Enhanced Contingency Analysis—A Power System Operator Tool

Guest Editorial: Situational awareness of integrated energy systems

Gerilim kararlılığı, güç sistemlerinde incelenmesi gereken önemli bir konudur. Sürekli artan elektrik enerjisi talebi, artan talebi karşılamak için yeni elektrik üretim santrallerinin kurulumu ve güç sistem arızaları gerilim kararlılığını olumsuz yönde etkilemektedir. Günümüzde gerilim kararlılığını iyileştirmek için güç sistemlerinde Esnek Alternatif Akım İletim Sistemleri (FACTS cihazları) kullanılmakta olup, ayrıca enerji talebini karşılamak ve gerilim kararlılığını iyileştirmek için güç sistemlerine Dağıtık Üretim Kaynakları (DGs) entegre edilmektedir. Bu çalışmada, IEEE 30 baralı güç sistemi ile Kuzeybatı Anadolu (KBA) güç sistemi, gerilim kararlılığı bakımından temel (nominal), aşırı yüklenme ve hat kopması durumlarında incelenmiştir. İncelenen güç sistemlerine FACTS cihazlarından Statik Var Kompanzatör (SVC) ve Tristör Kontrollü Seri Kapasitör (TCSC), DGs’lerden ise Yakıt Hücresi (YH), Güneş Enerji Santrali (GES) ve Rüzgâr Enerji Santrali (RES) ayrı ayrı ve birlikte bağlanmıştır. Güç sistemlerinin bara gerilim değerlerinde ve yüklenme parametresinde meydana gelen değişimler analiz edilmiştir.

In February 1998 a new and unique form of power generator, the Powerformer TM , was introduced into the marketplace. The Powerformer can be operated at transmission system level voltage without the need for a step up transformer and consequently has unique system support capabilities. The aim of the research work, presented in this thesis, was to investigate the unique aspects of the Powerformer from a power system perspective, in order to facilitate optimal power system operation and security.The main focus of the research undertaken by the author of this thesis was to investigate the impact of Powerformer on the relative voltage stability of a power system. The driving force behind the author's desire to look at the impact of the Powerformer on power system voltage stability and security was the realisation that voltage stability issues are becoming increasingly important. The focus for today's modem power transmission systems is undergoing a radical shift from a purely publicly provided service to a financially driven commodity. This ultimately means that power systems are increasingly under pressure to be utilised to their fullest capacity without the need to install new, expensive and sometimes socially unpopular system equipment and facilities. The problem with this situation is that a system's stability can be affected greatly by loading conditions. When a system is heavily loaded the voltage stability limit, rather than the transient angle stability limit constrains its operation. It is for this reason that voltage stability concerns in particular are increasingly being discussed in the literature and becoming an issue for power system operators. A number of different static and dynamic system analysis techniques have been reviewed and compared in this thesis, with the results of these comparisons presented and discussed in this thesis. Amongst the methods discussed in this thesis, methods for use in voltage stability analysis and assessment have been proposed and presented by the author of this thesis. The most suitable techniques are being utilised to determine how the Powerformer can aid in safe system operation under a number of different system contingencies in a number of different test systems, including the 600+ bus Queensland Transmission System. Particular emphasis has been placed in this research on long-term system dynamics. Two major aspects of the Powerformer that differ it from conventional are the lack of a step up transformer and the ability of the Powerformer stator to be overloaded for longer periods than conventional power generators. The lack of a step up transformer means that power that would normally be lost can be supplied to the system and that the high voltage bus, normally on the opposite side of the step up transformer, is directly controlled. The overload capability of the Powerformer means that it can supply reactive power support to the power system for a longer period than a conventional generator. Investigations undertaken into the Powerformer have shown it to have a potentially beneficial ability to allow greater system loading capability and therefore system security margins and a potentially beneficial impact on the time to voltage collapse following a system contingency.

