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Influence of extreme weather and climate change on the resilience of power systems: Impacts and possible mitigation strategies
Abstract
A key driver for developing more sustainable energy systems is to decrease the effects of climate change, which could include an increase in the frequency, intensity and duration of severe weather events. Amongst others, extreme weather has a significant impact on critical infrastructures, and is considered one of the main causes of wide-area electrical disturbances worldwide. In fact, weather-related power interruptions often tend to be of high impact and sustained duration, ranging from hours to days, because of the large damage on transmission and distribution facilities. Hence, enhancing the grid resilience to such events is becoming of increasing interest. In this outlook, this paper first discusses the influence of weather and climate change on the reliability and operation of power system components. Since modelling the impact of weather is a difficult task because of its stochastic and unpredicted nature, a review of existing methodologies is provided in order to get an understanding of the key modelling approaches, challenges and requirements for assessing the effect of extreme weather on the frequency and duration of power system blackouts. Then, the emerging concept of resilience is discussed in the context of power systems as critical infrastructure, including several defense plans for boosting the resilience of power systems to extreme weather events. A comprehensive modelling research framework is finally outlined, which can help understand and model the impact of extreme weather on power systems and how this can be prevented or mitigated in the future.
... Resiliency assessment works are mainly focused on presenting metrics and quantitative frameworks to evaluate the resilience of the power system to HILP events. For example, [12]- [14] present comprehensive discussions on different metrics and techniques for resiliency assessment against weather-oriented HILP events. Short-term studies focus on the preventive and corrective actions before, i.e., days or weeks, during, and after the events [15]. ...
... Papers on long-term planning, which are also the focus of this paper, are related to power system planning for providing robustness and adaptability against major future events [14]. In [20], a two-stage stochastic formulation is proposed for transmission and generation expansion planning to increase the power system resiliency to earthquakes. ...
... In the above equations, (7) represents the supply-demand power balance constraint, (8)-(11) determine the power flows in the existing and new lines plus their flow limitations, and (12))- (14) limit the production of conventional generators, load shedding, and wind curtailment, respectively. The emergency condition constraints for each normal scenario s, emergency scenario s , emergency day t , and time interval h are as follows [20], [22], [23], [27]. ...
This paper presents a two-stage stochastic planning model for transmission system and distributed energy resources (DERs) considering power system resiliency. The model takes into account both normal and emergency conditions as well as the duration of each condition. The proposed model is formulated as a mixed integer linear program to minimize the total investment cost of transmission lines and DERs, expected values of generators operation cost in normal and emergency situations, and load shedding cost in emergency condition. The emergency scenarios are considered as moderate, severe, and complete damage states, which have different recovery time for transmission assets. The Benders decomposition technique is utilized to solve the optimization problem. Numerical results are demonstrated based on the IEEE 118-bus test system.
... The increasing frequency of extreme weather events, affecting both distribution and transmission networks, pushes distribution system operators (DSOs) and transmission system operators (TSOs) to evaluate the impact of multiple dependent outages of components, possibly leading to blackouts, and to deploy preventive or corrective countermeasures aimed at absorbing the effects of such disruptive events and quick recovery, i.e., to increase system resilience [1][2][3][4][5][6]. In this context, for instance, the current Italian regulation [4] imposes operators to publish and update a plan for resilience enhancement on a yearly basis. ...
... s r , x j , and the conditional probability function of failure over ∆t, P V,j (∆t) t 0 , s 1 . . . s r , x j , respectively, as in (1). ...
Nowadays, distribution network operators are urged by regulatory authorities to reduce the load disruptions due to extreme weather events, i.e., to enhance network resilience: in particular, in Italy they are required to present a yearly plan (called “resilience plans”) describing the interventions aimed to improve network resilience. To this purpose, they need new methodologies and tools to assess the network resilience and to quantify the benefits of countermeasures. This paper proposes the application of a risk-based framework and tool to assess the impacts of extreme weather events in T&D grids, which anticipate critical network situations in presence of incumbent weather threats. To do this, the forecasting of weather events is combined with the component vulnerability models in order to predict which components are more prone to fail. Based on this set of components, the set of most risky contingencies is identified and their impacts on the distribution network in terms of unsupplied load are quantified. The major advantage of the applied methodology is its generality: in fact, it is applicable to both distribution and transmission systems as well as integrated transmission and distribution (T&D) systems, considering the peculiarities of each type of grid, in terms of operation, maintenance and component vulnerabilities. In particular, the application refers to a distribution network connected to a portion of high voltage transmission system in a mountainous zone, with focus on two major threats in the area, i.e., wet snow and fall of trees induced by combined wind and snow. The methodology also quantifies the benefits brought to the system resilience by countermeasures such as reconductoring, optimal reconfiguration or new right-of-way maintenance procedures. Simulations demonstrate the ability of the methodology to support T&D operators in an operational planning context in case of different incumbent threats.
... Environmental stressors are among the main causes of power system disturbances worldwide [1]. High temperatures, for instance, can limit the transfer capability of transmission lines by increasing energy losses and line sagging [1]. ...
... Environmental stressors are among the main causes of power system disturbances worldwide [1]. High temperatures, for instance, can limit the transfer capability of transmission lines by increasing energy losses and line sagging [1]. Rising temperatures also disrupt demand patterns, as they have been proved to be positively correlated with extreme temperatures during summertime [2]. ...
The stable and efficient operation of the transmission network is fundamental to the power system's ability to deliver electricity reliably and cheaply. As average temperatures continue to rise, the ability of the transmission network to meet demand is diminished. Higher temperatures lead to congestion by reducing thermal limits of lines while simultaneously reducing generation potential. Due to prohibitive costs and limited real estate for building new lines, it is necessary to consider capacity expansion as well to improve the functioning and efficiency of the grid. Optimal control, however, requires many discrete choices, rendering fully accurate models intractable. Furthermore, temperature changes will impact different regions and climate differently. As such, it is necessary to model both temperature changes and transmission flows with high spatial resolution. This work proposes a case study of the transmission grid centered in Arizona, using a DC optimal power flow mathematical formulation to plan for future transmission expansion and capacity expansion to efficiently meet demand. The effects of rising temperatures on transmission and generation are modeled at the regional level. Several classes of valid inequalities are employed to speed up the solution process. Multiple experiments considering different temperature and demand trends are considered which include each of the above technologies.
... Analytical methods are more often utilized with resilience analysis considering that these disruptive events are high impact but with low probability of occurrence, in addition, it is cost-effective compared to experimental methods as well as eliminates bias and uncertainty with judgemental methods. It is worth mentioning that analytical techniques are also preferred for small-scale system configurations because of their simplicity and low computational burden [145]. ...
Power systems incur considerable operational and infrastructural damages from high impact low probability events such as natural disasters. It therefore becomes imperative to quantify the impact of these disruptive events on power system performance so that adaptive actions can be effectively applied. This impact can be evaluated using resilience metrics which should be able to assess the transitions between the different phases in which a power system resides when subjected to an extreme event. Also, the metrics can aid in the evaluation of the effectiveness of the power system adaptive strategies. However, challenges exist since developed metrics do not proceed with a standardized framework, and due to the vast variety of existing metrics, it becomes ambiguous for researchers and power utilities to find converging information pertinent to their work. In an effort to address these challenges, this paper provides a comprehensive review of quantitative power system resilience metrics which are standardized and evaluated with diverse categorizations. The review methodology employs the Axiomatic Design Process that utilizes the axiomatic design theory and design structure matrix to decompose resilient power system functional requirements into metric design parameters. By integrating the Axiomatic Design Process, we provide a standard for comparison and analysis of the adequacy of reviewed quantitative metrics in power systems resilience quantification, while the categorizations aim to identify the specifications of these metrics. This aids in directing further studies towards resilience requirements that have received less literary attention. In addition, the paper furnishes statistical analysis of reviewed metrics in venues and years of publication by percentage, recurring resilience indicators, and power system levels/stages in which the metrics are applicable. Finally, this paper presents common requirements for resilience quantification, discusses gaps and challenges related to power system resilience metrics as well as implications posed by these challenges, while making recommendations towards filling these gaps, and posing pertinent questions to the power system resilience community.
... Besides, researchers working in this area have also offered their own definitions. The resilience of future energy networks is defined in [33] as 'The ability of a power system to withstand extraordinary and high impact-low probability events such as due to extreme weather, rapidly recover from such disruptive events and absorb lessons for adapting its operation and structure to prevent or mitigate the impact of similar events in the future.' Later, this definition is generalised for all systems, including power system as 'The ability of a system to anticipate and withstand external shocks, bounce back to its pre-shock state as quickly as possible and adapt to be better prepared to future catastrophic events.' [34]. ...
Modern societies these days are more dependent on electrical energy and they expect a continuous supply as per
demand. In this regard, the complex power system is designed to supply electrical energy with a certain level of quality and
continuity though it is still susceptible to vandalism, natural disasters, and extreme weather. The black sky event where the
power grid goes down is more of a possibility nowadays than ever due to more frequent severe weather events. This in turn has
increased the need to study resilience in the context of the power system. This study presents a comprehensive review of the
literature on power system resilience from various perspectives. First, well-developed power system safety concepts are
discussed and critically reviewed in view of large-scale power outages. Then, the various definitions and confounding features
of resilience in the power system domain are presented. Subsequently, several frameworks, resilience curves, and quantitative
metrics proposed in recent years for power system resilience are investigated, followed by a summary of hardening strategies.
Next, a case study is presented to illustrate how the resilience of a 69-bus system is assessed against a hurricane. Finally, the
study highlights challenges and proposes several future works to achieve a resilient power grid.
... When the global temperature rises, the potential rate of failure also increases. Research has shown that the long-term average global temperature is increasing, which has a significant negative impact on the performance of the machines [4]. Earlier, conventional methods use manual configurations and visual inspection to monitor the quality of the power supply in the system. ...
... In the engineering field, the TSO could prioritise the reconnection of some loads or some generators or utilise all infrastructure facilities [42][43][44][45]. Certain assets may not be available if they have suffered irreversible damage. ...
Power systems face failures, attacks and natural disasters on a daily basis, making robustness and resilience an important topic. In an electrical network, robustness is a network’s ability to withstand and fully operate under the effects of failures, while resilience is the ability to rapidly recover from such disruptive events and adapt its structure to mitigate the impact of similar events in the future. This paper presents an integrated framework for jointly assessing these concepts using two complementary algorithms. The robustness model, which is based on a cascading failure algorithm, quantifies the degradation of the power network due to a cascading event, incorporating the circuit breaker protection mechanisms of the power lines. The resilience model is posed as a mixed-integer optimisation problem and uses the previous disintegration state to determine both the optimal dispatch and topology at each restoration stage. To demonstrate the applicability of the proposed framework, the IEEE 118-bus test network is used as a case study. Analyses of the impact of variations in both generation and load are provided for 10 simulation scenarios to illustrate different network operating conditions. The results indicate that a network’s recovery could be related to the overload capacity of the power lines. In other words, a power system with high overload capacity can withstand higher operational stresses, which is related to increased robustness and a faster recovery process.
