Single-layer Model Research Articles

Quantifying the reciprocal impacts of capital and logistics networks in the supply chains: A cyber–physical system approach

Capital flow and logistics are closely linked in the supply chain. Current research primarily assesses their independent resilience during disruptions, but this study reveals an iterative bidirectional impact. The interconnection of shareholding, mutual guarantees, and borrowing within the supply chain has created financial relationships that were previously nonexistent. However, the evolution of logistics has not aligned with these financial developments, leading to a growing heterogeneity between capital flow and logistics. While the heterogeneity between capital flow and logistics within supply chains is crucial, previous studies, especially those rooted in complex networks, often overlook its impact. Drawing inspiration from the cyber–physical system, this paper abstracts logistics and capital flow into respective networks. A model of mutual dependence quantifies reciprocal impacts between capital and logistics networks, exploring the consequences of heterogeneity on the supply chain. Simulation results are compared with traditional single-layer network models. Finally, an innovative edge removal method is proposed to enhance supply chain robustness. Our simulation results indicate that the influence of the capital network on the logistics network and their relationship to heterogeneity is nonlinear. The edge removal method enhances supply chain network robustness in a nonlinear manner, with optimal effectiveness achieved at p_l=0.2.

The Sharing Energy Storage Mechanism for Demand Side Energy Communities

Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES’s high investment costs and long payback period, energy management with shared ESs becomes a suitable choice for the demand side. This work investigates the sharing mechanism of ES units for low-voltage (LV) energy prosumer (EP) communities, in which energy interactions of multiple styles among the EPs are enabled, and the aggregated ES dispatch center (AESDC) is established as a special energy service provider to facilitate the scheduling and marketing mechanism. A shared ES operation framework considering multiple EP communities is established, in which both the energy scheduling and cost allocation methods are studied. Then a shared ES model and energy marketing scheme for multiple communities based on the leader–follower game is proposed. The Karush–Kuhn–Tucker (KKT) condition is used to transform the double-layer model into a single-layer model, and then the large M method and PSO-HS algorithm are used to solve it, which improves convergence features in both speed and performance. On this basis, a cost allocation strategy based on the Owen value method is proposed to resolve the issues of benefit distribution fairness and user privacy under current situations. A case study simulation is carried out, and the results show that, with the ES scheduling strategy shared by multiple renewable communities in the leader–follower game, the energy cost is reduced significantly, and all communities acquire benefits from shared ES operators and aggregated ES dispatch centers, which verifies the advantageous and economical features of the proposed framework and strategy. With the cost allocation strategy based on the Owen value method, the distribution results are rational and equitable both for the groups and individuals among the multiple EP communities. Comparing it with other algorithms, the presented PSO-HS algorithm demonstrates better features in computing speed and convergence. Therefore, the proposed mechanism can be implemented in multiple scenarios on the demand side.

Graph Neural Network-Based Speech Emotion Recognition: A Fusion of Skip Graph Convolutional Networks and Graph Attention Networks

In speech emotion recognition (SER), our research addresses the critical challenges of capturing and evaluating node information and their complex interrelationships within speech data. We introduce Skip Graph Convolutional and Graph Attention Network (SkipGCNGAT), an innovative model that combines the strengths of skip graph convolutional networks (SkipGCNs) and graph attention networks (GATs) to address these challenges. SkipGCN incorporates skip connections, enhancing the flow of information across the network and mitigating issues such as vanishing gradients, while also facilitating deeper representation learning. Meanwhile, the GAT in the model assigns dynamic attention weights to neighboring nodes, allowing SkipGCNGAT to focus on both the most relevant local and global interactions within the speech data. This enables the model to capture subtle and complex dependencies between speech segments, thus facilitating a more accurate interpretation of emotional content. It overcomes the limitations of previous single-layer graph models, which were unable to effectively represent these intricate relationships across time and in different speech contexts. Additionally, by introducing a pre-pooling SkipGCN combination technique, we further enhance the ability of the model to integrate multi-layer information before pooling, improving its capacity to capture both spatial and temporal features in speech. Furthermore, we rigorously evaluated SkipGCNGAT on the IEMOCAP and MSP-IMPROV datasets, two benchmark datasets in SER. The results demonstrated that SkipGCNGAT consistently achieved state-of-the-art performance. These findings highlight the effectiveness of the proposed model in accurately recognizing emotions in speech, offering valuable insights and a solid foundation for future research on capturing complex relationships within speech signals for emotion recognition.

