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The Lower Cambrian Longwangmiao Formation in the Penglai gas area has promising carbonate reservoirs and exhibit strong gas indications, making itself a pivotal exploration zone following the Anyue gas field in the central Sichuan region. However, the lack of clarity regarding reservoir development patterns has constrained exploration deployment. Through comprehensive analysis of the most recent drilling and testing data, this study systematically examines the reservoir characteristics and primary controlling factors of the Longwangmiao Formation in Penglai gas area. Results reveal a mixed sedimentary background of terrigenous debris and carbonate, with reservoirs predominantly situated in the middle and upper strata of the Longwangmiao Formation. The reservoir rock types are predominantly (residual) granular dolomite and crystalline dolomite. Main types of reservoir space include intergranular dissolved pores, intragranular dissolved pores, intercrystalline dissolved pores, and micro-fractures. The reservoir is characterized by low porosity and permeability, with thickness typically ranging between 10-42 m. Reservoir development is controlled by sequence stratigraphy, lithology and diagenesis, particularly evident in the granular dolomite and crystalline dolomite within the middle and upper sections of the progradational parasequence set. Key constructive diagenetic processes include atmospheric freshwater dissolution, oil and gas dissolution and fracture. A comparison with the Moxi-Gaoshiti area in the main area of Anyue gas field suggests similar controlling factors, primarily revolving around granular dolomite, dissolution and fracturing. Nonetheless, the Longwangmiao Formation reservoir in Penglai gas area exhibit distinctive traits such as high ash content, elevated terrigenous clastic content and limited penecontemporaneous exposure. Consequently, identifying high-energy granular dolomite and fine-grained dolomite exposed during the penecontemporaneous period, as well as dolomite modified by supergene karst, emerges as imperative in targeting reservoirs within the Penglai gas area.

With the growing emphasis on sustainable development, there has been increasing attention given to measures aimed at promoting environmental improvements and reducing carbon emissions, including the adoption of intelligent industry. Recent studies have analyzed the influence of industrial intelligence on urban carbon emission performance while ignore the spatial spillover effects and lack in-depth discussion of the mechanisms, which reduces the reliability of the assessment of industrial intelligence's impact on carbon emission performance. To address this issue, the paper examines direct effect, spatial spillover effects, and mechanisms, utilizing a balanced panel data from 2008 to 2019 for 238 Chinese cities. The findings reveal that a 1 % improvement in industrial intelligence results in a 2.747 % enhancement of local carbon emission performance. Moreover, through the spatial spillover analysis, we determined that industrial intelligence has a notable negative impact on the carbon emission performance of surrounding areas. The mechanism analysis demonstrated that industrial intelligence affects the carbon emission performance of local and neighboring areas by influencing the agglomeration of productive services. Furthermore, our study illustrates that the industrial intelligence's enhancement effect on carbon emission performance shows more significantly in eastern, resource-dependent, and ordinary prefecture-level cities. Finally, endogeneity and robustness tests conducted yielded consistent conclusions. Our study provides a new perspective on industrial intelligence's carbon reduction effect and contributes to the development of policies related to industrial upgrading and green development.

In this paper, the environmental and economic indicators based on the reactive power pricing are proposed so as to formulate the novel environmental economic power dispatch (NEED) problems for electricity trading. Here, a new way of calculating total cost is proposed based on the reactive power pricing instead of the pure fuel cost, which leads to the NEED problems. In addition, an improved tunicate swarm algorithm (ITSA) is proposed to deal with the NEED problems. Different from the original tunicate swarm algorithm (TSA), adaptive weight parameter and mutation process are used to improve the disadvantages of TSA which converges too fast and easily falls into local optimum. In combination with the Pareto dominance rule, the ITSA can be more effective in dealing with the NEED problems. In order to examine the performance of the ITSA, simulation tests of ITSA, TSA, and particle swarm optimization (PSO) are conducted in IEEE30 node system. During the experimental process, optimized objective functions include total cost considering reactive cost, pollution emission and power loss. The final comparison results show that the ITSA is able to get better distributed Pareto front (PF) and superior best compromise solution (BCS), which proves the high efficiency of the ITSA in dealing with the NEED problems.

