Energy Generation Research Articles

Analysis of New Energy Generation Technology Based on the "Dual Carbon" Goal

With the increasingly severe global climate change issues, achieving the "dual carbon" goal has become a common pursuit for China and the world. The development of new energy power generation technology is of great significance for reducing greenhouse gas emissions and optimizing energy structure. This article first outlines the background and significance of the "dual carbon" goal, and analyzes its driving effect on new energy power generation technology. Subsequently, this paper discusses in detail several mainstream new energy power generation technologies, including wind energy, solar energy, biomass energy, and geothermal energy, and systematically analyzes their development status and challenges. In the part of technological innovation, this paper focuses on the application and innovation trend of smart grid technology, energy storage technology and power electronics technology in the field of new energy power generation. In addition, this paper also evaluates the economy of new energy power generation technology, including cost-benefit analysis and the impact of policy environment. Finally, this paper summarizes the research results and puts forward the direction and suggestions for future research.

Design, modeling and cost analysis of 8.79 MW solar photovoltaic power plant at National University of Sciences and Technology (NUST), Islamabad, Pakistan

Climate change, as a critical global concern, has fueled our efforts to address it through different strategies. In response to the critical worldwide issue of climate change, we suggested a Photovoltaic (PV) system at the National University of Sciences and Technology (NUST) in Islamabad, Pakistan (latitude: 33.724530 N, longitude: 73.046869, terrain elevation: 552 m). Islamabad is located in a region blessed with enormous solar resources, boasting a daily horizontal solar irradiance of 1503.45 kWh/m2 and an average daily solar irradiance of 5.89 kWh/m2, with an exceptional solar fraction of 98.99%. The ambient air temperature, averaging 23.21 °C, reaches its maximum in June and its minimum in December. Our research thoroughly evaluates the system’s performance, accounting for various losses and utilizing modern PVsyst software. Over the course of 18 years, our PV system is expected to save 75,478.60 tons of CO2, the equivalent of planting 348,754 teak trees. Furthermore, the cost of energy generation is an affordable 0.0141 US $/kWh, much lower than traditional rates, including the Sherif cost of 0.028$/kWh. Along with the performance research, we conducted a detailed cost analysis, projecting the starting cost and cash flow, and discovered that the plant would be in surplus within 12 years of installation. Our system is positioned to generate 11,270,771 kWh/year with a respectable performance ratio (PR) of 76.2% and a Capacity Utilization Factor (CUF) of 16%. Our findings not only highlight the potential of renewable energy but also provide important insights for future sustainable energy programs.

Investigating the sustainable energy generation potential of an invasive weed: Lantana camara.

Lantana camara, one of the world's top ten most invasive species, was initially cultivated for ornamental use. However, it spread uncontrollably across the fallow areas and agricultural lands, threatening approximately 44% of Indian forests. Its invasion disrupts ecosystems by suppressing nearby plant growth through allelopathy and poses toxicity risks to grazing ruminants. It significantly increases forest fire risk by adding large amounts of combustible biomass, particularly dried L. camara. Despite efforts to control it using mechanical, chemical, and biocontrol methods, the results have been largely unsatisfactory, with associated costs estimated at $18,000 per square kilometre. Considering these challenges, recent research explored the potential of L. camara as a bioenergy resource. TheL. camarabriquettes exhibit a heating value of approximately 20MJ/kg with a low sulphur (0.5%), nitrogen (1%), and ash content (2%), making them suitable for decentralised energy production. Furthermore, bioethanol production fromL. camarahydrolysate has shown promising results, yielding 0.33g/g withPichia stipitisand0.47g/g with Saccharomyces cerevisiae, which is comparable to other lignocellulosic feedstocks. Additionally, the gasification of L. camara using a downdraft gasifier produced syngas with a lower heating value (LHV) of 6.4MJ/Nm3. These findings demonstrate that using L. camara for bioenergy production presents a dual solution, addressing the growing demand for renewable energy and managing invasive species. This review aims to critically evaluate the potential and challenges associated with the different energy production pathways for L. camara, highlighting its role in sustainable energy generation.