Toward a robust physical and chemical characterization of heterogeneous lines of sight: The case of the Horsehead nebula

Dense and cold molecular cores and filaments are surrounded by an envelope of translucent gas. Some of the low- J emission lines of and isotopologues are more sensitive to the conditions either in the translucent environment or in the dense and cold one because their intensities result from a complex interplay of radiative transfer and chemical properties of these heterogeneous lines of sight (LoSs). We extend our previous single-zone modeling with a more realistic approach that introduces multiple layers to take account of possibly varying conditions along the LoS. We used the IRAM-30m data from the ORION-B large program toward the Horsehead nebula in order to demonstrate our method's capability and effectiveness. We propose a cloud model composed of three homogeneous slabs of gas along each LoS, representing an outer envelope and a more shielded inner layer. We used the non-LTE radiative transfer code RADEX to model the line profiles from the kinetic temperature ( the volume density ( kinematics, and chemical properties of the different layers. We then used a fast and robust maximum likelihood estimator to simultaneously fit the observed lines of the and isotopologues. To limit the variance on the estimates, we propose a simple chemical model by constraining the column densities. A single-layer model cannot reproduce the spectral line asymmetries that result from a combination of different radial velocities and absorption effects among layers. A minimal heterogeneous model (three layers only) is sufficient for the Horsehead application, as it provides good fits of the seven fitted lines over a large part of the studied field of view. The decomposition of the intensity into three layers allowed us to discuss the distribution of the estimated physical or chemical properties along the LoS. About 80<!PCT!> of the integrated intensity comes from the outer envelope, while $ of the integrated intensity of the and lines of comes from the inner layer. For the lines of the and the isotopologues, integrated intensities are more equally distributed over the cloud layers. The estimated column density ratio NCeiO in the envelope increases with decreasing visual extinction, and it reaches $25$ in the pillar outskirts. While the inferred of the envelope varies from $25$ to K$ that of the inner layer drops to K$ in the western dense core. The estimated in the inner layer is pccm$ toward the filament, and it increases by a factor of ten toward dense cores. Our proposed method correctly retrieves the physical and chemical properties of the Horsehead nebula. It also offers promising prospects for less supervised model fits of wider-field datasets.

Periodic solutions, breather, lump and interaction solutions of a generalized (2+1)-dimensional Hirota bilinear equation via bilinear neural network method

Abstract The main focus of this paper is to address a generalized (2+1)-dimensional Hirota bilinear equation utilizing the bilinear neural network method. The paper presents the periodic solutions through a single-layer model of [3-4-1], followed by breather, lump and their interaction solutions by using double-layer models of [3-3-2-1] and [3-3-3-1], respectively. A significant innovation introduced in this work is the computation of periodic cross-rational solutions through the design of a novel [3-(2+2)-4-1] model, where a specific hidden layer is partitioned into two segments for subsequent operations. Three-dimensional and density figures of the solutions are given alongside an analysis of the dynamics of these solutions.

Estimating Models and Evaluating their Efficiency under Multicollinearity in Multiple Linear Regression: A Comparative Study

Multicollinearity between independent variables occurs in multiple linear regression analysis characterized by high correlations, which complicates discerning individual variable effect, impacting model accuracy, stability, and interpretation of relationships. The research aims to diagnose the multicollinearity problem between explanatory variables in the linear regression model and identifying the variables causing this problem based on the variance inflation factor (VIF), then estimation and evaluate the performance of three alternative methods, which are Ridge regression, principal components analysis, and Feedforward Neural Networks (FFNN) models with one and two hidden layers and application of the models to compressive strength data for high-performance concrete. The results showed that Ridge regression and PCA effectively addressed multicollinearity problem, but the single hidden layer model FFNN showed superior predictive accuracy in estimating the compressive strength of high-performance concrete when comparing RMSE, MAE, and R2 values.

