Many renewable sources for electricity generation are distributed and volatile by nature, and become inefficient and difficult to coordinate with traditional power transmission paths. As a part of the transition from fossil fuel to renewable sources, local energy markets allow an efficient allocation and distribution of energy from local sources to nearby households. When using a discrete time double auction model, bids in such markets reflect the supply and demand of energy. However, since the energy
demand of a household contains personal information, such markets are not in line with privacy legislation.
In this paper, we investigate the influence of anonymization methods on local energy markets. In particular, we anonymize the bids of the order book, and we compare the CO2 emissions and the expenses of market participants of this allocation with a non-anonymous one. We have modeled the flows of personal data for a local energy auction platform, and we have
developed a model for the supply and demand of electricity of a small town in the near future. Our experiments show that with elementary anonymization methods, the impact of anonymization on the costs and on the CO2 emissions is small.