Transmission Line Capacity Research Articles

Chinese power structure in 2050 considering energy storage and demand response under high renewable power penetration ratio.

Energy storage and demand response offer critical flexibility to support the integration of intermittent renewable energy and ensure the stable operation of the power system. Using the ERA5 dataset and hourly power load data, this study develops an hourly-based dynamic optimization model to assess the roles of energy storage and demand response in Chinese (2050) power structure under high renewable energy penetration. The results revea; that: (1) Energy storage and demand response significantly contribute to reducing power transition cost, carbon emission, and power curtailment. Specifically, 2h storage duratin and 10% demand response capacity are found to reduce transition costs by 6.07 trillion CNY, carbon emissions by 11.38 billion tons, and annual power curtailment by 2.21 PWh. (2) By 2050, Chinese power structure will be dominated by wind and PV, with installed capacity exceeding 7000GW. Regional differences will be evident, and energy storage will account for 8%-20% of the total installed renewable power capacity, while facilitating the retirement of over 250GW of thermal power plants. (3) Complex substitution effects between energy storage, demand response, CCS, and renewable energy are observed, varying according to the duration of energy storage and the capacity of transmission lines across regions. (4) The operational mechanisms of energy storage and demand response align closely with PV generation patterns, showing high utilization from Feb to May. In contrast, thermal power generation and CCS mainly complement renewable power generation during the peak power demand period of Jul to Sep.

Reliability impact of dynamic thermal line rating and electric vehicles on wind power integrated networks

Wind power, as a sustainable clean energy source, has been widely used in power systems. However, the intermittency of wind power poses a threat to the reliability of these systems. One solution to address this issue is the integration of electric vehicle (EV) scheduling. By using EVs with Vehicle-to-Grid (V2G) technology, grid reliability can be improved: EVs can charge when there is excess wind power and discharge when there is a shortage. Another technology that enhances wind power system reliability is Dynamic Thermal Line Rating (DTLR). The DTLR system increases transmission line capacity based on actual weather conditions, helping to alleviate network congestion. Despite the benefits of these technologies, their coordinated operation has not been jointly modeled for reliability purposes before. This study presents, for the first time, a reliability model for the coordinated operation of these two technologies and introduces a new reliability index, the Smart Regulation Reliability Index (SRRI). Simulations were conducted using the IEEE 24-bus reliability test system. The results show that the coordinated operation of these technologies reduces Expected Energy Not Supplied (EENS) by nearly 28.8 % and increases SRRI by about 12.9 %.

An ADMM approach for unit commitment with considering dynamic line rating

To enhance the transmission capabilities in power system scheduling, this paper develops a unit commitment model that incorporates dynamic transmission line capacities and proposes an efficient solving algorithm. A multi-scenario unit commitment model that integrates dynamic transmission line capacities is introduced, using quantile regression to construct a data-driven capacity increase model based on historical environmental data. The model is solved using Lagrangian relaxation and the Alternating Direction Method of Multipliers (ADMM) to decouple dynamic constraints, allowing the dual problem to be decomposed into sub-problems and solved iteratively. The proposed model and algorithm are validated using the IEEE-118 and IEEE-300 test cases, demonstrating their effectiveness in handling dynamic transmission line capacities and improving scheduling performance.The approach provides a robust and flexible solution for power system scheduling, enhancing reliability and economic efficiency.

Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.

Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

AbstractThe economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs.

Research on supply-demand balance in China’s five southern provinces amidst fluctuations in regional wind and solar power generation and transmission faults

To investigate the supply-demand balance of regional power systems under extreme scenarios, this study employs the high-resolution power optimization model SWITCH-China to simulate the regional heterogeneity and randomness of extreme weather events in detail. Focusing on the five southern provinces, this study explores various impacts on the power generation side and the grid side under scenarios of reduced wind and solar power output, transmission line failures, and combined scenarios, proposing strategies for constructing a new power system. The main conclusions are: the reduction in wind and solar power output significantly affects provinces with a high proportion of these installations, like Guizhou, necessitating other stable power generation forms to compensate. Transmission line failures notably impact provinces like Guangdong, which rely heavily on imported electricity, requiring increased investment in new wind and solar installations and more self-generated power to offset the reduction in imported electricity. The combination of these factors amplifies their individual impacts, leading to the highest carbon reduction and electricity costs. The simulation results of this study are valuable for China’s five southern provinces in coping with extreme scenarios. As these provinces work on building a new power system and gradually retire fossil fuel units, they should expand the number and capacity of inter-provincial high-voltage transmission lines while considering system economics. Additionally, accelerating the deployment of energy storage is crucial for maintaining power system stability.