... A review of the research addressing the impacts of extreme weather on the power systems' resilience is presented in [4], where a framework for the modelling of weather related impacts on power systems is proposed. A methodology based on this framework is developed in [5], where the effects of windstorms on the transmission network's resilience are assessed, utilising real time weather conditions and calculating the weather dependent failure probabilities. ...
Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data are shown to support fault prediction on distribution networks. Operational data are temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showing the ability to predict the occurrence of a weather related fault at a given substation considering only meteorological observations. Unifying a diverse range of previously identified fault relations in a single ensemble model and accompanying the predicted network conditions with an uncertainty measure would allow a network operator to manage their network more effectively in the long term and take evasive action for imminent events over shorter timescales.
... High reliability, all-time system availability, low environmental impact, and human safety are key points for the profitability and competitiveness in industry [1]. Operational changes related to challenging weather conditions, operational modes, set point modifications, and, especially, non-reported hash conditions are real concerns to engineers [2]. The current trend of effective management strategies from unexpected events has been sharpened to avoid major economical, environmental, and security defects [3]. ...
Maintenance is the process of preserving the good condition of a system to ensure its reliability and availability to perform specific operations. The way maintenance is nowadays performed in industry is changing thanks to the increasing availability of data and condition assessment methods. Soft sensors have been widely used over last years to monitor industrial processes and to predict process variables that are difficult to measured. The main objective of this study is to monitor and evaluate the condition of the compressor in a particular industrial gas turbine by developing a soft sensor following an autoencoder architecture. The data used to monitor and analyze its condition were captured by several sensors located along the compressor for around five years. The condition assessment of an industrial gas turbine compressor reveals significant changes over time, as well as a drift in its performance. These results lead to a qualitative indicator of the compressor behavior in long-term performance.
... The United Nations International Strategy for Disaster Reduction defines resilience as "The capacity of a system, community or society potentially exposed to hazards to adapt, by resisting or changing to reach and maintain an acceptable level of functioning and structure" [6]. However in a more specific context from that of extreme weather events, the definition of resilience can be thought of as "the network ability to withstand high impact low probability events, rapidly recovering and improving operations and structures to mitigate the impact of similar events in the future" [7]. Efforts placed on quantification of resilience analysis have been limited and have only been tested in the last 20 years. ...
The primary objective of resilience engineering is to analyse and mitigate the risk of a system once a vulnerability has been triggered by an attack. Resilience is a multidimensional concept in the field of engineering and incorporates restoration in the form of a performance and time. Nodal restoration is a key factor in the analysis of resilience in systems, and the properties of the nodes can be analysed to assess the states on the system. The model proposed for the power grid to demonstrate the failure of the network has been used to simulate probability of contingencies on the system and applies a Sequential Monte Carlo simulation to simulate the energy supplied. Additionally, a weather model incorporating the effects of both severe winds and lightning storms has been applied to act as a trigger to the contingency. Once failure of one component has occurred, it cannot be repaired until the network’s performance reaches zero. Given failure of all components, the network will immediately start its restoration phase, and using the same algorithm for optimal power flow calculations, a DC power flow approach is implemented to assess the energy supplied to the whole network in a transient model until the network’s loads meet the demand criteria completely.
... The authors of [23] have presented a conceptual resilience curve and have stated the actions related to each part in the two classes of short-term and long-term actions in [24]. In this paper, this curve is developed in the form of Fig. 1 by highlighting the role of short-term resilience actions and the actions taken after fast restoration. ...
In order to increase the resilience of distribution systems against high-impact low-probability (HILP) events, it is important to prioritize the damaged assets so that the lost loads, especially critical and important loads, can be restored faster. In addition, correctly predicting the number of repair teams during critical times contributes to restoring the network to the initial resilience level. For this reason, this paper discusses the prioritization of electricity supply lines for evaluating the number of required repair teams. To this end, the economic value of distribution system lines has been considered as a criterion representing the sensitivity of the network to hurricanes. The modeling is based on value, in which the load value, failure probability of the poles, fragility curve, duration of line repair by the maintenance team, and the topology factor have been considered. This is so that the significance of the demand side, the failure extent and accessibility of the lines, the importance of time, and the network configuration are considered. The results provide a list of line priority for fault resolution, in which the topology factor has a larger effect. The number of repair teams required to restore critical and important loads is determined from this model. This modeling has been tested on an IEEE 33-bus network. Keywords: Resilience, distribution systems, asset evaluation, restoration prioritization, repair team
... The Careful review of the literature reveals that most of the resilience studies are mainly concentrated on the absorption, adaption, and recovering aspects of resilience while the fundamental studies associated with anticipating a 2 natural catastrophe and its impact on power system components have not received much attention. In addition, most of the resilience studies anticipating the occurrence of an event are concentrated on events associated with extreme weather [19] and other events are rarely studied. ...
Power systems are stretched across thousands of miles of diverse territories, often in remote locations, to generate and transfer the energy to geographically dispersed customers. The system is therefore subjected to a wide range of natural hazards which could potentially damage critical system components and cause interruption of electricity supply in some areas. To improve system resilience against natural hazards, management frameworks are required to identify hazardous areas and prioritize reinforcement activities in order to take the most out of the limited resources. Landslide is a natural disaster that involves the breakup and downhill flow of rock, mud, water, and anything caught in the path. It is a phenomenon frequently occurred in some parts of the world that could result in the failure of power transmission networks. Consequently, in this paper, a novel approach has been proposed that quantifies the landslide hazard, its damage to power system components, and the impacts on the overall system performance to prioritize reinforcement activities and mitigate the landslide vulnerability. The proposed approach is applied to a real power system and the obtained results are discussed in detail.
... Climate-induced risks, many due to extreme climate events [6], should be recognized before the further development of energy infrastructures [7]. Extreme weather events are one of the main reasons for energy disturbances [8]. Rather than just aiming for decarbonizing the energy systems and climate change mitigation, it is essential to plan for climate change adaptation as well, especially in urban areas with complex energy flows and interactions. ...
This report is about synthesizing future climate data sets for the demosites investigated in the Flexi-Sync project. Several long-term weather data sets using regional climate models (RCMs) have been used to synthesize representative weather data sets for near-term, mid-term and long-term future climatic conditions. The generated weather data sets will be used in the next stages of the project to assess the climate flexibility and resilience of energy solutions.
... Power system equipment are vulnerable to physical stresses such as high wind speeds, the extent of which is directly related to the intensity and continuity of the stress. The outage probability of a distribution line due to the strong winds of a hurricane is a function of wind speed, typically shown by a fragility curve, such that higher wind speeds result in higher failure probability [25]. The hurricane wind speed as experienced by objects on the ground is also a function of multiple meteorological and geographical parameters. ...
This paper utilizes deep reinforcement learning (DRL) to develop an intelligent resilience controller (IRC) that devises fast real-time operation decisions to strategically dispatch distributed generation and energy storage units for restoring power to customers after sudden outages. The proposed IRC learns the failure development pattern of uncertain high impact events and is able to explore a large action space in the partially observable state space of distribution grids under widespread outages. A spatio-temporal hurricane impact analysis model is presented as an example of uncertain high impact events, and its parameters are used in training the IRC model, and prepare it for similar events. In the proposed model, the distribution grid operation under uncertainty is modeled as a Markov decision process (MDP), and actions taken by the operator are rewarded based on operation costs. Since the number of distributed energy resources can be significant, the scalability issue of the method is addressed by reformulating the problem as a sequential MDP. The proposed model is implemented on a test distribution grid undergoing a hurricane, and its performance is compared with common operation strategies, indicating the superiority of the proposed model in terms of reduced outage cost and close to zero running time. Further analysis shows the robustness of the proposed model to deviations from the training set.
... A review specific to the impacts of natural hazards on power systems can be found in Wang et al. (2016). Panteli and Mancarella (2015) review extreme weather events on power system transmission networks and propose methods for future research including resilience attributes of network assessment. Salman and Li (2018) look at the resilience of power grids, including identifying the importance of individual links relative to the prevalence of blackout occurrences. ...
Critical infrastructure failures from natural hazard events affect the economic and social well-being of communities. This is particularly true in lower income countries, where infrastructure may be less resistant to natural hazards and disaster recovery is often limited by available resources. The interconnectivity of these systems can strongly affect the services they deliver, and the failure of one infrastructure system can result in cascade failures with wide-reaching consequences. Unfortunately, interconnectivity has been particularly difficult to measure. We present a method for identifying service-oriented interdependencies in interconnected networks. The approach uses well-established methods for network analysis and is demonstrated for healthcare services in the Commonwealth of Dominica, a small island state in the Caribbean. We show that critical links in road networks necessary for healthcare service delivery are important for more than just patient access to a facility, but also on the supply chains that enable the hospitals to function (e.g., water, fuel, medicine). Once identified, the critical links can be overlaid with known hazard vulnerabilities to identify the infrastructure segments of highest priority, based on the risk and consequences of failure. An advantage of the approach presented is that it requires relatively little input data when compared to many network prioritization models and can be run using open-source geospatial data such as OpenStreetMap. The method can be expanded beyond road networks to assess the service-oriented criticality of any infrastructure network.
... During the years 2003 to 2012, severe weather-related events contributed to roughly 58% outages in the USA, which is estimated to have an average burden of 18-33 billion $ annually to the economy of USA [2]. The swelling dependency on power systems coupled with an increased number of natural disasters has drawn the attention of both the research community and industry to reduce the system vulnerability [3][4][5][6]. One of the stimulating difficulties faced by power system operators nowadays is enhancing the vulnerability level in the system. ...
Power systems are one of the most multifaceted systems and have a large significance in present society. For stable and continuous operation of such systems, numerous protection methods are compulsory. Although, modern power systems are fortified with numerous protection schemes with the goal of evading the unexpected events, they are still impacted by various emergency and mal-operation conditions. The most severe disturbances put the entire or at least a part of the network at the risk of blackout. If the emergency is not dealt with timely and accurately, the power system is probable to have cascading failures, which ultimately lead to a blackout. Due to the severe impacts, many nations around the world have research teams whose main task is to circumvent blackouts on their systems. Moreover, due to ecological concerns and expensive system expansion, power systems generally operate closer to their limits, which upsurges their vulnerability and possibility of blackouts. With the continuous development of power systems, rise in grid intricacy, and the drift towards deregulated market, vulnerability assessment is critical. It is of great significance to include vulnerability in power system planning and operating procedures, as it is the key to accurate assessment of power system security and stability. Thus, this paper aims to review the concept of vulnerability assessment in power systems and the associated research. This review can be a great starting point for researchers in the domain of power system security and vulnerability.
