Capital market opening and labour investment efficiency

The purpose of this research is to explore the impact of capital market opening on inefficient labour investment of enterprises and its impact path. This paper takes 2010–2019 A-share nonfinancial listed companies in Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE) as research objects and samples, and uses DID method to examine the impact of capital market opening on labour investment efficiency of listed companies.We collected 22567 pieces of data.The results show that the capital market opening system significantly reduces inefficient labour investment of enterprises, mainly through reducing the information asymmetry and the agency costs as the main paths. This research shows that the capital market opening is of positive significance to the sustainable development of enterprises, and it proposes targeted suggestions for the government, listed companies and market investors to effectively reduce the inefficient labour investment of enterprises. The research provides more feasible references for capital market opening and corporate governance, and also offers theoretical evidence for the implementation of ‘Shanghai-Hong Kong Stock Connect’ program

Transcriptome Profiling to Identify Genes Involved in Mesosulfuron-Methyl Resistance in Alopecurus aequalis

Non-target-site resistance (NTSR) to herbicides is a worldwide concern for weed control. However, as the dominant NTSR mechanism in weeds, metabolic resistance is not yet well-characterized at the genetic level. For this study, we have identified a shortawn foxtail (Alopecurus aequalis Sobol.) population displaying both TSR and NTSR to mesosulfuron-methyl and fenoxaprop-P-ethyl, yet the molecular basis for this NTSR remains unclear. To investigate the mechanisms of metabolic resistance, an RNA-Seq transcriptome analysis was used to find candidate genes that may confer metabolic resistance to the herbicide mesosulfuron-methyl in this plant population. The RNA-Seq libraries generated 831,846,736 clean reads. The de novo transcriptome assembly yielded 95,479 unigenes (averaging 944 bp in length) that were assigned putative annotations. Among these, a total of 29,889 unigenes were assigned to 67 GO terms that contained three main categories, and 14,246 unigenes assigned to 32 predicted KEGG metabolic pathways. Global gene expression was measured using the reads generated from the untreated control (CK), water-only control (WCK), and mesosulfuron-methyl treatment (T) of R and susceptible (S). Contigs that showed expression differences between mesosulfuron-methyl-treated R and S biotypes, and between mesosulfuron-methyl-treated, water-treated and untreated R plants were selected for further quantitative real-time PCR (qRT-PCR) validation analyses. Seventeen contigs were consistently highly expressed in the resistant A. aequalis plants, including four cytochrome P450 monooxygenase (CytP450) genes, two glutathione S-transferase (GST) genes, two glucosyltransferase (GT) genes, two ATP-binding cassette (ABC) transporter genes, and seven additional contigs with functional annotations related to oxidation, hydrolysis, and plant stress physiology. These 17 contigs could serve as major candidate genes for contributing to metabolic mesosulfuron-methyl resistance; hence they deserve further functional study. This is the first large-scale transcriptome-sequencing study to identify NTSR genes in A. aequalis that uses the Illumina platform. This work demonstrates that NTSR is likely driven by the differences in the expression patterns of a set of genes. The assembled transcriptome data presented here provide a valuable resource for A. aequalis biology, and should facilitate the study of herbicide resistance at the molecular level in this and other weed species

Stochastic dynamic optimal power flow under the variability of renewable energy with modern heuristic optimization techniques.

With the increasing penetration of renewable energy to power systems, such as wind power, more challenges have been brought to system operations due to the intermittent nature of wind. Such influence can be reflected on ancillary services of systems such as frequency control, scheduling and dispatch, and operating reserves. To tackle those challenges, wind power forecast has become an important tool. Nowadays, forecasters typically have access to information scattered through a huge number of observed wind power time-series data from a large number of wind farms. However traditional multivariate time-series models can only process small number of data and capture only the temporal correlation in wind. In this work we utilized a probabilistic forecast model, dynamic factor model (DFM), to predict wind power. The DFM is able to capture both the spatial and temporal correlation of data, and generate as many scenarios as possible to represent the uncertainty of wind power forecast. This work also focuses on the optimization of the system integrated with wind power and storage devices over 24 hours. Thus we formulate such problem as a stochastic dynamic optimal power flow (DOPF) problem. The essence of solving stochastic problem is to make a decision that performs well on average under almost all possible scenarios. In all, the objective functions are to optimize the expected value over all scenarios generated by DFM. Once the stochastic optimization problem is formulated, a proper methodology is required to solve the problem. Static optimal power flow (OPF) is a highly non-linear, mixed-integer, non-convex and non-smooth problem, and traditional techniques such as nonlinear programming, quadratic programming, interior point method simplifies the problem which sacrifices the accuracy of the solution, and fails to consider the non-smooth, non-differentiable and non-convex objective functions. Therefore, to circumvent these downsides we proposed a novel heuristic method called artificial bee colony (ABC) to tackle the static OPF without approximation. In this study, the ABC has been tested on small, medium and large power system for OPF (IEEE-30, IEEE-57 and IEEE-118 buses) and then it was modified and extended to solve a dynamic optimization problem recursively

