Enhanced Smart Meter Privacy Protection Using Rechargeable Batteries

Practically Private Smart Meters with Energy Storage: An Experience-Driven Approach

“…Yang et al [20] developed dynamic programming using the Lyapunov theory, which incurs its own limitations and becomes impractical to preserve the load when the variation is considerably large. The framework developed in [21] adopted AFSO (Artificial Fish Swarm Optimization) and that in [22] exploited progressive averaging, but, [21] and [22] are ineffective when the energy demand fluctuates across customer types. Moreover, all of the aforementioned works [13]- [15] and [20]- [22] lacks the degree of adaptability required to keep track of the change in load variations and their fluctuations over time to improve the cost-friendly privacy of SMs.…”

“…Eq. ( 18), ( 19), ( 20), (21) By taking into account the states and behaviours of an RB, we develop the MDP model. The state-space of the MDP environment for the suggested technique by taking various SoC of an RB into consideration.…”

“…For tractability, to quantify the capability of privacy preservation of SMs, we employ Mutual Information (MI) as a metric [14], [21], [36], [37].Our measure of MI is an extended version of [37]. We use realistic tariffs of SG [38], the off-peak price is 5 cents/kWh (00:00 am to 12:00 pm), the on-peak price is 20 cents/kWh (12:00 pm to 20:00 pm, and the medium-peak price is 10 cents/kWh (20:00 pm to 00:00 am).…”

Review of results on smart‐meter privacy by data manipulation, demand shaping, and load scheduling

“…Such a model has been used in, e.g. [89–93]. However, other models, such as Markov chains, can also be used [94].…”

“…because of a charging rate constraint on the battery). Such a constraint has been used in [89, 90, 92].…”