Demand-side energy management (EM) is studied from a privacy-cost trade-off perspective, considering time-of-use pricing and the presence of an energy storage unit. Privacy is measured as the variation of the power withdrawn from the grid from a fixed target value. Assuming non-causal knowledge of the household's aggregate power demand profile and the electricity prices at the energy management unit (EMU), the privacy-cost trade-off is formulated as a convex optimization problem, and a low-complexity backward water-filling algorithm is proposed to compute the optimal EM policy. The problem is studied also in the online setting assuming that the power demand profile is known to the EMU only causally, and the optimal EM policy is obtained numerically through dynamic programming (DP). Due to the high computational cost of DP, a low-complexity heuristic EM policy with a performance close to the optimal online solution is also proposed, exploiting the water-filling algorithm obtained in the offline setting. As an alternative, information theoretic leakage rate is also evaluated, and shown to follow a similar trend as the load variance, which supports the validity of the load variance as a measure of privacy. Finally, the privacy-cost trade-off, and the impact of the size of the storage unit on this trade-off are studied through numerical simulations using real smart meter data in both the offline and online settings.

Delay constrained linear transmission (LT) strategies are considered for the transmission of composite Gaussian measurements over an additive white Gaussian noise fading channel under an average power constraint. If the channel state information (CSI) is known by both the encoder and decoder, the optimal LT scheme in terms of the average mean-square error distortion is characterized under a strict delay constraint, and a graphical interpretation of the optimal power allocation strategy is presented. Then, for general delay constraints, two LT strategies are proposed based on the solution to a particular multiple measurements-parallel channels scenario. It is shown that the distortion decreases as the delay constraint is relaxed, and when the delay constraint is completely removed, both strategies achieve the optimal performance under certain matching conditions. If the CSI is known only by the decoder, the optimal LT strategy is derived under a strict delay constraint. The extension to general delay constraints is elusive. As a first step toward understanding the structure of the optimal scheme in this case, it is shown that for the multiple measurements-parallel channels scenario, any LT scheme that uses only a one-to-one linear mapping between measurements and channels is suboptimal in general.

Delay constrained linear transmission (LT) of a mixture of Gaussian measurements over an additive white Gaussian noise (AWGN) fading channel is considered. At each time slot (TS), the control center (CC) asks for the measurement of a particular system parameter from the sensor, which is capable of measuring multiple independent system parameters. The average mean-square error (MSE) distortion is studied for Gaussian parameters and a Gaussian fading channel under an average power constraint. The optimal LT scheme is characterized under a strict delay constraint, and a graphical interpretation for the power allocation strategy is presented. Then, two achievable LT strategies are proposed for general delay constraints. It is shown that the performance improves as the delay constraint is relaxed, and when the delay constraint is completely removed, both strategies achieve the optimal performance under certain matching conditions.

Smart meters are key elements for the operation of smart grids. By providing near realtime information on the energy consumption of individual users, smart meters increase the efficiency in generation, distribution and storage of energy in a smart grid. The ability of the utility provider to track users' energy consumption inevitably leads to important threats to privacy. In this paper, privacy in a smart metering system is studied from an information theoretic perspective in the presence of energy harvesting and storage units. It is shown that energy harvesting provides increased privacy by diversifying the energy source, while a storage device can be used to increase both the energy efficiency and the privacy of the user. For given input load and energy harvesting rates, it is shown that there exists a trade-off between the information leakage rate, which is used to measure the privacy of the user, and the wasted energy rate, which is a measure of the energy-efficiency. The impact of the energy harvesting rate and the size of the storage device on this trade-off is also studied.