Voltage instability and voltage collapse have been considered as a major threat to present power system networks due to their stressed operation. It is very important to do the power system analysis with respect to voltage stability. Flexible AC Transmission System (FACTS) is an alternating current transmission system incorporating power electronic-based and other static controllers to enhance controllability and increase power transfer capability. A FACTS device in a power system improves the voltage stability, reduces the power loss and also improves the load ability of the system. FACTS have made the power systems operation more flexible and secure. Amongst the several FACTS controllers, the Unified Power Flow Controller (UPFC) is most effective to improve the enhancement of voltage stability and reduces the power loss. This study investigates the application of Particle Swarm Optimization (PSO) to find sizing of Unified Power Flow Controller (UPFC) device to minimize the voltage stability index, total power loss, load voltage deviation, and cost of FACTS devices to improve voltage stability in the power system. A new model is proposed in this thesis to improve existing power-based model by using the Norton Equivalent Theorem. The proposed model can be integrated with the Equivalent Current Injection (ECI) power flow model easily. By ECI algorithm, it is much quickly and precisely to implement power flow calculations. Finally It is observed from the results that the voltage stability margin is improved, the voltage profile of the power system is increased and real power losses also reduced by optimally sizing UPFC device in the power system. Index Terms: Voltage stability, Unified Power Flow Controller (UPFC), Equivalent -Current Injection (ECI), Modified particle swarm optimization (MPSO)

Intermittency of wind energy pose a great challenge for power system operation and control. Wind curtailment might be necessary at certain operating condition to keep the line flow within limit. Remedial Action Scheme (RAS) offers quick control action mechanism to keep reliability and security of the power system operation with high wind energy integration. In this paper, a new RAS is developed to maximize the wind energy integration without compromising the security and reliability of the power system based on a specific utility requirements. A new Distributed Linear State Estimation (DLSE) is also developed to provide the fast and accurate input data for the proposed RAS. A distributed computational architecture is designed to guarantee the robustness of cyber system to support RAS and DLSE implementation. The proposed RAS and DLSE is validated using modified IEEE-118 Bus system. Simulation results demonstrate the satisfactory performance of the DLSE and effectiveness of RAS.

Integrating large-capacity wind power into a power system has a great impact on the voltage stability of a power system. Especially, a fault in such a system can result in the tripping of a large amount of wind power generation or system collapse. This article studies the effect of large-capacity wind power generation on power system voltage stability under a fault condition. The adopted wind power model consists mainly of a wind turbine, drive device, asynchronous generator, and control system. The proposed analysis approach is applied to a practical power system with wind power, which is located in Gansu province in China. Through simulation and analysis, the characteristic and stability principle of an asynchronous wind generator group are recognized. A speed increase in a wind generator group is closely related to voltage drop after fault, which may cause voltage instability if they both impact each other after fault. The detailed results are presented in the article.

Voltage instability is a major issue that attracts worldwide interest because of its consequence of voltage collapses. These days power systems operate closer to their stability limits, transferring large amounts of powers from long distance and an inadequate supply of reactive power contribute to system voltage instability and eventually lead to voltage collapse. So estimating the margin in the loadability of the power system is essential in the real time system for voltage stability assessment. This paper investigates the enhancement of voltage stability using Static Compensator (STATCOM) in a wind integrated power system with storage. Wind energy is gaining the most interest among a variety of renewable energy resources and this requires energy storage because it is necessary to get a smooth output from a wind turbine. A battery energy storage system is connected to the system which enhances the voltage levels in weak buses. The combined action of battery storage and STATCOM enhances the voltage stability of the entire system. The analysis is done with power system analysis toolbox (PSAT), a powerful toolbox in MATLAB for power system analysis.

Stress on the generation and transmission capability of the power system increased as the energy demand increases. Distributed generation (DG), which is usually placed near the load centre has the capability to meet these energy increment in demands. Hence, for making the power system highly durable and reliable, Distributed generations are interconnected with the existing power system. Based on the nature of the primary source of generation and the energy conversion technology these types of generation have important impacts on power system voltage stability. One of the most significant threats considered for a secure operation of the power system is voltage instability problem. Voltage collapse might occur in an interconnected power system due to voltage instability and thus lead to power blackouts. Hence in an interconnected power system it is essential to know the strength of the buses to monitor and improve the stability of the system. This paper focuses on voltage stability analysis of an interconnected distributed photovoltaic system with IEEE 30 bus system. The voltage stability of the system assessed by evaluating voltage stability indicators at each bus of interconnected power systems. Fast Voltage Stability Index value of each bus is evaluated under normal and critical load conditions. The index value helps to identify the weakest or the bus which is likely to get voltage collapse due to voltage instability problem in the interconnected system.

