Evaluation of Various Objective Functions for Optimal Reactive Power Flow Including Transformer Tap Changer Optimisation

TL;DR

This paper investigates how to design effective objective functions for optimizing reactive power sources and transformer tap changers in renewable-rich distribution grids, balancing multiple conflicting goals.

Contribution

It proposes a data-driven, multi-objective optimization approach and derives a combined weighted objective function for reactive power and tap changer optimization.

Findings

A combined weighted objective function improves grid performance.

Optimizing conflicting objectives reveals trade-offs in grid operation.

The interior point method effectively solves the multi-objective optimization problem.

Abstract

Modern distribution grids with high penetration of renewable generation provide substantial flexibility through distributed reactive power sources and transformer tap changers. This high degree of freedom can be exploited for optimisation. However, choosing an objective function for optimisation is not trivial, e.~g. minimising grid losses may lead to overvoltages and minimising voltage deviations may lead to higher reactive power flows to neighbouring system operators. Thus, this paper deals with the design of an objective function for the centralised optimisation of distributed reactive power sources and transformer tap changers. Different objectives for characteristic network quantities are investigated for the optimisation and optimised in a combined manner and separately. The consequences of optimising conflicting target values are then analysed. For the optimisation, various grid usage cases of a 110 kV benchmark power grid from SimBench are examined. The investigated power grid is characterised by a high proportion of renewable energy plants. The optimisation is carried out in a data-driven, object-oriented manner using the interior point method with open source software. At the end of the paper, a meaningful optimisation function with combined weighted objectives is derived and the results are analysed.