Output Power Of Generator Research Articles

A Novel Fractional-Order Active Disturbance Rejection Load Frequency Control Based on An Improved Marine Predator Algorithm

Load frequency control (LFC) serves as a crucial component of automatic generation control in renewable energy power systems. Its primary objective is to maintain a balance between the output power of generators and the load demand, thereby ensuring system frequency stability. However, integrating renewable energy sources into power systems brings forth several challenges, such as low power quality and poor system stability due to their uncontrollable nature. To enhance the response speed, stability, and disturbance rejection capabilities of LFC, a novel fractional-order active disturbance rejection controller (NFOADRC) based on an improved marine predator algorithm (IMPA) has been designed in this paper. By leveraging the wide frequency-response range and non-local memory of NFOADRC, a more precise prediction and compensation of rapid oscillations in the system can be achieved. Additionally, the IMPA can be utilized for efficient parameter tuning, enabling a more accurate adjustment of the controller. Subsequently, the combined application of these approaches can be applied to two-area interconnected power systems with a solar thermal power plant (STPP) and a five-area interconnected power system including a wind turbine generator (WTG), photovoltaic (PV) cells, hydro turbine, and gas turbine. The simulation results confirm that the proposed control strategy effectively minimizes the undershoot and overshoot of frequency deviation in the power system. It achieves a faster stabilization of the load frequency, leading to enhanced power quality.

Dynamic behaviour of automobile thermoelectric waste heat recovery under different driving cycles

Different types of vehicles often run in different driving cycles, resulting in different dynamic behaviours of the automobile thermoelectric generator. The dynamic performance of a simplified automobile thermoelectric generator under different driving cycles, including HWFET, NEDC, WLTP, and CLTC, is evaluated to guide the application of automobile thermoelectric waste heat recovery by a hybrid transient CFD-TE model. The model is experimentally validated with an average output error of 10.92%, mainly caused by the instrument error. The findings indicate that the response of the dynamic output power of the automobile thermoelectric generator exhibits smoother fluctuations compared to the exhaust temperature, and also displays hysteresis in its response, while the dynamic conversion efficiency experiences sharp fluctuations. The behaviour of the automobile thermoelectric generator in dynamic situations is not entirely proportional to the vehicle speed but is instead dependent on changes in vehicle speed, more specifically, the change of exhaust heat. By comparing the steady and dynamic results, it is found that the steady-state model overestimates the output power but may underestimate the conversion efficiency. The automobile thermoelectric generator is more suitable for vehicles with frequent speed changes, such as passenger cars on urban and suburban roads, to enhance the dynamic output performance.

Optimization of Permanent Magnet Synchronous Generator Output Power in Wind Power Plants with ANN Back Propagation

The focus of this research is optimizing a wind power plant using a Permanent Magnet Synchronous Generator (PMSG). The backpropagation method of the artificial neural network system was chosen to optimize the output power of the wind power generator. Based on the simulation results, the backpropagation algorithm of the artificial neural network obtains the output power based on the input variable in the form of changing wind speed. The results show that the best value is learning rate = 0.5, error = 0.0001, max. epoch= 100000, neuron hidden layer = 5. The Mean Square Error (MSE) value obtained is 0.1026 reaching the goal at epoch 14845. The reverse training regretion reaches 0.99917. The optimization results are close to the specified error, which is 0.0001, while what is obtained is 0.0145. The power generated by the wind speed is 10.7 m/s before being optimized using the back propagation neural network method worth 321 watts, while the optimized power results are 409 watts. The difference in the average target power obtained is 88 watts compared to the power of the Artificial Neural Network (ANN).