... Resilience of electric power systems reflects their capability to withstand high impact and low probability disturbances, by quickly recovering from these disruptive events, adapting their operational conditions and continuing the energy supply [9]. Considering the high penetration of distributed resources, intentional islanding operation of distribution power systems is gradually acknowledged as an essential solution to increase the system reliability and resilience. ...
The alarming number of severe outages that have occurred over the last decades raised the awareness regarding the importance of resilient power systems. As fundamental component of the Smart City concept, the energy infrastructure must adapt to the new challenges in terms of major blackouts, natural or human-caused. In distribution networks, the intentional islanding operation proves itself an efficient solution to maintain the energy supply during emergency conditions, made possibile by the Smart Grid technologies, such as distributed generation and energy storage. However, the limited resources present at the distribution level require a good management strategy, in order to minimize the adverse effectes of a long-term interruption. The scope of this paper is to develop an efficient coordination model of load, generation units and energy storage devices under islanding conditions for a smart distribution network. In this regard, a mixed-integer second-order cone programming (MISOCP) method is approached in order to increase the network resilience based on the unsupplied load minimization, while maintaining proper operational parameters for the IEEE 33-bus test distribution network.
... As mentioned in the UC Department of Energy, 58% of disruptions are due to disruptive weather conditions, which are unintentional events, and their time is not predictable [1]. Therefore, the enhancement of resiliency in the operation of power systems and microgrids has become a significant issue for researchers/engineers in the last decade [2]. Generally, the concept of resiliency is defined as an ability of the system to maintain the load feeding considering the priority of them and stability of the network in the outage situations [3], [4]. ...
Integrating microgrids within distribution systems can significantly improve the power system’s reliability while reducing operating costs. However, due to the unintentional disaster conditions, sometimes distribution systems and microgrids cannot support each other, and the microgrids are forced to work in the islanded mode. Accordingly, we developed an optimal resilient scheduling scheme that guarantees networked microgrids (NMGs) reliable operation in the normal and islanding modes. To achieve this aim, the problem is decomposed into day-ahead normal operation (grid-connected) and real-time islanded examination by benders decomposition algorithm perspective. The specified scheme of NMGs in the normal operation will be scrutinized in the real-time islanded mode. According to the benders decomposition theory, the scheduling of NMGs would be revised in the next iteration if the current schedule is not feasible for possible real-time islanding conditions. The status of thermal units, charging, and discharging of energy storage systems respecting their other constraints are changed depending on the type and severity of mismatches between generation and demand. Three different interconnection topologies are tested for assessing the performance of the proposed method and the impact of transaction energy between NMGs on that. Numerical simulations illustrate the advantages of the proposed scheme and explain its merits.
... From these results, it is evident that the best attack plans are those in which load shedding occurs at node 4 since this is a critical load. A critical load is considered to be those infrastructures that are associated with the basic needs of human life, which include hospitals, public lighting, water utilities, telecommunications and others [37,38]. Figure 6 and Table 6 summarize the most relevant characteristics of the network operation after the execution of the best attack plan of the disruptor agent. ...
Electric power systems are subject to failures, due to both deliberate and fortuitous events. This paper addresses the first case in which a disruptive agent aims at maximizing the damage to the network (expressed through the total cost of operation), while the system operator takes the necessary measures to mitigate the effects of this attack. The interaction between these two agents is modeled by means of a bi-level optimization problem. On one hand, the disruptive agent is positioned in the upper-level optimization problem and must decide which elements to render out of service (lines and generators), given a limited destructive budget. On the other hand, the system operator, located in a lower-level optimization problem, reacts to the attack by deploying mitigation measures in order to minimize cost overruns in system operation. Based on the aforementioned dynamic, this paper proposes a novel approach to maximize the resiliency of the power system under intentional attacks through the implementation of distributed energy resources (DERs), namely, distributed generation (DG) and demand response (DR). Three metrics are proposed to assess resilience by assigning DERs in islands generated by the destruction of lines and generators. The results obtained in a didactic 5-bus test system and the IEEE RTS-24 bus test system demonstrate the applicability and effectiveness of the proposed methodology.
... Further, the wet snow threat model was used to demonstrate the capability of the proposed tool applied for a real scenario that occurred during February 2015 in the northern province of Italy. Panteli and Mancarella (2015) investigated the effect of extreme weather and climate change on the reliability and operation of power system components. Their study also provided a comprehensive review of the existing methodologies to assess the impact of weather on power systems. ...
Extreme weather events like tropical cyclone result in colossal catastrophe during landfall causing widespread inland flooding due to storm surge and also the post-landfall event result in extensive damage to infrastructural facilities and property hinterland. The state of Odisha located in east coast of India experienced a Very Severe Cyclonic Storm (VSCS) named Phailin during the post-monsoon season of October 2013. Timely warnings and alertness on storm surge coordinated with a massive evacuation effort by National Disaster Management Authorities (NDMA) were quite effective in minimizing the loss of human life. However, there was a trial of destruction due to extremely high winds and rainfall that followed during post-landfall causing extensive damage to property and major infrastructure facilities in the Odisha State. This study critically investigated the Phailin post-landfall phase focusing on the impact of high winds and rainfall on the power distribution network using the Weather Research and Forecasting (WRF) model. The study evaluated the spatial and temporal variability of wind speed and rainfall distribution from the WRF model configured for three different spatial domains and selecting the best available microphysics and land surface parameterization schemes. The proposed outer, intermediate, and inner domains had spatial resolutions of 27, 9, and 3 km respectively and that provided the best estimate for onshore wind speed, track forecast, and rainfall distribution highly relevant for the management of power distribution and transmission network. In context to weather model application for the Indian region, this effort is novel and probably for the first time that linked a suitable customized weather model output to evaluate its impact on observed tripping in transmission network of electric power grids. The dynamic model outputs from WRF were compared with data from synchrophasors used in electrical technology that monitored the transient and dynamic behavior of power systems in real-time operations. A close examination of the results signifies that the atmospheric model performed exceptionally well in capturing the tripping time of power lines, and the overall knowledge obtained from this study has a broader scope to develop a framework for efficient planning operations of the power network, resource allocation, and emergency preparedness.
... Assessing the resilience of energy systems, i.e., the ability to recover after an unexpected shock [1], is becoming more relevant in the context of rapidly changing energy systems and climate-change-related disasters [2]. Studies on resilience commonly measure how disasters impact a system's ability to serve the energy demand and how quickly the system can be restored to a minimum level of service [3]. ...
To assess the resilience of energy systems, i.e., the ability to recover after an unexpected shock, the system’s minimum state of service is a key input. Quantitative descriptions of such states are inherently elusive. The measures adopted by governments to contain COVID-19 have provided empirical data, which may serve as a proxy for such states of minimum service. Here, we systematize the impact of the adopted COVID-19 measures on the electricity demand. We classify the measures into three phases of increasing stringency, ranging from working from home to soft and full lockdowns, for four major electricity consuming countries of Europe. We use readily accessible data from the European Network of Transmission System Operators for Electricity as a basis. For each country and phase, we derive representative daily load profiles with hourly resolution obtained by k-medoids clustering. The analysis could unravel the influence of the different measures to the energy consumption and the differences among the four countries. It is observed that the daily peak load is considerably flattened and the total electricity consumption decreases by up to 30% under the circumstances brought about by the COVID-19 restrictions. These demand profiles are useful for the energy planning community, especially when designing future electricity systems with a focus on system resilience and a more digitalised society in terms of working from home.
... Like power system vulnerability, power system resilience systems to such disastrous events has fascinated many researchers lately (Wang et al. 2016;Panteli and Mancarella 2015b). According to (Amirioun, Aminifar, and Lesani 2018;Li et al. 2017;Chanda and Srivastava 2016), power system resilience is the ability of a power system to respond to HILP events; and it emphasises how quickly and resourcefully the power system can be reinstated to its pre-event Current operational state. ...
The electric power system is one of the most vital infrastructures, and its security is necessary for the proper functioning of society. The main goal for the electric power system has traditionally been continuity of the electrical power supply. However, in addition to this requirement, power systems must follow the requirements associated with vulnerability and resilience. Vulnerability deals with the assessment of risk, as it relates to physical and economic consequences, arising from the capability of the network to handle an undesirable incident. Resilience deals with the network capability to withstand unknown disturbances, and consequently, the ability to restore stable operating conditions. Despite some research on power system resilience and vulnerability, their basic concepts are still unexplored. This paper aims to discuss the essential concepts of vulnerability and resilience in electric power systems. Their assessment frameworks and quantification metrics are also described. Case studies, on standard test systems, to demonstrate the assessment of power system vulnerability and resilience, are also part of this research.
... Kwasinski, 2016;Mukherjee et al., 2018;M. Panteli et al., 2016;Mathaios Panteli & Mancarella, 2015a, 2015bY. Wang et al., 2016). ...
The value of critical infrastructure rests in the services they provide. Electricity in particular underpins the operation of hospitals, schools, financial systems, transportation assets, telecommunications, and water treatment, and affects many other aspects of daily life. There is an emerging recognition of power systems as complex adaptive systems and a proliferation of work on how to make them more resilient in the face of natural hazards and climate change. However, the term “resilience” is often used to encompass important, but distinct, aspects of infrastructure analysis including vulnerability, asset hardening, resistance to failure and risk management. As work in this domain has evolved through disciplines from risk management, economics, engineering and policy, the term “resilience” has often remained vague or referred to broad aspects of infrastructure performance under hazard events. Here we argue it is important to distinguish asset hardening and risk management from aspects of a system that allow it to deliver services even when some of its components fail. Understanding infrastructure vulnerability and risk are key components of resilient power system assessments. However, differentiating vulnerability from functional resilience (service delivery even when components fail) can help identify gaps in data, modeling and decision making. We propose that continued progress in this area can be made by unifying these areas of work through (1) understanding vulnerability and risk management in the context of hazards and climate change and (2) an orientation of resilient service delivery as a unifying concept for resilience analysis.
This article is categorized under:
• Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies
The term resilience describes the ability to survive and quickly recover from extreme and unexpected disruptions. A high energy system resilience is of utmost importance to modern societies that are highly dependent on continued access to energy services. This review covers the terminology of energy system resilience and the assessment of a broad landscape of threats mapped with the proposed framework. A more detailed discussion on two specific threats are given: extreme weather, which is the cause for most of the energy supply disruptions, and cyberattacks, which still are a minor, but rapidly increasing concern. The framework integrates various perspectives on energy system threats by showcasing interactions between the parts of the energy system and its environment. Weather-related threats are discussed distinguishing relevant meteorological parameters and different durations of disruptions, increasingly related to the impacts of the climate change. Extremes in space weather caused by solar activity are very rare, but are nonetheless considered due to their potentially catastrophic impacts on a global scale. Digitalization of energy systems, e.g. through smart grids important to renewable electricity utilization, may as such improve resilience from traditional weather and technical failure threats, but it also introduces new vulnerabilities to cyberattacks. Major differences between the internet and smart grids limit the applicability of existing cybersecurity solutions to the energy sector. Other structural energy system changes will likely bring new threats, which call for updating the threat landscape for expected system development scenarios.