An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem

EKE, ibrahim/0000-0003-4792-238XWOS: 000399628600014The increasing fuel price has led to high operational cost and therefore, advanced optimal dispatch schemes need to be developed to reduce the operational cost while maintaining the stability of grid. This study applies an improved heuristic approach, the improved Artificial Bee Colony (IABC) to optimal power flow (OPF) problem in electric power grids. Although original ABC has provided robust solutions for a range of problems, such as the university timetabling, training neural networks and optimal distributed generation allocation, its poor exploitation often causes solutions to be trapped in local minima. Therefore, in order to adjust the exploitation and exploration of ABC, the IABC based on the orthogonal learning is proposed. Orthogonal learning is a strategy to predict the best combination of two solution vectors based on limited trials instead of exhaustive trials, and to conduct deep search in the solution space. To assess the proposed method, two fuel cost objective functions with high non-linearity and non-convexity are selected for the OPF problem. The proposed IABC is verified by IEEE-30 and 118 bus test systems. In all case studies, the IABC has shown to consistently achieve a lower cost with smaller deviation over multiple runs than other modern heuristic optimization techniques. For example, the quadratic fuel cost with valve effect found by IABC for 30 bus system is 919.567 $/hour, saving 4.2% of original cost, with 0.666 standard deviation. Therefore, IABC can efficiently generate high quality solutions to nonlinear, nonconvex and mixed integer problems.Scientific and Technical Research Council of Turkey (TUBITAK) TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [1059B191300593]; Scientific Research Projects Coordination Unit (BAP) of Kirikkale University (TUBiTAK) [2012/112]I.Eke is supported by The Scientific and Technical Research Council of Turkey (TUBITAK) Turkey, through the postdoctoral research program 2219, under application number 1059B191300593. and Scientific Research Projects Coordination Unit (BAP - project number 2012/112) of Kirikkale University (TUBiTAK)

Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model

The deterministic methods generally used to solve DC optimal power flow (OPF) do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM)—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC) algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h

Improved Artificial Bee Colony Based on Orthognal Learning for Optimal Power Flow

2015 18th International Conference on Intelligent System Application to Power Systems (ISAP) -- SEP 11-17, 2015 -- Porto, PORTUGALEKE, ibrahim/0000-0003-4792-238XWOS: 000380395400055Optimal power flow (OPF) problem is to optimize an objective function (usually total cost of generation), while satisfying system constraints. The OPF is a non-linear and non-convex problem, and an artificial bee colony (ABC) algorithm is utilized to handle the problem. Heuristic methods are credited for their simplicity to solve complex non-linear optimization problem without simplifying approximation of the system. However, the original ABC has poor efficiency on exploitation search, thus in order to find better global optimum, this paper proposes an improved ABC (IABC) based on orthogonal learning. The IABC implements the idea of orthogonal experiment design (OED) based on the orthogonal learning. The validity and effectiveness of the method are tested in the IEEE-30 bus system.Distribuicao, Inesctec Technol & Sci Associate Lab Portugal, Fac Engn Univ Port, Dept Engn Elect & Comp, Fdn Ciencia & Tecnol, IEEE Power & Energy So

Heuristic Optimization for Wind Energy Integrated Optimal Power Flow

General Meeting of the IEEE-Power-and-Energy-Society -- JUL 26-30, 2015 -- Denver, COEKE, ibrahim/0000-0003-4792-238XWOS: 000371397503195Wind energy has been playing a critical role in modern electric power system due to the fact that wind is free of cost and environment-friendly. However the inherent intermittency of wind has complicated system operation such as optimal power flow (OPF). In this paper, the authors present a stochastic OPF model integrated with wind power (WOPF). Since WOPF is a highly non-linear and non-convex problem, a heuristic method, artificial bee colony (ABC), is utilized to handle the problem. Heuristic methods are credited for their simplicity to solve complicated non-linear optimization problem without approximating the system and the ability to find better global optimum. Two IEEE systems (30 and 118 bus) are used to test the validity and effectiveness of the method.IEEE Power & Energy So

Optimal Scheduling of Distributed Energy Resources by Modern Heuristic Optimization Technique

19th International Conference on Intelligent System Application to Power Systems (ISAP) -- SEP 17-20, 2017 -- San Antonio, TXEKE, ibrahim/0000-0003-4792-238XWOS: 000426989800045The increasing number and types of energy resources and prosumers has complicated the operation in microgrid greatly. Such problem becomes a hard-to-solve or even impossible-to-solve for traditional mathematical algorithms without necessary approximation. However, modern heuristic optimization techniques have proven their efficiency and robustness in complex non-linear, non-convex and large-size problems. In this paper, we propose a comprehensive microgrid which consists of renewables, distributed generators, demand response, marketplace, energy storage system and prosumers, and investigate the behaviors of such system. A novel heuristic method, artificial bee colony, is proposed to solve the day-ahead optimal scheduling of the microgrid. Case studies have shown that such algorithm is able to solve the problem fast, reliable with satisfactory solutions. For the first case, the computational time is 9 minutes compared with 19 hours by a traditional methodical tool which has not taken necessary approximation of the original problem

A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms

The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward–backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case.<br/

Optimal Allocation of PV Systems on Unbalanced Networks Using Evolutionary Algorithms

As the distributed energy resources (DERs) increasingly penetrate the unbalanced distribution network, it becomes challenging to accommodate such penetration technically and economically. Therefore, this paper tackles an optimal allocation of PV systems (locations and sizes) to maximize the penetration while minimizing voltage violation. It is challenging because the problem is a mixed integer nonlinear programming (MINLP) problem with non-linear and non convex properties. In addition, the network is unbalanced which brings burdens on solving load flows. Computational intelligent methods, particularly evolutionary algorithms (EAs) have proven its efficiency and robustness in large optimization problems and thus, this paper explores two EAs on the problem with the help of a robust unbalanced load flow algorithm. A comparative study is conducted on particle swarm optimization (PSO) and artificial bee colony (ABC) based on IEEE 13 and 37 bus systems. Optimal allocation based on peak hour and day ahead scenarios are considered. After 30 times run, the test cases have shown that both EAs are successful and yet ABC generally converges to better solution and yet with larger statistical deviations on solutions.<br/