Future Power Generation Research Articles

Optimal reservoir operation using the improved multi-step-ahead time-varying hedging rule under climate and land-use changes

ABSTRACT This study aims to optimize Yeywa Hydropower Reservoir (YHR) operation using the multi-step-ahead time-varying hedging (TVH) rule under climate change (CC) and land-use change (LUC) to improve summer power generation. The performance of three multi-objective algorithms – the Multi-objective Salp Swarm Algorithm (MOSSA), the Multi-objective Antlion Optimizer (MOALO) and the Non-dominated Sorting Whale Optimization Algorithm (NSOWA) – are compared. MOSSA provides the best solutions with a higher mean hypervolume in a shorter computation time, and it is utilized to optimize the TVH rules for four periods: monthly (TVH-1), quarterly (TVH-3), half-yearly (TVH-6) and yearly (TVH-12). The six-month-ahead TVH-6 rule and the five-month-ahead TVH-6 rule generate the highest summer power for the historical period (2011–2020) and the future period (2020–2059), respectively. The future decadal power generation is expected to be higher than the historical power generation. The future TVH-6 rule is more reliable and it has lower water deficits than the historical YHR operation rule.

A novel data-driven seasonal multivariable grey model for seasonal time series forecasting

Rapid growth in solar power generation, accompanied by random fluctuations, has important implications for the security, stability, and productivity of the grid. Therefore, accurate predictions of solar power generation provide an essential benchmark for relevant institutions to conduct grid planning and power dispatch. Thereby, a novel data-driven seasonal multivariable grey model is proposed for simulating seasonal fluctuations and non-linear trends in solar power generation. To be specific, the novel model contains the following refinements: first, the seasonal factors and time-power item are introduced to modify the insensitivity of traditional multivariable grey models to seasonal fluctuations and non-linear trends. Second, to enhance the generalizability and adaptability of the model, the parameter associated with the time-power item is determined by using the genetic algorithm. Third, on the premise of proving the model’s requirements for period length and sample size, the derivation of the time response function is expounded. For illustration and verification purposes, comprehensive experimental studies are conducted with quarterly and monthly time series samples. In addition, forecasts of the novel model are compared with five prevailing models, including grey prediction models, traditional econometric technology, and artificial intelligence. Case studies indicate that this novel model exhibits improved generalizability, stability, and reliability in the face of quarterly or monthly time series data, confirming it as an auspicious and powerful tool for future solar power generation forecasting.

Revolutionizing Solar Power Production with Artificial Intelligence: A Sustainable Predictive Model

Photovoltaic (PV) power production systems throughout the world struggle with inconsistency in the distribution of PV generation. Accurate PV power forecasting is essential for grid-connected PV systems in case the surrounding environmental conditions experience unfavourable shifts. PV power production forecasting requires the consideration of critical elements, such as grid energy management, grid operation and scheduling. In the present investigation, multilayer perceptron and adaptive network-based fuzzy inference system models were used to forecast PV power production. The developed forecasting model was educated using historical data from October 2011 to February 2022. The outputs of the proposed model were checked for accuracy and compared by considering the dataset from a PV power-producing station. Three different error measurements were used—mean square error, root-mean-square error, and Pearson’s correlation coefficient—to determine the robustness of the suggested method. The suggested method was found to provide better results than the most recent and cutting-edge models. The MLP and ANFIS models achieved the highest performance (R = 100%), with less prediction errors (MSE = 1.1116 × 10−8) and (MSE = 1.3521 × 10−8) with respect to MLP and ANFIS models. The study also predicts future PV power generation values using previously collected PV power production data. The ultimate goal of this work is to produce a model predictive control technique to achieve a balance between the supply and demand of energy.

Design and Optimization of an Integrated Resonant Inductor With High-Frequency Transformer for Wide Gain Range DC–DC Resonant Converters in Electric Vehicle Charging Applications

Large-scale adoption of wide band gap semiconductors in switched mode power converters enables miniaturization of power electronic systems. With switching frequencies approaching a few hundred kilohertz - megahertz range, efficient and compact design of magnetic components are crucial for future generation power supplies in data centers or electric vehicle (EV) applications. In a typical two-stage AC-DC power supply, the DC-DC stage is often constructed using a resonant converter with galvanic isolation provided by a high-frequency transformer. In such systems, the integration of an inductor in the resonant network with the transformer helps improve power density and reduces component count and cost. However, the integration of magnetic components can result in increased losses, deteriorating the performance of the power conversion system. In this paper, a magnetic structure along with an optimization methodology is presented to integrate a predefined resonant inductor as leakage inductance of the high-frequency transformer achieving minimum penalty on converter efficiency using planar core geometry and printed circuit board (PCB) based windings. The proposed methodology is applied to integrate a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$7.8 \mu \rm{H}$</tex-math></inline-formula> inductor with a 2:1 transformer for a 6.6 kW, 500 kHz LCL-T resonant DC-DC stage utilizing GaN transistors in an onboard battery charging converter. On the prototype converter using the optimized integrated magnetic component, a peak efficiency of 98.4% was obtained using PCB windings. Furthermore, the integrated magnetic component achieved 64 kW/L power density in the demonstrator prototype.

