There is a general consensus worldwide for a need to increase the capacities of transmission lines by not physically altering the transmission structures. Amidst this challenge, dynamic thermal rating (DTR) system appears as an appropriate option. Besides that, many countries have also pledged to integrate wind energy sources into their power networks. Taking these as motivations, the purpose of this thesis is to carry out the reliability analysis of DTR system for transmission lines. The two main standards - IEEE 738 and CIGRE, use for calculating the line rating of overhead lines has been analysed and compared. It was demonstrated that the two standards yield insignificant differences in their calculated line ratings. A new methodology that can systematically assess the reliability and risk of any DTR system designs was proposed in this thesis. In the reliability assessment part, methods such as the event tree analysis (ETA), risk reduction worth (RRW) and wide range method (WRM) were used. The risks of DTR systems were evaluated by implementing them in the IEEE 24-bus reliability test network (RTN). The network's loss of load and the percentages of the conductor loss of tensile strength constitute the risks of DTR systems. In the risk assessment part, a multiple-linear regression (MLR) model was proposed to estimate missing weather values during the failure of DTR sensors. Results in this thesis show that the MLR model is accurate and has only estimation error of less than 6%. It avoids overestimating the risk of DTR systems. An optimum DTR system design was also selected. The strategic placement of DTR sensors along a transmission line is an important reliability issue too. Hence, a new DTR sensor placement algorithm that considers the effects of DTR system ratings on the amount of line sagging, conductor annealing and the correlation between DTR system ratings and actual line ratings was proposed. Results in this thesis show that the newly proposed algorithm outperforms the currently published algorithm in terms of causing no spans to sag beyond their ground clearance limits and all spans experience lesser annealing effects. The 2-dimensional movement of line spans in their longitude and latitude directions, which the currently published algorithm also lacks, are considered in the proposed algorithm. This thesis also investigates the reliability behaviours of a power network that has DTR systems and wind farms. The proposed methodology for achieving that considers the reliability of transmission lines, DTR systems, conventional generators and wind turbines. The chronological behaviour of DTR ratings and wind farm power outputs was modelled using the auto-regressive and moving-average (ARMA) model. The correlations between the ARMA models were also considered. Results in this thesis show that by considering the correlation effects, the network's reliability indices are not over and under estimated. A new reliability index for describing the amount of wind power integration named as Expected Wind Power Delivered (EWPD) was proposed as well.

The global mean surface temperature rise was observed in the past century and proved the warming of the earth climate system. Global warming is believed to continue into the next decades due to unprecedented increases in greenhouse gas emissions. As a consequence of global warming, extreme weather scenarios are also expected to occur more frequently. In such a context, it is of vital importance to assess the impacts of climate change on the operational performance of power systems. This thesis investigates the impacts of climate change on the operational performance of power systems. The future climate is simulated based on emission scenarios and is then used as an input to the thermal models of power system components to assess their ratings and ageing, and further the reliability of the system. This research contributes to a number of areas in power system research. In the literature review, the risks that climate change may cause to power systems are identified. The models used for the simulation of future climate are firstly introduced. The weather variables that can be simulated from the models include air temperature, solar radiation, wind speed and direction, soil moisture and soil temperature. Among the models, the one for soil temperature is originally developed in this thesis. Following this, the component thermal models of overhead line, cable and transformer, from different standards are compared and selected. After that, the sensitivity of component ratings to individual weather variables is investigated, as a preliminary study for the later research in this thesis. Then, the impacts of climate change on component ratings (including both static and dynamic rating) are comprehensively and probabilistically assessed. The assessment results indicate the reduction of component ratings due to climate change. The impacts of climate change on system reliability is further examined on the IEEE Reliability Test System. Results demonstrate and quantify the reduction of both component ratings and system reliability, and prove that the dynamic rating can be used to mitigate the reduction. Finally, the preliminary exploration of transformer ageing is carried out and shows an increased ageing rate due to air temperature rises.

