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This paper proposes a control strategy for the stable operation of the micro-grid dluring different operating modes while providing the DC voltage control and well quality DC-Ioads supply. The proposed method adapts the battery energy storage system (BESS) to employ the same control architecture for grid-connected mode as well as the islanded operation with no need for knowing the micro-grid operating mode or switching between the corresponding control architectures. Furthermore, the control system presents effective charging of the battery in the micro-grid. When the system is grid connected and during normal operation, AC grid converter balances active power to ensure a constant DC voltage while the battery has the option to store energy for necessary usage. In order to achieve the system operation under islanding conditions, a coordinated strategy for the BESS, RES and load management including load shedding and considering battery state-of-charge (SoC) and battery power limitation is proposed. Seamless transition of the battery converter between charging and discharging, and that of grid side converter between rectification and inversion are ensured for different grid operating modes by the proposed control method. MATLAB/SIMULINK simulations and experimental results are provided to validate the effectiveness of the proposed battery control system.

Massive heterogeneous big data residing at different sites with various types and formats need to be integrated into a single unified view before starting data mining processes. Furthermore, in most of applications and research, a single big data source is not enough to complete the analysis and achieve goals. Unfortunately, there is no general or standardized integration process; the nature of an integration process depends on the data type, domain, and integration purpose. Based on these parameters, we proposed, implemented, and tested a big data integration framework that integrates big data in the biology domain, based on the domain ontology and using distributed processing. The integration resulted in the same result as that obtained from the local integration. The results are equivalent in terms of the ontology size before the integration; in the number of added items, skipped items, and overlapped items; in the ontology size after the integration; and in the number of edges, vertices, and roots. The results also do not violate any logical consistency rules, passing all the logical consistency tests, such as Jena Ontology API, HermiT, and Pellet reasoners. The integration result is a new big data source that combines big data from several critical sources in the biology domain and transforms it into one unified format to help researchers and specialists use it for further research and analysis.

With the rapid development of big data, numerous industries have turned their focus from information research and construction to big data technologies. Earth science and geographic information systems industries are highly information-intensive, and thus there is an urgent need to study and integrate big data technologies to improve their level of information. However, there is a large gap between existing big data and traditional geographic information technologies. Owing to certain characteristics, it is difficult to quickly and easily apply big data to geographic information technologies. Through the research, development, and application practices achieved in recent years, we have gradually developed a common geospatial big data solution. Based on the formation of a set of geospatial big data frameworks, a complete geospatial big data platform system called BiGeo was developed. Through the management and analysis of massive amounts of spatial data from Sichuan Province, China, the basic framework of this platform can be better utilized to meet our needs. This paper summarizes the design, implementation, and experimental experience of BiGeo, which provides a new type of solution to the research and construction of geospatial big data.

With the increasing share of distributed energy resources on the electric grid, utility companies are facing significant decisions about infrastructure upgrades. An alternative to extensive and capital-intensive upgrades is to offer non-firm interconnection opportunities to distributed generators, via a coordinated operation of utility scale resources. This paper introduces a novel flexible interconnection option based on the last-in, first-out principles of access aimed at minimizing the unnecessary non-firm generation energy curtailment by balancing access rights and contribution to thermal overloads. Although we focus on solar photovoltaic (PV) plants in this work, the introduced flexible interconnection option applies to any distributed generation technology. The curtailment risk of individual non-firm PV units is evaluated across a range of PV penetration levels in a yearlong quasi-static time-series simulation on a real-world feeder. The results show the importance of the size of the curtailment zone in the curtailment risk distribution among flexible generation units as well as that of the “access right” defined by the order in which PV units connect to the grid. Case study results reveal that, with a proper selection of curtailment radius, utilities can reduce the total curtailment of flexible PV resources by up to more than 45%. Findings show that non-firm PV generators can effectively avoid all thermal limit-related upgrade costs.

Big data useful for epidemiological research can be obtained by integrating data corresponding to individuals between databases managed by different institutions. Privacy information must be protected while performing efficient, high-level data matching. Privacy-preserving distributed data integration (PDDI) enables data matching between multiple databases without moving privacy information; however, its actual implementation requires matching security, accuracy, and performance. Moreover, identifying the optimal data item in the absence of a unique matching key is necessary. We aimed to conduct a basic matching experiment using a model to assess the accuracy of cancer screening. To experiment with actual data, we created a data set mimicking the cancer screening and registration data in Japan and conducted a matching experiment using a PDDI system between geographically distant institutions. Errors similar to those found empirically in data sets recorded in Japanese were artificially introduced into the data set. The matching-key error rate of the data common to both data sets was set sufficiently higher than expected in the actual database: 85.0% and 59.0% for the data simulating colorectal and breast cancers, respectively. Various combinations of name, gender, date of birth, and address were used for the matching key. To evaluate the matching accuracy, the matching sensitivity and specificity were calculated based on the number of cancer-screening data points, and the effect of matching accuracy on the sensitivity and specificity of cancer screening was estimated based on the obtained values. To evaluate the performance, we measured central processing unit use, memory use, and network traffic. For combinations with a specificity ≥99% and high sensitivity, the date of birth and first name were used in the data simulating colorectal cancer, and the matching sensitivity and specificity were 55.00% and 99.85%, respectively. In the data simulating breast cancer, the date of birth and family name were used, and the matching sensitivity and specificity were 88.71% and 99.98%, respectively. Assuming the sensitivity and specificity of cancer screening at 90%, the apparent values decreased to 74.90% and 89.93%, respectively. A trial calculation was performed using a combination with the same data set and 100% specificity. When the matching sensitivity was 82.26%, the apparent screening sensitivity was maintained at 90%, and the screening specificity decreased to 89.89%. For 214 data points, the execution time was 82 minutes and 26 seconds without parallelization and 11 minutes and 38 seconds with parallelization; 19.33% of the calculation time was for the data-holding institutions. Memory use was 3.4 GB for the PDDI server and 2.7 GB for the data-holding institutions. We demonstrated the rudimentary feasibility of introducing a PDDI system for cancer-screening accuracy assessment. We plan to conduct matching experiments based on actual data and compare them with the existing methods.

