Power System Reliability Method

The reliability of power system will obviously be influenced by wind farms connected because of the volatility of wind power. With the increase of wind power penetration, it's necessary to research the influence of wind power integration on the reliability level of system and how much reserve capacity should we increase in order to stabilize the influence of wind power on power system reliability. In this paper a reliability evaluation model is built by Monte Carlo method and also a reserve optimization model, which can be used to analyze changes of system reliability in conditions of different grid-connected wind farms, and to find out the optimum of system reserve to achieve unifying the maximum utilization of new energy and meanwhile high reliability of power system.

Power system is one of the most complex systems ever designed. Determining its reliability is therefore a demanding process, which is mostly dealt with in a way that partial problems are stated and solved. A reliability method of loss of load expectation is selected and improved and the model of conventional power system is considered as a standpoint. Reliability of the initial conventional power system is evaluated. Then, the conventional power system is changed, where one conventional power plant is abandoned and new wind power plants stand to its place. The reliability of the initial and the changed power system is calculated and compared. Discussion shows the problems of decreased reliability of the power system with increasing the percentage of wind power plants within the power system replacing conventional plants, if not enough reserve is added to the power system at the same time. The other related problems are discussed in addition, to show that the problem is very complex and one cannot only consider a limited number of influencing parameters when deciding on power system configurations.

In the power system of the future, reliability will play an important role. This reliability is influenced by different developments like the increase in electricity consumption and the use of renewable and distributed generation. It is important to have a good insight into the reliability performance of the system. With probabilistic methods, the reliability of the power system can be analyzed and decisions can be made to maintain the high reliability level that we demand. This paper gives an overview of the different developments in power systems and describes how these developments influence the reliability of the future power system. The possibilities to implement these developments into the probabilistic reliability analysis in network planning and operational planning are discussed. By use of an example, the possibilities and challenges of probabilistic methods in operational planning are illustrated.

FACTS technologies can have major positive impacts on power system reliability performance and the actual benefits obtained can be assessed using suitable models and practice. Emerging techniques for composite power system reliability evaluation mainly focus on conventional generation and transmission facilities. In this paper, improvement of Reliability in composite electric power system is examined by incorporating Unified Power Flow Controller (UPFC). Two test systems, Reliability Test System (RTS) and Roy Billinton Test System (RBTS) are considered to show major improvement in reliability. A state space reliability model of multi-module UPFC has been developed and incorporated in the system. Load point & System indices performances are presented to examine the impact of UPFC on the composite electric power system reliability. Investigation results show a significant improvement in the Load point & system indices when utilizing UPFC.

Uncertainty parameters of battery energy storage integrated grid and their modeling approaches: A review and future research directions

Small power systems such as those found on offshore platforms are fragile; yet, they must operate reliably for economy and safety for personnel and the environment. These systems often include combustion turbine generators for main process power requirements and black-start diesel generators that can supply essential and emergency buses and be used to restart the main generation in the event of a power system collapse. The power distribution systems on these offshore platforms have built-in redundancy with multiple circuits to supply critical loads for fault tolerance and operating flexibility. A system to easily and safely synchronize islanded buses via many possible synchronization points can revolutionize the operational flexibility and therefore the safety and reliability of the power system. This paper reports on advanced synchronizing systems installed on two offshore platforms.

The complexity of power systems has accelerated and this has emphasized the use of sophisticated maintenance strategies that would help in enhancing reliability in power systems, minimize down-time as well as reduce operational costs. Conventional corrective and preventive maintenance strategies have been found inadequate in managing the dynamism of large power grids that are increasingly incorporating smart grids, renewable energy sources and smart-incorporated devices. Predictive maintenance has become an even better replacement since it allows one to predict failing equipment to handle before they fail and conduct it proactively. The analysis of Big Data is fundamentally relevant in meeting this paradigm since it offers the ability to collect, analyze and extract meaning of large amounts of heterogenous data collected using a variety of sources including IoT, SCADA systems, and smart meters. This paper explores the ways by which predictive maintenance in the new power systems can be reinforced through Big Data Analytics. It presents the framework that combines superior data collection, scalable storage systems, and machine learning model to help detect faults and predict system health. Analyzing the impacts that Big Data-based predictive maintenance has to offer, the paper focuses on three positive dimensions of the change, including increased resilience of systems, economic feasibility, and sustainability of operations. By overcoming the problems of extensibility, instant response, and accuracy in the decision making, the suggested solution allows Big Data Analytics to become one of the main pillars of smart future power system management. DOI :https://doi.org/10.52783/pst.2349

