Power Generation Units Research Articles

Addressing thermal challenges in hydrodynamic bearings: A contemporary review of strategies and technologies

Hydrodynamic bearings are integral components in various industrial sectors, including aerospace, defense technologies, and power generation units etc. Advancements in modern technology have driven the evolution of bearings to withstand the high temperatures, increased speeds, and additional load demands of contemporary operations. Some of the prime consequences of high temperatures include thermal deformation, misalignment, viscosity degradation, reduced oil film thickness, reduced load carrying capacity and premature failure due to bearing seizure. Researchers have rigorously addressed the persistent thermal challenges posed to hydrodynamic bearings, achieving significant advancements over the years. Key developments include innovative bearing pad designs, optimal materials, coating technologies, advanced computational software, novel lubrication methods, and efficient monitoring techniques. The introduction of materials with tailored properties such as high heat conductivity, high strength, and low-friction coatings has greatly enhanced bearing performance. Additionally, advanced computational software like ANSYS CFX, Fluent, and COMSOL, adaptive lubrication methods, and real-time temperature sensors have significantly improved the performance of critical systems, including hydro power plants. This review paper summarizes significant developments and investigations carried out to mitigate thermal effects in hydrodynamic bearings, and hence provides a comprehensive in depth details of the subject matter. Even though there has been a lot of progress in the field of hydrodynamic bearings, continuous research is important to tackle issues related to sustainability, industry-specific obstacles, developing technologies, and dynamic operating conditions.

An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems

The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO-λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.

Harvesting multidirectional wind energy based on flow-induced vibration triboelectric nanogenerator with directional tuning mechanism

Wind-induced vibration (WIV), as low-velocity wind energy utilization technology in agricultural environment, has significant advantages. Nevertheless, there is a mismatch between the variability of wind direction and the work mode of triboelectric nanogenerator (TENG), which leads to a sharp decline in the performance of triboelectric power generation. This work proposes a TENG based on multidirectional WIV (TENG-WIV), which mainly contains triboelectric power generation unit, guide-wing and triboelectric direction sensor. By introducing the directional tuning mechanism based on guide-wing, the TENG-WIV aims to break the constraints of single wind direction response and effectively respond to the wind direction within 360° range, thereby realizing multidirectional wind energy harvesting and sensor power supply. The empirical findings indicate that the output voltage and current of triboelectric power generation unit are in the ranges of 62–241 V and 0.25–1.21 μA, respectively, at the wind velocities of 1.23–5.13 m/s. At a wind velocity of 3.18 m/s, the unit achieves an out-power peak of 0.09 mW. Furthermore, the triboelectric direction sensor can respond to changes in 8 directions and has wind direction monitoring potentiality. The directional tuning mechanism endows flow-induced vibration energy harvesters with an all-around multidirectional sensitivity.

Closing the Loop between Plastic Waste Management and Energy Cogeneration: An Innovative Design for a Flexible Pyrolysis Small-Scale Unit

This study proposes a simplified unit that can be employed in an industrial facility for the utilization of its own abundant plastic waste, primarily from discarded packaging, to achieve full or partial energy autonomy. By converting this waste into synthetic pyrolysis oil equivalent to 91,500 L, the industry can power a combined heat and power generation unit. The proposed unit was designed with a focus on maintaining high temperatures efficiently while minimizing oxygen exposure to protect the integrity of hydrocarbons until they transform into new compounds. Pyrolysis stands as a foundational procedure, paving the way for subsequent thermochemical transformations such as combustion and gasification. This study delves into the factors affecting pyrolysis and presents analytically the mathematical formulations and relevant calculations in order to effectively design and apply a real-life system. On this basis, fuels from plastic waste can be produced, suitable for utilization in typical equipment meant to produce heat, estimated for six months’ operation and 800 MWh of electricity. This study enhances the transition towards a more circular and resource-efficient economy with technologies that unlock the latent energy contained within the discarded matter. Additionally, it demonstrates the feasibility of a moderate investment in a co-generation system for industries utilizing 568 tonnes of plastic waste per year. The design and accurate calculations of this study highlight the theoretical potential of this technology, promoting environmental sustainability and resource conservation.

