Review Special Issues

An overview of AC and DC microgrid energy management systems

  • Received: 10 July 2023 Revised: 06 October 2023 Accepted: 10 October 2023 Published: 02 November 2023
  • In 2022, the global electricity consumption was 4,027 billion kWh, steadily increasing over the previous fifty years. Microgrids are required to integrate distributed energy sources (DES) into the utility power grid. They support renewable and nonrenewable distributed generation technologies and provide alternating current (AC) and direct current (DC) power through separate power connections. This paper presents a unified energy management system (EMS) paradigm with protection and control mechanisms, reactive power compensation, and frequency regulation for AC/DC microgrids. Microgrids link local loads to geographically dispersed power sources, allowing them to operate with or without the utility grid. Between 2021 and 2028, the expansion of the world's leading manufacturers will be driven by their commitment to technological advancements, infrastructure improvements, and a stable and secure global power supply. This article discusses iterative, linear, mixed integer linear, stochastic, and predictive microgrid EMS programming techniques. Iterative algorithms minimize the footprints of standalone systems, whereas linear programming optimizes energy management in freestanding hybrid systems with photovoltaic (PV). Mixed-integers linear programming (MILP) is useful for energy management modeling. Management of microgrid energy employs stochastic and robust optimization. Control and predictive modeling (MPC) generates energy management plans for microgrids. Future microgrids may use several AC/DC voltage standards to reduce power conversion stages and improve efficiency. Research into EMS interaction may be intriguing.

    Citation: Mohamed G Moh Almihat. An overview of AC and DC microgrid energy management systems[J]. AIMS Energy, 2023, 11(6): 1031-1069. doi: 10.3934/energy.2023049

    Related Papers:

    [1] Haihua Xiao, Qiaokang Liang, Dan Zhang, Suhua Xiao, Gangzhuo Nie . A method for demand-accurate one-dimensional cutting problems with pattern reduction. Mathematical Biosciences and Engineering, 2023, 20(4): 7453-7486. doi: 10.3934/mbe.2023323
    [2] Xiaoyue Xie, Jian Shi . A distributed quantile estimation algorithm of heavy-tailed distribution with massive datasets. Mathematical Biosciences and Engineering, 2021, 18(1): 214-230. doi: 10.3934/mbe.2021011
    [3] Kwabena Owusu-Agyemang, Zhen Qin, Appiah Benjamin, Hu Xiong, Zhiguang Qin . Guaranteed distributed machine learning: Privacy-preserving empirical risk minimization. Mathematical Biosciences and Engineering, 2021, 18(4): 4772-4796. doi: 10.3934/mbe.2021243
    [4] Xiangfen Song, Yinong Wang, Qianjin Feng, Qing Wang . Improved graph cut model with features of superpixels and neighborhood patches for myocardium segmentation from ultrasound image. Mathematical Biosciences and Engineering, 2019, 16(3): 1115-1137. doi: 10.3934/mbe.2019053
    [5] Xueyuan Li, MiaoYu, Xiaoling Zhou, Yi Li, Hong Chen, Liping Wang, Jianghui Dong . A method of ultrasound diagnosis for unilateral peripheral entrapment neuropathy based on multilevel side-to-side image contrast. Mathematical Biosciences and Engineering, 2019, 16(4): 2250-2265. doi: 10.3934/mbe.2019111
    [6] Ming Zhu, Kai Wu, Yuanzhen Zhou, Zeyu Wang, Junfeng Qiao, Yong Wang, Xing Fan, Yonghong Nong, Wenhua Zi . Prediction of cooling moisture content after cut tobacco drying process based on a particle swarm optimization-extreme learning machine algorithm. Mathematical Biosciences and Engineering, 2021, 18(3): 2496-2507. doi: 10.3934/mbe.2021127
    [7] Hong Zhang, Zhenchao Xu, Yunxiang Wang, Yupeng Shen . An innovative parameter optimization of Spark Streaming based on D3QN with Gaussian process regression. Mathematical Biosciences and Engineering, 2023, 20(8): 14464-14486. doi: 10.3934/mbe.2023647
    [8] Xu Ji, Fan Bai, Jiang Jiang, Hongge Fu, Qingjie Sun, Weiyu Zhu . Numerical simulation and experimental study for ultrasonic vibration-assisted drilling of SiCp/AL6063. Mathematical Biosciences and Engineering, 2023, 20(2): 2651-2668. doi: 10.3934/mbe.2023124
    [9] Fengjie Liu, Monan Wang, Yuzheng Ma . Multiscale modeling of skeletal muscle to explore its passive mechanical properties and experiments verification. Mathematical Biosciences and Engineering, 2022, 19(2): 1251-1279. doi: 10.3934/mbe.2022058
    [10] Xin Zheng, Chenhan Liu, Yifei Gong, Qian Yin, Wenyan Jia, Mingui Sun . Food volume estimation by multi-layer superpixel. Mathematical Biosciences and Engineering, 2023, 20(4): 6294-6311. doi: 10.3934/mbe.2023271
  • In 2022, the global electricity consumption was 4,027 billion kWh, steadily increasing over the previous fifty years. Microgrids are required to integrate distributed energy sources (DES) into the utility power grid. They support renewable and nonrenewable distributed generation technologies and provide alternating current (AC) and direct current (DC) power through separate power connections. This paper presents a unified energy management system (EMS) paradigm with protection and control mechanisms, reactive power compensation, and frequency regulation for AC/DC microgrids. Microgrids link local loads to geographically dispersed power sources, allowing them to operate with or without the utility grid. Between 2021 and 2028, the expansion of the world's leading manufacturers will be driven by their commitment to technological advancements, infrastructure improvements, and a stable and secure global power supply. This article discusses iterative, linear, mixed integer linear, stochastic, and predictive microgrid EMS programming techniques. Iterative algorithms minimize the footprints of standalone systems, whereas linear programming optimizes energy management in freestanding hybrid systems with photovoltaic (PV). Mixed-integers linear programming (MILP) is useful for energy management modeling. Management of microgrid energy employs stochastic and robust optimization. Control and predictive modeling (MPC) generates energy management plans for microgrids. Future microgrids may use several AC/DC voltage standards to reduce power conversion stages and improve efficiency. Research into EMS interaction may be intriguing.



    Bone cutting is a common surgical method in bone surgery. In the process of bone cutting, the temperature generated by the cutting directly affects the biological activity of bone material and the degree of thermal damage to the surrounding soft tissue, especially when the temperature of the bone in direct contact with the cutter reaches 47℃ and remains for more than 1min, thermal necrosis will occur immediately due to high temperature [1]. Thermal necrosis of bone materials and surrounding soft tissues can prolong and delay the patient's postoperative recovery time [2]. In addition, excessive stress also leads to the damage of bone tissue, cartilage tissue and muscle in the surrounding area of cutting, resulting in secondary damage [3,4]. Therefore, reducing the temperature and stress in the process of bone cutting is the urgent research. Bone is a kind of anisotropic material with low thermal conductivity, 0.16~12.8 WM-1·K-1 [5,6]. These characteristics prevent heat dissipation during bone cutting, leading to the increase of bone temperature. Besides, the drilling mechanism is produced by a complex combination of cutting and extrusion at the drill point, and the cutting force, torque, and temperature must be kept below the critical level of osteonecrosis [7]. During bone drilling, there is the energy conversion, i.e., mechanical work (friction and shear deformation of bone) from cutters is converted into heat energy [1,8,9]. The cutting temperature and the cutting force generated depend on various cutting parameters, such as bit diameter, bit speed, and axial drilling force [10]. Davidson et al. investigated the effects of spindle speed, feed rate, screw angle, bit apex angle, and bit diameter on drilling temperature [11]. The results showed that spindle speed, feed rate and bit diameter had great influence on bone thermal properties, while screw angle and bit apex angle had relatively weaker influence on bone thermal properties. Chen et al. studied the influence of drilling parameters (feed rate and rotational speed) on bone temperature, and analyzed bone temperature distribution through experiments and numerical simulation of drilling process [12]. They pointed out that when the drilling speed was constant, the maximum temperature of bone decreased with the increase of feed rate, while when the feed rate was constant, the maximum drilling temperature increased with the increase of rotational speed, and the maximum temperature occurred in the cancellous bone near the cortical bone in their study. Karaca et al. found that the drilling temperature increased gradually in the process of increasing the drilling speed from 200 r/min to 1180 r/min when drilling the calf tibia [13]. However, the effects of cutting parameters on temperature were inconsistent. For example, some studies believed that when drilling speed was low, the temperature increased with the increase of drilling speed, while others believed that the temperature decreased with the increase of drilling speed [10].

