Conceptual framework for composite power system health analysis

This paper presents a neural network approach intended to aid electricity consumers or power system planner in the task of classification of harmonic load types using the measurements (data samples) collected from one of the power station in Malaysia. In order to allow the classification of the type of harmonic loads, harmonic currents order produced by aggregate harmonic loads and the level of emission are modelled using the Multi-Layer Extreme Learning Machine with autoencoder (hereinafter denoted as ML-ELM-AE). The feasibility of ML-ELM-AE on the classification of the Harmonic empirical data set will be probed, for when the classification results of the Harmonic load types become available, it can come in handy to power system analyst or engineers for analysis in later stage. They can use it to determine the harmonic distortion patterns and to characterize the harmonic currents at network buses. Depending on whether the aggregate load is residential, commercial, or industrial, the load characteristics in terms of its harmonic contents are likely to be different. The achieved results demonstrate the effectiveness of the investigated technique in dealing with the real world power system application of harmonic load type classification, that can be useful in providing good indication to the power system or distribution network planner.

Redundancy and hardening of components in transmission systems are practices to improve system resilience against possible adverse consequences caused by natural hazards. However, power systems planners do not usually recognize the natural hazards, so-called high-impact low-probability (HILP) events, within network investment decisions. The situation is exacerbated by an increase in the frequency and severity of power system blackouts due to HILP-type events. In this context, this paper proposes a planning problem for investment in transmission lines and hardening of substations to improve the resilience of power system considering the outage scenarios originated by earthquakes. The problem is formulated as a mixed-integer linear programming (MILP) approach. Furthermore, a probabilistic methodology is used to simulate the hazard and its impacts on the power system. Our proposal is applied on the Ecuadorian transmission system. The results demonstrate that hardening substations and investment in lines can significantly contribute towards enhancing resilience to high impact exogenous natural hazards.

In recent years, power demand has increased day by day while the expansion of power generation and transmission has been limited due to limited resources and environmental restrictions. Flexible Alternating Current Transmission Systems (FACTS) devices play an important role in improving the power system performance. Flexible AC transmission systems devices have been mainly used for solving various steady states, dynamic and transient control problems of power system. TCSC is an important member of FACTS family. In a power system, TCSC device has high potential in applications because it may improve power system performance including power flow control, transfer capability enhancement of the transmission system, transient stability enhancement and sub-synchronous resonance (SSR) mitigation etc. As other FACTS devices, TCSC is also a costly device. Therefore this is utmost essential to find its optimal size and location in a power system to achieve maximum benefit of this device. This complicated problem of finding optimal location and sizing of TCSC has been solved by researchers in different ways by considering different objectives. This paper presents a brief review of the work carried out by researchers in this field.

Reliability assessment techniques and programs handling is an important issue for both power systems planning and testing in existing power system configurations. The Assessment techniques are suitable for detecting weak points in the reliability assessment. The reliability study of bulk power systems indicates the ability of the composite generation and transmission system to satisfy the load demand at major load points. The major burden of the developed methods used is the computation time required to solve a large number of credible contingencies or outage states. This paper presents a novel approach capable of the reliability evaluation for real large-size networks using normal size computers. It calculates the reliability indices at individual load buses and reliability of the whole system. The effect of the loading factor between loads on reliability is also investigated. The proposed approach is expanded to calculate the reliability of composite generation and transmission system taking the following constraints into consideration: amount of reliability of system generation, amount of reliability of transmission lines, maximum system generation capacity, maximum transmission lines capacity and maximum connected load at each bus. The approach is based on the minimal path set and dual fault tree techniques. A new concept of constant reliability region, which is utilized in the field of optimum operation of power networks, is introduced. A special program is developed for the proposed technique. The comparison between the proposed and the previous techniques confirms that the proposed method is more accurate and precise.