The climate and environmental pollution problems caused by carbon dioxide and other harmful gases emitted from traditional fossil fuel thermal power plants are increasingly threatening the living environment of mankind. In September, 2020, the Chinese government clearly put forward the national strategic goal of “Carbon Peak and Carbon Neutrality”. Distributed generation is the main means to effectively reduce carbon emissions, especially the rapid development of wind power generation. Accurate and stable wind speed prediction can reasonably formulate power generation plans and optimize power dispatching, which is an effective means to reduce the overall carbon emissions of the power system.This paper designs and proposes a hybrid wind speed prediction model based on convolutional neural network and long short-term memory network deep learning model. Based on the historical wind speed data set collected at the location of multi-fan in the same wind farm, high-precision and stable short-term wind speed prediction is realized. Firstly, singular spectrum analysis aims to remove the noise components in the wind speed series and reduce the impact of noise on the prediction performance of the model. Secondly, convolutional neural network (CNN) is introduced to extract the features of the de-noised wind speed sequence, which provides more effective information for the training of the prediction network. Then, a prediction network based on sparrow search algorithm and long short-term memory network is constructed, and the modified sparrow search algorithm is used to optimize the selection of long short-term memory (LSTM) Hyper-parameters. Eventually, to verify the superiority of the proposed model, an evaluation system based on accuracy, stability and complexity indicators is constructed. The experimental results show that the index values of the wind speed prediction model proposed in this paper based on dataset 1 in the one-step prediction simulation experiment are the smallest among all comparison models, and the same is true for dataset 2.

Previous studies have focused on the reduction effect of regulation-based instruments and economic-based instruments on air pollution, ignoring the importance of environmental information disclosure. Based on the Ambient Air Quality Standards (AAQS), a quasi-natural policy implemented in 2012, this paper assesses the effect of environmental information disclosure on air pollution through a dynamic spatial difference-in-difference (DID) model using panel data of 269 cities from 2006 to 2017 in China. We find that the implementation of the AAQS results in a 3% reduction of local PM2.5 concentration and a 3.3% reduction of PM2.5 concentration in the surrounding cities. Further analysis suggests that environmental information disclosure reduces air pollution through enhancing public environmental concerns, green innovation, and industrial upgrading. We also explore the moderating effect of government environmental regulation and heterogeneity analysis in different regions. Our findings suggest that government should further develop the scope and quality of environmental quality information disclosure. Moreover, the local government should provide more support for the green transformation of enterprises and provide necessary support during the transition period. In addition, long-term sustainable environmental protection policies must be able to mobilize regional initiatives for green development.

IntroductionExisting studies have focused on the impact of economic development and urban expansion on public healthcare environment but has ignored the importance of regional integration. Regional integration reflects the spatial distribution of the labor force, which significantly affects healthcare workforce and healthcare infrastructure development.MethodsBased on panel nested data for 137 cities in 16 major city clusters in China from 2001 to 2019, this paper assesses the impact of regional integration on the public healthcare environment through a hierarchical linear model (HLM).ResultsOur findings indicate that a 1% increase in regional integration leads to a 6.6 and 1.9% improvement in healthcare workforce and healthcare infrastructure. The results of the mechanism analysis indicate that regional integration affects the public healthcare environment through improving transportation infrastructure and industrial upgrading. In addition, regional integration has a stronger effect on cities with lower levels of economic development and healthcare environments. Finally, the endogeneity test based on the difference-in-difference (DID) model and the robustness test based on high-dimensional fixed effects model conduct the consistent conclusions.DiscussionPolicies to improve the public healthcare environment through promoting regional integration are proposed. Government should develop a more comprehensive regional cooperation plan to improve the public healthcare environment. Also, financial spending on improving the healthcare environment in peripheral cities should be increased. In addition, regional integration policy development needs to consider differences across regions.