    The research showed that the cutting parameters also had great influence on the cutting force. Udiliak et al. evaluated the influence of bit tip angle and spindle speed on drilling force [14], and they concluded that bit tip angle was related to drilling force, while spindle speed had no obvious influence on it. At the same time, the feed speed was proportional to the drilling force. Alam et al. explored the influence of cutting depth, cutting speed and other parameters on cutting force in planar cutting of cortical bone based on FEA [15]. The results showed that lower cutting depth and sharp cutting tools can reduce cutting force. With the improvement of machining technology, the ultrasonic vibration method can effectively reduce the cutting temperature and cutting force. Wang et al. found that compared with conventional drilling methods, low-frequency vibration assisted drilling (frequency 5–20 Hz) had fewer and shorter microcracks, and the cutting heat was significantly reduced [16,17]. Zakrasas et al. conducted drilling experiments using pig ribs as samples, and the results showed that the maximum temperature generated at vibration frequencies of 60–120 Hz was 14% lower than that generated by conventional cutting [18]. Ostaševičius et al. also conducted 10 drilling experiments (frequency 60–140 Hz), and the results showed that the drilling temperature was reduced by 21℃ when the frequency was 80 Hz [19]. Gupta et al. proved through ultrasound-assisted pig bone drilling experiment that the rotational speed of the bit had the greatest influence on the temperature rise, accounting for 46% of the total proportion [20]. Paktinat and Amini [21], Nosouni [22] et al. compared the drilling effect of vibration-assisted drilling with that of ordinary drilling, and conducted simulation and experimental research on drilling force. The results showed that in the test range, the vibration-assisted drilling effect was better. The axial force produced by ultrasonic assisted drilling was obviously lower than that produced by ordinary drilling. Optimizing the structure of bone cutting tools is also an effective method to reduce cutting heat and bone stress [23]. For example, micro-texture tools can effectively improve the situation of excessive cutting heat and cutting force in the process of bone cutting [24,25,26].

    Human bone is composed of four layers of tissue, including periosteum, cortical bone (dense bone), cancellous bone (spongy or trabecular bone) and bone marrow [27,28]. In particular, cancellous bone is formed by porous structure similar to honeycomb and has a gradient distribution [29,30]. Cancellous bones have large interstitial spaces with porosity ranging from 50 to 90% [31]. Although cortical bone structure is nearly solid, it still has about 3–5% porosity [29]. In the existing literature, the bone model mostly adopts the solid model for finite element analysis. However, the continuum model is not fit for porous model such as bone. Because the Cauchy continuum model is the simplest mechanical model to describe the behavior of bone from a macroscopic point of view [32,33]. Indeed, the porous space inside bone tissue is filled with fluids, such as bone marrow, interstitial fluid, blood, etc., so bone tissue is a model of liquid-solid two-phase coexistence [33,34]. To be precise, bone tissue is considered to be an anisotropic material rather than an isotropic Cauchy continuum model. To correctly describe the behavior of bone tissue at the level of hundreds of micrometers, the geometric arrangement and porosity of bone interior are needed [33]. Therefore, it is inevitable to consider the effect of porous structure on cutting heat and force when conducting bone cutting research. The accuracy of the bone cutting model determines the described phenomena. But few studies have been reported on this topic. Therefore, a gradient porous structure of bone cutting model was constructed using Voronoi method in this paper, and the main research contents were as follows: 1) a gradient porous structure of bone cutting model was established based on Voronoi method, and the cutting temperature and cutting force were verified by FEA and cutting experiment; 2) orthogonal cutting experiments were designed to analyze the influence of cutting parameters on the Voronoi bone model; 3) The least square method is used to establish the prediction model of bone cutting temperature and cutting force.

    Voronoi diagram, also called Tyson polygon or Dirichlet diagram, is a space segmentation method based on seed points. Given a finite set of points in the Euler plane {..., Pi, ... Pn} [35,36], for each point Pi, the corresponding Voronoi unit contains all points in the Euler plane whose distance to Pi is less than or equal to any other point, and these points divide the Euler space into two parts. The Voronoi diagram is determined by the number and distribution of seed points, the control of which is critical to the successful modeling of irregular porous scaffolds for a set of points in m-dimensional Euclidean space:

    P={p1,,pn}Rm,2n<,pipj,i,jIn=1,,n (1)

    Given that Voronoi diagram can be generated at any point in space and irregular structures can be established, Wang's team successfully obtained gradient porous structures based on the top-down design method of Voronoi Mosaic [37]. Han et al. also designed porous bone structure with gradient by using Voronoi method [38]. Based on these studies, a three-dimensional model of porous bone cutting with gradient is established by using Voronoi method in this paper. Therefore, python language was used to program three-dimensional Voronoi structure, and Python script was run in finite element software Abaqus2020 to construct porous bone tissue models with different gradients, as shown in Figure 1. The gradient bone model refers to the center of each structure as the benchmark. The closer to the center, the sparser the Voronoi units are, inversely, the closer to the edge of the strucutre, the denser the Voronoi units are. In this study, three gradients were set as 0.7, 0.8 and 0.9, respectively. The smaller the number is, the more obvious the gradient is. These designed gradient structures were used for bone cutting simulation analysis to select replacement models that were similar to real bone issue.

    Figure 1.  The gradient bone model based on Voronoi method.

    FEA has been widely used in bone tissue cutting. In the FEA, it is often necessary to define the thermodynamic properties of the model materials, and accurate material parameters can more truly simulate the cutting process. In this paper, it is assumed that the bone material was isotropic and had elastic-viscoplastic behavior for the prediction of cutting force and temperature. The drilling bit was assumed to be a clinically similar stainless steel material. Table 1 lists the thermodynamic performance parameters of the bone model and the drilling bit [12]. To describe the mechanical behavior of bone tissue, the Johnson-Cook (J-C) constitutive model was used, which took nonlinear strain hardening and strain rate sensitivity into account [39]. J-C model can be used to describe bone as an elastoplastic material with bilinear strain hardening [15], as shown in Eq (2). J-C model parameters of bone materials are listed in Table 2 [15,16]:

    σ=(A+Bεn)(1+Cln˙ε˙ε0)[1(TTrTmTr)m] (2)
    Table 1.  Thermodynamic parameters of cancellous bone model and cutter [12].
    elastic and thermal properties cancellous bone cutter
    young modulus (MPa) 759 193000
    Poisson's ratio 0.30 0.25
    density (kg/m3) 640 7990
    thermal conductivity (W/m·K) 0.087 16.2
    specific heat (J/kg·K) 1477 500
    yield stress (MPa) 31.0 290
    ultimate stress (MPa) 31.1 579
    ultimate strain 0.07 0.003

     | Show Table
    DownLoad: CSV
    Table 2.  Parameters of cancellous bone Johnson-Cook model [15,16].
    A B n C m ε Tr Tm
    50 MPa 101 MPa 0.72 0.059 1.56 0.001 945 K 293 K

     | Show Table
    DownLoad: CSV

    In which σ is the material flow stress, ε is the equivalent plastic strain, Tm is the melting temperature of the material, T is the material temperature, Tr is the reference temperature, ˙ε is the plastic strain rate, ˙ε0 is the effective plastic strain rate of the quasi-static test and A, B, C, m, n are the material constants.

    Generally, bone cutting heat comes from three regions: shear deformation zone, friction zone Ⅰ and friction zone Ⅱ, as shown in Figure 2(a). Shear deformation is caused by plastic deformation of bone. However, both friction zones are generated by contact between the drilling bit and the bone, and friction between the bone chips and the front slope creates zone Ⅰ. Lateral friction with bone surface produces zone Ⅱ. The shear deformation zone and friction zone Ⅰ convert mechanical work to heat energy, but the energy of friction zone Ⅱ is negligible if a sharp bit is used [1,9].

    Figure 2.  Bone cutting model: (a) Two-dimensional cutting heat model; (b) Three-dimensional models including drilling cutter, solid bone and Voronoi bone.

    In the process of bone cutting, chip will be formed when the cutting edge contact with the bone surface. Consequently, it is necessary to set the failure criterion of bone material. In the simulation of bone drilling, shear damage failure criterion is usually selected. In this part, J-C damage model was used to separate the formation of wool chips, as shown in Eq (3). In the J-C damage model, damage occurs when parameter D exceeds 1 [22]. Table 3 shows parameters of the J-C damage model:

    D=Δεplεplf (3)
    Table 3.  Johnson-Cook damage material constants.
    d1 d2 d3 d4 d5
    -0.068 0.451 -0.952 0.036 0.697

     | Show Table
    DownLoad: CSV

    In which Δεpl is the plastic tension increase and Δεplf ­ is the tension needed for damage and is calculated from Eq (4):

    εplf=(d1+d2expd3pq)[1+d4lnεpl˙ε0](1+d5TTrTmTr) (4)

    In which p is the compressive stress, q is the von-misses stress, d1 is initial failure strain, d2 is exponential factor, d3 is triaxiality factor, d4 is strain rate factor, d5 is temperature factor.

    This research focuses on analyzing the effects of cutting rotational speed Vc (Hereinafter referred to as cutting speed), feed speed Vf and tip angle α on bone cutting temperature and cutting force. Therefore, the orthogonal experiment scheme of establishing three-factor three-level numerical simulation is shown in Table 4.

