This study addresses the feasibility, sizing, and scheduling of an island microgrid involving photovoltaic, wind turbine, diesel generator, and battery energy storage systems. In addition, the end-use demand is expanded beyond residential electricity loads, as the full electrification on domestic water supply and transportation, in the forms of desalination and electric vehicles (EVs), are proposed in this study with integration to a renewable energy system focused micro-grid. A mixed integer linear programming model is constructed to explore the mutual interactions of desalination plant, vehicle-to-grid enabled EVs and residential load deferring on a micro-grid. A case study in Bruny Island, Australia was incorporated in the study, based on the existing infrastructure and future planning. Multiple scenarios were run utilizing real-world, fine resolution time-series data. Results were compared to investigate the impact of the polygeneration configuration and potential economic savings on household affordability. Our study displayed the advantage of electrifying and integrating household resources into the micro-grid, such as transportation and water filtration. The integration created a greater diversity in electricity end-uses, allowed for better scheduling and Renewable System management. In addition, the cost-benefit analysis of stationary battery storage units was modeled against the other forms of energy storage, such as electric vehicles and seawater desalination plant, with findings suggesting the potential economic advantage of storage other than grid connected batteries in a residential precinct.
The dam displacement is related to multiple factors such as time, temperature, water level and etc. And it presents a strong nonlinear and certain randomness.Neural network model because of its inherent characteristics can better simulate the dam displacement.Nowadays,It has methods to estimate the displacement of the dam by constructing physical model and BP neural network model.But BP neural network's training time is too long and the forecast effect is not very good.So this paper introduces Elm neural network model,establishs Elm neural network model of dam displacement early warning considering multiple factors to estimate the displacement.By a simple example and compared with BP neural network model to reflect the rationality and scientificity of this method.
The Central Dry Zone (CDZ) supports 10 million people whose livelihoods depend on dry-land agriculture and small-scale livestock rearing. The CDZ has the highest livestock concentration in Myanmar, but characteristics of livestock production and health in this region have not been evaluated in detail. There is a need to understand the opportunities and limitations and for livestock production in the CDZ in order to develop methods to improve livestock production and disease control, to enhance the financial returns and living standards and, under the one-health paradigm, improve the nutrition and health status of farmers. Therefore, the objectives of this research were to describe husbandry and livestock health management and attitudes of small-scale cattle, small ruminant and village chicken farmers and to explore farmer's behaviours towards the prevention of livestock diseases and the risk of acquiring zoonotic diseases from livestock.Cross-sectional studies were conducted with 613 cattle, sheep and goat and village chicken farmers in 40 villages of the CDZ and with 63 stakeholders associated with livestock trading. Farming practices were compared between different livestock ownership groups and logistic, ordinal and multinomial regression models were used to quantify the association between husbandry practices on livestock rearing outcomes (such as livestock health, biosecurity and income generation). Path analysis and multilevel mixed modelling were applied to identify factors that affect small-scale livestock farmers' decisions to vaccinate their livestock against Foot and Mouth Disease (FMD) and Newcastle Disease (ND). In addition, attitudes, beliefs and barriers to the application of recommended zoonotic disease prevention approaches and social networks of livestock movements and trading density were explored to identify their impact on farmer's perceptions on the risk of acquiring zoonotic diseases.Multispecies rearing was a frequent occurrence with 51.7% (95%CI: 42-61%) of farmers rearing more than one livestock species. Rearing animals to be sold as adults for slaughter (meat production) was more common for small ruminants (98.1%) and chickens (99.8%) compared to cattle (69.8%). A substantial proportion of farmers in the CDZ derived their main income from crop production (43.2%), followed by livestock production (23.1%).Patterns of grazing differed between seasons (p<0.05) for cattle, but not for small ruminants.
Component sizing optimization and technoeconomic feasibility studies for stand-alone power systems have been actively discussed in recent years. However, most models and studies overlook the importance of individual component performance, input data resolution and their integration with the system. This article compares the rapidly evolving battery storage technology applications using the context of a stand-alone system, through a case study on Bruny Island, Tasmania, Australia, where the current electricity infrastructure is approaching its end of life. We constructed a mixed-integer linear programming model to achieve optimal balance between modeling accuracy and computational complexity. The model was constructed to obtain the optimal component sizing on the basis of minimum lifetime net present cost. It was shown in our work that any transition of islands to become fully stand alone should be carefully considered, where a limited grid connection could still provide significant value to the infrastructure, greatly reduce its stress over peak hours. This concept was illustrated further with another set of model scenarios aiming to explore the performance of battery storage systems with different power output allowances, limited by the time-series data resolution, were developed. It was shown that the selection of incomplete input data cycle, reliance on standardized average values and utilization of low resolution timeseries data could heavily skew the results of a model, as the use of incomplete dataset and oversimplified average values subtly conceal some of the critical sizing and operation issues in a technoeconomic feasibility study, impacting the accuracy of the model to reflect the real problem. K E Y W O R D Scase study, energy storage, microgrid, MILP, optimization
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