Grand challenges in electric power engineering: extreme system reliability

Proposed generalized models for estimating the reliability of a stand-alone solar photovoltaic power system

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.

Active worms are capable of compromising the cyber security of the supervisory control and data acquisition (SCADA) network in power systems within quite a short time. This may bring high vulnerability to the cyber-physical power system, which is becoming more dependent on the communication technologies deployed in the SCADA system. The reliability of the power system may be compromised by the worms spreading through the SCADA networks. In this paper, the impact of worm spreading attacks on power system reliability is analyzed. A forced outage rate (FOR) model is proposed considering the impacts of spreading worms on the reliability characteristics. The spreading and detection models of active worms through the network in the SCADA system lead to non-negligible effects on the FOR values. With different FOR values derived by the models of both worm spreading and detection, the loss of load probabilities (LOLP) in two reliability test systems are estimated. The simulation results demonstrated that the reliability of the power system is simultaneously influenced by both the spreading and detection intervals of active worms.

The paper highlights the reliability estimation tool for power system's reliability estimation study. The reliability estimation tool was applied to the different configurations of Uninterruptible Power Supply (UPS) systems Capacity, Isolated Redundant, Parallel Redundant, Distributed Redundant, and System plus System Redundant configurations. UPS reliability parameters such as failure rates, Mean Time Between Failures, and Availability can be estimated from the proposed system's reliability estimation methods. The suggested reliability estimation methods in this paper are the Reliability Block Diagram (RBD) and the Monte-Carlo simulation methods. The UPS system reliability estimation study found that the System plus System Redundant configuration gives the highest reliability and availability values compared to all other configurations. Studies has been conducted to investigate the effects of the reliability level of every component in the UPS system onto the overall system's reliability, thus making it possible to rank their contribution to system's reliability and thereby useful in establishing the area of improvement in the design of the highest UPS system's reliability.

Renewable energy sources (RESs), such as wind and solar energy, are an integral part of the current process of decarbonisation of electric power systems in the vast majority of countries. Due to the stochastic and variable nature of these resources, RESs could expose an electrical network to unexpected power outages. In this context, security studies are vital for the secure and day-to-day operation of the infrastructure. This article analyses the effects of integrating renewable energies into a power system from the perspectives of reliability and robustness. For this purpose, the IEEE 14-bus test system is modified, and two case studies are proposed with a high proportion of fossil-based power generation and a high share of renewable generation, respectively. In both cases, hourly load and electricity generation profiles are considered. The reliability study is completed using the Monte Carlo probabilistic method, and the robustness study is conducted by simulating cascading failures. These assessments are carried out in the two scenarios, where the electricity system without renewable energies is considered the base case. The effects of renewable energies are analysed comparatively in terms of Loss of Load Probability (LOLP), Expected Demand Not Supplied (EDNS) and Satisfied Demand (SD).

Oil provides 33% of the world’s energy (Wilcox, 2014) and transportation engines account for over 60% of the 70 million barrels of crude used each day. Engines power the world’s roughly one billion passenger vehicles, as well as trucks and heavy-duty vehicles. In theUSA, engines consume 14million barrels of oil per day or 2.5 gal/person. Since there are insufficient reserves, 62% is imported, and at current prices (e.g., $80 a barrel), the USA spends about 1 billion dollars a day on imported oil. is also impacts national security. It is unreasonable to think that this vast consumption of fuel is sustainable. But prospects for replacing the IC engine with more fuel efficient and cleaner power plants are not hopeful. Indeed, a recent report (NRC, 2011) concluded that “. . .the internal combustion engine (ICE) will be the dominant prime mover for light-duty vehicles for many years, probably decades. us, it is clearly important to perform R&D to provide a better understanding of the fundamental processes affecting engine efficiency and the production of undesirable emissions.” Also, there is no obvious alternative to the IC engine for mediumand heavy-duty commercial vehicles, which account for a quarter of all fuel used (mostly diesel). e fuel used by IC engines also has a major impact on our global environment. Burning one 1 kg of fuel consumes about 15 kg of air, and signi cant energy is required to pump it into and out of the engine. In addition, about 3 kg of CO2 is generated, which contributes to the world’s annual production of 37 billion tons of CO2, a major green house gas (GHG). Some fear that GHGs can cause climate change with unpredictable consequences. To address this problem, the International Energy Agency’s roadmap is to reduce fuel use by 30–50% in new road vehicles worldwide by 2030, and in all vehicles by 2050 (IEA, 2012). Although 2050 appears distant, the time required to bring new engines to production, together with the years needed for new technology to permeate the vehicle eet, means that major effort (and investment) will be required. us, the grand challenge faced by engine and automotive engineering researchers over the next decades will be to devise technological advances thatmaximize engine efficiency, minimize pollutant emissions, and optimize tolerance to a wider variety of fuels in power generation and transportation systems.