Reliability and economic evaluation of power system with renewables: A review

In order to ensure the reliability and security of electric power system, the faults must be removed exactly and quickly. The fault location method is one of key issues for the researches of electric power system. Extracting fault feature information from large amount of synchronized data will be a new challenge for locating of faulted components or faulted area. Multivariate analysis theory is superior in reducing dimensions and simplifying data structure featured without key information loss. In this paper, according to multivariate analysis theory, the reliability and security of complex power system has been accomplished.

Ensuring the provision of high-quality and dependable power supply to customers stands as a paramount responsibility within the realm of power systems. The concept of restructuring the power system emerges as an efficient approach to deliver economically viable and uninterrupted power supply. The assessment of power system reliability hinges on various factors, with the reliability index serving as a pivotal metric, dependent on both system security and adequacy. To enhance the reliability index, strategically locating FACTS devices becomes essential, a task facilitated through power flow analysis with specified constraints, pinpointing the weakest points through Genetic Algorithm-driven fast-acting device placement. The correlation between DG establishment and the reliability index was meticulously calculated, further bolstered by the introduction of a Derated Forced Outage Rate for enhanced performance assessment. In the pursuit of heightened power system reliability, sequential simulations prove invaluable, particularly in minimizing Expected Energy Not Supplied (EENS) and improving overall system reliability. The results obtained through sequential simulations underscore the effectiveness of this approach within bulk electrical systems. The installation of multiple FACTS devices in the system's weakest areas facilitated EENS computation, coupled with the assessment of related reliability indices such as system frequency and system duration. This approach not only enhances system reliability but also promotes the economic operation of both transmission and distribution.The simulation results substantiate the alignment of power system reliability objectives in deregulated power systems with the required standards, advocating for the restructuring of power networks. The reliability assessments, driven by the optimal placement of DGs and FACTS devices, reveal substantial improvements in system reliability, underscoring the pivotal role of these technologies in enhancing overall power system dependability.

This work deals with assessing the reliability of wind energy sources incorporated into a microgrid system to increase grid stability. As the share of power generated from wind energy continues to increase due to environmental concerns and to reduce carbon footprints, the case study proposed is designing an effective wind energy system for a smart building environment to reduce the burden on the grid. Incorporating wind energy systems into grid-connected buildings increases the reliability of the microgrid system for smart building environments. Applying smart techniques with integrated automation in the built environment can play a key role in decreasing peak demand for load development in smart cities. This can also enhance the overall efficiency of power grids, reduce waste, and enable the effective use of energy resources. Furthermore, researchers are finding new ways to collect energy from renewable sources and power-built environments due to dwindling fossil fuel resources and increased environmental awareness. The analysis of wind-connected power systems has been initiated to satisfy the increasing demand for reliable supply systems by consumers. This has prompted the use of wind energy systems with controlled techniques to enhance the reliability of power systems and reduce carbon footprints generated by fossil fuel-based power systems. Therefore, the main purpose of this work is to understand the economic prospects of wind energy sources for smart building environments, reliability analysis of wind energy sources-based power systems, reduce carbon footprints, and balance power demand through wind energy systems. The Progressive Wind Model Taylor-Sequence Proliferation (PWMTSP) and Innovative Six Parameters Based Progressive Beta Creative Model (ISPBPBCM) approaches are proposed to understand the reliability of wind energy-based power systems in smart building environments. The work also provides an economic analysis of the proposed low-cost wind power system with different capacities for residential consumers in selected geographic location. Eight cases are considered for the proposed effective wind energy system for smart building environments ranging from 2 MW to 32 MW. Hence, the economic analysis of the proposed systems is discussed for residential users. All eight cases are eco-friendly and reduce carbon emissions as these generated units from wind energy sources need not be purchased from fossil fuel-based plants, which increases the reliability of the proposed system. The expected energy not supplied (EENS) in the planned work increases as wind power capacity increases. Loss of load expectation (LOLE), loss of load probability (LOLP), and failure rate all decrease with the installation of more wind power systems. Moreover, it can be seen that mean time to repair (MTTR) improves when wind energy capacity rises incrementally from 2 MW to 32 MW. The proposed method is anticipated to be adaptable to various types of wind energy systems where dependability is a crucial component of system design and scalable to large wind integrated power systems.

