The aggregation of operational active and reactive power flexibilities as the feasible operation region (FOR) is a main component of a hierarchical multi-voltage-level grid control as well as the cooperation of transmission and distribution system operators at vertical system interconnections. This article presents a new optimization-based aggregation approach, based on a modified particle swarm optimization (PSO) and compares it to non-linear and linear programming. The approach is to combine the advantages of stochastic and optimization-based methods to achieve an appropriate aggregation of flexibilities while obtaining additional meta information during the iterative solution process. The general principles for sampling an FOR are introduced in a survey of aggregation methods from the literature and the adaptation of the classic optimal power flow problem. The investigations are based on simulations of the Cigré medium voltage test system and are divided into three parts. The improvement of the classic PSO algorithm regarding the determination of the FOR are presented. The most suitable of four sampling strategies from the literature is identified and selected for the comparison of the optimization methods. The analysis of the results reveals a better performance of the modified PSO in sampling the FOR compared to the other optimization methods.
This paper proposes a standardized simulation environment to evaluate current and to design future multi-level grid control strategies in terms of a safe and reliable operation in future converter-dominated grids. For this, the first step is to develop a taxonomy for the uniform description of multi-level grid control strategies, to define relevant design options and to derive the relevant evaluation and comparison criteria. Furthermore, aspects of new ICT-methods (e. g., machine learning decoders for aggregated flexibility description) are presented, which can help to tap the decentral flexibility potentials in future grid control strategies. Lastly, the major converter-related aspects are investigated. In particular, the stability of converter clusters in large-scale energy systems is analysed and new monitoring possibilities utilizing converter systems will be introduced.
In order to prevent conflicting or counteracting use of flexibility options, the coordination between distribution system operator and transmission system operator has to be strengthened. For this purpose, methods for the standardized description and identification of the aggregated flexibility potential of distribution grids are developed. Approaches for identifying the feasible operation region (FOR) of distribution grids can be categorized into two main classes: Random sampling/stochastic approaches and optimization-based approaches. While the former have the advantage of working in real-world scenarios where no full grid models exist, when relying on naive sampling strategies, they suffer from poor coverage of the edges of the FOR due to convoluted distributions. In this paper, we tackle the problem from two different sides. First, we present a random sampling approach which mitigates the convolution problem by drawing sample values from a multivariate Dirichlet distribution. Second, we come up with a hybrid approach which solves the underlying optimal power flow problems of the optimization-based approach by means of a stochastic evolutionary optimization algorithm codenamed REvol. By means of synthetic feeders, we compare the two proposed FOR identification methods with regard to how well the FOR is covered and number of power flow calculations required.
With an increasing share of distributed energy resources (DER) in the electrical energy system it is becoming more and more important that DER not only take part in active power provision but are also involved in the provision of ancillary services like frequency control or voltage regulation. Due to the large number of DER connected to the lower voltage levels via power-electronic converters the distribution grid evolves from a formerly mostly passive to a highly active system with a high number of actuating variables distributed over multiple stakeholders. The coordination and optimization of this kind of distribution grid requires new control and optimization approaches, not only with regard to the distribution grid itself, but also with regard to the coordination with the overlying transmission grid. This abstract presents first ideas of a PhD-project that aims to use machine learning surrogate models and decoder functions for agent-based dynamic optimization of local controller configurations particularly with regard to voltage regulation. Decoder functions derived from machine learning surrogate models abstract optimization problems from technical system specifications and allow for constraint-free optimization with standard optimization heuristics such as evolutionary optimization methods.
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