Optimal stochastic scenario-based allocation of smart grids' renewable and non-renewable distributed generation units and protective devices

The smart grid reliability is dramatically affected due to system uncertainties. Although much efforts have been devoted to developing the Monte Carlo simulation (MCS)-based or analytical methods for reliability-based optimal allocation distributed generation (DGs) and protective devices (PDs), there is a research gap about developing the probabilistic scenario-based optimization methods. This paper proposes a novel stochastic scenario-based reliability evaluation method for optimal allocation of smart grids' PDs and DGs. The scenario reduction is applied using the k-means algorithm and modified system state, including the clusters of renewable-based DGs. The malfunction of PDs is concerned, which is one of the most important contributions of the introduced method. The introduced clustering-based reliability evaluation method is applied to IEEE 33-bus test system. Test results infer that around 10% inaccuracy occurs in deterministic approaches without considering uncertainties of DGs and PDs. Obtained test results also imply that the impacts of renewable DGs' uncertainties are more considerable than eventual malfunctions of PDs. The MC S-based methods are used to verify the precision of the introduced method. Moreover, by comparing the introduced method with other available analytical methods, it is shown that the obtained results are 1.2% more precise than current analytical ones.

Automated summary

The paper proposes a stochastic scenario-based reliability evaluation method for optimal allocation of smart grids' PDs and DGs. The method applies scenario reduction using the k-means algorithm and focuses on PDs malfunction, showing 1.2% more precision than current analytical methods.