Integral predictive sliding mode control for high-speed trains: A dynamic linearization and input constraint-based data-driven scheme

A control scheme with high reliability and excellent tracking performance is essential for the automatic operation of high-speed trains (HSTs). In this study, a novel discrete-time data-driven predictive sliding mode control (DDPSMC) scheme is proposed for multi-power unit HSTs. Initially, a nonlinear integral terminal sliding mode surface was designed to replace the traditional linear sliding mode function, thereby achieving a rapid system error convergence and alleviating chattering. Then, receding horizon optimization was integrated into predictive control, which allowed the predicted sliding mode state to follow the expected trajectory of a predefined continuous convergence law. This scheme enabled the system to obtain higher output error accuracy and explicitly handle input constraints. Moreover, to enhance robustness, a parameter update law and disturbance delay estimation algorithm were introduced to calculate the control gain and total uncertainty, respectively. Finally, a comparative test of the proposed control scheme was conducted using a CRH380A HST simulation experimental platform in a laboratory setting. Simulation results demonstrate that the velocity error range of each power unit of the HST under the proposed control scheme is within [−0.176 km/h, 0.152 km/h], while the control force and acceleration are within [−55.7 kN, 44.8 kN] and [−0.564 m/s2, 0.496 m/s2], respectively, with stable variation, and other performance indicators are also better than other comparison methods. These results satisfy the safety, stability, and punctuality requirements of the train.

Improved Early Detection of Tube Leaks Faults in Pulverised Coal-fired Boiler Using Deep Feed Forward Neural Network

Boiler tube leaks significantly reduce the operational availability of power units, yet their early detection and prediction have not been fully realised in the industry. This paper introduces a novel approach employing deep feedforward neural networks for early detection of boiler tube leaks in pulverised coal-fired boilers. Early detection enhances repair planning, minimising downtime and production losses. It also improves monitoring and control of boiler tube failures, thereby optimising power plant operations and revenue. Diverse deep neural network models were developed and rigorously tested by leveraging 9 years of operational data (2012–2020). Exhaustive hyper-parameter optimisation, involving seven parameters, substantially improved predictive accuracy. By achieving training and testing accuracies of 82.8% to 99.3%, the study assessed their ability to detect boiler tube leaks over the same 9-year span, providing insights into leak detection capabilities. The models notably predicted all 12 identified tube leak events, averaging a 14-day lead time before boiler shutdown. In addition to leak prediction, a leak detection matrix was devised to analyse residual behaviour and reduce the likelihood of false alarms. However, the models’ predictive performance was observed to be limited to the following year, with satisfactory results for 2021 only. Incorporating the 2021 data into retraining significantly improved the predictions for 2022. The study concludes that while the models demonstrate robust short-term prediction capabilities, they require continuous retraining to maintain accuracy and relevance. This ongoing refinement is essential for keeping the models up-to-date and reliable in predicting future boiler tube leaks.

A Fuzzy Adaptive PID Coordination Control Strategy Based on Particle Swarm Optimization for Auxiliary Power Unit

Range extender hybrid vehicles have the advantages of better dynamics and longer driving range while reducing pollution and fuel consumption. This work focuses on the control strategy of an Auxiliary Power Unit (APU) operating in power generation mode for a range-extender mixer truck. When an operating point is switched, the engine speed and generator torque of the APU will switch accordingly. In order to ensure APU fast and stable adjustment to meet the power demand of the vehicle as well as operate at the lowest fuel consumption, a fuzzy adaptive PID coordination control strategy based on particle swarm optimization (PSO) is proposed to control the APU. The optimal operating curve of APU is calculated by coupling the engine and generator first. Then, the adaptive PID algorithm is used to control the speed and torque of the APU in a dual closed loop. The PSO is used to optimize the PID control parameter. Through hardware-in-the-loop tests under different working conditions, the control strategy is verified to be effective and real-time. The results show that the proposed control strategy can coordinate the operating of engine and generator and control the APU to track target power stably and quickly under minimum fuel consumption. Compared with traditional PID control strategy, the overshoot, regulation time and steady-state error are reduced by 55.1%, 11.1% and 77.3%, respectively.

