Maximum Power Generation Research Articles

Implementation of String Inverters for 50 MW Large Scale Solar Power Plant in Malaysia

The growth of large-scale solar photovoltaic (LSSPV) power plant site development in Malaysia has increased the demand for the reliability and potentiality of the system in achieving efficient monitoring, operation, and maintenance for the plants. A configuration of string inverters is applied in a 50 MW large-scale solar photovoltaic installation because of its flexibility and scalable technological advancements. This installation demonstrates that the plant can optimise the power output and monitor the performance of the system up to the string level with a cost-efficient implementation. The output from each photovoltaic (PV) string provides a guarantee of better maximum power point tracking (MPPT) capability. Instead of having shorter DC wires compared to the central inverter, this system offers the potentiality of monitoring the solar PV module at a string level, which also affords scalability for future expansion. The tests conducted verify that the string inverter configuration serves as a reliable outcome for grid code test (GCT) requirements. Furthermore, the string inverters provide a potential approach to achieving maximum power generation in different climate conditions, thus reducing the need for manpower in operation and maintenance activities. The string system offers a feasible implementation of effective configurations and decarbonisation for LSSPV in Malaysia without frequent shutdowns of plant generation due to defects or damage to PV equipment and modules.

Design and Implementation of a Control Strategy for a Dynamic Organic Rankine Cycle‐Based Power System in the Context of Industrial Waste Heat Recovery

Transient thermal waste heat sources, such as solar energy, industrial waste heat, and ship exhaust heat, pose significant challenges in terms of their reuse in other processes. Therefore, ensuring the safe and efficient design and operation of the organic Rankine cycle (ORC) system for these heat sources is imperative. Comprehending the ORC system's dynamic characteristics is necessary to formulate suitable control strategies. This article undertakes a comprehensive review of previous studies on dynamic modeling and control of the ORC system and discusses the prevalent dynamic modeling methodologies, the challenges frequently encountered in dynamic simulations, and a range of control strategies. This article comprehensively analyzes the overall control strategies used by ORC systems. Additionally, the energy conversion systems utilized in ORC are depicted in detail. Furthermore, using model predictive control in a simulation study allows for investigating various components’ disturbance and dynamic characteristics within the ORC system. These components include the expander, pump, condenser, and evaporator using setpoint tracking. Ultimately, this analysis aims to optimize the performance of the ORC cycle. The findings indicate that the utilization of superheating in the working fluid is the most effective method for achieving maximum power generation in an ORC system.

Numerical and experimental study of a non-frosting thermoelectric generation device for low temperature waste heat recovery

Liquefied natural gas (LNG) has great potential for power generation using thermoelectric generators (TEG) due to the large amount of cold energy released during LNG gasification, but frosting is a major issue which slows the heat transfer between the TEG and the environment and reduces the power generation efficiency. This study describes a non-frosting cryogenic thermoelectric generator for cold energy recovery. This device uses natural gas vapor on the hot side of the TEG to avoid frosting caused by direct contact of the TEG with air. The device characteristics were modeled using a thermodynamic analysis with the predictions verified by experiments to predict the output characteristics. The results show an optimal channel length for the selected design conditions that maximizes the power generation. Increasing the flow rate and enhancing the heat transfer on the hot side significantly increases the power output. For the design conditions of 1000 Nm3/h gasification capacity and 0.4 MPa working pressure, the optimum channel length is 9.8 m with 350 pairs of thermoelectric modules that gives the maximum power generation of 593.94 W with a 1.36% efficiency.

Reduction in Residential Electricity Bill and Carbon Dioxide Emission through Renewable Energy Integration Using an Adaptive Feed-Forward Neural Network System and MPPT Technique

Increasing electricity demand and the emergence of smart grids have given home energy management systems new potential. This research investigates the use of an artificial neural network algorithm for a home energy management system. The system keeps track of and organizes the use of electrical appliances in a typical home with the objective of lowering consumer electricity bills. An artificial-neural-network-based maximum-power-point-tracking scheme is applied to maximize power generation from photovoltaic sources. The proposed neural network senses solar energy and calculates load requirements to switch between solar and grid sources effectively. The implementation of improved source utility does not require numerical calculations. Traditional relational operator techniques and fuzzy logic controllers are compared with the suggested neural network. The model is simulated in MATLAB, and the results show that the artificial neural network performs better in terms of source switching following load demand, with an operating time of less than 2 s and a reduced error of 0.05%. The suggested strategy reduces electricity costs without affecting consumer satisfaction and contributes to environmental friendliness by reducing CO2 emissions.

