Coordinated EV charging and reactive power optimization for radial distribution network using mixed integer second-order cone programming

Li, Pengcheng; Duan, Xiaoyu; Yang, Jing; Zhang, Qiuyan; Zhao, Yuanliang; Hu, Zechun

doi:10.1109/itec-ap.2017.8080845

Cited by 7 publications

(8 citation statements)

References 12 publications

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“…The physical constraints should include power flow, voltage region and the capacity limits of equipment. Finally, the optimization model can be expressed as [58]: , , ( )…”

Section: A Deterministic Ev Optimal Charging Modelmentioning

confidence: 99%

Optimal Electric Vehicle Charging Strategy With Markov Decision Process and Reinforcement Learning Technique

Ding

Zeng

Bai

et al. 2020

IEEE Trans. on Ind. Applicat.

104

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Electric vehicles (EVs) have rapidly developed in recent years and their penetration has also significantly increased, which, however, brings new challenges to power systems. Due to their stochastic behaviors, the improper charging strategies for EVs may violate the voltage security region. To address this problem, an optimal EV charging strategy in a distribution network is proposed to maximize the profit of the distribution system operators while satisfying all the physical constraints. When dealing with the uncertainties from EVs, a Markov decision process (MDP) model is built to characterize the time series of the uncertainties and then the deep deterministic policy gradient based reinforcement learning technique is utilized to analyze the impact of uncertainties on the charging strategy. Finally, numerical results verify the effectiveness of the proposed method.

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“…The physical constraints should include power flow, voltage region and the capacity limits of equipment. Finally, the optimization model can be expressed as [58]: , , ( )…”

Section: A Deterministic Ev Optimal Charging Modelmentioning

confidence: 99%

Optimal Electric Vehicle Charging Strategy With Markov Decision Process and Reinforcement Learning Technique

Ding

Zeng

Bai

et al. 2020

IEEE Trans. on Ind. Applicat.

104

View full text Add to dashboard Cite

show abstract

“…The continuity of discrete variables in optimal power flow of distribution network has attracted many researchers' attention, and has achieved some satisfactory performances. But the convexity of the original problem may be changed with nonconvex penalty function or non-convex relaxation constraints added, which may result in local minima [62]. To solve above difficult problems second-order cone programming with Distflow branch model was proposed and it could transform the original optimal power flow model to second-order cone optimization problem by convex relaxation techniques [61].…”

Section: Solution Algorithmsmentioning

confidence: 99%

Review and prospect of active distribution system planning

Liu

Gao

et al. 2015

J. Mod. Power Syst. Clean Energy

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The approach to planning, design and operation of distribution networks have significantly changed due to the proliferation of distributed energy resources (DERs) together with load growth, energy storage technology advancements and increased consumer expectations. Planning of active distribution systems (ADS) has been a very hot topic in the 21st Century. A large number of studies have been done on ADS planning. This paper reviews the state of the art of current ADS planning. Firstly, the influences of DERs on the ADS planning are addressed. Secondly, the characteristics and objectives of ADS planning are summarized. Then, up to date planning model and some related research are highlighted in different areas such as forecasting load and distributed generation, mathematical model of ADS planning and solution algorithms. Finally, the paper explores some directions of future research on ADS planning including planning collaboratively with all elements combined in ADS, taking into account of joint planning in secondary system, coordinating goals among different layers, integrating detailed operation simulations and regular performance based reviews into planning, and developing advanced planning tools.

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“…The permeability of distributed generation (DG), static VAR compensation (SVC) and packet switching capacitors banks (CB) in the distribution network are increasing. The distribution network is evolving into an active distribution network (ADN) with many adjustable devices [1] . Therefore, it is very important to decrease the active power loss and improve the voltage quality of the distribution network by certain regulation means.…”

Section: Introductionmentioning

confidence: 99%

Active and Reactive Power Coordination Optimization of the Active Distribution Network

Deng

Wang²,

Gao³

2023

J. Phys.: Conf. Ser.

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The operation and control of the active distribution network are faced with great challenges due to a mass of tunable and controllable devices connected to the network, resulting in large active power loss and voltage deviation. In this paper, a method of active and reactive power coordination optimization for the active distribution network based on a one-dimensional convolutional neural network (1D-CNN) is proposed. This method can mine valuable information from the historical data of distribution networks, and use one-dimensional convolutional neural networks to map the complex nonlinear relationship between node load and optimization strategy. The simulation results of the modified IEEE33 node distribution network system show that the active power loss and the node voltage deviation of the proposed method are significantly reduced.

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Coordinated EV charging and reactive power optimization for radial distribution network using mixed integer second-order cone programming

Cited by 7 publications

References 12 publications

Optimal Electric Vehicle Charging Strategy With Markov Decision Process and Reinforcement Learning Technique

Optimal Electric Vehicle Charging Strategy With Markov Decision Process and Reinforcement Learning Technique

Review and prospect of active distribution system planning

Active and Reactive Power Coordination Optimization of the Active Distribution Network

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