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Electric power systems have experienced the rapid insertion of distributed renewable generating sources and, as a result, are facing planning and operational challenges as new grid connections are made. The complexity of this management and the degree of uncertainty increase significantly and need to be better estimated. Considering the high volatility of photovoltaic generation and its impacts on agents in the electricity sector, this work proposes a multivariate strategy based on design of experiments (DOE), principal component analysis (PCA), artificial neural networks (ANN) that combines the resulting outputs using Mixture DOE (MDOE) for photovoltaic generation prediction a day ahead. The approach separates the data into seasons of the year and considers multiple climatic variables for each period. Here, the dimensionality reduction of climate variables is performed through PCA. Through DOE, the possibilities of combining prediction parameters, such as those of ANN, were reduced, without compromising the statistical reliability of the results. Thus, 17 generation plants distributed in the Brazilian territory were tested. The one-day-ahead PV generation forecast has been considered for each generation plant in each season of the year, reaching mean percentage errors of 10.45% for summer, 9.29% for autumn, 9.11% for winter and 6.75% for spring. The versatility of the proposed approach allows the choice of parameters in a systematic way and reduces the computational cost, since there is a reduction in dimensionality and in the number of experimental simulations. [ABSTRACT FROM AUTHOR]

The design of the control system in an inverter‐based microgrid (μGs) is a challenging problem due to the large number of parameters involved. Different optimisation methods based on obtaining an approximated mathematical model of the μG can be found in the literature. In these approaches, the non‐linearities and uncertainties of the real system are typically not considered, which may result in a non‐optimal tuning of the control parameters. In addition, in most applications, the problem has been simplified, assuming that all controllers have the same value for their control parameters. However, in this case, the behaviour of the system is sub‐optimal since the particularities of each node of the μG are not taken into account. In this study, an experimental approach for tuning the control parameters of an inverter‐based μG is introduced. The approach is based on the methodology of design of experiments and it considers different values for the control parameters of all controllers. In this study, this methodology is applied to the design of a droop‐free control scheme; however, it can be easily extended to other control schemes. The validity of the proposal is verified through selected experimental results. [ABSTRACT FROM AUTHOR]

Response Surface Methodology is an effective framework for performing modelling and optimization of industrial processes. The Central Composite Design is the most popular experimental design for response surface analyses given its good statistical properties, such as decreasing prediction variance in the design center, where it is expected to find the stationary points of the regression models. However, the common practice of reducing center points in response surface studies may damage this property. Moreover, stationary and optimum points are rarely the same in manufacturing processes, for several reasons, such as saddle-shaped models, convexity incompatible with optimization direction, conflicting responses, and distinct convexities. This means that even when the number of center points is appropriate, the optimal solutions will lie in regions with larger prediction variance. Considering that, in this paper, we advocate that the prediction variance should also be considered into multiobjective optimization problems. To do this, we propose a multi-criteria optimization strategy based on capability ratios, wherein (1) the prediction variance is taken as the natural variability of the model and (2) the differences of expected values to nadir solutions are taken as the allowed variability. Normal Boundary Intersection method is formulated for performing the optimization of capability ratios and obtaining the Pareto frontiers. To illustrate the feasibility of the proposed approach, we present a case study of the turning without cutting fluids of AISI H13 steel with wiper CC650 tool. The results have supported that the proposed approach was able to find a set of optimal solutions with satisfactory prediction capabilities for both responses of interest (tool life T and surface roughness Ra), for a case with reduced number of center points, a saddle-shaped function for T and a convex function for Ra , with conflicting objectives. Although it was a response more difficult to control, the optimization benefited more Ra , which was a desired result. Finally, we also provide the sample sizes to detect differences between Pareto optimal solutions, allowing the decision maker to find distinguishable solutions at given levels of risk. • A multi-criteria optimization strategy based on model capability ratios is developed. • Normal boundary intersection method is used to optimize all the capability ratios. • A case study of the hard turning without cutting fluids of AISI H13 steel is proposed. • Distinguishable Pareto optimal solutions with acceptable prediction capabilities are obtained. • The optimization strategy benefited the most difficult response to control. [ABSTRACT FROM AUTHOR]