Parallel Statistical Model Checking for Safety Verification in Smart Grids

TL;DR

This paper introduces a parallel statistical model checking approach and a software tool for verifying that time-dependent electricity tariffs in smart grids effectively manage demand peaks despite user deviations, enhancing grid reliability.

Contribution

It presents a novel parallel statistical model checking method and the APD-Analyser tool for verifying demand response strategies in smart grids.

Findings

Feasibility demonstrated on a Danish distribution network scenario.

Effective verification of tariffs ensures demand peaks are avoided.

Parallel approach improves computational efficiency.

Abstract

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 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 paper we address 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. We show feasibility of the proposed approach through a realistic scenario from a medium voltage Danish distribution network.