Power Operation Research Articles

X-point radiator control and its dynamics in ASDEX Upgrade and JET deuterium–tritium discharges

Abstract Control of heat exhaust is essential for the operation of power producing fusion reactors. Here, we present results of heat exhaust feedback control experiments in JET and AUG. In JET, we demonstrate the first X-point radiator (XPR) control in DD and DT discharges using argon seeding. In AUG, we improve the XPR control with nitrogen seeding, resulting in achieving the first detached L-H and H-L transition (in a single discharge). The controllers are designed using a model-based design procedure. The required models are obtained experimentally using perturbative (system identification) experiments. We study the dynamic response of the XPR to various seeding species and varying operating conditions. We find that the sensitivity (relative gain) of the XPR varies as function the height of the radiator inside the confined region but that the relative phase is consistent for all operating points. In AUG, the XPR is also less sensitive to impurity seeding changes for higher heating powers. In JET, we show that the XPR dynamics are the same for DD and DT plasmas. However, we observe that XPR control is only possible with argon and not with neon. The results show that a controller might well be designed in earlier stages of operation of a future device, but remains applicable and can be further tuned for full power operation.

The low-carbon economic scheduling method for regional integrated energy systems considering the joint integration of electric vehicles and concentrated solar power plants for wind power consumption

In northern China, there is a serious problem of source-load imbalance and significant wind curtailment, mainly due to the limitations of the “heat determines electricity” mode of operation of combined heat and power (CHP) units in winter. To further absorb excess wind power and overcome these limitations, this paper proposes an optimized scheduling method for a regional integrated energy system (RIES) based on source-load coordination. Firstly, on the supply side, the paper introduces the concept of a concentrated solar power (CSP) plant and an electric boiler for combined heat and power generation to provide heating, partially decoupling their operation mode from “heat determines electricity.” Secondly, on the demand side, the impact of electric vehicles (EV) is considered. A formula for the fluctuation ratio of peak-to-off-peak electricity prices is established, and EV charging and discharging are guided by electricity prices to fully exploit the demand response capability of dispatchable resources on the demand side. Charging loads during peak electricity consumption periods are effectively shifted to off-peak periods, aiding in peak shaving and valley filling. Finally, a tiered carbon trading mechanism is introduced. With the goal of minimizing the total cost of the RIES, an optimization scheduling model for the RIES is established, and the CPLEX solver is used for solving. Simulation results show that under source-load coordination scheduling, the overall efficiency of the system is significantly improved. It can further overcome the limitations of CHP units, enhance wind power consumption capacity, reduce carbon emissions, and improve the economic benefits of the RIES.

5 kW single-mode oscillating-amplifying integrated fiber laser through tightly bent confined ytterbium-doped fiber

We demonstrated a 5 kW single-mode oscillating-amplifying integrated fiber laser by tightly bending a homemade 25/400 µm confined YDF. Simulation results showed that tightly bending the confined YDF could decrease the mode coupling coefficient and increase the TMI threshold. By optimizing the doping ratio and bending radius of the confined YDF, a TMI-free 5.1 kW laser output was achieved based on the confined YDF and non-wavelength stable 976 nm LD. The beam quality factor M2 was 1.2 and maintained in the 5.5 hours full power operation. In comparison, the 976 nm pumped oscillating-amplifying integrated fiber laser with the conventional 25/400 YDF was limited by the quantum defect induced TMI at 3.1 kW, while the 915 nm pumped oscillating-amplifying integrated fiber laser with the conventional 25/400 YDF experienced the photodarkening induced TMI at 3.8 kW. To our knowledge, this is the highest output power of the single-mode confined doped all-fiber laser. This work also proves that tightly bending the confined YDF is an effective method to suppress the TMI induced by the quantum defect and the photodarkening.