Abstract Background Cohen syndrome (CS; OMIM 216550) is a rare syndrome with evident clinical heterogeneity. The diverse phenotype comprises early‐onset hypotonia and developmental delays, intellectual disabilities, microcephaly, hypermobile joints, neutropenia, myopia, and characteristic facial features. The disease is rarely reported. Vacuolar Protein Sorting 13 Homolog B (VPS13B; OMIM 607817) is the only causative gene of CS. Methods Blood samples sourced from both siblings and parents were sent to identify mutations by trio‐WES, and changes in the patient's condition were understood through consultation data and follow‐up. Results We reported two siblings affected by developmental delay, microcephaly, intellectual disability, and facial features. The siblings' WES detected compound heterozygous variants in the exon region of VPS13B (NM_017890): c.9337A>T and c.8551A>C. Conclusion Two individuals were diagnosed with CS by genetic testing and clinical features. In addition, we conduct a brief review of the reports on the Chinese population with CS and reinforce the understanding of the correlation between genotype–phenotype.

Abstract This paper proposes a new scheme for locating the faults of three‐terminal double‐circuit transmission line without transposer in the phase domain that is not affected by type of fault. Synchronous voltage and current were measured by phasor measurement units (PMUs) installed at three busbars. Considering the coupling effect of parallel lines, the distribution line model was used to obtain more accurate results and third‐order Taylor series expansion to decouple distributed line model. The phase and branch of all faults on the transmission lines were effectively identified, and various types of faults were accurately located including normal shunt fault, evolving fault and cross‐country fault. The proposed algorithm was tested under different fault locations, resistances, initial angles and types (including cross‐country faults and evolving faults). In addition, the effects of different sampling rates, measurement errors, earth resistivities and transmission line parameter errors were also considered. The simulation calculation was performed in MATLAB software and three‐terminal transmission lines were simulated in PSCAD/EMTP software. The results indicate that maximum estimated error was less than 1.8631%.

Existing studies have focused on the impact of innovation on carbon emission performance but ignore the importance of government support for innovation. To overcome this challenge, this paper adopts a spatial difference-in-difference (DID) model to assess the impact of government support for innovation on urban carbon emission performance based on a quasi-natural experiment of innovative city pilots (ICP) in China. Using the high-resolution carbon emission data of 1 km × 1 km for 238 cities from 2008 to 2019 in China, this paper employees an extended stochastic frontier analysis (SFA) model to measure urban carbon emission performance. Our findings indicate that ICP implementation leads to a 1.3% improvement in local carbon emission performance. Meanwhile, there is a significant spatial spillover effect of ICP implementation, with a 3.3% improvement in the carbon performance of the surrounding areas. The results of the mechanism analysis suggest that government innovation support affects carbon emission performance by promoting total factor productivity improvement, green innovation, and industrial upgrading. Further analysis shows that ICP has the strongest impact on carbon performance in the eastern region, and the impact is stronger for large cities and resource-dependent cities. Finally, the paper carries out a series of robustness tests to ensure the reliability of the analytical results, including parallel trend tests, placebo tests and re-estimation of different methods. Based on the findings, this paper proposes feasible policy recommendations in terms of continuous promotion of government innovation support, regional cooperation and differentiated innovation support formulation.

The pore structure is an important factor affecting reservoir capacity and shale gas production. The shale reservoir of the Longmaxi Formation in the Changning area, Southern Sichuan Basin, is highly heterogeneous and has a complex pore structure. To quantitatively characterize the shale’s pore structure and influencing factors, based on whole rock X-ray diffraction, argon ion polishing electron microscopy observations, and low-temperature nitrogen adsorption-desorption experiments, the characteristics of the shale pore structure are studied by using the Frenkel-Halsey-Hill (FHH) model. The research reveals the following: 1) The pores of the Longmaxi Formation shale mainly include organic pores, intergranular pores, dissolution pores and microfractures. The pore size is mainly micro-mesoporous. Both ink bottle-type pores and semiclosed slit-type pores with good openness exist, but mainly ink bottle-type pores are observed. 2) The pore structure of the Longmaxi Formation shale has self-similarity, conforms to the fractal law, and shows double fractal characteristics. Taking the relative pressure of 0.45 (P/P0 = 0.45) as the boundary, the surface fractal dimension Dsf and the structural fractal dimension Dst are defined. Dsf is between 2.3215 and 2.6117, and the structural fractal dimension Dst is between 2.8424 and 2.9016. The pore structure of micropores and mesopores is more complex. 3) The mineral components and organic matter have obvious control over the fractal dimension of shale, and samples from different wells show certain differences. The fractal dimension has a good positive correlation with the quartz content but an obvious negative correlation with clay minerals. The higher the total organic carbon content is, the higher the degree of thermal evolution, the more complex the pore structure of shale, and the larger the fractal dimension. The results have guiding significance for the characterization of pore structure of tight rocks.

