Contemporary Power Research Articles

Judging voluntariness: abortion assistance around 1900

ABSTRACT In this article, I focus on a practice that is legally referred to as assisting or abetting abortion. Using the example of Margarethe Hardegger (1882–1963), a Swiss women’s rights activist and socialist who was charged with this crime in 1915, I analyze how she defended her assistance before the criminal court of the city of Bern. I seek to show how her conception of voluntary action as feminist women’s solidarity entered into a battle of interpretation with patriarchal thought and legal-economic ideas of order, whose exponents sought to impute a commercial character to her engagement. Additionally, I explore how the medical profession positioned itself in this dispute and what the women who had abortions with her help had to say. In light of this case study, the article aims to bring out the multiform interpretations of a voluntary social practice and thus gain insight into the contemporary power dynamics and forms of resistance.

Multi-Objetive Dispatching in Multi-Area Power Systems Using the Fuzzy Satisficing Method

The traditional mathematical models for solving the economic dispatch problem at the generation level primarily focus on minimizing overall operational costs while ensuring demand is met across various periods. However, contemporary power systems integrate a diverse mix of generators from both conventional and renewable energy sources, contributing to economically efficient energy production and playing a pivotal role in reducing greenhouse gas emissions. As the complexity of power systems increases, the scope of economic dispatch must expand to address demand across multiple regions, incorporating a range of objective functions that optimize energy resource utilization, reduce costs, and achieve superior economic and technical outcomes. This paper, therefore, proposes an advanced optimization model designed to determine the hourly power output of various generation units distributed across multiple areas within the power system. The model satisfies the dual objective functions and adheres to stringent technical constraints, effectively framing the problem as a nonlinear programming challenge. Furthermore, an in-depth analysis of the resulting and exchanged energy quantities demonstrates that the model guarantees the hourly demand. Significantly, the system’s efficiency can be further enhanced by increasing the capacity of the interconnection links between areas, thereby generating additional savings that can be reinvested into expanding the links’ capacity. Moreover, the multi-objective model excels not only in meeting the proposed objective functions but also in optimizing energy exchange across the system. This optimization is applicable to various types of energy, including thermal and renewable sources, even those characterized by uncertainty in their primary resources. The model’s ability to effectively manage such uncertainties underscores its robustness, instilling confidence in its applicability and reliability across diverse energy scenarios. This adaptability makes the model a significant contribution to the field, offering a sophisticated tool for optimizing multi-area power systems in a way that balances economic, technical, and environmental considerations.

Novel nine level switched capacitor multi-level inverter based STATCOM for distribution system

FACTS based devices are mostly used in power systems due to their capacity to improve system stability. The Static Synchronous Compensator (STATCOM), a shunt-connected device in the FACTS device family, is used for power compensation, power balancing, and enhancing dynamic stability in contemporary power systems. In the proposed work, STATCOM based novel nine level switched capacitor based multi-level inverter (MLI) is used to reduce power quality issues. The proposed novel inverter has a few number of switches with one voltage source. The placement of STATCOM based inverters in the wrong places may have detrimental effects on power loss and electricity quality. To overcome these issues, green anaconda optimization (GAO) is used to allocate the proposed system in an optimal place. The performance of the proposed inverter is examined using the MATLAB/Simulink tool. This inverter requires fewer switches and achieves DC link voltage balances in comparison to any other existing conventional topologies. STATCOM based inverter is validated under different faulty conditions to show the effectiveness of this inverter. The proposed new inverter has nine levels and compensates for the poor state while improving power quality. The proposed method has a substantially lower total harmonic distortion (THD) of 1.04 % while maintaining an efficiency of 99.02 %. Experimental validation is established using a dSPACE RTI1104 controller to validate the proposed method. Experimental results obtained 1.04 % of harmonics with the same output voltage as the simulation.