    Table 4.  Orthogonal experimental scheme.
    cutting parameters Level 1 Level 2 Level 3
    cutting speed Vc (mm/s) 10 15 20
    feed speed Vf (mm/s) 0.5 1.0 1.5
    tip angle 2α (°) 105 115 125

     | Show Table
    DownLoad: CSV

    FEA was carried out using Abaqus2020 explicit dynamics, and all models are shown in Figure 2(b). In the material setting, bone model and drilling cutter material attributes were assigned according to the content in Table 1, respectively. Temperature displacement coupling was selected in the analysis step, and the cutting time was set to 0.02 s. In order to save calculation time, mass scaling of the model was required. The target time increment was set to 1E-006 in time increment mode. Temperature and force were selected from both the field and historical variables. Because this paper mainly studied the cutting temperature and cutting force in the process of bone drilling, and did not involve the tool wear, the drilling bit was set as rigid body. The tangential behavior between the drilling bit and the bone model was set as penalty function with the friction coefficient 0.3, and the normal behavior was set as hard contact. In the boundary condition setting, the six degrees of freedom of the bone model were completely fixed. The Z-axis direction of the drilling bit was set as the feed movement speed and the cutting speed rotating around the Z-axis, and the other degrees of freedom were fixed. The mesh type of drilling bit was tetrahedral element mesh C3D4T, and the number of mesh was 145,322. The mesh types of all bone models were hexahedral mesh unit C3D8R, wherein the mesh number of Voronoi bone model with gradient 0.7, 0.8 and 0.9 was 1,819,800, 1,842,800, 1,826,248, respectively, and the mesh number of solid bone model was 1,070,680.

    To verify that the constructed Voronoi bone tissue model with gradient was more similar to the real cancellous bone model, pig hind leg bone was selected for bone drilling experiment in computer numerical control (CNC) machining center. The drilling bit specification used in the experiment was consistent with the finite element model. The cutting force was collected by Kistler 2825A-02 piezoelectric dynamometer, and the cutting temperature generated by bone drilling was monitored by infrared temperature sensor in real time. The cutting parameters of bone drilling experiment and FEA were set as cutting speed Vc = 20 r/s, feed speed Vf = 1.5 mm/s, and tip angle 2α = 115°. Table 5 shows the comparison of cutting temperature and cutting force between bone drilling experiment and FEA. The error calculation formula was as follows:

    Error=|ResultfiniteResultexperimentResultexperiment|100% (5)
    Table 5.  Comparison of the cutting temperature and force.
    Cutting Temperature (℃) Cutting Force (N) Error (%)
    Temp. Force
    Cutting experiment 47.7 34.5 -- --
    Solid bone 57.1 47.4 19.70% 37.39%
    Voronoi = 0.9 bone 53.8 38.9 12.79% 15.36%
    Voronoi = 0.8 bone 51.6 31.3 8.17% 9.28%
    Voronoi = 0.7 bone 43.2 29.8 9.43% 13.62%

     | Show Table
    DownLoad: CSV

    Where Resultfinite is the result of finite element analysis, including cutting temperature and cutting force; Resultexperiment is the result of cutting experiment, including cutting temperature and cutting force.

    According to the data in Table 5, the cutting temperature and the cutting force generated by Voronoi bone models with different gradients in the FEA were both smaller than those of solid bone models, while the Voronoi bone models with gradient 0.8 was closer to the real bone structure by comparing with bone experiment. Because the interior of the real bone model was porous and dense, these voids reduced friction with the tool surface, which reduced friction heat and friction between bone and drilling bit. In the numerical analysis, there was no porous structure in the solid bone model, the contact area between the drilling bit surface and the solid bone model was large in the cutting process, which led to the large friction. Friction not only caused heat increase, but also resulted in greater friction force. The increase of heat directly led to the increase of cutting temperature, and the friction force, as a part of the source of cutting force, also triggered the increase of cutting force. In the finite element simulation of Voronoi bone models with different gradient, the bone tissue constructed by Voronoi method had porous structure inside, which was similar to the real bone tissue model. The results of FEA showed that the cutting temperature of Voronoi bone tissue was lower than that of solid model and closer to that of bone cutting experiment. Because these porous structures created by the Voronoi method were similar to the internal structure of real bone tissue, the interaction between the drilling bit surface and the bone model was reduced. In addition, by adjusting the gradient, the distribution of porous structures can be altered to mimic more real bone tissue. The FEA showed that the cutting temperature and cutting force of Voronoi bone model with gradient 0.8 was closer to the cutting experiments. The error values in Table 5 clearly verified this conclusion. Therefore, the cancellous bone model established by Voronoi method was of certain significance to the study of bone cutting. The subsequent cutting simulation of bone drilling was based on the Voronoi bone model with gradient 0.8. Because FEA technology has been widely used to simulate biomechanics, its results are reliable. In order to save the experiment time and cost, the cutting experiment comparison and verification of the finite element model is no longer carried out.

    According to the results in Section 3.1, under the same cutting conditions, the error between the cutting force and cutting temperature generated by Voronoi bone structure with gradient 0.8 and the results of bone drilling experiment was the smallest. Therefore, in the following orthogonal experiment of cutting parameters, the porous bone model with gradient 0.8 was taken as the research object for FEA. According to the orthogonal cutting experiment scheme, Table 6 lists the cutting temperature results. In the range analysis table, Ki is the mean value of each factor at a certain level i, and R is range. The greater the range, the greater the influence of this factor on the test results. According to Table 7, the primary and secondary order of influence of each factor on cutting temperature was cutting speed > tip angle > feed speed. According to the FEA results of orthogonal experiment, the influence analysis of cutting parameters on cutting temperature was shown in Figure 3. As shown in Figure 3(a), when the tip angle was constant, the larger the cutting speed and feed speed were, the higher the cutting temperature would be in the process of bone drilling. As shown in Figure 3(b), when the feed speed was constant, the cutting temperature increased with the increase of the cutting speed. On the contrary, it tended to decrease with the increase of the tip angle. However, the combination of tip angle and feed speed had no stable effect on the cutting temperature of bone drilling.

    Table 6.  Finite element simulation results of cutting temperature.
    group cutting speed Vc (r/s) feed cutting Vf (mm/s) tip angle 2α (°) temperature (℃)
    1 10 0.5 105 44.6
    2 10 1.0 115 43.2
    3 10 1.5 125 42.9
    4 15 0.5 115 47.8
    5 15 1.0 125 46.1
    6 15 1.5 105 49.2
    7 20 0.5 125 48.7
    8 20 1.0 105 53.2
    9 20 1.5 115 51.6

     | Show Table
    DownLoad: CSV
    Table 7.  Cutting temperature range analysis.
    Ki cutting speed Vc (r/s) feed speed Vf (mm/s) tip angle 2α (°)
    K1 130.7 141.1 147.0
    tempeture K2 143.1 142.5 142.6
    K3 153.5 143.7 137.7
    R 22.8 2.6 9.3
    Weight of influence factor W Vc > > Vf

     | Show Table
    DownLoad: CSV
    Figure 3.  Analysis of influence of cutting parameters on cutting temperature: (a) cutting speed and feed speed-cutting temperature surface diagram; (b) cutting speed and tool tip angle-cutting temperature surface diagram; (c) feed speed and tip angle-cutting temperature surface diagram.

    By analyzing the influence of drilling parameters on cutting temperature, and combining with range method, the conclusion that cutting speed > tip angle > feed speed is obtained. The increase of cutting speed leads to the fracture of bone tissue caused by large shear deformation in a short time. As the cutting temperature of bone mainly comes from three regions, the shear deformation zone as shown in Figure 2(a), the deformation heat generated by the shear deformation of bone is the main source of heat. Larger shear deformation results in the rapid accumulation of cutting heat and the formation of higher cutting temperature. The tip angle mainly affects the contact area between the drilling bit and bone tissue and the heat dissipation area. The larger the tip angle is, the sharper the drilling bit is. At this time, the area of contact between the bit and bone tissue will be smaller, as shown in zone Ⅰ and zone Ⅱ in Figure 2(a). Therefore, tip angle affects the cutting temperature by changing the friction area between the bit and bone tissue. Feed speed, mainly by changing the speed of the cutting axial motion, has little influence on the shear deformation of bone tissue and the contact friction zone, so it has the least influence on the cutting temperature. Figure 4 also shows the finite element results of orthogonal experiments on Voronoi bone model drilling with gradient 0.8.

    Figure 4.  The cutting temperature and force of finite simulation.

    According to the orthogonal cutting experiment scheme, Table 8 lists the cutting force results. The proportion of influence of cutting parameters on cutting force is also analyzed by range method, as shown in Table 9. The order of influence of each factor on cutting force is tip angle > feed speed > cutting speed. According to the finite element simulation results of orthogonal experiment, the influence analysis of cutting parameters on cutting force is shown in Figure 5. When the speed is 15 r/s, the cutting force increases first and then decreases with the feed speed. Similarly, when the feed speed is 1 mm/s, the cutting force also increases first and then decreases with the increase of the cutting speed. Among other parameters, there is no obvious law of cutting force variation, as shown in Figure 5(a). From Figure 5(b), (c), it can be observed that when the tip angle is 125°, the cutting force is still greater than that when the tip angle is 115° and 105°, although the cutting speed and feed speed change. Specifically, when the tip angle is 125°, the cutting force increases with the increase of speed, but decreases with the increase of feed speed. By analyzing the influence of cutting parameters on cutting force, it can be found that the influence of multiple cutting parameters on cutting force should be considered simultaneously to obtain a relatively small cutting force.