Efficient thermal management and all-season energy harvesting using adaptive radiative cooling and a thermoelectric power generator

Passive daytime radiative cooling (PDRC) is useful for thermal management because it allows an object to emit terrestrial heat into space without the use of additional energy. To produce sub-ambient temperatures under direct sunlight, PDRC materials are designed to reduce their absorption of solar energy and to enhance their long-wavelength infrared (LWIR) emissivity. In recent years, many photonic structures and polymer composites have been studied to improve the cooling system of buildings. However, in cold weather (i.e., during winter in cold climates), buildings need to be kept warm rather than cooled due to heat loss. To overcome this limitation, temperature-responsive radiative cooling is a promising alternative. In the present study, adaptive radiative cooling (ARC) film fabricated from a polydimethylsiloxane/hollow SiO2 microsphere/thermochromic pigment composite was investigated. We found that the ARC film absorbed solar radiation under cold conditions while exhibiting radiative cooling at ambient temperatures above 40 °C. Thus, in outdoor experiments, the ARC film achieved sub-ambient temperatures and had a theoretical cooling power of 63.2 W/m2 in hot weather. We also demonstrated that radiative cooling with an energy harvesting system could be used to improve the energy management of buildings, with the thermoelectric module continuously generating output power using the ARC film. Therefore, we believe that our proposed ARC film can be employed for efficient thermal management of buildings and all-season energy harvesting in the near future.

Development of forecasting method of time variation of net solar output over wide area using grand data based neural network

This paper focuses on the short-term forecasting of the temporal variation in the net output of photovoltaic power generation across a wide area. Due to the unstable output fluctuations of photovoltaic power generation, thermal power generation is necessary. However, to handle unpredictable power fluctuations, thermal power often operates in a no-load standby mode, resulting in wasteful energy consumption. To address this issue, we have developed a novel prediction method that utilizes neural networks for short-term forecasting of the net output of photovoltaic power generation in a wide area. The key aspect of this method is the utilization of the distributed solar power generation itself as a sensor within the target area, enabling the use of BIG DATA derived from the sensor to predict future net output of solar power generation using a neural network. To expedite calculations, we have incorporated an autoencoder and a decoder. We applied this methodology to northern Kyushu and conducted thorough verification. Furthermore, we compared the persistent model with the smart persistent model and demonstrated their effectiveness as viable solutions.

Predictive evaluation of solar energy variables for a large-scale solar power plant based on triple deep learning forecast models

The advanced development of large-scale solar power plants (LSSPs) has made it necessary to improve accurate forecasting models for the output of solar energy. Solar energy is still hampered by the lack of predictability in its output, which remains a major hurdle in the solar industry. This paper focuses on triple deep learning (DL) techniques such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolutional Neural Network- Long-Short Term Memory CNN-LSTM to address this problem. These techniques are utilized in solar energy variables (SEVs) such as power generation (MWh), soiling loss (%), and performance ratio (PR %) to determine the optimal forecast model. The novelty of this research is that it is the first time that important solar system parameters such as PR and soiling loss have been studied to predict a feasible forecast model using a different DL scheme. The SEVs real-time dataset is procured from the largest solar plant in Pakistan, titled “Quaid-e-Azam Solar Park” (QASP). The main significance of the study is that ANN, RNN, and CNN-LSTM-based models were developed in the DL process through feature generation, data scaling, training, and testing steps to predict the optimal model. The prediction values were compared with the solar plant's actual values over the last 7 years, and then a comparison was made to predict the future forecast trend over the next 20 years. The aim and goal is to develop three models to investigate the accurate results of time-series forecasting on the SEVs dataset, as well as to evaluate the performance measure errors to determine the appropriate model. Based on the forecasting/prediction graphic and error results, it was demonstrated that the CNN-LSTM hybrid model is a more capable forecasting model for the output of power generation and PR values and a more accurate predictor of the future trend over the ANN and RNN models. However, the ANN model is slightly better performed in predicting of soling loss value than CNN-SLTM and RNN. Thus, the CNN-LSTM hybrid model is an optimal model for SEVs, which can guarantee a variety of LSSPs of similar nature in the following time-series forecasting investigations which shows key findings and its importance for the solar industrial forecast issue.

