Accurate load modeling is essential for power system stability analysis and control. This topic has regained interest, due to the high penetration of new types of loads and the increased availability of measurements in extended power grids. In this paper, an aggregated load model based on measurement data is formulated for dynamic simulations of large power systems. The proposed model employs variable-order transfer functions, enabling the accurate simulation of complex load dynamics. A complete methodology for the automatic derivation of the minimum-required model order is proposed with the model parameters calculated via a robust multi-signal identification procedure. For this purpose, the Vector Fitting method is introduced as a technique for measurement-based load modeling. Several simulations are performed using the NEPLAN software to investigate the accuracy and the generalization capabilities of the proposed model. The model performance is thoroughly compared with other conventional load models, using also measurements recorded on a laboratory-scale microgrid.

In this letter, a new methodology is proposed to extend the applicability of conventional power flow methods to islanded microgrids (MGs), operating with the droop control method. A slack bus is considered to be connected at a random MG node. The operational conditions of this slack bus as well as of the rest MG nodes are determined through an iterative procedure. The objective of this procedure is to force the active and reactive power flowing through the slack bus to zero, representing the islanded MG operation. The proposed approach has a similar accuracy with time-domain simulations, while it reduces significantly the required computational burden.

Aggregated equivalent models for the dynamic analysis of active distribution networks (ADNs) can be efficiently developed using dynamic responses recorded through field measurements. However, equivalent model parameters are highly affected from the time-varying composition of power system loads and the stochastic behavior of distributed generators. Thus, equivalent models, developed through in-situ measurements, are valid only for the operating conditions from which they have been derived. To overcome this issue, in this paper, a new method is proposed for the derivation of generic aggregated dynamic equivalent models, i.e., for equivalent models which can be used for the dynamic analysis of a wide range of network conditions. The method incorporates clustering and artificial neural network techniques to derive robust sets of parameters for a variable-order dynamic equivalent model. The effectiveness of the proposed method is evaluated using measurements recorded on a laboratory-scale ADN, while its performance is compared with a conventional technique. The corresponding results reveal the applicability of the proposed approach for the analysis and simulation of a wide range of distinct network conditions.

In this paper, a centralized control strategy for low-voltage networks is proposed, aiming to coordinate the droop characteristics of the distributed renewable energy sources. The main objectives are the overvoltage mitigation and the optimal network operation in terms of maximum power injection and uniform power curtailment. The distinct features of the proposed method are the incorporation of droop-based control techniques into the conventional optimal power flow formulation and the use of the sensitivity theory as a means to significantly decrease the computational burden. The validity of the developed method is justified using a genetic algorithm, while extensive comparisons with conventional centralized and decentralized control strategies indicate its superior performance in the operation of low-voltage networks.