New energy grid connection power control method based on predictive tuning performance and embedded system

Nowadays, due to the cleanliness and high efficiency of grid-connected new energy, it has become more and more popular in the market. However, there are still some problems in grid-connected power control and cannot be well supervised. Therefore, this paper studies a new energy grid-connected power control method based on predictive regulation performance and embedded systems, aiming to control new energy grid-connected power through predictive regulation performance and embedded systems. In this paper, the predictive regulation performance and energy conversion rate of the embedded system new energy grid connection are tested. In the experiment, the energy conversion rate was between 60% and 70%, while the traditional new energy grid connection rate was between 40% and 60%. The maximum power generation efficiency of new energy grid-connected with predictive regulation performance and embedded systems was 83%, while the maximum power generation efficiency of traditional new energy grid-connected was 68%. It can be seen from these experimental results that predictive regulation performance and embedded systems have good effects on new energy grid-connected power control.

Research on dust deposition of PV modules based on three-dimensional numerical simulation and its application in installation parameter optimization

Dust deposition on the surface of photovoltaic (PV) modules leads to a decrease in photoelectric conversion efficiency and output power. To identify the dust deposition factors and laws, scholars have conducted numerous field experiments or numerical simulations. However, few scholars have studied the mechanism of dust deposition with three-dimensional (3D) simulation models. The parameters affecting dust deposition vary in different geographical regions, which limits the applicability of research findings on dust deposition to specific geographical areas. To address these issues, this paper first proposes dust adsorption conditions on the surface of PV modules. Based on the main dust deposition factors, a 3D simulation model for dust deposition is established, and the accuracy and effectiveness of the adsorption conditions and simulation model are verified. Then, with a combined approach of orthogonal experiments and 3D simulation analysis, the main dust deposition factors and laws are analyzed. Finally, regional clustering is conducted by dust deposition factors, and the simulation results are applied to the regional optimization of installation parameters. The case study results show that the PV modules have a maximum power generation of 231.91 kWh with an optimal installation angle of 17° and an installation height of 0.5 m in the I category region.

Effect of non-Gaussian coloured noise on ambient energy harvesting

Using nonlinear stochastic dynamics, the impact of non-Gaussian noise is investigated for vibration to electric energy conversion schemes. For the purpose of utilizing noise in energy harvesting, which offers helpful tips for the development of micropower generators of practical importance, we consider that noise can either be additive or multiplicative in nature. We found that when the property of the noise deviates from the Gaussian characteristics, maximum power generation due to autonomous stochastic resonances disappears. Furthermore, in terms of power generation, non-Gaussian noise outperforms the Gaussian noise.

An Investigation on the Electrochemical and Thermal Characteristics of LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 Composite Cathode Materials for Lithium-Ion Batteries in Different Health States

This paper studied the electrochemical performance and heat generation characteristics of 18650 LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 (LMFP/NCM) composite cathode materials for lithium-ion batteries under different states of health (SOH) (98%, 90% and 82%). The discharge performance, temperature rise and heat generation of these three batteries were investigated at different discharge rates and different temperatures. The electrochemical impedance spectroscopy (EIS), internal resistance and entropy heat coefficient at different depths of discharge (DOD) were analyzed. The results showed the internal resistance and irreversible heat generation increased with the decrease of battery SOH value. The entropy heat coefficient significantly increased at 0.5 DOD, and the maximum total heat generation power was also obtained at 0.5 DOD. The maximum heat generation power of 90% and 82% SOH batteries at 0.5 DOD increased by 175% and 208% than 98% SOH battery when discharging at 2 C, respectively. The battery with 82% SOH had the highest temperature rise (7.5 °C) and total heat generation power (3.12 W), and the ratio of reversible to irreversible heat generation was the lowest (0.152) at −10 °C. It provided a theoretical basis for optimizing the thermal management of LMFP/NCM batteries.

Experimental Study on High-Efficiency Microtubular Solid Oxide Fuel Cell Stacks Using Various Fuels