Research on Combination of Distributed Generation Placement and Dynamic Distribution Network Reconfiguration Based on MIBWOA

This paper aims to address the combination of distributed generation placement and dynamic distribution network reconfiguration. Herein, a multi-strategy multi-objective improved black widow algorithm is proposed. A model is established, which considers the objectives of minimizing active power loss, voltage deviation, and carbon emission. The proposed algorithm significantly enhances the traversal capability and search speed by employing Cubic–Tent chaotic mapping, involving a novel formula with the fusion of optimal genes, and employing an adaptive mutation of Wald mutation and elite reverse learning mixing. The DeepSCN is employed to forecast the distributed generation (DG) output power and distribution network load. Through various test functions, the capability of the proposed algorithm is demonstrated. Whether single-objective or multi-objective, the algorithm has excellent performance. To showcase the practicality and effectiveness of the model and approach, a simulation experiment was performed on the IEEE-33 node configuration. The solution set provided by MIBWOA can reduce active network loss to improve operating efficiency, increase voltage offset to make operation more stable, and reduce carbon emissions to make operation more environmentally friendly. The proposed algorithm shows excellent performance in distributed generation placement and distribution network reconfiguration compared with the comparison algorithms. The results show that the solution proposed by MIBWOA can enhance the real-time operational parameters of the distribution network with considerable efficiency.

Supply‐demand optimizer for economic emission dispatch incorporating price penalty factor and variable load demand levels

AbstractThe Economic and Emission Dispatch (EED) method is widely used to optimize generator output in a power system. The goal is to reduce fuel costs and emissions, including carbon dioxide, sulphur dioxide, and nitrogen oxides, while maintaining power balance and adhering to limit constraints. EED aims to minimize emissions and operating costs while meeting power demands. To solve the multi‐objective EED problem, the supply‐demand optimization (SDO) algorithm is proposed, which employs a price penalty factor approach to convert it into a single‐objective function. The SDO algorithm uses a swarm‐based optimization strategy inspired by supply‐demand mechanisms in economics. The algorithm's performance is evaluated on seven benchmark functions before being used to simulate the EED problem on power systems with varying numbers of units and load demands. Established algorithms like the Grey Wolf Optimizer (GWO), Moth‐Flame Optimization (MFO), Transient Search Optimization (TSO), and Whale Optimization Algorithm (WOA) are compared to the SDO algorithm. The simulations are conducted on power systems with different numbers of units and load demands to optimize power generation output. The numerical analyses demonstrate that the SDO technique is more efficient and produces higher quality solutions than other recent optimization methods.

See‐Through Flexible Photovoltaic Modules of Ultrathin Silicon Solar Microcells Enhanced by Synergistic Luminescent and Reflective Near‐Infrared Backplanes

AbstractUltrathin single‐crystalline silicon solar microcells assembled into see‐through and flexible modules have exhibited broadly applicable cost‐effective photovoltaics with reliable durability and efficiency. However, their module output power has suffered from inherent limitations caused by the inferior photon absorption property and the inactive see‐through windows. Herein a strategy is presented to enhance the performance of see‐through flexible ultrathin silicon microcell modules by employing visibly transparent but near‐infrared‐manipulatable optical backplanes, such as a luminescent waveguide gathering near‐infrared photons and a backside reflector mitigating near‐infrared photon escape. Simultaneous integration of these near‐infrared backplanes provides more photovoltaic gains to silicon solar modules than the sum of each backplane capability, and this synergy is due to the promoted properties of the photoluminescence process and waveguiding. The experimental module with ≈3‐µm thick silicon solar microcells can achieve up to ≈40%‐boosted output power generation when assisted by these backplanes. Detailed studies of optical and photovoltaic characterizations for near‐infrared backplanes and their integration modules elucidate the effectiveness of designed backplanes for see‐through flexible silicon photovoltaics.

Study on Abnormality Sign Detection Method of Photovoltaic Power Generation System using Frequency Difference Distribution Index in Power Generation Region

This paper proposes a new method for detecting electrical abnormal signs of photovoltaic module only by measuring the voltage value and current value, which are the power generation output values on the DC side in a photovoltaic power generation system. The proposed method is an abnormal sign detection method for photovoltaic power generation systems that uses the frequency difference distribution index of power generation output in the power generation region for evaluation. This paper first outlines the proposed power generation region as an evaluation window in the anomaly detection method. Next, as an example of an anomaly sign detection method using the proposed power generation region, we show the effectiveness of a quantitative evaluation using a frequency difference distribution index focusing on the occurrence of measured values in the power generation region. Finally, the validity and effectiveness of the proposed method is demonstrated using measured data from actual mega solar data.