    Table 8.  Finite element simulation results of cutting force.
    Group Cutting speed Vc (r/s) Feed cutting Vf (mm/s) Tip angle 2α (°) Force (N)
    1 10 0.5 105 41.3
    2 10 1.0 115 33.5
    3 10 1.5 125 37.8
    4 15 0.5 115 34.2
    5 15 1.0 125 44.8
    6 15 1.5 105 29.3
    7 20 0.5 125 52.6
    8 20 1.0 105 36.5
    9 20 1.5 115 31.3

     | Show Table
    DownLoad: CSV
    Table 9.  Cutting force range analysis.
    Ki Cutting speed Vc (r/s) Feed speed Vf (mm/s) Tip angle 2α (°)
    K1 112.6 128.1 107.1
    Force K2 108.3 114.8 99.0
    K3 120.4 98.4 135.2
    R 12.1 29.7 36.2
    Weight of influence factor W 2α > Vf > Vc

     | Show Table
    DownLoad: CSV
    Figure 5.  Analysis of influence of cutting parameters on cutting force: (a) cutting speed and feed speed-cutting force surface diagram; (b) cutting speed and tool tip angle-cutting force surface diagram; (c) feed speed and tip angle-cutting force surface diagram.

    Through the results of FEA, the influence of tip angle on cutting force is the largest. As tip angle changes affect the degree sharp of drilling bit. The larger the tip angle is, the sharper the drilling bit is. Bone tissue under the action of sharp drilling bit occurs large deformation, resulting in the interaction force between the drilling bit and bone tissue becomes larger, that is, the cutting force becomes larger.

    The feed speed mainly changes the cutting force by affecting the axial action of the drilling bit surface and bone tissue. Due to the porous structure inside the bone tissue, the distribution of porosity interacts with the bit surface in the axial direction, and the feed speed is too small, resulting in the bit interacting with more bone solid structures in the axial direction, which leads to a larger axial force. As the feed speed increases, the drilling bit tip passes through the pores, leaving more surface in the pores of bone tissue, which reduces the axial force generated when the drilling bit cuts bone tissue. The cutting speed mainly affects shear force of bone model. The tool rotates in the bone tissue, forming shear force to make bone tissue shear deformation. The drilling bit produces serious shear action on bone tissue at a faster cutting speed, thus forming a larger shear force, so that the cutting force becomes larger.

    According to the experimental scheme of Table 4, the cutting temperature prediction model and the cutting force prediction model were established based on cutting parameters such as cutting speed, feed speed and tip angle, respectively. Since there is not a simple linear relationship between cutting temperature and cutting parameters, cutting force and cutting parameters, the prediction model of bone cutting temperature and cutting force were established:

    T=VacVbf(2α)c (6)

    Where T is the cutting temperature; a, b, and c are coefficients, respectively.

    F=VdcVef(2α)h (7)

    Where F is the cutting force; d, e, and h are coefficients, respectively.

    Since Eqs (6) and (7) are non-highly nonlinear functions and complicated to calculate, they are converted into linear functions by means of the least square method. Here, the cutting temperature prediction model Eq (6) is taken as an example to calculate. Take the logarithm of Eq (6):

    lnT=alnVc+blnVf+cln2 (8)

    Let Y=lnT, x1=lnVc, x2=lnVf, x3=ln2α, b1=a, b2=b, b3=c, so Equation (8) can be converted to Eq (9):

    Y=b1x1+b2x2+b3x3 (9)

    There are independent variables x1, x2 and x3 in Eq (9). Since there are 9 experimental groups, the independent variables of group i are xi1, xi2 and xi3. Similarly, the cutting temperature Y obtained by finite element calculation is expressed as Yi. The specific expression is as follows:

    yi=bixi1+bixi2+bixi3(i=1,2,3,4,5,6,7,8,9) (10)

    To simplify the calculation, Eq (10) is changed into a matrix, as follows:

    Y=(y1y9)B=(b1b2b3)
    X=[x11x12x13x91x93] (11)

    Finally, Eq (11) can be obtained:

    B=(XTX)1XTY (12)

    The data in Table 6 are substituted into Eq (12) to calculate the coefficient matrix b.

    B=(0.26700.01350.6632)

    Therefore, the coefficient of the cutting temperature Eq (6) and the function relationship between the cutting temperature and the cutting parameters are finally obtained:

    a=1.3061,b=1.0136,c=1.9410
    T=V1.3061cV1.0136f(2α)1.9410

    Similarly, the function relation between cutting force and cutting parameters is obtained by the same method.

    F=V1.0347cV0.8010f(2α)2.0955

    In this section, based on the porous Voronoi bone models, the least square method was used to establish the prediction models of drilling parameters-cutting temperature and drilling parameters-cutting force, respectively.

    Since bone drilling is a common surgical procedure, it is important to create a model that can mimic real bone tissue. Therefore, this study mainly established a bone model with gradient structure and dense porous interior based on Voronoi method. By using finite element analysis (FEA) technology, the cutting temperature and the cutting force generated by Voronoi bone model with gradient 0.8 were determined to be close to the real bone issue by simulating three bone models with different gradients. Furthermore, the cutting parameters of bone drilling had a great influence on the cutting force and the cutting temperature. Orthogonal experiments were established to analyze the drilling parameters of Voronoi bone models in detail. Combined with range method, the proportion of influence of cutting parameters on cutting temperature and cutting force was obtained. Specifically, cutting speed had the greatest influence on cutting temperature, followed by tip angle. However, the tip angle has the greatest influence on the cutting force, followed by the feed speed. It can be seen that reasonable cutting parameters were very important to cutting temperature and cutting force. Finally, the least square method was used to predict the cutting temperature-cutting parameters, and cutting force-cutting parameters.

    Although this study provides a theoretical analysis of bone drilling and validates the reliability of Voronoi bone structure, there are still some defects. In the future research, we will make use of biological 3D printing technology to create Voronoi bone models in vitro for mechanical experimental analysis and biological properties.

    We would like to thank School of Mechanical and Vehicle Engineering of Changchun University for providing CNC machining center, dynamometer and other equipment. Thanks for the guidance of finite element simulation provided by colleagues.

    The authors declare there is no conflict of interest.



