Optimization techniques are widely applied in the power system planning and operation to achieve more efficient and reliable power supply. With the introduction of new technologies, the complexity of today’s power system increased significantly. Intelligent optimization techniques, such as Particle Swarm Optimization (PSO), can efficiently deal with the new challenges compared to conventional optimization techniques. This thesis presents applications of discrete PSO in two specific environments. The first one is for day-ahead optimal scheduling of the reconfigurable gird with distributed energy resources. The second one is a two-step method for rapid reconfiguration of shipboard power system. Effective techniques, such as graph theory, optimal power flow and heuristic mutation, are employed to make the PSO algorithm more suitable to application environments and achieve better performance.

Optimising task allocation in networked systems helps in utilising available resources. When working with unstable and heterogeneous networks, task scheduling can be used to optimise task completion time, energy efficiency and system reliability. The dynamic nature of networks also means that the optimal schedule is subject to change over time. The heterogeneity and variability in network design also complicate the translation of setups from one network to another. Discrete Particle Swarm Optimisation (DPSO) is a metaheuristic that can be used to find solutions to task scheduling. This thesis will explore how DPSO can be used to optimise job scheduling in an unstable network. The purpose is to find solutions for networks like the ones used on trains. This in turn is done to facilitate trajectory planning calculations. Through the use of an artificial neural network, we estimate job scheduling costs. These costs are then used by our DPSO meta heuristic to explore a solution space of potential scheduling. The results focus on the optimisation of batch sizes in relation to network reliability and latency. We simulate a series of unstable and heterogeneous networks and compare completion time. The baseline comparison is the case where scheduling is done by evenly distributing jobs at fixed sizes. The performance of the different approaches is then analysed with regards to usability in real-life scenarios on vehicles. Our results show a noticeable increase in performance within a wide range of network set-ups. This is at the cost of long search times for the DPSO algorithm. We conclude that under the right circumstances, the method can be used to significantly speed up distributed calculations at the cost of requiring significant ahead of time calculations. We recommend future explorations into DPSO starting states to speed up convergence as well as benchmarks of real-life performance. / Optimering av arbetsfördelning i nätverk kan öka användandet av tillgängliga resurser. I instabila heterogena nätverk kan schemaläggning användas för att optimera beräkningstid, energieffektivitet och systemstabilitet. Då nätverk består av sammankopplade resurser innebär det också att vad som är ett optimalt schema kan komma att ändras över tid. Bredden av nätverkskonfigurationer gör också att det kan vara svårt att överföra och applicera ett schema från en konfiguration till en annan. Diskret Particle Swarm Optimisation (DPSO) är en meta heuristisk metod som kan användas för att ta fram lösningar till schemaläggningsproblem. Den här uppsatsen kommer utforska hur DPSO kan användas för att optimera schemaläggning för instabila nätverk. Syftet är att hitta en lösning för nätverk under liknande begränsningar som de som återfinns på tåg. Detta för att i sin tur facilitera planerandet av optimala banor. Genom användandet av ett artificiellt neuralt nätverk (ANN) uppskattar vi schemaläggningskostnaden. Denna kostnad används sedan av DPSO heuristiken för att utforska en lösningsrymd med potentiella scheman. Våra resultat fokuserar på optimeringen av grupperingsstorleken av distribuerade problem i relation till robusthet och letens. Vi simulerar ett flertal instabila och heterogena nätverk och jämför deras prestanda. Utgångspunkten för jämförelsen är schemaläggning där uppgifter distribueras jämnt i bestämda gruperingsstorlekar. Prestandan analyseras sedan i relation till användbarheten i verkliga scenarion. Våra resultat visar på en signifikant ökning i prestanda inom ett brett spann av nätverkskonfigurationer. Det här är på bekostnad av långa söktider för DPSO algoritmen. Vår slutsats är att under rätt förutsättningar kan metoden användas för att snabba upp distribuerade beräkningar förutsatt att beräkningarna för schemaläggningen görs i förväg. Vi rekommenderar vidare utforskande av DPSO algoritmens parametrar för att snabba upp konvergens, samt undersökande av algoritmens prestanda i verkliga miljöer.

