Automated Scheduling Approach under Smart Contract for Remote Wind Farms with Power-to-Gas Systems in Multiple Energy Markets

被引:0
|
作者
Ji, Zhenya [1 ]
Guo, Zishan [1 ]
Li, Hao [1 ]
Wang, Qi [1 ]
机构
[1] Nanjing Normal Univ, Sch Elect & Automat Engn, 2 Xueyuan Rd, Nanjing 210046, Peoples R China
基金
中国国家自然科学基金;
关键词
integrated energy system; scheduling; energy trade; smart contract; INTEGRATED-SYSTEM; DEMAND RESPONSE; BLOCKCHAIN; STRATEGY; DISPATCH; INTERNET; STORAGE;
D O I
10.3390/en14206781
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The promising power-to-gas (P2G) technology makes it possible for wind farms to absorb carbon and trade in multiple energy markets. Considering the remoteness of wind farms equipped with P2G systems and the isolation of different energy markets, the scheduling process may suffer from inefficient coordination and unstable information. An automated scheduling approach is thus proposed. Firstly, an automated scheduling framework enabled by smart contract is established for reliable coordination between wind farms and multiple energy markets. Considering the limited logic complexity and insufficient calculation of smart contracts, an off-chain procedure as a workaround is proposed to avoid complex on-chain solutions. Next, a non-linear model of the P2G system is developed to enhance the accuracy of scheduling results. The scheduling strategy takes into account not only the revenues from multiple energy trades, but also the penalties for violating contract items in smart contracts. Then, the implementation of smart contracts under a blockchain environment is presented with multiple participants, including voting in an agreed scheduling result as the plan. Finally, the case study is conducted in a typical two-stage scheduling process-i.e., day-ahead and real-time scheduling-and the results verify the efficiency of the proposed approach
引用
收藏
页数:17
相关论文
共 37 条
  • [31] A tactical scheduling framework for wind farm-integrated multi-energy systems to take part in natural gas and wholesale electricity markets as a price setter
    Nasiri, Nima
    Zeynali, Saeed
    Ravadanegh, Sajad Najafi
    Marzband, Mousa
    [J]. IET GENERATION TRANSMISSION & DISTRIBUTION, 2022, 16 (09) : 1849 - 1864
  • [32] Optimal capacity configuration model of power-to-gas equipment in wind-solar sustainable energy systems based on a novel spatiotemporal clustering algorithm: A pathway towards sustainable development
    Lv, Shuaishuai
    Wang, Hui
    Meng, Xiangping
    Yang, Chengdong
    Wang, Mingyue
    [J]. RENEWABLE ENERGY, 2022, 201 : 240 - 255
  • [33] A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty
    Al-Aboosi, Fadhil Y.
    El-Halwagi, Mahmoud M.
    [J]. SUSTAINABILITY, 2019, 11 (18)
  • [34] Optimal scheduling strategy of district integrated heat and power system with wind power and multiple energy stations considering thermal inertia of buildings under different heating regulation modes
    Wang, Dan
    Zhi, Yun-qiang
    Jia, Hong-jie
    Hou, Kai
    Zhang, Shen-xi
    Du, Wei
    Wang, Xu-dong
    Fan, Meng-hua
    [J]. APPLIED ENERGY, 2019, 240 : 341 - 358
  • [35] A multi-agent game based joint planning approach for electricity-gas integrated energy systems considering wind power uncertainty
    Yang, Nan
    Qin, Tao
    Wu, Lei
    Huang, Yu
    Huang, Yuehua
    Xing, Chao
    Zhang, Lei
    Zhu, Binxin
    [J]. ELECTRIC POWER SYSTEMS RESEARCH, 2022, 204
  • [36] A day-ahead dispatching strategy for power pool composed of wind farms, photovoltaic generations, pumped-storage power stations, gas turbine power plants and energy storage systems based on multi frequency scale analysis
    Ma, Jing
    Shi, Jianlei
    Li, Wenquan
    Wang, Zengping
    [J]. Dianwang Jishu/Power System Technology, 2013, 37 (06): : 1491 - 1498
  • [37] A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses
    Ju, Liwei
    Li, Peng
    Tan, Qinliang
    Tan, Zhongfu
    De, GejiriFu
    [J]. ENERGIES, 2018, 11 (11)