The electrical power grid is one of the most critical infrastructures (CIs) in many developed and developing countries and must be planned, operated, maintained, and managed to ensure reliable, secure, and resilient service supply. Being coupled to the transport network, water distribution, Internet, food, etc., all are highly mutually dependent, including through information and communication technologies (the so-called cyber-based systems). We examine the threats to the electrical CIs of natural and man-made disasters that cause loss of power systems for several weeks over vast urban or statewide areas, affect many millions, and result in intersystems cascading failures.
Key future questions include: how high and reliable to build flooding defenses; how to enhance backup power systems for all CI fail-safe interconnections; and establishing increased investment. What next must and can only be better systems, improved reliability, more effective emergency management, through decision-making guided by quantified risk and resilience of coupled CI. What we have learned from past real events discussed in this chapter has national as well as systems engineering implications.
This paper presents a new online scheme for estimating critical contingencies caused by extreme windstorms aiming at improving the microgrid (MG) resiliency. The proposed scheme benefits from an online and non-model-based index by which the fragility metrics of MGs and the loads are evaluated at each time moving window. Also, four indices based on dynamic evaluation of the network are proposed which two indices correspond to the failure period and the two other are related to the network recovery period. By evaluation of these indices, correction actions aiming at improving the resilience of MG are provided. Finally, the proposed method is assessed in the case of different generation locations and the outage probability of system components. The results evidence that the proposed scheme shows a 15% reduction in the total load failure index and a 7% reduction in the critical load failure index if compared to the base case. It also shows a 23% improvement in the total load recovery in the presence of severe storms.
Electric failures are a problem for customers and grid operators. Identifying causes and localizing the source of failures in the grid is critical. Here, we focus on a specific power grid in the Arctic region of North Norway. First, we collect data pertaining to the grid topology, the topography of the area, the historical meteorological data, and the historical energy consumption/production data. Then, we exploit statistical and machine-learning techniques to predict the occurrence of failures. We interpret the variables that mostly explain the classification results to be the main driving factors of power interruption. We are able to predict 57% (F1-score 0.53) of all failures reported over a period of 1 year with a weighted support-vector machine model. Wind speed and local industry activity are found to be the main controlling parameters where the location of exposed power lines is a likely trigger. In summary, we discuss causing factors for failures in the power grid and enable the distribution system operators to implement strategies to prevent and mitigate incoming failures.
Disturbances such as natural disasters or man-made attacks exert serious influences on power system, resulting in an increasing awareness of resilience enhancement strategies. As the integration of microgrids, distributed generators (DGs) and power electronic devices adds to the vulnerability of power distribution network to disruptions, it is highly required to enhance the resilience against failure. Soft open points (SOPs) are flexible power electronic devices, which can balance power distribution under normal operation, and supply restoration under abnormal conditions. Thus, the application of SOPs can boost resilience both in pre-failure prevention and post-failure recovery. In this paper, to maximize the effect of SOPs on the boost of resilience of distribution network, a mixed integer non-linear optimization problem is proposed to schedule the siting and sizing of SOPs based on multi-stage elastic mechanical model. A bi-level algorithm is used to tackle the SOP planning problem. The selection of location and capacity is solved by genetic algorithm, after which the control strategies of SOP are optimized by particle swarm algorithm with given planning scheme to obtain the maximum resilience. Finally, case studies on the IEEE 33-bus system and IEEE 123-node test feeder are used to verify the effectiveness and efficiency of the proposed method.
In this paper, the performance analysis of a 30 MW wind power plant is performed. The farm consists of fifteen (T1-T15) G9 7/2000/GAMESA 2 MW grid-connected turbines. The farm is in operation mode installed 28 km south of Nouakchott city in Mauritania. The analyzed data are monitored from July 1st, 2015 (the first operation day of the power plant) to December 31st, 2019. The parameters of performance evaluation are power generation, capacity factor, machine availability, grid availability, and system availability. It is observed from data analysis, the wind farm supplies a total energy of 507.39 GWh to the power grid and have a high average capacity factor of 42.55%. T1 produces the highest amount of electrical energy among the other turbines with a total energy output of 35.46 GWh, an average capacity factor of 44.97%, and operating hours of 33,814 hours. While T12 produced the minimum amount of energy in this period, the difference in energy compared to T1 is 4.563 GWh. It is observed that the availability of the network is unstable and needs improvement, varying between 90.86% in 2016 and 93.16% in 2018. In the first year of operation, 97.06% of the turbines were available. However, the average availability of the wind farm is approximately 94% during the total study period. Keywords: Capacity factor Machine Power grid Power system availability Wind power plant This is an open access article under the CC BY-SA license.
Intense winds are the leading cause of the topple of towers of electric power transmission lines, which may be of synoptic origin (e.g. frontal systems) or convective (e.g. microburst). The goal of this work is to gather the accidents that occurred
in Paraná at the FURNAS company's towers (1980-2017), due to the action of the winds and to investigate the atmospheric patterns present in these events. The statistical tool Self Organizing Map (SOM) was applied to meteorological variables
(ERA5 Reanalysis) of different atmospheric levels to identify the synoptic patterns. For the analysis of the mesoscale, meteorological data from surface stations were used to identify the presence of meso-highs and soundings for the calculation of instability indices in a specific case where the wind seemed to have a purely convective origin. The results showed that the synoptic-scale was significant in most cases because even though there was no direct synoptic contribution to wind generation, it played a fundamental role in convection. All classifications indicated a flow pattern of
850 hPa similar to the low-level jet. The atmospheric indexes of case of study pointed to strong vertical wind shear and also low level heating due to advection. About the seasonality, most accidents occurred during springs.
The case of unlocking energy access finance through crowdfunding This chapter displays an emerging Islamic approach for the energy sector in pursuit of addressing affordable and clean energy access gap with renewable energy alternatives. The traditional energy sector reform agenda could not deliver the desired outcomes to have global electricity access nor a substantially more efficient sector. It is also not aligned to new technological trends. The technological trend favors renewable energy over gas flaring or other fossil fuel alternatives for provision of energy, cooling, and heating. With some fine-tuning with the emerging Islamic approach showcased, there is a strong potential for the provision of clean and affordable energy for all with greasing of Islamic finance. The technological trend and environmental concerns favor electricity over other alternatives in energy sector.
Wind loading on a transmission tower structure is jointly influenced by the wind field, structural parameters, and the geo-spatial configuration of the transmission line. Considering the multi-parametric effect, this paper aims at developing a limit capacity model for transmission towers under strong winds. To this end, the limit capacity of the tower is expressed via two equivalent means: one is the limit wind speed as a function of the wind angle of attack and the span of transmission line; the other is a limit capacity surface with three fundamental wind load components as the principal axes. An adaptive kriging surrogate modeling is constructed to approximate the function/surface with structural uncertainties considered. The performance of the surrogate model is improved by adding support points and then evaluated by the overall accuracy validation and local error check. A numerical example demonstrating the feasibility of the surrogate modeling for the limit capacity of the transmission tower under winds is presented. Finally, a fragility assessment concerning a practical transmission line and towers subjected to typhoons is accomplished using the established limit capacity model of the tower.
Concerns about climate change and energy security, and related behaviour may be impacted by experiences such as flooding and power outages and we consider that impacts may be different for individual and social actions. Our first study, using online survey data from a quota sample in the UK (N = 1543) found that concerns about climate change and energy security differed for people who had recent power outage experience compared to those who did not; with small but significant effects. A mediation model analysis found that people who had experienced power outages were more likely to intend to engage in social energy saving behaviours, partially mediated by concerns about climate change and energy security. Our second study used survey data from a convenience sample in Mexico City (N = 661). Here a further mediation analysis indicated that people who had experienced higher levels of power outages or flooding were more likely to intend to engage in social energy saving behaviours. In aggregate no significant impacts of experiences on individual energy saving behaviours were found. We conclude that shared adverse experiences may promote prosocial interactions around environmental issues and that there is a key role for communications around environmental experiences in order to promote sustainable behaviour.
This paper presents the findings about the impact of heat waves on a real urban distribution system. A data-driven methodology is proposed to simulate the portion of faults that can be associated to normal conditions (and hence to reliability) and the portion correlated to the heat wave occurrence. Based on real data collected in the years 2012-2017, the fault rates associated to reliability and resilience have been calculated and then used to feed a Monte Carlo simulation aiming to manage the uncertainty in the fault occurrence. Finally, based on the Italian legislation, the benefits deriving by the substitution of the faulted portion of the system have been calculated.
The operational reliability analysis to events with low probability and high impact is crucial for power system. The cyber-physical threats to a power system possess several challenges to a reliable and efficient power supply to its customers. In this paper, operational reliability of a microgrid is quantified using the “resilience trapezoid.” This paper aims to study the time-dependent metrics that capture distribution system degradation phases due to any external threat and its recovery process based on the trapezoid method. It illustrates the sensitivity of the distribution system due to the effect of abnormal weather conditions or cyber threats and how it can recover back to its original form in a fast and efficient way. The operational reliability analysis of a distribution network consisting of lines, loads, and wind turbine generator is done to identify its performance during such high impact conditions. Loss of load probability and expected energy not served are calculated to have a proper assessment of the system and its measures to keep the network intact for a comparatively more time duration thus preventing further damage.
This paper presents a decentralized methodology in a parallel manner to provide an outage management strategy with the scheduling of networked microgrids (NMGs). Integrating MGs require a trustworthy strategy to determine the optimal amount of transaction power among them. Besides that, they are vulnerable versus unintentional tie-lines outages. The proposed approach addresses these challenges. The MGs' optimization problem is decomposed into the day-ahead normal operation scheduling as a master problem (MP) and real-time islanded examination as a sub-problem (SP) using Benders decomposition algorithm perspective. Decentralized scheduling with a parallel solution based on analytical target cascading algorithm is implemented on both MP and SP to coordinate the operation of NMGs. In the master problem, all NMGs are typically (grid-connected mode) and separately scheduled. The obtained results are also separately examined in the sub-problem to compute mismatches between generation and demand in the possible real-time islanding situations. Then the MP is revised in the next iteration according to existing mismatches in the previous iteration. This iterative procedure continues until all of the mismatches reach zero at the same iteration. The proposed methodology's performance and effectiveness are investigated and validated by four different case studies.
