One of the major challenges facing the world's electric power companies is data quality, which I have blogged about on multiple occasions (here, here, here and here). At Autodesk University Frank Markus provided an instructive overview of how Swissgrid has addressed the data quality issue using an approach based on a rules engine.
The Electricity Supply Act (StromVG) of 2007 reorganized the Swiss electricity market, resulting in the separation of electricity generation, trading, distribution, and transmission. Swissgrid became the monopoly operator of the Swiss transmission system with responsibility for the operation, security, and expansion of 6,700 km of high voltage transmission lines, 130 substations, and 15,000 pylons.
Most electric power generation in Switzerland is either hydro or nuclear, so that electric power generation does not contribute significantly to the world's GHG emissions. About 56% of power generation is hydro, 39% is nuclear, renewable is about 2%, and only 3% is fossil fuels. However, Switzerland has announced plans to shut down its nuclear power plants. In 2009 Switzerland's power consumption was 61,800 GWh and it produced about 63,900 GWh, but demand is uneven. In 2009 54,159 GWh were exported, and 52,002 GWh were imported.
Swissgrid has ambitious plans. By 2012 Swissgrid will be the sole owner of the transmission system which is currently jointly owned by eight operating companies. By 2014 Swissgrid will be the integrated service provider for energy supply in Switzerland, and by 2016 Swissgrid intends to be one of the five leading grid operators in an integrated pan-European electriclty market.
Swissgrid began the process of taking over the transmission network from eight regional providers. It decided that to operate the grid reliably and cost-effectively, it needed to manage the transmission network using an integrated CAD and geographic information system (GIS).
Swissgrid decided to implement a system that provided a single point of truth for both CAD and GIS data. They used a rules-based desktop solution with an Oracle Spatial database to store asset attributes and geospatial data and to provide an open data environment that could be integrated with SAP.
Data quality
The biggest challenge that Swissgrid found when they assessed the facilities data that they received from the regional operating companies was the uneven quality of the data. In some cases, high voltage and low voltage lines were directly interconnected.
The Swissgrid team found that the rules engine of their solution was critical for addressing the data quality issue efficiently. It allowed Swissgrid to develop and apply business rules based on power transmission standards to the data and spot and correct errors intelligently. Based on this approach Man and Machine helped Swissgrid develop a process for data cleaning and loading. Data received from regional operators was loaded and the implementation team ran data quality checks using the rules engine. Most of the errors could be corrected automatically, but a few required field inspection.
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