Control Technology of Smart Grid Considering Intermittent Power Uncertain Dispatch

The phenomenon of “anti-peak regulation” occurs when large-scale new energy is connected to the grid. The strange phenomenon of wind, light, and water curtailment at low loads occurs in areas with weak power regulation capacity, and the problem of peak regulation of the power system has become increasingly prominent. The typical scenario method is adopted to simplify the description of photovoltaic power generation output and hydropower station runoff. Then, considering the fluctuation of power production and output of the water and photovoltaic complementary system, the short-term multi-objective optimal dispatching model of the water and the photovoltaic complementary system is constructed with the objective of maximizing the average capacity coefficient and power generation load rate of the system on the premise of meeting the corresponding hydropower constraints. Finally, the simulation experimental platform is built. The multi-objective algorithm is used to solve the model. Through the case study, the complementary effect of intermittent power uncertain dispatching is analyzed, and the effectiveness of the model is verified. The dispatching mode can fully guarantee the collaborative optimization decision-making problem of a multi-microgrid with clean power. It can also effectively promote the acceptance level of clean power in the distribution network, and reduce the carbon emission of the distribution network.

Wind farm layout optimization through optimal wind turbine placement using a hybrid particle swarm optimization and genetic algorithm.

The placement and configuration of wind turbines (WTs) are the key factors in determining the performance and energy output of a wind farm (WF). This involves considering various elements such as wind speed, wind direction, and the interspacing between turbines in the design process. To achieve an optimized and consistent wind farm layout optimization (WFLO) for maximum output power, a novel hybrid algorithm hybrid particle swarm optimization and genetic algorithm (HPSOGA), combining particle swarm optimization (PSO) and genetic algorithm (GA), is proposed. HPSOGA can effectively handle problems with multiple local optima, as PSO explores multiple regions and GA refines solutions found by PSO. The framework has two phases, where PSO improves initial parameters in the first phase, and parameters are adjusted in the second phase for improved fitness. The wake effect is analyzed using the Jenson-Wake model, and the objective function considers the total cost of WTs and the power output of the WF. The interspacing of WTs is evaluated by the rule of thumb. HPSOGA outperforms other methods such as GA, BPSO-TVAC, L-SHADE, BRCGA, and EO-PS, producing better results in terms of total output power generation. The simulation results validate the reliability of HPSOGA in WFLO.

Proposing linear structure for annular thermoelectric generators; thermal, exergetic, mechanical, and economic analysis

Annular thermoelectric generators have been the subject of many studies in recent years due to their structure which improves the efficiency when recovering heat from round surfaces. A novel linear arrangement of legs is proposed in this paper which greatly increases the flexibility of design compared to the conventional π structure of annular thermoelectric generators. Every geometric feature of the legs in the linear annular thermoelectric generator can be changed and optimized independently. Numerical simulations are carried out to study the effect of various parameters such as height ratio (θh), total height (h), angle ratio (θφ), total angle (φ), thickness ratio (θt), total thickness (t), hot and cold side temperature on the output power, efficiency, mechanical and economic performance of the linear shape annular thermoelectric generator. Results indicated that the height ratio, angle ratio, and thickness ratio of 0.5 lead to the highest conversion and exergy efficiencies while producing the lowest thermal stress on the legs and having the least cost per watt of output power. Additionally, it is found that the increment of the total combined height, total angle, or total thickness of the thermoelectric legs increases the costs per watt of output power. For example, the dollar/watt value is grown by 7% when the total thickness is increased from 8 mm to 16 mm for an angle ratio of 0.5. The increase of the heat source temperature can boost the output power, and conversion efficiency, and lower the cost but also impose larger stress as well.

Transregional electricity transmission and carbon emissions: Evidence from ultra-high voltage transmission projects in China

Ultra-high voltage (UHV) transmission projects provide an effective way to alleviate the reverse distribution of energy in China, but do they reduce regional carbon emissions? This paper takes UHV transmission projects as a quasi-natural experiment and adopts a time-varying difference-in-differences (DID) method to test the effect of trans-regional electricity transmission on carbon emission performance and its transmission path. The empirical results show that (1) UHV transmission projects have significantly reduced the amount and intensity of regional carbon emissions, and this conclusion is still valid after a series of robustness tests. (2) UHV transmission projects can reduce regional carbon emissions by promoting energy substitution, power generation capacity, industrial agglomeration, and output efficiency. (3) The carbon reduction effect of UHV transmission projects is more significant in regions with high energy dependence and regions with clean power generation at power output. The findings of this paper not only enrich the literature on infrastructure investment and carbon emission reduction but also provide an empirical reference for the government to orderly promote the new infrastructure investment direction.

