Coupled dispatching of regional integrated energy system under an electric-traffic environment considering user equilibrium theory

Wei, Wei; Xu, Lin; Xu, Jierui; Liu, Chang; Jiang, Xiaofeng; Liu, Kai

doi:10.1016/j.egyr.2022.07.008

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Traffic flow assignment can be divided into static traffic assignment (STA) semi-dynamic traffic assignment (SDTA), and dynamic traffic assignment (DTA) [11][12][13] STA and SDTA are often used to allocate traffic loads in the traffic planning process, but traffic flow changes in real-time during traffic jams. Scholars prefer using DTA to accurately depict users' dynamic changes in traffic flow [14][15][16].…”

Section: Collaborative Optimization Frameworkmentioning

confidence: 99%

Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation

Zhang¹,

Chen²,

Wang

et al. 2023

Energies

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With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam charging is introduced and the dynamic loading process of traffic flow is described using a cellular transmission model. The charging load is related to traffic flow and serves as a bond between the power distribution network and traffic network. The traffic flow achieves balanced allocation under dynamic user equilibrium conditions, and cooperatively optimizes the power flow of the power distribution network in conjunction with charging loads. Numerical analysis shows that this model can accurately depict the congestion situation during peak travel periods, and alleviate traffic congestion and distribution network voltage out of range.

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Section: Collaborative Optimization Frameworkmentioning

confidence: 99%

Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation

Zhang¹,

Chen²,

Wang

et al. 2023

Energies

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“…Each particle can be regarded as a longicorn, and the process of setting its initial position and speed is the same as that of the standard PSO. Combining the search idea of the longicorn whisker looking algorithm, on the basis of increasing the judgment of each individual in the population on the environment space, we can determine a better result by comparing the healthy function values reflecting the left and right whiskers during each iteration, so as to obtain the updated direction and coordinates of the longicorn herd [29]. The calculation process of BAS-PSO optimization algorithm is seen in Figure 2.…”

Section: Bas-pso Algorithmmentioning

confidence: 99%

BAS-PSO Algorithm for Integrated Energy System Optimization of Multiple Energy Supply Facilities

Xiong¹,

Chen²,

Liu³

et al. 2023

SPEE

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Regional low carbon will play an important role in the path of achieving the “double carbon” goal, and there are still many key technologies to be broken in its planning research. In this paper, an optimal scheduling model of integrated energy system with multiple energy supply devices is established by using Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) algorithm. First, in the scheduling model, a carbon trading mechanism is introduced and a stepped carbon trading cost model is constructed to constrain the carbon emissions of the plant. Then, using drosophila algorithm, the premise of whether wind power generation and photovoltaic power generation need to be built is determined by judging the economics of wind power generation and photovoltaic power generation in the construction area, and then the target power consumption curve and new energy power supply output curve are fitted. Then use the life cycle analysis method to analyze the carbon emissions generated by electric energy storage equipment, consider the carbon trading mechanism in the system economic operation model, and solve the model by BAS-PSO algorithm to overcome the problems of local optimum and slow convergence speed. Finally, a typical park integrated energy system is simulated to analyze the economic operation conditions and energy efficiency level of the system before and after participation in demand response. The innovation of this paper lies in considering the carbon trading mechanism and solving with the optimized BAS-PSO algorithm. Considering carbon trading can effectively improve the system wind power consumption capacity, and the optimized BAS-PSO algorithm improves the defects of traditional PSO. The simulation analysis results show that the established optimal scheduling model with multiple energy supply devices can realize the optimal operation of the integrated energy system of the park in the demand response environment; the improved BAS-PSO finds the lowest energy consumption within 100 iterations in 21 out of 50 iterations; the improved BAS-PSO algorithm reduces the energy consumption by 27.1 kW on average.

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“…(Valery et al, 2022). Compared with the traditional energy utilization system, IES can realize the coupling of different types of energy in different links such as source, network and load side, effectively improving the comprehensive utilization rate of energy (Wei et al, 2022). At present, as the basis for guiding the optimal scheduling of the system, IES load prediction is important for the accurate prediction of multivariate loads, considering the interaction of various relevant factors in IES and the complex mechanism (Wang et al, 2021;Jizhong et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Ultra-short-term load prediction of integrated energy system based on load similar fluctuation set classification

Yang

et al. 2023

Front. Energy Res.

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Due to the strong coupling characteristics and daily correlation characteristics of multiple load sequences, the prediction method based on time series extrapolation and combined with multiple load meteorological data has limited accuracy improvement, which is tested by the fluctuation of load sequences and the accuracy of Numerical Weather Prediction (NWP). This paper proposes a multiple load prediction method considering the coupling characteristics of multiple loads and the division of load similar fluctuation sets. Firstly, the coupling characteristics of multivariate loads are studied to explore the interaction relationship between multivariate loads and find out the priority of multivariate load prediction. Secondly, the similar fluctuating sets of loads are divided considering the similarity and fluctuation of load sequences. Thirdly, the load scenarios are divided by k-means clustering for the inter-set sequences of similar fluctuating sets, and the Bi-directional Long Short-Term Memory (BI-LSTM) models are trained separately for the sub-set of scenarios and prioritized by prediction. Finally, the effectiveness of the proposed method was verified by combining the multivariate load data provided by the Campus Metabolism system of Arizona State University.

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Coupled dispatching of regional integrated energy system under an electric-traffic environment considering user equilibrium theory

Cited by 6 publications

References 29 publications

Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation

Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation

BAS-PSO Algorithm for Integrated Energy System Optimization of Multiple Energy Supply Facilities

Ultra-short-term load prediction of integrated energy system based on load similar fluctuation set classification

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