Integrated optimization for sizing, placement, and energy management of hybrid energy storage systems in renewable power systems

Power systems reliant on renewable energy sources (RES) encounter supply-demand imbalances and stability challenges due to their inherent uncertainties. Hybrid energy storage systems (HESS) have emerged as a flexible and cost-effective solution to address these issues. This paper proposes an integrated optimization method for the capacity, location, and energy management of a HESS in RES-based power systems. The method optimizes battery energy storage system (BESS), electrolyzer (EL), fuel cell (FC), and hydrogen storage tank (HST) to minimize total costs, including power purchase, curtailment compensation, operation and maintenance (O&M), and investment costs, alongside voltage profile deviation index (VPDI) and active power loss index (APLI). A novel hybrid framework is proposed, utilizing mixed-integer linear programming (MILP) optimization at the master level and non-dominated sorting genetic algorithm II (NSGA-II) optimization at the slave level. This framework ensures that MILP provides robust solutions, while NSGA-II derives balanced optimal solutions from the optimal Pareto front. The proposed method was validated using real seasonal data from Korea and tested in IEEE 33 and IEEE 69 bus systems against various benchmark cases. Results indicate significant improvements, with VPDI enhanced by 16.00 % and 10.11 %, APLI by 15.64 % and 12.15 %, and total costs reduced by 58.67 % and 50.56 %, respectively. The sensitivity analysis demonstrated the robustness of the proposed method, showing zero sensitivity to iterations and positive correlations with increased levels of RES. Further verification against other optimization algorithms showed that the proposed method excels in cost efficiency, voltage stability, and line loss reduction, providing highly reliable results.

The energy management strategy of two-by-one combined cycle gas turbine based on dynamic programming

The complexity and nonlinearity of components in large-scale thermal facilities have resulted in a lack of recognized energy management models, and simple rule-based energy management strategies are still the main approach, which reduces their operating efficiency. In this study, a dynamic programming (DP) method for globally optimal power distribution and operation mode decision for two-by-one combined cycle gas turbine is proposed. First, the energy management model of system is established. Then the drum pressure of the heat recovery steam generator, which indicates the thermal energy storage of the system, is chosen as the state variable, while the control variables are the gas turbine power, turbine power and operation mode. In addition, the system response time is considered to re-evaluated the mode switch command. The simulation results show that the DP optimizes the thermal storage management, which allows the gas turbine to run in the high-efficiency operating range for a longer time. The DP-based strategy saves 6.25%, 5.89%, and 4.92% of fuel at initial drum pressure 8MPa, 9MPa, and 10MPa, respectively, compared to the rule-based strategy. The results of this study can be used as a benchmark to evaluate online energy management strategies in future work.

Language Model Crossover: Variation through Few-Shot Prompting

This article pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, language models of sufficient scale demonstrate in-context learning, i.e., they can learn from associations between a small number of input patterns to generate outputs incorporating such associations (also called few-shot prompting). This ability can be leveraged to form a simple but powerful variation operator, i.e., to prompt a language model with a few text-based genotypes (such as code, plain-text sentences, or equations), and to parse its corresponding output as those genotypes’ offspring. The promise of such language model crossover (which is simple to implement and can leverage many different open source language models) is that it enables a simple mechanism to evolve semantically rich text representations (with few domain-specific tweaks), and naturally benefits from current progress in language models. Experiments in this article highlight the versatility of language-model crossover, through evolving binary bit-strings, sentences, equations, text-to-image prompts, and Python code. The conclusion is that language model crossover is a flexible and effective method for evolving genomes representable as text.

Joint peak power and carbon emission shaving in active distribution systems using carbon emission flow-based deep reinforcement learning

Distribution optimal power flow (D-OPF) with peak load shaving function is crucial for guaranteeing economical and reliable operations of active distribution grids with various distributed energy resources. However, conventional D-OPF methods reduce only the power operation cost without considering carbon emission reduction, which may lead to a slowdown in achieving global carbon neutrality. To resolve this issue, this study proposes a deep reinforcement learning (DRL)-assisted D-OPF framework realizing dual-peak shaving of power and carbon emission for low-carbon active distribution system operations based on the notion of carbon emission flow (CEF). The proposed framework aims to minimize the total power operation costs of substation and gas-turbine (GT) generators. It also aims to reduce the total carbon emission cost via mitigation of peak power and carbon emission in the CEF-based D-OPF framework with both power and carbon emission peak constraints. A key feature of the proposed framework is the adoption of the DRL method for the CEF-based D-OPF problem to determine economical and eco-friendly peaks of power and carbon emission under dynamically changing distribution system operations. Furthermore, a D-OPF optimization-based reward function for the DRL agent is designed to yield no constraint violations for the D-OPF problem during the agent’s training phase. Numerical examples conducted on the IEEE 33-node and IEEE 69-node distribution systems with GT generators, solar photovoltaic systems, and energy storage systems demonstrate that, in contrast with CEF-free and CEF-integrated optimization methods with fixed power and/or carbon emission peaks, the proposed method further reduces the total carbon emission and cost.

