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► Mathematical modeling and simulation of a standalone hybrid energy system. ► Multi-objective optimization (MOO) considering techno-economical and environmental aspects. ► Using ...Fuzzy TOPSIS as a multi-criterion decision making (MCDM) technique. ► Proposing a decision making tool to combine MCDM, MOO and level diagrams. ► A case study using novel technique.
Hybrid energy systems (HESs) are becoming popular for standalone applications due to global concern regarding green house gas (GHG) emissions and depletion of fossil fuel resources. Research in the optimal design of HESs is ongoing, with numerous optimization techniques giving special emphasis to Pareto optimization, incorporating conflicting objectives. The subsequent decision-making process including the non-dominant set of solutions has yet to be addressed.
This work focuses on combining multi-objective optimization with a multi-criterion decision making (MCDM) technique to support decision makers in the process of designing HESs. Four different objectives, i.e., levelized energy cost (LEC), unmet load fraction, wasted renewable energy (WRE) and fuel consumption are used to obtain the Pareto front. A decision support tool based on Fuzzy TOPSIS and level diagrams is proposed to analyze the Pareto front and support the subsequent decision-making activity. A case study is used to illustrate the applicability of the proposed method. The study shows that the novel method is useful when determining the relative weights of objectives, providing a detailed picture of the objective space to the designer when coming up with the optimum system. The technique proposed in this study can be further extended to analyze similar problems in energy system design where MCDM is necessary after multi-objective optimization.
HES (hybrid energy system)s are becoming energy systems of choice for standalone applications due to ever increasing fuel costs and global concern on GHG (Green House Gas) emissions. However, it is ...difficult to justify the higher ICC (Initial Capital Cost) of renewable energy components, especially for rural electrification projects in developing countries. This paper illustrates the modeling and simulation of HESs, and multi-objective optimization carried out in order to support decision-making in such instances. LEC (Levelized Energy Cost), ICC and GHG emission were taken as objective functions in the optimization and the sensitivity of market prices and power supply reliability was further evaluated. Results depict that Pareto front of LEC, ICC and GHG emission can be simplified as a combination of ICC–LEC and LEC–GHG emission Pareto fronts making the decision-making process simpler. Gradual integration of renewable energy sources in a number of design stages is proposed for instances where it is difficult to bear the higher ICC. Finally, importance of planning integration of renewable energy sources at early design stages of the project is highlighted in order to overcome the difficulties that need to be faced when coming up with the optimum design.
•Multi-objective optimization considering LEC (Levelized Energy Cost), ICC (Initial Capital Cost) and GHG (Green House Gas) emission.•3-D Pareto front can be simplified as a combination of two 2-D Pareto fronts.•Sensitivity of the fuel price is notable in LEC–ICC Pareto front.•Gradual integration of renewable energy sources is suggested in order to bear higher ICC.
Pasta is a widely consumed food in all over the world. Coarse semolina obtained from durum wheat and water are the main ingredients of conventional pasta products. The amount of gluten and quality ...level of durum wheat, are two important factors for the superiority of finished pasta. Market price of durum wheat is higher than the common wheat and it contributes no more than 5% of the world wheat production. Thus, to come across the challenge of emerging pasta consumption, new field of research that is dealing with the incorporation of nonconventional ingredients to the conventional formula of pasta has initiated. The compositions of raw materials which are used for pasta preparation directly affect the physical, chemical, and textural properties of the product. Therefore, incorporation of nonconventional ingredients can lead to a contradictory effect of pasta quality. This review will focus on the various types of nonconventional ingredients that are being incorporated in pasta products and their effect on the quality attributes of different pasta products.
•Obtained Pareto fronts of LEC, power supply reliability (PSR) and ICC/GHG emission.•Pareto surface was observed for smaller ICGs when considering LEC–PSR–GHG.•Shape of the LEC–PSR–ICC Pareto front ...gradually changes with ICG capacity.•Importance of multi-criterion decision-making after multi objective optimization.
Expanding existing Internal Combustion Generator (ICG) systems by combining renewable energy sources is getting popular due to global concern on emission of green house gases (GHG) and increasing fossil fuel costs. Life cycle cost, initial capital cost (ICC), power supply reliability of the system, and GHG emission by ICG are factors to be considered in this process. Pareto front of Levelized Energy Cost (LEC)–Unmet Load Fraction (ULF)–GHG emission was taken in this study for four different expansion scenarios. Furthermore, Pareto front of ICC–LE–ULF was taken for three different expansion scenarios in order to analyze the impact of renewable energy integration. The results clearly depict that characteristics of the Pareto front varies with the scale of expansion and objectives taken for the optimization. A detailed analysis was conducted for a scale up problem with a 4kVA ICG by using the Pareto fronts obtained.
