An operational power management method for the grid containing renewable power systems utilizing short-term weather and load forecasting data

被引:0
|
作者
Aula, Fadhil T. [1 ]
Lee, Samuel C. [1 ]
机构
[1] Univ Oklahoma, Sch Elect & Comp Engn, Norman, OK 73019 USA
关键词
Grid power management; wind and photovoltaic power systems; weather and load forecasting; load curve;
D O I
10.1117/12.2012127
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
This paper addresses the problems associated with power management of the grid containing renewable power systems and proposes a method for enhancing its operational power management. Since renewable energy provides uncertain and uncontrollable energy resources, the renewable power systems can only generate irregular power. This power irregularity creates problems affecting the grid power management process and influencing the parallel operations of conventional power plants on the grid. To demonstrate this power management method for this type of grid, weather-dependent wind and photovoltaic power systems are chosen an example. This study also deals with other uncertain quantities which are system loads. In this example, the management method is based on adapting short-term weather and load forecasting data. The new load demand curve (NLDC) can be produced by merging the loads with the power generated from the renewable power systems. The NLDC is used for setting the loads for the baseload power plants and knowing when other plants are needed to increase or decrease their supplies to the grid. This will decrease the irregularity behavior effects of the renewable power system and at the same time will enhance the smoothing of the power management for the grid. The aim of this paper is to show the use of the weather and load forecasting data to achieve the optimum operational power management of the grid contains renewable power systems. An illustrative example of such a power system is presented and verified by simulation.
引用
收藏
页数:4
相关论文
共 50 条
  • [1] A data mining method for short-term load forecasting in power systems
    Mori, H
    Kosemura, N
    ELECTRICAL ENGINEERING IN JAPAN, 2002, 139 (02) : 12 - 22
  • [2] A NOVEL PROBABILISTIC SHORT-TERM LOAD FORECASTING METHOD FOR LARGE POWER GRID
    Li, Canbing
    Fu, Meiping
    Shang, Jincheng
    Cheng, Peng
    2010 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2010,
  • [3] Short-term Forecasting for Integrated Load and Renewable Energy in Micro-grid Power Supply
    Mwanza, Naomi Nthambi
    Moses, Peter Musau
    Nyete, Abraham Mutunga
    2020 IEEE PES & IAS POWERAFRICA CONFERENCE, 2020,
  • [4] A New Short-term Load Forecasting in Power Systems
    Li Hui
    Sun Hong-bin
    CURRENT DEVELOPMENT OF MECHANICAL ENGINEERING AND ENERGY, PTS 1 AND 2, 2014, 494-495 : 1631 - 1635
  • [5] A novel method for the short-term power load forecasting
    Lu, Min
    Wu, Xinrong
    Chen, Yi
    Lin, Kai
    Huang, Ruochen
    Lin, Qiongbin
    Xie, Lifu
    2022 IEEE 9TH INTERNATIONAL CONFERENCE ON POWER ELECTRONICS SYSTEMS AND APPLICATIONS, PESA, 2022,
  • [6] Short-term bus load forecasting of power systems by a new hybrid method
    Amjady, Nima
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2007, 22 (01) : 333 - 341
  • [7] Short-term power load forecasting based on big data
    State Grid Information & Telecommunication Branch, Xicheng District, Beijing
    100761, China
    不详
    100070, China
    不详
    100031, China
    Zhongguo Dianji Gongcheng Xuebao, 1 (37-42):
  • [8] Data Characteristics and Short-term Forecasting of Regional Power Load
    Cheng X.
    Wang L.
    Zhang P.
    Yan Q.
    Shi H.
    Dianwang Jishu/Power System Technology, 2022, 46 (03): : 1092 - 1099
  • [9] Neural systems for short-term forecasting of electric power load
    Bak, Michal
    Bielecki, Andrzej
    ADAPTIVE AND NATURAL COMPUTING ALGORITHMS, PT 2, 2007, 4432 : 133 - +
  • [10] A Hybrid Method for Short-term Load Forecasting in Power System
    Zhu, Xianghe
    Qi, Huan
    Huang, Xuncheng
    Sun, Suqin
    PROCEEDINGS OF THE 10TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2012), 2012, : 696 - 699