Our society currently faces complex ecological, social, and economic challenges that require collaborative and interdisciplinary approaches. Although interest in interdisciplinary research is growing, it is still confronted with significant obstacles stemming from difficulties in establishing a common understanding. While others have evaluated interdisciplinary in a top-down funding-oriented ex ante manner or a result-oriented ex post manner, this focus paper intends to create a framework that enables the measurement of interdisciplinary understanding within a group. Therefore, using the case of the interdisciplinary Boysen-TU Dresden Research Group, a framework for measuring interdisciplinary understanding (MIU) is presented. The basis of the framework is the development of dimensions based on the specific composition and requirements of the interdisciplinary group. With this framework, two main contributions are provided. First, interdisciplinary understanding within a research group can be measured with respect to the defined dimensions. Through discussions on the differences of the understanding of projects over the dimensions, it is possible to establish a common understanding of projects. Second, the interdisciplinarity within a group or interdisciplinary distance between two specific research projects can be evaluated in order to identify possible difficulties in understanding, as well as recognizing potentials for interdisciplinary research. The MIU framework and the dimensions established in this paper can be adapted and transferred to any interdisciplinary research group to improve joint understanding of researchers and enable them to tackle complex techno-societal problems of the future.
Wind energy is one of the most competitive and efficient energy sources and, as a result, usage of it is continuously increasing worldwide. Although wind energy is relatively cheaper among the other renewables, it is also variable and uncertain. Therefore, the high penetration of wind energy causes several technical problems related to security, stability, power quality and operation of power
systems. Transmission and Distribution System Operators (TSOs and DSOs) are issued to develop required grid codes for wind farms (WFs) considering a number of parameters to overcome those difficulties. This paper represents a Real-Time Embedded Particle Swarm Optimization (RTEPSO) to define optimal settings of WFs to meet reactive power requirements of a typical German 110 kV-sub-transmission grid at any time.In other words, the proposed approach searches the exact operation points of WFs to calculate required amount of absorbed/generated reactive power at Connection Point (CP) under consideration of Consumer Counting Arrow System.
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