A Novel Hybrid Approach for Optimal Placement of Non-Dispatchable Distributed Generations in Radial Distribution System

The objective of the present paper is to study the optimum installation of Non-dispatchable Distributed Generations (NDG) in the distribution network of given sizes under the given scheme. The uncertainty of various random (uncertain) parameters like load, wind and solar operated DG besides uncertainty of fuel prices has been investigated by the three-point estimate method (3-PEM) and Monte Carlo Simulation (MCS) based methods. Nearly twenty percent of the total number of buses are selected as candidate buses for NDG placement on the basis of system voltage profile to limit the search space. Weibull probability density function (PDF) is considered to address uncertain characteristics of solar radiation and wind speed under different scenarios. Load uncertainty is described by Standard Normal Distribution Function (SNDF). To investigate the solution of optimal probabilistic load flow (OPLF) three-point PEM-based technique was applied. For optimization, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GA-PSO hybrid-based Artificial Intelligent (AI) based optimization techniques are employed to achieve the optimum value of the multi-objectives function. The proposed multi-objective function comprises loss and different costs. The proposed methods have been applied to IEEE 33- bus radial distribution network. Simulation results obtained by these techniques are compared based on loss minimization capability, enhancement of system bus voltage profile and reduction of cost and fitness functions. The major findings of the present study are the PEM-based method which provides almost similar results as MCS based method with less computation time and as far as loss minimization capacity, voltage profile improvement etc. is concerned, the hybrid-based optimization methods are compared with GA and PSO based optimization techniques.

Optimal location of UPFC based on sensitivity of CDF with power system’s stochasticity

In this paper, a stochastic sensitivity algorithm is introduced to optimize the location of unified power flow controller (UPFC) in large scale power grid. The stochastic sensitivities are defined as the total operation cost of power system to control parameters of UPFC. Firstly, probability optimal power flow (POPF) model with power system’s randomness is established. Then point estimate method (PEM) is utilized to solve the above problem, so that the stochastic sensitivities of UPFC in all possible transmission lines could be obtained. Finally, by sorting the influence degree of UPFC at different locations to cumulative distribution function (CDF) of operation cost, the optimal location for UPFC could be selected correspondingly. To this end, IEEE-5 and IEEE-14 systems are employed to verify our proposed approach. The results show that installing UPFC by the method in this paper could significantly reduce the probability distribution of operation cost in higher region.

Bulk power system availability assessment with multiple wind power plants

The use of renewable non-conventional energy sources, as wind electric power energy and photovoltaic solar energy, has introduced uncertainties in the performance of bulk power systems. The power system availability has been employed as a useful tool for planning power systems; however, traditional methodologies model generation units as a component with two states: in service or out of service. Nevertheless, this model is not useful to model wind power plants for availability assessment of the power system. This paper used a statistical representation to model the uncertainty of power injection of wind power plants based on the central moments: mean value, variance, skewness and kurtosis. In addition, this paper proposed an availability assessment methodology based on application of this statistical model, and based on the 2m+1 point estimate method the availability assessment is performed. The methodology was tested on the IEEE-RTS assuming the connection of two wind power plants and different correlation among the behavior of these plants.

Probabilistic Power Flow Analysis Based on Point-Estimate Method for High Penetration of Photovoltaic Generation in Electrical Distribution Systems

Environmental awareness and energy policies led to decarbonization targets, fostering the adoption of distributed energy resource in the distribution network. Particularly, photovoltaic systems have been gaining momentum due to cost-competitive option and financial benefits. However, traditional distribution networks were not designed for intermittency in power generation. This poses technical issues such as reverse power flow, overvoltage, and thermal overloading. Furthermore, the growth in intermittency and variability of distributed energy resources increases the uncertainty, hence, it brings challenges for the operation, planning, and investment decisions. In this context, probabilistic methods to cater for these uncertainties are essential to address this issue. This paper presents a probabilistic power flow method based on point estimate method combined Edgeworth expansion for high penetration of photovoltaic generation in distribution networks. Normal distribution and Beta distribution are considered for load and solar irradiation modelling, respectively. The method is assessed for different cases using the IEEE 33-bus distribution test system with photovoltaic systems installation. The point estimate method combined Edgeworth expansion provided satisfactory results with lower computational effort and high fitting accuracy of statistical information compared to Monte Carlo simulation.

Water Cycle Algorithm for Probabilistic Planning of Renewable Energy Resource, Considering Different Load Models

This work introduces multi-objective water cycle algorithm (MOWCA) to find the accurate location and size of distributed energy resource (DERs) considering different load models for two seasons (winter, and summer). The impact of uncertainties produced from load and renewable energy resource (RES) such as wind turbine (WT) and photovoltaic (PV) on the performance of the radial distribution system (RDS) are covered as this is closer to the real operation condition. The point estimate method (PEM) is applied for modeling the RES uncertainties. An optimization technique is implemented to find the multi-objective optimal allocation of RESs in RDSs considering uncertainty effect. The main objectives of the work are to maximize the technical, economic and environmental benefits by minimizing different objective functions such as the dissipated power, the voltage deviation, DG cost and total emissions. The proposed multi-objective model is solved by using multi-objective water cycle algorithm (MOWCA), considering the Pareto criterion with nonlinear sorting based on fuzzy mechanism. The proposed algorithm is carried out on different IEEE power systems with various cases.

Global Sensitivity Analysis applied to Model Inversion Problems: A Contribution to Rail Condition Monitoring

Rising demands on railroad infrastructure operator by means of profitability and punctuality call for advanced concepts of Prognostics and Health Management. Condition based preventive maintenance aims at strengthening the rail mode of transport through an optimized scheduling of maintenance actions based on the actual and prognosticated infrastructure condition, respectively. When applying model-based algorithms within the framework of Prognostics and Health Management unknown model parameters have to be identified first. Which of these parameters should be known as precisely as possible can be figured out systematically by a sensitivity analysis. A comprehensive global sensitivity analysis, however, might be prohibitive by means of computation load when standard algorithms are implemented. In this study, it is shown how global parameter sensitivities can be calculated efficiently by combining Polynomial Chaos Expansion and Point Estimate Method principles. The proposed framework is demonstrated by a model inversion problem which aims to recalculate the track quality by measurements of the vehicle acceleration, i.e. analyzing the dynamic railway track-vehicle interaction.