: Microtubular solid oxide fuel cells (mtSOFCs) are a clean and low carbon emission power generation technology that has great prospects in the field of portable power generation. Improving the power generating efficiency and broadening the variety of fuels available can significantly improve the performance of portable power generation devices based on mtSOFCs. In this study, mtSOFC stacks assembled by mounting modular substacks in an array on a designed fuel/air distribution base were tested. By changing the connection of the substacks’ current collection nodes, the influence of circuit connection modes on the overall output performance of the stack was studied. At a temperature of 700℃ and a fuel feed of hydrogen, the mtSOFC stack composed of 6 substacks in series can achieve a maximum power of 296 W. Fuel cascade utilization was realized by transferring the spent fuel gas from the upstream substacks to the downstream substacks. A maximum power generation efficiency of 45.7% was achieved in the two-cascade stack. The feasibility of using liquified fuel gas (ammonia, propane and diesel) as fuel for the mtSOFC stack has also been experimentally verified. A total continuous discharge time of more than 100 h was achieved at an average power ≥ 180 W under different fuel conditions.Key words: Microtubular SOFC; Substack array; Fuel cascade utilization; Liquified fuel gas; Ammonia

Performance evaluation and parametric analysis of nighttime electricity generation system integrating thermogalvanic cells with radiative sky cooling

While distributed power generation has been made possible by photovoltaic cells during the day, there is no comparable option available at night, especially in off-grid areas around the world. In our work, a cost-effective nighttime electricity generation approach based on thermogalvanic cells and radiative sky cooling is proposed. Based on the developed thermal and electrical coupled model, we determine the maximum power point and optimal operating conditions, including the optimal load ratio of 1.2 and area ratio of 1. The calculated results show that, with systematic optimization, a high power density of 900 mW/m2 is achievable for the device by enhancing (suppressing) heat transfer between the hot (cold) side and ambient air, operating at hot and dry conditions, and optimizing the geometry of individual cells. Furthermore, the presented device significantly outperforms the nighttime thermoelectric generator and could directly power various electric devices to meet the power needs of people in remote or off-grid regions. This work identifies how to achieve maximal electrical power generation for nighttime radiative-cooling-assisted thermogalvanic devices, providing a feasible approach for constructing high-performance nighttime electricity generation devices.

Short-term coordinated hybrid hydro-wind-solar optimal scheduling model considering multistage section restrictions

With the large-scale integration of wind and solar power in China, the consumption of these intermittent renewable energies is severely restricted by the capacity of the transmission channel, which leads to massive renewable energy curtailment. Therefore, it would be beneficial to use limited transmission channels to absorb as much renewable energy as possible. In this paper, we propose a chance constraint-based multistage nested hydro-wind-solar coordinated optimal scheduling model to aid peak shaving while ensuring maximum power generation. First, multistage partitioned section chance constraints are introduced to mitigate the power congestion of the transmission network. Then, based on the principle of using hydropower to complement the uncertainty of wind and solar power, the compensation chance constraints are considered. To quantify the uncertainty of wind and solar power, their prediction errors are analyzed using the Gaussian mixture model. Finally, the model is recast and linearized into a mixed integer linear programming model. A case study of a hydro-wind-solar base in Southwest China demonstrates that the proposed model can effectively leverage the regulation ability of hydropower to coordinate multiple power sources with the restrictions of multistage transmission sections, effectively alleviating the congestion of transmission channels and reducing the curtailment of renewable energy.

A LSTM-based approximate dynamic programming method for hydropower reservoir operation optimization

Dynamic programming (DP) is a classical method developed to address the multi-stage hydropower reservoir operation problem, but still suffers from the serious dimensionality problem where the computational burden increases exponentially with the number of state variables. To improve the DP performance, this paper proposes a LSTM-based approximate dynamic programming (ADP) method for complex hydropower reservoir operation optimization. In ADP, the long short-term memory (LSTM) is treated as the response surface model to reduce redundant computations of power outputs in DP’s recursive equation, making obvious improvements in the execution efficiency. To fully assess its feasibility, the ADP method is used to find the scheduling schemes of a real-world reservoir system in China. Simulation results show that compared with the standard DP method, ADP effectively reduces the execution time while guarantee the solution quality in different cases. In the 1000-state and wet-year scenario, the ADP method achieves approximately 86.7% and 85.8% reductions in DP’s computation time for Longyangxia and Laxiwa reservoir with the goal of maximizing power generation. Thus, the LSTM-based response surface model is an effective tool to improve the DP performance in the hydropower reservoir operation problem.

An energy harvesting shock absorber for powering on-board electrical equipment in freight trains

To realize smart detection and safe operation of freight trains, a continuous and stable energy source is required for electrical equipment on the train. It is a feasible scheme to harvest the vibration energy of train suspension to supply power for on-board electrical equipment. This paper presents an energy-harvesting shock absorber (EHSA) based on the slider-crank mechanism and ratchet-pawl mechanism, which provide a vibration reduction effect and renewable electricity. To determine the damping performance and the power generation performance of EHSA, a dynamic model is established based on MATLAB. According to the tests on the mechanical testing and sensing (MTS) bench, the maximum power generation mechanical efficiency of the EHSA is 67.75%, and the maximum output power of the EHSA is 1.65W. In addition, the charging tests on the MTS bench show that the proposed device is applicable to power on-board electrical equipment on freight trains.

