Increase In Power Loss Research Articles

Enhancing line loadability in urban rail systems: evaluating the impact of distributed generation on voltage stability, power losses and generation costs

Alternative source of electrical energy for urban railway electrification system is Direct Current (DC). Traction Power Substations (TPS) were essentially connected to the electrical grid network managed by the electrical utility company for operating and powering purposes. Power unbalance frequently occurs in the high voltage railway network due to the load of the traction system. The aim of this project is to find the impact of distribution generation (DG) for line loadability in urban rail systems. The output that needs to be considered are the voltage profile, power losses and generation cost. The simulation was considered to prove the positive impact of distributed generation for line loadability in urban rail system. Electrical energy was crucial for Direct Current (DC) railway electrification systems, as it provided the traction power necessary for train operations via Traction Power Substations (TPS) that connected to the electrical grid network. However, power imbalances frequently arose due to the varying loads imposed by traction systems, resulting in inefficiencies, suboptimal voltage profiles, increased power losses and higher operational costs. With the expansion of urban rail networks and the growing need for efficient power management, it became vital to explore solutions that could enhance loadability and system performance. This project examined the impact of integrating Distributed Generation (DG) on the line loadability of urban rail systems, concentrating on improvements in voltage profiles, reductions in power losses and the evaluation of generation costs. The methodology involved developing a detailed simulation model of the urban rail system, incorporating DG scenarios, analyzing their effects on voltage stability, power losses and costs. The simulation results were anticipated to demonstrate that DG could improve voltage stability, reduce power losses, lower generation costs and increase line loadability. The implications of these findings encompassed enhanced operational efficiency, potential economic benefits despite high initial investments, increased sustainability through renewable energy and valuable guidance for future infrastructure development and investment decisions.

Electric vehicle charging stations load impact on the distribution network

Integrating large numbers of electric vehicles will lead to operational and planning difficulties for distribution networks. The installation of electric vehicle charging stations (EVCS) increases power losses, affects stability, and degrades the reliability of the distribution network, resulting in dissatisfaction among customers. This work describes the impacts of the integration of electric vehicle charging stations on power losses, voltage stability index, and the main reliability indices as a function of the position and size of electric vehicle charging stations. This investigation was carried out on the IEEE 33 bus test system. According to the results, a high concentration of EVs increases peak demand, leading to greater energy losses, adversely affecting stability, and degrading reliability indices of the distribution network. The choice of installation points for EVCS is therefore very important, as some points induce greater energy losses than others and affect stability and reliability more than others. Measures to mitigate the effects of EVCS installation on distribution networks are crucial.

The Choice of a Construction Arrangement for Cable Lines with a Voltage of 10 kV According to the Criterion of the Minimum Expected Cost

The article considers the issue of choosing the optimal construction arrangement of cable lines with a voltage of 10 (20, 35) kV with cross-linked polyethylene insulation. The options for using three or single-core cables laid in a triangle or in a plane are analyzed. The methodological basis of the work is the principle of minimizing the expected costs, taking into account both capital investments and annual costs, including losses of electrical energy in cables. Within the framework of the study, a nomogram of economic intervals was scheduled, which makes it possible to determine the optimal cable core sections and boundary conditions for the use of threeand single-core cables, depending on the calculated current load. It is shown that with the existing nomenclature of three-core cables with a maximum core cross-section up to 630 mm2, single-core cables can be economically feasible only with a core cross-section of 800 mm2 and above. It has been discovered that the expected costs for laying single-core cables in the plane with two-way grounding of screens always turn out to be higher than when laying a triangle. This effect is due to an increase in both capital costs and power losses in cable screens. To increase the efficiency of cable lines, it is proposed to expand the range of three-core cables by including cables with the maximum possible core cross-section. This will make possible to increase the range of current loads in which the use of three-core cables will be economically justifiable. The results presented in the article can be used in the design of new cable lines, the analysis of the effectiveness of existing ones, as well as in the modernization and reconstruction of urban medium-voltage cable networks.

