An open-source tool for reliability analysis in radial distribution grids

We present an open-source software implementation of an approximate contingency enumeration approach for calculating reliability in distribution grids based on RELRAD. The tool is coded using the efficient programming language Julia, to ensure fast and scaleable calculations. The network topology is mapped as a graph. This allows us to efficiently determine load points affected by contingencies by using standard graph algorithms. The tool is demonstrated on a simple synthetic test system and an actual Norway distribution grid.acceptedVersio

Probabilistic operational planning using dynamic programming with time-domain simulations

A dynamic programming model with time-domain simulations of contingencies is created to find the least-costly operational strategies according to a probabilistic operational criterion with selected preventive and corrective actions. The results show that the operational model can identify strategies which appear to be satisfactory according to a static analysis, but where the system response in the time-domain is violating the systems operational requirements. The model calculates the costs related to many possible operating strategies compared to models which only search parts of the solution space. This can be useful for TSOs that want to use the model for decision support. However, computational times are very limiting due to the time-domain simulations. Consequently, approaches for limiting the number of scenarios or speeding up the time-domain simulations should be investigated.publishedVersio

Modelling of corrective actions in power system reliability analysis

Consequence analysis, including the modelling of corrective actions, is an important component when performing power system reliability analyses. Using an integrated methodology for power system reliability analysis, we investigate the impact of different modelling choices for the consequence analysis on estimates for the energy not supplied. These investigations corroborate the large impact modelling assumptions for corrective actions have on the resulting reliability indices. We have also identified other features of the consequence analysis, such as islanding and distributed slack, that can be important to take into account. The findings and the underlying structured approach contribute to improving the accuracy of power system reliability analyses.Modelling of corrective actions in power system reliability analysisacceptedVersio

A Novel Approach for Solving Security Constrained Optimal Power Flow Using the Inverse Matrix Modification Lemma and Benders Decomposition

With the increasing complexity of power systems, faster methods for power system reliability analysis are needed. We propose a novel methodology to solve the security constrained optimal power flow (SCOPF) problem that reduces the computational time by using the Sherman-Morrison-Woodbury identity and Benders decomposition. The case study suggests that in a 500 node system, the run time is reduced by 83.5% while ensuring a reliable operation of the system considering short- and long-term post-contingency limits and reducing the operational costs, compared to a preventive `N-1' strategy

Future control architecture and emerging observability needs

The paper presents the first findings from workpackage 'Increased Observability' in EU FP7 project ELECTRA. Accommodation of intermittent generation into the network and its reliable operation require a gradual evolution of the network structure and in particular improvement of its monitoring or observing. The present practices of observing distribution networks are quite limited and vary from country to country. New network architectures are expected to evolve in the close future, including web-of-cells (concept defined in ELECTRA), which will result in new control schemes, significantly different from the existing. Several new observability needs have to be met in order to secure operation of the future networks. © 2015 IEEE

Graph Convolutional Networks for probabilistic power system operational planning

Probabilistic operational planning of power systems usually requires computationally intensive and time consuming simulations. The method presented in this paper provides a time efficient alternative to predict the socio-economic cost of system operational strategies using graph convolutional networks. It is intended for fast screening of operational strategies for the purpose of operational planning. It can also be used as a proxy for operational planning that can be used in long term development studies. The performance of the model is demonstrated on a network inspired by the Nordic power system.Graph Convolutional Networks for probabilistic power system operational planningacceptedVersio

Frequency control and stability requirement on Hydro plants: System identification for performance and stability assessment