Advancements in solar technology, markets, and investments – A summary of the 2022 ISA World Solar Reports

This paper provides a summary of the Annual World Solar Reports on Technology, Markets, and Investments published by the International Solar Alliance (ISA) in October 2022. Solar has emerged as the technology of choice to drive the renewable energy transition. This preference for solar has been driven by technology maturity and improvements, cost reductions, and improved methods for grid integration of solar generation. Globally, solar has grown nearly 20 fold in the last decade to reach 920 GW of installed capacity in 2021. As solar approaches and crosses into Terawatt scale of deployment, a number of technological innovations are emerging to continue improving generation efficiency, power output, and material consumption. Additionally, manufacturing capacity is growing rapidly to meet demand for installations. The market for solar installations continues to grow around the world but will need to scale rapidly to meet net zero requirements. The growth in solar markets will also require a significant scale up in solar investments across the world through a wide range of instruments. The deployment of the correct solar related interventions can ensure that the world can achieve multi terawatt scale of solar deployment in the coming decades, thus supporting the global energy transition.

Design of Cost-Effective Microcontroller Based Load Controller with Data Logging for Standalone Electric Plant

Maintaining the voltage and frequency fluctuation using a hydraulic governor in Standalone Micro- Hydro Projects (SMHEP) is costlier than the generator itself. So, an Electronics Load Controller (ELC) is used to regulate output power of generator used in the SMHEP. Developments in the ELCs will play an important role in increasing the efficiency and life span of the power plant thereby providing a lot of space for the further improvements. This paper analyzes the current trends and tries to identify their limitations and suggest a novel approach in designing a cost effective ELC system with the capabilities of data logging. In this study software simulation was carried out in Proteus, while the experimental model was designed using microcontroller and integrated sensor system. Both the software and hardware implementations showed positive results. The experiment was carried out under laboratory conditions in which the proposed model successfully diverted the excess power to connected dump loads and allowed the generator to generate the power at full load with efficient data logging and graphical user interface. Keyword: Data Acquisition, Electronic Ballasts, Load Management, Sensor Systems and Applications, Standalone Power Supply

Photovoltaic power generation and charging load prediction research of integrated photovoltaic storage and charging station

Aiming at the obvious randomness and intermittent problems of photovoltaic power generation output and charging load of photovoltaic storage and charging station, a photovoltaic power generation prediction model based on long short-term memory neural network and a charging load prediction model based on BP neural network are proposed respectively. In the photovoltaic power generation prediction model, the Pearson correlation coefficient is used to analyze the influencing factors of photovoltaic power generation power, and the factors with high correlation are used as the input variables of the long short-term memory neural network model. Then the data is divided into four types of seasons by K-means cluster analysis, and the processed data sets are put into the prediction model for training. In the charging load prediction model, the processed historical load data is put into the time series-based BP neural network for iterative training, meanwhile the weights and thresholds of the model are adaptively optimized. Finally, the simulation results verify the extremely high prediction accuracy of the proposed prediction model. The results also show that compared with PSO-BP, GA-BP neural network, this charging load prediction model has higher precision and stability.

Wind curtailment mitigation in presence of battery energy storage and electric vehicle: A comprehensive multi-objective decision-support framework