Parabolic Mirror‐Assisted Thermoelectric and Radiative Cooling System for Maximizing Power Generation Utilizing Solar and Outer Space Thermodynamic Resources

AbstractRadiative cooling technology is extensively researched as a green technology, leveraging outer space as a thermodynamic resource. Recently, integrating thermoelectric generators (TEGs) and radiative coolers (RCs) are proposed to generate power using the temperature difference between the ambient and the radiative cooler. However, current TEG‐RC systems only utilize one thermodynamic resource, resulting in suboptimal efficiency. It proposes a parabolic mirror‐assisted TEG‐RC system that fully utilizes both the Sun and outer space as thermodynamic resources. The system places the hot side of the TEG, covered by a solar absorber (SA), at the focal point of the parabolic mirror, while the RC is located on the cold side of the TEG. Theoretical and experimental results reveal the optimal ratio between the RC and SA sizes to balance the power mismatch between cooling and heating powers. It also finds that the number of TEG stacks significantly affects power generation efficiency and determines the optimal number. Outdoor measurements demonstrate exceptional power generation during the daytime, which is an unprecedented achievement. This study also demonstrates the further enhancement in power generation efficiency when the proposed system is integrated with concentrated solar cells instead of the SA.

Experimental investigation of the micro-power generation system based on porous burning

This study presents a micro-power generation system based on porous combustion and gasification of biomass energy. The efficient heat exchange between porous combustion-based technology and a free-piston Stirling engine generator (FPSEG) was investigated to convert biomass energy into electric energy. Alumina ceramic particles were used to build a porous medium area within a heat transfer power generation experimental platform to explore the influence of the porous medium bed height and particle diameter, as well as the effect of inlet gas conditions on the system power generation performance. Specifically, the speed of power generation of the system increased and then decreased with an increase in the height of the bed, with maximal power generation speed observed when the height of the bed was 45 mm. The change in the alumina ceramic particle diameter was not significantly related to the rising speed of the system; however, when the diameter of the alumina ceramic particles was less than 15 mm, the flame front could not be stabilized in the porous medium, indicating that a critical particle size existed in the porous medium. Increased gas intake resulted in the power generation rate of the system to first increase and then decrease. When the gas intake was 9.3 m3/h, the power generation rate of change reached 1.2 W/s.

Real-time economic and safe operation diagnosis of hydropower units

In order to ensure the safety and economy of the operation of hydropower units under the big data technology application platform and the automatic generation control system, and to tap the maximum power generation potential of the units, an effective method of economic operation evaluation of hydropower units combined with state diagnosis is proposed. Taking Unit 1 of Longtan Hydropower Station as an example, the economic evaluation and status diagnosis of the daily operating conditions are carried out, the real-time efficiency of the turbine is queried through the unit operating conditions data, and the economic operation of the unit is comprehensively evaluated using the standard water consumption index. In addition, the real-time operation condition of the unit shall be diagnosed according to the working condition zoning diagram, and the economic operation condition shall be combined with the unit state operation condition for joint assessment. It is strictly forbidden to operate the unit in the vibration zone and the vortex zone and it is determined that the unit can maximize the power generation efficiency after operating in the safe operation zone.

Alleviated ammonia crossover in thermally regenerative ammonia-based batteries by optimizing the introduced intermediate-chamber

Thermally regenerative ammonia-based batteries (TRABs) are promising for harvesting low-grade waste heat and their performance is seriously limited by the general phenomenon of ammonia crossover. The feasibility of further promoting the performance of an intermediate-chamber TRAB was investigated in this work. It is highlighted that these investigations are centered around the physical properties and chemical reactions of NH3. To further enhance the alleviation of ammonia crossover, the ammonia absorption capacity of the buffer solution is very crucial. After introducing an intermediate chamber (TRAB-C) between the anode and cathode, ammonia crossover was alleviated in TRAB-C although the voltage output decreased due to the increased ohmic resistance. Concerning the intermediate chamber, the different operating conditions including flow condition, reactor placement, and electrolyte components would not only affect the ammonia crossover but also influence the electricity generation. To obtain the optimized condition, a performance evaluation criterion (PEC) was proposed to assess the electricity generation characteristic based on the maximum power density, energy density, and power generation. The results indicated that TRAB-C with flowing supporting electrolyte had the highest PEC value, suggesting the optimized design for TRABs.