The Nordic transmission system operators (TSOs) have proposed new draft requirements for the providers of frequency containment reserves. These requirements include extensive tests to determine the dynamics of hydro power plants. The dynamics of the hydro power plant are used to verify whether or not the power plants qualify to provide these reserves. To be more precise, the tests require the power plant owners to measure the plants’ response when operating in open loop and with various sine sweep signals modulating the turbine governor setpoint. This is an intrusive approach and alternatives should therefore be investigated. In this work, three novel methods have been investigated as alternatives to the one proposed by the TSOs. The main novelties of these methods are that plants are allowed to continue closed loop operation during testing and that the added excitation is limited. The three methods are characterised by different requirements on input data, as follows: 1. Phasor measurement units (PMU) measurements close to the power plant, without added extra excitation. 2. Control system measurements from the power plant, without added extra excitation. 3. Control system measurements from the power plant, with added extra excitation. For all of the proposed methods it was analysed under which conditions the results are consistent (non biased). Although consistency cannot in general be guaranteed, it was argued that the bias due to lack of consistency is small. Moreover, the bias can be further reduced by adding external excitation to the identification procedure. To validate the methods, tests at two different power plants in the Norwegian power system were performed. The first test compared the PMU method to the one proposed in the coming draft requirements. From this test it can be seen that the PMU method yields similar results to the one proposed in the draft requirements. It was also shown that only one dataset is needed per operating state under investigation. That is true even if the method outlined in the draft requirements is used. This is an important observation as the draft proposes to use 10 tests per operating state under investigation. During the test at the other power plant it was demonstrated that the proposed method using control system measurements without added excitation could detect changes in the settings of the plant’s turbine governor (PID) parameters. Moreover, it was shown that this method is capable of estimating steady state gains of the governor controller that correspond very closely with the actual permanent droop setting of the plant . The methods were also demonstrated using the simulation softwares SIMULINK and PSS/E and a Monte Carlo Simulation (MCS) approach. This approach was used to investigate how large nonlinearities could be present before the results became too biased as well as some other aspects presented below. When using a PMU for the identification, the power system frequency is used as an estimate of the angular speed of the machine. Consequently, a natural question is, how large an error will this lead to? The MCS approach showed that frequency is indeed a good approximation of speed. This is perfectly true when studying the slowest turbine and governor dynamics, but for the faster dynamics there will be a bias in the estimate. When it comes to the performance of the three methods, the best results are obtained when the turbine governor uses angular speed of the rotor as feedback signal, and at the same time measurements from the power plant control system is utilized and extra excitation is added to the governor setpoint. It is possible to obtain a good estimate in the other cases too, but then some bias in the estimation should be expected, especially for the faster dynamics

Investigating G-parameters Modelling of Power Electronc Devices

In a world with an ever increasing population, the demand for energy is constantly on the rise. Most countries also agree, that a sustainable development should be ensured. To accomplish this, a higher penetration of renewables in the present energy system is needed. Unlike traditional power production like thermal and hydro power plants, many of the renewable energy production units can not produce electricity at the desired frequency without power electronic devices. HVDC grids have also been suggested, as a mean to electrically connect geographically distant areas, to reduce the problem of local weather systems influencing the output of many renewable sources. It should thus be quite evident, that good models of power electronic devices are needed, to analyse the system's behaviour, and in particular its stability. In this report a black-box measurement based modelling technique will be investigated, with its biggest advantage being, that no a priori knowledge of the device under investigation is needed. The thesis starts off with an introduction to the relevant theory, and an introduction to the methodology. In the theory part a suggestion for a new equation set to calculate the model and a stability criterion for the model is presented. The new equation set for calculating the model is also proven to be equivalent with the old one. In the rest of the thesis the focus is on developing a test bench for obtaining the model. Following a step by step approach, starting with simulations on a VSI developed in Simulink, to get acquainted with the methodology. Before a test bench was developed for testing the methodology on dc-dc converters, ultimately leading to tests conducted on a physical VSI

Vector fitting for estimation of turbine governing system parameters

With the introduction of more and more renewables into the power system both the inertia and the primary frequency reserves are expected to decrease. It is therefore a growing concern that the frequency quality will deteriorate. One way of mitigating these problems may be a more detailed monitoring of the generators providing the primary reserves. A promising approach for monitoring the generators is to identify turbine governing system parameters using system identification. This will allow for estimating the droop and the bandwidth of the governor, parameters that are important for the primary control. Furthermore, if this can be reliably done on ambient data, updated estimates of these parameters can be obtained relatively fast. In this paper we will look into how vector fitting can be used for this purpose. The algorithm possesses some interesting properties for automatically constructing models from ambient data. How this can be done will be presented together with results obtained using real data from the Norwegian power system. A simple criterion for reducing the obtained model order is also proposed