Multi-scale spatio-temporal framework for characterising design and operational parameters of the electrical power system
Keywords: spatio-temporal characterisation of design and operational parameters of the electrical power system
monte carlo simulation
smart meter data time granularity
load-dependency loss factor
load aggregation
load variability
power losses

This thesis explores the key research question as stated below:
• KEY RESEARCH QUESTION: Given the increasing adoption of distributed variable energy resources and smart appliances onto the electrical power system, what methods can be developed to help electrical power system planners and operators gain insight into the design and operational parameters of the electrical power system for purposes of enabling greater adoption of distributed variable energy resources and smart appliances in an efficient and sustainable way?
This key research question covers a number of important elements that are essential for enabling the adoption of efficient and sustainable design and operational techniques to be applied on the modern electrical power system. Therefore, in order to arrive at logical answers in respect of the key research question, three further sub-questions were explored, namely:
• SUB-QUESTION 1: In view of increasing presence of distributed variable energy resources and smart appliances on the electrical power system, how do the planning and design parameters of a modern electrical power system vary when such parameters are characterised at different customer aggregation levels?
• SUB-QUESTION 2: With the objective to accurately estimate the planning and design parameters of a modern electrical power system, what effect does the use of time-series customer load data with different time granularities have on the estimated parameters?
• SUB-QUESTION 3: When considering a real electrical power system with a high presence of distributed variable energy resources and smart appliances, how do the design and operational parameters of a modern electrical power system vary when such parameters are characterised at different customer aggregation levels when using time-series customer load data with different time granularities?
It is important to note that sub-questions 1 and 2 relate to characterising planning and design parameters of the power system, which are derived from characterisations based on customer load profiles. Sub-question 1 relates to performing such characterisations at different customer aggregation levels, whereas sub-question 2 relates to the characterisations considered when using customer load profiles with different time granularities. The difference in the focus of sub-question 3 in relation to sub-questions 1 and 2 is that sub-question 3 seeks to characterise design and operational parameters on a real network with a composition of customer load profiles added to and/or removed from the network at different customer aggregation levels when using customer load profiles with different time granularities. So, sub-question 3 pertains to characterisations based on a real network with the inclusion of distributed variable energy resources and smart appliances.
The research work covered in this thesis pertains to the characterisation of design and operational parameters of the electrical power system, with a particular focus on distribution networks with significant penetration of distributed variable renewable energy and smart appliances.
The characterisation technique contributed by this thesis bears two major focus areas, namely: 1) to characterise the planning and design parameters of the electrical power system, and also, 2) to characterise the operational parameters of the electrical power system. For both focus areas, i.e., planning & design and operation, a characterisation of specific parameters is considered at different customer aggregation levels, using time-series customer load data with different time granularities. It is because of the spatial/sizing consideration of customer load aggregation and the temporal aspect of the time granularity of time-series customer load data that the over-arching characterisation technique which forms the integral contribution of this thesis is termed spatio-temporal characterisation framework.
CHARACTERISATION BASED ON LOAD PROFILES
The spatio-temporal characterisation framework is thus applied to several load profiles datasets, namely: a synthetic dataset (generated using an Excel Workbook model developed by the Centre for Renewable Energy Systems Technology (CREST) of Loughborough University) and real datasets from four different jurisdiction areas (i.e., the United Kingdom (UK, Northern Grid), United States of America (USA, Texas), Belgium (Mons), and Australia (Ausgrid)). This study characterises planning and design parameters such as the after-diversity maximum demand (ADMD, a measure for diversity), load variance (a measure for load variability), and the load factor (which is useful for calculating the expected network power losses). All of these parameters are characterised in relation to changing aggregation levels and time-scales. All the time-series customer load profiles datasets used in this thesis are representative of residential customers. Both the ADMD (i.e. per customer capacity requirement) and load variance were found to asymptotically decrease toward a settling value, when both the size of customer groupings and averaging time intervals approached large numbers. Conversely, the load factor asymptotically increases toward a settling value, when both the size of home groupings and averaging time intervals increase.
CHARACTERISATION BASED ON A DISTRIBUTION NETWORK
In order to understand the impact of the size of customer load aggregation and the time granularities of load profiles datasets on the design and operational parameters of a distribution network, the spatio-temporal characterisation framework was further applied to a UK low voltage (LV) network with a high presence of distributed solar PV based renewable energy and plug-in electric vehicles (PEVs) based flexible demand. This study characterises the network diversified peak demand, load variability, power losses, and the load-dependency loss factor, when different sizes of home groupings are either added to or disconnected from the network at a time, using time-series profiles (for the load, solar PV, and PEV charging) with different time granularities. The load-dependency loss factor is a newly introduced theoretical parameter, defined as the differential change in losses at a given aggregation level divided by the total demand at full load, which quantifies how the change in losses implicitly compares to the differential change in network load when varying aggregation levels for a given time granularity. The results for the characterisations based on a real distribution network are very comprehensive and they shed light on the appropriate time granularity of customer load data that can be used for purposes of accurately planning and designing a power system with a high composition of distributed variable energy resources and smart appliances. The results also give insight into the requisite time scales of managing and operating the modern power system. A detailed summary of these results is presented in Chapter 6 under sub-section 6.2.2. What is most noteworthy is that, additional to the aspects of customer aggregation levels and the time granularity of customer load profiles, the inclusion of solar PV and electric vehicles had a great impact on the characterisations of network diversified peak demand, load variability, power losses, and the load-dependency loss factor, particularly when the composition of such resources is varied on the network at different aggregation levels.
The spatio-temporal characterisation framework developed in this thesis provides a useful tool for distribution network planners and operators to derive planning and design parameters of a distribution network with a particular load size on the basis of the per-customer capacity requirement, and devise network-specific active management schemes with carefully tailored control time scales on the basis of load variability. The characterisation of network power losses allows the distribution network operators to assess the network performance and implement appropriate network interventions for optimal network operation.