Concern to maintain exploitation of power system in secure mode, the wind curtailment (WR) caused by the perpetual fluctuating of wind power (WP) can menace the operational power system schedule. The mentioned phenomenon will intensify by high WP penetration into the power system. In addition, incorporating plugged-in hybrid electric vehicles (PHEVs) into the power system along with the WP intermittent will increase the vagueness rating of the problem. Due to the expansion and development of battery energy storage (BES), the possibility of power shortage compensating and accumulating additional power produced by wind farms (WFs) has been engendered. Providing a stochastic scenario-based structure with different correlation levels can increase the robustness of the model. Here, a two-stage model has been proposed to overcome such a high-dimensioned problem. The first stage determined the scheduled power output of thermal generators (TGs) as the unit commitment (UC) problem to minimize operational costs. Next, by reckoning the supplementary constraints, the scheduled WP, which is re-optimized, will be obtained. The second stage has employed to detect the BES capacity. In this stage, the intelligent parking lots (IPLs) assign price-based signal impetus to the PHEVs to participate in economic dispatch. Due to the prominence of being equipped with the supreme decision-making facility for the intelligent decision-maker operator (IDMO), the augmented epsilon-constraint method has been employed to capture the Pareto sets of some multi-objective functions. The optimum solution has been selected by the decision-making method (DMM) according to the operator's preferences. The results of proposed Multi-Objective Decision-Support (MODS) model show that the total operation cost and BES sizing have dropped by 68 and 6.1 percent, respectively. The performance of the proposed model has been testified through the IEEE-30 bus system via GAMS software.

A Probabilistic Modeling Method for Joint Output of Multi Energy Combined Generation System

Existing methods cannot accurately establish the joint output probabilistic model of the Wind-PV-CSP multi-energy complementary generation system. Therefore, based on the analysis of the correlation and complementarity among wind power, PV, and CSP output, a method of establishing the joint output probability model of Wind-PV-CSP multi-energy complementary system by using Copula theory and Gaussian mixture model is proposed. Considering the operational history data of wind power, PV, and CSP, the Copula function based on the correlation between PV and CSP output is adopted to establish the probabilistic model of CSP-PV combined output. Moreover, the probabilistic model for the joint output of PV-wind power generation is set up using the mixed Gaussian model based on the complementarity of wind power and PV output. Subsequently, based on the probabilistic models for the combined outputs of wind power-PV and CSP-PV, the probabilistic model for the combined output of wind power-PV-CSP mixed Gaussian model was established. Moreover, three evaluation metrics-the Nash efficiency coefficient (NSE), root mean square error (RMSE), and Euclid coefficient, are used to judge the quality of the model. The operational history data of CSP-PV-wind power in Jiuquan high proportion renewable energy base is taken as examples; the simulation results verify the validity and usability of the proposed method.

Flexible Wearable Electronic Fabrics with Dual Functions of Efficient EMI Shielding and Electric Heating for Triboelectric Nanogenerators

Traditional conductive fabrics are prepared by the synthesis of conductive polymers and the coating modification of metals or carbon black conductive materials. However, the conductive fabrics cause a significant decline in performance after washing or mechanical wear, which limits their application. Moreover, the single function of the traditional conductive fabric is also the reason that limits its wide application. In order to prepare a wearable, stable, high-performance, washable, multifunctional conductive fabric, we have carried out related research. In this work, polydopamine was used as a bonding layer, an adsorption reduction layer, and a protective layer to improve the bonding between silver nanoparticles and carbon nanotubes (CNTs) on the polyester fabric surface so as to prepare a multifunctional conductive fabric with a high-stability "sandwich" structure, in which a Ag-NPS@CNT structure acting as an intermediate conductive layer formed on the inner layer PDA@CNT by electroless silver plating and the outermost layer PDA@CNT coated on the surface of the intermediate conductive layer by the impregnation-drying method. The sheet resistance of an E-Fabric can reach 2.11 Ω/□ due to the uniform and dense conductive path formed by the special structure Ag-NPs@CNT. At a low voltage of 1.5 V, the E-Fabric can reach 117 °C in 50 s and remain stable. The electrical conductivity and current heating properties of the E-Fabric remain good even after multiple washing or bending tests. Due to its stable and outstanding electrical conductivity, the E-Fabric has an electromagnetic shielding efficiency (SET) of 35.3 dB in the X-band (8.2-12.4 GHz). In addition, E-Fabric-based spin-coated poly(methyl methacrylate) or polydimethylsiloxane electrodes exhibit excellent performance in nanogenerators. Through the low-frequency friction of the human body, transient voltages up to 4 V can be generated from a 2 cm × 2 cm electrode sample. The output power of a single generator can reach about 12 nW/cm2. Therefore, an E-Fabric is considered to have great potential in the fields of electric heating, electromagnetic shielding, and smart wearable devices.