Reduced Mechanism for Combustion of Ammonia and Natural Gas Mixtures

A fuel mixture of ammonia and natural gas as a low-carbon alternative for future power generation and transportation is an attractive option. In this work, a 50-species reduced mechanism, NH3NG, suitable for computational fluid dynamics simulations (CFD), is developed for ammonia–natural gas cofiring while addressing important emission issues, such as the formation of nitrogen oxides (NOx), soot, carbon monoxide, and unburnt methane/ammonia. The adoption of reduced mechanisms is imperative not only for saving computer storage and running time but also for numerical convergence for practical applications. The NH3NG reduced mechanism can predict soot emission because it includes soot precursor species. Further, it can handle heavier components in natural gas, such as ethane and propane. The absolute error is 5% for predicting NOx and CO emissions compared to the full Modified Konnov mechanism. Validation with key performance parameters (ignition delay, laminar flame speed, adiabatic temperature, and NOx and CO emissions) indicates that the predictions of the reduced mechanism NH3NG are in good agreement with published experimental data. The average prediction error of 13% for ignition delay is within typical experimental data uncertainties of 10–20%. The predicted adiabatic temperatures are within 1 °C. For laminar flame speed, the R2 between prediction and data is 0.985. NH3NG over-predicts NOx and CO emissions, similar to all other literature methods, but the NOx predictions are closer to the experimental data.

Multi-objective optimal droop control of solid oxide fuel cell based integrated energy system

Solid oxide fuel cell (SOFC) based integrated energy system (IES) is promising in the future low-carbon power generation market, due to the high efficiency and flexibility. However, it is challenging for the dynamic control design in dealing with the conflicting objectives in terms of fast power tracking and overall efficiency during the transient process of load response. To this end, this paper develops a multi-objective optimal droop control strategy for the real-time power dispatch of the IES. Firstly, a nonlinear implicit dynamic model consisting of SOFC, lithium-ion battery, photovoltaic array and DC-DC converter is developed. Then, a multi-objective optimization is formulated to balance the power tracking performance and transient efficiency. Non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to search the optimal parameters for droop controller. Simulation results demonstrates that the electricity loss of the proposed method can be reduced by 96.26% with a slight compromise in power tracking performance.

Dynamics between earthquakes, volcanic eruptions, and geothermal energy exploitation in Japan

Intruding magma brings high temperatures close to the surface, thus offering possibilities for harnessing large amounts of heat for geothermal exploitation. Mount Aso in southern Japan showed frequent volcanic activity during 2016, accompanied by significant earthquake activities with tens of thousands of aftershocks (Kumamoto sequence). Here we investigate the influence of earthquake/volcanic activity on the future productivity of nearby geothermal power plants to determine whether the activity is detrimental or beneficial to energy exploitation. Model results show an increase in {text {CO}}_2 pressure and temperature with a spatio-temporal correlation between modeled earthquake locations and aftershock decay rates along the entire sequence, showing that seismic activity opened pre-existing vertical cracks providing pathways for the ascending magma. Interestingly, the minor but still significant eruption of Mount Aso in October 2021 may have enhanced future geothermal power generation, indicating a vigorous and active system, possibly increasing the future geothermal power production.

Low‐Cost and Biodegradable Thermoelectric Devices Based on van der Waals Semiconductors on Paper Substrates

We present a method to fabricate handcrafted thermoelectric devices on standard office paper substrates. The devices are based on thin films of WS2, Te, and BP (P‐type semiconductors) and TiS3 and TiS2 (N‐type semiconductors), deposited by simply rubbing powder of these materials against paper. The thermoelectric properties of these semiconducting films revealed maximum Seebeck coefficients of (+1.32 ± 0.27) mV K−1 and (−0.82 ± 0.15) mV K−1 for WS2 and TiS3, respectively. Additionally, Peltier elements were fabricated by interconnecting the P‐ and N‐type films with graphite electrodes. A thermopower value up to 6.11 mV K−1 was obtained when the Peltier element were constructed with three junctions. The findings of this work show proof‐of‐concept devices to illustrate the potential application of semiconducting van der Waals materials in future thermoelectric power generation as well as temperature sensing for low‐cost disposable electronic devices.