    [1] Schaab DA, Sauer A (2020) Stability implications for the design process of an industrial DC microgrid. 2020 International Conference on Smart Energy Systems and Technologies (SEST), 1–6. https://doi.org/10.1109/SEST48500.2020.9203022 doi: 10.1109/SEST48500.2020.9203022
    [2] Zhang D, Zhang Z, Ren Q, et al. (2022) Research on application mode of HYBRID microgrid AC-DC microgrid in large industrial enterprise park based on energy router. 2022 China International Conference on Electricity Distribution (CICED), 1715–1721. https://doi.org/10.1109/CICED56215.2022.9929073 doi: 10.1109/CICED56215.2022.9929073
    [3] Li Y, Sun Q, Dong T, et al. (2018) Energy management strategy of AC/DC hybrid microgrid based on power electronic transformer. 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2677–2682. https://doi.org/10.1109/ICIEA.2018.8398163 doi: 10.1109/ICIEA.2018.8398163
    [4] Kazemi M, Salehpour S, Shahbaazy F, et al. (2022) Participation of energy storage-based flexible hubs in day-ahead reserve regulation and energy markets based on a coordinated energy management strategy. Int Trans Electr Energy Syst 2022: 1–17. https://doi.org/10.1155/2022/6481531 doi: 10.1155/2022/6481531
    [5] Xia Y, Wei W, Yu M, et al. (2018) Power management for a hybrid AC/DC microgrid with multiple subgrids. IEEE Trans Power Electron 33: 3520–3533. https://doi.org/10.1109/TPEL.2017.2705133 doi: 10.1109/TPEL.2017.2705133
    [6] Li Z, Xie X, Cheng Z, et al. (2023) A novel two-stage energy management of hybrid AC/DC microgrid considering frequency security constraints. Int J Electr Power Energy Syst 146: 108768. https://doi.org/10.1016/j.ijepes.2022.108768 doi: 10.1016/j.ijepes.2022.108768
    [7] Calpbinici A, Irmak E, Kabalcı E, et al. (2021) Design of an energy management system for AC/DC microgrid. 2021 3rd Global Power, Energy and Communication Conference (GPECOM), 184–189. https://doi.org/10.1109/GPECOM52585.2021.9587523 doi: 10.1109/GPECOM52585.2021.9587523
    [8] Kang J, Fang H, Yun L (2019) A control and power management scheme for photovoltaic/fuel cell/hybrid energy storage DC microgrid. 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), 1937–1941. https://doi.org/10.1109/ICIEA.2019.8833994 doi: 10.1109/ICIEA.2019.8833994
    [9] Ferahtia S, Djeroui A, Rezk H, et al. (2022) Optimal control and implementation of energy management strategy for a DC microgrid. Energy 238. Available from: https://ideas.repec.org//a/eee/energy/v238y2022ipbs0360544221020259.html.
    [10] Ali S, Zheng Z, Aillerie M, et al. (2021) A review of DC microgrid energy management systems dedicated to residential applications. Energies 14: 4308. https://doi.org/10.3390/en14144308 doi: 10.3390/en14144308
    [11] Wu Y, Lau YY, Wu JA (2022) Integration of electric vehicles into microgrids: Policy implication for the industrial application of carbon neutralisation in China. World Electr Veh J 13: 96. https://doi.org/10.3390/wevj13060096 doi: 10.3390/wevj13060096
    [12] Konečná E, Teng SY, Máša V (2020) New insights into the potential of the gas microturbine in microgrids and industrial applications. Renewable Sustainable Energy Rev 134: 110078. https://doi.org/10.1016/j.rser.2020.110078 doi: 10.1016/j.rser.2020.110078
    [13] Torkan R, Ilinca A, Ghorbanzadeh M (2022) A genetic algorithm optimization approach for smart energy management of microgrids. Renewable Energy 197: 852–863. Available from: https://ideas.repec.org//a/eee/renene/v197y2022icp852-863.html.
    [14] Jung S, Yoon Y (2019) Optimal operating schedule for energy storage system: focusing on efficient energy management for microgrid. Processes 7: 80. https://doi.org/10.3390/pr7020080 doi: 10.3390/pr7020080
    [15] Albarakati AJ, Boujoudar Y, Azeroual M, et al. (2022) Microgrid energy management and monitoring systems: A comprehensive review. Front Energy Res 10. https://doi.org/10.3389/fenrg.2022.1097858 doi: 10.3389/fenrg.2022.1097858
    [16] Zahraoui Y, Alhamrouni I, Mekhilef S, et al. (2021) Energy management system in microgrids: A comprehensive review. Sustainability 13: 10492. https://doi.org/10.3390/su131910492 doi: 10.3390/su131910492
    [17] Brandao D, Santos R, Silva W, et al. (2020) Model-free energy management system for hybrid AC/DC microgrids. IEEE Trans Ind Electron PP: 1–1. https://doi.org/10.1109/TIE.2020.2984993
    [18] Prodanovic M, Rodríguez-Cabero A, Jiménez-Carrizosa M, et al. (2017) A rapid prototyping environment for DC and AC microgrids: Smart energy integration Lab (SEIL). 2017 IEEE Second International Conference on DC Microgrids (ICDCM), Nuremburg, Germany, 421–427. https://doi.org/10.1109/ICDCM.2017.8001079
    [19] Kim M, Choi BY, Kang KM, et al. (2020) Energy monitoring system of AC/DC hybrid microgrid systems using LabVIEW. 2020 23rd International Conference on Electrical Machines and Systems (ICEMS), 489–493. https://doi.org/10.23919/ICEMS50442.2020.9290836 doi: 10.23919/ICEMS50442.2020.9290836
    [20] Xiao J, Zhao T, Hai KL, et al. (2017) Smart energy hub—Modularized hybrid AC/DC microgrid: System design and deployment. 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China, 1–6. https://doi.org/10.1109/EI2.2017.8245453
    [21] Abdolrasol M, Mohamed A, Hannan MA (2017) Virtual power plant and microgrids controller for energy management based on optimization techniques. J Electr Syst 13: 285–294. Available from: https://www.proquest.com/openview/9aa5d28dce901943fd9de88988f32e42/1?pq-origsite = gscholar & cbl = 4433095.
    [22] Basantes JA, Paredes DE, Llanos JR, et al. (2023) Energy management system (EMS) based on model predictive control (MPC) for an isolated DC microgrid. Energies 16: 2912. https://doi.org/10.3390/en16062912 doi: 10.3390/en16062912
    [23] Freire VA, de Arruda LVR, Bordons C, et al. (2019) Home energy management for a AC/DC microgrid using model predictive control. 2019 International Conference on Smart Energy Systems and Technologies (SEST), 1–6. https://doi.org/10.1109/SEST.2019.8849077 doi: 10.1109/SEST.2019.8849077
    [24] Fathy Y, Jaber M, Nadeem Z (2021) Digital twin-driven decision making and planning for energy consumption. J Sens Actuator Netw 10: 37. https://doi.org/10.3390/jsan10020037 doi: 10.3390/jsan10020037
    [25] Thirunavukkarasu GS, Seyedmahmoudian M, Jamei E, et al. (2022) Role of optimization techniques in microgrid energy management systems—A review. Energy Strategy Rev 43: 100899. https://doi.org/10.1016/j.esr.2022.100899 doi: 10.1016/j.esr.2022.100899
    [26] Arrar S, Li X (2022) Energy management in hybrid microgrid using artificial neural network, PID, and fuzzy logic controllers. Eur J Electr Eng Comput Sci 6: 38–47. https://doi.org/10.24018/ejece.2022.6.2.414 doi: 10.24018/ejece.2022.6.2.414
    [27] Al-Saadi M, Al-Greer M, Short M (2021) Strategies for controlling microgrid networks with energy storage systems: A review. Energies 14: 7234. https://doi.org/10.3390/en14217234 doi: 10.3390/en14217234
    [28] Hu J, Shan Y, Xu Y, et al. (2019) A coordinated control of hybrid AC/DC microgrids with PV-wind-battery under variable generation and load conditions. Int J Electr Power Energy Syst 104: 583–592. https://doi.org/10.1016/j.ijepes.2018.07.037 doi: 10.1016/j.ijepes.2018.07.037
    [29] Nejabatkhah F, Li YR (2014) Overview of power management strategies of hybrid AC/DC microgrid. IEEE Trans Power Electron 30: 7072–7089. https://doi.org/10.1109/TPEL.2014.2384999 doi: 10.1109/TPEL.2014.2384999
    [30] Sahoo B, Routray SK, Rout PK (2021) AC, DC, and hybrid control strategies for smart microgrid application: A review. Int Trans Electr Energy Syst 31: e12683. https://doi.org/10.1002/2050-7038.12683 doi: 10.1002/2050-7038.12683
    [31] Singh P, Paliwal P, Arya A (2019) A review on challenges and techniques for secondary control of microgrid. IOP Conf Ser Mater Sci Eng 561: 012075. https://doi.org/10.1088/1757-899X/561/1/012075 doi: 10.1088/1757-899X/561/1/012075
    [32] Ramos F, Pinheiro A, Nascimento R, et al. (2022) Development of operation strategy for battery energy storage system into hybrid AC microgrids. Sustainability 14: 13765. https://doi.org/10.3390/su142113765 doi: 10.3390/su142113765
    [33] Allwyn RG, Al-Hinai A, Margaret V (2023) A comprehensive review on energy management strategy of microgrids. Energy Rep 9: 5565–5591. https://doi.org/10.1016/j.egyr.2023.04.360 doi: 10.1016/j.egyr.2023.04.360
    [34] Mohamed MA (2022) A relaxed consensus plus innovation based effective negotiation approach for energy cooperation between smart grid and microgrid. Energy 252: 123996. https://doi.org/10.1016/j.energy.2022.123996 doi: 10.1016/j.energy.2022.123996
    [35] Rangarajan SS, Raman R, Singh A, et al. (2023) DC Microgrids: A propitious smart grid paradigm for smart cities. Smart Cities 6: 1690–1718. https://doi.org/10.3390/smartcities6040079 doi: 10.3390/smartcities6040079
