Abstract
Achieving carbon neutral power systems is pushing for higher penetration of distributed energy resources (DER) in existing distribution systems. Accordingly, sophisticated, yet, practical tools for the optimal operation and management of active distribution systems (ADS) are in high need. In response to this necessity, this paper presents a novel and scalable tool for ancillary services procurement by distribution system operators (DSOs). The developed tool takes into consideration the inter-temporal and variable nature of DER in an uncertainty-aware approach. This tool is also suited for real-world implementation with large ADS, as it adopts a sequential linearization approach. As such, it allows DSOs to procure flexibility optimally from DERs embedded in ADS in the day-ahead operation planning timeframe, where congestion and voltage issues are managed.

Abstract
The increasing number of Power-Electronic (PE) interfaced devices in the new generation of distribution systems results in concerns about the power quality of modern grids. Besides the loads, the harmonic-injecting devices are increasingly penetrating the generation, storage, and delivering levels of energy dispatch systems in the microgrids and the LV networks which can be easily reflected in the primary distribution systems. As an economic, applicable, and efficient solution, the passive filters can be optimized and added to the grid to absorb the harmonics. Furthermore, in the presence of controllable devices such as PE-interfaced DGs and storage units, a coordination strategy can be implemented to actively decrease the effect of the nonlinear loads. Accordingly, the idea of a virtually-hybrid filter can be developed by the use of passive filters and the coordinated active harmonic filtering strategy. In this paper, by providing an explanation for the developed coordination strategy of active filters, the probabilistic techno-economic planning of virtually-hybrid filters is studied considering the different combinations of the linear and nonlinear loads in a modern primary distribution system. Simulation results have proved that the proposed method is capable of minimizing harmonic distortions and grid loss by the use of the optimal passive filters and the suggested coordination strategy of the active devices. © 2023 IEEE.

Abstract
The deviation of the demand levels of the modern LV distribution systems due to the more loads and distributed generations connected in the same grid leads to the loss of acceptable quality of voltage. These voltage quality problems occur in case of the high difference between the power of the loads and distributed generations in the same area. Accordingly, the high loading conditions lead to the bus voltage decrease while the bus voltage increment occurs in scenarios with the excess of generation. In this condition, the successful voltage stabilization in MV/LV substation can effectively suppress the deviations of the grid voltage values and increase the hosting capacity of the network. There are different custom power devices introduced in the literature which can provide the stabilization of voltage in the grids. In this paper, among the available tools, the application of Open-UPQC is examined in hosting capacity improvement maintaining a desired power quality level; this capability is provided through the successful voltage regulation in the different probable high/low loading scenarios in the grid. According to the results, while the uncoordinated operation of the series and shunt devices does not have the capability of stabilization of the base grid, the Open-UPQC has successfully maintained the voltage profile inside the limits in both the base case and in the presence of high load and PV penetration levels. It should be emphasized that the services of the Open-UPQC are provided in an economical and effective way making the solution strategy applicable in real-world cases. © 2023 IEEE.

Abstract
Modern electrical distribution networks are prone to more severe voltage fluctuations due to the presence of variable loads such as electric vehicles and renewable energy generation units. These fluctuations decrease both the quality of power and the hosting capability of the grid. In such a condition, a Dynamic Voltage Compensator (DVC) can be used to stabilize the voltage of the LV networks. DVC is generally designed to resolve voltage fluctuations reflected from MV systems maintaining the voltage on a constant value. However, it will more effectively improve the voltage quality in the grid if the reference voltage is dynamically adjusted based on measurements inside the LV system. On the other hand, the more complex measurement and coordination strategy may lead to the inapplicability of the methods. Hence, voltage reference adjustment strategies should be developed to conform to the availability of data and measurements inside the grid. Accordingly, in this paper, novel voltage reference adjustment strategies have been developed for DVC based on the measurements at the installation point of the device. In order to examine the proposed methods, they are applied to an LV grid with real measured data and the results are discussed. Based on the provided simulation results, the developed dynamic reference voltage adjustment strategies can successfully improve the quality of voltage and improve the hosting capacity of the LV network. © 2023 IEEE.

Abstract
Traditional use of predictive control techniques require the knowledge of the systems model to control and the use of constant cycle-time. In the case of a switched reluctance motor its model is highly nonlinear and time-varying with current magnitude and rotor position. The use of look-up tables has been one solution, but requires a complete knowledge of the motor and mismatches from the original model used in the design can happen due temperature variation or changes in operating regimes. To address these issues as well as to increase the tracking performance of current control, a model-free predictive algorithm is developed by updating the next cycle time of the next time step of the predictive control. A new parameter estimation method is proposed that identifies the parameters of the switched reluctance model with low computational burden. Based on knowledge of the parameters at real time, not only the ideal voltage vector is applied at each cycle but the ideal time that each cycle must have is also calculated. As result, the advanced current controller requires almost no knowledge of the motor in use. The performance of the proposed schemes is validated through simulation and by a prototype experimental setup. Experimental data shows a decreasing in prediction error around 78 per cent, when comparing to the predefined model controller.

Abstract
Storing energy efficiently is one of the main factors of a more sustainable world. The battey management system in energy storage plays an extremely important role in ensuring these systems' efficiency, safety, and performance. This battery management system is capable of estimating the battery states, which are used to give better efficiency, a long life cycle, and safety. However, these states cannot be measured directly and must be estimated indirectly using battery models. Therefore, accurate battery models are essential for battery management systems implementation. One of these models is the nonlinear grey box model, which is easy to implement in embedded systems and has good accuracy when used with a good parameter identification method. Regarding the parameter identification methods, the nonlinear least square optimization is the most used method. However, to have accurate results, it is necessary to define the system's initial states, which is not an easy task. This paper presents a two-outputs nonlinear grey box battery model. The first output is the battery voltage, and the second output is the battery state of charge. The second output was added to improve the system's initial states identification and consequently improve the identified parameter accuracy. The model was estimated with the best experiment design, which was defined considering a comparison between seven different experiment designs regarding the fit to validation data, the parameter standard deviation, and the output variance. This paper also presents a method for defining a weight between the outputs, considering a greater weight in the output with greater model confidence. With this approach, it was possible to reach a value 1000 times smaller in the parameter standard deviation with a non-biased and little model prediction error when compared to the commonly used one-output nonlinear grey box model.