With higher depletion rates of fossil fuels and the ever growing environmental concerns on Green House Gas (GHG) emissions it has become important to investigate the impact of Internal Combustion ...Generator (ICG) capacity in Hybrid Energy System (HES)s for standalone applications. In order to accomplish this objective HES modeling, simulation, and optimization was done for three different system configurations based on the renewable energy source. Both mono and multi objective optimization was carried out using Evolutionary Algorithm considering Levelized Energy Cost (LEC) and unmet load fraction as objective functions. Results clearly depicts that seasonable variation of renewable energy sources having a strong impact on system component selection under higher power supply reliability which gradually reduce with the increase of ICG capacity.
► Capability of Internal Combustion Generator (ICG) to reduce the impact of seasonal variation in renewable energy sources. ► ICG capacity is having a significant impact of system configuration, which also depends upon the power supply reliability. ► Necessity of improving energy storage technology and dispatch strategy to reduce waste of renewable energy.
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•A novel method introduced to optimize Electrical Hubs.•Novel dispatch based on fuzzy control and finite state machines.•Evaluating sensitivity of three performance indices for system ...autonomy.•Multi objective optimization considering system autonomy-cost.•Electrical Hubs can cover above 60% of the demand using wind and Solar PV.
A paradigm change in energy system design tools, energy market, and energy policy is required to attain the target levels in renewable energy integration and in minimizing pollutant emissions in power generation. Integrating non-dispatchable renewable energy sources such as solar and wind energy is vital in this context. Distributed generation has been identified as a promising method to integrate Solar PV (SPV) and wind energy into grid in recent literature. Distributed generation using grid-tied electrical hubs, which consist of Internal Combustion Generator (ICG), non-dispatchable energy sources (i.e., wind turbines and SPV panels) and energy storage for providing the electricity demand in Sri Lanka is considered in this study. A novel dispatch strategy is introduced to address the limitations in the existing methods in optimizing grid-integrated electrical hubs considering real time pricing of the electricity grid and curtailments in grid integration. Multi-objective optimization is conducted for the system design considering grid integration level and Levelized Energy Cost (LEC) as objective functions to evaluate the potential of electrical hubs to integrate SPV and wind energy. The sensitivity of grid curtailments, energy market, price of wind turbines and SPV panels on Pareto front is evaluated subsequently. Results from the Pareto analysis demonstrate the potential of electrical hubs to cover more than 60% of the annual electricity demand from SPV and wind energy considering stringent grid curtailments. Such a share from SPV and wind energy is quite significant when compared to direct grid integration of non-dispatchable renewable energy technologies.
Continued timing observations of the double pulsar PSR J0737–3039A/B, which consists of two active radio pulsars (A and B) that orbit each other with a period of 2.45 h in a mildly eccentric ...(e=0.088) binary system, have led to large improvements in the measurement of relativistic effects in this system. With a 16-yr data span, the results enable precision tests of theories of gravity for strongly self-gravitating bodies and also reveal new relativistic effects that have been expected but are now observed for the first time. These include effects of light propagation in strong gravitational fields which are currently not testable by any other method. In particular, we observe the effects of retardation and aberrational light bending that allow determination of the spin direction of the pulsar. In total, we detect seven post-Keplerian parameters in this system, more than for any other known binary pulsar. For some of these effects, the measurement precision is now so high that for the first time we have to take higher-order contributions into account. These include the contribution of the A pulsar’s effective mass loss (due to spin-down) to the observed orbital period decay, a relativistic deformation of the orbit, and the effects of the equation of state of superdense matter on the observed post-Keplerian parameters via relativistic spin-orbit coupling. We discuss the implications of our findings, including those for the moment of inertia of neutron stars, and present the currently most precise test of general relativity’s quadrupolar description of gravitational waves, validating the prediction of general relativity at a level of 1.3×10^{-4} with 95% confidence. We demonstrate the utility of the double pulsar for tests of alternative theories of gravity by focusing on two specific examples and also discuss some implications of the observations for studies of the interstellar medium and models for the formation of the double pulsar system. Finally, we provide context to other types of related experiments and prospects for the future.
