An Identification Method for Anomaly Types of Active Distribution Network Based on Data Mining

Wang, Shaorui; Lu, Tianguang; Hao, Ran; Li, Jing; Guo, Yu; He, Xueqian; Han, Xueshan

doi:10.1109/tpwrs.2023.3288043

Cited by 9 publications

(1 citation statement)

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“…To combat pollution and promote sustainable development, an increasing number of distributed energy resources (DERs) are being integrated into distribution systems. While DERs offer remarkable advantages, they also present various challenges [1]. These challenges stem from the small capacity, large quantity, and uneven distribution of distributed generators (DGs), resulting in high costs for individual connections and complexities in operation and transaction management.…”

Section: Introductionmentioning

confidence: 99%

Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism

Sun,

2023

IET Generation Trans & Dist

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With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants’ benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system.

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Section: Introductionmentioning

confidence: 99%