Self-powered human movement measurement with a unidirectional ratchet driven wearable energy harvester

A large amount of energy is generated during human movement which is mostly not effectively utilized. To collect human movement energy and utilize it rationally, a wearable piezoelectric-electromagnetic energy harvester (WPEEH) driven by a unidirectional ratchet is proposed, which adopts the principle of up-conversion to convert mechanical energy into electrical energy. The electromagnetic power generator unit is used to obtain energy to power the sensing circuit, while piezoelectric generator unit can be used for both energy generation and self-powered sensing. A spring push rod and a ratchet structure are used to convert the swinging of the human limb into the unidirectional rotation of the device to enhance its energy harvesting efficiency. The theoretical model of the WPEEH is established, and the experimental test platform is built. The results show that the WPEEH is able to generate a maximum power of 17.2 mW at an oscillation frequency of 1.5 Hz. In the actual wearable test, the proposed energy harvester is able to supply the harvested energy to the low-power electronic devices through capacitors, and it enables to light up 42 LEDs as well as to drive the digital thermo-hygrometer to work continuously and stably. Apart from having excellent properties of a high output power and low drive frequency, the proposed energy harvester also exhibits a high energy density (7.1 × 10–5 W/cm3). What’s more, the speed of human movement can be predicted and calculated for different individuals by testing the results of the swing frequency with a maximum error of 2.71 %. The investigation proves that the proposed WPEEH can measure human movement and has great potential in the field of power supply for low-power electronic devices.

Underwater blade-free triboelectric nanogenerator for harvesting current energy in low-speed current

Ocean current energy bears the characteristics of wide distribution and high energy density. Harvesting current energy as the in-situ power for distributed ocean sensors will greatly enhance the sustainability of ocean sensing. In many parts of the ocean, the current speed is not desirable (low or unstable) for conventional current energy harvesting devices. Here, an underwater blade-free triboelectric nanogenerator (UBF-TENG) is developed to effectively utilize the low-speed currents through the flow-induced vibration (FIV). The power generation unit is fully enclosed inside the shell, effectively reducing the direct impact and underwater corrosion. The study demonstrates that within the established experimental parameters space, the optimized UBF-TENG can facilitate a start-up speed as low as 0.14 m/s. The UBF-TENG has achieved a maximum open-circuit voltage output of 250 V and a maximum short-circuit current output of 2.2μA at 0.76 m/s. The charging capacity of the UBF-TENG is tested with various capacitors, demonstrating the application potential of the UBF-TENG as an in-situ power supply underwater.

Fuel-economy-optimal power regulation for a twin-shaft turboshaft engine power generation unit based on high-pressure shaft power injection and variable shaft speed

Due to the high power-to-weight ratio characteristic of turboshaft engines, the power generation unit based on it holds immense potential in the hybrid electric propulsion system (HEPS) of flying cars. To mitigate environmental pollution and reduce fuel consumption, while enhancing the fuel economy of turboshaft engine power generation unit (TEPGU) across the entire power output range, this paper investigates a dual-motor electronic regulation architecture for twin-shaft TEPGU. According to the different variable combinations of TEPGU architecture, two modes and their respective fuel-economy-optimal power regulations are proposed. Initially, by considering the efficiency distribution of motors, we construct an Extended co-working balance model based on the Newton-Raphson method. Subsequently, different operating modes are proposed and discussed, studying the characteristic laws of the power generation unit under each mode. For the variable speed mode and the power split mode, this article proposes a shaft-speed-feature-projective searching (SFPS) method and a multi-dimensions-feature-projective searching (MFPS) method based on characteristic dimensionality reduction projection. Finally, a comparative analysis of the fuel-saving effects of the SFPS and MFPS regulation methods is conducted, and the reasons for enhancing fuel efficiency are discussed. The simulation results show that the model of TEPGU is in good agreement with the experimental data, and the error of specific fuel consumption(SFC) in the full power range is less than 5.7 %. Besides, comparative results indicate that the strategies based on MFPS and SFPS can achieve a maximum instantaneous fuel consumption reduction of 14.38 % and 10.82 %, respectively. The MFPS strategy exhibits a fuel consumption saving of 12.69 % throughout the driving cycle, demonstrating superior performance in fuel economy.

Coordinated planning of thermal power, wind power, and photovoltaic generator units considering capacity electricity price

AbstractWith the implementation of China's carbon reduction policies, the role of thermal power units will transition to a regulating power source. Hence, the electricity market fails to accurately reflect the capacity value of thermal power units, resulting in potential future losses for these units. Therefore, it is imperative to establish a rational capacity compensation mechanism that guarantees the revenue of thermal power units and offers effective investment and construction signals. Therefore, this paper proposes a capacity compensation mechanism for thermal power units based on effective capacity. To achieve this, a two‐layer power source planning model is established. At the upper level of the model, the installed capacity and capacity price of various types of power sources are optimized, while minimizing the operating costs of the planning horizon year under constraints such as annual net profit of units. The lower level focuses on the operation of typical days, optimizing the output of various types of units. Through case analysis, it can be concluded that the proposed model can achieve coordinated planning of capacity and capacity prices for various types of units, effectively ensuring the economic efficiency of the system while safeguarding the revenue of each unit.