Lithium extraction with energy generation

Lithium extraction with energy generation

Solar Species: Energy Optimization of Urban Form Through an Evolutionary Design Process

This paper proposes design guidelines to enhance energy efficiency and energy generation potential in active solar buildings. Additionally, it presents a variety of optimized urban forms characterized by attributes such as shape, layout, and number of buildings on the plot. These urban configurations are classified into solar species, each associated with a distinct range of high passive and active solar potential. These results were achieved by developing and applying a simulation-driven, multi-objective optimization technique for the early-stage design of a residential building cluster in a temperate climate. This method leverages both passive and active energy indicators, employing a genetic algorithm to identify optimal forms that maximize active solar potential while also minimizing operational energy demand. The approach utilizes a parametric modelling routine that relies on vertical cores and horizontal connections to produce design iterations featuring irregular geometry, while ensuring structural continuity and means of egress. The findings reveal a significant variability in onsite energy generation, with optimized solutions differing by a factor of 2.5 solely based on shape, underscoring the critical role of active solar potential. Taken together, these results hint at the descriptive and predictive capabilities of these solar species, making them a promising heuristic model for characterizing urban form in relation to energy performance.

Data-driven stochastic dynamic economic dispatch for combined heat and power systems using particle swarm optimization

In modern power systems, the uncertain behavior of renewable energy sources (RESs) may result in deviations from the optimal operating dispatches for the various power sources. The electric power generation from Combined Heat and Power (CHP) units is characterized by high efficiency with less pollution. To use CHP units more efficiently, the dynamic economic dispatch problem is applied to obtain the optimal power and heat sources’ outputs to satisfy heat and power demands while meeting the different operational constraints. In this paper, data-driven stochastic optimization is used to model these uncertainties utilizing the Generative Adversarial Networks in scenario generation that are accurate and do not require fitting models or modeling probability distributions. Then, the Fast Forward Selection technique is used to decrease the number of scenarios to improve system tractability. The results obtained indicate that the variability in electrical load, photovoltaic (PV), and wind turbine (WT) output significantly impacts the overall operational costs of the power system. Therefore, it is essential to incorporate the uncertainties associated with electrical load, PV, and WT when aiming for accurate results in economic dispatch. the study found that the overall operation cost of the dynamic economic dispatch of the electrical system for 24 hours after integrating RES achieved a daily savings of 2.5 % in the first scenario. Furthermore, the findings demonstrate that RESs positively contribute to reducing the total operational costs of the power system. The economic dispatch of CHP systems is influenced by the integration of RESs and the uncertainties associated with electrical parameters.

Photocatalytic hydrogen production and sulfamerazine degradation via a novel dual S-scheme photocatalyst: Nanocomposite synthesis, characterization and mechanism insights

Creating highly effective photocatalysts is crucial for harnessing solar energy to degrade pollutants and produce hydrogen (H₂). In this study, we successfully synthesized a novel dual S-scheme iron oxide (Fe₂O₃)/bismuth oxide (Bi₂O₃)/titanium dioxide (TiO₂) ternary photocatalyst using a straightforward method. This photocatalyst was employed for efficient photocatalytic water splitting and the degradation of the antibiotic sulfamerazine (SMZ) under visible light. Various characterization and photoelectrochemical techniques, including scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), Brunauer–Emmett–Teller surface area analysis (BET), photocurrent measurements, Mott-Schottky analysis, photoluminescence (PL), electrochemical impedance spectroscopy (EIS), and electron spin resonance (ESR), were utilized to analyze the synthesized materials. Among the synthesized nanocomposites, the 15 wt% Fe₂O₃/Bi₂O₃/TiO₂ (15FeBi/TiO₂) composite demonstrated exceptional photocatalytic efficiency, achieving 98 % SMZ degradation and a hydrogen production rate of 590.36 μmol/g·h. Experimental results, including scavenging tests and ESR findings, highlighted the crucial role of hydroxyl radicals (•OH) and superoxide radicals (•O₂−) in the photocatalytic process. Moreover, liquid chromatography-mass spectrometry (LC-MS) results proposed three degradation pathways, and quantitative structure-activity relationship (QSAR) analysis showed that the toxicity of intermediates was effectively reduced. The 15FeBi/TiO₂ photocatalyst also exhibited excellent reusability, retaining about 85 % of its initial activity after five cycles, and proved effective against various pollutants and in real water matrices. This research contributes to the design and development of high-activity heterojunction photocatalysts for superior clean energy generation and pollutant degradation under visible light.

