Phase imbalance is a widespread problem in the UK's three-phase four-wire and five-wire low voltage (LV, 415V) distribution networks. More than 70% of these networks suffer from moderate or severe degrees of phase imbalance. For instance, it is not uncommon to see that one phase has a peak current of 300 A and another phase only has 150 A. This problem is also prevalent in European LV distribution networks as well as the medium-voltage distribution networks in the US, China, and other countries in the world. This problem will be aggravated by the increase of photovoltaic generation, wind generation, and electric vehicles, if they are not properly controlled.
In practice, the major causes for phase imbalance are: (1) an uneven allocation of customers across the three phases (I will refer to the phases as a, b and c) and (2) random customer behavior.
Phase imbalance has three main consequences:
1) Phase imbalance wastes network capacity. For instance, consider the common situation where phase a carries, say 72 kW with no spare capacity and the other two phases (b and c) carry 36 kW with 50% spare capacity. Customers on phase a cannot connect any additional appliances without overloading the system, yet the large margins on phases b and c cannot be transferred to phase a – they are effectively wasted by phase imbalance.
2) Energy losses are increased by more than 20% (equivalent to 4 ~ 5 TWh, or worth more than £4 bn by 2050 throughout the UK, compared to the ideal phase balanced case).
3) It causes “a current of imbalance”, also known as the zero-sequence current. An additional conductor, the neutral wire, is required to carry this neutral current. This incurs an additional investment cost compared to the ideal phase balanced case.
In attempting to cure the problem, researchers are confronted by three main challenges: (i) too little data available in LV networks, which I refer to as blindness, (ii) scalability (millions of LV networks need to be phase balanced), and (iii) adaptability (the imbalance condition changes over time because of random customer behavior and the connections of electric vehicles).
My research involves understanding the characteristics and consequences of phase imbalance and developing data-efficient (requiring a minimal amount of data), scalable, adaptive solutions to rebalance the three phases.
Funded by Royal Society, UK. From Dec 1st 2016 to Mar 29th 2019. PI: Dr Kang Ma
Funded by Northern Powergrid. From Oct 19th 2017 to Oct 18th 2022. Co-I: Kang Ma