Sharif Digital Repository

This work, based on the standard unscented Kalman filter (UKF), proposes a modified UKF for highly non-linear stochastic systems, assuming that the associated probability distributions are normal. In the standard UKF with 2n + 1 sample points, the estimated mean and covariance match the true mean and covariance up to the third order, besides, there exists a scaling parameter that plays a crucial role in minimising the fourth-order errors. The proposed method consists of a computationally efficient formulation of the unscented transform that incorporates N - 1, N ≥ 2, constant parameters to scale 2n(N - 1) + 1 sample points such that the 2Nth order errors are minimised. The scaling parameters... 

The unprecedented increasing penetration of distributed energy resources, mainly harvesting renewable energies, is a direct result of environmental concerns. These types of energy resources bring about more uncertainties in the power system and, consequently, necessitate probabilistic analyses of the system performance. This paper develops a new approach for probabilistic optimal power flow (P-OPF) by adapting the unscented transformation (UT) method. The heart of the proposed method lies in how to produce the sampling points. Appropriate sample points are chosen to perform the P-OPF with a high degree of accuracy and less computational burden in comparison with the features of other... 

The unprecedented increasing penetration of distributed energy resources, mainly harvesting renewable energies, is a direct result of environmental concerns. These types of energy resources bring about more uncertainties in the power system and, consequently, necessitate probabilistic analyses of the system performance. This paper develops a new approach for probabilistic optimal power flow (P-OPF) by adapting the unscented transformation (UT) method. The heart of the proposed method lies in how to produce the sampling points. Appropriate sample points are chosen to perform the P-OPF with a high degree of accuracy and less computational burden in comparison with the features of other... 

As a matter of course, the unprecedented ascending penetration of distributed energy resources, mainly harvesting renewable energies, is a direct consequence of environmental concerns. This type of energy resource brings about more uncertainties in power system operation and planning; consequently, it necessitates probabilistic analyses of the system performance. This paper develops a new approach for probabilistic load flow (PLF) evaluation using the unscented transformation (UT) method. The UT method is recognized as a powerful approach in assessing stochastic problems with/without correlated uncertain variables. The capability of the UT method in modeling correlated uncertain variables is... 

This paper investigates the optimal operation of distribution feeder reconfiguration (DFR) strategy in the smart grids with high penetration of plug-in electric vehicles (PEVs) and correlated wind power generation. The increased utilization of PEVs in the system with stochastic volatile behavior along with the high penetration of renewable power sources such as wind turbines (WTs) can create new challenges in the system that will affect the DFR strategy greatly. In order to reach the most efficiency from the PEVs, the idea of vehicle-to-grid (V2G) is employed in this paper to make a bidirectional power flow (either charging/discharging or idle mode) strategy when providing the main charging... 

In natural gas transmission networks, the efficiency of daily operation is strongly dependent on our knowledge about the customer future demands. Unavailability of accurate demand forecasts makes it more important to be able to characterize the loads and quantify the corresponding uncertainty. A planning strategy for natural gas transmission networks under future demand uncertainty is addressed, which involves coupling of a gas transmission network dynamic simulator with the stochastic optimization framework. Loads from a gas-fired power plant are studied where the loads are characterized by a number of uncertain parameters, and unscented transform is utilized for uncertainty propagation.... 

As energy resource planning associated with environmental consideration are getting more and more challenging all around the world, the penetration of distributed energy resources (DERs) mainly those harvesting renewable energies (REs) ascend with an unprecedented rate. This fact causes new uncertainties to the power system context; ergo, the uncertainty analysis of the system performance seems necessary. In general, uncertainties in any engineering system study can be represented probabilistically or possibilistically. When sufficient historical data of the system variables is not available, a probability density function (PDF) might not be defined, while they must be represented in another... 

As a matter of course, the unprecedented ascending penetration of distributed energy resources, mainly harvesting renewable energies, is a direct consequence of environmental concerns. This type of energy resource brings about more uncertainties in power system operation and planning; consequently, it necessitates probabilistic analyses of the system performance. This paper develops a new approach for probabilistic load flow (PLF) evaluation using the unscented transformation (UT) method. The UT method is recognized as a powerful approach in assessing stochastic problems with/without correlated uncertain variables. The capability of the UT method in modeling correlated uncertain variables is... 

Hastening the power industry reregulation juxtaposed with the unprecedented utilization of uncertain renewable energies (REs), faces power system operation with sever uncertainties. Consequently, uncertainty assessment of system performance is an obligation. This paper reviews the probabilistic techniques used for probabilistic optimal power flow (P-OPF) studies and proposes a novel and powerful approach using the unscented transformation (UT) method. The heart of the proposed method lies in how to produce the sampling points. Appropriate sampling points are chosen to perform the P-OPF with a high degree of accuracy and less computational burden compared with features of other existing... 

The rapid rise in the grid integration of low-carbon technologies, e.g. renewable energy sources (RESs) and plug-in electric vehicles (PEVs), has led to several challenges in distribution networks (DNs). This is due to the intermittent generation of RESs and uncertain loads of PEVs, both of which necessitate enhancing the flexibility requirements at the distribution level so as to accommodate the high penetration of these clean technologies in the future. To address such issues, this study proposes a mixed-integer linear programming-based expansion planning model for DNs considering the impact of high RES and PEV penetration, the associated uncertainties, and providing flexibility...