Optimal Power Flow Formulations for Coordinating Controllable Loads in Distribution Grids: An Overview of Constraint Handling and Hyper Parameter Tuning When Using Metaheuristic Solvers

In the future, higher penetrations of electrical loads in low-voltage distribution grids are to be expected. To prevent grid overload, a possible solution is coordination of controllable loads. Typical examples might be charging of electric vehicles or operation of electric heat pumps. Such loads ar...

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Bibliographic Details
Main Authors: André Ulrich, Ingo Stadler, Eberhard Waffenschmidt
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Electricity
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Online Access:https://www.mdpi.com/2673-4826/6/2/31
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Summary:In the future, higher penetrations of electrical loads in low-voltage distribution grids are to be expected. To prevent grid overload, a possible solution is coordination of controllable loads. Typical examples might be charging of electric vehicles or operation of electric heat pumps. Such loads are associated with specific requirements that should be fulfilled if possible. However, at the same time, a safe grid operation must be ensured. To this end, a corresponding optimal power flow optimization problem might be formulated and solved. This article gives a comprehensive review of the state of the art of optimal power flow formulations. It is investigated which constraint handling techniques are used and how hyper parameters are tuned when solving optimal power flow problems using metaheuristic solvers and how controllable loads and fluctuating renewable production are incorporated into optimal power flow formulations. Therefore, the literature is reviewed for pre-defined criteria. The results show possible gaps to be filled with future research: extended optimal power flow formulations to account for controllable loads, investigation of effects of choosing constraint handling techniques or hyper parameter tuning on the performance of the metaheuristic solver and automated methods for determining optimal values for hyper parameters.
ISSN:2673-4826