Energy Reports (Mar 2023)
A flexible stochastic PV hosting capacity framework considering network over-voltage tolerance
Abstract
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.
