On the one hand, several developments make the operation of the distribution grids more and more complex. On the other hand developments in energy storage and the availability of loads which are not time critical bring up possibilities to provide more flexibility in the grid and to use the available system more efficiently. In this paper, these and other advantages of as well distributed energy storage as load management of not time critical loads are discussed. An approach is described to analyse the available capacity in the grids, to investigate how this capacity can be used by applying storage or load management and to compare the benefits of storage and load management. This approach is illustrated using a real medium voltage network and measured data. It shows the part of the capacity in the distribution grids which is now unused, but can be made available by applying storage or load management of non-critical loads. The size of the storage and the non-critical loads which are needed to use this capacity are determined. The characteristics of future loads, the need to support integration of distributed generation and the desired level of reliability of supply are factors that determine the size of the storage and how storage or load management can be applied best.Index Terms-Energy storage, load management, power distribution.
This paper describes the need and requirements for advanced decision support tools in future network planning from a distribution network operator perspective. The existing tools will no longer be satisfactory for future application due to present developments in the electricity sector that increase uncertainty. Advanced decision support tools can help network planners to select among the numerous alternatives for the optimal future distribution network layout and expansion schedule. This in contrast to the existing method, whereas the generation of planning alternatives is carried out heuristically by network planners based on design directives and experience. Advanced decision support tools require the most efficient optimization techniques, must be able to find the optimal investment path considering multiple scenarios, and should not only consider classic expansion strategies but also intelligent ones (e.g. energy storage and controlling active demand). Advanced decision support tools would greatly help network operators to make better decisions under uncertainty, which contributes to an affordable, sustainable, and reliable electric energy supply.
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