Participation of wind-storage combined system in the secondary frequency regulation control strategy of power grid

Abstract The secondary frequency modulation of the unit, that is, the automatic generation control (AGC), is the process of increasing or decreasing the power of the power generation unit by the power plant unit according to the dispatching command issued by the power grid dispatching center, and then using the frequency modulator to restore the frequency. Based on the principle of AGC, this paper establishes a frequency dynamic response model of the secondary frequency modulation of the traditional unit and the wind storage participation system including load frequency control (LFC) and traditional proportional-integral-derivative (PID) controllers, determines the frequency modulation control strategy of wind storage, and uses the distribution mode based on Area Control Error (ACE) signal to assist in the secondary frequency modulation. In order to solve the parameter optimization problem of the classical PID controller when the wind-storage combined system participates in the secondary frequency modulation under the large load disturbance, a parameter optimization model of the controller with PID was established, and the ITAE evaluation index was finally selected as the fitness function by comparing different fitness evaluation indexes. To overcome the problem that the conventional Particle Swarm Optimization (PSO) is prone to local extremum, the inertia weights and learning factors are adjusted nonlinearly to balance and optimize the global and local optimization capabilities and improve the search efficiency, and the parameters of the PID controller are optimized by the improved PSO algorithm. The simulation results show that the proposed algorithm improves the secondary frequency modulation effect under the background of a high proportion of renewable energy connected to the power grid.

Multi-objective Harris Hawks optimization algorithm for selecting best location and size of distributed generation in radial distribution system

Distributed Generating Units (DGUs) are widely used in backup power, renewable energy integration, voltage regulation, and energy storage applications. The DGUs are small-scale power-generating units located near the load centers of a power system, as opposed to large, centralized power plants located far away. However, the DGUs must reduce power losses and environmental impacts and improve the voltage profile. This was achieved by effective optimization of the position and rating of DGU. However, the conventional optimization methods failed to optimize the power losses, voltage-current balancing issues, and harmonic distortions. So, this article presents the selection of the best location and size of DGU in radial distribution systems using a nature-inspired multi-objective Harris Hawks optimization (HHO) algorithm while considering improvements in the voltage profile and reductions in active power losses. The HHO algorithm is inspired by the supportive actions of intelligent birds, such as Harris Hawks, while searching for prey. The distinguished hunting process of Harris Hawks using other family members makes it unique, which is useful for searching for better quality solutions while optimizing multi-objective fitness functions. Fitness function is determined by considering each bus's voltage profile and branches' active power losses. The proposed method is tested on two circle distribution systems (69 bus and 118 bus). The results are compared to those obtained using some of the more well-known optimization techniques given by scholars in recent years. The optimization of seven DGU in 118 bus radial distribution systems resulted in 405.32 kW of active power losses, a 68.78% decrease in losses, and 0.9723 Vs of minimum voltage.

A comparative thermodynamic assessment of microwave-assisted and conventional pyrolysis of biomass in poly-generation systems using coupled numerical and process simulations

Biomass is a unique renewable carbon resource that can be flexibly converted into biofuels, chemicals, and biomaterials using pyrolysis technology. Microwave-assisted pyrolysis (MAP) has gained increasing attention due to its faster and more uniform heating characteristics compared to conventional pyrolysis (CP). However, thermodynamic assessments of biomass MAP-based systems that consider detailed pyrolytic product information are lacking. This study simulated biomass-derived poly-generation systems employing MAP with microwave absorbers (MAP-S) and CP with solid heat carriers (CP-S). The proposed systems included pretreatment, pyrolysis, bio-oil rectification, hydrodeoxygenation, and combined cycle power units, producing biofuels, chemicals, and electricity. To obtain operating information that aligns more closely with engineering reality, pilot-scale numerical models integrating reaction kinetics and transport processes were established for the MAP and CP reactors. These models were validated against lab-scale experimental results after scaling down the models’ geometrical sizes. The results of product yields and energy consumption from numerical simulations were used to support the process simulation. Simulation results showed that the energy efficiency and net power generation efficiency of MAP-S were 59.37 % and 17.78 %, respectively, lower than the 67.19 % and 18.22 % of CP-S. In CP-S, the exergy of materials primarily moved towards the chemicals production units with an exergy efficiency (ηex) of 60.0 %, whereas in MAP-S, more exergy flowed towards the power generation units with a lower ηex of 58.46 %. Notably, the exergy destruction rate (Exd) for CP-S was 3.91 MW, higher than the 3.65 MW for MAP-S. Moreover, the CP-S achieves a higher carbon utilization efficiency at 42.32 %, compared to 35.03 % for the MAP-S. For the pyrolysis units, although the CP unit experienced higher heat losses (0.31 MW) compared to the MAP unit (0.1 MW), the ηex for the CP unit was higher due to a lower Exd. Additionally, CP-S consumed more hydrogen in the hydrodeoxygenation units, whereas MAP-S might achieve self-supply of hydrogen by adjusting operation conditions and technology processes.