Analysis of the Selected Design Changes in a Wheel Hub Motor Electromagnetic Circuit on Motor Operating Parameters While Car Driving

The drive system of an electric car must meet road requirements related to overcoming obstacles and driving dynamics depending on the class and purpose of the vehicle. The driving dynamics of modern cars as well as size and weight limitations mean that wheel hub motors operate with relatively high current density and high power supply frequency, which may generate significant power losses in the windings and permanent magnets and increase their operating temperature. Designers of this type of motor often face the need to minimize the motor’s weight, as it constitutes the unsprung mass of the vehicle. Another limitation for motor designers is the motor dimensions, which are limited by the dimensions of the rim, the arrangement of suspension elements and the braking system. The article presents two directions in the design of wheel hub motors. The first one involves minimizing the length of the stator magnetic core, which allows for shortening of the axial dimension and mass of the motor but involves increasing the thermal load and the need for deeper de-excitation. The second one involves increasing the number of pairs of magnetic poles, which reduces the mass, increases the internal diameter of the motor and shortens the construction of the fronts, but is associated with an increase in the motor operating frequency and increased power losses. Additionally, increasing the number of pairs of magnetic poles is often associated with reducing the number of slots per pole and the phase for technological reasons, which in turn leads to a greater share of spatial harmonics of the magnetomotive force in the air gap and may lead to the generation of higher power losses and higher operating temperatures of permanent magnets. The analysis is based on a simulation of the motor operation, modeled on the basis of laboratory tests of the prototype, while the car is driving in various driving cycles.

Novel variable charging pricing strategy applied to the multi-objective planning of integrated fast and slow electric vehicle charging stations and distributed energy resources

Electric vehicle charging stations are increasingly being deployed to accommodate the rising demand for electric vehicles. However, this additional load on the distribution system can lead to significant challenges, including increased power losses and undervolage issues. Therefore, this paper proposes the optimized planning of electric vehicle charging stations, along with the integration of distributed energy resources considering the power system operator’s perspective. The approach focuses on minimizing the costs associated with public charging stations and distributed generators, as well as reducing power losses and voltage deviation of the system. Additionally, this study incorporates both fast and slow chargers to address user requirements, while promoting the use of slow charging to help reduce the peak load demand caused by the electric vehicle charging stations. A novel variable pricing strategy for slow charging is also proposed to decrease the operator’s expenses while promoting this mode of charging. The proposed methodology is validated through the integration of a 33-bus distribution test system with a 25-node traffic system. The analysis was conducted using three multi-objective optimization methods: Multi-Objective Cuckoo Search, Multi-Objective Flower Pollination Algorithm, and Non-dominated Sorting Genetic Algorithm II. From the results, it is possible to conclude that the proposed approach provides benefits not only to the system operator but also to EV users, given the reduction in their travel costs. Despite the increased charging demand, the optimization results show a reduction in voltage deviation by 11.57%–16.94% and power losses by 19.71%–24.20% compared to the original system.

GENETIC ALGORITHM-BASED OPTIMIZATION OF DISTRIBUTED GENERATOR PLACEMENT IN DISTRIBUTION NETWORKS TO MINIMIZE POWER LOSSES

An increase in electrical energy can lead to an increase in power losses and a decrease in voltage in the system. One of the efforts made to reduce power losses that occur in the distribution network is by placing the optimal Distributed Generation (DG) in the right location. Installation of DGs with suboptimal capacity and placement location can result in greater active power losses and further reduce voltage stability. This study discusses the optimization of DG placement in the electric power distribution network using genetic algorithm methods that are known to be effective in solving complex optimization problems. The IEEE 33 bus distribution system, which is a standard system frequently employed in power flow research, was the subject of the case study. The Newton-Raphson method is a commonly used iterative method for power flow analysis due to its accuracy in calculating power flow in electrical networks. The research findings indicate that the most suitable location for two DG units with an injection power of 6,626 MW is on buses 3 and 4. The placement of this DG has the potential to substantially mitigate power losses within the distribution network. The placement of two DG units on buses 3 and 4 results in a more significant improvement in system power losses than the placement of DG units on other buses. Power losses of buses 3 and 4 experienced power losses of 67724.69 MW, while system losses were reduced by 1137621.21 MW, or 5.61 percent.

