are evaluated; V Optimising the operation of ESS considering multiple technical and commercial objectives to establish the technical benefits and revenues that can be obtained from an ESS deployment and the trade-off of benefits that applies for differing ownership types. The simulation results show that, under the scenarios investigated, ESS can be used as a technical solution for DNOs. They show that the ESS capital costs can be offset by aggregating benefits from both technical and commercial applications in distribution networks if regulatory and market changes are made. The conclusions offer a perspective to DNOs and third parties’ considering investing in ESS on the electricity grid as it evolves towards a more active, decarbonised system.Electricity North West Limited and Scottish Power Energy Networks sponsored my stud

Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions

Evolutionary Algorithms (EA) consist of several heuristics which are able to solve optimisation tasks by imitating some aspects of natural evolution. Two widely-used EAs, namely Harmony Search (HS) and Imperialist Competitive Algorithm (ICA), are considered for improving single objective EA and Multi Objective EA (MOEA), respectively. HS is popular because of its speed and ICA has the ability for escaping local optima, which is an important criterion for a MOEA. In contrast, both algorithms have suffered some shortages. The HS algorithm could be trapped in local optima if its parameters are not tuned properly. This shortage causes low convergence rate and high computational time. In ICA, there is big obstacle that impedes ICA from becoming MOEA. ICA cannot be matched with crowded distance method which produces qualitative value for MOEAs, while ICA needs quantitative value to determine power of each solution. This research proposes a learnable EA, named learning automata harmony search (LAHS). The EA employs a learning automata (LA) based approach to ensure that HS parameters are learnable. This research also proposes a new MOEA based on ICA and Sigma method, named Sigma Imperialist Competitive Algorithm (SICA). Sigma method provides a mechanism to measure the solutions power based on their quantity value. The proposed LAHS and SICA algorithms are tested on wellknown single objective and multi objective benchmark, respectively. Both LAHS and MOICA show improvements in convergence rate and computational time in comparison to the well-known single EAs and MOEAs

Grid flexibility by electrifying energy systems for sustainable aviation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDecarbonisation of aviation goals set by Flightpath 2050 Europe’s Vision for Aviation requires that the airports become emission-free by 2050. This thesis original contribution to knowledge is to explore the incorporation of aviation electrification technologies, including electric aircraft (EA), electrified ground support equipment (GSE), and airport parking electric vehicles (EVs), into power systems, evaluating their influence on grid infrastructure and operations, as well as their potential to support the grid operation. A comprehensive review of aviation electrification technologies revealed a research gap in the integration of these technologies into the power systems. The thesis contributes to electricity network infrastructure planning for electrification of aviation and airport-based distributed energy resources (DER) that provide ancillary services to the power grid. A multi-objective airport microgrid planning framework is developed, comparing EA charging strategies and revealing that battery swap performs better. Vehicle-to-grid (V2G) strategy with parking EVs improves the microgrid's performance. A techno-economic assessment of wireless charging systems for electric airport shuttle buses shows better economic performance than conventional buses and other charging options. A novel Aviation-to-Grid (A2G) flexibility concept provides frequency response services to the GB power system using EA battery charging systems, with typical A2G service capacity showing significant variation across eight UK airports. A deep reinforcement learning (DRL)-based A2G dispatch approach evaluates the impact of EA charger capacity on energy dispatch results, with higher capacities leading to higher revenue and lower operation costs. To summarise, this thesis addresses the research gaps in integrating aviation electrification technologies into power systems, offering valuable insights for airport operators aiming to decarbonise air transport activities through the adoption of these technologies. The study also provides an understanding of the impacts on grid operators in terms of infrastructure planning and operations. This comprehensive approach ensures a cohesive understanding of the challenges and opportunities presented by aviation electrification and its integration into power systems

Planning optimal load distribution and maximum renewable energy from wind power on a radial distribution system

Doctor of PhilosophyElectrical and Computer EngineeringRuth D. MillerOptimizing renewable distributed generation in distribution systems has gained popularity with changes in federal energy policies. Various studies have been reported in this regard and most of the studies are based on optimum wind and/or solar generation planning in distribution system using various optimization techniques such as analytical, numerical, and heuristic. However, characteristics such as high energy density, relatively lower footprint of land, availability, and local reactive power compensation ability, have gained increased popularity for optimizing distributed wind generation (DWG) in distribution systems. This research investigated optimum distributed generation planning (ODGP) using two primary optimization techniques: analytical and heuristic. In first part of the research, an analytical optimization method called “Combined Electrical Topology (CET)” was proposed in order to minimize the impact of intentional structural changes in distribution system topology, in distributed generation/ DWG placement. Even though it is still rare, DWG could be maximized to supply base power demand of three-phase unbalanced radial distribution system, combined with distributed battery energy storage systems (BESS). In second part of this research the usage of DWG/BESS as base power generation, and to extend the ability to sustain the system in a power grid failure for a maximum of 1.5 hours was studied. IEEE 37-node, three-phase unbalanced radial distribution system was used as the test system to optimize wind turbines and sodium sulfide (NaS) battery units with respect to network real power losses, system voltage profile, DWG/BESS availability and present value of cost savings. In addition, DWG’s ability to supply local reactive power in distribution system was also investigated. Model results suggested that DWG/NaS could supply base power demand of a threephase unbalanced radial distribution system. In addition, DWG/NaS were able to sustain power demand of a three-phase unbalanced distribution system for 1.5 hours in the event of a power grid failure