Optimal operation for P2H system based on a power–temperature–hydrogen coupling model for alkaline water electrolyzer plant

For the effective operation of a Power-to-Hydrogen (P2H) system that integrates renewable energy sources with an Alkaline Water Electrolyzer (AWE), precise modeling of the AWE plant is paramount. This research delves into the electrothermal dynamics of the AWE and constructs an advanced Power–Temperature–Hydrogen (P–T–H) coupling model of the AWE plant. The developed model intricately captures the pivotal role of temperature in modulating hydrogen production rates, power constraints, and system flexibility. Furthermore, to enable the application of the model to system-level optimization, we undertake a rational simplification process to transform it into a linear model. Building upon this model, we devise an optimized power scheduling strategy for the P2H system that enhances the system’s economic efficiency and adaptability. A comprehensive sensitivity analysis of crucial variables such as hydrogen demand, transmission line capacity, and AWE capacity underscores the strategic insights this model offers for system development and implementation. In a word, the developed P–T–H model is well feasible for the improvement of the system’s operation with AWE.

Сравнение характеристик линий высокого и сверхвысокого напряжений при выборе параметров управляемого устройства продольной компенсации

The use of series compensation devices leads to an increase of the transmission line capacity. However, it also increases the risk of an oscillatory violation of the stability of the electric power system. Cumulative hunting and self-excitation of the system is observed in the systems with controlled series compensation devices with a high degree of capacitive compensation. It is known that automatic excitation control of generators partially eliminates the negative effect of series capacitive compensation on oscillatory stability. Therefore, when choosing the tuning parameters of controlled devices, it is necessary to consider their combined effect on the system modes. The developed methods to study steady-state stability make it possible to analyze the modes of power systems with controlled power transmission of various voltage levels. Thus, it is advisable to conduct a comparative analysis of the influence of the adjustable parameters of the series compensation device on the steady-state stability of systems of various voltage levels, considering synchronous generators automatic excitation control. Methods of mathematical modeling of the electric power system, the theory of long-distance power lines and electromechanical transients, methods of analyzing the stability of electric power systems are used. The original software in the C ++ programming language has been used as a modeling tool. The choice of controlled series compensation device and the automatic excitation control regulation laws parameters is made, provided that steady-state stability is maintained. The stability regions of the studied electric power-engineering system are constructed depending on the tuning parameters of the considered devices of various voltage levels. A comparative analysis of the characteristics of the modes of high and extra-high voltage lines has been carried out. The regulation coefficients values of controlled series compensation device and automatic excitation of generators have been determined, under which there is no violation of oscillatory stability for different voltage levels. The conducted comparative analysis of the characteristics of high and extra-high voltage power lines demonstrates the need to consider the power transmission rated voltage when comprehensively selecting the tuning parameters of the controlled series compensation device and the automatic excitation control regulator.

Optimal Power Flow of Power System with Unified Power Flow Controller Using Moth Flame Optimization with Locational Marginal Price

Consideration of transmission line capacity and the optimal power flow (OPF) determines the locational marginal price (LMP), which in turn determines the performance and profitability of a producing unit. Reducing the total cost of the generators can lead to a drop in the market price of electricity. It is recommended to use numerical and repetition-based approaches for solving power flow equations due to their nonlinear nature. In order to achieve the ideal power flow at an affordable price, this paper employs a Moth Flame Optimization (MFO) to solve the equations. We then enhance the MFO’s structure to make it more effective at performing the simultaneous calculations of power passing through transmission lines. Flexible AC transmission systems (FACTS) tool that has been utilized to overcome this problem is the Unified Power Flow Controller (UPFC). Lastly, the proposed MFO algorithm would include the following parameters in its output: bus voltages, line losses, generated power, total generating expenditures, and generator profits. In this work, the proposed method is tested on the IEEE 57-bus network also shows that it improves upon the OPF problem.