In the context of increasing decentralised electricity generation, this paper evaluates the effect of different regulatory frameworks on the evolution of distribution networks. This problem is addressed by means of agent based modelling in which the interactions between the agents of a distribution network and an environment are described. The consumers and the distribution system operator are the agents, which act in an environment that is composed by a set of rules. For a given environment, we can simulate the evolution of the distribution network by computing the actions of the agents at every time step of a discrete time dynamical system. We assume the electricity consumers are rational agents that may deploy distributed energy installations. The deployment of such installations may alter the remuneration mechanism of the distribution system operator. By modelling this mechanism, we may compute the evolution of the electricity distribution tariff in response to the deployment of distributed generation.

To understand and solve various natural environmental problems, geoscience research activities are becoming increasingly dependent on the integration of knowledge, data, and algorithms from scientists at different institutes and with multiple perspectives. However, the facilitation of these integrations remains a challenge because such scientific activities require gathering numerous geoscience researchers to provide data, knowledge, algorithms, and tools from different institutes and geographically distributed locations. The pivotal issue that needs to be addressed is the identification of a method to effectively combine geoscience algorithms in a distributed environment to promote cooperation. To address this issue, in this paper, a scheme for building a distributed geoscience algorithm integration based on the Open Geospatial Consortium web service (OWS) specifications is proposed. The architecture of the geoscience algorithm integration, algorithm service management mechanism, XML description method for algorithm integration, and integrated model execution strategy are designed and implemented. The experiment implements the integration of geoscience algorithms in a distributed cloud environment and evaluates the feasibility and efficiency of the integrated geoscience model. The proposed method provides a theoretical basis and practical guidance for promoting the integration of distributed geoscience algorithms; this approach can help to aggregate the distributed geoscience capabilities to address natural challenges.

This paper evaluates the technical aspects of grid-connected photovoltaic (PV) systems and distributed energy resources (DERs) interconnection grid codes. The advanced functions of smart PV inverters and smart grid solutions are discussed as well as the gaps of the existing grid codes that hinder DER ancillary services. An online survey targeted the key stakeholders and industry experts have been conducted to investigate advanced inverters potential of providing DER ancillary services to distribution grids. The survey results are discussed in details and recommendations for the reactive power support of DER inverters and DER interconnection codes enhancements are presented.

Rural tourism has been developing vigorously, and rural community functions are becoming diversified in China. Therefore, this paper takes China as an example to explore how sustainable rural tourism affects rural community development in the long and short run over the period 1994–2020. Sustainable rural tourism can be measured using two indicators: total rural tourism revenue and number of rural tourists. Rural community development is measured by the number of rural community service institutions. Then, by incorporating other variables and using the autoregressive distributed lag bounds co-integration technique to perform an empirical analysis, we found that, whether in the long or short run, sustainable rural tourism always plays a positive and significant role in promoting rural community development. In particular, in the long run, rural infrastructure construction, rural ecological environment, agricultural fiscal expenditure, agricultural technological progress, and rural human capital are identified as the major forces behind rural community development. Meanwhile, in the short run, rural infrastructure construction, rural ecological environment, agricultural fiscal expenditure, agricultural technological progress, and rural human capital are also major drivers of rural community development. This paper contributes to the current literature by filling in the existing gaps in several aspects.

Rice is one of the most important staple foods for 70 percent of the population of the world. It is among the main cereal crops grown in different regions of Pakistan as food crop. Pakistan has very much potentials for growing the crop and the potential rice production sown area is estimated to be about 2724 thousand hectares. The purpose of this study is to examine the linkage between fertilizer consumption and rice production in Pakistan from 1984 to 2014. For checking the stationarity of the data, this study incorporated Augmented Dickey Fuller (ADF) and Phillips Perron (PP) unit root tests. Furthermore, the Johenson Co-integration test is used to detect the long-term relationship among the series. Likewise, on the basis of annual time series data the Autoregressive Distributed Lag (ARDL) model is employed to evaluate the impact of fertilizer consumption on the production of rice in Pakistan up to now. The results of ADF and PP unit root tests reveal that fertilizer consumption and water availability are integrated at I(0) whereas area and rice production are integrated at I(1).The empirical findings of Autoregressive Distributed Lag (ARDL) model indicate that area and fertilizer consumption for rice has a significant effect on the rice production in both short-run and long-run. In contrast, water availability has a significant effect on the rice production in the long-run but it was statistically insignificant in the short-run. The estimated equation remains stable from the period of 1984 to 2014 as showed by stability tests.