A Data-Driven Approach to Linearize Power Flow Equations Considering Measurement Noise

Liu, Yuxiao; Wang, Yi; Zhang, Ning; Lu, Dan; Chen, Kang

doi:10.1109/tsg.2019.2957799

Cited by 43 publications

(29 citation statements)

References 33 publications

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“…Standard power flow algorithms are not applicable for islanded operation due to two known reasons: the inexistence of slack bus and the dependence of active/reactive power on frequency/voltage due to droop action. In addition, the nonlinearity of basic power flow equations is known for their iterative nature and therefore slow in convergence [36], [39]. Since future microgrids will consist of a significantly large number of DERs (several orders higher than present state of the arts), a more computationally efficient algorithm will be beneficial.…”

Section: A Basic Equationsmentioning

confidence: 99%

An Optimal Secondary Multi-Bus Voltage and Reactive Power Sharing Control Based on Non-Iterative Decoupled Linearized Power Flow for Islanded Microgrids

et al. 2021

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The subject of optimal secondary control of power-electronic-interfaced distributed energy resources (DERs) in droop-controlled microgrids has garnered significant research attention in recent years. While the feasibility of optimal secondary control based on non-linear power flow has been proven, the power flow algorithm is essentially iterative in nature. This work proposes an optimal secondary control with non-iterative power flow to regulate multi-bus voltages and DERs' reactive powers. The control scheme incorporates a modified Decoupled Linearized Power Flow that is known to be superior in terms of reactive power and bus voltage magnitude estimation, as compared to classical DC power flow, into a constrained quadratic programming. Q-V droop is integrated into the linear power flow in place of the slack bus. The proposed optimal scheme is provably accurate for maintaining reactive power sharing while regulating multiple load-bus voltages. The additional degrees of freedom enabled by the weighting factors significantly improve the control flexibility of the secondary controller. The allowable bus voltages and DER kVar capacity limits have also been considered by the control algorithm. The work is proven through an accurate co-simulation study comprising an 18-bus network and a full primary control models in PowerFactory, interfaced through industrial communication tool MatrikonOPC.INDEX TERMS Microgrid, droop control, reactive power sharing, voltage regulation, optimal secondary control, decoupled linearized power flow.

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Section: A Basic Equationsmentioning

confidence: 99%

An Optimal Secondary Multi-Bus Voltage and Reactive Power Sharing Control Based on Non-Iterative Decoupled Linearized Power Flow for Islanded Microgrids

et al. 2021

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“…In the data-driven context, also the solution to the classical power flow problem with noisy input data has been addressed, leading to the linearisation of the power flow equations (Liu Y. et al, 2020 ). Anomaly detection is also applied to short-term load forecasting, with procedures based on robust statistical methods (Chakhchoukh et al, 2011 ; Guo et al, 2012 ) and dynamic regression model (Luo et al, 2018 ).…”

Section: Data Qualitymentioning

confidence: 99%

Data Consistency for Data-Driven Smart Energy Assessment

Chicco

2021

Front. Big Data

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In the smart grid era, the number of data available for different applications has increased considerably. However, data could not perfectly represent the phenomenon or process under analysis, so their usability requires a preliminary validation carried out by experts of the specific domain. The process of data gathering and transmission over the communication channels has to be verified to ensure that data are provided in a useful format, and that no external effect has impacted on the correct data to be received. Consistency of the data coming from different sources (in terms of timings and data resolution) has to be ensured and managed appropriately. Suitable procedures are needed for transforming data into knowledge in an effective way. This contribution addresses the previous aspects by highlighting a number of potential issues and the solutions in place in different power and energy system, including the generation, grid and user sides. Recent references, as well as selected historical references, are listed to support the illustration of the conceptual aspects.

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“…Some other extensions were discussed in [41]- [43]. The supervised and transfer learning were applied in [41] to estimate the Pareto front that is made up with a series of initial values.…”

Section: B Category 2 Optimization Option Selectionmentioning

confidence: 99%

“…Reference [42] proposed a linear power flow model to accelerate and approximate the power flow calculation. This work was further extended in [43] to tackle the challenge of hidden measurement noises. The authors formulated three quadratic programming models with several Jacobian-matrix-guided constraints to achieve this goal.…”

Section: B Category 2 Optimization Option Selectionmentioning

confidence: 99%

Review of Learning-Assisted Power System Optimization

Ruan,

Zhong,

Zhang

et al. 2020

Preprint

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Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates to what extent such data-driven analysis may benefit the rule-based optimization. A series of typical references are selected and categorized into four kinds: the boundary parameter improvement, the optimization option selection, the surrogate model and the hybrid model. This taxonomy provides a novel perspective to understand the latest research progress and achievements. We further discuss several key challenges and provide an in-depth comparison on the features and designs of different categories. Deep integration of machine learning approaches and optimization models is expected to become the most promising technical trend.

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A Data-Driven Approach to Linearize Power Flow Equations Considering Measurement Noise

Cited by 43 publications

References 33 publications

An Optimal Secondary Multi-Bus Voltage and Reactive Power Sharing Control Based on Non-Iterative Decoupled Linearized Power Flow for Islanded Microgrids

An Optimal Secondary Multi-Bus Voltage and Reactive Power Sharing Control Based on Non-Iterative Decoupled Linearized Power Flow for Islanded Microgrids

Data Consistency for Data-Driven Smart Energy Assessment

Review of Learning-Assisted Power System Optimization

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