Abstract
We perform a rare-event study on a simulated power system in which grid-scale batteries provide both regulation and emergency frequency control ancillary services. Using a model of random power disturbances at each bus, we employ the skipping sampler, a Markov Chain Monte Carlo algorithm for rare-event sampling, to build conditional distributions of the power disturbances leading to two kinds of instability: frequency excursions outside the normal operating band, and load shedding. Potential saturation in the benefits, and competition between the two services, are explored as the battery maximum power output increases. This article is part of the theme issue 'The mathematics of energy systems'.
Highlights
When combined with endogenous power disturbances due to various other system contingencies, these disturbances could in the worst case activate emergency responses in the power grid [4,5] which may propagate in a cascading fashion [6,7,8,9,10,11]
In this line of research we aim to combine time-domain simulation with random sampling, in order to study rare events occurring in power systems
The main challenges are that standard Monte Carlo simulation methods are unreliable in rare event settings [20], a problem compounded by the heavy computational expense of time-domain, simulation-based assessment of power system robustness [21]
Summary
In this study we employ the skipping sampler, a Markov Chain Monte Carlo (MCMC) rare-event sampler, to examine the benefits of BESS as both the battery maximum power output (MPO) and the distribution of power disturbances vary. Potential saturation in the benefits of BESS is examined as MPO increases, and potential interactions between the frequency regulation and contingency services which they provide. In this line of research we aim to combine time-domain simulation with random sampling, in order to study rare events occurring in power systems. As in the latter papers, any probability density may be taken as the model of power disturbances
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