    [36] Yin F, Hajjiah A, Jermsittiparsert K, et al. (2021) A secured social-economic framework based on PEM-Blockchain for optimal scheduling of reconfigurable interconnected microgrids. IEEE Access 9: 40797–40810. https://doi.org/10.1109/ACCESS.2021.3065400 doi: 10.1109/ACCESS.2021.3065400
    [37] Wang P, Wang D, Zhu C, et al. (2020) Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands. Energy Rep 6: 1338–1352. https://doi.org/10.1016/j.egyr.2020.05.019 doi: 10.1016/j.egyr.2020.05.019
    [38] Tummuru NR, Manandhar U, Ukil A, et al. (2019) Control strategy for AC-DC microgrid with hybrid energy storage under different operating modes. Int J Electr Power Energy Syst 104: 807–816. https://doi.org/10.1016/j.ijepes.2018.07.063 doi: 10.1016/j.ijepes.2018.07.063
    [39] Alluraiah NC, Vijayapriya P, Chittathuru D, et al. (2023) Multi-objective optimization algorithms for a hybrid AC/DC microgrid using RES: A comprehensive review. Electronics 12: 1–31. https://doi.org/10.3390/electronics12041062 doi: 10.3390/electronics12041062
    [40] Gabbar HA, El-Hendawi M, El-Saady G, et al. (2016) Supervisory controller for power management of AC/DC microgrid. 2016 IEEE Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 147–152. https://doi.org/10.1109/SEGE.2016.7589516
    [41] Silveira JP, dos Santos Neto P, Barros T, et al. (2021) Power management of energy storage system with modified interlinking converters topology in hybrid AC/DC microgrid. Int J Electr Power Energy Syst 130: 106880. https://doi.org/10.1016/j.ijepes.2021.106880 doi: 10.1016/j.ijepes.2021.106880
    [42] Lee H, Kang JW, Choi BY, et al. (2021) Energy management system of DC microgrid in grid-connected and standalone modes: Control, operation and experimental validation. Energies 14: 581. https://doi.org/10.3390/en14030581 doi: 10.3390/en14030581
    [43] Abbas FA, Obed AA, Qasim MA, et al. (2022) An efficient energy-management strategy for a DC microgrid powered by a photovoltaic/fuel cell/battery/supercapacitor. Clean Energy 6: 827–839. https://doi.org/10.1093/ce/zkac063 doi: 10.1093/ce/zkac063
    [44] Han Y, Ning X, Yang P, et al. (2019) Review of power sharing, voltage restoration and stabilization techniques in hierarchical controlled DC microgrids. IEEE Access 7: 149202–149223. https://doi.org/10.1109/ACCESS.2019.2946706 doi: 10.1109/ACCESS.2019.2946706
    [45] Yang F, Ye L, Muyeen SM, et al. (2022) Power management for hybrid AC/DC microgrid with multi-mode subgrid based on incremental costs. Int J Electr Power Energy Syst 138: 107887. https://doi.org/10.1016/j.ijepes.2021.107887 doi: 10.1016/j.ijepes.2021.107887
    [46] Liu X, Zhao T, Deng H, et al. (2022) Microgrid energy management with energy storage systems: A review. CSEE J Power Energy Syst, 1–21. https://doi.org/10.17775/CSEEJPES.2022.04290 doi: 10.17775/CSEEJPES.2022.04290
    [47] Xia Y, Wei W, Yu M, et al. (2017) Decentralized multi-Time scale power control for a hybrid AC/DC microgrid with multiple subgrids. IEEE Transactions on Power Electronics 33: 4061–4072. https://doi.org/10.1109/TPEL.2017.2721102 doi: 10.1109/TPEL.2017.2721102
    [48] Manbachi M, Ordonez M (2019) Intelligent agent-based energy management system for islanded AC/DC microgrids. IEEE Transactions on Industrial Informatics 16: 4603–4614. https://doi.org/10.1109/TII.2019.2945371 doi: 10.1109/TII.2019.2945371
    [49] Arunkumar AP, Kuppusamy S, Muthusamy S, et al. (2022) An extensive review on energy management system for microgrids. Energy Sources Part Recovery Util Environ Eff 44: 4203–4228. https://doi.org/10.1080/15567036.2022.2075059 doi: 10.1080/15567036.2022.2075059
    [50] Cecilia A, Carroquino J, Roda V, et al. (2020) Optimal energy management in a standalone microgrid, with photovoltaic generation, short-term storage, and hydrogen production. Energies 13: 1454. https://doi.org/10.3390/en13061454 doi: 10.3390/en13061454
    [51] Kumari N, Sharma A, Tran B, et al. (2023) A comprehensive review of digital twin technology for grid-connected microgrid systems: State of the art, potential and challenges faced. Energies 16: 5525. https://doi.org/10.3390/en16145525 doi: 10.3390/en16145525
    [52] Muqeet HA, Javed H, Akhter MN, et al. (2022) Sustainable solutions for advanced energy management system of campus microgrids: Model opportunities and future challenges. Sensors 22: 2345. https://doi.org/10.3390/s22062345 doi: 10.3390/s22062345
    [53] Baharizadeh M, Karshenas HR, Guerrero JM (2018) An improved power control strategy for hybrid AC-DC microgrids. Int J Electr Power Energy Syst 95: 364–373. https://doi.org/10.1016/j.ijepes.2017.08.036 doi: 10.1016/j.ijepes.2017.08.036
    [54] Pratomo LH, Matthias LA (2022) Control strategy in DC microgrid for integrated energy balancer: Photovoltaic application. Iran J Energy Environ 13: 333–339. https://doi.org/10.5829/ijee.2022.13.04.02 doi: 10.5829/ijee.2022.13.04.02
    [55] Volnyi V, Ilyushin P, Suslov K, et al. (2023) Approaches to building AC and AC–DC microgrids on top of existing passive distribution networks. Energies 16: 5799. https://doi.org/10.3390/en16155799 doi: 10.3390/en16155799
    [56] Qu Z, Shi Z, Wang Y, et al. (2022) Energy management strategy of AC/DC hybrid microgrid based on solid-state transformer. IEEE Access 10: 20633–20642. https://doi.org/10.1109/ACCESS.2022.3149522 doi: 10.1109/ACCESS.2022.3149522
    [57] Irmak E, Kabalcı E, Kabalci Y (2023) Digital transformation of microgrids: A review of design, operation, optimization, and cybersecurity. Energies 16: 4590. https://doi.org/10.3390/en16124590 doi: 10.3390/en16124590
    [58] Khubrani MM, Alam S (2023) Blockchain-Based microgrid for safe and reliable power generation and distribution: A case study of saudi arabia. Energies 16: 5963. https://doi.org/10.3390/en16165963 doi: 10.3390/en16165963
    [59] Azeem O, Ali M, Abbas G, et al. (2021) A comprehensive review on integration challenges, optimization techniques and control strategies of hybrid AC/DC microgrid. Appl Sci 11: 6242. https://doi.org/10.3390/app11146242 doi: 10.3390/app11146242
    [60] Kumar AA, Prabha NA (2022) A comprehensive review of DC microgrid in market segments and control technique. Heliyon 8: e11694. https://doi.org/10.1016/j.heliyon.2022.e11694 doi: 10.1016/j.heliyon.2022.e11694
    [61] Chen J, Alnowibet K, Annuk A, et al. (2021) An effective distributed approach based machine learning for energy negotiation in networked microgrids. Energy Strategy Rev 38: 100760. https://doi.org/10.1016/j.esr.2021.100760 doi: 10.1016/j.esr.2021.100760
    [62] Khan R, Islam N, Das SK, et al. (2021) Energy sustainability–survey on technology and control of microgrid, smart grid and virtual power plant. IEEE Access 9: 104663–104694. https://doi.org/10.1109/ACCESS.2021.3099941 doi: 10.1109/ACCESS.2021.3099941
    [63] Pabbuleti B, Somlal J (2020) A review on hybrid AC/DC microgrids: Optimal sizing, stability control and energy management approaches. J Crit Rev 7: 376–381. Available from: https://www.semanticscholar.org/paper/A-REVIEW-ON-HYBRID-AC-DC-MICROGRIDS%3A-OPTIMAL-AND-Pabbuleti-Somlal/db68b9f4b88a82fd5707656b721f96976acb8176.
    [64] Gutiérrez-Oliva D, Colmenar-Santos A, Rosales E (2022) A review of the state of the art of industrial microgrids based on renewable energy. Electronics 11: 1002. https://doi.org/10.3390/electronics11071002 doi: 10.3390/electronics11071002
    [65] Haidekker M, Liu M, Song W (2023) Alternating-Current microgrid testbed built with low-cost modular hardware. Sensors 23: 3235. https://doi.org/10.3390/s23063235 doi: 10.3390/s23063235
    [66] Arif S, Rabbi A, Ahmed S, et al. (2022) Enhancement of solar PV hosting capacity in a remote industrial microgrid: A methodical techno-economic approach. Sustainability 14. https://doi.org/10.3390/su14148921 doi: 10.3390/su14148921
    [67] Zhao T, Xiao J, Koh LH, et al. (2018) Distributed energy management for hybrid AC/DC microgrid parks. 2018 IEEE Power & Energy Society General Meeting (PESGM), 1–5. https://doi.org/10.1109/PESGM.2018.8586403 doi: 10.1109/PESGM.2018.8586403
    [68] Garcia-Torres F, Zafra-Cabeza A, Silva C, et al. (2021) Model predictive control for microgrid functionalities: Review and future challenges. Energies 14: 1296. https://doi.org/10.3390/en14051296 doi: 10.3390/en14051296
    [69] Francis D, Lazarova-Molnar S, Mohamed N (2021) Towards data-driven digital twins for smart manufacturing. In: Selvaraj, H., Chmaj, G., Zydek, D., Proceedings of the 27th International Conference on Systems Engineering, ICSEng 2020. Lecture Notes in Networks and Systems. 182: 445–454. https://doi.org/10.1007/978-3-030-65796-3_43
