The increased integration of intermittent and decentralised forms of power production has eroded the stability margins of power grids and made it more challenging to ensure reliable and secure power transmission. Reliable grid operation requires system-scale stability in response to perturbations in supply or load; previous studies have shown that this can be achieved by tuning the effective damping parameters of the generators in the grid. In this paper, we present and analyse the problem of tuning damping parameters when there is some uncertainty in the underlying system. We show that sophisticated methods that assume no uncertainty can yield results that are less robust than those produced by simpler methods. We define a quantile-based metric of stability that ensures that power grids remain stable even as worst-case scenarios are approached, and we develop optimisation methods for tuning damping parameters to achieve this stability. By comparing optimisation methods that rely on different assumptions, we suggest efficient heuristics for finding parameters that achieve highly stable and robust grids.
<p>Small, run of the river hydropower (SHP) has the potential to help provide rural regions in developing countries with access to power. Satellite rainfall products can be used in these often data sparse regions to drive a series of linked models to determine locations feasible SHP sites. However, the inherent uncertainty in satellite rainfall products are a significant source of error, and this must be quantified. Additionally, there is a trade-off between the benefits of power produced from SHP and the cumulative environmental impacts they may produce when multiple are implemented across a basin, and it is important to assess this trade off. &#160;</p>
<p>The first part of this study calculates the uncertainty in predictions of SHP potential due to satellite rainfall uncertainty across a data sparse catchment. Comparisons of predicted power and its uncertainty are then made at locations where known SHP sites are located, to evaluate the model&#8217;s usefulness. The second part of the study involves assessing the trade-off between the cumulative power output and cumulative environmental impact of a range of SHP portfolios, to assess at which locations it is best to construct in order to maximise power output benefits and minimise negative environmental impacts. &#160;</p>
<p>A calibrated, linked VIC&#8211;LISFLOOD hydrodynamic model driven by different satellite derived rainfall datasets was constructed at 5km resolution on the Pungwe Basin in Mozambique / Zimbabwe.&#8239;The VIC model was calibrated to a single available&#8239;GRDC gauging station.&#8239;A&#8239;LISFLOOD-FP hydraulic model&#8239;with sub grid channel representation of small rivers&#8239;was created&#8239;from the&#8239;HYDROSHEDs network, river widths&#8239;extracted from&#8239;multiple databases,&#8239;hydraulic geometry relationships for bed depth, and MERIT DEM.&#8239;Modelled&#8239;flow&#8239;from the 5km&#8239;VIC&#8239;cells&#8239;were routed into&#8239;each 90m&#8239;LISFLOOD-FP&#8239;river&#8239;pixel.&#8239;Power Duration Curves were then derived&#8239;for&#8239;each&#8239;river pixel across the basin, and the modelled power predictions were evaluated using six known SHP&#8239;sites in the upper reaches of the basin. Geostatistical techniques were then applied to generate ensembles of satellite rainfall realisations, which were propagated through the model chain, in order to establish the uncertainty in the modelled power.&#160;</p>
<p>Broad assessment of environmental impact has been made based on impacts SHP impacts on river connectivity, with subsequent multi-objective optimisation&#8239;to&#8239;analyse the&#8239;trade-offs&#8239;between&#8239;different portfolios based on&#8239;cumulative&#8239;power&#8239;output and&#8239;impact on river connectivity using the NSGAII algorithm, and thus suggest optimum locations. &#160;</p>
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