The uncertain nature of natural, man-made, and complex phenomena poses a challenge to microgrid (MG) functioning. In the face of such unexpected events, the power supply to the customer degrades and it becomes necessary to manage the performance of MG components. Therefore, the resilience of MG should be a priority. Resilience prepares the system to handle the operational loss and recover quickly to its pre-disturbance state. It is the ability to adapt to changing conditions, withstand it, and rapidly recover from uncertain natural disasters, man-made interruptions, and complex events termed as high-impact low-probability (HILP) events. MG planning and operation strategy for such HILP events enhances resilience. To analyze this strategy, the uncertain nature of MGs needs to be addressed. However, resilience study can be extended throughout the power system but is more suitable for MGs. It is due to the location of MGs at different terrains that makes it more vulnerable to HILP events. Crisp value of resilience parameter fails to capture the wide range of variations in MG behavior. To incorporate these significant variations, fuzzy-based resilience is required. The fuzzy-based resilience planning and operation is flexible and allows variabilities associated with changing environment. This chapter provides a comprehensive analysis of fuzzy-based resilience assessment for MG planning and operation against windstorm. Weibull wind assessment estimates the maximum likelihood of wind speed distribution in a particular region. Distribution lines are the exposed component during windstorm, so the probability of impacting the MG connectivity is very high. Therefore, this chapter focuses on distribution line fragility. The fragility curve of distribution lines depicts the wind speed-dependent failure probability. The region-specific wind profile of windstorms is mapped to the fragility curve of lines to obtain the time and hazard-dependent operational status. The Monte-Carlo probabilistic assessment measures this disruption status of lines by comparing the failure of lines as a function of weather parameter. To evaluate the influence of uncertain parameters on the operation and planning of MG, fuzzy-based system average interruption frequency index (FSAIFI), fuzzy-based system average interruption duration index (FSAIDI), and fuzzy-based average service availability index (FASAI) are calculated. For MG resilience planning, it is essential to assess the time-varying nature of these indices. The characteristics of these indices are thus assessed using the resilience triangle. It describes the resilience level of a system during each specific phase of the windstorm, which are pre-disturbance, degraded, and restorative stages. This analysis is tested on IEEE 33-bus system. Also, a comparative assessment of the resilience triangle and trapezoid approach for the IEEE 33-bus system is provided. This graphical representation of fuzzy-based performance parameters provides an insight into the impact of uncertainties on the MG under HILP events.
Facilities such as ports that are associated with goods movement face challenges in managing energy requirements including growing demands, maintaining economic competitiveness, increasing efficiencies of operation, and improving the resiliency, reliability, and security of the energy supply. Furthermore, ports face pressure to meet environmental goals including reducing the emissions of both pollutants and greenhouse gases and increasing the levels of renewable sources. Within this framework, many port operators are pursuing the development of microgrids supported by self-generation including fuel cell systems. Given the breadth of energy requirements of ports, the range of fuel cell applications is an attractive resource for stationary power generation, motive power and fuel generation, and backup/auxiliary power in concert with base load power. This work involves a review of the literature, from a technical perspective, to assess microgrids and fuel cell systems at ports including comparison with combustion-based power distributed generation sources. Additionally, novel simulations are presented to provide insight into economic and emission considerations associated with fuel cell deployment in critical facilities. Important distinctions of fuel cells for ports include flexibility of size and fuel, low to negligible emissions, capability to operate in grid-forming mode, and high electric-only efficiencies. While combined cooling, heating, and power improves performance and should be pursued, the mismatch in port electrical and thermal loads is a potential barrier and increases the importance of high electric-only efficiencies of fuel cells. Tri-generation systems have the potential to maximize benefits with the production of hydrogen along with electricity and, if needed, heat.
Electric power systems try to maximize resilience by various enhancement strategies as preventive and corrective actions against extreme weather events. This paper presents an operational network reconfiguration strategy during a high wind event to strengthen the resiliency of distribution networks. In the proposed resilience enhancement strategy, a bi-level optimization problem is formulated with two conflicting objectives (i) maximizing grid resilience and (ii) realize the primary objective by a minimum number of out-of-service lines (OSLs) switching operations. Furthermore, the algorithm considers the priority of loads, which is an important characteristic of modern distribution grids. The optimization problem is solved by the bi-level genetic algorithm (BiGA). The IEEE 33-bus network is utilized to demonstrate the application of the developed algorithm. The simulation results show a significant improvement in the resiliency of the tested network and reveal that switching operations can be used effectively to increase the resiliency of distribution networks against natural disasters. Furthermore, sensitivity analysis is performed to show the effectiveness of the proposed approach for finding the global optimal point. The proposed network reconfiguration strategy helps to prepare for severe weather conditions.
A power system developed solely based on reliability metrics is not expected to appropriately respond to the severe events' effects. Also, reinforcement of existing components may be very expensive if practical. One of the measures taken to have the proper tools to deal with a severe event is to consider the resilience index during generation and transmission expansion planning (GTEP). Microgrids can also increase the power system's resilience and reduce planning costs with the resilience index. In this paper, a hybrid resilient static framework for GTEP is presented, determining the microgrid' optimal penetration rate. The proposed framework is a two-stage framework that, in the first stage, the GTEP problem is solved. In the second stage, the results of planning are examined by the Monte Carlo simulation (MCS) to calculate the expected demand not served (EDNS) as the representative of the resilience index. For reaching the desired resilience, investment costs in the planning stage should be increased and used as a constraint in the next step. This process is repeated for each predetermined microgrids' penetration rate. The effect of the event on the transmission lines is modeled using the notion of fragility curve. In this framework, there is also the ability to use reinforced lines during GTEP as a planning option. The simulations performed on the two sample networks show the effectiveness of the proposed method.
We present scalable stochastic optimization approaches for improving power systems’ resilience to extreme weather events. We consider both proactive redispatch and transmission line hardening as alternatives for mitigating expected load shed due to extreme weather, resulting in large-scale stochastic linear programs (LPs) and mixed-integer linear programs (MILPs). We solve these stochastic optimization problems with progressive hedging (PH), a parallel, scenario-based decomposition algorithm. Our computational experiments indicate that our proposed method for enhancing power system resilience can provide high-quality solutions efficiently. With up to 128 scenarios on a 2,000-bus network, the operations (redispatch) and investment (hardening) resilience problems can be solved in approximately 6 min and 2 h of wall-clock time, respectively. Additionally, we solve the investment problems with up to 512 scenarios, demonstrating that the approach scales very well with the number of scenarios. Moreover, the method produces high quality solutions that result in statistically significant reductions in expected load shed. Our proposed approach can be augmented to incorporate a variety of other operational and investment resilience strategies, or a combination of such strategies.
We perform a rare-event study on a simulated power system in which grid-scale batteries provide both regulation and emergency frequency control ancillary services. Using a model of random power disturbances at each bus, we employ the skipping sampler, a Markov Chain Monte Carlo algorithm for rare-event sampling, to build conditional distributions of the power disturbances leading to two kinds of instability: frequency excursions outside the normal operating band, and load shedding. Potential saturation in the benefits, and competition between the two services, are explored as the battery maximum power output increases.
This article is part of the theme issue ‘The mathematics of energy systems’.
Acute impacts of past extreme climatic events on power distribution networks (PDNs) have highlighted the significance of resilience in PDNs. However, maintaining and enhancing the resilience of PDNs over an extended horizon is challenging and requires long-term planning. This chapter presents a decision-making framework for resilience enhancement of PDNs that undergo gradual deterioration and face the risk of exposure to multiple stochastic hurricane events over a decision horizon. The proposed framework integrates modeling of climatic stressors, performance of physical components of PDNs, and probabilistic resilience quantification to form a nonlinear constrained optimization problem with binary decision variables for replacement of deteriorating assets. The objective of the optimization problem is to maximize life-cycle resilience by determining the optimal sequence of preventive maintenance actions. The major complexity of this optimization problem arises from the large number of combinations of possible maintenance actions over an extended decision horizon as well as the existing constraints. To overcome these challenges, this study introduces the BICDE algorithm, which is developed via the integration of a binary differential evolutionary (BDE) algorithm with the improved (μ+λ)-constrained differential evolution (ICDE). The BICDE has the capability of the ICDE to solve constrained optimization problems and features of the BDE algorithm to handle nonlinear, non-differentiable, and multi-modal objective functions with binary decision variables. The presented framework is applied to a large-scale aging PDN. The BICDE-based strategy is compared to the strength-based strategy set by National Electric Safety Code (NESC) that is commonly used in practice. The results indicate that the optimal strategy based on the proposed framework leads to a significant cumulative improvement of 23.1% in the expected resilience of the PDN over a planning horizon of 100 years.
Given the important role that electricity plays in powering society, and the significant risk that extreme weather and other events pose in disrupting electricity supply, the idea of community-scale microgrids has come to the forefront to enhance electrical grid resiliency and provide critical services to local communities during extended outages. In this paper, a discrete choice experiment is used to evaluate willingness to pay (WTP) for services provided by a community microgrid during extended power outages. With a sample of 939 respondents from New York State, results indicate that, overall, there is a positive willingness to pay for microgrid services, including hospital and emergency services, potable water, shelters, and retail outlets; even if residents are not receiving their own residential electricity supply during an outage. The average willingness to pay for the full suite of evaluated microgrid services is approximately $14 per month per household. We also find that WTP varies with some sociodemographic and other characteristics. These results provide critical evidence for rate-makers and utilities in evaluating societal benefit when making investment decisions for microgrids and related infrastructure.
This paper describes active research on situation awareness (SA) as it applies to the power transmission and distribution (T&D) industry. Goal-Directed Task Analysis (GDTA) interviews were conducted with Specialist Reliability Analysis & Operation and Reliability Coordinator/System Operators from two large U.S. power companies to achieve a clear understanding of the power T&D domain. The resulting GDTA and lessons learned are presented.
INCREASING THE RESILIENCE of critical power infrastructures to high-impact, low-probability events, such as extreme weather phenomena driven by climate change, is of key importance for keeping the lights on. However, what does resilience really mean? Should we build a stronger and bigger grid or a smarter one? This article discusses a conceptual framework of power system resilience, its key features, and potential enhancement measures.
Electrical power systems have been traditionally designed to be reliable during normal conditions and abnormal but foreseeable contingencies. However, withstanding unexpected and less frequent severe situations still remains a significant challenge. As a critical infrastructure and in the face of climate change, power systems are more and more expected to be resilient to high-impact low-probability events determined by extreme weather phenomena. However, resilience is an emerging concept, and, as such, it has not yet been adequately explored in spite of its growing interest. On these bases, this paper provides a conceptual framework for gaining insights into the resilience of power systems, with focus on the impact of severe weather events. As quantifying the effect of weather requires a stochastic approach for capturing its random nature and impact on the different system components, a novel sequential Monte-Carlo-based time-series simulation model is introduced to assess power system resilience. The concept of fragility curves is used for applying weather- and time-dependent failure probabilities to system's components. The resilience of the critical power infrastructure is modeled and assessed within a context of system-of-systems that also include human response as a key dimension. This is illustrated using the IEEE 6-bus test system.
