Key technologies and applications of rural energy internet in China

Long, Hai; Fu, Xueqian; Kong, Wei; Chen, Hongyi; Zhou, Yazhong; Yang, Feifei

doi:10.1016/j.inpa.2022.03.001

Cited by 22 publications

(13 citation statements)

References 58 publications

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“…NSGA-II maintains population diversity by calculating the crowding distance. Crowding distance describes a group (Schott, 1995;Wang et al, 2010;Long et al, 2022). First, let the individual i be represented by d, and set d i 0.…”

Section: Crowding Distancementioning

confidence: 99%

Non-Pareto Genetic Algorithm for Optimal Planning of Multi-Type Energy Resources in Active Distribution Networks

Qin

Li²,

Chen

2022

Front. Energy Res.

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Based on summarizing and analyzing the typical applications of energy storage, the study established a model for an active distribution network, and analyzed the technical and economic benefits of its access to the distribution network. In addition, considering the economic and technical requirements of multiple types of energy, ensure the stable and continuous operation of multiple types of energy, and build an optimal configuration model for multiple types of energy. To achieve a reliable solution to the model, a non-Pareto genetic algorithm (NSGA-II) is designed to obtain the optimal Pareto solution set for multi-type energy location and volume schemes. The proposed solution algorithm has a rich individual update mechanism and an advanced Pareto solution set storage and screening mechanism, which can effectively solve the problem. Furthermore, idea point decision making (IPDM) has been designed to select the best compromise solution in Pareto non-dominated solution set. Finally, based on the IEEE-33 node standard test system, the input source-load uncertainty scenario set is used to construct the distribution network operation scenario, and the configuration model is solved. The results show that NSGA-II can obtain a Pareto front with better solution quality and a more uniform distribution. After accessing the battery energy storage systems (BESS), the annual total power fluctuation and peak-valley difference of daily maximum load have been reduced by 19.25% and 11.8% respectively.

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Section: Crowding Distancementioning

confidence: 99%

Non-Pareto Genetic Algorithm for Optimal Planning of Multi-Type Energy Resources in Active Distribution Networks

Qin

Li²,

Chen

2022

Front. Energy Res.

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“…Due to the concerns about reserves of fossil fuels, renewable energy has become an essential part of global energy (Wang et al, 2019a). Among renewable energy, wind energy is recognized as clean energy with a high conversion rate, large-scale development, and rich resources (Long et al, 2022). Moreover, wind energy can reduce greenhouse gas emissions to relieve the energy systems effectively.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

Huang

Hong²,

Wang³

2022

Front. Energy Res.

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With the rapid growth of wind power penetration into modern power grids, wind speed forecasting plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the existing wind speed forecasting methods are modeled as black boxes, which are very complicated and cannot be written down explicitly due to the complex fluctuation characteristics of wind speed series. To this end, this study proposes a novel direct method based on an explainable neural network (xNN) for deterministic and probabilistic wind speed forecasting. It can theoretically extract the nonlinear mapping features in wind speed, thereby providing a clear explanation of the relationship between the input and the output of the forecasting model. Then, the uncertainties in wind speed are statistically synthesized via the kernel density estimation method. Finally, we use wind speed data from real wind farms in Belgium to verify the feasibility and effectiveness of the proposed method. The simulation results demonstrate that it is not only able to accurately extract the non-stationary feature in the wind speed series but also superior to other benchmark algorithms in prediction accuracy. Therefore, the proposed method has a high potential for practical applications in real electric power and energy systems.

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“…By 2020, the cumulative installed capacity of photovoltaic power generation in China will reach 203 GW, ranking first in the world (Liu et al, 2021). Recently, China's National Development and Reform Commission has put forward the work requirements for the large-scale development of distributed photovoltaics in the whole county (city and district), which will further promote the development of distributed photovoltaic power generation and accelerate the clean and green transformation of energy structure (Long et al, 2022). In order to promote the green, safe, and efficient operation of distribution networks, it is necessary to focus on the key technologies of new distribution network modeling, planning, and regulation based on distributed photovoltaics.…”

Section: Introductionmentioning

confidence: 99%

Key technologies and applications of rural energy internet in China

Cited by 22 publications

References 58 publications

Non-Pareto Genetic Algorithm for Optimal Planning of Multi-Type Energy Resources in Active Distribution Networks

Non-Pareto Genetic Algorithm for Optimal Planning of Multi-Type Energy Resources in Active Distribution Networks

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

Statistical Machine Learning Model for Uncertainty Analysis of Photovoltaic Power

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