WS7 – DS2030 Stage Two and Three Reporting

Posted 22 May 2015 by Energy Networks Association in Smart Grid Forum, Smarter Networks

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The DS2030 Project for the ENA is being undertaken by a consortium led by Parsons Brinckerhoff and including The University of Manchester, PPA Energy, Chiltern Power and Grid Scientific. DS2030 will carry out the technical studies necessary to assess how smart solutions will operate in future distribution systems incorporating new LCT connections. The objective is to both confirm the technical viability of the future distribution system and provide an understanding of its characteristics, for example, to identify what control co-ordination may be required to ensure reliable and robust whole system operation. Most importantly, it will also highlight from a technical perspective any new roles and responsibilities that a DNO will be required to accept. To ensure that the DS2030 project provides the focus required by the industry, 24 questions compiled by the Smart Grid Forum WS7 committee are to be addressed.

The DS2030 project comprises 6 stages; Stage 2 includes the development of the Base Network models, the WS7 Scenarios and Use Cases. This report covers the first two aspects of Stage 2, whilst the Use Cases will be presented as part of the Stage 3 report along with a review of the aforementioned 24 questions. Stages 4 and 5 will apply the 2030 Scenarios to the Base Networks and carry out studies and analysis. Stage 6 will involve reporting and dissemination of the results.

Four Base Networks have been developed covering a range of typical distribution network types (specifically urban, rural and two forms of interconnection) as detailed in this report. In line with the project remit, we have sought depth in preference to breadth when developing these networks, using the 24 questions as our focus. Each Base Network represents actual GB distribution network configurations and data wherever possible, but with agreed refinements where needed. These achieve the most representative conditions to ensure that learning can be extrapolated more readily. Detailed assumptions and considerations made during the choice and development of the Base Networks are recorded in this report for the benefit of future users of the Base Network models and the study results.