Voltage enhancement and loss minimization in a radial network through optimal capacitor sizing and placement based on Crow Search Algorithm

In power systems, the distribution network is crucial as it serves as the final stage in delivering energy from generation plants to end users. However, reactive power demand from consumers can create challenges such as low power factor, voltage drops, and increased power losses depending on the distribution system used. This study aims to enhance the performance of radial distribution networks (RDNs) by optimizing capacitor placement and sizing based on the Crow Search Algorithm (CSA). Additionally, the study addresses the Optimal Power Flow (OPF) within the network by employing the Backward/Forward Sweep (BFS) method. The study accesses CSA’s effectiveness compared to other metaheuristic optimization techniques through simulation. The simulation results showed that the Voltage Stability Index improved from 0.61 for base case to 0.68 pu with CSA, compared to 0.66 for Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Teacher Learner Based Optimization (TLBO and 0.67 for Invasive Weed Optimization (IWO). CSA also achieved a 30.41 % reduction in active power losses, outperforming PSO, ABC, GA, and TLBO having 26.61 %, and IWO at 24.92 %. Additionally, application of CSA led to a 29.33 % reduction in reactive power losses, compared to 21.91 % for PSO, ABC, GA, and TLBO, 18.2 % for IWO. In the simulated IEEE 33 bus Radial Distribution Network (RDN) topology, the lowest bus voltage at bus 18 increased from 0.8820 in the base case to 0.9080 with CSA, indicating a 32.9 % improvement in voltage deviation. Furthermore, the optimization resulted in a significant reduction of 3.841 pu in capacitor cost, demonstrating CSA’s efficiency in both technical and economic aspects. CSA not only showed favorable results in minimizing power losses and improving voltage profiles but also provided substantial cost savings in capacitor implementation, making it a robust and cost-effective solution for enhancing RDN performance.

A comprehensive simultaneous allocation algorithm of charging stations and vehicle to grid operation in radial networks

The widespread adoption of electric vehicles (EVs) helps improve air quality by minimizing pollutants and promoting sustainable transportation practices. The integration of a substantial fleet of EVs leads to an increase in the installation of charging stations. The unplanned allocation of these loads impacts the distribution systems, such as increasing power loss. This paper introduces an algorithm that proposes an optimal allocation strategy for charging stations (CSs) in a distribution system. The allocation process aims to minimize the total apparent energy losses and ensure that the system voltage profile remains within limits. Load profile, charging and discharging profiles of CSs are considered. Vehicle-to-Grid (V2G) mode is one of the merits of integrating EVs in the grid, so this feature has been used to maintain system voltage stability and minimize energy loss. Power rating and locations of V2G mode are optimally determined to guarantee power quality indices. A hybrid algorithm of genetic algorithm (GA) and Self-Adaptive Multi-Population Elitist JAYA (SAMPE-JAYA) is developed to simultaneously allocate CSs and V2G in the systems. The proposed algorithm is verified on standard systems, IEEE 33, and 69-bus systems. The proposed algorithm is verified on standard IEEE 33-bus and 69-bus systems. V2G integration with CSs results in a reduction of energy losses by 6.33% and 22.25%, respectively, and voltage deviation improvements to 7.61% and 7.88% for the two systems.