Energy systems can be vulnerable to climate change. This paper summarizes the contribution of their authors to a few strategic studies, research workshops, development forum and international conferences related to Climate and Energy. It presents a review of the impacts that climate change may have throughout the energy chain and identifies current knowledge gaps and areas for future research development. One of the greatest challenges is how to assess impacts which may occur as a consequence of the projected increase in the intensity of extreme weather events: the majority of current methodologies rely on past experience but this may not be a sufficiently good guide for planning and operational activities in the coming decades. Also, climate impact assessments on energy planning and operation need to take into account a greater number of scenarios, as well as investigate impacts on particular energy segments. Therefore, we identify energy segments for which little climate impact research has been conducted. Finally, because climate impact assessment for energy systems is a relatively new research field, it is expected that methodological developments will increase in the near future with a consequent broadening of the knowledge base on the subject.
Climate change poses a grave threat to sustainability. The first section of Sustainability2009: The Next Horizon, therefore, is devoted to Climate Change and Adaptation. Contributions focus on the historical consequences of climate change for human societies, as well as the effects of current
climate change on sea level,
lightning intensity, fire, the El Niño—Southern Oscillation (ENSO), and hurricane intensity. Chapters on fisheries and coral reefs highlight the cascading effects climatic warming, rising sea level, and ocean acidification. Adaptation to climate change and its consequences will be necessary to buy time for mitigation and reversal of the effects of greenhouse‐gas emissions.
Increasing shares of fluctuating renewable energy sources induce higher and higher power flow variability at the transmission level. The question arises as to what extent existing networks can absorb additional fluctuating power injection without exceeding thermal limits. At the same time, the resulting power flow characteristics call for revisiting classical approaches to line temperature prediction. This paper presents a probabilistic modeling and simulation methodology for estimating the occur- rence of critical line temperatures in the presence of fluctuating power flows. Cumbersome integration of the dynamic thermal equations at each Monte Carlo simulation trial is sped up by a specific algorithm that makes use of a variance reduction tech- nique adapted from the telecommunications field. The substantial reduction in computational time allows estimations closer to real time, relevant to short-term operational assessments. A case study performed on a single line model provides fundamental insights into the probability of hitting critical line temperatures under given power flow fluctuations. A transmission system application shows how the proposed method can be used for a fast, yet accu- rate operational assessment.
The physical environment in which transmission and distribution systems reside has a major effect on the resulting reliability of the network. The physical stress placed on system components can be much higher in bad weather than that encountered under normal weather conditions. This paper briefly illustrates the conventional approach to predictive reliability assessment using a two-state weather model. In order to incorporate failures occurring in major adverse weather, a three state weather model is developed. The numerical analysis presented in this paper clearly shows that the conventional two state weather model does not reflect the increasing importance of major adverse weather and underestimates the potential error in predicting the reliability of transmission and distribution systems. In the analysis done, it is assumed that the repair of the failed components is carried out only in normal weather conditions.
One of the main problems is the assessment of “residual reliability” or “vulnerability” of a single existing structure, or
of a given class of similar structures, against seismic actions. The fragility curves describe the probability of a structure
to suffer a given damage level when it is subject to a given seismic excitation level. This research work is focused on an
analytical method for constructing fragility curves of existing structures based on a stochastic approach. Analysed structure
is modelled by a simple one degree of freedom system subject to a nonstationary Kanai-Tajimi process. The numerical values
of the parameters of its constitutive law, here suitably described by means of a hysteretic model, are selected using an identification
procedure with the non linear pushover analysis performed on the real assessed structure. To develop fragility curves, a displacement
based damage index is adopted. Fragility curves are obtained in terms of probability of exceeding a given damage level by
using an approximate crossings theory of stochastic processes.
The adoption of Smart Grid devices throughout utility networks will effect tremendous change in grid operations and usage of electricity over the next two decades. The changes in ways to control loads, coupled with increased penetration of renewable energy sources, offer a new set of challenges in balancing consumption and generation. Increased deployment of energy storage devices in the distribution grid will help make this process happen more effectively and improve system performance. This paper addresses the new types of storage being utilized for grid support and the ways they are integrated into the grid.
This paper is a summary of the IEEE Power System Relaying Committee report on the System Integrity Protection Schemes (SIPS) survey. The SIPS role is to counteract system instability, maintaining overall system connectivity, and/or to avoid serious equipment damage during major system events. The survey describes industry experiences with this category of protection schemes applied to protect the integrity of the power system. It is designed to provide guidance for SIPS users and implementers based on surveyed operating practices and lessons learned. The survey includes a global participation through the comprehensive effort of IEEE and CIGRE.
Weather information is an important factor in load forecasting models. Typically, load forecasting models are constructed and tested using actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. These weather forecast errors inevitably lead to a degradation in model performance. This is an important factor in load forecasting but has not been widely examined in the literature. The main aim of this paper is to present a novel technique for minimizing the consequences of this degradation. In addition, a supplementary technique is proposed to model weather forecast errors to reflect current accuracy. The proposed technique utilizes a combination of forecasts from several load forecasting models (sub-models). The parameter estimation may thus be split into two parts: sub-model and combination parameter estimation. It is shown that the lowest PMSE corresponds to training the sub-models with actual weather but training the combiner with forecast weather.
This paper, prepared by the Climate Change Technology Subcommittee, a subcommittee of the Power and Energy Society Energy Development and Power Generation Committee, identifies key technical issues facing the electric power industry, related to global climate change. The technical challenges arise from: 1) impacts on system operating strategies, configuration, and expansion plans of emission-reducing technologies; 2) power infrastructure response to extreme weather events; 3) effects of government policies including an expanded use of renewable and alternative energy technologies; and 4) impacts of market rules on power system operation. Possible lessons from other industries' responses to climate change are explored.
A methodology has been developed for assessing the sensitivity of electricity and natural gas consumption to climate at regional scales. The approach involves a multiple-regression analysis of historical energy and climate data, and has been applied to eight of the most energy-intensive states, representing 42% of the total annual energy consumption in the United States. Statistical models were developed using two sets of independent variables—primitive variables such as temperature, relative humidity, and wind speed, and derived variables including cooling degree days, heating degree days, and enthalpy latent days. The advantages and disadvantages of both modeling approaches are discussed in this paper, along with sample results for a combined analysis of residential and commercial consumption in eight states.
Electrical energy storage technologies for stationary applications are reviewed. Particular attention is paid to pumped hydroelectric storage, compressed air energy storage, battery, flow battery, fuel cell, solar fuel, superconducting magnetic energy storage, flywheel, capacitor/supercapacitor, and thermal energy storage. Comparison is made among these technologies in terms of technical characteristics, applications and deployment status. © 2008 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.
The ongoing evolution of the electric power systems brings about the need to
cope with increasingly complex interactions of technical components and
relevant actors. In order to integrate a more comprehensive spectrum of
different aspects into a probabilistic reliability assessment and to include
time-dependent effects, this paper proposes an object-oriented hybrid approach
combining agent-based modeling techniques with classical methods such as Monte
Carlo simulation. Objects represent both technical components such as
generators and transmission lines and non-technical components such as grid
operators. The approach allows the calculation of conventional reliability
indices and the estimation of blackout frequencies. Furthermore, the influence
of the time needed to remove line overloads on the overall system reliability
can be assessed. The applicability of the approach is demonstrated by
performing simulations on the IEEE Reliability Test System 1996 and on a model
of the Swiss high-voltage grid.
As modern society has become increasingly reliant on electricity, disturbances to the power supply system have become a worldwide industry concern. The range and impact of disturbances are addressed in this comprehensive account of the planning, operation and control of power systems during emergencies. The impact of a full range of power system emergency situations from adverse weather conditions and natural disasters to equipment failures, human errors and industrial action. Detailed coverage of the procedures, organisation, training and equipment provided by utilities in order to contain the incidence and impact of disturbances, both sudden and predicted. Survey of the measures adopted to restore electricity supply from various levels of failure. The development of abnormal operating conditions: descriptions of actual power system failures and their impacts. Discussion of the costs and benefits associated with emergency control. Emergency control in the future - the impact of industry restructuring and deregulation and the new challenges facing utilities and their staff. Offering a clear and concise treatment of the cause, effect and prevention of power system emergencies, this timely book will appeal to utility managers, power engineers, consultants and practitioners involved in, and reliant upon, the electricity supply industry.
High winds, especially when combined with precipitation from seasonal storms, can cause damage to electricity utility systems, resulting in service interruptions to large numbers of electricity customers. While most such power outages are caused by damage from trees and tree limbs falling on local electricity distribution lines and poles, major power outages tend to be caused by damage to electricity transmission lines which carry bulk power long distances. Depending on the severity of the storm and resulting impairment, power outages can last a few hours or extend to periods of several days, and have real economic effects. Power outages can impact businesses (primarily through lost orders and damage to perishable goods and inventories), and manufacturers (mainly through downtime and lost production, or equipment damage). Data from various studies lead to cost estimates from storm-related outages to the U.S. economy at between $20 billion and $55 billion annually. Data also suggest the trend of outages from weather-related events is increasing. Suggested solutions for reducing impacts from weather-related outages include improved tree-trimming schedules to keep rights-of-way clear, placing distribution and some transmission lines underground, implementing Smart Grid improvements to enhance power system operations and control, inclusion of more distributed generation, and changing utility maintenance practices and metrics to focus on power system reliability. However, most of these potential solutions come with high costs which must be balanced against the perceived benefits. A number of options exist for Congress to consider which could help reduce stormrelated outages. These range from improving the quality of data on storm-related outages, to a greater strategic investment in the U.S. electricity grid. Congress could empower a federal agency to develop standards for the consistent reporting of power outage data. While responsibility for the reliability of the bulk electric system is under the Federal Energy Regulatory Commission (as per the Energy Policy Act of 2005), no central responsibility exists for the reliability of distribution systems. One possible option could be to bring distribution systems under the Electric Reliability Organization for reliability purposes. Recovery after storm-related outages might be enhanced by a federal role in formalizing the review or coordination of electric utility mutual assistance agreements (MAAs). This would not necessarily mean federal approval of MAAs, but may help in the cooperative coordination of additional federal and state resources, especially in a wide, multi-state weather event. While there has been much discussion of transmission system inadequacies and inefficiencies, many distribution systems are in dire need of upgrades or repairs. The cost of upgrading the U.S. grid to meet future uses is expected to be high, with the American Society of Civil Engineers estimating a need of $673 billion by 2020. While the federal government recently made funding available of almost $16 billion for specific Smart Grid projects and new transmission lines under the American Recovery and Reinvestment Act of 2009, there has not been a comprehensive effort to study the needs, set goals, and provide targeted funding for modernization of the U.S. grid as part of a long-term national energy strategy. Such an effort would also require decisions about the appropriate roles of government and the private sector. Power delivery systems are most vulnerable to storms and extreme weather events. Improving the overall condition and efficiency of the power delivery system can only serve to improve the resiliency of the system, and help hasten recovery from weather-related outages. Ultimately, however, electric utilities are responsible for this infrastructure. They are in the business of selling electricity, and they cannot sell electricity if their power delivery systems are out of service.
