Investments in data quality: Evaluating impacts of faulty data on asset management in power systems

Data play an essential role in asset management decisions. The amount of data is increasing through accumulating historical data records, new measuring devices, and communication technology, notably with the evolution toward smart grids. Consequently, the management of data quantity and quality is becoming even more relevant for asset managers to meet efficiency and reliability requirements for power grids. In this work, we propose an innovative data quality management framework enabling asset managers (i) to quantify the impact of poor data quality, and (ii) to determine the conditions under which an investment in data quality improvement is required. To this end, an algorithm is used to determine the optimal year for component replacement based on three scenarios, a Reference scenario, an Imperfect information scenario, and an Investment in higher data quality scenario. Our results indicate that (i) the impact on the optimal year of replacement is the highest for middle-aged components; (ii) the profitability of investments in data quality improvement depends on various factors, including data quality, and the cost of investment in data quality improvement. Finally, we discuss the implementation of the proposed models to control data quality in practice, while taking into account real-world technological and economic limitations.