Facilitating the electrochemical characterization and biofilm enrichment through anode modification in microbial fuel cells

Poor anodic performance is a significant obstacle for scalable applications of air-cathode microbial fuel cells (MFCs), and it can be significantly improved with graphite anodic surface modifications. Morphological and electrochemical performance of modified anode with reduced graphene oxide rGO), polyaniline (PANI), carbon nanotubes (CNTs), each mixed with plant powder, showed improvement in electron transfer rate and anodic biocapacitance over unmodified anode in MFC. PANI-modified anode showed maximum power generation (149.7 mW/cm2) over other variations in MFCs. The highest COD percentage removal was obtained by the PAIN-modified anode (91.9%) followed by the CNTs-modified anode (86.7%) and the rGO-modified anode (82.4%). Electrochemical analyses revealed the enhancement in redox current generation with low charge transfer resistance with enrichment in electrogens in anodic biofilm using such anode modifiers, especially PANI, than unmodified anode in MFC. Biological and physiological analyses also support favoring electrogenic microbial activities and community structure towards the electroactive response and secretion of extracellular polymeric substances (EPS) for mediating electrons with modified anode conditions. Thus, the PANI-modified anode can be a promising and a sustainable anode candidate for MFC's long-term stability and scalable applications. Likewise, it could potentially be utilized as an innovative approach for developing a high-performance anode with extended operation for COD reduction in wastewater.

Enhancing the Hybrid Microgrid Performance with Jellyfish Optimization for Efficient MPPT and THD Estimation by the Unscented Kalman Filter

Power management in advanced grid systems requires the seamless integration of diverse renewable energy sources. This study investigates the optimization of a grid-connected system comprising a photovoltaic (PV) solar panel, energy storage system, fuel cell (FC), and diesel generator (DG) using the bioinspired metaheuristic technique called jellyfish optimization (JF). The objective is to maximize power generation from the PV system under normal and partial shading conditions. The performance of JF is compared against particle swarm optimization (PSO) using various parameters. As India heavily relies on solar PV, the results highlight JF’s exceptional effectiveness in extracting maximum power during partial shading scenarios. Inspired by the active and passive motions of jellyfish in the ocean, the JF algorithm is utilized. To further optimize the power output, the system is integrated with an efficient battery management system, PEM fuel cell stacking, and diesel generators. The system’s performance is analyzed using fast Fourier transform (FFT) to evaluate harmonic distortions, which consistently meet the limits specified in IEEE STD 1547-2018. Furthermore, unscented Kalman filter-based analysis is employed to assess total harmonic distortion (THD) and power rating for the grid system across various renewable energy scenarios. The contribution of the jellyfish optimization (JF) algorithm lies in its ability to efficiently and effectively maximize power generation from the PV system, regardless of normal or partial shading conditions. JF, a bioinspired metaheuristic optimization technique, successfully emulates the collective behavior of jellyfish in the ocean to identify optimal solutions. In this study, JF outperforms particle swarm optimization (PSO) in terms of power generation under partial shading conditions. Notably, JF exhibits remarkable capability in exploring the search space and discovering the global optimum, even when the system operates under challenging conditions. Overall, this study demonstrates the tremendous potential of JF in maximizing power generation in grid-connected systems with renewable energy sources while also highlighting the benefits of integrating additional components to further enhance the system performance.

Optimization Model of Photovoltaic Module Cleaning Strategy Based on Particle Swarm Algorithm

Photovoltaic (PV) power generation in desert areas has significant economic benefits. Accumulated dust on PV panels can significantly reduce their power output, so it is necessary to clean them after a certain period of operation. However, traditional periodic cleaning strategies often cannot maximize the economic benefits of PV power generation. Therefore, optimizing the cleaning strategy with equal intervals is essential. In this study, we establish a model to calculate PV power generation revenue and optimize the cleaning strategy based on environmental variables from some PV power stations in central Australia. We consider the cost of cleaning PV panels and the impact of environmental variables such as solar radiation intensity, relative humidity, and wind speed on dust accumulation. This model can calculate the dust accumulation on a specific day based on environmental data and then estimate the corresponding power generation revenue for that day. Meanwhile, a variant of the particle swarm algorithm is used to automatically adjust the cleaning interval under different environmental conditions, achieving the maximum power generation efficiency. After comparison and analysis with different datasets, it is verified that the optimization strategy generated by the proposed algorithm model can increase the economic benefits by more than 70 US dollars than Equal time interval cleaning strategy, which demonstrates its stability and reliability.