There are current worries surrounding climate change and with some of the effects already being seen, such as a rise in coastal temperatures and more extreme weather, greater research into how society will be affected is required. With the possibility of increased frequency and intensity of adverse or extreme weather there is a concern that this will affect the reliability of the electricity transmission network in GB which currently suffers from regular faults with a significant percentage of them caused by adverse or extreme weather. The research discussed in this paper focuses on extreme and adverse weather and its effects on the GB transmission network using data supplied by the three transmission companies that own and operate the GB network: Scottish Power Ltd., National Grid plc. and SSE plc. It also presents the beginnings of a relationship between weather types, mainly focusing on wind, and weather related faults.
Deterministic security criteria provide a degree of security that may insufficient under some operating conditions and excesive for others. To determine an appropriate level of security, one should perform a probabilistic cost/benefit analysis that balances the cost of security margin against its benefits, i.e. the expected societal cost of the avoided outages. This paper shows how a previously published method based on Monte Carlo simulation can be enhanced to take into account time-dependent phenomena such a cascade tripping of elements due to overloads, malfunctionof the protection system and potential power system instabilities. In addition, the importance of using failures rates that reflect the weather conditions is discussed. Studies based on the South-Western part of the transmission network of England and Wales demonstrate the validity of the models that have been developed.
The stability and reliability of electric power grids are essential to the continuous operation of modern cities as well as for the mitigation, preparedness, response and recovery in disaster management. Power systems must be assessed in order to identify and address component and system-level weaknesses while supporting their rapid restoration. This paper proposes a Bayesian Network (BN)-based framework to predict outages in an electric power grid that is exposed to a hurricane event. The inherent capabilities of BNs, including its intuitive and graphical representation of probabilistic information, and its ability to allow information updating with ease, make it an effective tool for this application. The framework is coupled with a DC-flow model that captures the physics of the electrical system and also reduces the computational complexity of building conditional probability tables needed in the BN model. The framework relies on component fragilities and topology of the grid, and predicts outages at substations and distribution points within the electric power system. Its application is demonstrated using Harris County's electric power system under the 2008 Hurricane Ike winds that battered the Gulf Coast of the United States. The model captured well the field system response, where low outage probabilities are observed in the transmission system while the outage risks at distribution load points are significantly higher. The developed BN framework can seamlessly integrate transmission and distribution systems, inform disaster management, and suggest restoration strategies, while supporting decision-making for pre- or post-event intervention actions.
Wood poles comprise a portion of the power utility's aging infrastructure that make a significant impact on customer's reliability. A large number may fail under the influence of severe wind storms and hurricanes, sometimes resulting in millions of dollars in replacement costs per year to the utilities. A holistic approach to risk assessment of wood poles in power distribution networks would therefore consider the simultaneous effects of decay and natural hazards on the failure probability of the poles. Toward this goal, this paper presents a framework for the development of age-dependent fragility curves of utility wood poles that relies on age-dependent probabilistic capacity models of wood poles and wind induced demand models. The framework considers uncertainties in the initial fiber strength of the wood poles, the age-dependent capacity loss model, the geometric features of wood poles of different classes, and the applied wind loads. The results of this study show that the decay process in wood poles may increase the fragility of the poles significantly as the age of the poles increase. The fragility curves provided in this research may constitute a major component of risk assessment approaches of power distribution networks against hurricanes and strong winds.
During the last decade, besides the rapid increase in the penetration level of Distributed Generation (DG) units of micro-grids, the connection of micro-grids as a developed technology to the existing distribution networks has also attracted much attention. One of the major challenges associated with the protection of micro-grids is to devise a proper protection strategy that is effective in the grid-connected as well as the islanded mode of operation. In order to deal with the challenge, many researchers have recently proposed various techniques. The purpose of the current study is to provide a comprehensive review of the available protection techniques that are applied to address micro-grid protection issues in both grid-connected and islanded mode. The most up to date relevant options are described and categorized into specific clusters. A comparative analysis is carried out in which the advantages and disadvantages to each technique are assessed. Lastly, after the appraisement of the existing protection techniques, some conclusions and suggestions are put forward for the protection of micro-grids in the future.
Situation awareness is a key factor in preserving power system security, as it enables effective and timely decision-making and reactions by the operators to an incident. Insufficient situation awareness results in a delayed, incorrect or deficient response, endangering power system stability. This factor was actually identified as one of the main causes of several electrical disturbances in the last decade. This paper identifies numerous factors that govern the formation of situation awareness in a power system control center. A multi-state model based on Markov modeling is proposed for assessing the impact of insufficient situation awareness on the probability of power system blackouts. The proposed model considers the level of situation awareness and the state of the information infrastructure. The workings of this model are illustrated using the IEEE 24-bus Reliability Test System.
A reliable public electricity supply depends in part on a reliable electricity grid system to transmit and distribute electrical power from generating stations to consumers. The grid system comprises many components that are exposed to the weather and can experience faults as a result of weather events. As climate change is expected to alter the number and severity of weather events, then the reliability of the grid and hence the reliability of electricity supplies can be affected. This paper reviews the effects of weather events on grid systems, illustrated by reference to experience on the grid systems in Europe and North America. It is shown that the effects on the high voltage transmission networks are different from the effects on lower voltage distribution networks and that generally the most significant extreme weather is high winds. Some remedial measures that can mitigate the effects of weather events are also described.
The concept of integration of distributed energy resources for formation of microgrid will be most significant in near future. The latest research and development in the field of microgrid as a promising power system through a comprehensive literature review is presented in this paper. It shows a broad overview on the worldwide research trend on microgrid which is most significant topic at present. This literature survey reveals that integration of distributed energy resources, operation, control, power quality issues and stability of microgrid system should be explored to implement microgrid successfully in real power scenario.
The needs of contemporary electric utility customers and expectations regarding energy supply require dramatic changes in the way energy is transmitted and delivered. A smart grid is a concept by which the existing and aging electrical grid infrastructure is being upgraded with integration of multiple applications and technologies; such as two way power transfer, two way communication, renewable distributed generation, automated sensors, automated & advanced controls, central control, forecasting system and microgrids. This enables the grid to be more secure, reliable, efficient, self-healing, while reducing greenhouse gases. In addition, it will provide new products & services and fully optimize asset utilization. Also, integration of these innovative technologies to establish a smart grid poses new challenges.
There will be need for new tools to assess and predict reliability issues. The goal of this research is both to demonstrate these new electrical system tools and to monitor and analyze the relationship of weather and electrical infrastructure interruptions. This goal will be accomplished by modeling weather and distribution system reliability issues, by developing forecasting tools and finally developing mathematical models for system availability with smart grid functionality. Expected results include the ability to predict and determine the number of interruptions in a defined region; a novel method for calculating a smart grid system’s availability; a novel method for normalizing reliability indices; and to determine manpower needs, inventory needs, and fast restoration strategies.
The reliability of modern power distribution systems is dependent on many variables such as load capacity, renewable distributed generation, customer base, maintenance, age, and type of equipment. This research effort attempts to study these areas and in the process, has developed novel models and methods to calculate and predict the reliability of a smart grid distribution system. A smart grid system, along with variable weather conditions, poses new challenges to existing grid systems in terms of reliability, grid hardening, and security.
The modern grid is comprised of various distributed generation systems. New methods are required to understand and calculate availability of a smart grid system. One such effort is demonstrated in this research. The method that was developed for modeling smart grid dynamic reconfigurations under variable weather conditions combines three modeling techniques: Markov modeling, Boolean Logic Driven Markov Process (BDMP) and the modeling of variable weather condition. This approach has advantages over conventional models because it allows complex dynamic models to be defined, while maintaining its easy readability.
This volume evaluates the different concepts, models, and techniques used to measure the reliability of power systems in both planning and operating phases. Applications of the techniques presented in the text are illustrated in numerical examples and diagrams. Areas discussed include basic probability plus frequency and duration methods for determining generating capacity, interconnected systems, operating reserve, composite generation and transmission systems, and plant and station availability.
Extreme adverse weather such as a hurricane can have a significant impact on composite power system reliability. Since hurricanes
can cause the simultaneous failures of multiple system components, common-cause failures (CCF) should be investigated in the
reliability evaluation of composite power systems when the effects of hurricanes are considered. A few techniques have been
proposed to evaluate the effects of CCF, but they are not suitable for composite power systems. This paper proposes a method
based on Bayesian networks (BN) to solve this problem. Basically, the proposed method uses the noisy OR-gate model to reduce
the dimensional dilemma of the conditional probability method. This model also considers the independent failures of transmission
lines and generating units during hurricanes. The functionality of a BN is determined by its configuration and the conditional
probability distributions (CPD) associated with the nodes. In this paper, the CPD of BN is obtained by using random sampling.
Since hurricanes usually last for only a limited period of time, a pseudo-repetitive temporal model is used to calculate the
time-specific system reliability indices. The proposed method is applied to the modified IEEE Reliability Test System (RTS).
The implementation demonstrates that the proposed method is effective and flexible in its applications.
KeywordsHurricane-Reliability-Common-cause failure-Bayesian network-Random sampling-Time-specific reliability index
In power system planning and operation, accurate assessment of reliability worth is essential for making informed decisions. One common simplification when modeling power system reliability is assuming constant failure rates and non time-varying restoration times. However, historical outages show differently; failure rates and restoration times for especially overhead lines are dependent upon time-varying factors as, for instance, weather conditions. When modeling this time dependence a two or three-state weather model is often used. The reliability model proposed in this paper does in contrast use the stochastic nature of the severe weather intensity and duration to model variations in failure rate and restoration time. Further, the model also considers when severe weather is likely to occur during the year by using a non-homogeneous Poisson process (NHPP). A time-sequential Monte Carlo technique is applied to a radial distribution system. By combining the proposed reliability model with a time-dependent interruption cost model, the effect of the inclusion of time-varying failure rates and restoration times is investigated and found to be of importance when assessing reliability worth.
This paper illustrates the development and analysis of several transmission system models which include varying weather conditions for composite system reliability assessment. The failure rate of an outdoor component can be much higher in stormy weather than that in normal weather periods. The probability of overlapping failures in stormy weather, therefore, can be much greater than that in normal weather periods. This phenomenon is often called `failure bunching¿ due to the fact that components are fully or partially exposed to a common weather condition. Several methods, designated as the constant weather model, the full Markov process approach, the 4-state approximate method and the line or area addition approach are presented. The impact of these weather models in composite generation and transmission system adequacy evaluation is examined and illustrated in this paper using the IEEE reliability test system.