Optimal micro-pump-storage system for the average Greek hotel with PV generation

In this paper, a modern micro-scale pumped hydro storage system that is specifically designed to operate in coordination with photovoltaics installed at the roof of an average Greek hotel is presented. The fictitious hotel chosen as a case study, displays the energy profile of an average sea-side hotel around the Mediterranean Sea, while photovoltaics’ energy generation is assumed to follow the typical production profile of such sites. Pumped hydro storage and photovoltaic generation, size and cost have been appropriately modeled so that they realistically simulate their operational scheme, while also considering the spatial and technical characteristics and limitations of such projects. Results derived from the implementation of such a scheme into the Average Greek hotel demonstrated significant monetary benefits, accompanied with a substantial net annual profit and low payback period of the investment.

Integration of Renewable Energy Solutions in Agricultural Operations

The integration of renewable energy solutions in agricultural operations presents a transformative approach to enhancing sustainability, reducing greenhouse gas emissions, and improving the economic viability of farming practices and explores the various renewable energy technologies applicable to agriculture, including solar, wind, biomass, and biogas energy solutions. Each technology is examined for its specific applications within the agricultural sector, highlighting its potential benefits and the challenges faced in implementation. Solar energy, through photovoltaic and thermal systems, offers significant potential for powering irrigation, greenhouse operations, and processing facilities, thereby reducing reliance on fossil fuels and grid electricity. Wind energy, with its ability to generate electricity through turbines, is particularly valuable in regions with consistent wind patterns, contributing to both onfarm energy needs and grid supply. Biomass energy, utilizing agricultural residues and dedicated bioenergy crops, provides a sustainable solution for heating and energy generation, while also addressing waste management challenges. Biogas production, through anaerobic digestion of organic waste, presents a dual benefit of generating renewable energy and improving nutrient recycling in farming systems. Government subsidies, feedin tariffs, research funding, and technical support play pivotal roles in encouraging farmers to transition to renewable energy sources. Case studies from various regions illustrate the practical implementation of these technologies, demonstrating their impact on enhancing agricultural productivity, reducing environmental footprints, and fostering energy independence, including policymakers, researchers, and farmers, about the opportunities and challenges in adopting sustainable energy practices. The findings underscore the importance of continued innovation, supportive policies, and collaborative efforts to achieve a resilient and sustainable agricultural sector.

Reductive dehalogenase of Dehalococcoides mccartyi strain CBDB1 reduces cobalt- containing metal complexes enabling anodic respiration.

Microorganisms capable of direct or mediated extracellular electron transfer (EET) have garnered significant attention for their various biotechnological applications, such as bioremediation, metal recovery, wastewater treatment, energy generation in microbial fuel cells, and microbial or enzymatic electrosynthesis. One microorganism of particular interest is the organohalide-respiring bacterium Dehalococcoides mccartyi strain CBDB1, known for its ability to reductively dehalogenate toxic and persistent halogenated organic compounds through organohalide respiration (OHR), using halogenated organics as terminal electron acceptors. A membrane-bound OHR protein complex couples electron transfer to proton translocation across the membrane, generating a proton motive force, which enables metabolism and proliferation. In this study we show that the halogenated compounds can be replaced with redox mediators that can putatively shuttle electrons between the OHR complex and the anode, coupling D. mccartyi cells to an electrode via mediated EET. We identified cobalt-containing metal complexes, referred to as cobalt chelates, as promising mediators using a photometric high throughput methyl viologen-based enzyme activity assay. Through various biochemical approaches, we show that cobalt chelates are specifically reduced by CBDB1 cells, putatively by the reductive dehalogenase subunit (RdhA) of the OHR complex. Using cyclic voltammetry, we also demonstrate that cobalt chelates exchange electrons with a gold electrode, making them promising candidates for bioelectrochemical cultivation. Furthermore, using the AlphaFold 2-calculated RdhA structure and molecular docking, we found that one of the identified cobalt chelates exhibits favorable binding to RdhA, with a binding energy of approximately -28 kJ mol-1. Taken together, our results indicate that bioelectrochemical cultivation of D. mccartyi with cobalt chelates as anode mediators, instead of toxic halogenated compounds, is feasible, which opens new perspectives for bioremediation and other biotechnological applications of strain CBDB1.

Integrated Transcriptomic Analyses of Liver and Mammary Gland Tissues Reveals the Regulatory Mechanism Underlying Dairy Goats at Late Lactation When Feeding Rumen-Protected Lysine.