Voltage stability has recently become a challenging problem for many power systems. Voltage instability can be ascribed to the lack of reactive power support needed to maintain the voltage profile at a specified value. It has been responsible for severe system disturbances including major blackouts. A key concept in the restructuring of the electric power industry is the ability to accurately and rapidly quantify the capabilities of transmission systems. Computation of available transmission capability (ATC) must take into account adequate static, dynamic, and voltage stability margins. Online voltage stability assessment tools will be required for the secure operation of interconnected power systems. This paper presents the application of artificial neural networks (ANN) for online voltage stability assessment. A key feature of the proposed method is the use of principal component analysis to project the input patterns in the original pattern space into a new subspace having fewer dimensions than the original pattern space. This enhances the efficiency of ANN training. The proposed neural network is used to determine the voltage stability margin of the IEEE 118 bus power system for different operating conditions. The possibility of including topology information in a single ANN is also investigated.

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

In practical engineering, large-scale wind power integration typically requires long-distance transmission lines to deliver power to load centers. The resulting weak sending-end systems lack support from synchronous power sources. Under extreme weather conditions, the rapid increase in active power output caused by high wind power generation may lead to voltage instability. In existing projects, a phenomenon of repeated voltage fluctuations has been observed under fault-free system conditions. This phenomenon is induced by the coupling of the characteristics of weak sending-end systems and low-voltage ride-through (LVRT) discrimination mechanisms, posing a serious threat to the safe and stable operation of power grids. However, most existing studies focus on the analysis of voltage instability mechanisms and the optimization of control strategies for single devices, with insufficient consideration given to voltage fluctuation suppression methods under the coordinated operation of wind power and energy storage systems. Based on the actual scenario of energy storage configuration in wind farms, this paper improves the traditional LVRT discrimination mechanism and develops a coordinated voltage ride-through control strategy for permanent magnet synchronous generator (PMSG) wind turbines and energy storage batteries. It can effectively cope with unconventional operating conditions, such as repeated voltage ride-through and deep voltage ride-through that may occur under extreme meteorological conditions, and improve the safe and stable operation capability of wind farms. Using a hardware-in-the-loop (HIL) test platform, the coordinated voltage ride-through control strategy is verified. The test results indicate that it effectively enhances the wind–storage system’s voltage ride-through reliability and suppresses repeated voltage fluctuations.

In recent decades, voltage instability has become an important contributor to large-scale power outages globally. Periodic voltage security assessments are key for the secure operation of power systems. There are various indicators which have been implemented in different types and complexity of systems to provide a measure of closeness to voltage collapse, but some of these algorithms may require advanced computational resources or may not perform well, depending on the prevailing conditions. Expanding interconnected power systems is needed like never before due to the increasing world demand for electricity, and voltage stability is therefore a worth consideration. The growing demand penetration of solar photovoltaic (PV) systems has some potential stability challenges due to their intermittent characteristics. Existing research evaluates the test systems of voltage stability and does not optimize using Intelligent models, which have variable power generation from Solar PV and Wind, as well as uncertainties in load demand and the initial step considering the evaluation of the model have to cover either inputs. A feed–forward back propagation artificial neural network (ANN) for voltage stability assessment to classify the operating scenario of a power system into one of two states, i.e. safe or unsafe. Neural network input data is generated from load flow analysis using Newton-Raphson (NR), and stability indices are computed over different scenarios. Line Stability Index (Lmn), counts the stability of a transmission line in power systems and provides an indication of its potential voltage collapse. The Fast Voltage Stability Index (FVSI), which also estimates line stability through reactive power and impedance characteristics. Voltage Collapse Proximity Index (VCPI) assesses system proximity to voltage collapse and identifies weak transmission lines. Values approaching 1 suggest voltage collapse conditions. The FVSI, VCPI, and Lmn are compared to a baseline NR stability index. Moreover, self-organizing map (SOM) neural network is used for stability results classification. Multi-objective grasshopper optimization algorithm (MOGOA) has been employed as an optimization network on the dual axis solar PV and wind turbine-based hybrid power generation system to analyze technical, economic, as well as environmental relations of energy conversion. The findings reveal that the adapted grid-tied solar-wind composite power system substantially mitigates the power deficiencies, reduces the real-power outages, diminishes the motion voltage small amendments, maximizes voltage prosperity, and improves power system resilience. The main findings show a significant reduction in real power loss, which went from 289,391.30 kW to only 5.43 kW, and a remarkable decrease in voltage deviation, which reduced from 0.56 p.u to 0.0493 p.u. Ensures Improved Service Quality and Enhanced Equipment Protection While gas generation costs increased by a small amount, it is important to note that this is an acceptable trade-off for a more robust and sustainable system, with costs per kWh of $0.142 vs $0.2881 per kWh, respectively. In addition, stability index was substantially decreased from 0.7666 to 0.0768, indicating better grid and resilience to disturbances. Findings emphasize the contribution of embedding MOGOA in conjunction with machine learning models including ANN and Self Organizing Maps (SOM) to fulfill the primal requisites of stability, efficiency and sustainability in modernized power systems, illustrating a coupled advancement in the realm of power systems engineering and optimization.