Primary intracerebral hemorrhage (ICH) is a significant cause of morbidity and mortality throughout the world. ICH is a multifactorial disease that emerges from interactions among multiple genetic and environmental factors. DNA methylation plays an important role in the etiology of complex traits and diseases. We used the Illumina Infinium Human Methylation 850k BeadChip to detect changes in DNA methylation in peripheral blood samples from patients with ICH and healthy controls to explore DNA methylation patterns in ICH. Here, we compared genomic DNA methylation patterns in whole blood from ICH patients (n = 30) and controls (n = 34). The ICH and control groups showed significantly different DNA methylation patterns at 1530 sites (p-value < 5.92E-08), with 1377 hypermethylated sites and 153 hypomethylated sites in ICH patients compared to the methylation status in healthy controls. A total of 371 hypermethylated sites and 35 hypomethylated sites were in promoters, while 738 hypermethylated sites and 67 hypomethylated sites were in coding regions. Furthermore, the differentially methylated genes between ICH patients and controls were largely related to inflammatory pathways. Abnormalities in the DNA methylation pattern identified in the peripheral blood of ICH patients may play an important role in the development of ICH and warranted further investigation.

The IFIH1 gene encodes melanoma differentiation-associated gene 5 (MDA5) and has been associated with Aicardi-Goutières syndrome (AGS), Singleton-Merten syndrome (SMS), and other autoimmune diseases. The mechanisms responsible for how a functional change in a single gene can cause so many different phenotypes remain unknown. Moreover, there is significant controversy as to whether these distinct phenotypes represent the same disease continuum or mutation-specific disorders. Here, we describe the case of a patient with a novel c.1465G > T (p.Ala489Ser) mutation in the IFIH1 gene. The patient presented with spastic paraplegia, dystonia, psychomotor retardation, joint deformities, intracranial calcification, abnormal dentition, characteristic facial features, lymphadenopathy, and autoimmunity. His phenotype appeared to represent an overlap of the phenotypes for AGS and SMS. The patient also experienced unexplained pancytopenia, suggesting that the hemic system may have been affected by a gain-of-function mutation in the IFIH1 gene. In summary, we provide further evidence that SMS and AGS exhibit the same disease spectrum following a gain-of-function mutation in the IFIH1 gene. Our data highlight the genetic heterogeneity of these conditions and expand our knowledge of differential phenotypes created by IFIH1 gain-of-function mutation.

Temporal lobe epilepsy (TLE) is the most frequent type of focal epilepsy in adults, typically resistant to pharmacological treatment, and mostly presents with cognitive impairment and psychiatric comorbidities. The most common neuropathological hallmark in TLE patients is hippocampal sclerosis (HS). However, the underlying molecular mechanisms involved remain poorly characterized. The dentate gyrus (DG), one specific hippocampal subarea, structural and functional changes imply a key involvement of the DG in the development of TLE. In this study, a isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomic technique was performed for the analysis of hippocampal DG obtained from patients with TLE-HS compared to control samples obtained from autopsy. Our proteomic data identified 5,583 proteins, of which 82 proteins were upregulated and 90 proteins were downregulated. Bioinformatics analysis indicated that differentially expressed proteins were enriched in “synaptic vesicle,” “mitochondrion,” “cell-cell adhesion,” “regulation of synaptic plasticity,” “ATP binding,” and “oxidative phosphorylation.” Protein-protein interaction network analysis found a pivotal module of 10 proteins that were related to “oxidative phosphorylation.” This study has investigated proteomic alterations in the DG region of TLE-HS patients, and paved the way for the better understanding of epileptogenesis mechanisms and future therapeutic intervention.

