With the increasing global energy crisis, wind power is gradually favored by people for its sustainability and cleanliness. When the wind power is connected with the power system, the problem of grid connection must be overcome. In order to better meet the demand load interactive response in smart grid environment, a multi-layer scheduling two-dimensional operation model was proposed. The mechanism of wind power uncertainty was analyzed. Thus, wind power prediction algorithm was established, and the concept of probability based multi-level scheduling was introduced to improve the adaptability of the model as a whole. In order to verify the reliability and effectiveness of the method, case experiments were carried out. The results show that the method can effectively help wind power consumption when ensuring economic and reliable conditions.
ZhangN., HuZ., ShenB., et al., A source–grid–load coordinated power planning model considering the integration of wind power generation [J], Applied Energy168 (2016), 13–24.
2.
MosayebianM.E., SoleymaniS., MozafariB., et al., Optimal operational management of a micro-grid, including high penetration level of renewable energy resources [J], Journal of Intelligent & Fuzzy Systems31(1) (2016), 565–577.
3.
García-SánchezE., García-MoralesV.J. and Martín-RojasR., Analysis of the influence of the environment, stakeholder integration capability, absorptive capacity, and technological skills on organizational performance through corporate entrepreneurship [J], International Entrepreneurship & Management Journal159 (2017), 1–33.
4.
LiX., HuangY., MaS., et al., Analysis on the Influence Factors of Wind Power Accommodation [J], Journal of Power & Energy Engineering03(4) (2016), 162–169.
5.
Rahbari-AsrN., OjhaU., ZhangZ., et al., Incremental Welfare Consensus Algorithm for Cooperative Distributed Generation/Demand Response in Smart Grid [J], IEEE Transactions on Smart Grid5(6) (2017), 2836–2845.
6.
RafieiM., NiknamT., KhoobanM.H., et al., A novel intelligent strategy for probabilistic electricity price forecasting: Wavelet neural network based modified dolphin optimization algorithm [J], Journal of Intelligent & Fuzzy Systems31(1) (2016), 301–312.
7.
MaT., WangF., WangJ.Z., et al., A combined model based on seasonal autoregressive integrated moving average and modified particle swarm optimization algorithm for electrical load forecasting [J], Journal of Intelligent & Fuzzy Systems32(5) (2017), 3447–3459.
8.
SafdarianA., Fotuhi-FiruzabadM. and LehtonenM., Optimal Residential Load Management in Smart Grids: A Decentralized Framework [J], IEEE Transactions on Smart Grid7(4) (2016), 1836–1845.
9.
RekaS.S. and RameshV., A demand response modeling for residential consumers in smart grid environment using game theory based energy scheduling algorithm [J], Ain Shams Engineering Journal7(2) (2016), 835–845.
10.
KhanA.R., MahmoodA., SafdarA., et al., Load forecasting, dynamic pricing and DSM in smart grid: A review [J], Renewable & Sustainable Energy Reviews54 (2016), 1311–1322.
11.
LiW., LuC., LiuS., et al., The research on electric load forecasting based on nonlinear gray bernoulli model optimized by cosine operator and particle swarm optimization [J], Journal of Intelligent & Fuzzy Systems30(6) (2016), 3665–3673.
12.
ErvuralB.C. and ErvuralB., Improvement of grey prediction models and their usage for energy demand forecasting [J], Journal of Intelligent & Fuzzy Systems34(4) (2018), 2679–2688.
13.
GriloM.M., de MoraesC.H., et al., Modified imperialist competitive optimization to high resolution spatial electric load demand forecasting [J], Journal of Intelligent & Fuzzy Systems35(5) (2018), 5495–5506.
14.
MoonS. and LeeJ.W., Multi-Residential Demand Response Scheduling with Multi-Class Appliances in Smart Grid [J], PP (99), IEEE Transactions on Smart Grid (2016), 1–1.
15.
VijayakumarK., RajeshK., VishnuvardhananG., et al., Evolutionary algorithm based control strategy for enhanced operation of multifunction grid connected converters [J], Journal of Intelligent & Fuzzy Systems36(5) (2019), 4461–4478.
16.
HaiderH.T., SeeO.H. and ElmenreichW., A review of residential demand response of smart grid [J], Renewable & Sustainable Energy Reviews59 (2016), 166–178.
17.
CoelhoV.N., CoelhoI.M., CoelhoB.N., et al., A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment [J], Applied Energy169 (2016), 567–584.
18.
GollouA.R. and GhadimiN., A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets [J], Journal of Intelligent & Fuzzy Systems32(6) (2017), 4031–4045.
