Multi-level evolutionary algorithms resource allocation utilizing model-based systems engineering

This research presents an innovative approach to solve the resource allocation problems using Multi-level Evolutionary Algorithms. Evolutionary Algorithms are used to solve resource allocation problems in different domains and their results are then incorporated into a higher level system solution using another Evolutionary Algorithm to solve base camp planning problems currently faced by the U.S. Department of Defense. Two models are introduced to solve two domain specific models: a logistics model and a power model. The logistic model evaluates routes for logistics vehicles on a daily basis with a goal of reducing fuel usage by delivery trucks. The evaluation includes distance traveled and other constraints such as available resource levels and priority of refilling. The Power model incorporates an open source electrical distribution simulator to evaluate the placement of structures and generators on a map to reduce fuel usage. These models are used as the fitness function for two separate Evolutionary Algorithms to find solutions that reduce fuel consumption within the individual domains. A multi-level Evolutionary Algorithm is then presented, where the two Evolutionary Algorithms share information with a higher level Evolutionary Algorithm that combines the results to account for problem complexity from the interfacing of these systems. The results of using these methods on 5 different base camp sizes show that the techniques provide a considerable reduction of fuel consumption. While the Evolutionary Algorithms show significant improvement over the current methods, the multi-level Evolutionary Algorithm shows better performance than using individual Evolutionary Algorithms, with the results showing a 19.25 % decrease in fuel consumption using the multi-level Evolutionary Algorithm --Abstract, page iii

Comparison of different redispatch optimization strategies

In den letzten Jahren hat die Häufigkeit des Auftretens von Engpässen in den elektrischen Übertragungsnetzen stark zugenommen, weil die Stromnetze ursprünglich für die aktu-elle Energiemenge und deren starke Schwankung nicht ausgelegt sind. Darüber hinaus bringen die weiter steigende Nutzung der erneuerbaren dezentralen Energiequellen, die zunehmende Netzkomplexität, die Abschaltung konventioneller Kraftwerke, Progno-sefehler und der starke Wettbewerb auf dem Strommarkt die elektrischen Netze immer öfter an ihre Übertragungsgrenzen. Daher ist die Gefahr von Engpässen permanent ge-stiegen, insbesondere in Mitteleuropa. Wenn ein Engpass im Stromnetz entstanden ist, sind die Übertragungsnetzbetreiber ver-pflichtet, eine geeignete Abhilfemaßnahme so schnell wie möglich anzuwenden, um ihn zu beseitigen, z. B. durch den deutschlandweit verbreiteten Redispatch. Allerdings kann diese Gegenmaßnahme hohe Kosten für die Übertragungsnetzbetreiber verursachen, die zum Schluss die Stromverbraucher zahlen müssen. Deswegen ist die Realisierung eines kosten- und technisch effizienten Redispatches ein sehr wichtiges Thema des Netzbe-triebs geworden. Daher ist das Hauptziel dieser Arbeit, unterschiedliche Möglichkeiten und Ansätze für eine kostengünstige Redispatchumsetzung bei Entstehung der Engpässe zu entwickeln. Dafür werden verschiedene numerische und metaheuristische Optimierungsmethoden hinsichtlich ihrer Komplexität, Effizienz, Verlässlichkeit, Detaillierung und Rechenzeit verglichen und durch ein kleines Netzmodell sowie durch ein vereinfachtes ENTSO-E-Netzmodell verifiziert. Schließlich werden die Übertragungsnetzbetreiber durch die Erkenntnisse in dieser Arbeit in die Lage versetzt, ihre Stromnetze effizienter zu betreiben, in dem der Redispatchpro-zess verbessert wird. Dabei werden die hohen Redispatchkosten, insbesondere in Deutschland, deutlich gesenkt.In the recent years, line congestions in the electric transmission networks occur quite fre-quently due to the power grids were not originally designed for the current amount of energy and its strong fluctuation. Furthermore, the increasing utilization of renewable distributed energy sources, growth of the network complexity, reduction of the conven-tional power plant utilization, forecast errors and strong electricity market competition frequently bring the power grids to their transmission limits as well. Therefore, the risk of congestions has permanently increased, especially in central Europe. If a line congestion occurs in the electric network, the transmission system operator has to apply a suitable remedial action to overcome the problem as fast as possible, e.g by utilization of redispatch, which is very common in Germany. However, this measure can cause high costs for the transmission network operators. For this reason, the realization of an economically efficient and optimal redispatching has become very important issue in the power system operation. The main goal of this work is a consideration and development of various possibilities and methods for realization of a technically sound and cost-efficient redispatch in case of network congestions. Therefore, different numerical and metaheuristic optimization tech-niques are implemented, compared with respect to their complexity, efficiency, reliabil-ity, simulation time etc. and verified through a small test grid and simplified ENTSO-E network model. Furthermore, it is shown which technical and economic aspects of redispatching have a major influence on its realization and should always be taken into account or can be ne-glected while solving the redispatch optimization problem. Here, different approaches of the network sensitivity analysis are evaluated and compared as well. Finally, the transmission network operators can use the knowledge and results of this work to improve the current redispatch realization in their power grids, and thus to reduce the redispatch costs, which are especially high in Germany

Short Term Unit Commitment Using Genetic Algorithms

Unit commitment is a complex decision-making process because of multiple constraints which must not be violated while finding the optimal or a near-optimal commitment schedule. This paper discusses the application of genetic algorithms for determining short term commitment order of thermal units in power generation. The objective of the optimal commitment is to determine the on/off states of the units in the system to meet the load demand and spinning reserve requirement at each time period such that the overall cost of generation is minimum, while satisfying various operational constraints. The paper examines the feasibility of using genetic algorithms, and reports preliminary results in determining a near-optimal commitment order of thermal units in a studied power system. A version of this report will appear in the Proceedings of 5th IEEE International Conference on Tools with Artificial Intelligence, November 8-11, 1993, Boston, USA. 1 Introduction In power industries, fuel e..