Advanced investigation of adsorption process of the five main responses of taste molecules onto the gerbil receptor sites (GCaMP3): Steric, molecular docking, and energy interpretations

In this research work, an adsorption phenomenon putatively in the process of taste at a molecular level for understanding the five principal tastes using experimental dose-gustatory responses on receptors sites of gerbil (GCaMP3). In order to perform theoretical interpretations of the experimental data of dose gustatory response curve were fitted with the single-layer physical model (PMM) for sucrose, cycloheximide, quinine and MSG. However, a two-layer with two-energies model for NaCl and citric acid. The modeling discussion indicated that all six molecules were linked with the gerbil receptor sites of tasting surface by a parallel and a no parallel orientations. In addition, the linking adsorption energies are ranged from 3.15 to 14.19 kJ/mol for all tasting molecules, which suggested that this process is performed by a reversible physical adsorption. These physico-chemical model parameters are utilized to determine the molar energy of adsorption for various taste-entities. By fitting the number of adsorbed molecules or molecular anchors per site, a stereographic and energetic characterization of interactions between six entities (sucrose, cycloheximide, MSG, citric acid, and NaCl) and the gerbil gustatory-receptor-sites is conducted. These distributions AED and PSD can be considered as a characterization of taste of not only not only not only of any gustatory entity but also a characterization of the gustatory receptor systems of the peripheric nerves. The docking computation between five gustatory molecules and the gerbil GCaMP3 cavity receptor sites is performed, showing a significant resemblance in the receptor-ligand detection process. Hence, the findings from docking confirm that the calculated binding affinities fell within the range of adsorption energies.

Seismic Wave Amplification Characteristics in Slope Sections of Various Inclined Model Grounds

The collapse of slopes caused by earthquakes can lead to landslides, resulting in significant damage to both lives and structures. Seismic reinforcement of these slopes can protect social systems during an earthquake. In South Korea, where more than 70% of the land is mountainous, the stability of slopes is of paramount importance compared to other countries. While many seismic designs are based on peak ground acceleration (PGA), there is relatively little consideration given to the extent of PGA’s influence, and few studies have been done. This study aims to assess the seismic amplification of slopes with multilayers using a 1 g shaking table and verify the results through numerical analysis after confirming the impact of PGA at specific points. Typically, slope model experiments are conducted on single-layered ground models. However, actual ground conditions consist of multiple layers rather than a single layer, so a multi-layered model was created with different properties for the upper and lower layers. Two multi-layered ground models consisting of two layers were created, one with a flat ground surface and the other with a sloped surface. The properties of the two layers in each model were configured as a single layer to create the slope models. The peak ground acceleration (PGA) of the four ground models was compared, revealing that seismic wave amplification increases as it moves upward, and the amplification is even greater when transitioning from the lower to the upper ground layers, leading to different dynamic behavior of the slope. Through the contour lines, the influence of PGA was further confirmed, and it was found that approximately 60% of the PGA impact occurs at the topmost part of the slope on average. Analysis of the earthquake waves showed that the top of the slope experienced an average amplification of about 31.75% compared to the input motion, while the lower part experienced an average amplification of about 27.85%. Numerical analysis was performed using the ABAQUS program, and the results were compared with the 1 g shaking table experiments through spectral acceleration (SA), showing good agreement with the experimental results.

A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties

The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.

NIR-ViS-UV broadband absorption in ultrathin electrochemically-grown, graded index nanoporous platinum films.

Nanoporous platinum broadband absorber has attracted interest in thermosensorics and IR photodetection due to its unique properties. In this work we report the physical mechanism underlying broadband absorption in electrochemically-grown, nanoporous Pt films by analyzing NIR-ViS-UV spectral ellipsometry data of nanoporous Pt films in dependence on the Pt film thickness (27, 35, 38, 48nm). For the two thinner films a single layer model with a graded optical index Pt surface layer was used. For the two thicker films a two-layer optical model with a constant optical index Pt substrate layer and a graded optical index Pt surface layer was used. The graded optical index of the Pt surface layer reduces surface reflectivity and the constant optical index Pt substrate layer supports multiple reflections in the Pt film. Finally, we relate the thickness dependent optical index with the nanostructure of the nanoporous Pt film, which can be controlled in the electrochemical growth process. We observed that while in the transverse plane the multilayer exhibits graded refractive index, in the top horizontal planes the multilayer assembly exhibits discontinuous refractive index values due to the distribution of platinum crystal islands in air, which allows a metamaterial behavior of the whole system.