Distribution network reconfiguration optimization based on genetic algorithm and its influence on operation and maintenance management

As one of the advanced application functions of distribution network automation, distribution network reconfiguration is an important optimization means to ensure the normal operation of the power grid. In order to further improve the power supply quality and operation and maintenance efficiency of the distribution network, this paper proposes a reconfiguration method based on improved genetic algorithm, establishes a network topology reconfiguration computational model, and validates the proposed method for the reconfiguration of the distribution network, and the results show that compared with the reconfiguration model of the distribution network constructed by basic genetic algorithm, the algorithm of this paper shows excellent performance in terms of both the comparison of the node voltages and the evolution of the population. The results show that compared with the basic genetic algorithm constructed distribution network reconfiguration model, this paper's algorithm exhibits excellent performance in both node voltage comparison and population evolution, and is capable of realizing optimal power transmission. Finally, the impact of distribution network reconfiguration on operation and maintenance management is analyzed.

544-nm Tb3+-doped LiYF4 laser pumped by 488-nm InGaN laser diodes and high peak power operation with cavitydumped Q-switching

544-nm Tb3+-doped LiYF4 laser pumped by 488-nm InGaN laser diodes and high peak power operation with cavitydumped Q-switching

Unlocking wind power potential to improve energy security in Ethiopia

Ethiopia possesses abundant wind resources that have the potential to revolutionize its energy sector by providing reliable and sustainable electricity through wind power. Despite the presence of a few operational wind farms, the country is facing challenges in generating sustainable electricity. The slow progress in wind power development raises concerns, as the impact of declining power generation from existing wind farms on economic development and return on investment remains insufficiently examined. The research paper aims to examine the status, challenges, and opportunities in developing, deploying, and sustaining wind power generation. This was accomplished through qualitative and quantitative analysis using 11 years of power generation data from operational wind farms and field research. Data sources include Ethiopian Electric Power, wind power operation reports, official government reports, field observations and discussions with wind power operators. The study reveals that challenges such as intermittent wind resources, high initial investment costs, inadequate transmission infrastructure, limited skilled workforce, and environmental concerns hinder the realization of wind energy's full potential. The article provides evidence-based recommendations for policymakers and the wider stakeholders to address the challenges and maximize benefits of wind energy in Ethiopia. This contributes to understanding the current situation and necessary actions to harness the country's abundant wind power potential.

A Hybrid Optimization Strategy for Minimizing Conversion Losses in Semi-Series-Resonant Dual-Active-Bridge Converter

To enhance the performance of resonant DC–DC converters, particularly under low-load conditions, a semi-series-resonant dual-active-bridge (Semi-SRDAB) converter with a hybrid optimization strategy is proposed. This strategy aims to reduce conduction-related losses and is designed for applications requiring a wide voltage range. The proposed Semi-SRDAB converter comprises a full-bridge inverter on the primary side and a hybrid-output bridge rectifier on the secondary side. It adopts phase-shift modulation combined with frequency modulation for power control. The hybrid optimization strategy for the Semi-SRDAB converter is investigated, beginning with the deduction of resonant current minimization using phasor analysis. Based on these analysis results, zero reactive power operation and soft-switching operation are achieved for both buck and boost modes. Successful validation has been demonstrated through experimental testing on a 300 W laboratory prototype. Enhanced conversion performance is confirmed by comparing the results with those from previous works.

The lycanthropic ambivalence within the biopolitical operations of the state power in Jo Nesbø’s Macbeth

Although lycanthropy is a medical term and a psychological phenomenon, depicted in myths and literature as the metamorphosis of divine or human beings into various animals, the political theory has viewed it as a space to explore the biopolitical strategies of assimilation or exclusion in a sovereign state. This research focuses on Jo Nesbø’s Macbeth (2018), an adaptation of Shakespeare’s play which addresses the anxieties about being human at a time when humanism has been deeply challenged. Informed by Agamben views on the ‘wolf-man’ and homo sacer, together with Derrida’s shrewd insights on ‘political bestiary’ which conveys the shared ‘outlaw’ status of the beast and the sovereign, this study explores Nesbø’s Macbeth in relation to Shakespeare’s infinite complexities to reveal the lycanthrope, a liminal being, who is pushed to the threshold of community to live permanently under threat of expulsion and annihilation or who transforms into a beast to survive in the contemporary political climate.

Dynamic graph structure and spatio-temporal representations in wind power forecasting

Wind Power Forecasting (WPF) has gained considerable focus as a crucial aspect of the successful integration and operation of wind power. However, due to the stochastic and unstable nature of wind, it poses a real challenge to effectively analyze the correlations among multiple time series data for accurate prediction. In our study, an end-to-end framework called Dynamic Graph structure and Spatio-Temporal representation learning (DSTG) framework is proposed to achieve stable power forecasting by constructing graph data to capture the critical features in the data. Specifically, a graph structure learning (GSL) module is introduced to dynamically construct task-related correlation matrices via backpropagation to mitigate the inherent inconsistency and randomness of wind power data. Additionally, a dual-scale temporal graph learning (DTG) module is further proposed to explore the implicit spatio-temporal features at a fine-grained level using different skip connections from the constructed graph data. Finally, comprehensive experiments are performed on the collected Xuji Group Wind Power (XGWP) dataset, and the results show that DSTG outperforms the state-of-the-art spatio-temporal methods by 10.12% on the average of root mean square error and mean absolute error, demonstrating the effectiveness of DSTG. In conclusion, our model provides a promising approach.

On the Rigorous Correspondence Between Operator Fractional Powers and Fractional Derivatives via the Sonine Kernel

Traditional operational calculus, while intuitive and effective in addressing problems in physical fractal spaces, often lacks the rigorous mathematical foundation needed for fractional operations, sometimes resulting in inconsistent outcomes. To address these challenges, we have developed a universal framework for defining the fractional calculus operators using the generalized fractional calculus with the Sonine kernel. In this framework, we prove that the α-th power of a differential operator corresponds precisely to the α-th fractional derivative, ensuring both accuracy and consistency. The relationship between the fractional power operators and fractional calculus is not arbitrary, it must be determined by the specific operator form and the initial conditions. Furthermore, we provide operator representations of commonly used fractional derivatives and illustrate their applications with examples of fractional power operators in physical fractal spaces. A superposition principle is also introduced to simplify fractional differential equations with non-integer exponents by transforming them into zero-initial-condition problems. This framework offers new insights into the commutative properties of fractional calculus operators and their relevance in the study of fractal structures.

Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions

With the rapid development of space exploration technology, the detection of extraterrestrial bodies has become increasingly important. Among these, path planning and target recognition and positioning technologies are particularly critical for applications in intelligent agents with low computational power operating in complex environments. This paper presents a novel approach to path planning on low-resolution lunar surface maps by introducing an improved A* algorithm with an adaptive heuristic function. This innovation enhances robustness in environments with limited map accuracy and enables paths that maintain maximum distance from obstacles. Additionally, we innovatively propose the Dynamic Environment Target Identification and Localization (DETIL) algorithm, which identifies unknown obstacles and employs spatiotemporal clustering to locate points of interest. Our main contributions provide valuable references for the aerospace industry, particularly in lunar exploration missions. The simulation results demonstrate that the improved A* algorithm reduces the maximum elevation difference by 55% and the maximum cumulative elevation difference by 68% compared to the traditional A* algorithm. Furthermore, the DETIL algorithm’s obstacle identification component successfully recognizes all the obstacles along the path, and its spatiotemporal clustering improves the average number of target discoveries by 152% over the conventional DBSCAN clustering approach.

The Spatiotemporal Evolution of the Mudflat Wetland in the Yellow Sea Using Landsat Time Series

Mudflat wetland, one of the 27 surface elements identified by the International Geographic Data Committee, has undergone substantial transformations with the rapid growth of the social economy and marine hazards, resulting in significant changes in its area and distribution. Quick identification of mudflat wetland evolution is vital to improve the mudflat ecological service value. We employed object-oriented and decision tree classification methods to map the mudflat wetland in the Yellow Sea using the Landsat time series from 1983 to 2020. The Improved Spectral Water Index (IWI) was established by combining the characteristics of many ratio indices and using ratio operation and quadratic power operation. The coefficient of variation (CV) of the IWI was calculated, and the range of the intertidal zone in 1983, 1990, 2000, 2010, and 2020 was obtained by using a threshold method. The results indicate that the mudflat wetland area decreased continuously from 1983 to 2020, with a reduction of 337.38 km2/10a. Among the total area, the natural wetland experienced a decline of 446.9 km2/10a, with the most drastic changes occurring between 2000 and 2010. In contrast, the area of the human-made wetland increased by 109.56 km2/10a. Over the 38 years, the tidal flat has undergone the most drastic reduction, with an average of 157.45 km2/10a. From 1983 to 2020, the intertidal zone area decreased, with a reduction of 429.02 km2/10a. Human activities were the key factors causing mudflat wetland loss. Based on these findings, we propose several policy suggestions. This study provides a scientific basis for understanding the synergetic evolution mechanism of coastal resources utilization and mudflat wetland protection under global change.

A Novel development of soft computing based hybrid power point tracking controllers for hydrogen vehicle application with new wide source DC-DC converter

From the present power generation scenario, most of the power production companies are focusing on renewable energy systems because their merits are easy to install, more reliable power sources, free from atmospheric pollution, and better adaptable to all weather conditions. In this article, a Polymer Electrolyte Membrane Fuel Cell (PEMFC) module is selected in the first objective to produce constant and continuous power to the hydrogen-based electrical vehicle systems. This fuel module provides fast power production and gives good energy density. However, the demerits of this module are more current production and low-level voltage generation. A new single-switch DC-DC circuit is introduced in the second objective of this work for optimizing the current levels of the fuel module thereby reducing the source power loss of the proposed system. Here, another problem of the fuel module is nonlinear power production which is linearized by proposing the Cuckoo Search Optimization (CSO)-based Adaptive Network-based Fuzzy Inference System (ANFIS) in the third objective to extract the maximum voltage from the fuel module. The merits of this introduced Maximum Power Point Tracking (MPPT) Controller are better tracking speed, low-level disturbances in the consumer load power, high robustness, more sustainability, and easy operation. The entire proposed system is investigated by selecting the MATLAB Simulink tool. The introduced converter circuit is analyzed by selecting the programmed DC supply.

Violent peace: effigies and the marking of the Armistice in Britain

ABSTRACT This article examines how effigies were used in Britain after the signing of the Armistice in November 1918. Effigies of Kaiser Wilhelm, the Crown Prince and members of the German High Command were paraded through the streets of villages, towns and cities as part of celebrations for the cessation of hostilities. These figures were physically and verbally abused, hanged on gibbets, displayed from shop windows, placed on trial and ‘executed’ by hanging and public burning. The violence that these effigies were subjected to is significant as it indicates the way the brutality of the war became part of civic life but it also demonstrates how civilians returned public spaces to public life. The war brought greater control of civic society by the operation of government to mobilise individuals and communities for the war effort. The carnivalesque celebrations that marked the end of the war was a means of acknowledging victory but also formed a means of dissent against the operation of power. Whilst these moments were fleeting and some harked back to traditional forms of celebration, the use of effigies transformed wartime spaces back into public ownership.

Optimal independent but coordinated operation of interconnected power, natural-gas, and district-heating distribution systems

Traditionally, power, natural-gas, and district-heating distribution systems have been planned and operated independently. However, the increasing presence of distributed natural-gas-fired generating units, heat pumps units, power-to-gas facilities, and combined heat and power units create an interdependency where the operational decisions of any system affect the operation of the other systems. We analyze centralized and decentralized operations of the three distribution systems and discuss under which conditions centralized and decentralized operations are equivalent. We find this equivalence by replacing each system operation problem with its optimality conditions and comparing them with the optimality conditions of a centralized operation. Using a realistic case study, we quantify the gains resulting from different levels of coordination. Equilibria, i.e., joint optimal solutions for the three systems, are found using an iterative convexification technique, which is novel in the context of this paper. An advantage of the proposed model lies in its potential to expand across a range of interconnected energy systems, which in turn, enables the identification of the equivalences between centralized and decentralized operations.

The influence of grid-forming energy storage and distributed synchronous condenser on the multiple renewable energy stations short circuit ratio

Abstract With the improvement of technical performance and reduction of cost, the scale of grid-forming energy storage and distributed synchronous condenser centralized access is constantly expanding, and their impact on the Multiple Renewable Energy Stations Short Circuit Ratio (MRSCR) is highly concerned by the power planning and operation departments. In this paper, the MRSCR calculation model considering distributed synchronous condenser and grid-forming energy storage is constructed, and the influence of distributed synchronous condenser and grid-forming energy storage on MRSCR is analyzed, which are very helpful for improving the MRSCR and the short-circuit capacity.