Discussion on multi-condition load capacity evaluation algorithm of transmission line based on fusion algorithm

ABSTRACT High temperature, strong wind, ice cover and other problems may lead to transmission line breakage, tripping, tower toppling and so on. In order to reduce the probability of such problems, it is necessary to predict the load capacity of transmission line in multiple working conditions. In order to improve the accuracy of load capacity prediction of transmission lines under multiple working conditions and reduce the occurrence of bad transmission line problems, Particle Swarm Optimisation-Back Propagation neural network algorithm is used to simulate the prediction of transmission lines under different working conditions with different accuracy. The results show that the error of the optimised fusion algorithm is smaller than that of the pre-optimised algorithm. Combined with the prediction error results of the traditional neural network, it is found that the fusion algorithm can improve the prediction effect of transmission lines, has strong robustness, and can be popularised in practice.

Revolutionizing energy infrastructure: Automated route planning for underground transmission lines in Phnom Penh

The capacity of conventional overhead transmission lines (OTL) has been exceeded by the energy consumption of Cambodia's flourishing cities. Underground transmission lines (UTL) provide a unique solution due to their technical and environmental advantages. However, engineers must contend with high UTL costs and the need for effective conduit cable installation. This study presents a solution that automates the shortest route method for determining the optimal path for routing UTL power supply cables between any two substations. Multiple algorithms are evaluated to identify the most effective solution to this design's inherent shortest-path problem. Using Geographic Information System (GIS) data, potential UTL network routes in Phnom Penh, Cambodia can be investigated. The ultimate product is an automated route planning support tool that has the potential to revolutionize energy project planning by generating optimal routes for UTL. This will increase efficiency and lower costs, making UTL a more viable option for meeting the energy demands of modern infrastructure. This study contributes significantly to the field of energy infrastructure planning and has the potential to be replicated in cities around the world.

Typhoon-induced failure analysis of electricity transmission tower-line system incorporating microtopography

Safety of electricity transmission tower-line systems is always at risk from various natural hazards, especially typhoons, which have the potential to cause significant damage and disruption. However, previous studies have predominantly focused on typhoons occurring in open-flat terrains, disregarding the impact of actual terrains. To address this research gap, this paper comprehensively examines the typhoon-induced responses of an electricity transmission tower-line system while considering the influence of microtopography. Specifically, a virtual wind tunnel is first established to simulate the typhoon within a specific microtopography. The wind characteristics of the typhoon considering the influence of microtopography are then studied in terms of the average wind speed, gust factor, and turbulence intensity, and compared to those at open-flat terrains. Finally, the influences of microtopography on the typhoon-induced responses and collapse of the electricity transmission tower-line system are investigated. The results demonstrate that the introduction of microtopography can obviously increase local wind speed and enhance fluctuation of wind, resulting in large responses and collapse risk of electricity transmission tower-line systems. This paper could provide a valuable reference for assessing the wind-resistant capacity of electricity transmission lines located in complex terrains, e.g., mountains and valleys.

Integrating Floating Photovoltaics with Hydroelectricity

The transition process from fossil fuels to environmentally friendly renewable energy sources carries the risk of creating new environmental damages. Photovoltaic technology represents one of the alternatives with the least risk of harmful environmental impact. However, this technology has two important drawbacks: the significant land occupation for the installation of PV systems and the uncontrollability of production. By constructing floating photovoltaic plants on hydroelectric reservoirs, both of these problems can be reduced to an acceptable level. Some artificial reservoirs, originally built for hydroelectric power plants, have acquired a significant secondary function as recreational areas and fish breeding sites. Therefore, there is justified resistance from the local community to change the existing appearance and purpose of such reservoirs. This paper proposes a completely new concept of integrating the interests of the local community into such objects. In addition to preserving existing uses, the concept also offers new features. This can make the entire system environmentally friendly and sustainable. This paper details the technology behind the construction of floating photovoltaic power plants on artificial reservoirs and emphasizes their various advantages. These benefits include the non-utilization of cultivable land, the ease of assembly and construction, integration into existing power grids, and the potential to address electricity storage issues. For instance, Buško Lake, covering an area of 55.8 km2, may host 2.93 km2 of installed floating photovoltaic (FPV) facilities, enabling a total installed capacity of 240 MW. With an average of 5.5 h of daily sunshine, this totals 2007 annual hours, equivalent to a 55 MW thermal power plant. An analysis showed that, with losses of 18.2%, the average annual production stands at 302 GWh, translating to an annual production value of 18 million € at 60 €/MWh. The integration of this production into an existing hydroelectric power plant featuring an artificial reservoir might boost its output by 91%. The available transmission line capacity of 237 MW is shared between the hydroelectric power plant (HPP) and FPV; hence during the FPV maximum power generation time, the HPP halts its production. HPP Orlovac operates a small number of hours annually at full capacity (1489 h); therefore in combination with the FPV, this number can be increased to 2852 h. This integration maintains the lake’s functions in tourism and fishing while expanding its capabilities without environmental harm.