Geochemical investigation on the implications of fluid origin, subsurface processes and recharge on the Tangkuban Perahu geothermal conceptual model

Tangkuban Perahu and nearby volcano-hosted geothermal prospects are believed to be important for future power generation in West Java, Indonesia. Recharge areas of these prospects and geothermal fluid evolution are not yet fully understood. This study aims to describe the fluid origin, processes, and evolution of geothermal fluids concerning the recharge and the conceptual model of the Tangkuban Perahu Geothermal Systems (TPGS). Sampling and analyses of geothermal waters, rainwater, and gases collected in fumaroles and other manifestations were implemented in this study. Isotopic results of δ18O and δ2H collected from thermal and rainwater indicate deep-circulating meteoric origin, at an infiltration altitude range between 753 and 850 m in low terrain. Meanwhile, the high-standing terrain indicates the recharge zone to be within the wall of the Sunda Caldera and mountain slopes. Chondrite normalized patterns reveal an overall flat trendline that suggests the enrichment of Lower Rare Earth Elements (LREEs) in the central part of Tangkuban Perahu which is derived from the magmatic influx. The non-atmospheric gases origin was diagnosed by the lower N2/Ar (≤ 38) and is characterized by meteoric origin. The positive δ18O shifts from Ciater (CT), Batu Gede (BG), and Domas Crater (KwD) geothermal waters samples comparatively to the local meteoric lines are influenced by the interaction with wall rocks or magmatic influx. In these geothermal systems, the meteoric water infiltrated to the depth is heated, mixed with near-surface water, and followed by partial discharge as thermal springs along the faults. The resultant Tangkuban Perahu conceptual model has six distinct reservoirs with five geothermal systems hosted in the sedimentary (northern parts) and volcanic rock composition in the central and southern parts.

Wind Power Systems Distribution and Optimization in Mainland China

With the fast development and technology evolution, the energy consumption in China has gradually increased, and energy depletion has become a main concern. In recent years, with the promulgation of new energy policies, wind power plant, as a clean energy source, has gradually attracted people’s attention, and wind power generation technology has been progressed and developed. This paper will assess and summarize the wind capacities and results of this operation through observation and comparison of the outcome in different areas. Factors for the distribution of systems are analyzed, providing an explanation for the current state of capacity distributions in the provinces. Comparing the installed wind power systems in east and west China, east China was perceived to have a larger capacity. Further investigation into the reasoning has led to the high wind curtailment rate in different areas of China. Possible reasons were provided, and solutions were given. Wind curtailment and even energy distribution will also be discussed, as well as suggestions for future technology development and power generation planning.

Research on market trading strategy of multi-microgrid intelligent power distribution system based on Bi-level optimization

With the continuous promotion of the “dual carbon” idea, future power generation will rely heavily on renewable energy sources. As an effective utilization form of clean power sources, it is of positive significance to study the trading strategy of microgrids in the intelligent power distribution system under the influence of carbon quota. In this research, a bi-level optimization method is used to build a trading model of the distribution-side power market, with the upper-level planning aiming to reduce the cost of distribution system operators and the lower-level planning aiming to increase the revenue of microgrids. Secondly, the genetic algorithm and sequential quadratic planning algorithm are applied to the upper and bottom models to determine the optimal clearing strategy for the microgrid and the optimal scheduling scheme for the distribution system operator, respectively. Finally, a typical day is used as an example to analyze in detail the market trading strategy of a multi-microgrid intelligent distribution system under the influence of carbon quota, and the effectiveness of the method described in this paper is verified.

Integrating Machine Learning Algorithms for Predicting Solar Power Generation

In recent years, there has been a growing interest in using artificial intelligence (AI) techniques to predict solar power generation. One such technique is the use of an artificial neural network (ANN) with a genetic algorithm (GA) to optimize its parameters. This approach involves training an ANN to predict solar power generation based on historical data and using a GA to optimize the ANN’s architecture and activation function. The GA searches for the best combination of hidden layers and activation functions to minimize the error between the predicted and actual solar power generation. This paper presents an algorithm for implementing an ANN-GA for predicting solar power generation. The algorithm involves preprocessing the data, defining the ANN architecture, defining the fitness function, and implementing the GA to optimize the ANN’s parameters. The results of this approach can be useful for predicting future solar power generation and optimizing the performance of solar power systems.

