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Smart Grids

Funding body



Principal investigator: Gareth Taylor
Co-investigator: Prof Maozhen Li

Project description

This project is a collaborative smart grids research project involving researchers from both the UK and China.  Professor Gareth Taylor of Brunel University leads the research in the UK and Professor Yong-Hua Song of Tsinghua University leads the research in China.

In the UK, as the GB transmission operator, National Grid has established the 'Gone Green' scenario, which results in 35% of UK electricity being sourced from renewable energy sources by 2020, such as intermittent and highly variable wind power for example, compared with about 6-7% today. Therefore, in the UK National Grid will face significant operational challenges over the next decade and beyond. Similarly in the Guangzhou province of China there now already exists one of the most technologically advanced and operationally complex transmission systems in the world. From 2005 onwards China Southern Power Grid has already experienced operationally complex challenges due to the impact of large-scale renewable energy source deployment on the transmission system.

It is clear that similar operational challenges, as faced in the UK by the GB transmission system operator, are also being faced by transmission network operators in China and also in other parts of the world. Decision making in transmission system control centres is becoming more complex and control room actions are required in reducing timescales and with greater reliance on more accurate risk assessment in the future in order to enable optimal operation of transmission systems.

The proposed collaborative interdisciplinary smart grids research will investigate and develop scalable tools on secure high performance computing platforms that support large-scale, interoperable near to real-time data processing and data mining methods.

Novel near to real-time simulation techniques and computational analysis will be investigated with regard to deployment and performance at high computational speeds, using novel scalable tools and infrastructure such as trusted cloud computing platforms or dedicated cluster computing platforms. Recent developments in secure cloud computing that exploit improved processor, chipset and platform-level security will be investigated and developed to provide protected computational environments such that critical applications cannot be compromised.

The novel smart grid tools and techniques that will be developed in this project can provide and support much faster actions to securely control more complex transmission systems in shorter time scales and therefore accommodate greater renewable energy sources on an operational basis in such future transmission systems.

Transmission system operators in the UK, China and other parts of the world will benefit considerably from the future availability of such scalable, high performance and secure tools when operating more complex future transmission systems that accommodate greater amounts of renewable energy resources in 2020 and beyond, as they will be able to securely accommodate larger amounts of intermittent renewable energy sources and thereby enable the decarbonisation of the electricity supply industry in line with 2020 targets.

Final Reports and Deliverables

Tasks 1.1 & 1.2:  Comprehensive report and survey of current High Performance Computing and related power system analysis tools R&D    

Task 2.1 & 2.2:  Detailed reports on suitability of trusted cloud computing platforms at enterprise level and dedicated cluster computing platforms at an application level for scalable data processing and mining

Task 2.3:  Working paper critically evaluating suitability of approaches investigated in Tasks 1.1 & 1.2

Task 2.4:  Prototype application interfaces using emerging standards such as CIM at the transmission network application level

Task 3.1:  Working paper on interoperability, scalability, compliance and visualisation when gather substation data and relaying the control centre

Task 3.2:  Detailed report on risk assessment and identification of risk-relevant components

Task 3.3:  Report on dynamic state estimation and prediction technologies using hybrid measurements

Task 3.4:  Report on novel methods for preventative risk mitigation with active network component participation