Adaptive water resource planning using decision-rules

Dealing with uncertainty in water resource planning is problematic because insufficient or underused infrastructure can have social and environmental costs. Multistage stochastic optimisation provides a mechanism to deal with this challenge in water supply capacity expansion planning. However, for real systems it can be mathematically hard and computationally expensive. The ‘Decision-rule’ formulation represents an attempt to remedy this by approximating the multistage problem where decisions at each stage are a function of the uncertainty and the state of the system. We introduce a family of rules to show how they approximate the multistage problem and investigate the implications of the approximation for adaptive water resources planning

Climate, buildings' envelope design and energy patterns: improving energy performance of new buildings in Kuwait

PURPOSE: The two main contributing factors that control the overall buildings’ energy performance are the heating ventilation and air conditioning (HVAC) system and the envelope design. Environmental design guidelines that consider these two factors aim to lower energy consumption. However, they are regional and climate-sensitive. This study aims to investigate how three main buildings’ envelope design variables (orientation, compactness and window to wall ratio) impact the overall building’s energy consumption within Kuwait’s regional and climate conditions. DESIGN/METHODOLOGY/APPROACH: This study simulate the energy consumption of typically shaped buildings by varying their geometry between a square to a rectangular floor plan. This study analyse the associated energy usage and provide early-stage envelope design guidance specific to the country’s conditions, to make informed decisions towards environmentally conscious buildings. FINDINGS: The analysed envelope variables have the potential to reduce energy consumption by 40%, and the possibility to reduce HVAC system capacity by 30%. In contrast to the general guidance in literature and standards, the simulation results demonstrate that less compact building forms perform on occasions better than the most compact ones. ORIGINALITY/VALUE: The objective of this paper is to quantify the energy consumption rates for buildings located within the Arabian Peninsula, an under-studied region with potentially high interest considering three main envelope design variables. The buildings’ yearly energy consumption patterns are unique and suggest different envelope design considerations, compared to other regions with different climate conditions. This emphasises the importance of regional guidelines for the different factors associated with energy and buildings’ environmental performance

Self-adaptive agent modelling of wind farm for energy capture optimisation

© 2017, Springer-Verlag GmbH Germany. Typical approaches to wind turbines placement problem take into account the wind distribution and wake effects to maximise the total aggregate farm’s energy production in a centralised top–down optimisation problem. An alternative approach, however, is yet to be addressed as the problem can be instead modelled in a decentralised bottom–up manner emulating a system of self-adaptive agents. The potential advantages of this is that it offers easier scalability for high dimension problems as well as it enables an easier adaptation to the complex structure of the design problem. This paper contributes to this and presents an evolutionary algorithm to model and solve the wind farm layout design problem as a system of interrelated agents. The framework is applied to problems with different complexities where the quality of the results is examined. The convergence and scalability of the suggested technique indicate promising results for small to large scale wind farms, which, in turn, encourage the application of such an evolutionary based algorithm for real world wind farm design problem

Influence of shock wave propagation on dielectric barrier discharge plasma actuator performance

Interest in plasma actuators as active flow control devices is growing rapidly due to their lack of mechanical parts, light weight and high response frequency. Although the flow induced by these actuators has received much attention, the effect that the external flow has on the performance of the actuator itself must also be considered, especially the influence of unsteady high-speed flows which are fast becoming a norm in the operating flight envelopes. The primary objective of this study is to examine the characteristics of a dielectric barrier discharge (DBD) plasma actuator when exposed to an unsteady flow generated by a shock tube. This type of flow, which is often used in different studies, contains a range of flow regimes from sudden pressure and density changes to relatively uniform high-speed flow regions. A small circular shock tube is employed along with the schlieren photography technique to visualize the flow. The voltage and current traces of the plasma actuator are monitored throughout, and using the well-established shock tube theory the change in the actuator characteristics are related to the physical processes which occur inside the shock tube. The results show that not only is the shear layer outside of the shock tube affected by the plasma but the passage of the shock front and high-speed flow behind it also greatly influences the properties of the plasma

Water Quality and Pollution Trading: A Sustainable Solution for Future Food Production

Nitrogen, an essential nutrient for plant growth, is commonly added to food crops in the form of manure and synthetic fertilizers. Fertilizer use has significantly increased in the past decades to meet the food demands from a rising population. Although this has boosted food production, it has come at a cost to the environment. Indeed, excess fertilizer ends up in water bodies, a pollution that causes losses in aquatic biodiversity. Better fertilizer management is therefore essential to maintaining water sustainability. Here, we develop and evaluate a nitrogen water quality trading scheme to address this challenge. Nitrogen trading incentivizes farmers to work together to invest in pollution reduction measures in order to keep nitrogen water pollution levels within a standardized limit. We build a mathematical model to represent the nitrogen trading and use it to assess the pollution reduction, the effect on the crop yield, and economical outcomes. The model is applied among local farms in the agricultural county of Suffolk, eastern England. We calculate the nitrogen load to the river from each farm and incorporate the abatement cost into the model. The results show how nitrogen water pollution could be reduced cost-effectively while simultaneously increasing the benefit for the whole catchment. Although the benefit does not increase for all the farms, the increase in benefit for the whole catchment is enough to compensate for this loss. The surplus benefit is equally distributed between all the farms, thus increasing their overall benefit. We discuss how the proposed trading model can create a platform for farmers to participate and reduce their water pollution

Trading Off Environmental and Economic Scheduling of a Renewable Energy Based Microgrid Under Uncertainties