    [70] Rosero DG, Sanabria E, Díaz NL, et al. (2023) Full-deployed energy management system tested in a microgrid cluster. Appl Energy 334: 120674. https://doi.org/10.1016/j.apenergy.2023.120674 doi: 10.1016/j.apenergy.2023.120674
    [71] Leonori S, Paschero M, Frattale Mascioli FM, et al. (2020) Optimization strategies for Microgrid energy management systems by Genetic Algorithms. Appl Soft Comput 86: 105903. https://doi.org/10.1016/j.asoc.2019.105903 doi: 10.1016/j.asoc.2019.105903
    [72] Vásquez LOP, Ramírez VM, Thanapalan K (2020) A comparison of energy management system for a DC microgrid. Appl Sci 10: 1071. https://doi.org/10.3390/app10031071 doi: 10.3390/app10031071
    [73] Islam H, Mekhilef S, Shah NBM, et al. (2018) Performance evaluation of maximum power point tracking approaches and photovoltaic systems. Energies 11: 365. https://doi.org/10.3390/en11020365 doi: 10.3390/en11020365
    [74] Shafiullah M, Refat AM, Haque ME, et al. (2022) Review of recent developments in microgrid energy management strategies. Sustainability 14: 14794. https://doi.org/10.3390/su142214794 doi: 10.3390/su142214794
    [75] Çimen H, Bazmohammadi N, Lashab A, et al. (2022) An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring. Appl Energy 307: 118136. https://doi.org/10.1016/j.apenergy.2021.118136 doi: 10.1016/j.apenergy.2021.118136
    [76] Elsied M, Oukaour A, Gualous H, et al. (2014) An advanced energy management of microgrid system based on genetic algorithm. 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), 2541–2547. https://doi.org/10.1109/ISIE.2014.6865020 doi: 10.1109/ISIE.2014.6865020
    [77] El Makroum R, Khallaayoun A, Lghoul R, et al. (2023) Home energy management system based on genetic algorithm for load scheduling: A case study based on real life consumption data. Energies 16: 2698. https://doi.org/10.3390/en16062698 doi: 10.3390/en16062698
    [78] Ali M, Hossain MI, Shafiullah M (2022) Fuzzy logic for energy management in hybrid energy storage systems integrated DC microgrid. 2022 International Conference on Power Energy Systems and Applications (ICoPESA), 424–429. https://doi.org/10.1109/ICoPESA54515.2022.9754406 doi: 10.1109/ICoPESA54515.2022.9754406
    [79] Bianchini I, Kuhlmann T, Wunder B, et al. (2021) Hierarchical network management of industrial DC-microgrids. 2021 IEEE Fourth International Conference on DC Microgrids (ICDCM), 1–6. https://doi.org/10.1109/ICDCM50975.2021.9504619 doi: 10.1109/ICDCM50975.2021.9504619
    [80] Ahmed M, Abbas G, Jumani T, et al. (2023) Techno-economic optimal planning of an industrial microgrid considering integrated energy resources. Front Energy Res 11: 12. https://doi.org/10.3389/fenrg.2023.1145888 doi: 10.3389/fenrg.2023.1145888
    [81] Dzyuba A, Solovyeva I, Semikolenov A (2022) Prospects of introducing microgrids in Russian industry. J New Econ 23: 80–101. https://doi.org/10.29141/2658-5081-2022-23-2-5 doi: 10.29141/2658-5081-2022-23-2-5
    [82] Ghasemi M, Kazemi A, Mazza A, et al. (2021) A three‐stage stochastic planning model for enhancing the resilience of distribution systems with microgrid formation strategy. IET Gener Transm Distrib 15. https://doi.org/10.1049/gtd2.12144 doi: 10.1049/gtd2.12144
    [83] Han Y, Shen P, Coelho E, et al. (2016) Review of active and reactive power sharing strategies in hierarchical controlled microgrids. IEEE Trans Power Electron 32: 2427–2451. https://doi.org/10.1109/TPEL.2016.2569597 doi: 10.1109/TPEL.2016.2569597
    [84] Nardelli P, Hussein M, Narayanan A, et al. (2021) Virtual microgrid management via software-defined energy network for electricity sharing: benefits and challenges. IEEE Systems, Man, and Cybernetics Magazine 7: 10–19. https://doi.org/10.1109/MSMC.2021.3062018 doi: 10.1109/MSMC.2021.3062018
    [85] Borisoot K, Liemthong R, Srithapon C, et al. (2023) Optimal energy management for virtual power plant considering operation and degradation costs of energy storage system and generators. Energies 16: 2862. https://doi.org/10.3390/en16062862 doi: 10.3390/en16062862
    [86] Lombardi P, Sokolnikova T, Styczynski Z, et al. (2012) Virtual power plant management considering energy storage systems. IFAC Proc Vol 45: 132–137. https://doi.org/10.3182/20120902-4-FR-2032.00025 doi: 10.3182/20120902-4-FR-2032.00025
    [87] Jithin S, Rajeev T (2022) Novel adaptive power management strategy for hybrid AC/DC microgrids with hybrid energy storage systems. J Power Electron 22. https://doi.org/10.1007/s43236-022-00506-x doi: 10.1007/s43236-022-00506-x
    [88] Bhattar CL, Chaudhari MA (2023) Centralized energy management scheme for grid connected DC microgrid. IEEE Syst J 17: 3741–3751. https://doi.org/10.1109/JSYST.2022.3231898 doi: 10.1109/JSYST.2022.3231898
    [89] Balapattabi S, Mahalingam P, Gonzalez-Longatt F (2017) High‐gain–high‐power (HGHP) DC‐DC converter for DC microgrid applications: Design and testing. Int Trans Electr Energy Syst 28. https://doi.org/10.1002/etep.2487 doi: 10.1002/etep.2487
    [90] Modu B, Abdullah MP, Sanusi MA, et al. (2023) DC-based microgrid: Topologies, control schemes, and implementations. Alex Eng J 70: 61–92. https://doi.org/10.1016/j.aej.2023.02.021 doi: 10.1016/j.aej.2023.02.021
    [91] Kang KM, Choi BY, Lee H, et al. (2021) Energy management method of hybrid AC/DC microgrid using artificial neural network. Electronics 10: 1939. https://doi.org/10.3390/electronics10161939 doi: 10.3390/electronics10161939
    [92] Friederich J, Francis DP, Lazarova-Molnar S, et al. (2022) A framework for data-driven digital twins of smart manufacturing systems. Comput Ind 136: 103586. https://doi.org/10.1016/j.compind.2021.103586 doi: 10.1016/j.compind.2021.103586
    [93] Bazmohammadi N, Madary A, Vasquez JC, et al. (2022) Microgrid digital twins: concepts, applications, and future trends. IEEE Access 10: 2284–2302. https://doi.org/10.1109/ACCESS.2021.3138990 doi: 10.1109/ACCESS.2021.3138990
    [94] Yu P, Ma L, Fu R, et al. (2023) Framework design and application perspectives of digital twin microgrid. Energy Rep 9: 669–678. https://doi.org/10.1016/j.egyr.2023.04.253 doi: 10.1016/j.egyr.2023.04.253
    [95] Sifat MdMH, Choudhury SM, Das SK, et al. (2023) Towards electric digital twin grid: Technology and framework review. Energy AI 11: 100213. https://doi.org/10.1016/j.egyai.2022.100213 doi: 10.1016/j.egyai.2022.100213
    [96] Nasiri G, Kavousi-Fard A (2023) A digital twin-based system to manage the energy hub and enhance the electrical grid resiliency. Machines 11: 392. https://doi.org/10.3390/machines11030392 doi: 10.3390/machines11030392
    [97] Essayeh C, Raiss El-Fenni M, Dahmouni H, et al. (2021) Energy management strategies for smart green microgrid systems: A systematic literature review. J Electr Comput Eng 2021: e6675975. https://doi.org/10.1155/2021/6675975 doi: 10.1155/2021/6675975
    [98] Kannengießer T, Hoffmann M, Kotzur L, et al. (2019) Reducing computational load for mixed integer linear programming: An example for a district and an island energy system. Energies 12: 2825. https://doi.org/10.3390/en12142825 doi: 10.3390/en12142825
    [99] Lagouir M, Badri A, Sayouti Y (2019) Development of an intelligent energy management system with economic dispatch of a standalone microgrid. J Electr Syst 15: 568–581. Available from: https://www.proquest.com/openview/74e70711c074dc123b54081dab08fa5f/1?pq-origsite = gscholar & cbl = 4433095.
    [100] Bishnoi D, Chaturvedi H (2021) Emerging trends in smart grid energy management systems. Int J Renewable Energy Res (IJRER) 11: 952–966. Available from: https://www.ijrer.org/ijrer/index.php/ijrer/article/view/11832.
    [101] Dwivedi SD, Ray PK (2022) Energy management and control of grid-connected microgrid integrated with HESS. 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), 1–6. https://doi.org/10.1109/ICICCSP53532.2022.9862374 doi: 10.1109/ICICCSP53532.2022.9862374
    [102] Rahman M, Hossain MJ, Rafi F, et al. (2016) A multi-purpose interlinking converter control for multiple hybrid AC/DC microgrid operations. 2016 IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia), Melbourne, VIC, Australia, 221–226. https://doi.org/10.1109/ISGT-Asia.2016.7796389
    [103] Yang P, Xia Y, Yu M, et al. (2017) A decentralized coordination control method for parallel bidirectional power converters in a hybrid AC/DC microgrid. IEEE Trans Ind Electron 65: 6217–6228. https://doi.org/10.1109/TIE.2017.2786200 doi: 10.1109/TIE.2017.2786200
    [104] Helal S, Hanna M, Najee R, et al. (2019) Energy management system for smart hybrid AC/DC microgrids in remote communities. Electr Power Compon Syst 47: 1–13. https://doi.org/10.1080/15325008.2019.1629512 doi: 10.1080/15325008.2019.1629512