Directional Adaptive Triboelectric Nanogenerator with Wind-Wave Synergy.

To enhance the adaptability and synergy of the triboelectric nanogenerator (TENG) in a wave environment, this paper introduces a directional adaptive triboelectric nanogenerator (DA-TENG) with wind-water synergistic action for wave energy collection. An innovative design combining a wind vane on the top and fan-shaped blade electrodes internally allows the DA-TENG to adjust its swinging direction adaptively to align with the direction of wave motion. The internal multiple power generation units work in coordination, effectively addressing the issues of low efficiency associated with spherical TENGs in capturing multidirectional wave energy. The DA-TENG demonstrates superior performance under various wind speed conditions, showcasing its practical application potential. Experimental results show that the DA-TENG, equipped with a single tail wind vane and a 700 g mass block, can achieve an output voltage, current, and charge of 374.97 V, 84.77 μA, and 622.69 nC under a mild wind environment. Its peak power density reaches 7.51 W m-3, enabling successful data transmission for a wireless temperature and humidity sensor and powering 248 light-emitting diodes (LEDs). This research expands the possibilities of omnidirectional wave energy collection and the collaborative operation of multiple power generation units, offering an effective method for powering low-power maritime devices.

PENGARUH INOVASI PRODUK DAN STRATEGI PEMASARAN TERHADAP PENDAPATAN PADA KELOMPOK BINAAN INDONESIA POWER DI DESA RANUKLINDUNGAN GRATI PASURUAN

Product innovation and marketing strategy are considered important factors that can increase the competitiveness and income of the target group. Problems that occur in groups assisted by PT. PLN Indonesia Power Generation Unit (PGU) Grati in Ranuklindungan Village is located at an unstable income that even decreases every month. This research aims to analyze the influence of Product Innovation and Marketing Strategy on Income in the Indonesia Power Assisted Group in Ranuklindungan Village, Grati, Pasuruan. The type of research used in this research is quantitative. The population used is members of the Indonesia Power assisted group in Ranuklindungan Grati Pasuruan Village. The sampling technique used a saturated sampling technique using a sample of 37 respondents. The analysis used is research instrument testing in the form of validity and reliability testing, descriptive analysis, classical assumption testing, multiple linear regression analysis, hypothesis testing and determinant coefficient testing. The research results of the Product Innovation variable partially have a significant effect on Income in the Indonesia Power assisted group in Ranuklindungan Grati Pasuruan Village, this means that Product Innovation plays a very important role in Income. And the Marketing Strategy variable partially has a significant effect on Income in the Indonesia Power assisted group in Ranuklindungan Grati Pasuruan Village, this means that the Marketing Strategy variable also plays a very important role in Income. The determinant coefficient (R2) obtained a value of 0.862 which shows that the influence of Product Innovation and Marketing Strategy can influence Revenue by 86.2%. while other variables outside the scope of the research were 13.8%. Keywords: Product Innovation, Marketing Strategy and Opinion.

