Dynamic water allocation policies improve the global efficiency of storage systems

Water impoundment by dams strongly affects the river natural flow regime, its attributes and the related ecosystem biodiversity. Fostering the sustainability of water uses e.g., hydropower systems thus implies searching for innovative operational policies able to generate Dynamic Environmental Flows (DEF) that mimic natural flow variability. The objective of this study is to propose a Direct Policy Search (DPS) framework based on defining dynamic flow release rules to improve the global efficiency of storage systems. The water allocation policies proposed for dammed systems are an extension of previously developed flow redistribution rules for small hydropower plants by Razurel et al. (2016). The mathematical form of the Fermi-Dirac statistical distribution applied to lake equations for the stored water in the dam is used to formulate non-proportional redistribution rules that partition the flow for energy production and environmental use. While energy production is computed from technical data, riverine ecological benefits associated with DEF are computed by integrating the Weighted Usable Area (WUA) for fishes with Richter's hydrological indicators. Then, multiobjective evolutionary algorithms (MOEAs) are applied to build ecological versus economic efficiency plot and locate its (Pareto) frontier. This study benchmarks two MOEAs (NSGA II and Borg MOEA) and compares their efficiency in terms of the quality of Pareto's frontier and computational cost. A detailed analysis of dam characteristics is performed to examine their impact on the global system efficiency and choice of the best redistribution rule. Finally, it is found that non-proportional flow releases can statistically improve the global efficiency, specifically the ecological one, of the hydropower system when compared to constant minimal flows. (C) 2017 Elsevier Ltd. All rights reserved

Modeling macroroughness contribution to fish habitat-suitability curves

Improved water management strategies necessitate a solid understanding of environmental impacts associated with various flow release policies. Habitat suitability models use hydrodynamic simulations to generate weighted usable area curves, which are useful in characterizing the ecological suitability of flow release rules. However, these models are not conveniently run to resolve the hydrodynamics at the smaller scales associated with macroroughness elements (e.g., individual stones), which produce wakes that contribute significantly to habitat suitability by serving as shelter zones where fishes can rest and feed. In this study, we propose a robust environmental indicator that considers the habitat generated by the wakes downstream of stones and can thus be used to assess the environmental efficiency of flow release rules for impounded streams. We develop an analytical solution to approximate the wake areas behind macroroughness elements, and the statistical distribution of wake areas is then found using the derived distribution approach. To illustrate the concept, we apply our theory to four exemplary river streams with dispersed stones having different statistical diameter size distributions, some of which allow for an analytical expression of the weighted usable area. We additionally investigate the impact of spatiotemporal changes in stone size distributions on the usable area and the consequent threshold flows. Finally, we include the proposed environmental indicator to solve a multiobjective reservoir optimization problem. This exemplifies its practical use and allows stakeholders to find the most favorable operational rules depending on the macroroughness characteristics of the impounded stream

Frontiers of (Pareto) optimal and sustainable water management for hydropower and ecology

River regulation alters the natural flow regime of streams with consequent impacts on terrestrial and aquatic habitats of the riparian zone. The severity of such impacts can be modulated by changing the redistribution rules at water intakes and reservoirs. Contrary to minimal-flow policies, non-proportional and proportional redistribution policies result in variable environmental flow releases, namely Dynamic Environmental Flows (DEFs), which improve the global (i.e., ecological and economic) efficiency of water use practice, e.g., for energy production. DEF assessment is based on different indicators. However, the choice and aggregation method of different hydrological and fish habitat indicators affects the assessment of the global power plant performance, i.e., the Frontier of efficient solutions (sensu Pareto). This study investigates DEF assessment, and shows the extent to which the choice and method of aggregation of different indicators impacts the Frontier of Pareto-efficient solutions. The findings are supported by six case studies of hydropower practice that differ in terms of river morphology, energy production amount and technique. The relative importance of several types of indicators is examined as is their influence on optimal and sustainable water allocation solutions that lie on the Pareto Frontier. The analysis shows that DEFs arising from either proportional or non-proportional redistribution rules can positively impact strategies of sustainable management of freshwater resources

Analytical Modeling of Wind Farms: A New Approach for Power Prediction

Wind farm power production is known to be strongly affected by turbine wake effects. The purpose of this study is to develop and test a new analytical model for the prediction of wind turbine wakes and the associated power losses in wind farms. The new model is an extension of the one recently proposed by Bastankhah and Porté-Agel for the wake of stand-alone wind turbines. It satisfies the conservation of mass and momentum and assumes a self-similar Gaussian shape of the velocity deficit. The local wake growth rate is estimated based on the local streamwise turbulence intensity. Superposition of velocity deficits is used to model the interaction of the multiple wakes. Furthermore, the power production from the wind turbines is calculated using the power curve. The performance of the new analytical wind farm model is validated against power measurements and large-eddy simulation (LES) data from the Horns Rev wind farm for a wide range of wind directions, corresponding to a variety of full-wake and partial-wake conditions. A reasonable agreement is found between the proposed analytical model, LES data, and power measurements. Compared with a commonly used wind farm wake model, the new model shows a significant improvement in the prediction of wind farm power

A new analytical model for wind farm power prediction

In this study, a new analytical approach is presented and validated to predict wind farm power production. The new model is an extension of the recently proposed by Bastankhah and Porte-Agel for a single wake. It assumes a self-similar Gaussian shape of the velocity deficit and satisfies conservation of mass and momentum. To estimate the velocity deficit in the wake, this model needs the local wake growth rate parameter which is calculated based on the local turbulence intensity in the wind farm. The interaction of the wakes is modeled by use of the velocity deficit superposition principle Finally, the power curve is used to estimate the power production from the wind turbines. The wind farm model is compared to large-eddy simulation (LES) data and measurments of Horns Rev wind farm for a wide range of wind directions. Reasonable agreement between the proposed analytical model, LES data and measurments is obtained. This prediction is also found to be substantially better than the one obtained with a commonly used wind farm wake model

Analytical Modeling of Wind Farms: A New Approach for Power Prediction

Wind farm power production is known to be strongly affected by turbine wake effects. The purpose of this study is to develop and test a new analytical model for the prediction of wind turbine wakes and the associated power losses in wind farms. The new model is an extension of the one recently proposed by Bastankhah and Porté-Agel for the wake of stand-alone wind turbines. It satisfies the conservation of mass and momentum and assumes a self-similar Gaussian shape of the velocity deficit. The local wake growth rate is estimated based on the local streamwise turbulence intensity. Superposition of velocity deficits is used to model the interaction of the multiple wakes. Furthermore, the power production from the wind turbines is calculated using the power curve. The performance of the new analytical wind farm model is validated against power measurements and large-eddy simulation (LES) data from the Horns Rev wind farm for a wide range of wind directions, corresponding to a variety of full-wake and partial-wake conditions. A reasonable agreement is found between the proposed analytical model, LES data, and power measurements. Compared with a commonly used wind farm wake model, the new model shows a significant improvement in the prediction of wind farm power