Although low-protein diets can improve the nitrogen utilization efficiency and alleviate economic pressures in ruminants, they may also negatively impact dairy performance. Rumen-protected lysine (RPL) supplementation can improve the health status and growth performance of ruminants without compromising nitrogen utilization efficiency and feed intake. In this study, a total of thirty-three multiparous dairy goats in the late-lactation period were randomly divided into three groups that were separately fed the control diet (namely the protein-adequacy group), the low-protein diet (namely the protein-deficient group), and the RPL-supplemented protein-deficient diet (namely RPL-supplementation group) for five weeks. Here, we investigated the molecular mechanisms regarding how low-protein diets with RPL supplementation compromise lactation phenotypes in dairy goats through cross-tissue transcriptomic analyses. Dietary protein deficiency caused an imbalance in amino acid (AA) intake, disrupted hepatic function, and impaired milk synthesis. Transcriptomic analyses further showed that RPL supplementation exhibited some beneficial effects, like mitigating abnormal lipid and energy metabolism in the liver, elevating hepatic resistance to oxidative stress, improving the mammary absorption of AAs, as well as activating mammary lipid and protein anabolism primarily through peroxisome proliferator-activated receptor (PPAR) and janus kinase-signal transducer (JAK)-signal transducer and activator of transcription (STAT) signaling, respectively. RPL supplementation of a low-protein diet contributes to maintaining late lactation in dairy goats primarily through mitigating hepatic energy disturbances and activating both lipid and protein metabolism in the mammary glands. Since RPL supplementation initiated a series of comprised events on mammary protein and lipid metabolism as well as the hepatic function and energy generation in dairy goats under protein deficiency during late lactation, these findings thus provide some insights into how RPL supplementation helps maintain milk production and health in dairy mammals especially at late lactation.

Advancement of Finite Element Method Solver Used in Dam Safety Monitoring System by Interpolation of Pore Pressure and Temperature Values

In this paper, we focused on the advancement of Dam Monitoring Software that incorporates the Finite Element Method (FEM), as these large infrastructure constructions are crucial for ensuring a dependable water supply, irrigation, flood control, renewable electric energy generation, and safe operation, which is of utmost importance to any country. However, the material properties and geotechnical environments of dams can change (deteriorate) over time, while the standards and legal norms that govern them become more and more rigorous, so in order to accurately assess the state of a dam and detect any concerning behavior, the software must be updated as well. The custom-developed FEM solver, unlike many commercial alternatives, is adaptable and can be reconfigured to function within a Dam Monitoring System. In this paper, we present the procedure for interpolating numerical values at measurement points, when the position of the measurement point does not align with the node of the element, allowing for additional instrument locations to be added to the monitored system without the need for remeshing the numerical model. This procedure is used to compare the actual pore pressures and temperature values of the concrete dam structure with the prediction of the numerical model, and the agreement is much greater with the new interpolation algorithm in comparison to the nearest nodal values, with the average relative difference for pore pressure reduced from 8.89% to 8.10%, justifying this implementation.

Charge transfer behavior of triboelectric polymers and triboelectric sensors operated under ultrahigh pressure

AbstractThe energy generation performance of triboelectric materials under ultrahigh pressure remains to be investigated. Here, the variations in molecular structure and built‐in electric field of triboelectric polymers under ultrahigh pressure have been thoroughly studied. The attenuation of built‐in electric field and the escaping of triboelectric charges under ultrahigh pressure are observed in different triboelectric polymers, whereas the existence of deep traps allows the built‐in electric field to be recoverable with the release of pressure. Moreover, the macromolecular conformational changes, including twisting molecular chains and crystal structure changes, can also induce the redistribution of deep traps, leading to a sudden increase in built‐in electric field under specific pressure. Finally, a triboelectric sensor for ultrahigh pressure condition is fabricated with excellent cycle repeatability and a total thickness of 2 mm, which has a sensitivity of 0.07 V MPa−1 within a linear region of 1–100 MPa. This study offers in‐depth insight into the physical understanding of charge behavior both on interface and in bulk of triboelectric materials, whereas the proposed ultrahigh pressure sensors can promote various potential applications of triboelectric sensor in extreme environments.

