Load Reduction Method and Optimisation for Demand Side Management in Distribution Networks

Abstract

Optimisation of Load Reduction Management to Support National Grid Management To mitigate the low frequency problem in a transmission system in the event of a power station failure or during low renewable generation production, the UK National Grid (NG) Electricity System Operator has a balancing mechanism in place with generators to provide temporary extra power, or with large energy users to reduce load demand or so-called fast reserve services. This thesis presents an alternative method to aggregately control the existing distribution network primary on-load transformer tap changers to achieve voltage-led load demand reduction as customer load active services. The main benefits of this laod demand reduction method are (i) to unlock the distribution network load demand flexibility without causing any negative impact on customers, and (ii) to provide the lowest cost of fast reserve service from a distribution network to the transmission network. In this thesis, network load reduction capability studies have been carried out through the use of tap changers. And an optimal load demand reduction based on genetic algorithm is proposed and developed to achieve an optimised voltage-led customer load active service with the aim of finding the optimal dispatch of on-load transformer tap changers by minimising each transformer tap switching operation as well as network losses. Two practical UK distribution networks have been modelled and used to investigate the network load reduction capacity, and demonstrate and compare the effectiveness of the proposed control methods under different operating conditions. The performances of the proposed method are also compared with both the rule-based and the branch-and-bound methods. The results show that the proposed optimal control strategy based on the genetic algorithm is more effective by achieving more accuracy and a faster solution for a large distribution network than the other two methods.

Date of Award	7 Sept 2022
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Haiyu Li (Main Supervisor)