6.1 Introduction

In electric power systems, the advantages of reactive power dispatching or optimisation include [1]:

• improved utilisation of reactive power sources and hence reduction in reactive power flows and real losses of the system;

• unloading of the system and equipment as a result of reactive flow reduction; the power factors of generation are improved and system security is enhanced;

• reduced voltage gradients and somewhat higher voltages which result across the system from improved operation;

• deferred capital investment in new reactive power sources as a result of improved utilisation of existing equipment; and

• for the National Grid Company plc (NGC), the main advantage is reduced out-of-merit operation.

The problem of reactive power control has been studied and widely reported in the literature. Non-linear programming methods as well as linear programming techniques for constraint dispatch have been described. Static optimisation of reactive power sources by the use of sensitivity analysis was described by Kishore and Hill [2]. Long range optimum var planning has been considered and the optimum amount and location of network reactive compensation so as to maintain the system voltage within the desired limits, while operating under normal and various insecurity states, have also been studied using several methods [3 7].

The objective of this chapter is therefore to review conventional methods as well as AI techniques for reactive power control.

6.2 Algorithmic methods