Security constrained unit commitment in smart energy systems: A flexibility-driven approach considering false data injection attacks in electric vehicle parking lots

In this paper, a new structure, the so-called FBSCUCFDIP-P/EV is proposed as flexibility-based security constrained unit commitment (SCUC) in the presence of false data injection (FDI) attack into the communication infrastructure of electric vehicle parking lot (EVPL). Herein, the uncertain EVPLs are integrated into the SCUC problem with the aim of reducing the operation cost. It is notable that growing integration of unspecified EVPLs can introduce novel challenges to the power system, significantly impacting its flexibility. In this study, electric vehicles are leveraged as a means to enhance system flexibility. Meanwhile, the FDI attacks in EVPLs can distort the system’s flexibility and lead to inaccurate assessments of the power system’s ability to adapt to changing conditions. In order to model the FDI attack, a bi-level optimization problem based on mixed integer linear programming is formulated. At the upper level, the impact of EVPLs on the flexibility indices of the SCUC is evaluated, and the false data injected into the EVPL is calculated at the lower level. Since both levels of the proposed FBSCUCFDIP-P/EV include discrete variables, a reformulation and decomposition technique is utilized to achieve the optimal solution. Instead, an extreme gradient boosting (XGBoost)-based machine learning method is considered to detection and correction of FDI attack. The proposed approach is tested on the IEEE 24-bus system. The simulation results initially indicate the improvement of the flexibility of the power system in proposed structure. Further, injecting false data into all available EVPLs causes to increase the system operation cost. Besides, false data leads to distorted charging and discharging scheduling of EVPLs; likewise, scheduling and commitment of power generation units also changes. Subsequently, the application of the XGBoost algorithm effectively mitigates the impact of FDI attacks, achieving a maximum accuracy of 85.41%.

Portable Multi-Layer Capsule-Shaped Triboelectric Generator for Human Motion Energy Harvesting.

This paper introduces a novel portable multi-layer capsule-shaped triboelectric generator (CP-TEG), aimed at optimizing the performance of triboelectric generator technology in terms of miniaturization, modularity, and efficient energy collection. The CP-TEG utilizes a unique multi-layer, stacked structure and an elliptical cylindrical design to increase the effective frictional area and enhance power generation efficiency. Its portable design allows for flexible application in various environments and scenarios. Experimental results demonstrate that the CP-TEG can maintain stable and efficient electrical output under various motion amplitudes and frequencies, and it shows good adaptability to the direction of motion excitation. With a motion amplitude of 7 cm and a frequency of 1.94 Hz, the CP-TEG can charge a 220 μF capacitor to 1.3 V within 100 s. The power generation unit's output voltage and current are more than three times higher than that of traditional single-layer contact-separation mode triboelectric devices. Particularly, its performance in harvesting energy from human motion underscores its effectiveness as a renewable energy solution for wearable devices. Through its innovative structural design and optimized working mechanism, the CP-TEG demonstrates excellent energy collection efficiency and application potential, offering new options for sustainable energy solutions and development.

A multi-timescale and chance-constrained energy dispatching strategy of integrated heat-power community with shared hybrid energy storage

The community in the future may develop into an integrated heat-power system, which includes a high proportion of renewable energy, power generator units, heat generator units, and shared hybrid energy storage. In the integrated heat-power system with coupling heat-power generators and demands, the key challenges lie in the interaction between heat and power, the inherent uncertainty of renewable energy and consumers’ demands, and the multi-timescale scheduling of heat and power. In this paper, we propose a game theoretic model of the integrated heat-power system. For the welfare-maximizing community operator, its energy dispatch strategy is under chance constraints, where the day-ahead scheduling determines the scheduled energy dispatching strategies, and the real-time dispatch considers the adjustment of generators. For utility-maximizing consumers, their demands are sensitive to the preference parameters. Taking into account the uncertainty in both renewable energy and consumer demand, we prove the existence and uniqueness of the Stackelberg game equilibrium and develop a fixed-point algorithm to find the market equilibrium between the community operator and community consumers. Numerical simulations on integrated heat-power community show that the proposed solution outperforms the constant curtailment strategy with the daily cost reduced by 14.38%.

CO2 emission characteristics modeling and low-carbon scheduling of thermal power units under peak shaving conditions

To accommodate the high penetration of renewable power, the operational flexibility of thermal power units is highly required by the power system, so thermal power units frequently operate under peak shaving conditions. The CO2 emission characteristics of a thermal power unit are significantly influenced by the load factor, and also by the installed capacity and design parameters of the thermal power unit. Therefore, detailed CO2 emission characteristics of thermal power units should be modeled and considered in the low-carbon scheduling of the power system. In this study, thermal power units are classified by live steam parameters, installed capacities, reheat stages, cooling modes, and operation modes, and the models of detailed CO2 emission characteristics are developed and validated. The turbine isentropic efficiency, boiler thermal efficiency, and auxiliary power consumption are comprehensively considered in the CO2 emission model. A new indicator is defined to evaluate the reduction of carbon emissions in a power system when a TPU accommodates per unit of renewable power generation, and an optimization method for low-carbon scheduling based on it is proposed. Finally, a reference case study is conducted. Results show that the total carbon emissions for a day of the proposed low-carbon scheduling method are 0.06 %−0.20 % less than the efficiency priority method, and 1.53 %−2.36 % less than the capacity weighting method.