Environmental concerns and long term energy security are the key drivers behind most current electric energy policies whose primary aim is to achieve a sustainable, reliable and affordable energy system. In a bid to achieve these aims many changes have been taking place in most power systems such as emergence of new low carbon generation technologies, structural changes of power system and introduction of competition and choice in electricity supply. As a result of these changes, the level of uncertainties is growing especially on generation side where the locations and available capacities of the future generators are not quite clear-cut. The transmission network needs to be flexibly and economically robust against all these uncertainties. The traditional operation of the network under preventive control mode is an inflexible practice which increases the total system cost. Corrective control operation strategy, however, can be alternatively used to boost the flexibility, to expedite the integration of the new generators and to decrease the overall cost. In this thesis, the main focus is on development of new techniques and methodologies that can be used for modelling and solving a transmission planning problem under the assumption that post-contingency corrective actions are plausible. Three different corrective actions, namely substation switching, demand response and generation re-dispatch are investigated in this thesis. An innovative multi-layer procedure deploying a genetic algorithm is proposed to calculate the required transmission capacity while substation switching is deployed correctively to eradicate the post-fault network violations. By using the proposed approach, a numerical study shows that the network investment reduces by 6.36% in the IEEE 24 bus test system. In another original study, generation re-dispatch corrective action is incorporated into the transmission planning problem. The ramp-rate constraints of generators are taken into account so that the network may be overloaded up to its short-term thermal rating while the generation re-dispatch action is undertaken. The results show that the required network investment for the modified IEEE 24 bus test system can be reduced by 23.8% if post-fault generation re-dispatch is deployed. Furthermore, a new recursive algorithm is proposed to study the effect of price responsive demands and peak-shifting on transmission planning. The results of a study case show that 7.8% of total investment can be deferred. In an additional study on demand response, a new probabilistic approach is introduced for transmission planning in a system where direct load curtailment can be used for either balancing mechanism or alleviating the network violations. In addition, the effect of uncertainties such as wind power fluctuation and CO2 emission price volatility are taken into account by using Monte Carlo simulation and Hypercube sampling techniques. Last but not least, a probabilistic model for dynamic thermal ratings of transmission lines is proposed, using past meteorological data. The seasonal correlations between wind power and thermal ratings are also calculated. £26.7 M is the expected annual benefit by using dynamic thermal ratings of part of National Grid's transmission network.

Increasing amounts of solar power connected to the low-voltage network will adversely affect the performance of the network. The two impacts that will most often set the limit are overvoltage with the customers and overloading the distribution transformer. In this work, alternative methods have been studied for determining when a transformer is overloaded, to allow more solar power to be connected to the low-voltage network, i.e., increasing the hosting capacity for solar power.A limit-based method on the highest temperature inside the transformer (the hotspot temperature) and a method based on the loss-of-life of the transformer insulation due to hotspot temperatures above the design temperature are those alternative methods in this study. These methods are known as "dynamic transformer rating", a technology proposed in the literature but with very little practical experience in distribution networks.Two models were developed and implemented in MATLAB: a thermal model of the transformer calculating the hotspot temperature for a given time series of loading and ambient temperature; and a model for the loss-of-life of the winding insulation for given time series of the hotspot temperature. These models have been applied to existing distribution networks: measured consumption patterns with high time resolution (10-minute time step) for nine different distribution transformers for 1.5 years (network operator); measured ambient temperature (SMHI); and solar-power production calculated from satellite measurements (Renewables Ninja).For these nine distribution transformers, the time series of the hotspot temperature and the loss-of-life over the 1.5 years have been calculated for different values of the solar power installed capacity on the low-voltage side of the distribution transformer. The resulting time series are used to estimate the hosting capacity for solar power of a 200 kVA transformer. Using the existing design methods, the hosting capacity is 200 kW. Once that value is reached, the further connection of solar power should be stopped until a larger transformer is available. According to IEC design methods, the hosting capacity is about 270 kW using a limit to the hotspot temperature. This value somewhat depends on the loading patterns of the transformer before the connection of solar power. Once that value is reached, the further connection should again be stopped. Even for installed capacity exceeding 270 kW, the loss of life of the transformer insulation is still small and acceptable. This allows for further connection of PV without the immediate need to replace the transformer. Even values up to 350 or 400 kW may be acceptable, but a limit based on loss-of-life will require a detailed risk analysis as the pre-solar loading of the transformer is shown to play an important role.This work has shown that dynamic transformer rating allows more solar power to be connected to a distribution network than using classical rating methods without unacceptable risk for transformer loss-of-life.