Enhancing the Performance of Oxide‐Based Transverse Thermoelectric Generators by Optimized Internal Geometry

AbstractIn transverse thermoelectric generators (TTEGs), comprising an artificially anisotropic material formed by a layered and tilted structure of two materials, the induced thermoelectric voltage is generated perpendicular to the applied temperature gradient. The performance of TTEGs composed of alternate layers of La1.99Sr0.01CuO4 (LSCO) and silver (Ag), tilted at an angle φ, with metal‐to‐ceramic thickness ratio νt, is investigated. Improvement in performance is achieved by optimizing the internal geometry parameters, φ and νt of the tilted layered structure. The transverse power factor PFtr and figure‐of‐merit ZtrT of the artificially tilted material composite are analytically calculated and presented in color contours. Experimentally, a power output Pout of 14.5 mW at ΔT = 140 K is measured for a TTEG with φ = 66°, compared to 2.8 mW and 11 mW for generators with φ of 20° and 45°, respectively. Moreover, TTEGs with different values of νt are prepared. Furthermore, transverse multilayer thermoelectric generators (TMLTEG) with output power of 14.3 mW at ΔT = 225 K are fabricated utilizing the ceramic multilayer technology. It is demonstrated that the transverse thermoelectric effect using an artificial anisotropic material consisting of ceramic and metal can be explored for autonomous thermoelectric energy generation for low‐power applications.

A cooperative approach for generation and lines expansion planning in microgrid‐based active distribution networks

AbstractWith the growth of the load in the electricity networks, sufficient investment in the generation and lines expansion should be made in order to provide the energy needed by consumers with the lowest possible investment and operation costs. This issue is especially important in distribution networks, which are faced with the uncertainties of renewable energy generation and the development of microgrids and related issues.In this article, the planning of generation and lines expansion has been modeled with the aim of minimizing the total costs of microgrids, based on the cooperative approach. For this purpose, a bi‐level model has been developed; on the upper level, microgrids make investment decisions with a cooperative approach, and a constrained stochastic formulation has been developed with considering operational uncertainties on the lower level. Also, in this article, in order to ensure the supply of critical loads in island conditions, the self‐sufficiency index is defined. Three case studies have been considered to ensure the effectiveness of the developed model. In case 1, each microgrid will be able to supply its load only by generating of its units and purchasing from the retail market. In case 2, the possibility of trading with other microgrids in a non‐cooperative approach will also be available to the microgrids operators, and in case 3, microgrids can exchange energy with other microgrids in a cooperative manner. The simulation results showed that due to the possibility of using nearby microgrid resources, the cost of microgrid load supply in case 2 was reduced by 4.84% compared to case 1. Also, this cost in case 3 was reduced by 5.23% and 0.38%, respectively, compared to cases 1 and 2, due to the use of a cooperative manner in microgrid load providing.

Circulation types and their relationship with extreme wind energy generation events in Poland

Circulation types and their relationship with extreme wind energy generation events in Poland

Mixed Thermal and Renewable Energy Generation Optimization in Non-Interconnected Regions via Boolean Mapping

Global efforts aiming to shift towards renewable energy and smart grid configurations require accurate unit commitment schedules to guarantee power balance and ensure feasible operation under different complex constraints. Intelligent systems utilizing hybrid and high-level techniques have arisen as promising solutions to provide optimum exploration–exploitation trade-offs at the expense of computational complexity. To ameliorate this requirement, which is extremely expensive in non-interconnected renewable systems, radically different approaches based on enhanced priority schemes and Boolean encoding/decoding have to take place. This compilation encompasses various mappings that convert multi-valued clausal forms into Boolean expressions with equivalent satisfiability. Avoiding any need to introduce prior parameter settings, the solution utilizes state-of-the-art advancements in the field of artificial intelligence models, namely Boolean mapping. It allows for the efficient identification of the optimal configuration of a non-convex system with binary and discontinuous dynamics in the fewest possible trials, providing impressive performance. In this way, Boolean mapping becomes capable of providing global optimum solutions to unit commitment utilizing fully tractable procedures without deteriorating the computational time. The results, considering a non-interconnected power system, show that the proposed model based on artificial intelligence presents advantageous performance in terms of generating cost and complexity. This is particularly important in isolated networks, where even a-not-so great deviation between production and consumption may reflect as a major disturbance in terms of frequency and voltage.