This thesis aims to provide insight into the necessary power system operation and control developments to facilitate a sustainable, safe and reliable electric power supply now and in the future. The primary objective is to enhance the interconnected power system situational awareness with the aim of reinforcing the reliability of power systems. First, the thesis elaborates on the existing and emerging operational challenges of modern power systems and identifies the required power system developments to overcome them. Next, it focuses on state-of-the-art Synchronised Measurement Technology (SMT) supported Wide-area Monitoring Protection and Control (WAMPAC) of power systems. In this context, a cyber-physical experimental testbed for online evaluation of the emerging WAMPAC applications under realistic conditions is developed. Following, to fill the scientific gap between the IEEE Std. C37.118-2005 (communication part) and IEEE Std. C37.118.2-2011 specifications and their implementation, the MATLAB supported Synchro-measurement Application Development Framework is developed. Next, to improve situational awareness of power systems, two SMT-supported algorithms are proposed. The first algorithm is suitable for online detection of disturbances, observed as excursions in SMT measurements, in AC and HVDC power grids. Whereas the second algorithm is suitable for online identification of grouping changes of slow coherent generators in an interconnected power system during quasi-steady-state and the electromechanical transient period following a disturbance. Finally, further research directions towards the Control Room of Future are presented.

In electric power systems, the generated power should be equal with the demand and power of network is mainly controlled through generation system. In such operation, the demand is satisfied through changing the generated power and afterward, the safe operation of power system is reached. But during recent years, a new concept has been developed in electric power systems namely demand side management (DSM). DSM is the modification of consumer demand for energy through various methods such as financial incentives and education. Usually, the goal of demand side management is to encourage the consumer to use less energy during peak hours, or to move the time of energy use to off-peak times such as nighttime and weekends. One of the important models of DSM is interruptible loads. Interruptible loads are the right for an electricity utility to interrupt supply to a customer, typically during a system emergency, to relieve short term network constraints up to a couple of hours. Interruptible loads can be deployed in one of two ways. The network operator gives notice of an interruptible load event to the customer, then relies on the customer to reduce their electricity usage; or unilaterally interrupts supply to the customer. In this paper, the effect of interruptible loads on the power system reliability is investigated. A multi machine power system is considered as cases study. Simulation results show the great effects of interruptible loads on the power system reliability.

Typically, consumers experience power outages as a result of transmission system issues. Transmission lines are one of the most used technical systems for transporting large amounts of electricity from one part of a country to the farthest point in the opposite direction. The transmission lines traverse many terrain types and geographic regions, which makes them more sensitive to various types of atmospheric disasters and causes frequent line faults and power outages. Therefore, power system reliability and power loss are important, since if these losses are higher than the industry norm, the amount of power accessible to customers will be reduced. Power from a distributed generator (DG) system is incorporated into a power system in this study to improve the power system reliability, average energy not supplied (AENS), and reduce transmission line losses. The results of an optimal DG installation are gained from the penetration of distributed generation in an IEEE 14-bus system by using the Evolutionary Programming (EP) optimization technique. The results prove that with the use of EP optimization, power system reliability can be improved post optimization.

Three-parameter interval number theory and its application in power system reliability evaluation