Phase balancing algorithms

Wang, Kai; Skiena, Steven; Robertazzi, Thomas G.

doi:10.1016/j.epsr.2012.10.012

Cited by 36 publications

(22 citation statements)

References 3 publications

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“…T. H. Chen et al developed a genetic-algorithm-based approach to achieve both phase rebalancing and loss reduction for radial distribution networks, where the control variables are the phase connectivity of the transformers connected to a primary feeder [47]. K. Wang et al applied six algorithms to the phase rebalancing problem and identified dynamic programming as a promising algorithm because of its optimality [48]. S. H. Soltani et al developed a dynamic re-phasing strategy, which automatically re-phases loads at any time of a day when the degree of unbalance exceeds a threshold through automatic re-phasing switches [49].…”

Section: Existing Solutions To Phase Unbalancementioning

confidence: 99%

See 1 more Smart Citation

Review of Distribution Network Phase Unbalance: Scale, Causes, Consequences, Solutions, and Future Research Direction

Ma¹,

Fang²,

Kong³

2020

Preprint

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Phase unbalance is widespread in the distribution networks in the UK, continental Europe, US, China, and other countries. First, this paper reviews the mass scale of phase unbalance and its causes and consequences. Three challenges arise from phase rebalancing: the scalability, data scarcity, and adaptability (towards changing unbalance over time) challenges. Solutions to address the challenges are: 1) using retrofit-able, maintenance-free, automatic solutions to overcome the scalability challenge; 2) using data analytics to overcome the data-scarcity challenge; and 3) using phase balancers or other online phase rebalancing solutions to overcome the adaptability challenge. This paper categorizes existing phase rebalancing solutions into three classes: 1) load/lateral re-phasing; 2) using phase balancers; 3) controlling energy storage, electric vehicles, distributed generation, and micro-grids for phase rebalancing. Their advantages and limitations are analyzed and ways to overcome the limitations are recommended. Finally, this paper suggests future research topics: 1) long-term forecast of phase unbalance; 2) whole-system analysis of the unbalance-induced costs; 3) phase unbalance diagnosis for data-scarce LV networks; 4) technocommercial solutions to exploit the flexibility from large threephase customers for phase balancing; 5) the optimal placement of phase balancers; 6) the transition from single-phase customers to three-phase customers.

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Section: Existing Solutions To Phase Unbalancementioning

confidence: 99%

“…Advantages Limitations and challenges Ways to overcome limitations and challenges 1. Re-phasing [11], [34], [44], [2], [45], [46], [47], [48], [49], [ Offline re-phasing is unable to address this. Online re-phasing can address this by frequent switching.…”

Section: Solutionsmentioning

confidence: 99%

Review of Distribution Network Phase Unbalance: Scale, Causes, Consequences, Solutions, and Future Research Direction

Ma¹,

Fang²,

Kong³

2020

Preprint

View full text Add to dashboard Cite

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“…Power transformers are critical expensive assets in distribution networks, hence special attention must be given to their operation and maintenance [41,42]. Nonetheless, detailed information is scarce at the LV network level, such as the particular phase where a single-end user or DER unit is connected [43], despite successful smart grid pilot projects such as Smartcity Malaga [44], INTEGRIS [5], IDE4L [45], or MONICA [46] have dedicated special efforts in distribution network digitalization and state estimation. Therefore, technical solutions are available today to address congestion in singular elements such as LV feeder phases, avoiding bottlenecks that may arise even earlier in the case of significant DER penetration or demand growth [47,48], since technical constraint violations may occur at an earlier stage [49].…”

Section: Congestion In LV Distribution Networkmentioning

confidence: 99%

Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks

2019

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A growing presence of distributed energy resources (DER) and the increasingly diverse nature of end users at low-voltage (LV) networks make the operation of these grids more and more challenging. Particularly, congestion and voltage management strategies for LV grids have usually been limited to some elemental criteria based on human experience, asset oversizing, or grid reinforcement. However, with the current massive deployment of sensors in modern LV grids, new approaches are feasible for distribution network assets operation. This article proposes a multi-objective particle swarm optimization (MOPSO) approach, combined with data analytics through affinity propagation clustering, for congestion threshold determination in LV grids. A real case study from the smart grid of Smartcity Malaga Living Lab is used to illustrate the proposed approach. Within this approach, distribution system operators (DSOs) can take decisions in order to prevent situations of risk or potential failure at LV grids.

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“…Such inefficient uses of assets cause the ARCs. Quantifying ARCs on a utility scale would help DNOs to appraise whether it is sensible to take the passive network investments approach or a proactive phase balancing approach [3][4][5]. However, the challenges towards the ARC estimation at a utility scale are: 1) the extensiveness of LV networks with varying characteristics across different regions [6]; and 2) the limited monitoring data available [7].…”

Section: Introductionmentioning

confidence: 99%

Utility-Scale Estimation of Additional Reinforcement Cost From Three-Phase Imbalance Considering Thermal Constraints

2017

IEEE Trans. Power Syst.

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Widespread three-phase imbalance causes inefficient uses of low voltage (LV) network assets, leading to additional reinforcement costs (ARCs). Previous work that assumed balanced three phases underestimated the reinforcement cost throughout the whole utility by more than 50%. Previous work that quantified the ARCs was limited to individual network components, relying on full sensory data. This paper proposes a novel methodology that will scale the ARC estimation at a utility level, when the data concerning the imbalance of circuits or transformers are scarce. A novel statistical method is developed to estimate the volume of assets that need to be invested by identifying the relationship between the triangular distribution of circuit imbalance and that of circuit utilization. When there are more data available in future, accurate probability distributions can be constructed to reflect the network condition across the whole system. In light of this, two novel generalized ARC estimation formulas are developed that account for generic probability distributions. The developed methodology is applied to a real utility system in the UK, showing that: 1) three-phase imbalance leads to ARCs that are even greater than the reinforcement costs in the balanced case; 2) a 1% increase in the demand growth rate, the maximum degree of imbalance (DIB) and the maximum nominal utilization rate leads to over 10%, approximately 1% and 2% increases in the ARCs, respectively; and 3) the ARC is not sensitive to the minimum DIB values and the minimum nominal utilization rates.

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Phase balancing algorithms

Cited by 36 publications

References 3 publications

Review of Distribution Network Phase Unbalance: Scale, Causes, Consequences, Solutions, and Future Research Direction

Review of Distribution Network Phase Unbalance: Scale, Causes, Consequences, Solutions, and Future Research Direction

Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks

Utility-Scale Estimation of Additional Reinforcement Cost From Three-Phase Imbalance Considering Thermal Constraints

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