Space Transformation-Based Interdependency Modelling for Probabilistic Load Flow Analysis of Power Systems

Dependence among random input variables affects importantly the results of probabilistic load flow(PLF),system economic operation,and system security.To solve this problem,the main objectiveness of the paper is to analyze the performance of several schemes for simulating correlated variables combined with the point estimate method(PEM).Unlike the existing works that considering one single scheme combined with Monte Carlo simulation(MCS) or PEM,by neglecting the correlation among random input variables,four schemes were presented for disposing the dependence of correlated random variables,including Nataf transformation /polynomial normal transformation(PINT) combined with orthogonal transformation(OT) / elementary transformation(ET).Combining with the 2m+1 approach of PEM,a space transformation-based formulation was proposed and adopted for solving the PLF.The proposed approach is applied in the modified IEEE 30-bus system while considering correlated wind generations and load demands.Numerical results show the effectiveness of the proposed approach compared with those obtained from the MCS.Results also show that the scheme of combining Nataf transformation and ET with PEM provides the best performance.

Uplifting the complexity of analysis for probabilistic security of electricity supply assessments using artificial neural networks

The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 min to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.

Evaluation of Uncertainties Related to the Near Field Model of Geological Repository for Spent Nuclear Fuel

In Slovakia, a direct disposal of spent nuclear fuel in a deep geological repository within the country after a certain period of interim storage is a preferred option. This paper briefly describes near field model of radionuclide migration developed in GoldSim simulation code environment and analyses the calculated results on time-dependent release rates of safety relevant radionuclides. Given the fact that GoldSimalso enables to perform probabilistic simulations using the Monte Carlo method, a probabilistic approach was chosen to assess the influence of selected near field parameter uncertainties related to radionuclide migration on the radionuclide release rates from the bentonite buffer to the surrounding host rock. Based on the results, release rates of nuclides which exceed their solubility limits are effectively lowered and many of nuclides are significantly sorbed on the buffer material. It can be seen that the variance of the total release rate in the case of solubility uncertainty is almost two orders of magnitude within a long period of time.

Probabilistic production simulation of a power system with wind power penetration based on improved UGF techniques

Universal Generating Function(UGF)techniques have been applied to Multi-State System(MSS)reliability analysis,such as long term reserve expansion of power systems with high wind power penetration.However,using simple steady-state distribution models for wind power and large generating units in reliability assessment can yield pessimistic appraisals.To more accurately assess the power system reliability,UGF techniques are extended to dynamic probabilistic simulation analysis on two aspects of modelling improvement.Firstly,a principal component analysis(PCA)combined with a hierarchal clustering algorithm is used to achieve the salient and time-varying patterns of wind power,then a sequential UGF equivalent model of wind power output is established by an apportioning method.Secondly,other than the traditional two-state models,the conventional generator UGF equivalent model is established as a four discrete-state continuous-time Markov model by Lztransform.In the construction process of such a UGF model,the state values are transformed into the integral multiples of one common factor by choosing proper common factors,thus effectively restraining the exponential growth of its state number and alleviating the explosion thereof.The method is suitable for reliability assessment with dynamic probabilistic distributed random variables.In addition,by acquiring the clustering information of wind power,the system reliability indices,such as fuel cost and CO_(2) emissions through different seasons and on different workdays,are calculated.Finally,the effectiveness of the method is verified by a modified IEEE-RTS 79 system integrated with several wind farms of historical hourly wind power data of Zhangbei wind farm in North China.

Resource pre-allocation algorithms for low-energy task scheduling of cloud computing

In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.