This paper describes the use of survival analysis and simulation to model the lifetime of high voltage instrument transformers in the Dutch transmission system. To represent asset aging, the non-parametric Kaplan-Meier method is used to enable the fitting of Weibull distribution. Such an approach is implemented on three different voltage levels, namely 110 kV, 150 kV, and 220/380 kV. Real failure and inspection data is used to achieve a realistic failure model of the instrument transformers. Failure and maintenance data occurring between 1989 and 2021 have been used for this study. In spite of missing and low-quality data, a rich failure database could still be prepared. This study also offers insights into factors (i.e., voltage level, in-service age) influencing the remaining life from both graphical survival function and parametric Weibull distribution analysis. Based on the derived statistics, future possible maintenance planning scenarios are simulated under a complex system modelling framework in a digital twin enabled platform. Eventually, the scenarios are evaluated in terms of replacement costs (CAPEX), inspection hours, and unavailability hours.

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With massive wind power integration, the spatial distribution of electricity load centers and wind power plants make it plausible to study the inter-spatial dependence and temporal correlation for the effective working of the power system. In this paper, a novel multivariate framework is developed to study the spatio-temporal dependency using vine copula. Hourly resolution of load and wind power data obtained from a US regional transmission operator spanning 3 years and spatially distributed in 19 load and two wind power zones are considered in this study. Data collection, in terms of dimension, tends to increase in future, and to tackle this high-dimensional data, a reproducible sampling algorithm using vine copula is developed. The sampling algorithm employs k-means clustering along with singular value decomposition technique to ease the computational burden. Selection of appropriate clustering technique and copula family is realized by the goodness of clustering and goodness of fit tests. The paper concludes with a discussion on the importance of spatio-temporal modeling of load and wind power and the advantage of the proposed multivariate sampling algorithm using vine copula.

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This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises “How to extract information from large chunk of data?” The concept of “rich data and poor information” is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.

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Location of wind power plants and demand centres are not always close by; hence, the transmission of energy puts a burden on existing grid infrastructure. This unwanted burden necessitates transmission lines to operate more and more frequently close to their operating limits. To alleviate such situations, this research addresses the advantages of modelling spatio-temporal dependence of load and wind power using vine copula. Probabilistic AC optimal power flow is performed on a modified IEEE 39-bus system with significant wind penetration. Real load and wind power data from a U.S. utility is mapped onto the test-case to achieve realistic results. Load flow calculation can help in performing steady-state voltage and overload evaluations for post-disturbance system conditions. Because the security level of a power system is determined by the likelihood and severity of security violation. In this research, the probability of line overload is calculated from load flow and the severity function describes the risk of line overloading. Two case studies depicting future operating conditions of massive wind power penetration with reduced fossil fuel and nuclear power generation are considered. Simulation results prove the advantage of addressing spatio-temporal dependency to quantify the overload risk index, which is treated as a security indicator.@en

This paper proposes a multivariate modeling approach to tackle spatio-temporal dependency of various variables accounted in electric power system operation and planning. The stochasticity of system load as well as power generation from renewable energy sources poses special challenges to power system planners. Increasing penetration levels of wind exacerbate the uncertainty and variability that must be addressed in coming years, and can be extremely relevant to power system planners. Inefficiency of univariate models and relying on correlation is seen as a future bottleneck. A joint multivariate modeling approach using vine copula is proposed in this work considering load and wind data from US utility.@en

Long-term electricity load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment on the construction of excess power facilities, while an underestimate of the future load will result in insufficient generation and inadequate demand. As a first of its kind, this research proposes the use of a multiplicative error model (MEM) in forecasting electricity load for the long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, as accessed from a United States (U.S.) regional transmission operator, and recession data, accessed from the National Bureau of Economic Research, are used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. Historical volatility is used to account for implied volatility. To incorporate future volatility, backtesting of MEM is performed. Two performance indicators used to assess the proposed model are: (i) loss functions in terms of mean absolute percentage error and mean squared error (for both in-sample model fit and out-of-sample forecasts) and (ii) directional accuracy.

