Volt Var Watt Optimization in Distribution Network with High Penetration of Renewable Energy Sources and Electric Vehicles

Svenda, Goran; Krstić, Ivana Ilić; Kanjuh, Sonja; Jajcanin, Milena; Vuletić, Dejan

doi:10.1109/isgt-europe54678.2022.9960647

Cited by 2 publications

(2 citation statements)

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“…The online decision-making effects of different algorithms in the reconfigured and original IEEE 118-bus systems are compared in Table III. 1) In contrast to the IEEE 33-bus system, the task difficulty of the IEEE 118-bus system increases significantly, as reflected by the higher voltage violation rates.…”

Section: ) Evaluation Of Decision-making Effectsmentioning

confidence: 99%

“…T HE large-scale integration of intermittent distributed generation such as renewable energy sources (RESs) presents prospects for enhancing the energy decarbonization and flexibility of active distribution networks (ADNs). However, the uncertainty of RES output also challenges the operation of ADNs, including more frequent voltage violations and increased network loss [1]. Therefore, optimizing the ADN operation against the backdrop of high renewable energy penetration has emerged as a pressing issue that must be addressed.…”

Section: Introductionmentioning

confidence: 99%

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Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

Xu,

Gao,

Wang

et al. 2024

Journal of Modern Power Systems and Clean Energy

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The increasing integration of intermittent renewable energy sources (RESs) poses great challenges to active distribution networks (ADNs), such as frequent voltage fluctuations. This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning (MADRL), which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization. After deploying the calculated optimal switch topologies, the distribution network operator will dynamically adjust the distributed energy resources (DERs) to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm. Owing to the model-free characteristics and the generalization of deep reinforcement learning, the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments. Additionally, integrating parameter sharing (PS) and prioritized experience replay (PER) mechanisms substantially improves the strategic performance and scalability. This framework has been tested on modified IEEE 33-bus, IEEE 118bus, and three-phase unbalanced 123-bus systems. The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

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Section: ) Evaluation Of Decision-making Effectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%