This paper introduces a new black-box approach for time domain modeling of commercially available single-phase photovoltaic (PV) inverters in low voltage networks. An artificial neural network is used as a nonlinear autoregressive exogenous model to represent the steady state behavior as well as dynamic changes of the PV inverter in the frequency range up to 2 kHz. The data for the training and the validation are generated by laboratory measurements of a commercially available inverter for low power applications, i.e., 4.6 kW. The state of the art modeling approaches are explained and the constraints are addressed. The appropriate set of data for training is proposed and the results show the suitability of the trained network as a black-box model in time domain. Such models are required, i.e., for dynamic simulations since they are able to represent the transition between two steady states, which is not possible with classical frequency-domain models (i.e., Norton models). The demonstrated results show that the trained model is able to represent the transition between two steady states and furthermore reflect the frequency coupling characteristic of the grid-side current.

This article presents a non-destructive measurement-based identification of the dc-link capacitance in single-phase power electronic devices for modeling purposes. These devices are typically found in public low voltage networks for low power applications. A large number of manufacturers of these devices with individual approaches to the design of the components of the power electronic devices challenges the modeling and thus the analysis of their interaction with other devices and the low voltage network. Since the manufacturers typically do not disclose their design, only little information based on the assumptions can be made with regard to an individual device. This article provides a new method to identify the dc-link capacitance of single-phase power electronic devices based on analyzing the ripple part of the dc-link voltage, which contributes to the present research activities toward realistic modeling of such devices for power systems studies. The analytical background is provided and a simulative validation is performed. Finally, a laboratory application to a commercially available single-phase inverter for photovoltaic systems is demonstrated in terms of real measurement.

With increasing adoption of supply-dependent energy sources like renewables, Energy Storage Systems (ESS) are needed to remove the gap between energy demand and supply at different time periods. During daylight there is an excess of energy supply and during the night, it drops considerably. This paper focuses on the possibility of energy storage in vertically stacked blocks as suggested by recent startups. An algorithm is proposed based on conceptual constraints, to allow for removal and storage of excess electrical energy in the form of gravitational potential energy. To improve these results further, the concepts of wasted energy and unmet demand are used to develop a new mathematical model which aims to minimize the maximum unmet demand in all time periods using lowering and stacking of blocks at specific locations. Simulation results show that for time periods up to a week long, this storage system is able to shift blocks stored in a tower of vertically stacked blocks to reduce unmet demand significantly. This is augmented by storing extra energy from a photovoltaic system, taking account of stochasticity and temporal variability. The authors therefore conclude based on a sensitivity analysis that this system and its associated mathematical optimization can be feasible when scaled up to meet ancillary-level grid storage applications.

Across the world, the impact of increasing electric vehicle (EV) adoption requires a better understanding. The authors hypothesize that the introduction of EV’s will cause significant overloading within low voltage distribution grids. To study this, several low voltage networks were reconstructed based on the literature and modelled using DigSilent Powerfactory, taking into account the stochastic variability of household electricity consumption, EV usage, and solar irradiance. The study incorporates two distinct usage scenarios—residential loads with varying EV penetrations without and with distributed grid tied generation of electricity. The Monte-Carlo simulation took into account population demographics and showed that in urban networks, EV introduction could lead to higher cable loading percentages than allowed, and in rural networks, this could lead to voltage drops beyond the allowed limits. Distributed generation (DG) in the form of solar power could significantly offset both these overloading characteristics, as well as the active and reactive power demands of the network, by between 10–50%, depending on the topology of the network.

Harmonic resonance is increasingly observed in public low-voltage (LV) networks of central European countries. Adverse effects, such as harmonic disturbance amplification, can be caused by harmonic resonance, which necessitates and emphasizes the importance of their continuous monitoring and detection. This article proposes a noninvasive harmonic resonance detection technique for public LV networks that rely only on the measurement of voltage and current at the LV busbar. The proposed technique is formulated in three stages, each identifying a different characteristic of harmonic resonance: the presence of harmonic resonance, resonant harmonic order and bandwidth, and resonant intensity. The performance of each stage is first verified by extensive simulation. Then, using measurement data at three different LV networks, it is validated that the proposed technique can reliably track the three different characteristics of harmonic resonances accurately and continuously.