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Integration of PV resources in distribution networks has become a world-wide trend. This has been necessitated by the ever-decreasing photovoltaic (PV) technology cost and the need for clean, carbon-emission-free power generation. Furthermore, the current situation which has resulted in high oil and gas prices has paved extra way for more integration of renewable energy (REs) technologies such as solar PV and wind. However, high proliferation of PV power has several negative effects for the grid. For example, there is a high likelihood of over-voltage occurrences in the network, potential reverse power flow (which can affect protection system operation) and line congestion which may lead to thermal capitulation of conductors and cables. It is, therefore, important to establish the limit of PV that can be injected in the network without stretching the system operating performance indicators into extreme violation, here-in called the PV hosting capacity (PVHC). PV hosting capacity is estimated either by deterministic means or by stochastic methods. Deterministic methods are very good at obtaining the siting and optimal sizing of mega PV plants. The weakness of deterministic methods is that they do not incorporate uncertainty in the load demand nor the uncertainty in the PV output. Thus, their models are mostly unrealistic. The stochastic methods are very good at incorporating uncertainties and model the system input random variables in a more realistic way. However, stochastic methods also suffer from unrealistically too many scenarios to be considered for an accurate analysis. This results in a huge computational burden and large memory requirement. Furthermore, in most of these estimations, the voltage limit is set as a hard constraint for estimating the PVHC. In this paper, we propose a 2-stage method employing both the deterministic and the stochastic approaches. The deterministic stage is used to obtain the optimum PV plant locations and the optimum sizing ratios. The stochastic stage is used for incorporating uncertainty in the input random variables. We also propose the use of probabilistic voltage violation framework to explore extra PV installation for slight voltage violation tolerant feeders or networks. This is then used to obtain output probabilistic maximum node voltages for different PV sizes, which are used to estimate the PV hosting capacity of a network. The efficacy and validity of the approach is demonstrated through numerical simulations conducted on IEEE test distribution networks.

The introduction of a large number of photovoltaic systems to distribution systems has increased the complexity of the voltage profile. It is thus necessary to manage voltage at a higher system level. For this purpose, the voltage profile of the distribution system can be calculated using state estimation and the voltage can be controlled based on the estimated values. Many methods for estimating voltage profiles using state estimation have been developed. However, the estimation accuracy of voltage profiles required for proper voltage control has not been discussed. If the assumed required estimation accuracy is too high, then more meters than necessary will be installed, unnecessarily increasing costs. Conversely, if the assumed required estimation accuracy is too low, voltage control equipment will not operate properly, and voltage violations may occur. Thus, it is important to determine the required estimation accuracy of voltage profiles for proper voltage control. In this paper, the estimation error of the voltage profile is expressed using a probability density function, and the estimation accuracy of voltage profiles required for proper voltage control is examined. A numerical case study was performed on a distribution network model with 2160 consumers, and revealed the following. To prevent voltage violations, an estimation accuracy of voltage profile that is approximately 10 times lower than that of the estimation accuracy based on current measurement equipment data is sufficient. To minimize the number of tap operations, an estimation accuracy needs to be improved by 70% compared with the estimation accuracy based on current measurement equipment data. [ABSTRACT FROM AUTHOR]

With many distribution networks adopting photovoltaic (PV) generation systems in their networks, there is a significant risk of over-voltages, reverse power flow, line congestion, and increased harmonics. Therefore, there is a need to estimate the amount of PV that can be injected into the distribution network without pushing the network towards these threats. The largest amount of PV a distribution system can accommodate is the PV hosting capacity (PVHC). The paper proposes an efficient method for estimating the PVHC of distribution networks that combines particle swarm optimization (PSO) and the gradient descent algorithm (GD). PSO has a powerful exploration of the solution space but poor exploitation of the local search. On the other hand, GD has great exploitation of local search to obtain local optima but needs better global search capabilities. The proposed method aims to harness the advantages of both PSO and GD while alleviating the ills of each. The numerical case studies show that the proposed method is more efficient, stable, and superior to the other meta-heuristic approaches. [ABSTRACT FROM AUTHOR]