Experimental Characterization of a Bladeless Air Compressor

Abstract The Tesla compressor is an innovative technology that offers a unique approach to fluid compression. Unlike traditional compressors that use rotating blades, bladeless compressors utilize closely spaced disks to create compression. The purpose of this article is to design a prototype Tesla air compressor with optimal design parameters and investigate the performance and loss characteristics based on numerical analysis and experimental demonstration. The prototype model has been numerically investigated at different rotational speeds, and the results have been compared with those obtained in experiments. Computational fluid dynamics (CFD) simulations indicate that the rotor-only efficiency is greater than 90% at very low mass flowrates, while the coupling of the rotor and volute leads to a total-to-static efficiency of approximately 58% (without losses) at 14 g/s. At a nominal mass flow of 4 g/s, the highest total-to-static pressure ratio would be around 1.27. Experimental results indicate leakage losses greatly reduce net mass flow, while pressure ratio values are in good agreement with CFD predictions. During this experiment, a maximum isentropic efficiency of 32.4% is measured. Indeed, the prototype included ventilation and leakage losses, which were not modeled in the CFD analysis. It is remarkable that the compressor does not show any unstable behavior down to zero mass flow (closed valve test), where the CFD and the experiment show consistent pressure ratios. An estimation of the losses from end-wall friction and leakage flow is carried out using numerical simulations at different exit radial clearances. Increasing radial clearance results in an increase in leakage and end-wall power loss, the latter being driven mainly by the axial clearance with the casing, which remained unchanged. To minimize leakage, a Teflon ring has been used as a first measure. Numerical calculations have indicated that the leakage rate is approximately 6 g/s at design speed. A brush seal-type solution can improve the sealing system to reduce leakage.

Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

In recent times, there has been increasing interest in renewable power generation and electric vehicles within the domain of smart grids. The integration of electric vehicles with hybrid systems presents several critical challenges, including increased power loss, power quality issues, and voltage deviations. To tackle these challenges, researchers have proposed various techniques. Effective management of energy systems is essential for maximizing the benefits of integrating a hybrid system with a microgrid at an electric vehicle charging station. This research specifically aims to optimize the location and sizing of such a hybrid system within the microgrid. Additionally, an improved binary quantum-based Elk Herd optimizer approach is proposed. This approach addresses for optimally managing renewable energy sources and load uncertainty. The proposed system also considers the stochastic nature of electric vehicles and operational restrictions, encompassing diverse charging control modes. The proposed technique performance is implemented in MATLAB platform and compared against existing approaches. The analysis demonstrates the effectiveness in achieving optimal location and sizing for a hybrid system with an electric vehicle charging station. Additionally, the proposed approach contributes to minimizing power loss, electricity costs, and average waiting time. Furthermore, the proposed approach reduces computing time, net present cost, and emissions are 12.5 s, 1.1×106 dollar, 2.21×108 g year−1, respectively.

Implementasi Metode Artificial Bee Colony Untuk Penempatan Distributed Generator Pada Penyulang Rayon Tanjung

Distributed Generation (DG) is a power plant that is spread over the power distribution network. In principle, electrical power output is generated by power plants located far from the load center and electrical power is transmitted to the load center through transmission and distribution networks. The longer the cable from the power plant or substation, the lower the voltage at the end load. Sub-optimal DG placement and size can result in increased power losses and affect the system voltage profile. In addition, suboptimal generation capacity can lead to voltage instability, reduced system security, and potential impacts on system frequency and emissions. Therefore, this study will use the Artificial Bee Colony (ABC) method to determine the optimal DG capacity and location. In the base case condition, the power losses in the system are 118 KW and 99 Kvar. Power losses after the installation of 1 DG active power loss becomes 37.4 KW and reactive power loss becomes 31.65 Kvar. Placement of 2 DGs reduces active power loss to 17 KW and reactive power loss to 15 Kvar. While the placement of 3 DG reduces active power loss to 33 KW and reactive power loss to 28 KVar.

Power Loss Minimization and Voltage Profile Improvement on Nigeria Distribution Network Using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

Optimal EV Charging and PV Siting in Prosumers towards Loss Reduction and Voltage Profile Improvement in Distribution Networks

In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction.