Comparison and Analysis of Three Different Power Generation Methods under the Context of Dual Carbon Goals

Traditional thermal power generation is characterized by mature technology, wide application, and high carbon emissions. Under the context of dual carbon goals, there is an urgent need for technological innovation in decarbonization. This paper aims to systematically analyze and compare three power generation methods, namely coal-fired power generation by steam turbines, power generation by natural gas turbines and fuel cell power generation. An outlook for future power generation technologies is presented at the end.

Energy storage performance improvement of phase change materials-based triplex-tube heat exchanger (TTHX) using liquid–solid interface-informed fin configurations

The future of power generation is predicted to be greener with greater uptake in renewable energy which will be more intermittent and make matching demand harder. Latent thermal energy storage using phase change materials (PCMs) could provide a solution to that problem. PCMs can store large amounts of energy in small volumes, however, the main issue is the low conductivity of PCMs, which limits the rate that energy can be stored due to the slow melting and solidification processes. In the present study, we design and optimise a flow structure-informed fin configuration for triplex-tube heat exchangers (TTHXs) that accelerates the PCM melting process to allow for faster energy storage rates. The computational fluid dynamics modelling is developed to evaluate different TTHX geometries and find the most efficient fin geometries. The results show that an optimal fin geometry can provide a 57.4 % reduction in melting time, which demonstrates that fins offer a very effective way of reducing the melting times of PCMs without taking up much volume. We also found that fins in the lower half of the PCM are more effective in reducing total melt time than fins in the upper half. It is revealed that curved fins offer better performance when compared to similar straight fins. Lastly, it is observed that horizontal or angled surfaces allow for more natural convection in TTHX. The findings can be used to inform future studies on energy storage performance improvement of PCMs.

Versatile Economic Risk Tool

The foundation of the Versatile Economic Risk Tool is based on the methodologies of the Generation Risk Assessment (GRA) and uses a systematic approach to prioritizing systems, structures, and components based on estimated operation reliability and the impact on future power generation. The GRA provides management with insight that helps with decisions on maintenance, component repair/replacement, design modifications, and other issues associated with electricity generation and equipment reliability programs. Although the benefit of the GRA is understood in the nuclear community, there are negative aspects associated with the current approaches. Traditional GRA modeling is time consuming, convoluted, and considers only the static portion of generation risks. The Versatile Economic Risk Tool addresses the drawbacks of the traditional GRA through simplifying and/or automating the requirements for an effective GRA model. The Versatile Economic Risk Tool also integrates in-depth parametric variability within models to evaluate system, structure, and component generation risks as a function of time. The Versatile Economic Risk Tool addresses the shortcomings of the GRA by automating the study of equipment reliability and degradation from an electricity generation perspective by coupling fault tree analysis and statistical analysis software that support repetitive, parametric analyses. Versatile Economic Risk Tool case studies are presented to provide insight into the potential benefit provided.

The Role of Micro Gas Turbines in Energy Transition

In the progressively rising decentralized energy market, micro gas turbines (MGT) are seen with great potential owing to their low emissions, fuel flexibility, and low maintenance. The current transformation in the landscape of electricity supply with an increasing share of fluctuant renewable energy resources and increasing complexity requires a reliable and energy-efficient power generation source to support the grid. In this scenario, small-scale power plants that are constructed based on micro gas turbines with up to 250 kW power range can play a substantial role in meeting the challenges of the modern electricity grid. Micro gas turbines provide a reliable and cost-effective power source with a quick load-following ability which can respond to demand peaks and compensate for intermittent renewable sources when they are not available. MGT units can work as a system together with renewables, or function as a stand-alone unit in off-grid operations. The features of micro gas turbines are compatible with the energy transition that is the carbon-free modern energy grid. The technology underlying MGTs offer hybridization with renewable energy sources, flexibility in operations and type of fuel, and promising low emission solutions that align with environmental concerns. However, there is a continuous need to improve energy efficiency with a pressing urge for reducing emissions. This paper provides a review of micro gas turbines’ characteristics which promote their role in future power and heat generation systems. A brief overview of the challenges to improving operational flexibility, reliability, and availability of MGTs while maintaining low environmental impact and lowering the costs is presented. A model for an active monitoring and control system of the micro gas turbines is proposed which could improve the reliability of MGT operation in the grid by means of AI methods.

