• Energy Research

Assessment of aging characteristics of conductors and other components of power transmission networks plays an important role in asset management systems. Due to adverse effects of conductor aging caused by annealing, the conductors lose their tensile strength. Although the loss of strength is gradual, it accumulates over time and increases the probability of outages and blackouts. Therefore, the most important factor affecting the strength of transmission conductors is the operating temperature of the line. For this reason, it is important to keep track of conductor temperatures over time, in order to identify segments of power transmission network that may require more close attention, and possibly repairs. This paper describes and illustrates a new methodology for estimating conductor thermal aging using load information and weather conditions derived from historical weather reanalysis, and interpolated to locations of power transmission lines. Conductor temperature is first determined using IEEE 738 standard, and then used to estimate loss of tensile strength in a conductor. The process is illustrated for a single location of a sample transmission line, using assumed load current and historical weather information spanning a period of five years. The simulation results show that the proposed approach provides information vital for transmission asset management and transmission network operating procedures.

High levels of penetration of distributed photovoltaic generators can cause serious overvoltage issues, especially during periods of high power generation and light loads. There have been many solutions proposed to mitigate the voltage problems, some of them using battery energy storage systems (BESS) at the PV generation sites. In addition to their ability to absorb extra power during the light load periods, BESS can also supply additional power under high load conditions. However, their capacity may not be sufficient to allow charging every time when power absorption is desired. Therefore, typical PV/BESS may not fully prevent over-voltage problems in power distribution grids. This work develops a cooperative state of charge control scheme to alleviate the BESS capacity problem through Monte Carlo tree search based reinforcement learning (MCTS-RL). The proposed intelligent method coordinates the distributed batteries from other regions to provide voltage regulation in a distribution network. Furthermore, the energy optimization process during the day hours and the simultaneous state of charge control are achieved using model predictive control (MPC). The proposed approach is demonstrated on two test cases, the IEEE 33 bus system and the practical medium size distribution system in Alberta Canada.

Power transmission lines are often used very inefficiently. A possible way to increase their effectiveness is the use of dynamic thermal rating, instead of the more common static rating. Dynamic thermal rating can be calculated from actual conductor load and ambient weather conditions using methodology described in IEEE Std. 738-2006. Main purpose of the presented study is to perform a sensitivity analysis of individual variables participating in dynamic thermal rating calculations. The effects of selected input variables on conductor current-carrying capacity and temperature are described as functional dependence using 2D plots. Individual parameters are then ranked according to their influence on the conductor thermal state.

This study presents a comprehensive comparative analysis of the operational strategies for multi-microgrid systems that integrate battery energy storage systems and electric vehicles. The analyzed strategies include individual operation, community-based operation, a cooperative game-theoretic method, and the alternating direction method of multipliers for multi-microgrid systems. The operation of multi-microgrid systems that incorporate electric vehicles presents challenges related to coordination, privacy, and fairness. Mathematical models for each strategy are developed and evaluated using annual simulations with real-world data. Individual operation offers simplicity but incurs higher costs due to the absence of power sharing among microgrids and limited optimization of battery usage. However, individual optimization reduces the multi-microgrid system cost by 47.5% when compared to the base case with no solar PV or BESS and without optimization. Community-based operation enables power sharing, reducing the net cost of the multi-microgrid system by approximately 7%, as compared to individual operation, but requires full data transparency, raising privacy concerns. Game theory ensures fair benefit allocation, allowing some microgrids to achieve cost reductions of up to 13% through enhanced cooperation and shared use of energy storage assets. The alternating direction method of multipliers achieves a reduction in the electricity costs of each microgrid by 6–7%. It balances privacy and performance without extensive data sharing while effectively utilizing energy storage. The findings highlight the trade-offs between cost efficiency, fairness, privacy, and computational efficiency, offering insights into optimizing multi-microgrid operations that incorporate advanced energy storage solutions.

Electrical load prediction has become an integral part of power system operation. Deep learning models have found popularity for this purpose. However, to achieve a desired prediction accuracy, they require huge amounts of data for training. Sharing electricity consumption data of individual households for load prediction may compromise user privacy and can be expensive in terms of communication resources. Therefore, edge computing methods, such as federated learning, are gaining more importance for this purpose. These methods can take advantage of the data without centrally storing it. This paper evaluates the performance of federated learning for short-term forecasting of individual house loads as well as the aggregate load. It discusses the advantages and disadvantages of this method by comparing it to centralized and local learning schemes. Moreover, a new client clustering method is proposed to reduce the convergence time of federated learning. The results show that federated learning has a good performance with a minimum root mean squared error (RMSE) of 0.117kWh for individual load forecasting. Accepted in Internet of Things; Engineering Cyber Physical Human Systems

The operational efficiency of remote environmental wireless sensor networks (EWSNs) has improved tremendously with the advent of Internet of Things (IoT) technologies over the past few years. EWSNs require elaborate device composition and advanced control to attain long-term operation with minimal maintenance. This article is focused on power supplies that provide energy to run the wireless sensor nodes in environmental applications. In this context, EWSNs have two distinct features that set them apart from monitoring systems in other application domains. They are often deployed in remote areas, preventing the use of mains power and precluding regular visits to exchange batteries. At the same time, their surroundings usually provide opportunities to harvest ambient energy and use it to (partially) power the sensor nodes. This review provides a comprehensive account of energy harvesting sources, energy storage devices, and corresponding topologies of energy harvesting systems, focusing on studies published within the last 10 years. Current trends and future directions in these areas are also covered.

Abstract The growing demand for electricity and the restructuring of power markets is forcing the power industry to change the way that power systems are planned and operated. Traditionally, transmission lines have been operated based on fixed deterministic thermal ratings, causing underutilization of their potential capacity. Efforts to overcome this limitation led to the development of alternative rating strategies based on probabilistic and dynamic methods. In this paper, a probabilistic static thermal rating method based on typical weather conditions along a transmission line is described and analyzed. The results of load and energy throughput analyses show that the use of this rating approach can significantly increase line throughput compared to traditional deterministic rating methods. However, this approach can also substantially increase the risk of thermal overload. To identify the problems associated with the use of a probabilistic static thermal rating method, we performed a sensitivity study. Statistical analysis of weather parameters shows that line ratings calculated from typical weather data are inflated. Additional results confirm that values of risk tolerance and wind direction incorporated into the rating method significantly affect the resulting rating values. We suggest values for these parameters that minimize the risk of line overloading.

The growing penetration of electric vehicles can pose several challenges for power systems, especially distribution systems, due to the introduction of significant uncertain load. Analysis of these challenges becomes computationally expensive with higher penetration of electric vehicles due to various preferences, travel behavior, and the battery size of electric vehicles. This problem can be addressed using clustering methods which have been successfully used in many other sectors. Recently, there have been several studies published on applying clustering methods for various aspects of electric vehicles. To summarize the existing efforts and provide future research directions, this contribution presents a three-step analysis. First, the existing clustering methods, including hard and soft clustering, are discussed. Then, the recent literature on the application of clustering methods for different aspects of electric vehicles is reviewed. The review concentrates on four major aspects of electric vehicles: the behavior of the user, driving cycle, used batteries, and charging stations. Then, several representative studies are selected from each category and their merits and demerits are summarized. Finally, gaps in the existing literature are identified and directions for future research are presented. They indicate the need for further research on the impact on distribution circuits, charging infrastructure during emergencies, equity and disparity in rebate allocations, and the use of big data with cluster analysis to assist transportation network management.