Iterative convex relaxation of unbalanced power distribution system integrated multi-energy systems

Sharma, Abhimanyu; Padhy, Narayana Prasad

doi:10.1016/j.energy.2024.130974

Energy

2024

DOI: 10.1016/j.energy.2024.130974

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Iterative convex relaxation of unbalanced power distribution system integrated multi-energy systems

Abhimanyu Sharma,

Narayana Prasad Padhy

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2024

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Cited by 2 publications

References 37 publications

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Multi-energy flow calculation in integrated energy system via topological graph attention convolutional network with transfer learning

Wu,

2024

Energy

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Multi-energy flow calculation in integrated energy system via topological graph attention convolutional network with transfer learning

Wu,

2024

Energy

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Dynamic arithmetic optimization algorithm control of distributed generations for demand balancing and enhancing power quality of unbalanced distribution systems

Eid,

Alsafrani

2024

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Unbalanced power systems cause transformers and generators to overheat, system losses to climb, and protective devices to trigger. An optimization-based control technique for distributed generators (DG) balances demand and improves power quality in three imbalanced distribution systems with 10, 13, and 37 nodes. Each system phase has its own DG. Particle Swarm Optimization (PSO) and Dynamic Arithmetic Optimization Algorithm (DAOA) determine each phase’s best locations, sizes, and power factors. The PSO and DAOA algorithms optimize the three imbalanced distribution systems at full load and throughout the day. The three DG sources are at the same node for easy operation, maintenance, and control. Each system’s voltage, power, and current imbalance factors (VUF, PUF, CUF) are determined according to ANSI and IEEE standards. Optimization techniques lower VUF to meet the criteria for all studied systems. PUF values drop from 116%, 28%, and 17% to virtually zero for the 10-, 13-, and 37-bus systems, while CUF improves similarly. Power losses are minimized by 80%, 51%, and 52% for each system. The voltage profile improves, reducing voltage variance across all three systems.

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Iterative convex relaxation of unbalanced power distribution system integrated multi-energy systems

Cited by 2 publications

References 37 publications

Multi-energy flow calculation in integrated energy system via topological graph attention convolutional network with transfer learning

Multi-energy flow calculation in integrated energy system via topological graph attention convolutional network with transfer learning

Dynamic arithmetic optimization algorithm control of distributed generations for demand balancing and enhancing power quality of unbalanced distribution systems

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