In this study we present a comprehensive forward-looking portfolio simulation methodology for assessing the correlated impacts of market risk, private sector and Sovereign credit risk, and inter-bank default risk. In order to produce better integrated risk assessment for banks and systemic risk assessments for financial systems, we argue that reasonably detailed modeling of bank asset and liability structures, loan portfolio credit quality, and loan concentrations by sector, region and type, as well as a number of financial and economic environment risk drivers, is required. Sovereign and inter-bank default risks are increasingly important in the current economic environment and their inclusion is an important model extension. This extended model is demonstrated through an application to both individual Brazilian banks (i.e., 28 of the largest banks) and groups of banks (i.e., the Brazilian banking system) as of December 2004. When omitting Sovereign risk, our analysis indicates that none of the banks face significant default risk over a 1-year horizon. This low default risk stems primarily from the large amount of government securities held by Brazilian banks, but also reflects the banks' adequate capitalizations and extraordinarily high interest rate spreads. We note that none of the banks which we modeled failed during the very stressful 2007-2008 period, consistent with our results. Our results also show that a commonly used approach of aggregating all banks into one single bank, for purposes of undertaking a systemic banking system risk assessment, results in a misestimate of both the probability and the cost of systemic banking system failures. Once Sovereign risk is considered and losses in the market value of government securities reach 10% (or higher), we find that several banks could fail during the same time period. These results demonstrate the well known risk of concentrated lending to a borrower, or type of borrower, which has a non-zero probability of default (e.g., the Government of Brazil). Our analysis also indicates that, in the event of a Sovereign default, the Government of Brazil would face constrained debt management alternatives. To the best of our knowledge no one else has put forward a systematic methodology for assessing bank asset, liability, loan portfolio structure and correlated market and credit (private sector, Sovereign, and inter-bank) default risk for banks and banking systems. We conclude that such forward-looking risk assessment methodologies for assessing multiple correlated risks, combined with the targeted collection of specific types of data on bank portfolios, have the potential to better quantify overall bank and banking system risk levels, which can assist bank management, bank regulators, Sovereigns, rating agencies, and investors to make better informed and proactive risk management and investment decisions.

The basic purpose of an electric power system is to supply its consumers with electric energy as parsimoniously as possible and with a sensible degree of continuity and quality. It is expected that the solicitation of power system reliability assessment in bulk power systems will continue to increase in the future especially in the newly deregulated power diligence. This paper presents the research conducted on the three areas of incorporating multi-state generating unit models, evaluating system performance indices and identifying transmission paucities in complex system adequacy assessment. The incentives for electricity market participants to endow in new generation and transmission facilities are highly influenced by the market risk in a complex restructured environment. This paper also presents a procedure to identify transmission deficiencies and remedial modification in the composite generation and transmission system and focused on the application of probabilistic techniques in composite system adequacy assessment

This paper proposes a novel approach for Optimal Power Flow (OPF) problems in power systems by integrating the optimal placement of Flexible Alternating Current Transmission System (FACTS) devices with a hybrid renewable energy system comprising wind and photovoltaic (PV). The objective is to optimize the placement of FACTS devices to simultaneously minimize power loss, voltage deviation and reduce gross cost. To achieve this goal, the Enhanced Multi – Strategies Sparrow Search Algorithm (EMSSA) is employed, which combines multiple optimization strategies inspired by the collaborative foraging behavior of sparrows. By considering multiple objectives, including power loss reduction, voltage deviation reduction, and gross cost minimization, the proposed methodoloty aims to identify optimal locations for installing FACTS devices such as SVC (Static Var Compensator), TCSC (Thyristor Controlled Seris Compensator), TCPS (Thyristor Controlled Phase Shifter). The results showcase the capability of the proposed methodology to achieve significant improvements in power system performance, thereby contributing to the efficient and sustainable operation of modern power grids. This research also provides valuable insights for power system operators and planners to make decisions regarding the deployment of FACTS devices and the integration of renewable energy sources.

In the traditional risk assessment of power system, the analysis of transmission system and distribution system were conducted separately. However, the power supply reliability of the distribution network users is not only related to the reliability of the distribution network components, but also to the power supply capability of the transmission system to the distribution system. The risk assessment method of transmission and distribution system that conducted separately cannot reflect the impact of changes in the transmission network on the reliability of power supply in distribution system. In this paper, a risk assessment method for transmission-distribution integrated system is proposed. Risk assessment is carried out in the transmission system first according to the load state of the system to evaluate the operation risk of the transmission system, and to obtain the probability distribution of the bus power of the transmission network. The probability distribution of bus power of transmission network is substituted into the root node of distribution network. Considering the possible island operation mode with the distributed PV connected in the distribution network, the loss of load in the case of component fault of distribution network is analyzed, and the risk index of load loss of the system is calculated. Finally, correctness and effectiveness of the proposed method are validated by a modified IEEE RTS and IEEE 33-bus system.

Modern electric power systems are large and complicated, and, in many regions, the generation and transmission systems are operating near their limits. Eigenanalysis is one of the tools used to analyze the behavior of these systems. Standard eigenvalue methods require that simplified models be used for these analyses; however, these simplified models do not adequately model all of the characteristics of large power systems. Thus, new eigenanalysis methods that can analyze detailed power system models are required. The primary objectives of the work described in this report were I) to determine the availability of eigenanalysis algorithms that are better than methods currently being applied and that could be used an large power systems and 2) to determine if vector supercomputers could be used to significantly increase the size of power systems that can be analyzed by a standard power system eigenanalysis code. At the request of the Bonneville Power Administration, the Pacific Northwest Laboratory (PNL) conducted a literature review of methods currently used for the eigenanalysis of large electric power systems, as well as of general eigenanalysis algorithms that are applicable to large power systems. PNL found that a number of methods are currently being used for the this purpose, and all seem to work fairly well. Furthermore, most of the general eigenanalysis techniques that are applicable to power systems have been tried on these systems, and most seem to work fairly well. One of these techniques, a variation of the Arnoldi method, has been incorporated into a standard power system eigenanalysis package. Overall, it appears that the general purpose eigenanalysis methods are more versatile than most of the other methods that have been used for power systems eigenanalysis. In addition, they are generally easier to use. For some problems, however, it appears that some of the other eigenanalysis methods may be better. Power systems eigenanalysis requires the computation of eigenvalues of nonsymmetric matrices. Such computations are fairly difficult, however, and they constitute an area of active research. Thus, research in this area should be closely monitored, and, as new methods become available, they should be tested on large power systems. PNL also investigated the use of vector supercomputers to enlarge the size of power systems that can be analyzed by MASS, a standard power system eigenanalysis code. MASS was converted to run on a Cray supercomputer. On a conventional computer, MASS is limited to power system problems with about 500 states because of computer time constraints. Running MASS on a Cray X-MP EA/232, however, PNL found that a problem with about 2200 states could be solved in about 26 minutes. Furthermore, by moving to a larger Cray, it should be possible to use MASS to analyze power systems with 5,000 to 10,000 states. Problems with 5,000 states would probably take about 5 Cray hours, while a problem with 10,000 states would probably take about 43 Cray hours, though the actual execution times will depend on the type of Cray used. Problems requiring 5 Cray hours are not uncommon. Problems requiring 43 Cray hours, however, would be fairly expensive. Thus, power systems with about 5000 states probably represent an upper limit on the size of problems that one would want to routinely solve using a Cray version of MASS.

Power systems can be analyzed using either a deterministic or a probabilistic approach. The deterministic analysis centers on studying the quantities and indicators that characterize the operating states of the power system under strictly defined conditions. However, the long-term horizon of planning analyses, the changes of marketing mechanisms, the development of renewable electricity sources, the leaving from large-scale generation, the growth of smart technology and the increase in consumer awareness make the development of transmission networks a non-deterministic problem. In this article, we propose a planning procedure that takes the probabilistic elements into account. This procedure was developed to take into account the high variability of power flows caused by the generation of renewable sources and international exchange. Such conditions of the power system operation forced a departure from deterministic planning. The new probabilistic approach uses the existing tools and experience gained during subsequent development projects. As part of the probabilistic approach, simulations were carried out using the Latin Hypercube Sampling and Two Point Estimation Method algorithms. These methods effectively reduce the computation time and, at the same time, give satisfactory results. The verification was carried out on a test grid model developed in accordance with the technical standards used in the Polish Power System. Effects were assessed using a deterministic and probabilistic approach. This analysis confirmed the practical possibility of using the probabilistic approach in planning the development of transmission network in Poland. When using a probabilistic approach to predict power flow, the criteria of technical acceptability for a given development variant and the manner in which the strategy is determined are of particular importance.

