Research on network reconfiguration (NR) considering distributed generations (DG) is typically concerns on the issues of power loss, voltage deviation, DG sizing as well as its placement, which are important and required in the planning stage. On the other hand, another important aspect which often neglected in this stage is coordination of protection devices which is essential to prevent the network from damages following system breakdown. Without sufficient attention given to the protection coordination during NR, there is a possibility for the protective devices to miscoordinate and consequently lead to system blackout, due to changes in current flow and fault level. Therefore, this paper proposed an NR method for distribution networks with DG, incorporating protection devices. The proposed method aims to find the optimal configuration and DG size with minimum power loss, and at the same time ensuring protective devices operate correctly during normal and fault condition. Constraints on protection coordination and DG size are explicitly formulated in the proposed method. The validity of the proposed method is analyzed on three commonly used IEEE 33-bus, 69-bus and 118-bus distribution systems, employing the firefly algorithm (FA) and evolutionary programming (EP) algorithm. Comparative studies are done to prove the validity and robustness of the proposed method.

The conservation voltage reduction (CVR) and load modelling have acquired a renewed interest in reassessing the CVR capabilities and load model parameters for renewable-rich distribution networks. The underlying theoretical views behind the changes in load model parameters and subsequent CVR capabilities in the distribution networks caused by the high penetration of renewable generation have yet to be explored. This research has presented a mathematical analysis of equivalent network impedance with integrated distributed generations (DG) and further established a relationship of the equivalent network impedance with the static load model parameters and CVR to accurately assess the impact of DGs on the CVR. Then, this paper has demonstrated various case studies with IEEE 33 bus network to analyse and validate the changes in static load model parameters due to the integration of renewable DGs and batteries into the balanced and unbalanced power systems. The obtained results verify the increased CVR capabilities of a distribution network, caused by the higher active power exponent in the load model parameters, as affected by the integration of renewable DGs.

The authors present an innovative approach for probabilistic coupled electric vehicle (EV) and solar photovoltaics (PV) hosting capacity analysis in low‐voltage (LV) distribution networks. The challenges posed by system uncertainties and correlations between different parameters, such as PV generation and EV charging demand, are addressed using probabilistic modelling. To appropriately incorporate the geographical distribution and time‐variant patterns of EV charging demand, a comprehensive spatio‐temporal (ST) model is developed to capture the trip distance, EV arrival, and charging time. The correlation between the PV generation and EV charging demand is effectively captured by copula theory. The proposed models have been validated using actual EV charging and PV generation data from 36 Australian EV users over 1 year. Power flow simulation with actual data and modelled data have identified EV‐only and coupled EV‐PV hosting capacities in an Australian LV test network. The coupled EV‐PV model presents a higher level of accuracy, having an average mean absolute percentage error (MAPE) of 5.97% compared to independent EV profiles having a MAPE of 10.12%. A voltage profile analysis with the EV and PV profiles also validates the same trend, having MAPE of 1.5% and 1.95%, respectively, for coupled EV‐PV and independent EV profiles.

The growing number of residential Electric vehicles (EVs) are posing a significant challenge for grid operators around the world. This paper presents spatiotemporal (ST) modeling approach to address system uncertainties and copula theory to model correlations of EV charging demand with EV users' travel parameters (such as EV departure time, trip distance, and EV arrival time) and solar PV generation. Copula theory is employed to represent the correlations between these parameters, validated using the Kolmogorov-Smirnov (KS) test and quantile-quantile (q-q) plots. The case study presents EV hosting capacity in an Australian distribution network, adapting the modeled EV charging demand with real-world measured load demand profiles. The network EV hosting capacity is determined subject to voltage violation risk (VVR), line overloading, and transformer capacity violation. According to the results, the EV hosting capacity was found to be limited to 25% EV penetration due to the transformer capacity limit primarily. However, the EV hosting capacity could reach 50% without violating the VVR limit. The simulation results also indicate that for the chosen test network and load profile, the EV hosting capacity is slightly higher on weekends than on weekdays of the simulation period.

Higher penetration of solar PV and wind generation in distribution networks may change the conservation voltage reduction (CVR) capabilities. The uncertainties associated with the renewable generations and system loads are neglected in the deterministic CVR assessment. In this paper, a probabilistic framework for CVR assessment has been presented to assess the impact of uncertainties associated with renewable generations and system loads. A theoretical framework has been presented by establishing a mathematical relationship between the probability distribution of renewable generations (solar PV and wind) and the probability distribution of static exponential load model parameters. The simulation results have confirmed that the penetration of non-Gaussian solar PV and wind generation leads to non-Gaussian static exponential load model parameter distribution, which is validated by the normality tests (quantile-quantile plot, skewness, and kurtosis). Subsequently, the magnitude and probability distribution of the CVR capabilities of the network changes with the penetration of renewable generations, where the higher renewable penetration scenarios lead to higher CVR values and non-Gaussian (asymmetric) distribution.