ПЕРСПЕКТИВИ ПРИЄДНАННЯ УКРАЇНСЬКИХ СПОТОВИХ РИНКІВ ЕЛЕКТРИЧНОЇ ЕНЕРГІЇ ДО MARKET COUPLING

The article is devoted to the study of the market coupling impact on the Ukrainian electricity market spot segments and the electricity market as a whole, taking into account its current state, where all stakeholders are constantly dealing with war risks, with no adequate possibility to hedge its own security of supply. It is emphasized that today the Ukrainian government and the electricity market stakeholders with the support of European partners and experts, are actively working on the implementation of the European Union energy regulatory acts, in particular, regarding the integration of the Ukrainian electricity market spot segments to market coupling – Commission Regulation (EU) 2015/1222 of 24 July 2015 establishing a guideline on capacity allocation and congestion management. It is noted that the implementation of other European Union energy regulatory acts was in 2018, which resulted in adoption of Law of Ukraine “On the Electricity Market” that established a playing field for competition of different stakeholders on electricity market and possibility of end consumers to be a part of the market as well, which all in its entirety introduced the liberalized model of electricity market Ukraine from July 01, 2019. The current state of the electricity market was analyzed, according to which it was concluded that the electricity market is in the stage of formation and development, and also needs measures to further liberalize the market, increase liquidity, introduce the financial instruments market and debt repayment. In stages, taking into account the selected basic rules and principles of market coupling, a study of the market coupling impact on the Ukrainian electricity market was conducted. As a result, positive conclusions were obtained regarding the improvement of liquidity and competition; ensuring optimal distribution of interstate power transmission line capacities as well as of obtaining an additional source for the development of interstate power transmission lines. It is emphasized that integration to market coupling can become an incentive for electricity market participants to use financial hedging tools to reduce credit and market risks.

A Study on the lack of Transmission and Distribution Line Capacity in the Domestic Power System

A Study on the lack of Transmission and Distribution Line Capacity in the Domestic Power System

МОДЕРНИЗАЦИЯ ЛИНИЙ ЭЛЕКТРОПЕРЕДАЧИ ИРКУТСКОЙ ОБЛАСТИ ДЛЯ ПОВЫШЕНИЯ ПРОПУСКНОЙ СПОСОБНОСТИ

The issue of increasing the capacity of power transmission lines (PTL) during the modernization of power supply systems of the Irkutsk region using power transmission lines manufactured using Aero-Z technology is considered

Day-ahead optimization of integrated electricity and thermal system combining multiple types of demand response strategies and situation awareness technology

Under the dual pressure of energy shortage and environmental pollution, relying only on increasing the installed capacity of units and line transmission capacity cannot cope with the conflict between the growth of power demand and the difficulty of grid expansion in the long run. Demand response conducts users to change their energy consumption habits through system-issued electricity prices or incentives, so that the demand of the load side can be adjusted flexibly, which can further enhance the consumption of wind power and improve system economics. Based on the background of diversified energy use, this paper proposes a day-ahead optimal scheduling strategy for integrated electricity and thermal system considering multiple types of demand response. Firstly, the dispatch framework of integrated electricity and thermal system with the situation awareness technology is constructed to address uncertainties of Renewable Energy Sources, thus helping system mitigate uncertain risks. Secondly, the demand response mechanism of power system and regional thermal inertia of thermal system are modeled, respectively, to uncover the principles of load regulation of different energy systems; Then, a day-ahead optimal scheduling model for the integrated thermal and electricity system is developed, and the consumption evaluation index is integrated to indicate energy utilization efficiency; Finally, a combined electric-heat system model with 39-node grid and 6-node heat network is developed, and the positive effects of considering multiple types of demand response and situation awareness technology on promoting the consumption of renewable energy and improving the energy efficiency of the system are verified through the case study.

