AMR as one key enabler of Smart Grid - how to take the full advantage of AMR investment

8.9.2015

Smart Grid has two main functions for being an enabler of energy-efficient and environmentally friendly energy market (e.g. integration of active distributed resources, demand response and comprehensive ICT solutions) and a critical infrastructure of society offering uninterrupted power supply (e.g. major disturbance management, self-healing networks, microgrids). The concept of Smart Grids may be characterized by words like flexible, intelligent, integration and co-operation for integration of large-scale distributed energy resources. Intelligence means here simply investments in protection, controllability and ICT technologies instead of pure passive lines, cables, transformers and switchgears. ICT with common interfaces and data models is an essential enabler of the Smart Grid. By making the customer connection point more flexible and interactive, the demand response functions are more achievable and the efficient use of the existing network and energy resources by market mechanisms can be improved.

In the visions of Smart Grids it has been emphasized that Automated Meter Reading (AMR), often named also as AMM or AMI, will play an essential role in the realization of interactive customer gateway. AMR is often referred also to a phrase “Smart Metering". It is concerned with residential smart meters and metering systems for collecting and analyzing energy usage and other measurements. Smart Metering is currently being installed in many DSOs in Europe. The legal and regulatory drivers for Smart Metering are partly country-specific, and also the details related to metering functions may vary in different countries.

Finland is a forerunner in large-scale AMR roll-out in worldwide, not only in coverage of installations but also in functionality and utilization of AMR system in various business processes. In 2009 the Finnish Government passed a new act, which states that at least 80 % of the customers of each DSO must have AMR implemented by December 31, 2013. In practice almost all customers (~98 %) are provide by a new AMR meter. The law requires the AMR meter that features hourly energy measurement as well as registrations of quality of supply and demand response functionality.

AMR system installation is not only energy remote reading, but it enables real time two-way communication between customers and other actors and offers huge amount of data for developing new functions for Smart Grids. AMR is an essential enabler for improving competition in electricity market by enhancing flexible change of energy retailer. However, the task of AMR system is not only to provide real time energy consumption data to the utility, but it offers several ways to improve electricity distribution and energy retail businesses. The cost of retrofitting the energy metering system may not be justified if the meters are used merely only for reading energy consumption data. The implementation of AMR systems has changed the function of basic energy meter as a smart terminal unit and gateway for multiple service providers and enabled real time two-way communication between customers and utilities. From that viewpoint the AMR system can also be seen as an extension of SCADA (Supervisory Control And Data Acquisition) and Distribution Management system (DMS) for controlling and monitoring the last parts of the network (i.e. the low voltage network) between medium voltage network and customer. The possibilities of using AMR include, for example, real-time energy information, customer service, demand side management, disconnection and reconnection of electricity supply, determination of load profiles for network calculations, network planning and secondary transformer condition monitoring, more accurate interruption statistics, more sophisticated power quality monitoring facilities, and the management of low voltage (LV) distribution networks. The use of AMR data in various functions increase cost effectiveness of AMR investments. AMR system with relating ICT systems and business processes form a larger entity to create added-value for customers, DSO, energy retailer and service providers.

It has been a tradition already for over some decades in Finland to use hourly load profiles for modelling customer behaviour e.g. in distribution network calculations. Now the large scale AMR roll-out in Finland offers a large amount of measurement data to determine more detailed load profiles, even customer-specific, and to improve modelling of temperature correlation of electrical loads. Present load models have been formed from AMR –measurements with an assumption that every customer has only normal energy consumption. However, electrical loads and customer behaviour are changing e.g. due to the customer's own small-scale production (e.g. PV systems), demand response, charging of electrical vehicles and energy storage possibilities. This sets new challenges for load profiling based on AMR measurements. Loads can be controlled by demand response functionality based on e.g. power limit or energy price. Modelling of combination of load and demand response is a challenging task. For example variation of energy price can be used to estimate demand response in a case when load is controlled by energy price.

As part of the “New Energy" -research programme of Finnish Academy a research project of “Improved Modelling of Electric Loads for Enabling Demand Response by Applying Physical and Data-Driven Models (RESPONSE)" is realised by the consortium having four participating units from Tampere University of Technology, University of Eastern Finland and VTT. The aim of the project is to develop enhanced models for load and control response forecasting required by dynamic on-line optimisation of demand response actions and network operation in a future sustainable energy system.The research project focuses on the following objectives:

  1. Developing improved models for short term load forecasting and optimisation of electric load dynamic responses to load control actions and weather variations.
  2. Analysing and developing criteria for comparing the performance of the short term load forecasting.
  3. Enhancing utilisation of smart metering data and other spatial information in updating and verification of load response models

Text: Pertti Järventausta

Viimeksi muokattu 9.3.2016
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