A Linear AC Power Flow Model for Unbalanced Multi-Phase Distribution Networks Based on Current Injection Equations

Heidari-Akhijahani, Adel; Safdarian, Amir; Vrakopoulou, Maria

doi:10.1109/tpwrs.2021.3073839

Cited by 15 publications

(2 citation statements)

References 7 publications

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“…The real-time model can be improved with model-based reinforcement learning techniques. The power flow formulation can be changed to address the current sharing problem in distribution systems by utilizing equations based on current injection in the linear power flow model [62].…”

Section: Discussionmentioning

confidence: 99%

Multi-Stage Volt/VAR Support in Distribution Grids: Risk-Aware Scheduling With Real-Time Reinforcement Learning Control

et al. 2023

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The ever-increasing penetration of intermittent renewable resources in low-voltage power grids necessitates efficient operational strategies for voltage regulation as well as power scheduling of the available resources. In this paper, a risk-aware Volt/VAR support framework followed by a real-time reinforcement learning controller is presented for three-phase distribution systems. In the risk-aware stochastic scheduling stage, the legacy voltage regulating assets along with inverter-based photovoltaics (PVs) and energy storage system (ESS) are optimized considering day-ahead and intra-day markets. Moreover, demand response (DR) and voltage reduction plans are included in the proposed scheduling framework. By incorporating voltagedependent load modeling in this study, the implementation of the voltage reduction plan reduces energy consumption in feeders by running the network at lower permissible voltage limits. The shiftable loads under the DR program are employed for peak shaving and to reduce operational costs. The result shows that DR implementation also reduces dependencies on the operation of traditional devices. The stochasticity of abrupt changes in PV generations is represented as the Gaussian Mixture Model (GMM), indicating a non-unimodal probability distribution in day-ahead PV forecasting errors. The scenario sets for uncertain variables are then reduced using a fuzzy clustering technique. Decisions made in the scheduling, associated with PV inverters and ESS operation, are revised with a real-time controller, i.e., Deep Deterministic Policy Gradient (DDPG) reinforcement learning. The DDPG is adopted in the control stage of the framework considering the detailed modeling of unbalanced three-phase distribution grids to minimize the voltage deviation and power ramping of ESS. The performance of the proposed multi-stage scheme is verified using a three-phase active distribution grid under different scenarios. INDEX TERMSDemand response program, Gaussian mixture model, reinforcement learning, risk-aware scheduling, Volt/VAR support. NOMENCLATURE P loss , Q loss Active/reactive Loss of feeders.

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Section: Discussionmentioning

confidence: 99%

Multi-Stage Volt/VAR Support in Distribution Grids: Risk-Aware Scheduling With Real-Time Reinforcement Learning Control

et al. 2023

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“…Furthermore, different neutral designs must be considered, since the design varies from country to country: networks without neutral, networks with an isolated neutral, networks with multiple neutral grounding, networks with resistance neutral grounding and networks with neutral compensated via Petersen coil. In the last decades, different approaches have been presented in literature for distribution systems: backward-forward sweep technique [19]- [20], Newton-Raphson method [21]- [22], current injection method [23]- [24] and Holomorphic embedding load flow [25]. In order to solve the unbalanced load flow problem, some approaches have been developed specifically for distribution networks [26]- [27].…”

Section: B Literature Reviewmentioning

confidence: 99%

PFPD_MCA: A Multiconductor Power Flow

Rusalen,

Benato

2024

IEEE Access

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In this paper, the multiconductor extension of the previously developed three-phase power flow algorithm (named PFPD_3P) is presented. This multiconductor formulation has a general validity for both distribution and transmission networks. An iterative matrix formulation for the solution is throughout expounded. The present method allows computing the electrical quantities of all the network conductors: the active conductors and the passive ones (i.e., OHL ground wires, IC metallic screens/armours, and GIL enclosures). These electrical quantities can be evaluated both at the network busbars and also along the lines. The knowledge of these quantities can be useful to perform: safety issues, power quality and electromagnetic compatibility studies. The electrical substations are carefully modelled, by considering the links between the passive conductors of different busbars and the earthing resistance of the meshed earth electrode. The algorithm is implemented in Matlab environment and tested by several fictitious networks. Eventually, in order to confirm the approach accuracy, the multiconductor results are compared with the equivalent single-phase ones and the three-phase power flow commercial software DIgSILENT PowerFactory.

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Hybrid renewable/nonrenewable/storage resources in electrical grid considering active‐reactive losses and depth of discharge

Hemmati

Mahdavi

2021

Int J Energy Res

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This paper presents a linear model for optimal operation of distributed generations (DGs) including renewable and nonrenewable DGs in the electrical grids.The accurate model of active-reactive losses is formed by two quadratic terms that are linearized by piecewise linear approximation of the quadratic curves.The DGs' technical/economic formulations are also presented based on the linear model. The proposed linear models are combined to implement linear optimal power flow (OPF) in the electrical grid integrated with hybrid diesel/wind/ solar/battery. The model is associated with uncertainties and expressed as stochastic mixed integer linear programming. Both the active and reactive powers of the grid and resources are incorporated. The model optimizes the following design variables: sitting and sizing of energy storage systems and DGs, depth of discharge, energy of battery, operation pattern of battery, operation pattern of non-renewables units, and investment/operational costs. The proposed model is tested on 69-bus distribution grid. The simulation results show that the depth of discharge is optimized on 30% to 95%. Active and reactive losses achieved by the proposed model are 72% and 47% more accurate compared to the other existing models. The voltage profile is improved by about 8%. The network losses are reduced by 24%. The peak shaving is performed at hours 18 to 24.

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A Linear AC Power Flow Model for Unbalanced Multi-Phase Distribution Networks Based on Current Injection Equations

Cited by 15 publications

References 7 publications

Multi-Stage Volt/VAR Support in Distribution Grids: Risk-Aware Scheduling With Real-Time Reinforcement Learning Control

Multi-Stage Volt/VAR Support in Distribution Grids: Risk-Aware Scheduling With Real-Time Reinforcement Learning Control

PFPD_MCA: A Multiconductor Power Flow

Hybrid renewable/nonrenewable/storage resources in electrical grid considering active‐reactive losses and depth of discharge

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