Smart power grids are transitioning towards effective employment of distributed energy resources including renewable energy sources to address the growing environmental concerns related to the pollutant emissions of fossil fuels. In this context, this paper proposes the directed search domain (DSD) method to compute the combined environmental and economic dispatch in a microgrid with battery energy storage systems, photovoltaic plants, wind turbines, fuel cells, and microturbines. The DSD algorithm is implemented for a multiobjective problem to obtain evenly-distributed Pareto optimal points by shrinking the original search domain into hypercone. This paper computes the optimal unit commitment and the related power dispatch while simultaneously minimizing the total pollutant emissions and operating costs. The best trade-off solution among the entire set of Pareto optimal points is computed by using the Fuzzy satisfying technique. The uncertainties associated with the forecasting of prices, load demand, wind, and photovoltaic power outputs are accounted for by employing the stochastic programming. The empirical results indicate the potential of the presented DSD algorithm in terms of the objective values, solution times, and quasi-even distribution of the Pareto set

Trade-off informed adaptive and robust real options water resources planning

Planning water resource systems is challenged primarily by two realities. First, uncertainty is inherent in the predictions of future supplies and demands due for example to hydrological variability and climate change. To build societal resilience water planners should seek to enhance the adaptability and robustness of water resource system interventions. Second, water resource developments typically involve competing interests which implies considering the trade-offs and synergies implied by the highest performing combinations of development options is useful. This work describes a real options based planning framework that generates adaptive and robust water system design alternatives able to consider and trade-off different goals. The framework can address different types of uncertainties and suggests the highest performing designs across multiple evaluation criteria, such as financial costs and water supply service performance metrics. Using a global city's water resource and supply system as a demonstration of the approach, we explore the trade-offs between a long-term water management plan's infrastructure services (service resilience, reliability, vulnerability) and its financial costs under supply and demand uncertainty. The set of trade-off solutions consist of different investment plans which are adaptive and robust to future changing conditions. Results show that the highest performing plans lower net present value (NPV) of needed investments by up to 18%, while maintaining similar performance across the other objectives. The real option value of delaying investments as much as possible approaches up to 14% of total NPV

Optimisation of multiple encapsulated electrode plasma actuator

The standard dielectric barrier discharge plasma actuator, in which an asymmetric arrangement of electrodes leads to momentum coupling into the surrounding air, has already demonstrated its capability for flow control. The new design of such an actuator exploits the multi-encapsulated electrodes to produce higher velocities providing more momentum into the background air. As the number of encapsulated electrodes increases and other variables such as the driving frequency and voltage amplitude are considered, finding the optimum actuator configuration for increasing the induced velocity becomes a challenge. Specially the task is prohibitive if it is implemented on an ad hoc basis. This paper uses D-optimal design to identify a handful of experiments, for which the velocity is obtained by Particle Imaging Velocimetry measurement. Afterwards, the velocity is modelled through a surrogate modelling practice, and the model is validated both experimentally and statistically. To find the optimum actuator configuration, numerical optimisation is conducted and the results are investigated through experiment. The results show that the surrogate modelling approach provides a cheap and yet efficient method for systematically investigating the effect of different parameters on the performance of the plasma actuator. © 2012 Published by Elsevier Masson SAS

Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation

Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems pertaining classification and identification tasks. A subclass of QML methods is quantum generative adversarial networks (QGANs) which have been studied as a quantum counterpart of classical GANs widely used in image manipulation and generation tasks. The existing work on QGANs is still limited to small-scale proof-of-concept examples based on images with significant downscaling. Here we integrate classical and quantum techniques to propose a new hybrid quantum-classical GAN framework. We demonstrate its superior learning capabilities by generating $28 \times 28$ pixels grey-scale images without dimensionality reduction or classical pre/post-processing on multiple classes of the standard MNIST and Fashion MNIST datasets, which achieves comparable results to classical frameworks with three orders of magnitude less trainable generator parameters. To gain further insight into the working of our hybrid approach, we systematically explore the impact of its parameter space by varying the number of qubits, the size of image patches, the number of layers in the generator, the shape of the patches and the choice of prior distribution. Our results show that increasing the quantum generator size generally improves the learning capability of the network. The developed framework provides a foundation for future design of QGANs with optimal parameter set tailored for complex image generation tasks

Global vaccine equity demands reparative justice-not charity

By late April, more than 80% of the world’s COVID-19 vaccines had gone to people in wealthy countries, with just 0.3% to people in low-income countries.1 This reprehensible imbalance is no accident. High-income countries have used neocolonial negotiating power, global policy leverage and capital to procure enough doses to cover 245% of their citizens while leaving few doses for poorer countries.2 As a result, lower-income countries may not be able to vaccinate their populations until 2023.3 Such inequity is yet another example of how the interests of racial capitalism run roughshod over the golden rule of global solidarity—attend to the highest risk first.4 Currently, older and medically vulnerable individuals are dying from COVID-19 disproportionately in poor countries, while young, healthy individuals are getting vaccinated in wealthy ones.5 Vaccine apartheid is a not novel phenomenon. The notion that only certain corners of the world get to benefit from life-saving treatments is an everyday reality of a global health system driven by a capitalist, philanthropic model.6 7 But in times of crises—and as new variants threaten the vaccination plans of wealthy countries—these inequities and their solutions come to the forefront of global debate.8 Policy-makers in rich nations are aware of these issues. But the solutions they have proposed so far do nothing to address the underlying structural problems. They offer charitable donations and partial, temporary fixes that are designed to deflect the substantive demands for reform that global South countries are fighting for, including challenges to unethical intellectual property (IP) regimes.9 This approach will not work, because it is not designed to ‘work.’ If we want to end vaccine apartheid, we need to target the root causes of global health inequities. We need reparative justice. There are currently three approaches to reduce inequity in