K-means based cluster analysis of residential smart meter measurements

A clustering module based on the k-means cluster analysis method was developed. Smart meter based residential load profiles were used to validate the clustering module. Several case studies were implemented using daily and segmented load profiles of individual and aggregated smart meters. Simulation results defined in terms of the relationship between the clustering ratio and the segmentation time window reveal that the minimum clustering ratio is obtained for the shortest time window of segmentation. Results also show that a small number of clusters is recommended for highly correlated load profiles

Local Energy Systems in Iraq: Neighbourhood Diesel Generators and Solar Photovoltaic Generation

Iraqis experience interruptions of the public electricity supply of up to 18 hours a day. In response, private entrepreneurs and the Local Provincial Councils (LPCs) have installed an estimated 55,000–80,000 diesel generators, each rated typically between 100 and 500 kVA. The generators supply neighbourhoods through small, isolated distribution networks to operate lighting, fans and small appliances when power is not available from the public supply. A single radial live conductor connects each customer to the generator and payment for the electricity is based on a monthly charge per ampere. The operation and regulation of the neighbourhood diesel generator networks was reviewed through a comprehensive literature survey, site visits and interviews conducted with local operators and assemblers of the generator sets. The electricity is expensive, the generators can only supply small loads, have considerable environmental impact and the unusual single wire distribution practice is potentially hazardous. However, the use of the generators is likely to continue in the absence of any alternative electricity supply. The diesels and networks are poorly regulated and there is scope to enforce existing standards and develop a new standard to address the hazards of the connection practice. The chapter goes on to assess the possibilities of using small photovoltaic systems for power generation in Iraq

State estimation of medium voltage distribution networks using smart meter measurements

Distributed generation and low carbon loads are already leading to some restrictions in the operation of distribution networks and higher penetrations of e.g. PV generation, heat pumps and electric vehicles will exacerbate such problems. In order to manage the distribution network effectively in this new situation, increased real-time monitoring and control will become necessary. In the future, distribution network operators will have smart meter measurements available to them to facilitate safe and cost-effective operation of distribution networks. This paper investigates the application of smart meter measurements to extend the observability of distribution networks. An integrated load and state estimation algorithm was developed and tested using residential smart metering measurements and an 11 kV residential distribution network. Simulation results show that smart meter measurements, both real-time and pseudo measurements derived from them, can be used together with state estimation to extend the observability of a distribution network. The integrated load and state estimation algorithm was shown to produce accurate voltage magnitudes and angles at each busbar of the network. As a result, the algorithm can be used to enhance distribution network monitoring and contro

k-means based load estimation of domestic smart meter measurements

A load estimation algorithm based on kk-means cluster analysis was developed. The algorithm applies cluster centres – of previously clustered load profiles – and distance functions to estimate missing and future measurements. Canberra, Manhattan, Euclidean, and Pearson correlation distances were investigated. Several case studies were implemented using daily and segmented load profiles of aggregated smart meters. Segmented profiles cover a time window that is less than or equal to 24 h. Simulation results show that Canberra distance outperforms the other distance functions. Results also show that the segmented cluster centres produce more accurate load estimates than daily cluster centres. Higher accuracy estimates were obtained with cluster centres in the range of 16–24 h. The developed load estimation algorithm can be integrated with state estimation or other network operational tools to enable better monitoring and control of distribution networks

Integrated load and state estimation using domestic smart meter measurements

The UK Government is promoting the decarbonisation of the power sector. The electrification of transport and heating, installation of distributed generators, development of smart grids and creation of an electricity and gas smart metering system are in progress. Higher penetrations of distributed generation and low carbon loads may lead to operational difficulties in distribution networks. Therefore, increased real-time monitoring and control becomes a necessary requirement. Distribution network operators will have available to them smart meter measurements to facilitate safe and cost-effective operation of distribution networks. This thesis investigates the application of smart meter measurements to extend the observability of distribution networks. Three main aspects were covered in this work: 1. The development of a cluster analysis algorithm to extract consumption patterns from smart meter measurements. Th

Feasibility of peer-to-peer energy trading in low voltage electrical distribution networks

Peer-to-peer (P2P) energy trading is referred to as flexible energy trades between peers, where the excessive energy from many small-scale Distributed Energy Resources (DERs) including those in dwellings, offices, factories, etc., is traded among local customers. To assess the feasibility of P2P energy trading, where local electricity demand and supply balancing is desired, a so-called P2P index was developed. By clustering the historical smart metering data using the k-means method, customers were categorized by their electricity consumption patterns and representative demand profiles of low voltage electrical distribution networks were produced. A linear programming optimization was carried out to find the optimal capacity of different DERs to maximize the local demand and supply balancing. PV systems and combined heat and power units were considered as the renewable resources. This work provides network planners with guidelines of appropriate shares of DERs for better constructing their future networks, and facilitates a P2P energy trading market paradigm

Load estimation of domestic smart meter measurements using moving average and moving median techniques

In this paper, the performance of the moving average and moving median load estimation techniques is investigated using aggregated measurements of domestic smart meters. The load estimation techniques were tested using forward and backward walk approaches. Forward walk aims to estimate future load measurements using past measurements while backward walk estimates missing past measurements of the load using more recent measurements. Simulation results show that the moving average combined with forward walk produces load estimates with higher accuracies than the moving median and backward walk