A novel battery network modelling using constraint differential evolution algorithm optimisation

The use of battery storage devices has been advocated as one of the main ways of improving the power quality and reliability of the power system, including minimisation of energy imbalance and reduction of peak demand. Lowering peak demand to reduce the use of carbon-intensive fuels and the number of expensive peaking plant generators is thus of major importance. Self-adaptive control methods for individual batteries have been developed to reduce the peak demand. However, these self-adaptive control algorithms of are not very efficient without sharing the energy among different batteries. This paper proposes a novel battery network system with optimal management of energy between batteries. An optimal management strategy has been implemented using a population-based constraint differential evolution algorithm. Taking advantage of this strategy the battery network model can remove more peak areas of forecasted demand data compared to the self-adaptive control algorithm developed for the New York City study case

Benchmarking (multi)wavelet-based dynamic and static non-uniform grid solvers for flood inundation modelling

This paper explores static non-uniform grid solvers that adapt three raster-based flood models on an optimised non-uniform grid: the second-order discontinuous Galerkin (DG2) model representing the modelled data as piecewise-planar fields, the first-order finite volume (FV1) model using piecewise-constant fields, and the local inertial (ACC) model only evolving piecewise-constant water depth fields. The optimised grid is generated by applying the multiresolution analysis (MRA) of multiwavelets (MWs) to piecewise-planar representation of raster-formatted topography data, for more sensible grid coarsening based on one user-specified parameter. Two adaptive solvers are also explored that apply the MRA of MWs and of Haar wavelets (HWs) to, respectively, scale and adapt the DG2 (MWDG2) and FV1 (HWFV1) modelled data dynamically in time. The performance of the non-uniform grid and adaptive solvers is assessed in terms of flood depth and extent, velocities, and CPU runtimes, with reference to the raster-based DG2 model predictions on their finest resolution grid. The assessments considered three large-scale flooding scenarios, involving rapid and slow-to-gradual flows. MWDG2 is found to be the most favourable choice when modelling rapid flows, where it excels in capturing small velocity variations. For slow-to-gradual flows, the adaptive solvers deliver less accurate outcomes, and their efficiency can be hampered by overhead costs of the dynamic MRA. Instead, non-uniform DG2 is recommended to capture urban flow interactions more accurately. Non-uniform ACC is 5 times faster to run than non-uniform DG2 but delivers close flooding depth and extent predictions, thus is more attractive for fluvial/pluvial flood simulation over large areas

Risk Assessment of Post-Fire Floods on Dams and Their Floodplains

The rapidly changing global climate and the increased dependence on infrastructure networks make our society vulnerable to natural disasters. Decision makers need to understand the intensity of potential natural disasters to take necessary actions to minimize their impacts. In Southwest United States, wildfires are increasing in frequency and magnitude. The literature review shows limited studies in evaluating the impacts of post-wildfire floods on civil infrastructures and residential zones. Earth dams are vulnerable to post-wildfire floods. The increased post-wildfire runoff volumes due to changes in soil characteristics and reduced vegetation could result in overtopping failure of an earth dam (dam-break scenario), and the accumulation of sediment and debris flow could reduce the capacity of the reservoir (no-dam break scenario). In this study, a framework to evaluate the impacts of post-fire floods on earth dams is proposed. First, pre and post-wildfire runoff volumes are estimated considering a distribution of runoff coefficients found in the literature, different watershed burnt areas and historic rainfall data. Second, based on these runoff volumes, potential dam overtopping failure is modeled using WMS: SMPDBK developed by National Weather Services. The model predicts downstream flooding due to dam failure. The dam-break results are interpolated with HAZUS (developed by Federal Emergency Management Agency) inventory data to assess the downstream economic, environmental and social impacts. Finally, the impacts of dam failure and no-dam failure scenarios are evaluated with inputs from Hazus results and from an interview to a dam safety manager about disaster response alternatives and procedures. The framework is demonstrated using three earth dams in the Southwest United States. The results showed that with increased fire intensity and post-fire rainfall, increase in impacts on earth dams due to increased runoff and sediment yields resulting in a potential dam failure and thereby increased impacts on its floodplain. These impacts are integrated into a decision matrix and a decision tree that could be used to prioritize dams and high hazard zones in the watershed