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A substantial increase in renewable energy in-feed to the primary grid as well as demand growth poses a challenge for transmission system operators (TSOs) to perform maintenance activities while addressing security of supply. A computationally efficient outage scheduling algorithm which is customizable in terms of area and time selection is proposed in this paper. Benders decomposition approach under different demand and wind scenarios, spanning two-stage stochastic programming approach is used. An accurate schedule while fulfilling both maintenance and network constraints is validated on a modified IEEE RTS-24 bus system in GAMS environment. A cost comparison analysis is also performed in this study.@en

Among the existing Renewable Energy Sources (RES), wind power has become significantly important to Transmission System Operators (TSOs) because of two reasons, namely: i. large Wind Power Plants (WPPs) can be connected to bulk power system at transmission level, and ii. large WPPs are being built or planned in regions with a high potential for the extraction of wind energy and TSOs must facilitate their integration. This comes at a time when the electric power industry is undergoing an energy transition due to the increasing penetration of RES and decentralized generation while discarding fossil fuels to achieve a greener future in the form of a low-carbon power system. To accommodate the high penetration of wind power into the existing electrical grid infrastructure while TSOs are facing stranded expansion of transmission infrastructure, TSOs are investing knowledge and money into safe-guarding grid reliability and to meet the required security of supply. As the location of WPPs and demand sites are not always close by, transmission of energy has placed a burden on transmission links in the existing grid infrastructure. Complexity in terms of interspatial dependence and temporal correlation of load and wind power impose a challenging operational threat to TSOs. Thus, it is important to emphasize spatial and temporal dependency to assess system security as TSOs are paving the way for the transition from deterministic to probabilistic reliability management. It is to be noted that system security is one of the two aspects of power system reliability, with the other being system adequacy. The security level of a power system is determined by the likelihood and severity of violations...@en

Asset management is one of the key components in a transforming electric power industry. Electric power industry is undergoing significant changes because of technical, socio-economical and environmental developments. Also, because of restructuring and deregulation, the focus has been on transmission and distribution assets that include transmission lines, power transformers, protection devices, substation equipment and support structures. This study aims to provide a detailed exposure to asset management classification, various interesting maintenance methods and theories developed. The work encompasses the issue of data management in recent years. Because of the use of various smart metering devices, large amounts of information are being collected. The advent of data-mining techniques has changed the asset management scenario, and it has been covered in this survey paper. In the end, it also discusses various risk assessment techniques in asset management developed and used for academic research and industries. It is accompanied with survey results from pan-European Transmission System Operator (TSOs) on various aspects in asset management.@en

Electrical load forecasting in long-term horizon of power systems plays an important role for system planning and development. Load forecast in long-term horizon is represented as time-series. Thus, it is important to check the effect of volatility in the forecasted load time-series. In short, volatility in long-term horizon affects four main actions: risk management, long-term actions, reliability, and bets on future volatility. To check the effect of volatility in load series, this paper presents a univariate time series-based load forecasting technique for long-term horizon based on data corresponding to a U.S. independent system operator. The study employs ARIMA technique to forecast electrical load, and also the analyzes the ARCH and GARCH effects on the residual time-series.@en

In the planning and operation of power systems, actions are taken in different processes and time-horizons. The purpose of these actions is to secure a high reliability level. Although the three main processes (grid development, asset
management, and system operation) are described in literature, there has been no explicit study on the time-horizons (long-term, mid-term, and short-term) and actual time-scale (decades, years, months, etc.) that these processes focus
on. This study aims at making a review of the various activities performed by transmission system operators while reviewing the concept of each time-horizon and methodologies developed in literature. As decisions taken in different
time-horizons can influence each other, the interactions and overlapping are discussed.@en

Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid- and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid- and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.@en

Load forecasting is considered vital along with many other important entities required for assessing the reliability of power system. Thus, the primary concern is not to forecast load with a novel model, rather to forecast load with the highest accuracy. Short-term load forecast accuracy is often hindered due to various load impacting factors. Two of the major impacting factors are day-ahead weather forecast and subsequent variation in electricity demand that is independent of weather. To tackle the uncertainty in short-term load forecasting, this paper presents a neural network-based load forecasting technique for short-term horizon based on data corresponding to a U.S. independent system operator. With the real life data, a better understanding of forecasting error is carried out while further identifying the time periods when the load is supposedly to be over- or under-forecast.@en

This paper aims at assessing the power system reliability by estimating loss of load (LOL) index using mutual information based Bayesian approach. Reliability analysis is a key component in the design, analysis and tuning of complex structure like electrical power system. Consideration is given to rare events while constructing the Bayesian network, which provides reliable estimates of probability distribution function of LOL with lesser computing effort. Also, the ranking of load components due to loss of load is evaluated. The RBTS and IEEE RTS-24 systems are used as test cases.@en

This paper presents a literature study on asset management in electrical power transmission and distribution system. Due to restructure and deregulation of electric power industry in recent times, the focus has been on transmission and
distribution assets that include transmission lines, transformers, power plants, substations and support structures. The study aims to provide a first of its kind exposure to asset management classification, various interesting maintenance methods and theories developed in last two decades. In the end, it also discusses various risk assessment techniques in asset management developed and used for academic research and industries.@en