Evaluating the Harmonic Effects on the Thermal Performance of a Power Transformer

Harmonics in the power grid contribute to increased power losses in both the core and windings of power transformers. These losses lead to abnormal rises in temperature causing overheating and reduce the efficiency of the transformer. If the losses and temperature exceed the values set during the design stage for linear load conditions, it can damage the transformer’s insulating materials and shorten its lifespan. To assess the thermal impact of power system harmonics on transformers under steady-state and transient conditions, the rated losses and harmonic losses of the transformer are calculated. These losses are then inputted into a developed thermal 3D finite element method (FEM) performance model to determine the temperature distribution of transformer components. The numerical results from the thermal model will be compared with data from a Hyundai test report and real measurements from Egypt’s Kureimat power plant, specifically a 750 MW combined cycle power plant. The thermal modeling is focused on a step-up (16.5/240 kV), 240 ± 4 × 2.5%, 180/240/300 MVA power transformer operating in ONAN, ONAF1, and ONAF2 modes. This paper shows that the developed model aligns closely with actual measurements and the HYUNDAI test report. The loss calculations reveal that the discrepancy in total losses, with and without accounting for harmonics, becomes more pronounced as the load increases. Using this model, the presence of grid harmonics results in a higher temperature distribution across transformer components, leading to an increase in the hot spot temperature.

Probabilistic Assessment of the Impact of Electric Vehicle Fast Charging Stations Integration into MV Distribution Networks Considering Annual and Seasonal Time-Series Data

Electric vehicle (EV) fast charging stations (FCSs) are essential for achieving net-zero carbon emissions. However, their high power demands pose technical hurdles for medium-voltage (MV) distribution networks, resulting in energy losses, equipment performance issues, overheating, and unexpected tripping. Integrating FCSs into the grid requires considering annual and seasonal variations in EV fast-charging energy consumption. Neglecting these variations can lead to either underestimating or overestimating the impacts of FCSs on the networks. This paper introduces a probabilistic method to assess voltage profile violations, overload capacity, and increased power losses due to FCSs. By incorporating annual and seasonal time-series data, the method accounts for uncertainties related to EV fast charging. Applied to an MV feeder in Brazil, our evaluations highlight the impact of annual power consumption seasonality on EV-grid integration studies. Considering seasonal dependency is crucial for precise impact assessments of MV distribution networks. The proposed method aids utility engineers and planners in quantifying and mitigating the effects of EV fast charging, contributing to more reliable MV grid integration strategies.

Optimal location and sizing of renewable distributed generations and electric vehicle charging stations

Many countries are aiming to replace gasoline-based vehicles with electric vehicles (EVs). The increasing adoption of EVs has led to a surge in the number of charging stations, significantly impacting the electrical grid with issues such as power quality degradation, increased losses, and voltage fluctuations. In response to these challenges, there is a growing interest in integrating distributed generation from unconventional and renewable sources into the grid to power EV Charging Stations (EVCSs). However, this integration poses new complexities, including increased power losses and voltage instability. Consequently, the optimal allocation and sizing of Renewable Distributed Generations (RDGs) and EVCSs have become critical planning considerations. This paper addresses these issues by formulating a multi-objective optimization problem aimed at minimizing power losses and improving the voltage profile of distribution systems. The study introduces several constraints, including the placement of EVCSs and RDGs at separate buses, and employs particle swarm optimization and cuckoo search algorithm to simultaneously determine the optimal locations and sizes of the RDGs and the optimal locations of the EVCSs. The effectiveness of the proposed techniques is demonstrated through simulations on IEEE 33 radial and meshed distribution systems, illustrating their capability to identify optimal configurations for integrating RDGs and EVCSs.

Reconfigurable topology assessment for efficient allocation of photovoltaic generators in unbalanced power delivery networks

The three-phase power delivery systems are reliable and efficient means of delivering electric power to customer node points. However, the unequal distribution of loads over the three phases leads to unbalance voltage between phases, resulting in increased power losses in distribution transformers and lines. It is highly challenging to install photovoltaic (PV) generators in unbalanced power distribution networks (PDNs) to restore power supply efficiency. This paper proposes dynamic reconfiguration (DR) in unbalanced PDNs to foster efficient integration of PV generators and improve system performance. Since the demand for electricity at residential node points and the PV-generated power varies with the season, this study proposes a seasonal DR of the PDNs. A framework with multiple objectives has been proposed for the DR-coupled PV deployment problem. A novel value-adaptive weight-aggregated (VAWA) grey-wolf optimizer (GWO) has been synthesized to contribute to system performance enhancement. The proposed strategy was validated and established through comparative investigations on an Indian 19-bus and modified IEEE 123-bus unbalanced radial distribution systems (URDSs).