Mechanical analysis of al/foam composite sandwich panels under elastic and elastoplastic states

This study performs mechanical analysis for Al/Foam composite sandwich panels under 3-point bending using numerically and experimentally. The flexural rigidity, elastic deflections, and normal, shear stresses are obtained by analytical calculations of the Timoshenko beam equation and compared finite element (FE) models for 3-point bending loading conditions. The FE models are constructed using 2D single-layer shell and 3D solid discrete-layer models. The validity of FE models at the analysis is evaluated for Al/PVC Foam sandwich composites for the elastic state. The experimental bending results of Al/XPS Foam sandwich composites are compared with numerical models at elastic and elastoplastic states. The elastic results indicate that the out-of-plane deflection results agree well across numerical and analytical models. Normal stresses at the core are higher in 3D discrete-layer solid models compared to laminated shell theory-based models for thick plates, due to the more accurate characteristics of the discrete-layer solid models. The Timoshenko beam theory-based analytical bending results show a good correlation with the results from laminated shell theory-based finite element method (FEM) analyses. Elastoplastic FEM analysis indicates that discrete-layer-based 3D solid FEM models effectively predict local effects dependent on indentation failure.

How Membrane Flexibility Impacts Permeation and Separation of Gas through Nanoporous Graphenes.

In recent years, extensive research has used molecular dynamics simulations to investigate gas separation through nanoporous graphene (NPG) membranes. However, most studies have considered graphene membranes as rigid, overlooking the impact of their inherent flexibility. This study systematically quantifies the effect of graphene flexibility on gas permeation by comparing the diffusion of various gases through flexible and rigid single-layer NPG models. The results demonstrate that flexibility notably increases permeance, particularly for gases with larger molecular diameters/pore size ratios, by allowing gas molecules greater mobility within the pore. Interestingly, the effect of flexibility boils down to the expansion of the average pore size, and the detail of the membrane's vibrational dynamics is of little importance in quantifying permeance. Our work shows that accounting for flexibility in molecular models improves the alignment of simulation results with experimental data, emphasizing the importance of considering membrane flexibility in predictive models of NPG membrane performance.

Optical Transmission in Single-Layer Brain Tissues under Different Optical Source Types: Modelling and Simulation.

The human brain is a complex organ controlling daily activity. Present technique models have mostly focused on multi-layer brain tissues, which lack understanding of the propagation characteristics of various single brain tissues. To better understand the influence of different optical source types on individual brain tissues, we constructed single-layer brain models and simulated optical propagation using the Monte Carlo method. Based on the optical simulation results, sixteen optical source types had different optical energy distributions, and the distribution in cerebrospinal fluid had obvious characteristics. Five brain tissues (scalp, skull, cerebrospinal fluid, gray matter, and blood vessel) had the same set of the first three optical source types with maximum depth, while white matter had a different set of the first three optical source types with maximum depth. Each brain tissue had different optical source types with the maximum and minimum full width at half maximum. The study on single-layer brain tissues under different optical source types lays the foundation for constructing complex brain models with multiple tissue layers. It provides a theoretical reference for optimizing the selection of optical source devices for brain imaging.

A convolutional neural network–back propagation based three-layer combined forecasting method for spare part demand

Accurate spare part demand forecasting for the key components of ships is one of the key factors to ensure normal ship operation. Large errors in demand forecasting not only bring challenges to the normal operation of ships but also cause an inventory backlog of key components and thus increase the operation and maintenance costs. A three-level spare parts combination prediction method based on historical data has been proposed, aiming to solve the problem of insufficient data in existing prediction methods. First, three types of individual direct forecasting models are used for predictions. Secondly, we used convolutional neural networks to perform convolutional operations on the prediction results, and then constructed a three-layer combined prediction model using backpropagation neural networks (BP). Experimental results have shown that the predictive performance of this model is significantly better than that of single-layer models. This study used spare parts data from shipping companies to predict the three-layer combination model, and the results fully demonstrated its significant advantages over single-layer models.

