  This paper proposes a framework to investigate the value of sharing
privacy-protected smart meter data between domestic consumers and load serving
entities. The framework consists of a discounted differential privacy model to
ensure individuals cannot be identified from aggregated data, a ANN-based
short-term load forecasting to quantify the impact of data availability and
privacy protection on the forecasting error and an optimal procurement problem
in day-ahead and balancing markets to assess the market value of the
privacy-utility trade-off. The framework demonstrates that when the load
profile of a consumer group differs from the system average, which is
quantified using the Kullback-Leibler divergence, there is significant value in
sharing smart meter data while retaining individual consumer privacy.
