Bin Chen¹, Ziyang Li¹, Shipeng Li¹, Qingzhou Zhao¹, Xingdou Liu^2,*

Energy Engineering, Vol.121, No.5, pp. 1291-1307, 2024, DOI:10.32604/ee.2024.046374

Abstract To reduce carbon emissions, clean energy is being integrated into the power system. Wind power is connected to the grid in a distributed form, but its high variability poses a challenge to grid stability. This article combines wind turbine monitoring data with numerical weather prediction (NWP) data to create a suitable wind power prediction framework for distributed grids. First, high-precision NWP of the turbine range is achieved using weather research and forecasting models (WRF), and Kriging interpolation locates predicted meteorological data at the turbine site. Then, a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion… More >

Jialin Yu^1,*, Xiaodi Zhang², Qi Zhong¹, Jian Feng¹

Energy Engineering, Vol.121, No.3, pp. 789-806, 2024, DOI:10.32604/ee.2023.043299

Abstract With a further increase in energy flexibility for customers, short-term load forecasting is essential to provide benchmarks for economic dispatch and real-time alerts in power grids. The electrical load series exhibit periodic patterns and share high associations with metrological data. However, current studies have merely focused on point-wise models and failed to sufficiently investigate the periodic patterns of load series, which hinders the further improvement of short-term load forecasting accuracy. Therefore, this paper improved Autoformer to extract the periodic patterns of load series and learn a representative feature from deep decomposition and reconstruction. In addition, a novel multi-factor attention mechanism… More >

Xueting Cheng¹, Ziqi Zhang^2,*, Yueshuang Bao¹, Huiping Zheng¹

Energy Engineering, Vol.120, No.11, pp. 2517-2529, 2023, DOI:10.32604/ee.2023.042633

Abstract At present, the proportion of new energy in the power grid is increasing, and the random fluctuations in power output increase the risk of cascading failures in the power grid. In this paper, we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering (DEC) algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids. First, considering the real-time operation status and system structure of new energy power grids, the scenario cascading failure risk indicator is established. Based on… More >

Huiru Zhao¹, Manyu Yao^1,*, Zhenqi Bai¹, Yue Zhang², Zhihua Ding³, Zhenda Hu⁴

Energy Engineering, Vol.120, No.10, pp. 2211-2231, 2023, DOI:10.32604/ee.2023.029371

Abstract As an essential infrastructure, the rural power grid is vital in promoting agricultural and rural carbon sequestration and improving rural energy electrification. It is necessary to carry out in-depth research on its comprehensive efficiency. Based on the requirements of “double carbon” and rural revitalization strategy for the rural Power Grid, this paper focuses on the modernization and low-carbon transformation of the rural Power Grid. It constructs an input-output index system for the investment efficiency of the rural Power Grid in China under the new situation. It uses the primary data of the rural Power Grid of 30 prefecture-level cities in… More >

Shun Ma¹, Na Yu¹, Xiuna Wang², Shiyan Mei¹, Mingrui Zhao^2,*, Xiaoyu Han²

Energy Engineering, Vol.120, No.10, pp. 2369-2392, 2023, DOI:10.32604/ee.2023.028727

Abstract Using the improved prospect theory with the linear transformations of rewarding good and punishing bad (RGPBIT), a new investment ranking model for power grid construction projects (PGCPs) is proposed. Given the uncertainty of each index value under the market environment, fuzzy numbers are used to describe qualitative indicators and interval numbers are used to describe quantitative ones. Taking into account decision-maker’s subjective risk attitudes, a multi-criteria decision-making (MCDM) method based on improved prospect theory is proposed. First, the [−1, 1] RGPBIT operator is proposed to normalize the original data, to obtain the best and worst schemes of PGCPs. Furthermore, the… More >

Bo Yang¹, Yulin Li¹, Yaxing Ren², Yixuan Chen³, Xiaoshun Zhang⁴, Jingbo Wang^5,*

Energy Engineering, Vol.120, No.6, pp. 1287-1293, 2023, DOI:10.32604/ee.2023.027801

Abstract This article has no abstract. More >

Jingtang Luo¹, Shiying Yao¹, Changming Zhao^2,*, Jie Xu³, Jim Feng⁴

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4207-4216, 2023, DOI:10.32604/cmc.2023.034439

Abstract The power grid operation process is complex, and many operation process data involve national security, business secrets, and user privacy. Meanwhile, labeled datasets may exist in many different operation platforms, but they cannot be directly shared since power grid data is highly privacy-sensitive. How to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart grid. Therefore, this paper proposes federated learning named entity recognition method for the power grid field, aiming to solve the problem of building a… More >

Ahmed Al-Abri¹, Kenneth E. Okedu^1,2,*

Energy Engineering, Vol.120, No.2, pp. 409-423, 2023, DOI:10.32604/ee.2023.020375

Abstract Lately, in modern smart power grids, energy demand for accurate forecast of electricity is gaining attention, with increased interest of research. This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand. In addition, proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network. As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman, new opportunities may arise considering the efficiency and… More > Graphic Abstract

Qian Wang¹, Xiaolong Yang^2,*, Di Pu³, Yingying Fan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 269-286, 2022, DOI:10.32604/cmes.2022.016437

Abstract This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine (DRBM) optimized by the Lion algorithm (LA). Firstly, two factors including transmission and distribution price reform (TDPR) and 5G station construction were comprehensively incorporated into the consideration of influencing factors, and the fuzzy threshold method was used to screen out critical influencing factors. Then, the LA was used to optimize the parameters of the DRBM model to improve the model's prediction accuracy, and the model was trained with the selected influencing factors and investment. Finally, the LA-DRBM model was used to predict the… More >