Parallel Statistical Model Checking for Safety Verification in Smart Grids

Toni Mancini, Federico Mari, Igor Melatti, Ivano Salvo, Enrico Tronci, J.K. Gruber, B. Hayes, M. Prodanovic, L. Elmegaard. In proceedings of the 2018 IEEE International Conference on Smart Grid Communications (SmartGridComm 2018). Aalborg, Denmark. IEEE, 2018.

Toni Mancini, Federico Mari, Igor Melatti, Ivano Salvo, Enrico Tronci, J.K. Gruber, B. Hayes, M. Prodanovic, L. Elmegaard.
Parallel Statistical Model Checking for Safety Verification in Smart Grids.
In proceedings of the 2018 IEEE International Conference on Smart Grid Communications (SmartGridComm 2018). Aalborg, Denmark.
IEEE, 2018.
DOI: 10.1109/SmartGridComm.2018.8587416

BibTex entry

By using small computing devices deployed at user premises, Autonomous Demand Response (ADR) adapts users electricity consumption to given time-dependent electricity tariffs. This allows end-users to save on their electricity bill and Distribution System Operators (DSOs) to optimise (through suitable time-dependent tariffs) management of the electric grid by avoiding demand peaks.

Unfortunately, even with ADR, users power consumption may deviate from the expected (minimum cost) one, e.g., because ADR devices fail to correctly forecast energy needs at user premises. As a result, the aggregated power demand may present undesirable peaks.

In this article we addressed such a problem by presenting methods and a software tool (APD-Analyser) implementing them, enabling Distribution System Operators to effectively verify that a given time-dependent electricity tariff achieves the desired goals even when end-users deviate from their expected behaviour.

Results

  • We proposed Aggregated Power Demand Analyser (APD-Analyser), a High Performance Computing (HPC)-based software system aimed at supporting DSOs in analysing the possible aggregated effects, on the EDN substation, of individualised price policies for electricity end-users, designed for highly dynamic ADR schemes.
  • APD-Analyser allows DSOs to compute a portfolio of Key Performance Indicators (KPIs) over the probability distribution of the APD, subject to probabilistic deviations of end-users from their predicted behaviours under the given price policies.
  • APD-Analyser can then act both as a Decision Support System allowing what-if analyses, and as an automated verifier for the safety of an envisioned set of price policies. In particular, APD-Analyser enables safety assessment of the price policies computed through automated methods.
  • Feasibility of APD-Analyser was shown through a realistic scenario from a medium voltage Danish distribution network.
Monthly APD (in kW) probability distributions, as computed by our algorithm (ε = δ = 5%, γ = 20 kW) on our case study scenarios.