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Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

AbstractIf the energy‐density of prey bodies is not uniform, predators usually should maximize their rate of energy intake while foraging by selectively consuming energy‐rich regions of their prey and discarding other parts. In this study, the hypothesis of selective body‐part consumption was tested using two species of dasyurid marsupials, Sminthopsis youngsoni and Ningaui ridei, and their invertebrate prey in arid central Australia. Energy‐densities were similar for several divergent types of prey, including whole insects, centipedes, and spiders (20.2 ± 2.19 (sd) J/mg ash‐free dry mass), but there were marked differences in energy‐density among orders of insects and also between different body regions of invertebrates. In captivity, the two dasyurid species consumed different body regions of prey in the same way. For beetles and cockroaches, the rank order for selection of body parts was abdomen, thorax, head, then legs; for centipedes it was body, then head + legs; and for spiders it was opisthosoma, prosoma and then legs. Selection of prey body regions by the marsupials correlated closely with the energy‐densities of these regions, and also with the rates of energy intake that they yielded. In the field, selection of body parts of arthropod prey by dasyurids probably occurs primarily when prey is abundant. This selection should allow maximization of rates of energy intake in favourable periods and should, in turn, allow dasyurid predators to effectively exploit pulses of prey resources in the temporally variable desert environment.

Microgrids are increasingly being used as a platform to integrate distributed generation such as renewable energy sources and (RESs) conventional sources in both grid-connected and isolated power systems. Due to the inherent intermittent nature of RESs, energy storage systems (ESSs) that can absorb fluctuations have become inevitable. Nevertheless, large capacities of ESSs increase the initial cost while small capacities lead to instabilities and increase in the cost of conventional fuels. Therefore, finding the optimal size of the ESS for a given application is essential for the reliable, efficient and economical operation of a microgrid. Once the battery size is decided, maintaining its energy at appropriate levels is essential to ensure stable and safe operation of the microgrid. This paper presents a novel expert fuzzy system - grey wolf optimization (FL-GWO) based intelligent meta-heuristic method for battery sizing and energy management. The proposed energy management operation is carried out by a Grey Wolf Optimiser (GWO) that is helped to set the membership functions and rules of the fuzzy logic expert system. The unit commitment (UC) issue, which is essential for the proper operation of the isolated microgrid, has been additionally considered in this paper. To verify the performance of the proposed method, results are compared with the rules-based method and traditional GWO algorithm. It has been proven from the results that the FL-GWO has a significant convergence property and capability to minimize the Levelized Cost Of Electricity (LCOE) by 14.13% and 24.15% compared with conventional GWO algorithm and rules-based method, respectively. The weather conditions for different climates is used to verify the performance of the intelligent energy management method under different operating scenarios. The results show that the intelligent online multi-objective energy management strategy is capable of managing a smooth power flow with the same optimal configuration in the isolated microgrid, minimising the fossil fuel utilisation and reducing the CO2 emission level.

Abstract Breastshot water wheels are gravity hydraulic machines employed in low head sites. The scope of this work is to test the performance of a breastshot water wheel with two geometric inflow configurations: a sluice gate at different openings and two vertical overflow weirs. With the sluice gate, the maximum efficiency of the plant is 75%, constant over a wide range of flow rates, while the efficiency with the weir is increasing in the same flow rate range. Therefore, the wheel with the weir can exploit higher water volumes, and also it performs better at high power input. In practical applications, the inflow configuration can be effectively controlled to optimize the operative working conditions of breastshot water wheels, depending on the external hydraulic ones. The experimental results are also discussed in dimensionless terms, in order to support engineers in the design of similar breastshot water wheels.

Seagrass systems of the Western Pacific region are biodiverse habitats, providing vital services to ecosystems and humans over a vast geographic range. SeagrassNet is a worldwide monitoring program that collects data on seagrass habitats, including the ten locations across the Western Pacific reported here where change at various scales was rapidly detected. Three sites remote from human influence were stable. Seagrasses declined largely due to increased nutrient loading (4 sites) and increased sedimentation (3 sites), the two most common stressors of seagrass worldwide. Two sites experienced near-total loss from of excess sedimentation, followed by partial recovery once sedimentation was reduced. Species shifts were observed at every site with recovering sites colonized by pioneer species. Regulation of watersheds is essential if marine protected areas are to preserve seagrass meadows. Seagrasses in the Western Pacific experience stress due to human impacts despite the vastness of the ocean area and low development pressures.

Abstract In many visions and roadmaps, there is a broad agreement that fuel cells – both for stationary and mobile applications – are the key technology to allow the development of a hydrogen infrastructure. Furthermore, this development is generally thought to be based on a gradual, decentralised evolution. Nevertheless, in this paper it is argued that, taking into account the entire hydrogen chain (production, transport, storage, distribution and end-use), this decentralised fuel-cell based philosophy shows some serious flaws. Therefore, a new hydrogen-transition approach was pushed forward: mixing in of hydrogen into the natural-gas bulk. Using Flanders – the Northern part of Belgium – as a case study, the development of a transitory hydrogen infrastructure has been studied, taking into account the entire hydrogen chain and its dynamics, from production to end use. In a next step, this transition is being quantified. An optimisation model has been developed using Matlab and the commercial solvers GAMS and CPLEX. Following a mixed-integer linear-programming approach, this model is able to determine the economically optimal hydrogen-production mix and operational behaviour of each hydrogen-production plant separately. The model then allows gaining valuable insights in the importance of storage and the influence of fuel prices and carbon taxes with regard to the development of an early hydrogen economy.

Abstract In the new competitive electricity supply industry, there is a renewed interest in algorithms that can provide savings in operation costs. An optimal scheduling of generators can provide substantial annual savings in fuel costs, but this highly constrained non-linear mixed integer optimisation problem can only be full v solved by complete enumeration, a process which is not computationally feasible for realistic power systems. In the recent past, evolutionary computation techniques have been applied to the solution of the unit commitment problem, but when implemented as stand alone systems, they suff er from computational time limitations, especially when the systems are scaled up. This paper proposes a hybridgenetic algorithm incorporating a priority list unit ordering scheme to solve the generator scheduling problem. Test results on networks with up to 110 generators are presented and the results demonstrate the viability of the hybrid GA method for unit commitment in realistic power systems.