Three-Phase Unbalanced Distribution Network Dynamic Reconfiguration: A Distributionally Robust Approach

An-ping, Zhou; Zhai, Hefeng; Yang, Ming; Lin, You

doi:10.1109/tsg.2021.3139763

Cited by 41 publications

(7 citation statements)

References 33 publications

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“…Figure 1 depicts the primary distribution feeder. It shows a radial industrial distribution feeder (13.8 kV) supplying twelve distribution transformers (13.8/0.4 kV), feeding factories, in the Riyadh region, Saudi Arabia [14], [15].…”

Section: B Distribution Feeder Modelmentioning

confidence: 99%

Primary Distribution Feeder Reinforcement Model With Network Constraints

Almehizia,

Al-Ismail

2023

IEEE Access

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The paper evaluates the primary distribution feeder reinforcement options due to increased electrical demand. A sizing and allocation optimization model based on stochastic Mixed Integer Nonlinear Programming (MINLP) is proposed to realize and asses various electrical distribution feeder upgrade options. The options include transformer reinforcement, adding new cables, installing Photovoltaic (PV) systems, and Battery Energy Storage systems (BESSs). Scenario generation and clustering address the demand and PV power uncertainties. A normal distribution is used to model the demand uncertainty and provide a sensitive insight into the system. The developed model was tested on an 18-bus distribution feeder from an industrial area in Riyadh, Saudi Arabia. The results identified BESS and PV systems as viable reinforcement options.

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Section: B Distribution Feeder Modelmentioning

confidence: 99%

Primary Distribution Feeder Reinforcement Model With Network Constraints

Almehizia,

Al-Ismail

2023

IEEE Access

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“…It is well known that the deterministic probabilistic distribution needed in stochastic optimization can only be observed indirectly through finite datasets and robust optimization has an over-conservativeness feature because it ignores probability distribution information. Recently, a distributionally robust optimization (DRO) method that remedies the SO's concreteness of probability information and RO's ignorance of probability information was proposed in [156][157].…”

Section: ) Distributionally Robust Optimizationmentioning

confidence: 99%

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Zhang

2022

IEEE Access

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Microgrid (MG) is one of the most effective solution to integrate distributed renewable energy into power system. However, modern MG has several technical challenges such as migration to multi vector energy system, increasing renewable energy penetration, and the uncertainties that arise from the large-scale incorporation of renewable energy. Recent development of multi-microgrid (MMG) could potentially mitigate these challenges. MMG has obvious advantages in improving the system stability, reliability, economy, and energy efficiency of power grid operation through autonomous management and coordinated control among networked MGs. In the meantime, uncertainties from many elements and operators in the MMG system also pose huge challenges in modeling. This paper will present and review typical architecture of MMG, including physical layer, information layer and application layer. Moreover, this paper will critically review and analyze challenges in uncertainty modelling and solution in MMGs. Finally, the paper will also discuss future research areas and development trends of MMG.

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“…In a different set of studies, mathematical models are utilized to solve the DNR optimization [12]. For example, a distributionally robust chance-constrained DNR approach for a three-phase unbalanced distribution network is proposed in [13]. It optimizes the switching cost and the expected power supply cost from an upstream grid.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling

2023

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Distribution network reconfiguration (DNR) is one of the most important methods to cope with the increasing electricity demand due to the massive integration of electric vehicles. Most existing DNR methods rely on accurate network parameters and lack scalability and optimality. This study uses model-free reinforcement learning algorithms for training agents to take the best DNR actions in a given distribution system. Five reinforcement algorithms are applied to the DNR problem in 33-and 136-node test systems and their performances are compared: deep Q-learning, dueling deep Q-learning, deep Q-learning with prioritized experience replay, soft actor-critic, and proximal policy optimization. In addition, a new deep Q-learning-based action sampling method is developed to reduce the size of the action space and optimize the loss reduction in the system. Finally, the developed algorithms are compared against the existing methods in literature.

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Three-Phase Unbalanced Distribution Network Dynamic Reconfiguration: A Distributionally Robust Approach

Cited by 41 publications

References 33 publications

Primary Distribution Feeder Reinforcement Model With Network Constraints

Primary Distribution Feeder Reinforcement Model With Network Constraints

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling

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