AbstractPhase imbalance is a widespread and long-outstanding problem for low voltage (LV, 415V) networks. According to the data from Western Power Distribution (WPD, one of the UK’s distribution network operators), more than 50% of their LV networks suffered from severe phase imbalance – the current on the “heaviest” phase exceeded that on the “lightest” phase by 50% most of the time, increasing energy losses by up to 30%. Moreover, phase imbalance leads to inefficient use of network assets. If leaving phase imbalance unsolved, distribution network operators have to reinforce LV networks when the “heaviest” phase goes overloaded, despite having unused capacities on the “lightest” phase. Suppose an LV network suffers from significant phase imbalance where its “heaviest” phase exceeds the “lightest” phase by 50%, and its “heaviest” phase uses up the capacity of that phase. If leaving phase imbalance as it is, the LV network requires reinforcement immediately. However, supposing the annual load growth rate is 2%, fully rebalancing this LV network can defer network reinforcements for at least 15 years.
A number of references had studied imbalance-induced consequences and phase balancing solutions, supposing they had substation-side time-series phase current data and customer-side smart meter data. However, in reality, those data are not collected due to the lack of advanced monitoring devices for the majority of LV networks in the UK. It, therefore, raises a solid challenge: distribution network operators cannot implement existing phase imbalance assessments and phase swapping to over 900,000 LV networks in the UK because of data scarcity. This thesis, for the first time, addresses this challenge by developing statistical methodologies to estimate imbalance-induced energy losses and making phase swapping (one classic phase balancing method by moving customers from one phase to the other) guidance for data-scarce LV networks. Compared to existing solutions, this thesis’ developed methodologies only require existing data from data-scarce LV networks, thus accommodating the reality and being practical for industrial implementation.
This thesis originally develops three methodologies to get around the data limitations in implementing phase imbalance diagnosis and phase swapping. First, this thesis estimates imbalance-induced energy losses on the phase residual path and the three phases, separately, using only yearly average and maximum phase current data. The estimation accuracies are over 80% and 82%, respectively. Second, this thesis develops phase swapping guidance for LV networks without the requirement of year-round substation-side time-series phase current data and customer-side smart meter data. Case studies reveal that my approach achieves effective reductions of the phase imbalance degrees for data-scarce networks. And the reduction of phase imbalance degree is only 14.3% lower than that for data-rich networks.
Given the developed approaches, this thesis brings the following solid contributions for the industry: 1) it provides one significant component of the decision making of phase balancing investments, imbalance-induced energy losses, for data-scarce LV networks; and 2) it significantly improves the practically of phase swapping for the industrial implementation. In short, this thesis turns massive industrial application of the decision making for phase balancing investments and phase swapping planning into reality.
|Date of Award||8 Sept 2021|
|Supervisor||Kang Ma (Supervisor), Furong Li (Supervisor) & Ran Li (Supervisor)|