Stock market index prediction based on market trend using LSTM

The stock market data analysis has received interest as a result of technological advancements and the investigation of new machine learning models, since these models provide a platform for traders and business people to choose gaining stocks. The business price prediction is a challenging and extremely complex process due to the impact of several factors on company prices. The numerous patterns that the stock market goes, they have been the focus of extensive research and analysis by numerous experts. There are several large data sets accessible, an artificial intelligence and machine learning techniques are developing quickly, and because of the machine’s improved computational power, complex stock price prediction algorithms can be developed. This paper presents stock market index prediction based on market trend using long short-term memory (LSTM). Using built-in application programmable interface (API), Yahoo Finance offers a simple method to programmatically retrieve any historical stock prices of an organization using the ticker name. The standard and poor’s 500 index (S&P 500 index) include the firms that have been taken into consideration here. Utilizing the selected input variable, single-layer and multi-layer LSTM models are implemented, and the measurement parameters of mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) are used to compare each performance. Nearly all of the real closing price’s curve and the prediction curve’s closing price for test data overlap. A potential stock investor may benefit significantly from such a prediction by using it to make well-informed choices that would increase his earnings.

Combined impacts of aerosols and urbanization on a highly threatened extreme precipitation event in Beijing, China

On July 21, 2012, a catastrophic precipitation event occurred in Beijing, highlighting the serious threat of extreme precipitation on socio-economic development and human health under climate change. Nevertheless, whether, how and to what extent aerosols and urbanization, as the two main influencing factors of urban extreme precipitation, have affected this highly damaging extreme event remains largely unexplored. Here, we employed the weather research and forecasting model coupled with chemistry (WRF-Chem) and a single-layer urban canopy model to investigate the influences of urbanization, aerosols and their interactions on this extreme precipitation event. We found that the joint intensification effects of urbanization and aerosols on extreme precipitation events greatly enhance its negative influence on megacities. The results indicate that aerosols are enhanced by increasing cloud droplet numbers, thereby intensifying the feedback between precipitation and latent heating. Consequently, the total precipitation increased by 22.6%, raising the precipitation in the Beijing area increase by at least 50 mm. By stimulating atmospheric instability and strengthening vertical air motion (over 0.25 m s−1), the urban heat island effect considerably influences the temporal and spatial distributions of extreme precipitation events, resulting in an increase in warm cloud precipitation (80%) and a decrease (30%) in frontal precipitation. Consequently, joint intensification effects resulted in more concentrated precipitation in the southwest of Beijing, leading to a substantial increase (more than 40%, ∼80 mm). This condition may be an important reason for the most severe disasters in the southwest of Beijing.

Uncovering the multiplex network of global container shipping: Insights from shipping companies

Shipping companies are key drivers of maritime trade and crucial in the development of container shipping networks. Each company's strategy shapes differences in port services and shipping routes, creating a complex and interconnected global container shipping network that is difficult to analyze using single-layer or aggregated models. This paper introduces a novel multiplex network modeling approach that leverages a very large set of AIS data and applies a series of structural indices to reveal the unique characteristics and multiple key roles of various shipping company networks. By applying methods such as overlap ratio analysis, HITS analysis, and community detection, the study identifies differences in overlap between ports and routes, hub and authority properties of individual ports, as well as local and regional patterns within the global network. Furthermore, it examines structural differences between the multiplex network, the aggregated network, and individual layers, highlighting the importance of multiplex container networks in understanding ports' roles on both regional and global scales. The findings clarify how different shipping companies shape shipping network patterns, enhance the multiplex network's functional roles, and provide a better understanding of the complex structure of the Global Container Shipping Network (GCSN). Finally, it provides authorities with valuable insights on how to better understand maritime shipping networks.

Effects of 2.5-D ultra-low and ultra-high velocity zones on flip-reverse-stacking (FRS) of the ScS wavefield