Computational model for thermohydraulic analysis of an integral pressurized water reactor with mixed oxide fuel (Th, Pu)O2

The use of advanced generation III+ and IV nuclear reactors, and their applications, has become important, seen as a means capable of contributing to the global transition to more sustainable, affordable and reliable energy systems. This technology, which could be integrated into future carbon-free electric power generation systems with high proportions of different renewable energy sources, includes Small Modular Reactors (SMR). There are about 100 different proposed projects of Generation III+ and IV, of which about 50 are SMR concepts, in various stages of development and of different types of technologies. Other important issues for achieving the long-term sustainability of nuclear energy are the proper use of its fuel sources and the improvement of nuclear waste management. Therefore, fuels based on a mixture of oxides have been used successfully in several countries. In addition, the incorporation of thorium-based fuel is a current challenge for the new designs of advanced reactors. The present paper focuses on the analysis of a small modular integral pressurized water reactor (iPWR) with Thorium-Uranium Oxide (Th-U MOX) mixtures. A thermohydraulic model is developed using the Ansys CFX program, which allows the calculation of the temperature distribution in the section where the highest power is produced within the SMR IPWR core (critical section). The temperature distributions in the fuel, clad and coolant were calculated with the objective of verifying that they were within the safety limits.

Future wind speed trends in the Indian offshore region

Climate models help assess the future availability of wind speeds to extract wind power; however, these climate models are mathematical models that include uncertainty in wind forecasts. Finding an appropriate climate model for analyzing future wind power generation is critical. Therefore, in the present work, six Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) climate models and their ensembles were statistically examined in the Indian offshore region using in-situ buoy readings and ERA5 reanalysis data. Statistical parameters like correlation coefficient, bias, RMSD, and standard deviation are computed to examine the applicable model at buoy locations. With ERA5 wind speeds, the overlapping percentage of climate models is later analyzed for the Indian offshore regions. The ensemble model statistically outperforms individual climate models at buoy sites, with 77% overlap with ERA5 wind speeds in the offshore region. Further, the trends and cumulative variations in wind speeds are calculated for ensemble models under two emission scenarios RCP (Representative Concentration Pathway) 4.5 and RCP 8.5. In the future, the North-East (NE) zone will have the most advantageous change in wind speeds (0.21 to 8.68%), whereas the North-West (NW) area will have a negative cumulative change in wind speeds.

A novel flamelet manifold parametrization approach for lean CH4–H2-air flames

Hydrogen is a carbon-free energy carrier that can substantially support the decarbonization of the power generation and transportation sector in the near future. Blending H2 into natural gas represents a feasible option to continue using the current infrastructure, allowing a smooth transition to pure H2 combustion technologies. In the present study, a flamelet model is proposed to describe lean CH4–H2-air laminar Bunsen flames with inert gas as a coflow, simultaneously taking into account multiple complex physical phenomena such as differential diffusion, heat losses at the burner wall and mixing between the main flow and coflow. In most previous works based on manifold-based reduction methods, transport equations are solved for the progress variable, mixture fraction and enthalpy. In contrast, transport equations for several species and enthalpy are solved in the present study and species diffusivities are evaluated with a mixture-averaged diffusion model. The control variables of the manifold are then reconstructed with those transported variables. In order to consider the mixing between the main flow and the inert coflow, two different approaches based on a three-dimensional and a four-dimensional manifold, respectively, are formulated and assessed. Utilizing a “linear mixing” assumption, the three-dimensional manifold is parameterized by a progress variable, an approximate Bilger mixture fraction and enthalpy. The four-dimensional manifold relaxes this assumption and additionally includes the inert gas mass fraction as the fourth control variable. The accuracy of the two approaches is evaluated by comparison with detailed chemistry results and experimental measurements. Overall, results obtained with both approaches show good agreement with the reference data, both qualitatively and quantitatively, indicating that all the effects mentioned above are well captured. Slightly better agreement is achieved with the four-dimensional manifold, which shows superiority in the mixing layer between the main flow and the inert coflow.

Future scenarios and power generation of marine energy deployment around Great Britain

Renewable energy from wave energy converters and tidal stream turbines could play an important role in the future power system of Great Britain. However, scenarios of future deployment are highly uncertain and rarely spatially or temporally detailed enough to be used as suitable inputs to power system models. This paper presents a new resource for power system modellers which combines published projections for wave and tidal stream installed capacities with discrete geospatial coordinates of potential deployment sites from the literature to develop ‘build scenarios’ of future deployment around GB. Three build scenarios (low, intermediate and high) are presented for both wave and tidal stream generation. These scenarios can be used by power system modellers to analyse the contributions of marine energy to the future power system of Great Britain.