DNA hypermethylation has been widely observed in temporal lobe epilepsy (TLE), in which NR4A1 knockdown has been reported to be able to alleviate seizure severity in mouse model, while the underlying methylation-imaging pathway modulated by aberrant methylation levels of NR4A1 remains to be clarified in patients with TLE. Here, using multi-site canonical correlation analysis with reference, methylation levels of NR4A1 in blood were used as priori to guide fusion of three MRI features: functional connectivity (FC), fractional anisotropy (FA), and gray matter volume (GMV) for 56 TLE patients and 65 healthy controls. Post-hoc correlations were further evaluated between the identified NR4A1-associated brain components and disease onset. Results suggested that higher NR4A1 methylation levels in TLE were related with impaired temporal-cerebellar and occipital-cerebellar FC strength, lower FA in cingulum (hippocampus), and reduced GMV in putamen, temporal pole, and cerebellum. Moreover, findings were also replicated well in both patient subsets with either right TLE or left TLE only. Particularly, right TLE patients showed poorer cognitive abilities and more severe brain impairment than left TLE patients, especially more reduced GMV in thalamus. In summary, this work revealed a potential imaging-methylation pathway modulated by higher NR4A1 methylation in TLE via data mining, which may impact the above-mentioned multimodal brain circuits and was also associated with earlier disease onset and more cognitive deficits.

Glycosylphosphatidylinositol (GPI) is a membrane anchor for cell surface proteins. Inherited GPI deficiencies are a new subclass of congenital disorders of glycosylation. Phosphatidylinositol glycan class S (PIGS) is a subunit of the GPI transamidase which plays important roles in many biological processes. In this study, we present a Chinese boy with infantile spasms (ISs), severe global developmental delay, hearing loss, visual impairment (cortical blindness), hypotonia, and intellectual disability and whose whole-exome sequencing (WES) identified compound heterozygous variants in PIGS (MIM:610271):c.148C > T (p.Gln50∗) and c.1141_1164dupGACATGGTGCGAGTGATGGAGGTG (p.Asp381_Val388dup). Flow cytometry analyses demonstrated that the boy with PIGS variants had a decreased expression of GPI-APs. This study stresses the importance of including the screening of PIGS gene in the case of pediatric neurological syndromes and reviews the clinical features of PIGS-associated disorders.

Benign adult familial myoclonic epilepsy (BAFME) is an autosomal dominant disorder characterized by adult-onset cortical myoclonus with or without seizures. Recently, it was reported to be associated with intronic TTTTA/TTTCA expansions. To investigate whether these abnormal expansions are involved in our new pedigree from China, whole exome sequencing (WES) and repeat-primed polymerase chain reaction (RP-PCR) analysis were performed to detect potential mutation in pedigree members. Neither causal mutations cosegregated with the disease in the family nor any novel mutation was identified through WES, while an abnormal TTTCA expansion in SAMD12 was identified by RP-PCR and then proved to be cosegregated in the pedigree. All the 12 alive affected individuals (M/F = 4/8; average age = 46.7 years old, range from 27 to 66) showed typical characteristics of BAFME. In addition, maternal clinical anticipation was observed in six mother/child pairs. In conclusion, our study offered the evidence of intronic pentanucleotide expansions in SAMD12 from a new Chinese BAFME pedigree, which further confirmed the association between this expansion and the pathogenesis of BAFME.

