Petr Janecek

Transmission line identification using PMUs

The paper is devoted to the identification of transmission lines from synchronized measurements of current and voltage phasors provided by the phasor measurement units (PMUs). The series and shunt line parameters are recursively estimated using the extended Kalman filter (EKF). Actual line temperature and reference resistance are estimated instead of actual series resistance, which is then computed from these estimates. This approach can accommodate more information and, therefore, produce better estimates of the line resistance than the traditional approaches based on the weighted least squares method. Two case studies based on data from South Moravia and East Bohemia are presented to demonstrate the effectiveness of the proposed approach.

Monte-Carlo simulation of electricity transmission system operation ⋆

One of basic system services that an electricity transmission system operator guarantees is power balance maintenance. The aim of this service is to keep power export/import to/from surrounding interconnected electricity transmission systems at a proposed value. The power quantity is a random variable. Therefore, a control strategy of the quantity consists in keeping amplitudes of a power balance deviation in specified boundaries. The operator uses so called ancillary services for this purpose. In order to verify that power reserves of particular ancillary services are large enough for guaranteeing reliable operation of the electricity transmission system a Monte-Carlo simulator has been developed and implemented. The simulator models dynamical behavior of the system under the power reserves constraints.

Backward/forward probabilistic network state estimation tool and its real world validation

Increasing penetration of renewable energy sources into conventional power networks causes increase of the uncertainty level which reveals new research and technological challenges. High fidelity network state estimation in environment with significant share of intermittent energy sources is required for planning and operation activities performed by system operators. Probabilistic load flow methods stand for promising estimation techniques which can be applicable for environment with strong presence of uncertainty. Paper describes an estimation tool based on probabilistic backward/forward method. Special emphasis is given to the description of particular components of the estimation tool. Reliability and robustness of the tool is demonstrated on the results from real world validation.

On-line Ampacity Monitoring from Phasor Measurements

Abstract The knowledge of transmission line parameters involving series impedance and shunt admittance is crucial to power system analysis and power system state estimation. Particularly in areas of power systems protection it is critical to have accurate values of these parameters to determine the line ampacity. The line parameters change according to the load level and weather conditions, so it is necessary to identify the line parameters in real time. The paper deals with the on-line parameter identification of transmission lines from synchronized measurements of current and voltage phasors provided by the phasor measurement units. The series and shunt line parameters are recursively estimated from the phasor measurements and weather conditions using the extended Kalman filter. Contrary to the other works, an actual line temperature and reference resistance are estimated, which makes possible to monitor the actual state of the transmission line in order to determine an actual reserve or prediction of future transmission capacity. A case study based on data measured from a 110kV overhead transmission line is presented to demonstrate the effectiveness of the proposed approach.

Probabilistic assessment of the impact of dispersed generation on voltage quality

Dispersed generation sources are slowly penetrating into distribution power networks. Renewables represent the majority of newly installed energy sources, which are of stochastic nature. With this phenomenon, new technical challenges are rising up, where appropriate impact assessment of volatile dispersed generation on voltage quality stands for one of them. Current planning tools and optimization methods mostly rely on the deterministic key performance indicators (KPI), which assesses the power quality in a distribution network. These indicators may not be sufficient for power network with high presence of uncertainty. In this paper, power network model with emphasis on probabilistic description of power injections is described. Correlation among stochastic power injection is discussed and consequently this correlation is categorized into several groups. Main focus of the paper is on design of probabilistic key performance indicators for assessment of power quality in a distribution network. Two probabilistic load flow methods used for computation of introduced KPIs are presented and their performance is analyzed on selected test case. On the test case, KPIs evaluation is demonstrated and the results are analyzed.

A method for assessment of impact of RES integration onto ancillary services

High penetration of the renewable energy sources (RES) into the transmission power systems poses a number of new challenges for power network operators (e.g. TSO). Impact assessment of RES integration to power transmission system can constitute one of these challenges. In this paper, we present a method for impact assessment of the RES integration to determination of the volume of ancillary services reserves. The method updates the statistical properties (mean value, standard deviation) of local RES power prediction (namely prediction error), aggregates these properties and evaluates the impact of the prediction error of RES production to determination of volume of ancillary services reserves. Here the method is theoretically described and afterwards the application of the method is demonstrated in various energy market environments which are described in this paper as well.

The use of Coxian distribution in closed-form treatment of stochastic Petri nets

In this paper we propose a method for delay approximation in stochastic Petri nets. The method is based on utilization of Coxian distribution and least square fitting criteria. The problem is reduced to a direct parameters identification. Two examples illustrate the method application. The first one demonstrates approximation of power outage duration at a 200 MW power plant unit. Degree of a phase-type distribution resulting from a fork-join structure is reduced in the second example.