A Study on the Lubrication Characteristics and Parameter Influence of a High-Speed Train Herringbone Gearbox

To investigate the lubrication characteristics in high-speed train gearboxes, a two-stage herringbone gearbox with an idle gear was analyzed. The lubricant flow and distribution were shown using the moving particle semi-implicit (MPS) method. A liquid film flow model was brought in to enhance the non-slip wall boundary conditions, enabling MPS to predict the film flow characteristics. This study investigates the influence of gear rotating speed, lubricant volume, and temperature on lubricant flow, liquid film distribution, lubrication state in the meshing zone, and churning power loss. The results indicate that lubrication characteristics depend on the splashing effect of rotating gears and lubricant fluidity. Increasing gear rotating speed and lubricant temperature can improve liquid film distribution on the inner wall, increase lubricant volume, and thus enhance film thickness. The lubricant particles in the meshing zone correlate positively with the gear rotating speed and lubricant volume, correlate negatively with a temperature above 20 °C, and decrease notably at low temperatures. Churning power loss mainly comes from the output gear. As lubricant volume and gear rotating speed increase, churning torque and power loss increase. Above 20 °C, viscosity decreases, reducing power loss; low temperatures lessen lubricant fluidity, reducing churning power loss.

Multiscale multiphysics modeling of ammonia-fueled solid oxide fuel cell: Effects of temperature and pre-cracking on reliability and performance of stack and system

Ammonia is a promising carbon-free fuel for solid oxide fuel cells (SOFCs). However, direct feeding of ammonia into the SOFC may lead to serious degradation due to the nitriding. Conversely, the pre-cracking of ammonia introduces complexity to the system design and causes increased power losses in the system. Therefore, it is crucial to explore all these aspects simultaneously. In this context, this study introduces a novel multiscale modeling approach by integrating a 3D multiphysics simulation of the ammonia-fueled SOFC stack with system-level modeling to investigate the reliability and performance of the stack and system. Two different cell technologies developed for low temperature (LT) and high temperature (HT) operation are investigated at LT (600 – 700°C) and HT (700 – 800°C) ranges. The results indicate that fuel inlet temperature should be 55°C and 18°C higher than the minimum temperature in 0% pre-cracking case for HT and LT cases, respectively. The increase in the required air flow rate for cooling in the 100% pre-cracking case compared to the 0% pre-cracking case is around 100% and 216% for the HT and LT cases, respectively. However, stack power production and power losses in the system components are comparable for LT and HT cases which leads to similar system performance. A larger share of the active area is affected by nitriding in LT cases than HT ones. However, a smaller cracking ratio at LT (∼ 82%) compared to HT conditions (∼ 92%) is needed for elimination of nitriding. While the LT and HT cases are comparable in terms of system power production, the lower stack outlet temperatures in LT cases require novel and more expensive catalysts for ammonia pre-cracking and HT cases need more expensive steels.

An optimal operation strategy of wind farm for frequency regulation reserve considering wake effects

When wind farms (WFs) participate in power system frequency regulation, deloaded control can increase the stored rotational kinetic energy in the wind turbines (WTs), thereby enhancing their frequency support capability. However, due to the wake effects and deloaded control of turbines, this method can also lead to increased power losses. An optimal operation strategy has been proposed to balance power generation and frequency regulation reserve in WFs, taking wake effects into account. Initially, a WF deloaded control model that considers wake effects, and an adaptive combined frequency control model based on kinetic energy reserve, are established for participation in power system frequency regulation. Subsequently, the Covariance Matrix Adaptation Evolution Strategy (CMAES) is utilized to determine the optimal operation allocation of maximum WF output power within the FLORIS wake model, meeting frequency regulation reserve requirements. A comparative analysis of the optimal allocation results under various wind conditions is conducted to test the frequency support capabilities. The results indicate that the proposed strategy can achieve varying levels of frequency regulation reserve, reduce wake losses to maximize output power under deloaded control, and improve frequency support capability.