A multi-objective performance optimisation framework for video coding

Digital video technologies have become an essential part of the way visual information is created, consumed and communicated. However, due to the unprecedented growth of digital video technologies, competition for bandwidth resources has become fierce. This has highlighted a critical need for optimising the performance of video encoders. However, there is a dual optimisation problem, wherein, the objective is to reduce the buffer and memory requirements while maintaining the quality of the encoded video. Additionally, through the analysis of existing video compression techniques, it was found that the operation of video encoders requires the optimisation of numerous decision parameters to achieve the best trade-offs between factors that affect visual quality; given the resource limitations arising from operational constraints such as memory and complexity. The research in this thesis has focused on optimising the performance of the H.264/AVC video encoder, a process that involved finding solutions for multiple conflicting objectives. As part of this research, an automated tool for optimising video compression to achieve an optimal trade-off between bit rate and visual quality, given maximum allowed memory and computational complexity constraints, within a diverse range of scene environments, has been developed. Moreover, the evaluation of this optimisation framework has highlighted the effectiveness of the developed solution

Multi-Criteria Performance Evaluation and Control in Power and Energy Systems

The role of intuition and human preferences are often overlooked in autonomous control of power and energy systems. However, the growing operational diversity of many systems such as microgrids, electric/hybrid-electric vehicles and maritime vessels has created a need for more flexible control and optimization methods. In order to develop such flexible control methods, the role of human decision makers and their desired performance metrics must be studied in power and energy systems. This dissertation investigates the concept of multi-criteria decision making as a gateway to integrate human decision makers and their opinions into complex mathematical control laws. There are two major steps this research takes to algorithmically integrate human preferences into control environments: MetaMetric (MM) performance benchmark: considering the interrelations of mathematical and psychological convergence, and the potential conflict of opinion between the control designer and end-user, a novel holistic performance benchmark, denoted as MM, is developed to evaluate control performance in real-time. MM uses sensor measurements and implicit human opinions to construct a unique criterion that benchmarks the system\u27s performance characteristics. MM decision support system (DSS): the concept of MM is incorporated into multi-objective evolutionary optimization algorithms as their DSS. The DSS\u27s role is to guide and sort the optimization decisions such that they reflect the best outcome desired by the human decision-maker and mathematical considerations. A diverse set of case studies including a ship power system, a terrestrial power system, and a vehicular traction system are used to validate the approaches proposed in this work. Additionally, the MM DSS is designed in a modular way such that it is not specific to any underlying evolutionary optimization algorithm

An Efficient Bi-objective Genetic Algorithm for the Single Batch-Processing Machine Scheduling Problem with Sequence Dependent Family Setup Time and Non-identical Job Sizes

This paper considers the problem of minimizing make-span and maximum tardiness simultaneously for scheduling jobs under non-identical job sizes, dynamic job arrivals, incompatible job families,and sequence-dependentfamily setup time on the single batch- processor, where split size of jobs is allowed between batches. At first, a new Mixed Integer Linear Programming (MILP) model is proposed for this problem; then, it is solved by -constraint method.Since this problem is NP-hard, a bi-objective genetic algorithm (BOGA) is offered for real-sized problems. The efficiency of the proposed BOGA is evaluated to be comparedwith many test problemsby -constraint method based on performance measures. The results show that the proposed BOGAis found to be more efficient and faster than the -constraint method in generating Pareto fronts in most cases

Genetic Algorithms in Stochastic Optimization and Applications in Power Electronics

Genetic Algorithms (GAs) are widely used in multiple fields, ranging from mathematics, physics, to engineering fields, computational science, bioinformatics, manufacturing, economics, etc. The stochastic optimization problems are important in power electronics and control systems, and most designs require choosing optimum parameters to ensure maximum control effect or minimum noise impact; however, they are difficult to solve using the exhaustive searching method, especially when the search domain conveys a large area or is infinite. Instead, GAs can be applied to solve those problems. And efficient computing budget allocation technique for allocating the samples in GAs is necessary because the real-life problems with noise are often difficult to evaluate and require significant computation effort. A single objective GA is proposed in which computing budget allocation techniques are integrated directly into the selection operator rather than being used during fitness evaluation. This allows fitness evaluations to be allocated towards specific individuals for whom the algorithm requires more information, and this selection-integrated method is shown to be more accurate for the same computing budget than the existing evaluation-integrated methods on several test problems. A combination of studies is performed on a multi-objective GA that compares integration of different computing budget allocation methods into either the evaluation or the environmental selection steps. These comparisons are performed on stochastic problems derived from benchmark multi-objective optimization problems and consider varying levels of noise. The algorithms are compared regarding both proximity to and coverage of the true Pareto-optimal front, and sufficient studies are performed to allow statistically significant conclusions to be drawn. Finally, the multi-objective GA with selection integrated sampling technique is applied to solve a multi-objective stochastic optimization problem in a grid connected photovoltaic inverter system with noise injected from both the solar power input and the utility grid