probabilistic load flow

The increasing rate of distributed generation resources expansion into power systems and the random nature of these resources have altered the operation and design of these networks, and reactive power management in distribution networks belongs to this category. The use of these resources in distribution networks is not without challenges and the lack of optimal management of reactive power may not bring economic efficiency for the network. Energy storage systems have the potential to solve this problem. Therefore, in this article, reactive power management in a microgrid connected to the main grid, taking into account distributed generation sources, energy storage systems and discrete reactive power compensating equipment, including capacitor banks, taking into account uncertainty in network load and Wind and solar power generation has been done. Finally, the efficiency of the method is demonstrated by numerical examinations on the distribution networks of 33 and 69 IEEE buses and in the GAMS optimization software.

Growth of wind distributed generation (DG) in the active distribution grids can impact on the operation and planning aspects of network. Therefore, this paper presents a probabilistic load flow problem to evaluate the impacts of wind unit uncertainties on the distribution systems. Proposed method is based on the cumulants technique that is a straightforward approach. This analytical method can assess the bus voltages of system in a probabilistic manner. Using correlated cumulants, a suitable methodology is presented to consider the correlation between wind generations. Furthermore, Weibull distribution is considered for modelling wind speed. Thereby, wind power curve is obtained. The proposed method is applied to the IEEE standard 33 bus test system, and the results are discussed.

Estimation of aggregated electric vehicle charging demand is essential for better design of future distribution networks. Given that the penetration level of electric vehicles is currently very low, Hence, the precise statistical data are not available to determine the initial state of charge related to electric vehicles. this paper proposes a new model for calculating the initial state of charge in electric vehicles. The proposed model is a function of fuel consumption of the conventional gasoline vehicles and all driving behaviors such as the use of air conditioning, vehicle speed, road slope etc. have been considered. Also, unlike most of the papers that have specific outputs for the distribution network parameters, in this paper, the mentioned parameters are presented as probability functions. Finally, the proposed method is applied to the IEEE-37 node test feeder and simulation results are presented to illustrate its performance.

Today's with the increasing development of distributed energy resources, power system analysis has been entered a new level of attention. Since the majority of these types of energy resources are affected by environmental conditions, the uncertainty in the power system has been expanded; so probabilistic analysis has become more important. Among the various methods of probabilistic analysis, point estimation methods have always been of interest to researchers. Using unscented transformation (UT) is one of these methods which have been introduced recently for probabilistic analysis. UT-based methods are powerful algorithms for probabilistic studies in which correlation between random variables could be considered.
In this paper, the use of spherical unscented transformation (SUT) in probabilistic load flow (PLF) is proposed. In order to evaluate the method and compare with existing procedures, a test power system is used. Simulation results show the proper performance of the proposed method as well as the higher speed than the existing methods. As a result, the proposed SUT-based PLF algorithm could be used as a fast and accurate method especially in large and complicated power systems.

Uncertainty analysis of voltage in distribution system is the main feature of current study. This paper presents a probabilistic analysis of radial distribution networks considering wind power resources. The wind turbines are modeled as probability distribution functions. Also, the loads are modeled with Gaussian and gamma distributions. Monte Carlo simulation is used for the probabilistic load flow process. Proposed method is applied to the IEEE 33-bus distribution test system and the results are analyzed. The results study validates the correctness and efficiency of the proposed technique.

Due to the development of photovoltaic distributed generation (DG) in distribution networks, these DGs can impact on the operation, planning and reliability of system. In this context, this paper has used a probabilistic load flow algorithm to assess the effects of photovoltaic generation uncertainties on the distribution networks. This method is based on the cumulants. It does not need complex computations like convolution method or high computational burden like Monte Carlo simulation. Proposed methodology can evaluate the correlation between input stochastic variables. In this paper, correlation between near wind units is analyzed. To estimate the probabilistic density functions of bus voltages, one of the most precise methods (maximum entropy) is used. Finally, suggested method is applied on the IEEE 33-bus distribution test system and the results are examined. The results analysis demonstrates the accuracy and effectiveness of the proposed method.

One of the basic components of future distribution networks are renewable energy resources (RER). The uncertainty in power production of renewable resources such as wind and solar as well as load is another characteristic of such networks. Conventional power flow methods as a basic tool for power system studies may not be suitable for active distribution networks such as microgrids. In this paper a heuristic load flow method considering the effects of intermittent behavior of RERs and load is modeled in probabilistic load flow algorithm. The method is suitable for both radial and weakly meshed distribution networks with RER for operation and planning of microgrids. Imperialist competitive algorithm (ICA) as heuristic based optimization algorithm is applied to formulate and solve the Probabilistic Load Flow (PLF). Based on PLF technique calculated parameters of the system such as bus voltages and feeders'' current¡ are extracted as random variables. A modified version of conventional IEEE 33-bus test system with RER is used as a weakly meshed network to evaluate efficiency and capability of the algorithm. Results are compared with Monte Carlo Simulation (MCS) method. The PDF and CDF of bus voltages¡ power of feeders'' and network total loss are compared. Based on the results the presented approach can solve the PLF problem regardless of the type of distribution network.