The objective of this research was to determine the adequacy of the rule-of-thumb reserve margin for Indiana's electricity generation system and the adjustment of the reserve margin. To achieve the objective, a statistical model is constructed to evaluate system reliability. Monte Carlo Simulation is applied to build the capacity availability probability distribution as the generation model. Hourly loads and daily peak loads of 2003, 2004 and 2005 are used to form the load models. Then, the generation model and the load model are convoluted to calculate the loss of load probability (LOLP) for Indiana's electricity generation system. Results show that the reliability standard is more than satisfied for 2003 and 2004. However, for 2005, the estimated LOLPs do not satisfy the standard, suggesting capacity expansion for the system is needed. By uniformly shifting load curves up or down to adjust the estimated LOLPs until the industry standard is satisfied, the new reserve margin for each load model is found. This analysis suggests that even for 2005 only a small increase in generating capacity is needed.

This paper describes a methodology based on safety factor concept and peak load considerations for a reliability evaluation of a generation system. This paper also refers to major research papers presented for the evaluation of reliability of power systems, reliability indices and modelling of power system components. Load models are considered as a straight line and a stepped load duration curve is used for reliability evaluation of system using Simpson's 1/3rd rule. The probability of load exceeding the generating capacity has also been considered in Loss of Load Probability (LOLP) evaluation.

This report discusses the current issues of distributed energy systems, in particular in the context of Ukraine, which is facing challenges in maintaining reliable electricity supply due to the war. Particular attention is paid to the use of the ‘energy island’ concept to ensure the autonomy of energy supply in the face of possible emergencies or attacks on infrastructure. The term ‘energy island’ covers self-sufficient systems that can operate autonomously from the main power grid, combining distributed energy sources (RES), including renewable energy sources, and consumers within microgrids. The text provides examples of countries that have already successfully implemented such an isolated power system and analyses the potential benefits of introducing similar technologies in Ukraine. The report emphasises the strategic importance of distributed generation (DG), which can operate independently of the centralised power system in crisis conditions. Based on a literature review and international experience, a structured concept of a microgrid based on distributed generators with autonomous control systems is proposed. The article describes the technical features of automation systems that ensure stable functioning of the microgrid in the conditions of disconnection from the main grid. In particular, it describes the requirements for automatic frequency and voltage regulators, reclosers that ensure safe transition to the island mode, as well as generator monitoring and synchronisation systems. The study is aimed at improving the reliability of the Ukrainian power system through the introduction of distributed energy sources and energy storage systems (ESS), which will increase the resilience of distribution networks to external influences. An important element of the study is the emphasis on the advantages of hydroelectric power plants, which can be easily started without an external power source, as they require minimal energy for auxiliary systems, so the report uses a hydroelectric power plant as an example, although the principles are generally relevant to other DGS

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.

Reliability evaluation of stand-alone hybrid photovoltaic energy system for rural healthcare centre

Power quality gains more and more attentions because disturbances in power quality may damage equipment security, power availability and system reliability in power system. Detection and classification of the power quality disturbances is the first step before taking measures to lessen their harmful effects. Common methods to classify power quality disturbances includes signal processing methods, machine learning methods and deep learning methods. Signal processing methods are good at feature extraction, while machine learning methods and deep learning methods are expert in multi-classification tasks. Via combing their respective advantages, this paper proposes a combined method based on variational mode decomposition and convolutional neural networks, which needs a small quantity of samples but achieves high classification precision. The proposed method is proved to be a qualified and competitive scheme for the detection and classification of power quality disturbances.

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.

Integration of renewable energy sources, such as wind power and solar power, into the generation system can enhance country's energy security. These alternative energy help diversify sources of primary energy used to produce electricity. However, the biggest disadvantage of using these types of renewable energy power plants is that it might reduce power system reliability because these intermittent renewable energy sources have low dependable capacity. This paper aims to evaluate the dependable capacity of wind power and solar power generation systems. It considers uncertainties due to intermittent wind speed, solar irradiance, ambient temperature, and unavailability of their corresponding generators. Additionally, load uncertainty is also taken into account. The dependable capacity of the wind power and solar power generation systems are determined from the principle of generation system reliability evaluation. The reliability index such as Loss of Load Probability (LOLP) will be used as a key indicator to define the dependable capacity.