This paper describes active research on situation awareness (SA) as it applies to the power transmission and distribution (T&D) industry. Recent emphases on situation awareness in the power T&D industry have highlighted the lack of SA-related research in this domain. This gap has been recognized by several organizations, leading to the development of new commercial energy management systems, reactive reserve monitoring tools, and visualization systems designed to assist T&D operators in monitoring, predicting, anticipating, and preventing potential problems that could lead to major power outages. The essential element in each of these endeavors is a focused effort on understanding and increasing operator SA in T&D control centers. The power T&D domain presents multiple vantage points upon which operator SA can be improved. The power T&D industry continues to seek a solution to its situation awareness gap. The work described in this paper helps to identify areas where SA needs are lacking within the industry, and provides valuable insights to inform the development of future technology to support SA in the power T&D domain.
In distribution system planning and operation, accurate assessment of reliability performance is essential for making informed decisions. Also, performance-based regulation, accompanied by quality regulation, increases the need to understand and quantify differences in reliability performance between networks. Distribution system reliability performance indices exhibit stochastic behavior due to the impact of severe weather. In this paper, a new reliability model is presented which incorporates the stochastic nature of the severe weather intensity and duration to model variations in failure rate and restoration time. The model considers the impact of high winds and lightning and can be expanded to account for more types of severe weather. Furthermore, the modeling approach considers when severe weather is likely to occur during the year by using a nonhomogeneous Poisson process (NHPP). The proposed model is validated and applied to a test system to estimate reliability indices. Results show that the stochasticity in weather has a great impact on the variance in the reliability indices.
Adverse weather such as hurricanes can have significant impact on power system reliability. One of the challenges of incorporating weather effects in power system reliability evaluation is to assess how adverse weather affects the reliability parameters of system components. In this paper, a fuzzy inference system (FIS) built by using fuzzy clustering method is combined with the regional weather model to solve the preceding problem. The composite power system is assumed to be partitioned into different regions and the FIS maps the nonlinear functional relationship between hurricane parameters and the increment multipliers of the failure rates (IMFR) of the transmission lines in different regions. The possible case that transmission lines traverse bordering regions is investigated by using the weighted average method. Since hurricanes last only a limited time period, the short-term reliability indices over the duration of hurricane instead of the steady-state ones are calculated by using the minimal cut-set method (MCSM). The proposed method is applied to the modified IEEE Reliability Test System (RTS). The implementation demonstrates that the proposed method is effective and efficient and is flexible in applications.
The system at Balls Gap did require minor adjustment in the sensing circuits to ensure proper coordination of the reclosers and automated feeder switches. Actual islanding events have occurred with successful operation of the batteries in islanding mode.
Failure rate of a system component is usually assumed to be constant in conventional reliability evaluation of power systems. It has been realised from the real-time operation that a component will experience more failures during heavy loading condition than those during light loading condition, which means that the failure rate of a component in real-time operation is not constant and varies with loading condition. In order to evaluate system operational reliability related to load condition, the factors affected condition-dependent failure rate (CDFR) are investigated and a basic CDFR model is proposed in this study. In predictive operational reliability study, equipment loading condition for a given load period is determined using AC power flow based on the corresponding load level with considering load uncertainty. A four-state model has been proposed to represent a system component. The equations for determining the probability of each state for the four-state model have been derived. A technique based on these equations and models has been developed to evaluate operational and annual reliability indices of components, load points and system. The IEEE-RTS has been analysed to illustrate the proposed models and technique.
In power system reliability evaluation, usually component failures are assumed independent and reliability indices are calculated using methods based on the multiplication rule of probabilities. But in some cases, for instance when the effects of fluctuating weather are considered, the previous assumption is invalid. Generally, two kinds of methodologies are adopted to solve this problem, namely analytical and simulation. This paper proposes a DC-OPF based Markov cut-set method (DCOPF-MCSM) to evaluate composite power system reliability considering weather effects. The proposed method uses DC-OPF approach to determine minimal cut sets (MCS) up to a preset order and then uses MCSM to calculate reliability indices. In the second step, Markov process is applied, at a time, to the components of the determined MCS (and their unions) instead of the entire system. Since enumerating all MCS (and their unions) of a power system is impractical and unnecessary, this paper proposes an algorithm to calculate the bounds of reliability indices and it can automatically generate transition rate matrix (TRM) of the determined MCS (and their unions). The proposed method is tested on the modified IEEE Reliability Test System (RTS) and the results are compared with those of the next-event sequential simulation (NESS). The implementation demonstrates that the proposed method is effective and efficient and can conveniently incorporate more system operational considerations.
Large tropical cyclones cause severe damage to major cities along the United States Gulf Coast annually. A diverse collection of engineering and statistical models are currently used to estimate the geographical distribution of power outage probabilities stemming from these hurricanes to aid in storm preparedness and recovery efforts. Graph theoretic studies of power networks have separately attempted to link abstract network topology to transmission and distribution system reliability. However, few works have employed both techniques to unravel the intimate connection between network damage arising from storms, topology, and system reliability. This investigation presents a new methodology combining hurricane damage predictions and topological assessment to characterize the impact of hurricanes upon power system reliability. Component fragility models are applied to predict failure probability for individual transmission and distribution power network elements simultaneously. The damage model is calibrated using power network component failure data for Harris County, TX, USA caused by Hurricane Ike in September of 2008, resulting in a mean outage prediction error of 15.59% and low standard deviation. Simulated hurricane events are then applied to measure the hurricane reliability of three topologically distinct transmission networks. The rate of system performance decline is shown to depend on their topological structure. Reliability is found to correlate directly with topological features, such as network meshedness, centrality, and clustering, and the compact irregular ring mesh topology is identified as particularly favorable, which can influence regional lifeline policy for retrofit and hardening activities to withstand hurricane events.
In today's world, there is a continuous global need for more energy which, at the same time, has to be cleaner than the energy produced from the traditional generation technologies. This need has facilitated the increasing penetration of distributed generation (DG) technologies and primarily of renewable energy sources (RES). The extensive use of such energy sources in today's electricity networks can indisputably minimize the threat of global warming and climate change. However, the power output of these energy sources is not as reliable and as easy to adjust to changing demand cycles as the output from the traditional power sources. This disadvantage can only be effectively overcome by the storing of the excess power produced by DG-RES. Therefore, in order for these new sources to become completely reliable as primary sources of energy, energy storage is a crucial factor. In this work, an overview of the current and future energy storage technologies used for electric power applications is carried out. Most of the technologies are in use today while others are still under intensive research and development. A comparison between the various technologies is presented in terms of the most important technological characteristics of each technology. The comparison shows that each storage technology is different in terms of its ideal network application environment and energy storage scale. This means that in order to achieve optimum results, the unique network environment and the specifications of the storage device have to be studied thoroughly, before a decision for the ideal storage technology to be selected is taken.
This paper explores potential impacts of climate change on natural gas, electricity and heating oil use by the residential and commercial sectors in the state of Maryland, USA. Time series analysis is used to quantify historical temperature–energy demand relationships. A dynamic computer model uses those relationships to simulate future energy demand under a range of energy prices, temperatures and other drivers. The results indicate that climate exerts a comparably small signal on future energy demand, but that the combined climate and non-climate-induced changes in energy demand may pose significant challenges to policy and investment decisions in the state.
In this paper, the major benefits and challenges of electricity demand side management (DSM) are discussed in the context of the UK electricity system. The relatively low utilisation of generation and networks (of about 50%) means that there is significant scope for DSM to contribute to increasing the efficiency of the system investment. The importance of the diversity of electricity load is discussed and the negative effects of DSM on load diversity illustrated. Ageing assets, the growth in renewable and other low-carbon generation technologies and advances in information and communication technologies are identified as major additional drivers that could lead to wider applications of DSM in the medium term. Potential benefits of DSM are discussed in the context of generation and of transmission and distribution networks. The provision of back-up capacity by generation may not be efficient as it will be needed relatively infrequently, and DSM may be better placed to support security. We also present an analysis of the value of DSM in balancing generation and demand in a future UK electricity system with significant variable renewable generation. We give a number of reasons for the relatively slow uptake of DSM, particularly in the residential, commercial and small business sectors. They include a lack of metering, information and communication infrastructure, lack of understanding of the benefits of DSM, problems with the competitiveness of DSM when compared with traditional approaches, an increase in the complexity of system operation and inappropriate market incentives.
Hurricane hazard modeling has become a commonly used tool for assessing hurricane risk. The type of hurricane risk considered varies with the user and can be an economic risk, as in the case of the insurance and banking industries, a wind exceedance risk, a flood risk, etc. The most common uses for hurricane hazard models today include:(i)Simulation of wind speed and direction for use with wind tunnel test data to estimate wind loads vs. return period for design of structural systems and cladding.(ii)Estimation of design wind speeds for use in buildings codes and standards.(iii)Coastal hazard risk modeling (e.g. storm surge elevations and wave heights vs. return period).(iv)Insurance loss estimation (e.g. probable maximum losses, average annual losses).This paper presents an overview of the past and present work in hurricane modeling. The wind model is the key input to each of the examples presented above and is the focus herein. We discuss the evolution and current state of wind field modeling, modeling uncertainties, and possible future directions of the hurricane risk modeling process.
This paper focuses on the potential upcoming impacts of climate change in the 21st century on electricity demand at regional/national levels for regions where topography and location result in large differences in local climate. To address this issue, a regional climate model, PRECIS, has been used to predict future climatic conditions under different emissions scenarios (namely A2 and B2 of the IPCC special report on emissions scenarios (SRES)) as an input to a multiple regression model of the sensitivity of electricity demand in the Greek interconnected power system to climate and socio-economic factors. The economic development input to the multiple regression model follows the same storylines of the SRES scenarios upto 2100 and includes sub-scenarios to cover larger and smaller economic development rates. The results of the analysis indicate an increase of the annual electricity demand attributable solely to climate change of 3.6–5.5% under all scenarios examined, most of which results from increased annual variability with substantial increases during the summer period that outweighs moderate declines estimated for the winter period. This becomes more pronounced if inter-annual variability, especially of summer months, is taken into consideration. It was also found that in the long run, economic development will have a strong effect on future electricity demand, thus increasing substantially the total amount of energy consumed for cooling and heating purposes. This substantial increase in energy demand with strong annual variability will lead to the need for inordinate increases of installed capacity, a large percentage of which will be underutilized. Thus, appropriate adaptation strategies (e.g. new investments, interconnections with other power systems, energy saving programmes, etc.) need to be developed at the state level in order to ensure the security of energy supply.