Deep neural network accelerated-group African vulture optimization algorithm for unit commitment considering uncertain wind power

The unit commitment (UC) of power systems serves to plan out the starting and shutdown of units and the power generation of units for a future period of time. However, the UC problem for large-scale systems faces the problems of long solution time and insufficiently accurate solution. Uncertainty in wind power generation poses a great challenge in solving the unit combination problem. This work proposes a deep neural network accelerated-double layer optimization method (DNNA-DOM) to improve the solution efficiency, accuracy, and economy of UC. The outer layer of DNNA-DOM utilizes the proposed deep neural network accelerated-group African vulture optimization algorithm (DNNA-GAVOA) to optimize the on-off condition of conventional coal-fired power units. The inner layer of DNNA-DOM adopts quadratic programming to optimize the solution of the economic dispatch problem of load distribution. The GAVOA optimizes the simulated African vultures in four groups to obtain the optimal solution after diverse exploration, exploitation, and exploitation activities, with low complexity and high accuracy. This work first evaluates the performance of GAVOA by solving seven unimodal functions. Furthermore, the 10-unit simulation by incorporating wind power curves and related constraints is optimized through the DNNA-DOM. The results show that GAVOA outperforms the African vulture optimization algorithm (AVOA) and traditional metaheuristics like the particle swarm algorithm and gray wolf algorithm in terms of lower operating cost and optimization stability. The DNNA-DOM combined with DNNA-GAVOA in the outer layer results in an average daily cost reduction of $36.30 and a 45.1997 % speed increase compared to AVOA in the outer layer.

ANALYSIS OF MICRO GAS TURBINE PERFORMANCE USING MODELLING APPROACH

Micro-gas turbine (MGT) is compact and self-sufficient distributed power generation units that exhibit reliability and has the capability to mitigate carbon monoxide (CO) emissions while conserving energy together with biomass used as a fuel sources. It is expected that they will have a noteworthy impact on securing the provision of upcoming energy resources for isolated areas, disaster condition and off-grid zone regardless of their connection to the power grid. However, the MGT performance using the various fuel still questionable. Databases and procedure of the MGT performance analysis should be established well to understand the overall MGT performance. In this study, MGT performance is determined by utilising the computer aided design (CAD) and solver software such as SOLIDWORKS and ANSYS-FLUENT. CAD drawing is used to design the combustion chamber and simulation performance is visualised in specific inside the combustion chamber (CC) of the MGT. The solver software was employed for the purpose of conducting computational fluid dynamics (CFD) analyses. The optimal configuration contained a flame holder with a diameter of 50 millimetres, a chamber height of 60 centimetres, four holes with diameters of 6, 8, and 10 millimetres, and a dead zone separating the combustion and dilution zones. The air inlet boundary conditions were established by utilising the specifications and compressor maps of the Garrett GT25 turbocharger in the simulation. The simulation employed the standard k-ε model of viscous model and energy equation to establish an optimal maximum airflow rate of 0.15 kg/s at a pressure of 1.4 bar, resulting in a compressor efficiency of approximately 70%. Three different fuels, diesel-air, wood-volatiles-air, and coal-hv-volatile, were used to determine the MGT's optimal performance. Successful investigation of this study opts to significant contribution to understand the MGT performance thus, give the awareness to choose as one of the alternative power generations which can be used in the future.

High performance hydrovoltaic devices based on asymmetrical electrode design

As a promising method for energy harvesting, water evaporation can generate electrical energy without additional mechanical energy input. In the evaporation power generation unit based on the principle of flow potential, the low charge collection efficiency is the key factor limiting the energy output, which limits the practical application of water evaporation equipment. Based on the principle of evaporation potential, an asymmetric sandwiched hydrovoltaic device based on metallic aluminum plate and carbon cloth electrodes is designed, which not only matches the electrode, but also reduces the carrier migration route and realizes fast charge transfer and collection. The device achieves an open-circuit voltage (Voc) of 730 mV and a short-circuit current density (Jsc) of 558 μA cm−2, capable of producing output power density exceeding 61.7 μW cm−2. Micro-nano electronic devices with low power consumption can be powered by integration of multiple devices. This study proposes a strategy for clean energy collection based on water evaporation.