Publications

Access to high-resolution smart meter data has many operational benefits for energy suppliers, network operators, and the energy system as a whole. However, access also raises privacy concerns, hindering the adoption of smart meters and the sharing of high-resolution smart meter data. This thesis addresses this dilemma by employing an interdisciplinary approach to designing a privacy-preserving data market mechanism for smart meter data as a means of balancing privacy and data access. The first research direction determines the design criteria of a data market for smart meter data. Specifically, we map the data dependence of benefits as well as the potential privacy infringements and risks associated with smart meter data. Data resolution, both spatial and temporal play a significant role in determining both benefits and privacy risks. We investigate consumers’ privacy concerns and their willingness-to-pay/accept for anonymisation through a novel survey and discrete choice experiment. Significant heterogeneity and endowment effects are observed with information asymmetries leading to depressed valuations for privacy protection. Finally, we assess the suitability of different privacy-preserving techniques for smart meter data, finding differential privacy to be a flexible, transparent, and easily integrated mechanism for ensuring privacy while allowing access to data. The second research direction develops a novel data market framework, using the design criteria determined in the first. A novel data valuation mechanism is developed based on the Wasserstein distance, which embodies the drivers of smart meter data value, including the privacy-utility trade-off induced by differential privacy. This is integrated into a novel procurement mechanism, developed using incentive mechanism design theory, which can model data buyers’ and consumers’ preferences, while preserving privacy. A joint energy and market is developed, which through case studies is shown to be a viable proposition to balance privacy and access to smart meter data, given our estimations of consumers’ willingness-to-accept.

We provide an exact expressions for the 1-Wasserstein distance between independent location-scale distributions. The expressions are represented using location and scale parameters and special functions such as the standard Gaussian CDF or the Gamma function. Specifically, we find that the 1-Wasserstein distance between independent univariate location-scale distributions is equivalent to the mean of a folded distribution within the same family whose underlying location and scale are equal to the difference of the locations and scales of the original distributions. A new linear upper bound on the 1-Wasserstein distance is presented and the asymptotic bounds of the 1-Wasserstein distance are detailed in the Gaussian case. The effect of differential privacy using the Laplace and Gaussian mechanisms on the 1-Wasserstein distance is studied using the closed-form expressions and bounds.

Digitalising the energy system is expected to be a vital component of achieving the UK’s climate change targets. Smart meter data, in particular, is seen a key enabler of the transition to more dynamic, cost-effective, cost-reflective, and decarbonised electricity. However, access to this data faces a challenge due to consumer privacy concerns. This Briefing Paper investigates four key elements of smart meter data privacy: existing data protection regulations; the personal information embedded within smart meter data; consumer privacy concerns; and privacy-preserving techniques that could be incorporated alongside existing mechanisms to minimise or eliminate potential privacy infringements.

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.

The Covid-19 pandemic has resulted in drastic and abrupt changes to daily life. One area where this change has been particularly stark is energy consumption. Lockdowns across the globe resulted in a reduction of overall energy consumption, carbon emissions and air pollution. We consider the possibilities and the implications of remote working in a longer-term perspective for the energy sector and emissions post pandemic

Assessment of the UK Smart Meter Roll-Out including pathways to ensure adoption targets.