Electric microgrids require accurate dynamic models for operation, control, stability, and protection studies, then adequate load modeling plays an important role. This paper presents a two-stage adaptive approach to improve the generalization capability of load models obtained with the measurement-based modeling. The load model and their respective parameters are obtained through machine learning tools like decision trees (DTs) and optimization algorithms as ant colony (ACO). In the off-line stage of the proposed approach, several parameterized load models are optimally obtained using a database of microgrid disturbances. Then, the best model to represent each disturbance is defined using a similarity criterion. This model and the disturbance characteristics are integrated into a DT (classifier), while the characteristics and the model parameters are related in a second DT (predictor). These DTs are used in an on-line stage to swiftly determine the adequate parameterized load model in the case of a new disturbance in the microgrid. The approach's performance is compared with the conventional measurement-based load modeling in a modified CIGRE benchmark low voltage microgrid. The results evidence the advantages of the proposed adaptive approach for dynamic load modeling.

Performance analysis is challenging in modern power distribution networks due to the increasing penetration of distributed energy resources (DERs) interfaced by voltage source converters (VSCs). Parameter uncertainty, black-box models of the components, and low inertia are some of the issues that must be addressed. The latter can represent high vulnerabilities to sudden load changes in a microgrid (MG). Additionally, the islanded operation represents a challenge for inverter-based (DERs), where secondary control must regulate the microgrid frequency and voltage to its nominal values. When a secondary control strategy is implemented, it is necessary to validate its performance under several conditions. Most existing research papers focus on the microgrid’s small-signal and transient stability. At the same time, little has been done on the influence of the load model on voltage stability. This type of analysis is required to ensure an adequate transition between the grid-connected and stand-alone modes. This paper provides a voltage stability analysis of a microgrid, considering secondary control based on receding horizon and frequency-dependent load models. Simulation results demonstrate the robust performance of the secondary control and validate the importance of considering or adapting voltage stability indices to ensure adequate microgrid performance.

Load modeling is an important task in power system stability analysis and control. Taking this into account, the development of dynamic load models using a measurement-based load modeling strategy and an improved particle swarm optimization algorithm is presented in this paper. To accomplish this objective, a measurement-based parameter estimation method is used for identification of an exponential recovery load model. Measurements are obtained performing dynamic simulation of an IEEE 30-bus test system under several disturbances, and additionally, cross validation technique is applied for an analysis of load model generalization capability. An adequate load modeling improves the comprehension of load behavior and the capability of reproduce transient events on power systems.

This paper presents a methodology for under voltage load shedding using a metaheuristic optimization technique and a stability criterion. Two strategies are proposed to find the minimal size and location of load to shed for the recovery of normal operation conditions. The first one is based on a classical criterion for the under voltage load shedding, identifying the load to disconnect by considering bus voltage level; the second includes a simplified voltage stability index SVSI, which identifies critical buses in the system. The proposed methodology is implemented in an IEEE 14 bus test system, considering a heavy loading condition with andwithout contingency to validate its efficiency.

This paper proposes an iterative approach to adjust the control parameters of multiple power converters within a microgrid, which operates in grid-connected and grid-islanded modes. The adequate control parameters are determined using an optimisation-based strategy, local measurements, and results obtained in previous algorithm iterations. The proposed objective function is based on the integral time-weighted absolute error (ITAE), modified to improve the microgrid control performance. The proposed approach addresses the control tuning complexity by considering an incremental strategy, starting from adjusting basic microgrids, continuing to the setting of intermediate microgrids, and finalising when the target microgrid control is fine adjusted. The results obtained at the CIGRE LV benchmark microgrid validate the proposal, obtaining during faults, the maximum deviations from rated frequency around 2.2% and 0.3% in grid-islanded or grid-connected cases, respectively. Also, voltage and power references are adequately followed during steady-state regardless of the microgrid operation mode, where the steady voltage profile is below 10% variation. The obtained results demonstrate the proposed approach advantages, which straightforwardly adjust multiple converters integrated into a microgrid. The main contributions are as follows: (a) the microgrid model is not required, and (b) only measurements at the point of common coupling of the distributed energy resource are used; consequently, the proposed tuning approach is especially applicable to complex microgrids. Finally, finely adjusted microgrids are required for further operation or protection studies where complex system configurations are commonly required.

Technology development and decentralized operations create changes in conventional electric systems, where load modeling has been a challenge in dynamic analysis. Consequently, accurate dynamic load models are required to ensure the quality of the studies in current systems. This paper presents an automatic strategy based on clustering, classification, and optimization algorithms, to obtain the load models in the case of several system operating conditions. The obtained load models are helpful for the planning, operation, and protection of electric power systems. The proposed approach validation is performed using the IEEE 14-bus test system, where high performance is obtained. The average obtained cross-validation error for the load models assigned to the 13 clusters of disturbances is $5.36\times 10^{-3}$ . The cross-validation error is used as a tolerance value to determine when an online assigned load model is suitable to represent the measured disturbance. The proposed tests show the strategy’s capabilities of defining the load model online, making this approach suitable for field applications.