Optimal power flow of power system with Thyristor Controlled Series Compensation using moth flame optimization with locational marginal price

Consideration of transmission line capacity and the optimal power flow (OPF) determines the locational marginal price (LMP), which in turn determines the performance and profitability of a producing unit. Reducing the total cost of the generators can lead to a drop in the market price of electricity. It is recommended to use numerical and repetition-based approaches for solving power flow equations due to their nonlinear nature. In order to achieve the ideal power flow at an affordable price, this paper employs a Moth Flame Optimization (MFO) to solve the equations. We then enhance the MFO's structure to make it more effective at performing the simultaneous calculations of power passing through transmission lines. One FACTS tool that has been utilized to overcome this problem is the Thyristor Controlled Series Compensation (TCSC). Lastly, the proposed MFO algorithm would include the following parameters in its output: bus voltages, line losses, produced power, total generating expenditures, and generator profits. Testing the proposed method on the IEEE 57-BUS network also shows that it improves upon the OPF problem.

On the issue of applying the dynamic thermal rating in electric power distribution system

The article considers separate studies and review articles on dynamic thermal rating system in power transmission lines and electrical equipment. Available researches of foreign authors are mainly devoted to forecasting algorithms, model-ing methodologies and implementation of dynamic thermal rating in order to increase the capacity of power transmis-sion lines. It is shown that for the tasks of planning and management of the electric power system in Russia, the need to study and coordinate domestic and foreign developments on the assessment of the capacity of power transmission lines and power distribution is currently relevant. The expansion of residential areas increases the load on electric distribution network due to the shift of maximum power consumption. The currently dynamically changing conditions of electricity consumption direct attention to the development of new technological concepts and the applicability of the existing approaches to assessing the capacity of electricity transmission lines to distribution networks. Available research on dynamic thermal rating in electric distribution networks has focused much attention on both the potential problems and benefits of its implementation in the integration of distributed generation based on the renewable energy sources, in particular wind generation. The ambiguous impact of dynamic thermal rating systems on the reliability of the electric distribution network and the need for their reasonable use, as well as a significant lack of research in relation to cable distribution networks and consistency in assessing their capacity depending on the cable insulation material are being not-ed. Few studies have verified the adaptation of the state-of-the-art approach of dynamic thermal rating of overhead transmission lines to cable lines for predicting parameters corresponding to operational objectives. The research results can be useful in the development and modernization of metering systems, in which new functions of power loss moni-toring and automated determination of distribution network reliability indicators are implemented, which will allow, within the framework of “smart grids”, to ensure the required level of reliability of power supply to consumers.

Multi-Agent Reinforcement Learning for Power System Operation and Control

This study investigates the use of Multi-Agent Reinforcement Learning (MARL) to enhance the efficiency of power system operation and control. The simulated power system environment is represented as a multi-agent system, where intelligent agents are used to mimic generators and loads. The MARL framework utilizes Q-learning algorithms to allow agents to independently adjust their activities in accordance with changing operating circumstances. The resulting simulated data represents a wide-ranging power grid scenario, including buses with different generator capacity, load needs, and transmission line capacities. The findings indicate a significant improvement in the stability of the system via Multi-Agent Reinforcement Learning (MARL), since the agents’ capacity to learn and adapt enables them to quickly alter the outputs of generators and meet the needs of the load, so ensuring that voltage and frequency levels remain within acceptable limits. The MARL framework significantly improves economic efficiency by enabling actors to optimize their behaviors in order to reduce the total costs of the system. The agility of the MARL-based control method is emphasized by the decrease in response time to dynamic disturbances, as agents demonstrate quick and efficient reactions to unforeseen occurrences. The favorable results highlight the potential of MARL as a decentralized decision-making model in power systems, providing advantages in terms of stability, economic efficiency, and the capacity to respond to disruptions. Although the research uses artificial data in a controlled setting, the observed enhancements indicate the flexibility and efficacy of the MARL framework. Future research should prioritize the integration of more practical situations and tackling computational obstacles to further confirm the suitability and expandability of Multi-Agent Reinforcement Learning (MARL) in actual power systems.