The power system components connected to renewable energy sources, such as transformers, are often oversized and conservatively loaded. The design of transformers normally ignores the intermittent nature of the connected renewable energy sources (e.g. solar, wind). Due to the variations in weather conditions and operation states, the transformer load oscillates and the actual hot spot temperature is significantly lower than the designed thermal rating. For wind farms, the oversized transformer causes extra resourcematerial waste and a higher wind power price. Dynamic thermal rating can be applied to determine the rating of the transformers based on real-time environmental conditions (e.g. ambient temperature, wind speed). However, in order to optimize the operation of the transformers with dynamic thermal rating, the prediction of the load profile of transformers is an obstacle. The load of wind farm export transformers oscillates due to thechange of load conditions (e.g. turbine availability, power curtailment) and environmental conditions (e.g. wind speed, wind direction and ambient temperature). This thesis proposes a new methodology to improve the utilization of wind farm export transformers by estimating their load profile more accurately and assessing their aging rate. The estimation of the load profile takes the wake effect and turbine availability into account. Specifically, the variation in the wind turbine failure and repair rates, which is influenced by the wind, is considered in the evaluation of turbine availability. Additionally, a correction method is proposed to improve the accuracy of the wake loss computation. The results demonstrate that the estimation accuracy of the transformer load profile is improved after considering the influence of the wake effect and turbine availability. The wake effect and the turbine availability reduce the generated wind power and to some extent, reduce the load and the aging rate of transformers. However, the wake effect has limited influence when the wind farm reaches peak power production while turbine availability influences the load profile of transformers especially when the load is close to the installed capacity of the wind farm. After considering these two factors, the prediction accuracy of the hot spot temperature in the transformers can be enhanced and dynamic thermal rating can be applied to transformers with improved reliability. / Kraftsystemkomponenter som är anslutna till förnybara energikällor, såsom transformatorer, är ofta överdimensionerade och konservativt belastade. Konstruktionen av transformatorer ignorerar normalt sett den intermittenta naturen hos anslutna förnybara energikällor (t.ex. sol och vind). På grund av variationer i väderförhållanden och drifttillstånd, oscillerar transformatorbelastningen och den faktiska hotspottemperaturen är betydligt lägre än den designade termiska bedömningen. För vindkraftsparker orsakar den överdimensionerade transformatorn extra resursmaterialavfall och högre vindkraftspriser. Dynamisk termisk bedömning kan tillämpas för att bestämma transformatorernas betyg baserat på realtidsmiljöförhållanden (t.ex. omgivande temperatur, vindhastighet). Men för att optimera driften av transformatorer med dynamisk termisk bedömning är förutsägelsen av transformatorernas belastningsprofil ett hinder. Belastningen på transformatorer för export av vindkraftsparkeroscillerar på grund av ändringar i belastningsförhållanden (t.ex. tillgänglighet för turbiner, effektreglering) och miljöförhållanden (t.ex. vindhastighet, vindriktning och omgivande temperatur). Denna avhandling föreslår en ny metod för att förbättra användningen av transformatorer för export av vindkraftsparker genom att uppskatta deras belastningsprofil mer noggrant och bedöma deras åldrande takt. Uppskattningen av belastningsprofilen tar hänsyn till wake-effekten och turbinernas tillgänglighet. Specifikt beaktas variationen i felfrekvensen och reparationsfrekvensen för vindturbiner, som påverkas av vinden, vid utvärderingen av turbinernas tillgänglighet. Dessutom föreslås en korrektionsmetod för att förbättra noggrannheten i beräkningen av wake-förlusten. Resultaten visar att uppskattningen av transformatorns belastningsprofil förbättras efter att ha beaktat wake-effekten och turbinernas tillgänglighet. Wake-effekten och turbinernas tillgänglighet minskar den genererade vindkraften och minskar till viss del belastningen och åldringstakten hos transformatorer. Wake-effekten har emellertid begränsad påverkan när vindkraftsparken når maximal produktionsnivå medan turbinernas tillgänglighet påverkar belastningsprofilen hos transformatorer, särskilt när belastningen är nära installerad kapacitet för vindkraftsparken. Efter att ha beaktat dessa två faktorer kan förutsägelsens noggrannhet för hotspot-temperaturen i transformatorerna förbättras och dynamisk termisk bedömning kan tillämpas på transformatorer med förbättrad tillförlitlighet. / <p>QC 20230414</p>

Over the last several decades, electricity consumption and generation have continually grown. Investment in the Transmission and Distribution (T&D) infrastructure has been minimal and it has become increasingly difficult and expensive to permit and build new power lines. At the same time, a growing increase in the penetration of renewable energy resources is causing an unprecedented level of dynamics on the grid. Consequently, the power grid is congested and under stress. To compound the situation, the utilities do not possess detailed information on the status and operating margins on their assets in order to use them optimally. The task of monitoring asset status and optimizing asset utilization for the electric power industry seems particularly challenging, given millions of assets and hundreds of thousands of miles of power lines distributed geographically over millions of square miles. The lack of situational awareness compromises system reliability, and raises the possibility of power outages and even cascading blackouts. To address this problem, a conceptual Power Line Sensor Network (PLSN) is proposed in this research. The main objective of this research is to develop a distributed PLSN to provide continuous on-line monitoring of the geographically dispersed power grid by using hundreds of thousands of low-cost, autonomous, smart, and communication-enabled Power Line Sensor (PLS) modules thus to improve the utilization and reliability of the existing power system. The proposed PLSN specifically targets the use of passive sensing techniques, focusing on monitoring the real-time dynamic capacity of a specific span of a power line under present weather conditions by using computational intelligence technologies. An ancillary function is to detect the presence of incipient failures along overhead power lines via monitoring and characterizing the electromagnetic fields around overhead conductors. This research integrates detailed modeling of the power lines and the physical manifestations of the parameters being sensed, with pattern recognition technologies. Key issues of this research also include design of a prototype PLS module with integrated sensing, power and communication functions, and validation of the Wireless Sensor Network (WSN) technology integrated to this proposed PLSN.