Este trabalho propõe uma metodologia baseada em técnicas inteligentes capaz de fornecer uma coordenação otimizada de relés direcionais de sobrecorrente instalados em sistemas de energia elétrica. O problema é modelado como um caso de programação não linear inteira mista, em que os relés permitem ajustes discretizados de múltiplos de tempo e/ou múltiplos de corrente. A solução do problema de otimização correspondente é obtida através de uma metaheurística nomeada como Discrete Particle Swarm Optimization. Na literatura técnico-científica esse problema geralmente é linearizado e aplicam-se arredondamentos das variáveis discretas. Na metodologia proposta, as variáveis discretas são tratadas adequadamente para utilização na metaheurística e são apresentados os resultados que foram comparados com os obtidos pelo modelo clássico de otimização implementado no General Algebraic Modeling System (GAMS). Tendo em vista os aspectos observados, o método permite ao engenheiro de proteção ter um subsídio adicional na tarefa da coordenação dos relés direcionais de sobrecorrente, disponibilizando uma técnica eficaz e de fácil aplicabilidade ao sistema elétrico a ser protegido, independentemente da topologia e condição operacional. / This work proposes a methodology that based on intelligent technique to obtain an optimized coordination of directional overcurrent relays in electric power systems. The problem is modeled as a mixed integer nonlinear problem, because the relays allows a discrete setting of time and/or current multipliers. The solution of the proposed optimization problem is obtained from the proposed metaheuristic named as Discrete Particle Swarm Optimization. In scientific and technical literature this problem is usually linearized and discrete variables are rounded off. In the proposed method, the discrete variables are modeled adequately in the metaheuristic and the results are compared to the classical optimization solvers implemented in General Algebraic Modeling System (GAMS). The method provides an important method for helping the engineers in to coordinate directional overcurrent relays in a very optimized way. It has high potential for the application to realistic systems, regardless of topology and operating condition.

Este trabalho propõe uma metodologia baseada em técnicas inteligentes capaz de fornecer uma coordenação otimizada de relés direcionais de sobrecorrente instalados em sistemas de energia elétrica. O problema é modelado como um caso de programação não linear inteira mista, em que os relés permitem ajustes discretizados de múltiplos de tempo e/ou múltiplos de corrente. A solução do problema de otimização correspondente é obtida através de uma metaheurística nomeada como Discrete Particle Swarm Optimization. Na literatura técnico-científica esse problema geralmente é linearizado e aplicam-se arredondamentos das variáveis discretas. Na metodologia proposta, as variáveis discretas são tratadas adequadamente para utilização na metaheurística e são apresentados os resultados que foram comparados com os obtidos pelo modelo clássico de otimização implementado no General Algebraic Modeling System (GAMS). Tendo em vista os aspectos observados, o método permite ao engenheiro de proteção ter um subsídio adicional na tarefa da coordenação dos relés direcionais de sobrecorrente, disponibilizando uma técnica eficaz e de fácil aplicabilidade ao sistema elétrico a ser protegido, independentemente da topologia e condição operacional. / This work proposes a methodology that based on intelligent technique to obtain an optimized coordination of directional overcurrent relays in electric power systems. The problem is modeled as a mixed integer nonlinear problem, because the relays allows a discrete setting of time and/or current multipliers. The solution of the proposed optimization problem is obtained from the proposed metaheuristic named as Discrete Particle Swarm Optimization. In scientific and technical literature this problem is usually linearized and discrete variables are rounded off. In the proposed method, the discrete variables are modeled adequately in the metaheuristic and the results are compared to the classical optimization solvers implemented in General Algebraic Modeling System (GAMS). The method provides an important method for helping the engineers in to coordinate directional overcurrent relays in a very optimized way. It has high potential for the application to realistic systems, regardless of topology and operating condition.