Energy systems undergo major transitions to facilitate the large-scale penetration of renewable energy technologies and improve efficiencies, leading to the integration of many sectors into the ...energy system domain. As the complexities in this domain increase, it becomes challenging to control energy flows using existing techniques based on physical models. Moreover, although data-driven models, such as reinforcement learning (RL), have gained considerable attention in many fields, a direct shift into RL is not feasible in the energy domain irrespective of the ongoing complexities. To this end, a top-down approach is used to understand this behavior by reviewing the current state of the art.
We classified RL papers in the literature into seven categories based on their area of application. Subsequently, publications under each category were further examined relative to problem diversity, RL technique employed, performance improvement (compared with other white and gray box models), verification, and reproducibility; many of the articles reported a 10–20% performance improvement with the use of RL. In most studies, however, deep learning techniques and state-of-the-art actor-critic methods (e.g., twin delayed deep deterministic policy gradient and soft actor-critic) were not applied. This has remarkably hindered performance improvements and problems related to complex energy flows have not been considered. Approximately half of the publications reported the use of Q-learning. Furthermore, despite the availability of historical data in the energy system domain, batch RL algorithms have not been exploited. Emerging multi-agent RL applications may be considered as a positive development that can enable the management of complex interactions among multiple parties. Most studies lack proper benchmarking compared to model-based approaches or gray-box models, and a majority cover energy dispatch problems and building energy management. Although RL can adequately solve problems that are considerably integrated in several sectors, only a limited number of publications have discussed its broad application. The present study clearly demonstrates that even without the full utilization of RL capacity, this technique has a considerable potential in resolving the continuously increasing complexity within the energy system domain.
•Publications of the energy system domain are divided into 11 subgroups and reviewed.•Many publications report 10–20% performance improvement.•Deep learning techniques and state-of-the-art actor-critic methods were not used by many articles.•Batch reinforcement learning algorithms have not been used besides wide availability of historical data.•Reinforcement learning has a notable potential which has not been utilized.
We report a microfluidic paper based analytical device implementing ion concentration polarization (ICP) for rapid pre‐concentration of Escherichia coli in water. The fabricated device consists of a ...paper channel with a Nafion® membrane and in‐built micro wire electrodes to supply electric voltage to induce the ICP effect. E. coli cells were stained with SYTO 9 and fluorescence was used as a sensing method. The device achieved high concentration factor up to 2 × 105 within minutes. The effect of total ion concentration, on ICP and fluorescence intensity was studied. The reported device and method are suitable and effective for detection of E. coli during ballast water quality monitoring, coastal water quality monitoring where high salinity water is present.
Severe undernutrition among under-5 children is usually assessed using single or conventional indicators (i.e., severe stunting, severe wasting, and/or severe underweight). But these conventional ...indicators partly overlap, thus not providing a comprehensive estimate of the proportion of malnourished children in the population. Incorporating all these conventional nutritional indicators, the Composite Index of Severe Anthropometric Failure (CSIAF) provides six different undernutrition measurements and estimates the overall burden of severe undernutrition with a more comprehensive view. This study applied the CISAF indicators to investigate the prevalence of severe under-5 child undernutrition in Bangladesh and its associated socioeconomic factors in the rural-urban context.
This study extracted the children dataset from the 2017-18 Bangladesh Demographic Health Survey (BDHS), and the data of 7661 children aged under-5 were used for further analyses. CISAF was used to define severe undernutrition by aggregating conventional nutritional indicators. Bivariate analysis was applied to examine the proportional differences of variables between non-severe undernutrition and severe undernutrition group. The potential associated socioeconomic factors for severe undernutrition were identified using the adjusted model of logistic regression analysis.
The overall prevalence of severe undernutrition measured by CISAF among the children under-5 was 11.0% in Bangladesh (rural 11.5% vs urban 9.6%). The significant associated socioeconomic factors of severe undernutrition in rural areas were children born with small birth weight (AOR: 2.84), children from poorest households (AOR: 2.44), and children aged < 36 months, and children of uneducated mothers (AOR: 2.15). Similarly, in urban areas, factors like- children with small birth weight (AOR: 3.99), children of uneducated parents (AOR: 2.34), poorest households (APR: 2.40), underweight mothers (AOR: 1.58), mothers without postnatal care (AOR: 2.13), and children's birth order ≥4 (AOR: 1.75), showed positive and significant association with severe under-5 undernutrition.
Severe undernutrition among the under-5 children dominates in Bangladesh, especially in rural areas and the poorest urban families. More research should be conducted using such composite indices (like- CISAF) to depict the comprehensive scenario of severe undernutrition among the under-5 children and to address multi-sectoral intervening programs for eradicating severe child undernutrition.
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