Data imbalance is often encountered in deep learning process and is harmful to model training. The imbalance of hard and easy samples in training datasets often occurs in the segmentation tasks from Contrast Tomography (CT) scans. However, due to the strong similarity between adjacent slices in volumes and different segmentation tasks (the same slice may be classified as a hard sample in liver segmentation task, but an easy sample in the kidney or spleen segmentation task), it is hard to solve this imbalance of training dataset using traditional methods. In this work, we use a pre-training strategy to distinguish hard and easy samples, and then increase the proportion of hard slices in training dataset, which could mitigate imbalance of hard samples and easy samples in training dataset, and enhance the contribution of hard samples in training process. Our experiments on liver, kidney and spleen segmentation show that increasing the ratio of hard samples in the training dataset could enhance the prediction ability of model by improving its ability to deal with hard samples. The main contribution of this work is the application of pre-training strategy, which enables us to select training samples online according to different tasks and to ease data imbalance in the training dataset.

In Northern China, due to the high penetration of coal-fired cogeneration facilities, which are generally equipped with extraction-condensing steam turbines, lots of wind power resources may be wasted during the heating season. In contrast, considerable coal is consumed in the power generation sector. In this article, firstly it is revealed that there exists a serious divergence in the ratio of electrical to thermal energy between end users’ demand and the cogenerations’ production during off-peak load at night, which may negate active power-balancing of the electric power grid. Secondly, with respect to this divergence only occurring during off-peak load at night, a temporary proposal is given so as to enable the integration of more wind power. The authors suggest that if the energy carrier for part of the end users’ space heating is switched from heating water to electricity (e.g., electric heat pumps (EHPs) can provide space heating in the domestic sector), the ratio of electricity to heating water load should be adjusted to optimize the power dispatch between cogeneration units and wind turbines, resulting in fuel conservation. With this proposal, existing infrastructures are made full use of, and no additional ones are required. Finally a numerical simulation is performed in order to illustrate both the technical and economic feasibility of the aforementioned proposal, under ongoing infrastructures as well as electricity and space heating tariff conditions without changing participants’ benefits. The authors aim to persuade Chinese policy makers to enable EHPs to provide space heating to enable the integration of more wind power.

With the mass introduction of wind power in Northern China, wind power variability has appeared. In this article, both existing electric heat pumps (EHPs) and coal-fired combined heat and power (CHP) facilities, which are generally equipped with extraction-condensing steam turbines coupled with district heating for space heating purposes, are proposed to incorporate the variability of wind power equivalently. The authors’ proposal arises from the facts that: (1) EHPs can provide space heating in the domestic sector with little thermal comfort change (e.g., energy carriers for space heating purposes can be switched from heating water to electricity); (2) coal-fired CHP units in Northern China can usually generate more electrical power corresponding to a shaved thermal power production. Thus, it is suggested that heating water from CHP units be shaved when the wind generation is low due to the variability of wind power, so as to enable more electrical power production and compensate for the corresponding insufficient wind generation. Following this, in the future and for some space heating loads at appropriate distances, electricity used as energy carrier should be converted by electric heat pumps for space heating. Thus, more electricity consumption will be achieved so as to avoid wasting wind power when the wind generation it is high. A numerical simulation is performed in order to illustrate the authors’ proposal. It is shown that the impact of variability of wind generation can be equivalently reduced to a great extent, which enable more wind power integration instead of curtailment and potential energy conservation. Moreover, in contrast to before, both the thermal and electrical power of coal-fired CHP units are no longer constants. In addition, the ratio of electrical to thermal power of CHP units is no longer constant either, and results in less energy consumption compared with fixed ratio. Finally, electricity consumed by end users’ EHPs, which are devoted to space heating for various spatial distances and time points, is figured out.

In the original version of the article published in Energies [1], insufficient acknowledgement was given for the contributors Mr. Li Guo and Prof. Kai Wu in Xi’an Jiaotong University. The main work was completed by the research group in Xi’an Jiaotong University led by Prof. Kai Wu. Mr. Li Guo was his master student who gave a lot of efforts in the measurement of daily loads of heating and electricity in different families, and his data was used in Section 2.1. Prof. Kai Wu was involved in the whole work; he put forward the basic idea, made the research plan and participated in the detailed discussion about the algorithm for calculation. [...]

