Study on orderly charging strategy of EV with load forecasting

Yin, Wenjun; Ji, Jianbo; Wen, Tao; Zhang, Chao

doi:10.1016/j.energy.2023.127818

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…Table 4 compares the results of three cases. Case 1: All EVs are charged in a disorderly fashion [16]. Case 2: All EVs are charged in an orderly fashion in the guidance of charging price decided by the particle swarm optimization (PSO) algorithm in [12].…”

Section: Performance Of the Proposed Approachmentioning

confidence: 99%

“…A microgrid deep Q-learning optimization strategy is presented based on the user charging behavior history data in [15], which aims to adopt the periodicity of user behavior. Reference [16] proposes the orderly scheduling model of EV charging with optimal grid loss. However, these studies are carried out under a fixed electricity price and direct control, which is passive regulation of EV users.…”

Section: Introductionmentioning

confidence: 99%

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Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

Hao,

Huang,

et al. 2023

Sustainability

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With increasing penetration of distributed generators (DG), the uncertainty and intermittence of renewable energy has brought new challenges to the economic dispatch and promotion of environment sustainability of microgrids. Active loads, especially in electric vehicles (EVs), are thought to be an efficient way to deal with the uncertainty and intermittence of renewable energy. One of the most important features of EVs is that their demand will vary in response to the electricity price. How to determine the real-time charging price to guide the orderly charging of EVs and operate with an uncertain renewable energy output represents an important topic for the microgrid operator (MGO). To this end, this paper formulates the optimal pricing and robust dispatch problem of the MGO as a Stackelberg game, in which the upper level minimizes the MGO’s cost, while the lower level minimizes the charging cost of each EV. In the problem, the approximate linear relationship between the node voltage and equivalent load is modeled, and the approximate linear expression of the node voltage security constraint is derived. Using dual optimization theory, the robust optimal dispatch model is transformed into a linear programming model without uncertain variables. Then, the Stackelberg game model is transformed into a mixed integer linear program by using the duality theorem of linear programming. Finally, the effectiveness of the proposed method is proved by simulation within the modified IEEE33-bus system.

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Section: Performance Of the Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

Hao,

Huang,

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…There is a lack of effective precise EVs charging forecast algorithms, so the disordered charging of electric vehicle cluster is currently popular; however, this method of charging is not conducive to meeting high-quality practical demands [4]. Currently, the EV OCP optimization issue has become a significant and difficult topic for the developing of Chinese economic and technological development zones, and an effective resolution is urgently needed [5].…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization

Che,

Chen,

Wang

et al. 2024

Algorithms

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This work discusses the electric vehicle (EV) ordered charging planning (OCP) optimization problem. To address this issue, an improved dual-population genetic moth–flame optimization (IDPGMFO) is proposed. Specifically, to obtain an appreciative solution of EV OCP, the design for a dual-population genetic mechanism integrated into moth–flame optimization is provided. To enhance the global optimization performance, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability, as well as the weight coefficient, are also designed. Additionally, opposition-based learning (OBL) is also introduced simultaneously. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance. Obviously, more ideal optimization solution of the EV OCP optimization problem can be obtained by using IDPGMFO.

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LA-RCNN: Luong attention-recurrent- convolutional neural network for EV charging load prediction

Mekkaoui,

Midoun,

Shen

2024

Appl Intell

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Study on orderly charging strategy of EV with load forecasting

Cited by 10 publications

References 21 publications

Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization

LA-RCNN: Luong attention-recurrent- convolutional neural network for EV charging load prediction

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