Colin J. Axon

Impacts of Raw Data Temporal Resolution Using Selected Clustering Methods on Residential Electricity Load Profiles

There is growing interest in discerning behaviors of electricity users in both the residential and commercial sectors. With the advent of high-resolution time-series power demand data through advanced metering, mining this data could be costly from the computational viewpoint. One of the popular techniques is clustering, but depending on the algorithm the resolution of the data can have an important influence on the resulting clusters. This paper shows how temporal resolution of power demand profiles affects the quality of the clustering process, the consistency of cluster membership (profiles exhibiting similar behavior), and the efficiency of the clustering process. This work uses both raw data from household consumption data and synthetic profiles. The motivation for this work is to improve the clustering of electricity load profiles to help distinguish user types for tariff design and switching, fault and fraud detection, demand-side management, and energy efficiency measures. The key criterion for mining very large data sets is how little information needs to be used to get a reliable result, while maintaining privacy and security.

Scalable high performance information and communications technology for smart distribution network operation

Scalable and secure information and communications technology will be essential to enable the interoperability of future electricity distribution networks. This will be mainly driven by the need to process, analyse and share increasing volumes of data produced by sensors monitoring the condition of network assets, smart meters from residential and commercial customers, distributed generation, and responsive loads. Complexity is further introduced as these diverse data-streams will be gathered at different rates and analysed for different purposes such as near-to-real-time system state estimation and on-line condition monitoring of network assets. However, the nature of active networks dictates that all relevant information will need to be exploited by interoperable applications across an enterprise or business as a whole. This paper presents novel Information and Communications Technology (ICT) and high performance computing tools and techniques to enable near-to-real-time state estimation across large-scale distribution networks whilst concurrently supporting on the same computational infrastructure for the condition monitoring of network assets and advanced network restoration solutions. These platforms are promoting and supporting the emergence of new distribution network management systems, with inherent security and intelligent communications, for smart distribution network operation and management. We propose cost-effective scalable ICT solutions and initial investigation of realistic distribution network data traffic and management scenarios involving state estimation. Furthermore, we review the prospects for off-line trials of our proposed solutions in three different countries.

Energy for Cities: Supply, Demand and Infrastructure Investment

Energy is essential to all activities in all regions of a country. However the density of energy use in, and our economic dependence on, cities means that it is more critical for urban areas. Nevertheless we suggest that the provision of energy for urban areas cannot be considered separately from the national context. We will demonstrate how to assess the ability of a nation to invest in energy infrastructure for the benefit of cities. Our approach exploits data sets which are available in most industrialised countries, and we select two quite different case studies to illustrate our method: the Colombia (Bogota) and UK (London). Our focus for energy sustainability in cities is quality of life and reduced fossil-fuel emissions. We will show that the main target for cities should be to improve air quality and reduce energy demand by improving energy efficiency.

Investigation of Data Communication Networks to Enable Demand Response and Dynamic Tariffs

Demand-side Response and dynamic tariffs are two examples of advanced functionality that may benefit both electricity consumers and distribution network operators. To be successful, the ICT infrastructure needs to be able to reliably cope with the data traffic. Within the wider UK context of the proposed centralised smart meter data transmission scenario, this paper demonstrates the scaling capability of two widely exploited communications protocols. Both payload (transmission overhead) and end-to-end delay times are examined.