As a clean and efficient renewable energy, wind power is increasingly valued by countries all over the world. However, due to the inherent volatility, intermittency, and uncontrollability of wind power, the secure, stable, and economical operation of the power grid will face unprecedented pressure with wind power integration on a vast scale. Wind power predictions and management are significantly impacted by wind speed. Therefore, it is crucial to propose an accurate, stable, and efficient wind speed prediction method. In this paper, the original wind speed data is denoised using singular spectrum analysis to solve the problem of strong randomness and volatility of wind speed data. Then, the denoised data is processed using wavelet analysis, and the high-frequency and low-frequency sequences are respectively predicted using CNN-BiLSTM and CNN-BP, while the weights are optimized using the PSO algorithm. Finally, the prediction accuracy is further improved by incorporating the error correction module. In the two datasets used for testing, the outcomes of the trial indicate that the wind speed prediction model proposed in this paper performs extremely well in short-term wind speed prediction, superior to other models. The five evaluation indices (MAE, MSE, MAPE, RMSE, and R²) for the first dataset are respectively 0.166, 0.055, 0.05, 0.234, and 0.953. Similar results were obtained for the second dataset. Furthermore, The ablation tests also show that each module suggested in this research is successful in enhancing prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, an improved remora optimization algorithm (IROA) is proposed to solve the multi-objective optimal power flow problem (MOOPF). The algorithm introduced the crossover strategy and variance strategy in the differential evolutionary (DE) algorithm. The use of these two strategies can increase the diversity of the remora optimization algorithm (ROA) population and jump out of the defect of being trapped in a local optimum. To better solve the MOOPF, this paper proposed constraint prioritization strategy (CPS), congestion distance ranking strategy (CDRS), and optimal compromise solution strategy (OCSS) to acquire a uniform Pareto optimal set (POS) and the best trade-off solution (BTS). Combined with practical applications, six kinds of objective functions are selected, namely, basic fuel cost, active power loss, emission, voltage deviation, voltage stability, and fuel cost with valve point. The above six objective functions are arranged and combined to obtain the MOOPF problems with dual or triple objectives for solving on IEEE30-bus, IEEE57-bus, and IEEE118-bus systems, which are used to demonstrate the capability of IROA. Furthermore, three performance metrics Hypervolume (HV), Spacing (SP), and Generational Distance (GD) were applied to verify the uniformity and diversity of the POS. The results of the IROA algorithm are compared with those of the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization algorithm (MOPSO), and it is obtained that the IROA algorithm has a better competitive advantage in solving the MOOPF. [ABSTRACT FROM AUTHOR]

In radial distribution networks, the unreasonable use of distributed generators (DGs) will enhance the effects of voltage quality reduction and network loss. Consequently, the optimal allocation of distribution groups (OADG) has far-reaching implications for the economical, secure, and stable operation of radial distribution networks. The purpose of the study on the optimal allocation of distributed generators is to allocate the locations and capacities of DGs scientifically and rationally, enhance voltage quality, and reduce power loss. This paper proposes an enhanced ISC-SSA algorithm with constraint strategy for the OADG problems, which addresses the disadvantages of traditional salp swarm algorithm (SSA), such as low solution accuracy and slow convergence speed, and introduces a sine cosine algorithm (SCA) to optimise the salp swarm followers. To reduce the limitation of only the leader guiding the followers for position updates, a symbiotic strategy with multiple subpopulations is introduced to simulate the sharing of information of the salp swarm population, relying on the decision-maker and the leader to jointly guide the population movement, thereby increasing the global optimization-seeking capability of the algorithm, and the method is implemented in IEEE-33, IEEE-69, and IEEE-119 systems. In addition, the paper extends the ISC-SSA algorithm to the MOISC-SSA algorithm to solve in IEEE-33, IEEE-69 test systems the multi-objective OADG problems. Multiple sets of simulation results demonstrate that the proposed method solves radial distribution system problems of varying sizes with greater search precision and greater significance. [ABSTRACT FROM AUTHOR]

