Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Fu, Xueqian; Wu, Xianping; Zhang, Chunyu; Fan, Shaoqian; Liu, Nian

doi:10.1186/s41601-022-00262-x

Cited by 43 publications

(22 citation statements)

References 157 publications

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“…1. Hierarchical, multi-timescale coordinated control configuration of the sustainable microgrid [1], [3], [5], [7], [25], [55].…”

Section: ⅱ Multi-timescale Hierarchical Controlmentioning

confidence: 99%

“…ith the increasing penetration of renewable and green distributed power generation in power systems, there are changes in the way the system generates, transmits, distributes, and uses electricity [1], [2]. A microgrid (MG) system, as a small-scale, low-and medium-voltage controllable distribution network, can operate in grid-connected and islanded mode, offering the possibility of efficient and flexible use of distributed energy, reducing the impact of intermittency of wind and solar energy on the main power grid, and making the power system more flexible, reliable, safe, clean and economical [1], [3] [7].…”

Section: ⅰ Introductionmentioning

confidence: 99%

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Multi-Timescale Modeling and Dynamic Stability Analysis for Sustainable Microgrids: State-of-the-Art and Perspectives

Zhang,

Han,

Liu

et al. 2024

Prot. Control Mod. Power Syst.

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The increasing trend for integrating renewable energy sources into the grid to achieve a cleaner energy system is one of the main reasons for the development of sustainable microgrid (MG) technologies. As typical power-electronized power systems, MGs make extensive use of power electronics converters, which are highly controllable and flexible but lead to a profound impact on the dynamic performance of the whole system. Compared with traditional large-capacity power systems, MGs are less resistant to perturbations, and various dynamic variables are coupled with each other on multiple timescales, resulting in a more complex system in-stability mechanism. To meet the technical and economic challenges, such as active and reactive power-sharing, volt-age, and frequency deviations, and imbalances between power supply and demand, the concept of hierarchical control has been introduced into MGs, allowing systems to control and manage the high capacity of renewable energy sources and loads. However, as the capacity and scale of the MG system increase, along with a multi-timescale control loop design, the multi-timescale interactions in the system may become more significant, posing a serious threat to its safe and stable operation. To investigate the multi-timescale behaviors and instability mechanisms under dynamic inter-actions for AC MGs, existing coordinated control strategies are discussed, and the dynamic stability of the system is defined and classified in this paper. Then, the modeling and assessment methods forthe stability analysis of multi-timescale systems are also summarized. Finally, an outlook and discussion of future research directions for AC MGs are also presented.

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“…1. Hierarchical, multi-timescale coordinated control configuration of the sustainable microgrid [1], [3], [5], [7], [25], [55].…”

Section: ⅱ Multi-timescale Hierarchical Controlmentioning

confidence: 99%

Section: ⅰ Introductionmentioning

confidence: 99%

Multi-Timescale Modeling and Dynamic Stability Analysis for Sustainable Microgrids: State-of-the-Art and Perspectives

Zhang,

Han,

Liu

et al. 2024

Prot. Control Mod. Power Syst.

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show abstract

“…Fuzzy power flow has been widely applied in the operation and planning of power systems since its proposal, including power transmission systems [20,21], distribution networks, and distributed generation systems [22][23][24][25]. This has led to new requirements for fuzzy power flow algorithms.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Fuzzy Newton Power Flow Convergence

Zhao,

Zheng,

Zhen

et al. 2023

Energies

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In order to address the convergence issue in fuzzy power flow calculations, this paper proposes an analytical approach based on the Levenberg–Marquardt method, aiming to improve the convergence of the fuzzy Newton power flow method. Firstly, a detailed analysis is conducted on the convergence theorem and convergence behavior of the fuzzy Newton method, revealing its poor convergence when the initial values are not properly selected. The Levenberg–Marquardt method is then selected as a means to enhance the convergence of the fuzzy Newton power flow calculations, specifically to tackle the problem of initial value deviation. Since the Jacobian matrix has a significant impact on the convergence region of the power flow, this paper reconstructs the Jacobian matrix based on the Levenberg–Marquardt method, effectively enlarging the convergence region. Through validation experiments on the IEEE 118 standard nodes and simulation comparative analysis, the results confirm the method’s effectiveness in resolving the problem of initial value deviation and notably enlarging the convergence region, thereby improving the convergence of power flow calculations.

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“…Selecting representative discrete scenarios to characterize the probability distribution is also an issue, which a large set of scenarios will greatly increase the computational burden. The literature (Fu et al, 2023b) proposes an approach to select the representative scenarios by neural networks to reduce the size of scenarios. Distributionally robust methods have attracted much attention in recent years, which combine the advantages of stochastic optimization and robust optimization by formulating expected optimal decisions under robust probability distributions, and have been applied to power system optimal scheduling (Shi et al, 2023) and control (Xu et al, 2023) issues.…”

Section: Introductionmentioning

confidence: 99%

Network-constrained flexible ramping product provision of prosumer aggregator: a data-driven stochastic bi-level optimization

Ai,

Hu,

et al. 2023

Front. Energy Res.

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Prosumers are expected to provide the flexible ramping product (FRP) in the power system. However, voltage violations and line congestion may arise in the distribution network, when FRP delivered by prosumers. Hence, this paper proposes a data-driven stochastic bi-level optimization model to coordinate the prosumer aggregator to decide FRP-offering while ensuring distribution network security under FRP delivery. In the proposed bi-level model, the upper-level is a min-max problem, representing the minimum expected cost under the worst-case scenario probability distribution for the prosumer aggregator. The lower-level is the operation cost minimization within the distribution network security for distribution network operator. The proposed model is converted into a single-level model using the Karush-Kuhn-Tucker condition and strong duality theory, and applied to the modified IEEE 33-bus network with three prosumers. The results demonstrate the effectiveness of the proposed model.

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Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Cited by 43 publications

References 157 publications

Multi-Timescale Modeling and Dynamic Stability Analysis for Sustainable Microgrids: State-of-the-Art and Perspectives

Multi-Timescale Modeling and Dynamic Stability Analysis for Sustainable Microgrids: State-of-the-Art and Perspectives

Improvement of Fuzzy Newton Power Flow Convergence

Network-constrained flexible ramping product provision of prosumer aggregator: a data-driven stochastic bi-level optimization

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