Jie Ma¹, Tong Zhao², Yuanzhao Hao³, Wenwen Qin², Haozheng Yu¹, Mingxuan Du², Yuanhong Liu⁴, Liang Zhang², Shixia Mu⁵, Cuiping Li², Junhui Li^2,*

Energy Engineering, Vol.121, No.9, pp. 2449-2477, 2024, DOI:10.32604/ee.2024.051244 - 19 August 2024

Abstract After the integration of large-scale Distributed Generation (DG) into the distribution network, the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network, exacerbating the phenomenon of wind and solar power wastage. As a novel power system model, the fundamental concept of Regional Autonomous Power Grids (RAPGs) is to achieve localized management and energy autonomy, thereby facilitating the effective consumption of DGs. Therefore, this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple… More > Graphic Abstract

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 - 30 April 2024

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 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 - 31 October 2023

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 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 - 29 November 2022

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… 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 - 29 November 2021

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… More >