The contemporary paradigm for developing novel distribution grids is centered on the integration of distributed generations (DGs) and shunt capacitors (SCs) within the distribution infrastructure. Unreasonable DG and SC configurations may have a negative impact on grid line loss and voltage quality. The goal of optimal allocation research of distribution network (OARDN) is to allocate distributed generators (DGs) and shunt capacitors (SCs) in the distribution network in a rational manner to minimize power losses, enhance voltage quality, and ensure secure and stable power supply to the loads. Aiming at the critical OARDN problems, such as minimizing power loss and improving voltage quality, this paper proposes an improved sand cat swarm optimization algorithm (ISCSO). To address the issue of the original SCSO being prone to local optima and low search accuracy, this method introduces a tent map-based chaotic strategy and reverse learning approach to augment the precision of the optimization, and proposes a cross-learning mechanism to expand the global search ability, and this method is used to solve the single-objective problem. Based on ISCSO, the sensitivity analysis method and pareto non-inferior sorting method are introduced, which is extended to the MOISCSO algorithm to solve multi-objective OARDN problem. Simulation experimental results on IEEE33, IEEE69, and IEEE119 test systems and comparison with other scholars' research results show that the proposed method is practical and competitive for solving single-objective and multi-objective OARDN problems. [ABSTRACT FROM AUTHOR]

Multi-objective reactive power optimization (MORPO) is a high-dimensional, nonlinear, multi-constraint problem. To solve this problem, an improved multi-objective honey badger algorithm (MOIHBA) is proposed. To address the shortcomings of the original algorithm, such as easy falling into local optimum and insufficient population diversity, the improved algorithm introduces a sine chaotic mapping strategy to expand the population diversity, a backward learning mechanism to narrow the range of high-quality solution sets, and a cross-learning mechanism to improve the precision of the algorithm optimization process. In addition, in order to obtain the pareto optimal set (POS), a method based on calculating individual rank and crowding distance is proposed to sort the non-inferior solution, and the best compromise solution (BCS) is obtained by using a fuzzy theory strategy. By introducing three objective functions of active power loss, voltage stability index, and voltage deviation, the multi-objective reactive power optimization is established. To investigate the robustness of the introduced improved algorithm and its ability to solve the MORPO problem, this paper uses IEEE30, IEEE57, and IEEE118 as test systems that optimize the dual objective and triple objective simultaneously. In order to study the comprehensive performance of the improved algorithm, the algorithm time complexity, GD index, and HV index are adopted for evaluation. The simulation results and performance index results show that compared with other algorithms, MOIHBA has better BCS and pareto fronts (PFs) with the best uniformity and convergence. Therefore, the MOIHBA algorithm has a greater competitive advantage in solving the MORPO problem. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence algorithms, using heuristic search optimization algorithms to solve optimal reactive power dispatch (ORPD) problem has become a hot topic for current research. ORPD is a multi-polar, non-convex and non-linear problem with a large number and type of constraints as well as discrete nature of control variables. This paper proposed a multi-strategy improved Aquila optimization (IAO) algorithm to solve the ORPD, which introduces a probabilistic perturbation strategy to improve the balance between global exploitation and local exploration, introduces a Cauchy operator to increase the population diversity of Aquila, introduces an elite group navigation strategy to further avoid falling into a local optimum, and analyzes the performance of IAO. To verify the validity of the proposed method in solving the ORPD problem, firstly, the simulation experiments of single-objective ORPD (SOORPD) are carried out on four test systems of different sizes, IEEE14-bus, IEEE30-bus, IEEE57-bus and IEEE118-bus, respectively. Then, to further verify the validity of the method, the simulation experiments of multi-objective ORPD (MOORPD) are carried out on the test system of IEEE30-bus with the method and other classical algorithms. Finally, Comparing the results of the proposed method, with the results of other classical optimization algorithms and the results of existing literature, the comparative analysis of the simulation results confirms the reliability and validity of the proposed method toward solving the ORPD problem. [ABSTRACT FROM AUTHOR]