Due to the statistical uncertainty of loads and power sources found in smart grids, effective computational tools for probabilistic load flow analysis and planning are now becoming indispensable. In this research, we describe a unified simulation framework that allows quantifying the probability distributions of a set of observation variables as well as evaluating their sensitivity to potential variations in the power demands. The proposed probabilistic technique relies on the generalized Polynomial Chaos algorithm and on a region-wise aggregation/description of the time-varying load profiles. It is shown how detailed statistical distributions of some important figures of merit, which includes voltage unbalance factor in distribution networks, can be calculated with a two-orders of magnitude acceleration compared to standard Monte Carlo analysis. In addition, it is highlighted how the associated sensitivity analysis is of guidance for the optimal allocation and planning of new loads.

This work aims to implement and analyze the effect of the Single Minute Exchange of Die (SMED) implementation in the bean packaging operation in a company in east Minas Gerais, Brazil. Design/Methodology/Approach: The research methodology used was action research. Two cycles of action research were conducted; the first to carry out phase one of SMED, and the second to execute phases two and three. Originality/Research gap: There are few studies on the application of Lean Manufacturing tools in agroindustry. Some works present case studies, mainly in the food supply chain aiming to fill this gap. Regarding SMED applied in agribusiness, no work was found. Key statistical results: The implementation of this methodology allowed the reduction of setup time by around 58%, the distance travelled by operators in the process by approximately 50%, in addition to gains in a production capacity of 14%. Practical Implications: It is concluded that the application of the methodology caused an increase in the company’s productivity, as it was possible to obtain gains in productive capacity without changing the amount of hours worked or the number of employees involved in the production process. Limitations of the investigation: This methodology was applied only once and the challenges encountered were not documented.

This paper presents a framework to analyze the problem of real-time management of Smart Grids. For this purpose, the energy management is integrated with the power system through a telecommunication system. The use of Multiagent Systems (MAS) leads the proposed algorithm to find the best-integrated solution, taking into consideration the operating scenario and the system characteristics. With this framework it was possible to evaluate the design of the energy management and the impact of the algorithm developed in the MAS. In the same way, the data sent from the power system to be used for energy management have a direct impact on his behavior. The proposed framework is tested with the help of a microgrid, so the results may be replicated.

As a mini city with a range of activities, a university campus can be used as a simulation and living lab. Smartness is a quality of contributing to sustainable development and resilience, and results achieved in a smart campus can be extrapolated to real and larger city as well. Indicators and targets not only represent important tools for smart cities planning but also for civil society participation and engagement. This paper reports the experience of incorporating data science and IoT in university activities, through a continuous and smart monitoring system. The results related to energy efficiency are presented, in order to share the positive and negative aspects of indicators measurement and control, improving decision making addressed to university campus sustainability and smartness.

This study aims to present an architecture for the planning of microgrids (MGs) in order to support system operator decision. In short, the proposed strategy is an iterative procedure that tries to find the optimal size of distributed energy resource (DER), which attends the necessities of stakeholders. The architecture has five distributed and correlated stages named MG coordination, MG operation optimisation, reliability assessment, contingency assessment, and searching mechanism. Since the DER selection involves multiple criteria and interests of different parts, it requires a multi‐attribute decision system providing a list of possible configurations based on their relative importance as denoted by the stakeholders. Owing to that, the particle swarm optimisation is used to create the multidimensional space of search in which the optimal solution will be selected by means of Pareto front decision criteria. As a result, the architecture provides a candidate solution to optimal size (optimal rated power) of each DER, that must be installed in the MG in order to have an optimal balance between technical, economical, social, and environmental aspects. To have realistic results, such a strategy is performed on a case study of a potential campus MG program.