Planning of Multi Energy-Type Micro Energy Grid Based on Improved Kriging Model

Liu, Di; Wu, Junyong; Lin, Kaijun; Wang, Mingli

doi:10.1109/access.2019.2894469

Cited by 12 publications

(7 citation statements)

References 31 publications

(32 reference statements)

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“…According to [10], [12] and [18], the related energy devices coupled by the MEG are modeled. In addition, ρ is 0.01 Yuan/kWh, and the TOU electricity sales price ϕ ge t of the power grid is demonstrated in Table 4 [31]. Fig.…”

Section: Numerical Results and Analysis A Simulation Settingsmentioning

confidence: 99%

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Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

Lin

Liu

2020

IEEE Access

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The application of time-of-use (TOU) pricing in the electricity market has significant value, which can improve economic efficiency and promote effective management of resources. However, this pricing mechanism has not been applied and promoted in the gas market in China. In this paper, we propose a bi-level transaction model considering TOU gas pricing for analyzing the economic efficiency of micro energy grid (MEG) operation based on Game Theory. The multiple energies trading is conducted between the natural gas company (NGC), the MEG and energy users (EUs), which forms a hierarchical Stackelberg game model. We first analyze utilities and strategies of aforementioned participants, and derive the Stackelberg equilibrium (SE) analytically as a balanced solution that captures the equilibrium strategies of participants. Then, a mixed integer nonlinear programming is formulated to determine the optimal TOU gas prices delivering maximum NGC profit, the optimal energy sales prices corresponding to the MEG' SE utility, and the optimal load pattern for EUs. Finally, numerical experiments are conducted in two scenarios, which reveal that the TOU pricing can well balance the gas supply and demand, and has significant potential for improving the economic efficiency of the MEG. INDEX TERMS Micro energy grid, Stackelberg game, economic efficiency, time-of-use pricing, equilibrium.

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Section: Numerical Results and Analysis A Simulation Settingsmentioning

confidence: 99%

“…is the production cost of the MEG, which is composed of the cost of purchasing gas from the NGC, the cost of electricity purchased from the grid, and the operating and maintenance costs of distributed energy devices [30], [31]. The expression of C meg t is as below.…”

Section: ) Utility Functionmentioning

confidence: 99%

Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

Lin

Liu

2020

IEEE Access

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“…This section compendiously explains the theoretical basis of Kriging-based reliability analysis. In general, Kriging model [18], [28], [29], including a polynomial regression and a Gaussian distribution, is a semi-parametric and accurate interpolation simulation method and it expresses the performance function as the following form:…”

Section: Kriging Methods For Reliability Analysismentioning

confidence: 99%

An Efficient Adaptive Reliability Analysis Method Based on Kriging and Weighted Average Misclassification Rate Improvement

et al. 2019

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In the past few years, the Kriging model based on the adaptive design of experiments (DoE) has attracted extensive attention in analyzing the reliability of structures involving time-consuming simulations or complicated implicit performance function. Although varieties of sampling strategies have been proposed, they update DoE mainly by selecting the sample at which its corresponding learning function value is the maximum or minimum. However, there are usually two drawbacks. First, the training samples in DoE are easily clustered or overlapped. Second, the existing strategies usually only consider the improvement of prediction accuracy at the new training sample rather than the accuracy improvement of the region near the new training sample. Unfortunately, these two drawbacks can cause some unnecessary performance function evaluations. Therefore, an efficient adaptive sampling strategy and reliability analysis algorithm based on Kriging model, weighted average misclassification rate, sampling uniformity and gradient of prediction uncertainty are proposed. Furthermore, an improved stopping criterion based on the relative error estimation of failure probability is also developed to further reduce iteration. Subsequently, two explicit examples from the literature are analyzed to verify the effectiveness and superiority of the proposed method. Finally, a truss structure subjected to six external loads is investigated to illustrate the feasibility of the proposed reliability analysis method in engineering applications.

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“…To improve the accuracy, we need to add some new sample points generated by the infill criteria to the initial Kriging model. The infill criterion adopted in this paper is the mean square error (MSE) infillsampling criterion, which can effectively improve the global precision of the Kriging model [21], [22]. The main idea of the MSE criterion is that the point with the maximum mean squared error in the design space is infilled into the Kriging model.…”

Section: Infill Criteriamentioning

confidence: 99%

Kriging Aerodynamic Modeling and Multi-Objective Control Allocation for Flying Wing UAVs With Morphing Trailing-Edge

Zhao

Yang

2021

IEEE Access

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Flexible skin and continuous deformable control surface are the basic of adaptive wing technology for future aircraft. This paper presents a continuous morphing trailing-edge and its control allocation method for the flying wing Unmanned Aerial Vehicle (UAV). Firstly, we apply the Kriging method to establish the aerodynamic model of the morphing trailing-edge, with the initial sample points generated by the non-uniform optimal Latin Hypercube Sampling (LHS). Then, based on the Kriging model, the multi-objective control allocation problem is converted into a standard optimization form. To solve such problem, we design a Comprehensive Multi-Objective Particle Swarm Optimization (C-MOPSO) algorithm and an improved Hierarchical MOPSO (H-MOPSO) algorithm, in which multiple optimization objectives are prioritized and hierarchically optimized using the PSO algorithm. As for performance analysis, an attitude angle tracking flight control system is established to validate the effectiveness of our proposed control allocation methods. Simulation results show that both the C-MOPSO and H-MOPSO methods have similar performance in attitude angle tracking, while H-MOPSO achieves better multi-objective allocation performance.INDEX TERMS Flying wing UAV, morphing trailing-edge, Kriging aerodynamic modeling, multiobjective control allocation, Particle Swarm Optimization.

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Planning of Multi Energy-Type Micro Energy Grid Based on Improved Kriging Model

Cited by 12 publications

References 31 publications

Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

An Efficient Adaptive Reliability Analysis Method Based on Kriging and Weighted Average Misclassification Rate Improvement

Kriging Aerodynamic Modeling and Multi-Objective Control Allocation for Flying Wing UAVs With Morphing Trailing-Edge

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