The increasing demand for electrical power contributes major challenges especially in power utilities as one of the main issues is voltage instability. The voltage instability occur in both transmission and distribution network may weaken the whole operation of power system that will significantly limit the growth of load. As a result, voltage collapse blackout occurs in the power system whereas at this point the voltage become uncontrollable. The penetration of Wind Power Plant (WPP) in distribution system may be a good alternative to accommodate this challenge. However, the location of WPP play the important role to fully utilize the power generated by WPP that will not only improve the voltage stability margin but also reduce the power losses and enhance voltage profile. This paper presents the analysis for the selection of the optimal location of WPP integration from voltage stability, voltage profile and total system losses overview. The study was conducted using IEEE 30 Bus test system as the benchmark system and DigSILENT PowerFactory 16 as simulation tools.

In some countries, the electric power systems are operating near to their limits due to the absence of investments in the transmission network expansion and natural growth of the electricity demand. This operation condition can also occur in electric power systems in which the transmission expansion is carried out in appropriate way. In this case, the excessive loading of the transmission network is usually originated by the loss of interconnections that transport large energy blocks. The two operation scenarios described above have caused Voltage Stability problems in the electric power systems. The voltage instability states are mainly characterized by the presence of two mechanisms: the unsolvability of the power flow equations and the controllability loss. The disturbances that originate these two mechanisms are of stochastic nature. Consequently, the voltage instability indices, used to analyze the unsolvability and controllability loss, are random variables. In this way, the voltage stability assessment would recognize the uncertainties associated with the parameters of the electric network, for example: load fluctuations and equipment availability. Generally, the uncertainties modeling in the voltage stability is carried out using the following probabilistic methods: the Monte Carlo Simulation and the State Enumeration. The main index estimated by these methods is the voltage instability risk. However, the voltage instability risk evaluation is usually carried out considering only one of the mechanisms that cause voltage instability scenarios. Furthermore, the severity of the unstable states has not been properly investigated. The aim of this thesis is to develop a method to carry out a probabilistic assessment of the voltage stability that take into account the two mechanisms that cause the voltage instability in the evaluation of its risk. Probabilistic indices, based on Well-Being Analysis, are also proposed to express the severity of the voltage instability states. The proposed method is based on the combination of the following techniques: State Enumeration Method, Monte Carlo Simulation, D' Matrix Method and Nonlinear Optimal Power Flow. The State Enumeration and Monte Carlo Simulation Methods are used to select the system states resulting of equipment failures and load forecast errors. The identification of the controllability loss and the solvability restoration of the power flow equations for the selected states are carried out by the D' Matrix Method and by the Nonlinear Optimal Power Flow, respectively. The combination of the methods cited above was used to obtain the following probabilistic indices: voltage instability risk, expected value of the voltage instability margin for the buses, and Well-Being states probabilities. The results of the tests with the proposed method revealed that the probabilities of unstable states, associated with the two voltage instability mechanism, are very significant. Additionally, the Well-Being Analysis was able to identify the root cause and the severity of the voltage instability problems.

In heavily loaded systems, voltage stability limit is usually dominant and voltage instability is usually observed following large disturbance. This is typically the case in the deregulated environment as the transmission systems are operating under more stressed condition due to increased transaction level associated with open access. In this paper simple voltage stability analysis is carried out for a multibus power system (26 bus system) using V-Q sensitivity analysis and modal analysis. The bus participation, line participation and generator participation in critical mode of voltage stability is determined. This is useful for implementation of contingencies in power system for further study of voltage stability. A novel idea of small increment in reactive power and distributing the same in proportional to bus participation factor for calculation of generator and line participation is presented. Also the correlation between the voltage sensitivity factor and bus participation factor is established for identification of weak buses.