    [105] Kumar S, Chinnamuthan P, Krishnasamy V (2018) Study on renewable distributed generation, power controller and islanding management in hybrid microgrid system. J Green Eng 8: 37–70. https://doi.org/10.13052/jge1904-4720.814 doi: 10.13052/jge1904-4720.814
    [106] Senfelds A, Bormanis O, Paugurs A (2016) Analytical approach for industrial microgrid infeed peak power dimensioning. 2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 1–4. https://doi.org/10.1109/RTUCON.2016.7763140 doi: 10.1109/RTUCON.2016.7763140
    [107] Mosa MA, Ali AA (2021) Energy management system of low voltage dc microgrid using mixed-integer nonlinear programing and a global optimization technique. Electr Power Syst Res 192: 106971. https://doi.org/10.1016/j.epsr.2020.106971 doi: 10.1016/j.epsr.2020.106971
    [108] Du H, Zhang X, Sun Q, et al. (2019) Power management strategy of AC-DC hybrid microgrid in island mode. 2019 Chinese Control And Decision Conference (CCDC), 2900–2905. https://doi.org/10.1109/CCDC.2019.8833467 doi: 10.1109/CCDC.2019.8833467
    [109] Dalai SK, Prince SK, Abhishek A, et al. (2022) Power management strategies for islanding and grid-connected DC microgrid systems with multiple renewable energy resources. 2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT), 1–6. https://doi.org/10.1109/GlobConPT57482.2022.9938187 doi: 10.1109/GlobConPT57482.2022.9938187
    [110] Kim TG, Lee H, An C-G, et al. (2023) Hybrid AC/DC microgrid energy management strategy based on two-step ANN. Energies 16: 1787. https://doi.org/10.3390/en16041787 doi: 10.3390/en16041787
    [111] Ullah S, Haidar A, Zen H (2020) Assessment of technical and financial benefits of AC and DC microgrids based on solar photovoltaic. Electr Eng 102: 1297–1310. https://doi.org/10.1007/s00202-020-00950-7 doi: 10.1007/s00202-020-00950-7
    [112] Ellert C, Horta R, Sterren T, et al. (2017) Modular ICT based energy management system for a LVDC-microgrid with local PV production and integrated electrochemical storage. 2017 IEEE Second International Conference on DC Microgrids (ICDCM), 274–278. https://doi.org/10.1109/ICDCM.2017.8001056 doi: 10.1109/ICDCM.2017.8001056
    [113] Senfelds A, Apse-Apsitis P, Avotins A, et al. (2017) Industrial DC microgrid analysis with synchronous multipoint power measurement solution. 2017 19th European Conference on Power Electronics and Applications (EPE'17 ECCE Europe), 1–6. https://doi.org/10.23919/EPE17ECCEEurope.2017.8099322 doi: 10.23919/EPE17ECCEEurope.2017.8099322
    [114] Sarda JS, Lee K, Patel H, et al. (2022) Energy management system of microgrid using optimization approach. IFAC-Pap 55: 280–284. https://doi.org/10.1016/j.ifacol.2022.07.049 doi: 10.1016/j.ifacol.2022.07.049
    [115] Dey P, Chowdhury MdM (2022) Developing a methodology for reactive power planning in an industrial microgrid. 2022 IEEE Region 10 Symposium (TENSYMP). https://doi.org/10.1109/TENSYMP54529.2022.9864406 doi: 10.1109/TENSYMP54529.2022.9864406
    [116] Zhou Z, Xiong F, Biyao H, et al. (2017) Game-theoretical energy management for energy internet with big data-based renewable power forecasting. IEEE Access 5: 5731–5746. https://doi.org/10.1109/ACCESS.2017.2658952 doi: 10.1109/ACCESS.2017.2658952
    [117] Sood VK, Ali MY, Khan F (2020) Energy management system of a microgrid using particle swarm optimization (PSO) and communication system. In: Ray P, Biswal M., Microgrid: Operation, Control, Monitoring and Protection, Singapore, Springer, 263–288. https://doi.org/10.1007/978-981-15-1781-5_9
    [118] Wei B, Han X, Wang P, et al. (2020) Temporally coordinated energy management for AC/DC hybrid microgrid considering dynamic conversion efficiency of bidirectional AC/DC converter. IEEE Access 8: 70878–70889. https://doi.org/10.1109/ACCESS.2020.2985419 doi: 10.1109/ACCESS.2020.2985419
    [119] Zafir S, Muhamad Razali N, Tengku J (2016) Relationship between loss of load expectation and reserve margin for optimal generation planning. J Teknol 78. https://doi.org/10.11113/jt.v78.8783 doi: 10.11113/jt.v78.8783
    [120] Diewvilai R, Audomvongseree K (2022) Optimal loss of load expectation for generation expansion planning considering fuel unavailability. Energies 15: 7854. https://doi.org/10.3390/en15217854 doi: 10.3390/en15217854
    [121] Li J, Cai H, Yang P, et al. (2021). A Bus-Sectionalized hybrid AC/DC microgrid: Concept, control paradigm, and implementation. Energies 14. 3508. https://doi.org/10.3390/en14123508. doi: 10.3390/en14123508
    [122] Pabbuleti B, Somlal J (2022) A hybrid AC/DC microgrid with multi-bus DC sub-grid optimal operation. Int J Intell Syst Appl Eng 10: 1–7. Available from: https://ijisae.org/index.php/IJISAE/article/view/2353.
    [123] Nguyen DH, Banjerdpongchai D (2016) Iterative learning control of energy management system: Survey on Multi-Agent System Framework. Eng J 20: 1–4. https://doi.org/10.4186/ej.2016.20.5.1 doi: 10.4186/ej.2016.20.5.1
    [124] Jasim AM, Jasim BH, Bureš V (2022) A novel grid-connected microgrid energy management system with optimal sizing using hybrid grey wolf and cuckoo search optimization algorithm. Front Energy Res 10. Available from: https://www.frontiersin.org/articles/10.3389/fenrg.2022.960141.
    [125] Li J, Cai H, Yang P, et al. (2021) A Bus-Sectionalized hybrid AC/DC microgrid: Concept, control Paradigm, and Implementation. Energies 14: 3508. https://doi.org/10.3390/en14123508 doi: 10.3390/en14123508
    [126] Yu D, Gao S, Zhao X, et al. (2023) Alternating iterative power-flow algorithm for hybrid AC–DC power grids incorporating LCCs and VSCs. Sustainability 15: 4573. https://doi.org/10.3390/su15054573 doi: 10.3390/su15054573
    [127] Kassa Y, Zhang J, Zheng D (2020) Optimal energy management strategy in microgrids with mixed energy resources and energy storage system. IET Cyber-Phys Syst Theory Appl 5: 80–85. https://doi.org/10.1049/iet-cps.2019.0035 doi: 10.1049/iet-cps.2019.0035
    [128] Zagrajek K, Paska J, Sosnowski L, et al. (2021) Framework for the introduction of vehicle-to-grid technology into the polish electricity market. Energies 14: 3673. https://doi.org/10.3390/en14123673 doi: 10.3390/en14123673
    [129] Katche M, Makokha A, Zachary S, et al. (2023) A comprehensive review of maximum power point tracking (MPPT) techniques used in solar PV systems. Energies 16: 2206. https://doi.org/10.3390/en16052206 doi: 10.3390/en16052206
    [130] Li Z, Tan, Ren J, et al. (2020) A two-stage optimal Scheduling Model of Microgrid Based on Chance-Constrained Programming in Spot Markets. Processes 8: 107. https://doi.org/10.3390/pr8010107 doi: 10.3390/pr8010107
    [131] Kantor I, Robineau JL, Bütün H, et al. (2020) A mixed-integer linear programming formulation for optimizing multi-scale material and energy integration. Front Energy Res 8. Available from: https://www.frontiersin.org/articles/10.3389/fenrg.2020.00049.
    [132] Ravichandran A (2016) Optimization-based microgrid energy management systems. Available from: https://www.semanticscholar.org/paper/Optimization-based-Microgrid-Energy-Management-Ravichandran/bc20a7cd69a6ce34652304ae2bcfd5499f534608.
    [133] Zia MF, Elbouchikhi E, Benbouzid M (2018) Microgrids energy management systems: A critical review on methods, solutions, and prospects. Appl Energy 222: 1033–1055. https://ideas.repec.org//a/eee/appene/v222y2018icp1033-1055.html
    [134] Naeem A, Ahmed S, Ahsan M, et al. (2016) Energy management strategies using microgrid systems. 2016 Conference: 2nd International Multi-Disciplinary Conference, 1–8. Available from: https://www.researchgate.net/publication/326534831_Energy_Management_Strategies_using_Microgrid_Systems.
    [135] Rodriguez-Diaz E, Palacios-Garcia EJ, Anvari-Moghaddam A, et al. (2017) Real-time Energy Management System for a hybrid AC/DC residential microgrid. 2017 IEEE Second International Conference on DC Microgrids (ICDCM), 256–261. https://doi.org/10.1109/ICDCM.2017.8001053 doi: 10.1109/ICDCM.2017.8001053
    [136] Spiegel M, Veith E, Strasser T (2020) The spectrum of proactive, resilient multi-microgrid scheduling: A systematic literature review. Energies 13: 4543. https://doi.org/10.3390/en13174543 doi: 10.3390/en13174543
    [137] Shahzad S, Abbasi M, Chaudhary M, et al. (2022) Model predictive control strategies in microgrids: A concise revisit. IEEE Access 10: 122211–122225. https://doi.org/10.1109/ACCESS.2022.3223298 doi: 10.1109/ACCESS.2022.3223298
  • This article has been cited by:

    1. Yamei Deng, Yonglu Chen, Qian He, Xu Wang, Yong Liao, Jue Liu, Zhaoran Liu, Jianwei Huang, Ting Song, Bone age assessment from articular surface and epiphysis using deep neural networks, 2023, 20, 1551-0018, 13133, 10.3934/mbe.2023585
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4297) PDF downloads(355) Cited by(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog