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

Metaheurísticas aplicadas ao problema de despacho econômico de energia elétrica

Resumo: Nesta dissertação é abordado um dos problemas de otimização em sistemas elétricos de potência, mais especificamente, o problema de despacho econômico de energia elétrica. Este é um problema bem estabelecido e conhecido em estudos de sistemas elétricos. Suas formulações simplificadas são facilmente resolvidas pelo método de otimização de Newton e suas variantes como o método dos pontos interiores primal-dual. Entretanto, variações destes problemas foram criadas com o intuito de tornar a modelagem mais realista, i.e., mais próxima das condições reais de operação dos sistemas modelados e portanto, mais complexa. Estas variações incluem taxas limites de rampa, zonas de operação proibidas, reserva de giro e funções de custo não-suaves, criando um espaço de busca altamente nãolinear, descontínuo, não-convexo e fortemente multimodal, onde o método de otimização de Newton falha em convergir. Por outro lado, métodos estocásticos de otimização, as metaheurísticas, livres de derivadas, são capazes de incorporar restrições e também de acomodar características nas funções de custo sem impedimentos de complexidade matemática, embora não possuam uma garantia de solução ótima. O objetivo principal desta dissertação é o levantamento de desempenho de metaheurísticas, através da aplicação e comparação em problemas de despacho econômico. Para isto, foi necessária a implementação de metaheurísticas como: algoritmo genético, evolução diferencial, otimização por enxame de partículas, algoritmo de seleção clonal, algoritmo de otimização por fogos de artifício, otimização big bang - big crunch, covariance matrix adaptation - evolution strategy, busca incremental baseada em população e simulated annealing. Estas metaheurísticas foram aplicadas a nove estudos de caso de despacho econômico de energia elétrica com efeito de ponto de válvula conhecidos na literatura, com o objetivo de otimização do custo de combustível dos geradores. A análise dos resultados obtidos compara o desempenho destes através de métricas como tempo de avaliação e melhor média obtida em diversos experimentos de otimização. Para validar estes resultados e verificar a significância de diferença entre os mesmos, foi utilizado o teste estatístico de Wilcoxon, que testa a hipótese nula que dados de duas amostras são amostras independentes de distribuições contínuas idênticas. Os resultados obtidos mostram que o Covariance Matrix Adaptation - Evolution Strategy e o Differential Evolution obtém os melhores resultados na otimização de problemas do despacho econômico. Dois pequenos experimentos foram adicionados a dissertação, um mostrando bons resultados na utilização de um gerador de folga variável e o outro a vantagem de processar avaliações da função objetivo no processador gráfico

A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering

Since its emergence, quantum computing has enabled a wide spectrum of new possibilities and advantages, including its efficiency in accelerating computational processes exponentially. This has directed much research towards completely novel ways of solving a wide variety of engineering problems, especially through describing quantum versions of many mathematical tools such as Fourier and Laplace transforms, differential equations, systems of linear equations, and optimization techniques, among others. Exploration and development in this direction will revolutionize the world of engineering. In this manuscript, we review the state of the art of these emerging techniques from the perspective of quantum computer development and performance optimization, with a focus on the most common mathematical tools that support engineering applications. This review focuses on the application of these mathematical tools to quantum computer development and performance improvement/optimization. It also identifies the challenges and limitations related to the exploitation of quantum computing and outlines the main opportunities for future contributions. This review aims at offering a valuable reference for researchers in fields of engineering that are likely to turn to quantum computing for solutions. Doi: 10.28991/ESJ-2023-07-01-020 Full Text: PD

Optimal Power Flow using Fuzzy-Firefly Algorithm

Development of Metaheuristic Algorithm in engineering problems grows really fast. This algorithm is commonly used in optimization problems. One of the metaheuristic algorithms is called Firefly Algorithm (FA). Firefly Algorithm is a nature-inspired algorithm that is derived from the characteristic of fireflies. Firefly Algorithm can be used to solve optimal power flow (OPF) problem in power system. To get the best performance, firefly algorithm can be combined with fuzzy logic. This research presents the application of hybrid fuzzy logic and firefly algorithm to solve optimal power flow. The simulation is done using the MATLAB environment. The simulations show that by using the fuzzy-firefly algorithm, the power losses, as well as the total cost, can be reduced significantly

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Modified rice husk and activated carbon filters for the removal of organics and heavy metals in water

Discharge of untreated industrial effluents containing heavy metals and organics is hazardous to the environment because of their toxicity and persistent nature. At the same time, agricultural waste poses disposal challenges, which can be converted into value added products like adsorbents that could serve as tools for contaminants abatement. Previous findings proved that, adsorption was a sustainable, economical and lucrative separation technique for the removal of such contaminants. This thesis presents the fabrication of a filter for the removal of organics and heavy metals in water which was prepared from treated rice husk and modified activated carbon (AC). The analysis of AC via Brunauer-Emmett-Teller (BET) surface area and scanning electron microscopy evidenced porosity of 707 m2/g as surface and a pore volume of 0.31 cm3/g. The elemental and thermogravimetric analysis proved that AC contain 48. 7% carbon, while the Fourier transform infrared spectroscopy shows that the surface contains functional groups such as O-H, C=C, C-O, C-O-C and C-H. The experimental results were fitted with fixed-bed adsorption models to understand the adsorbate-adsorbent relationship. Fixed-bed adsorption studies show that, the highest adsorption capacity of 248.2 mg/g and 234.12 mg/g for BPA and phenol respectively was obtained at 250 mg/L concentration and 9 mL/min flow rate. The results also revealed 73 % and 87 % as the highest removal capacity for heavy metal Pb and Cd respectively at 20 mg/L concentration and 9 mL/min flow rate. For sustainability, regeneration of the spent AC was carried out in a microwave which showed 75% yield after five cycles, while the rice husk was eluted with 0.lM hydrogen chloride and 37.8% efficiency was achieved after three successive cycles. The UV lamp incorporated in the filter shows total inactivation of E. coli after 7 minutes

A novel approach for coordinated design of TCSC controller and PSS for improving dynamic stability in power systems

The purpose of this article is to present a novel strategy for the coordinated design of the Thyristor Controlled Series Compensator (TCSC) controller and the Power System Stabilizer (PSS). A time domain objective function that is based on an optimization problem has been defined. This objective function takes into account not only the influence that disturbances have on the mechanical power, but also, and this is more accurately the case, the impact that disturbances have on the reference voltage. When the objective function is minimized, potential disturbances are quickly mitigated, and the deviation of the speed of the generator's rotor is limited; as a result, the system's stability is ultimately improved. Particle Swarm Optimization (PSO) and the Shuffled Frog Leaping Algorithm are both components of a composite strategy that is utilized in the process of determining the optimal controller parameters. (SFLA). An independent controller design as well as a collaborative controller design utilizing PSS and TCSC are developed, which enables a direct evaluation of the functions performed by each. The presentation of the eigenvalue analysis and the findings of the nonlinear simulation can help to provide a better understanding of the efficacy of the outcomes. The findings indicate that the coordinated design is able to successfully damp low-frequency oscillations that are caused by a variety of disturbances, such as changes in the mechanical power input and the setting of the reference voltage, and significantly enhance system stability in power systems that are connected weekly

Harmony Search Method: Theory and Applications

The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm, which has been employed to cope with numerous challenging tasks during the past decade. In this paper, the essential theory and applications of the HS algorithm are first described and reviewed. Several typical variants of the original HS are next briefly explained. As an example of case study, a modified HS method inspired by the idea of Pareto-dominance-based ranking is also presented. It is further applied to handle a practical wind generator optimal design problem