Performance Analysis of a Self-Decarbonizing Combustor

Abstract Hydrogen is envisioned to be a key decarbonization solution for fossil fuel-dependent power generation and aviation industries. At present, a significant fraction of the generated electrical power is derived from natural gas. As such, the external energy needed for hydrogen generation, often sourced from fossil fuels, results in CO2 emissions, compromising overall carbon neutrality. Instead, the processes of hydrogen generation can be energetically coupled with the combustion process, in situ, to eliminate external energy requirements. To that end, a novel self-decarbonizing combustor (SDC) has been conceptualized, integrating methane pyrolysis with the combustion process that can in principle decarbonize many contemporary power generation technologies. The underpinning methane pyrolysis process enables in situ pre-combustion capture of solid carbon, while simultaneously generating hydrogen. Consequently, CO2 emissions resulting from the combustion of processed, hydrogen-enriched fuel are mitigated. This study provides a comprehensive analysis, delineating the operating principle and the effect of some of the important governing parameters on the performance of the self-decarbonizing combustor. These parameters, including fuel temperature, residence time, pressure, and catalysis, are studied in the context of potentially applying the proposed concept to natural gas-based decarbonized electrical power generation. Investigating fuel chemistry, combustion exhaust, and carbon structure and morphology under varying process parameters enhances our comprehension of the SDC. Additionally, its self-sufficient nature eliminates the need for separate hydrogen production, storage, and transportation infrastructure, highlighting its potential as a scalable and realizable technology.

Using MCFC for capturing CO2 from flue gases and delivering to Sabatier reactor for SNG synthesis

In contemporary power generation, enhancing efficiency and mitigating environmental contamination are of paramount importance. The imperative to curtail greenhouse gas emissions stands as a preeminent challenge within this sector. Concurrently, there is a marked surge in the exploitation of renewable energy sources, which, due to their intermittent nature, precipitates the imperative for advanced energy storage solutions. This paper introduces an integrated system designed to address both the reduction of CO2 emissions and the storage of energy. The advocated system integrates a Molten Carbonate Fuel Cell (MCFC), Solid Oxide Electrolysis Cell (SOEC), and a Sabatier reactor. The MCFC is employed for its proficient CO2 capture capabilities at the cathode, exhibiting remarkable efficiency, operational flexibility, and a high CO2 separation quotient. The SOEC is recognized for its effective hydrogen production, leveraging high operational temperatures to augment hydrogen output while diminishing electrical energy consumption through thermal energy substitution. The Sabatier reactor is utilized for catalytic methanation, transforming CO2 into Substitute Natural Gas—a compound predominantly comprising methane and hydrogen with minimal CO2 and water traces. This system facilitates the capture and utilization of over 80% of CO2 from exhaust fumes, achieving an overall energy efficiency of 71%. The system's design and off-design operational parameters were meticulously modeled and analyzed.

Energy Community Resilience Improvement Through a Storage System

Energy communities serve as vital stakeholders within contemporary power grids. Nevertheless, managing these communities presents formidable challenges, owing to the intricate nature of the task, the presence of uncertainties, and competing objectives. This paper aims to demonstrate the positive impact of incorporating a storage system into an energy community, ensuring the welfare of every community member during grid malfunctions. The research investigates two separate energy communities, considering both uncontrollable and controllable devices within individual households as consumption sources, in addition to power supply sources like PV systems installed in community residences, as well as power derived from the main grid or the storage system.

Optimal scheduling of virtual power plants considering integrated demand response

Abstract In contemporary power system management, leveraging multilevel demand response strategies plays a pivotal role in balancing energy supply with demand, thereby enhancing system efficiency and flexibility. This paper introduces an innovative optimization model for virtual power plant operations that anticipates the scheduling needs several days in advance. It incorporates a comprehensive approach to demand response on the consumer side, highlighting two main types: price-based and substitution-based responses. Furthermore, the model integrates the functionalities of energy storage and conversion devices. This integration aims to facilitate effective load management, ensuring an optimized load distribution while minimizing the overall system costs.

Decision-focused learning under decision dependent uncertainty for power systems with price-responsive demand

Contemporary power systems are experiencing a growing integration of flexible resources on the consumer side. Different from flexible demand that submits specific bids to energy markets, price-responsive demand (PRD) adjusts its power consumption without notice, simply based on the resulting electricity prices as well as internal priorities and limitations. In this paper, we demonstrate how a high penetration of PRD (the behavior of which is invisible to the operator during the day-ahead unit commitment stage) results in systematic inefficiency costs and formulate the so-termed decision-focused learning problem of learning to provide a demand forecast which, once fed as an input to the operator’s economic dispatch optimization problem, results in an efficient dispatch. Interestingly, the prescribed demand forecast affects the resulting prices, which in turn affect the actual demand realization, giving rise to a decision-dependent uncertainty. Motivated by the problem’s hard-to-evaluate objective function, we solve it using Bayesian optimization. The empirical evaluations demonstrate significant savings in the effective real-time system cost, compared to the current practice of using the default demand forecast. Moreover, the method is shown to achieve a system cost that is fairly close to the one achieved by a system that fully integrates PRD into the day-ahead process; but without requiring any change in the operator’s existing dispatch algorithm while avoiding all efforts necessary for the integration of flexible demand, which is a widely pursued field of ongoing research.

Frequency control enhancement for hybrid microgrid using multi-terminal multi-function inverter

Renewable energy sources (RESs) are considered a crucial energy transformation to reduce carbon emissions, so more RESs are being integrated into contemporary power systems. Power electronic converters are extensively utilized to connect power grids with renewable generators to manage the fluctuations and unpredictability of these renewable energy sources. This paper introduces a multi-terminal multi-function inverter (MT-MF) designed for a battery energy storage system (BESS) to maintain the frequency stability of a hybrid microgrid (MG). The MG comprises a photovoltaic generation system, a diesel generator, BESS, and two loads: one constant load and the other variable, fed through a medium-voltage radial feeding system. An introduced approach involves utilizing a model predictive control controlled virtual synchronous generator (MPC-VSG) for BESS. This method offers inertia support during transient states and improves the dynamic characteristics of system frequency. In addition, it enables the connection of multiple batteries, provides individualized control for each, and supports the injection of reactive power into the MG. The required power from the BESS is shared between the two batteries using the low pass filter technique. The simulation outcomes affirm the proposed control strategy’s effectiveness and underscore the MT-MF inverter approach’s potential in integrating extensive RESs. This paper also explores how the proposed technique outperforms other methods in improving frequency stability.

Comprehensive Analysis of the Integration and Advanced Control of Photovoltaic Systems for Grid-Connected Applications

In-depth examination of photovoltaic (PV) system integration and enhanced control for grid-connected applications is presented in this study. It examines the difficulties, methods of control, and cutting-edge technologies needed to raise the dependability and effectiveness of PV systems in contemporary power networks. The study explores innovative Maximum Power Point Tracking (MPPT) algorithms, dynamic modeling methodologies, and different adaptive control strategies, emphasizing their importance in PV system performance optimization. It also looks at how high solar PV penetration affects grid stability and talks about creative ways to deal with these issues so that we can have robust and sustainable energy sources in the future. Key Words: photovoltaic Systems, pv integration, pv control strategies, energy storage systems, smart grid technologies, MPPT.

Evaluation of electrical load demand forecasting using various machine learning algorithms

The energy sector heavily relies on a diverse array of machine learning algorithms for power load prediction, which plays a pivotal role in shaping policies for power generation and distribution. The precision of power load prediction depends on numerous factors that reflect nonlinear traits within the data. Notably, machine learning algorithms and artificial neural networks have emerged as indispensable components in contemporary power load forecasting. This study focuses specifically on machine learning algorithms, encompassing support vector machines (SVMs), long short-term memory (LSTM), ensemble classifiers, recurrent neural networks, and deep learning methods. The research meticulously examines short-term power load prediction by leveraging Chandigarh UT electricity utility data spanning the last 5 years. The assessment of prediction accuracy utilizes metrics such as normalized mean square error (NMSE), root mean squared error (RMSE), mean absolute error (MAE), and mutual information (MI). The prediction results demonstrate superior performance in LSTM compared to other algorithms, with the prediction error being the lowest in LSTM and 13.51% higher in SVMs. These findings provide valuable insights into the strengths and limitations of different machine learning algorithms. Validation experiments for the proposed method are conducted using MATLAB R2018 software.

Inter- and sub-harmonics estimation: Treatise on raptor-inspired Harris Hawks collective wisdom

In contemporary power systems, harmonics as undesired electrical phenomena, have drawn considerable attention of the research community investigating in design and development of optimal electrical networks. Harmonics affect the power electronics devices with a number of negative impacts, including higher power losses, worse power quality, and interference with other delicate grid connected equipment. An objective function of inter- and sub-harmonics is constructed for joint estimation of amplitude and phase with known frequency. Accurate estimation of the harmonics model is carried out with stupendous knacks of raptor-inspired Harris Hawks optimization algorithm (HHOA), a revolutionary population-based, nature-motivated optimization approach. The primary sources of inspiration for HHOA are Harris Hawks cooperative demeanor and surprise pounce chasing style in nature. The HHOA’s performance is assessed for harmonics parameter estimation with several noise conditions, differing particle sizes and iterations. The proposed HHOA-based estimation technique provides accurate, convergent and robust performance for harmonics estimation. The reliable level of fitness is achieved consistently in the range of 10−3 to 10−10, an evidence of valuable precision, stability and robustness of the proposed algorithm.