SUMMARY Within the last decade, thin ultra-low velocity zone (ULVZ) layering, sitting directly on top of the core–mantle boundary (CMB), has begun to be investigated using the flip-reverse-stack (FRS) method. In this method, pre- and post-cursor arrivals that are symmetrical in time about the ScS arrival, but with opposite polarities, are stacked. This same methodology has also been applied to high velocity layering, with indications that ultra-high velocity zones (UHVZs) may also exist. Thus far, studies using the FRS technique have relied on 1-D synthetic predictions to infer material properties of ULVZs. 1-D ULVZ models predominantly show a SdS precursor that reflects off the top of the ULVZ and an ScscS reverberation within the ULVZ that arrives as a post-cursor. 1-D UHVZ models are more complex and have a different number of arrivals depending on a variety of factors including UHVZ thickness, velocity contrast, and lateral extent. 1-D modelling approaches assume that lower mantle heterogeneity is constant and continuous everywhere across the lower mantle. However, lower mantle features display lateral heterogeneity and are either finite in extent or display local thickness variations. We examine the interaction of the ScS wavefield with ULVZs and UHVZs in 2.5-D geometries of finite extent. We show that multiple additional arrivals exist that are not present in 1-D predictions. In particular, multipath ScS arrivals as well as additional post-cursor arrivals are generated. Subsequent processing by the FRS method generates complicated FRS traces with multiple peaks. Furthermore, post-cursor arrivals can be generated even when the ScS ray path does not directly strike the heterogeneity from above. Analysing these predictions for 2.5-D models using 1-D modelling techniques demonstrates that a cautious approach must be adopted in utilization and interpretation of FRS traces to determine if the ScS wavefield is interacting with a ULVZ or UHVZ through a direct strike on the top of the feature. In particular, traveltime delays or advances of the ScS arrival should be documented and symmetrical opposite polarity arrivals should be demonstrated to exist around ScS. The latter can be quantified by calculation of a time domain multiplication trace. Because multiple post-cursor arrivals are generated by finite length heterogeneities, interpretation should be confined to single layer models rather than to interpret the additional peaks as internal layering. Furthermore, strong trade-offs exist between S-wave velocity perturbation and thickness making estimations of ULVZ or UHVZ elastic parameters highly uncertain. We test our analysis methods using data from an event occurring in the Fiji-Tonga region recorded in North America. The ScS bounce points for this event sample the CMB region to the southeast of Hawaii, in a region where ULVZs have been identified in several recent studies. We see additional evidence for a ULVZ in this region centred at 14°N and 153°W with a lateral scale of at least 250 km × 360 km. Assuming a constant S-wave velocity decrease of −10 or −20 per cent with respect to the PREM model implies a ULVZ thickness of up to 16 or 9 km, respectively.

A double-layer optimization strategy for distribution networks considering 5G base station clusters

The reliability of the power supply for 5G base stations (BSs) is increasing. A large amount of BS backup energy storage (BES) remains underutilized. This study establishes a double-layer optimization distribution network (DN) considering BS clusters. An energy consumption characteristics and scheduling ability model of the BSs was established to address the differences in the characteristics of different traffic flows. A double-tier planning model for BS-joining grid market ancillary services is proposed. The upper-layer model addresses optimal tidal flow problems in DNs to minimize integrated operating costs, while the lower-layer model focuses on BES economic optimization. The double-layer model changes into a single-layer linear model using the Karush–Kuhn–Tucker (KKT) condition and the Big M method. Simulation validation using the IEEE-33 node DN proves that this approach can reduce DN operating costs, regulate voltage fluctuations, and guarantee economical and safe DN operation.

Information Gap Decision-Making Theory-Based Medium- and Long-Term Optimal Dispatching of Hydropower-Dominated Power Grids in a Market Environment

In the high-proportion hydropower market, the fairness of the execution of traded electricity and clean energy consumption are two issues that need to be considered in medium- and long-term dispatching. Aiming at the fairness of medium- and long-term optimal dispatching of hydropower-dominated grids and the problem of water abandonment in the power market environment, this paper proposes a medium- and long-term optimal dispatching method for hydropower-dominated grids based on the information gap decision-making theory (IGDT). Firstly, IGDT is used to establish a two-layer model of medium- and long-term optimal dispatching that considers runoff uncertainty, in which the lower layer solves the maximum value of the maximum difference in the contract power completion rate of the power stations, and the upper layer solves the maximum fluctuation range of the interval inflow. Then, a mixed-integer linear programming (MILP)-based single-layer optimization model is obtained through a variety of linearization techniques, and the model is solved via the CPLEX solver (version 12.10.0). The medium- and long-term optimal dispatching of 10 thermal power stations and 22 hydropower stations in Yunnan Power Grid, China, is taken as an example to verify the proposed model. The results show that the maximum difference in the contracted electricity completion rate of each power station is 0.412, and the amount of abandoned hydropower is reduced by 81.33% compared to when the abandoned water penalty function is not considered. It is proved that the proposed model can effectively alleviate the problems of excessive power generation, insufficient power generation and large-scale hydropower abandonment, which are of great significance for realizing the fair dispatching of hydropower-dominated power grids and promoting clean energy consumption in the market environment.