Microgrids open up a new arena of power generation, distribution, and consumption. Microgrids have the ability to deliver reliable and clean power. It enhances grid reliability through its ability to isolate from the utility grid under any abnormal conditions and work in standalone mode, then reconnect and synchronize again with the grid without load interruption. The growing installation of renewable energy resources creates higher interactions between transmission and distribution systems, which increases fears of the impact of power electronics devices on the stability and power quality metrics of the utility grid. Such studies require complete modeling and simulation of the power electronics interfacing microgrids with the power system. This paper presents a co-simulation framework for a microgrid connection with a transmission system. The framework uses Power System Simulator for Engineering (PSS/E) as a power system analysis tool and electromagnetic transient simulator (PSCAD) which is used to model the microgrid system and its control. The paper also investigates the impact of a large-scale microgrid on the power quality of the grid network. The total harmonic distortion (THD) in the current and voltage at the point of common coupling (PCC) is investigated and the obtained results are compared with the limits specified by the international standards. The co-simulation results are aimed to show that the inclusion of Distributed Energy Resources (DERs) in the transmission system works within the allowed limits and standards.

A long-term capacity expansion planning model for an electric power system integrating large-size renewable energy technologies

A high increase in the installed capacities of Renewable Energy Sources (RES) is expected by 2020 and beyond in Europe, and power systems planners and regulators have to make sure that the power systems are designed and operated to make optimal use of the potential of these clean energy sources. This paper investigates the consequences of integrating large-scale solar and wind power in the future Northwestern European power system. A zonal market model is used, where unit commitment and economic dispatch simulations for a given development scenario of load and generation, including wind power and solar power time series and inter-zonal transmission constraints, are with hourly resolution. The ability of the power system to absorb the available “green” energy is assessed. Moreover, factors that can hamper or improve the RES integration are identified by performing a sensitivity analysis on a subset of the scenario attributes.

For the research on crucial technologies of range-extended electric vehicle, the first problem to be solved is parameter matching and efficiency optimization for range-extended electric vehicle power and transmission system. Parameter matching and optimization of range-extended electric vehicle power and transmission system are multi-objective optimization problem. Evaluation and analysis of multi-objective optimization problem should be mutually independent and balanced. With the aim of guaranteeing vehicle’s comprehensive performance, a parameter matching and optimization method for range-extended electric vehicle power and transmission system is proposed in this paper. First, the house of quality model of range-extended electric vehicle is established to determine weight coefficient of vehicle performance indicator based on market requirements instead of experience. Based on co-simulation control model which is established in Matlab-Simulink and AVL-Cruise, 40 groups of orthogonal tests are performed, and the sensitivity of characteristic parameters is analyzed to explore the coupling law among vehicle performance indicators, so as to clarify the entry point for parameter matching and optimization. The simulation results show that the characteristic parameters not only have a significant influence but also have a coupling effect on the vehicle performance indicators. The analysis of variance shows that there is a limitation in optimal level combination of various factors only by range. Then, particle swarm optimization algorithm is selected to optimize the parameters of range-extended electric vehicle power and transmission system based on sensitivity analysis results obtained above. The study reveals that it is more efficient and reasonable to match the range-extended electric vehicle power and transmission system with a smaller battery capacity and a “medium-sized” auxiliary power unit which can achieve adequate dynamic performance, lower purchase cost, longer driving range and less energy consumption. Finally, a comparative simulation between the range-based analysis and particle swarm optimization-based analysis is conducted, the simulation results indicate that the optimized design parameters solution can significantly improve the technical indicators of the vehicle.

Intelligent search based techniques such as genetic algorithm (GA) have been proposed to deal with reliability evaluation of complex power systems recently. In this type of methods, the guided search is carried out on a population scale trying to find all the dominant failure states, based on which different reliability indices can be calculated accordingly. However, the process may be time-consuming when power flow analysis is involved in deciding the status of a system state in complex power systems such as composite systems. To speed up the computing process, parallel implementation of GA is proposed in this study by using multi-deme based search, where multiple subpopulations are distributed in different processors. In this way, simultaneous search is achieved through parallel implementation. An IEEE reliability test system is used for simulation studies. It turns out that the proposed parallel method is effective in increasing the computing efficiency of GA when it is used for reliability evaluation of composite power systems.