Stuck with Tourism: Space, Power and Labor in Contemporary Yucatán by Matilde Córdoba Azcárate (review)

Stuck with Tourism: Space, Power and Labor in Contemporary Yucatán by Matilde Córdoba Azcárate (review)

A hybrid long-term industrial electrical load forecasting model using optimized ANFIS with gene expression programming

Electric energy demand forecasting is vital in contemporary power systems, especially amidst market deregulation trends and the increasing influence of industrial customers on power dynamics. However, existing forecasting models encounter challenges such as slow convergence and high complexity. Addressing these issues, this study proposes a hybrid forecasting model that combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gene Expression Programming (GEP) to enhance predictions of electrical energy consumption. Validated using real-time monthly electrical load data from an industrial user in Uganda, the hybrid model outperforms individual ANFIS and GEP models, demonstrating reduced errors and minimal computation time. The application of this hybrid model presents promising results, showcasing exceptional predictive capabilities and offering potential improvements in efficiency and precision for electrical energy consumption forecasting amidst market deregulation and evolving industrial dynamics.

Enhancing power quality in contemporary utility systems: A comprehensive analysis of active power filters and control strategies

In contemporary power distribution systems, a substantial proportion of the loads not only utilize active power but also extract reactive power and harmonic currents from the AC source. The increased extraction of power components is a result of the widespread usage of fast-switching devices in utility systems. These devices contribute to an escalation in harmonic issues and problems related to reactive power. Furthermore, power quality disturbances have a range of negative repercussions on the utility system, impacting its overall performance and reliability.In order to address these difficulties and enhance the quality of electricity, Active electricity Filters (APFs) have been created and refined throughout the years. These filters are specifically engineered to identify sudden shifts in the reference current and counterbalance the harmonic currents produced by the voltage source inverter (VSI). The efficacy of these filters mostly relies on the selected current control procedures, which are essential for attaining adequate performance levels.This study provides a comprehensive examination of control mechanisms designed to improve power quality (PQ) in utility systems. Additionally, it examines the choice of filter components, taking into account both technical and economic factors. The paper explores the factors involved in selecting filters that are customized for individual applications, in order to achieve the best possible performance and cost efficiency.Based on an extensive analysis of more than 200 research publications, this study presents unique insights and discussions on the topic, providing readers with a full grasp of the difficulties and remedies associated with enhancing power quality in modern power distribution networks.

Vigil AI Assemble Leveraging Ensemble Technique for Enhanced Detection of Multi-Converter Synchronous Active Islanding Mode in Hybrid Power Distribution Systems

The increasing complexity of contemporary power distribution networks and the necessity to incorporate renewable energy sources have led to the rise in popularity of hybrid power distribution systems. A significant challenge for such systems is the reliable identification of multi-converter synchronous islanding mode, wherein numerous converters operate in synchronous islanded situations. Using state-of-the-art artificial intelligence ensemble techniques, we introduce "Vigil AI Assemble," a novel approach to detecting multi-converter synchronous islanding modes in hybrid power distribution systems. The proposed method enhances the precision and robustness of islanding situation recognition by combining various AI algorithms, each with its own distinct properties. By combining several AI-based techniques, the ensemble method can analyze system properties, waveform data, and communication in real-time.

Chaos-infused wind power integration in the grey wolf optimal paradigm for combine thermal-wind power plant systems

This research presents a novel methodology for tackling the combined thermal-wind economic load dispatch (ELD) issue in contemporary power system. The proposed approach involves hybridizing active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programming (SQP) into grey wolf optimization (GWO) algorithm, while effectively incorporating the intricacies associated with renewable energy sources (RES). A more accurate model is made possible by hybridization for complex systems with memory and hereditary characteristics. The GWO is used as a tool for global search while ASA, IPA and SQP methods are used for rapid local optimization mechanism. The performance evaluation of the design heuristics is carried out on 37 thermal and 3 wind power generating units and outcomes endorse the effectiveness of the proposed scheme over state-of-the-art counterparts. The worthy performance is further validated on statistical assessments in case of thermal-wind integrated ELD problem in terms of measure of central tendency and variation on cost and complexity indices.

