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

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

Bridging the gap between infrastructure capacity allocation and market-oriented railway: an algorithmic approach

The European Commission initiated the process of liberalization and introducing competition in the European railway sector more than twenty-five years ago. Despite the liberalization of the railway sector, train paths are currently administratively allocated in all EU countries using the train service priority criterion, which may not treat all train operators equally. This is especially evident in those network sections where demand exceeds the available capacity. In these situations, economic theory suggests the implementation of a market-based mechanism for allocation of capacity, such as auctions. However, due to its incompatibilities with priority criteria in the process of the capacity allocation, it is necessary to develop a new procedure to support the implementation of an auction. In this paper, the proposed algorithm fills the technological gap between train timetable design and train operator requests. The new algorithm for decentralized capacity allocation is the result of a multidimensional approach, which encompasses setting new relations between train operators and the infrastructure manager, train timetable drafting and resolving the conflicting request. In addition, the algorithm provides a feasible solution ensuring equal treatment of train operators and efficient allocation, in order to foster the development of the competition in the European rail market. First published online 10 September 201