Smart Grids and the data challenge

January 14, 2014
Posted by in Blog, Statistics & Data | Tagged , , , , , , , , , , , , , , , , , , , , |

The evolution of the electricity distribution network is at the heart of the debate about future of the electricity system. The distribution network, historically designed for unidirectional power flows and with very limited observability, will need to accommodate increased levels of small to medium scale distributed generation, including CHP-generated electricity, photovoltaic systems and wind farms.

Meanwhile, as heating and transport systems are electrified in the coming decades, peak demand will rise sharply, and this ‘may lead to a significant degradation of generation infrastructure and electricity network assets’, leading to an increase in the cost of system integration, according to a report from Imperial College’s Energy Futures Lab.

Reading about China’s big push for wind power, similar electricity network issues rear their head: The windiest regions tend to be extremely distant from the major conurbations, where electricity demand is greatest; the construction of wind farms regularly exceeds the building of the connections needed to link the turbines to the grid; and the grid itself, accustomed to handling the predictable output of power stations burning fossil fuels, has struggled to cope with the intermittency of wind. The result has been curtailment – where some wind farms have been ordered to shut down even on windy days. Sound familiar?

Throughout the world and not least the UK, the challenges of adapting the distribution network to new and variable levels of demand and generation require forward thinking and technical innovation – whilst bearing in mind the solution is not one of technology alone, as commercial and regulatory 
frameworks, and consumer engagement will be key enablers.

11729_smartgrid_fullMuch of the solution focus is on smart grids – responsive systems that can handle intermittent renewable energy sources and make it possible for users to vary their consumption patterns. The intention is that smart grids will pave the way for generators of all sizes and technologies to connect to the network, encourage electricity consumers and generators to play a part in improving the performance of the entire electricity system and give consumers fuller information on electricity consumption, choice of supplier and cost. In addition smart grids will improve reliability, and deliver the security of supply consumers expect.

To meet these challenges, a future ‘smart grid’ system needs to be more integrated and flexible than it is today, with the capacity to transfer significantly more energy between a diverse range of generators and consumers. The systems necessary to balance supply and demand will overlap and be integrated with network and demand control technologies. Significantly more system monitoring and intelligent control are expected to be needed to securely manage a much more complex system while meeting the demand for energy with the optimum level of generation and network capacity.

There is as yet no blueprint for the future networks: Smart Grids are an emerging philosophy with a new architecture, combining traditional and innovative techniques. In the UK, significant research on smart grid deployment is being coordinated by OFGEM under the auspices of The Low Carbon Network Fund which is providing supporting funding on projects sponsored by the Distribution Network Operators (DNOs) to try out new technology, operating and commercial arrangements. It is worth taking a look at the outputs of the various projects to understand th depth and breadth of work being undertaken.

One thing that is certain is the exponential volume of data arising from smart grid operations, which after all require a two-way flow of both energy and information. The data challenge is evident even when considering a handful of the Solution Sets defined by the DECC / Ofgem Smart Grid Forum working groups. These have been developed to form a core for practical business planning by the network companies and establish an approach that can support smart grid deployment. The Solutions Sets include:

Smart Community Energy: The objectives focus on enhancing network performance by forging closer links with those it services which includes: Building a local sense of energy identity, ownership, and engagement; Developing a Technical, Commercial, and Social functionality set; Energy from Waste and centralised CHP integration; enabling the trading of energy and services within and between local communities; Demand Response optimised with a Community group.

Smart Buildings & Connected Communities: The objectives include: Development of Private Networks; Building management systems with standard functional interfaces; Buildings and Private Networks providing DR services and DG services, as well as energy storage (heat/elec) services; 
 Network-integrated Communities managing their own energy.

Smart Ancillary Services (Local & National): the objectives here include: Aggregation of domestic downward and upward response and of Distributed Generation (e.g. PV) to provide Virtual Power Plant (VPP) capability.

Advanced Control Centres: this focuses on: the creation of visualisation and decision support tools;
Data processing at the lowest levels, with information passed upwards; Modelling & Forecasting tools for new demands, managed in operational timescales; generating a GB system view, integrating TSO and DNO network management; Architectures and Systems platforms that support hybrid combinations of distributed/centralised applications

At even the local network level fulfilling these objectives require a step change in the fusion of sensors and smart meters, data management and processing, automation and aggregation, and adaptive systems, control and protection. These will generate a vast amount of data – both raw and processed. This will also require a high degree of standardization and integration between data sets.

For organisations, businesses, communities and other groups, AM&T (automated metering and targeting) is already becoming a vital component to the process of imporving the productivity of energy resource use. Following a logical progression of capability, organisations will integrate data from a variety of sources (sub metering, sensors, BMS, business operational data, building asset ratings) with network systems and internal manual and automated control systems which will enable to them maximize productivity and reduce energy consumption and bills dramatically. This represents a challenge of data filtering, removing data silos and integrating different data types so as to meet the needs of disparate stakeholder groups.

One of the projects seeking to tackle these challenges is the DIMMER (District Information Modeling and Management for Energy Reduction) project, which is a collaboration between Manchester University and the University of Turin, as well as several organisations including ARUP and Clicks and Links. The Dimmer system “seeks to integrate BIM and district level 3D models with real-time data from sensors and user feedback to analyze and correlate buildings utilization and provide real-time feedback about energy-related behaviors. It allows open access with personal devices and Augmented Reality (A/R) visualization of energy-related information to client applications for energy and cost-analysis, tariff planning and evaluation, failure identification and maintenance, energy information sharing.” The first outputs should be available in 2015.

At the utility level the challenges that data management presents is evident in the roll out delays and systems integration requirements around smart meters: If the data is managed properly, smart metering will allow utility firms to forecast energy usage, to improve their performance on the settlement markets and to match supply and demand more closely, whilst also generating enhanced analytics to improve customer retention. However one of the challenges for the utilities is that while operational data and customer-facing systems remain separate, the utilities may fail to exploit the opportunities presented by integrating these two sides of the business, thereby creating silos of data rather than making more consistent availability of the data. The latter would enable Utilities to offer tariffs, which discourage consumption while supply is weak, and releasing stored energy in anticipation of peaks in demand, also reducing capital expenditure. Can the utilities, with their legacy IT systems, deliver?

ICT is recognized as being a key player in controlling the whole energy chain, but many questions need to be answered if it is to fulfill its potential: For example, will there be a single umbrella data model? As an enabler for all other aspects of energy systems, how do we ensure that data delivers action, not just information? Where is the intelligence in the system and who controls what the system does? And how do we ensure the right levels of data protection are in place? For the UK, the question is also one of whether we are moving fast enough to meet our near term energy challenges.


A massive thank you to our guest blogger, Andre Burgess, for this post.

This blog first appears on EMVC Solutions.

EMVC is an energy advisory business, delivering solutions in distributed, low carbon energy generation and energy efficiency services, including smart energy control and monitoring services.