The power to transform structures: power complexes and the challenges for realising a wellbeing economy

This article draws on different strands of existing scholarship to provide an analytical framework for understanding the barriers to achieving a well-being economy. It explores the interplay between agential and structural power, where some actor-coalitions can reproduce or transform pre-existing structures. Conversely, these structures are strategically selective, favouring some actors, interests, and strategies over others. Making sense of this interplay between agential and structural power, the article introduces the notion of power complexes—time-space-specific actor-coalitions with common industry-related interests and the power to reproduce or transform structures in a given conjuncture. To understand the historical “becoming” of today’s political-economic terrain, the article provides a regulationist-inspired history of the rise, fall, and re-emergence of four power complexes: the financial, fossil, livestock-agribusiness, and digital. They pose significant threats to pillars of a wellbeing economy such as ecological sustainability, equ(al)ity, and democracy. Subsequently, today’s structural context is scrutinised in more detail to understand why certain actors dominate strategic calculations in contemporary power complexes. This reveals strategic selectivities that favour multi- and transnational corporate actors over civil society, labour movements, and public bureaucracies. The article then examines firm-to-state lobbying as a strategy employed by corporate actors within today’s structural context to assert their interests. It presents illustrative cases of Blackstone, BP, Bayer, and Alphabet. Finally, it explores implications and challenges for realising a wellbeing economy based on post-/degrowth visions. It emphasises the double challenge faced by such a wellbeing-economy actor-coalition. On one hand, it has to navigate within contemporary modes of regulation that favour corporate strategies of capital accumulation while, on the other, it must confront the self-expanding and extractive logic of capital. In this context, three key challenges are outlined: the need to form unconventional strategic alliances, operate on various spatial dimensions simultaneously, and institutionalise alternatives to firm-to-state lobbying to influence policymaking.

Three-Phase Four-Wire Hybrid Active Power Filter for Mitigating Harmonic Problems Caused by CFLs Lamps based on a Shunt Active Power Filter SAPF in Parallel with a Passive Filter

The widespread integration of nonlinear loads across industrial, commercial, and residential settings has significantly exacerbated power quality issues within contemporary power distribution systems. An example of such nonlinear loads is the prevalent use of compact fluorescent lamps (CFLs), intended as replacements for incandescent lamps (ILs). CFLs have gained popularity owing to their reduced energy consumption and extended lifespan, contributing to their extensive use across various applications. But those lamps inject high harmonic current in the power system. To address this issue, a hybrid active power filter HAPF based on a shunt active power filter SAPF in parallel with a passive filter (PF) is implemented in this paper. The reference current is calculated based on the PQ theory, and the voltage source inverter VSI is controlled via a simple hysteresis current controller (HCC). The results show that the PF is suitable to compensate for the Hight harmonic generated by both the load and the switched device of the APF. Also, this HAPF is well designed and implemented to mitigate all harmonic generated by CFL lamps on the power system, and compensate the reactive power. The THD of the current is reduced from 90.75% before compensation to 0.75% after compensation. This implies that the main injects only the Fundamental current to the power.

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

AbstractIn contemporary power transmission systems, substation monitoring stands as a vital but challenging task. While robotics offers promise in this regard, its potential is still nascent, struggling to replicate human intelligence. This article's core aim was to optimize robot path planning (RPP). Employing the enhanced red deer algorithm (ERDA), we sought to bolster RPP for more efficient substation inspections. The key methods used seem to be modeling, experimentation, comparative analysis, and some elements of data benchmarking to systematically evaluate and validate their proposed technique and models both in simulation and the real world. Research aims to enhance substation inspection effectiveness and bolster the safety of power usage in society. Proposed hybrid approach, combining proportional–integral–derivative (PID) with ERDA (PID–ERDA), underpins an Intelligent Intelligent RPP framework tailored to substation inspections. Examining the PID–ERDA model's performance, it significantly improved path length by 18%–29% and reduced response times by 14%–26% compared with PID or ERDA alone. PID–ERDA consistently achieved optimal solutions in 40–60 trials out of 85, while PID and ERDA managed 20–40 trials with inconsistent optimization. Additionally, it reduced average response times to 17–20 s from 21 to 27 s observed when using PID and ERDA separately. PID–ERDA also demonstrated superior path accuracy, surpassing methods like improved adaptive control algorithm‐feedforward neural network, enhanced unified algorithm‐susceptible‐infected‐removed, and bounded behavior‐particle swarm optimization by 7%–13%. The study affirms that the PID–ERDA model significantly enhances path planning for substation inspections, representing a milestone in RPP for power station inspections within modern power transmission systems. The primary contribution of this research is the significant improvement it brings to RPP for power station inspections, especially in substation monitoring within modern power transmission systems.