Open AccessResearch on Optimal Load Scheduling for Multiple Scenarios
Author(s) -
Yifan Ding,
Molin Huo,
Hongxiang Jiang,
Yahui Ma,
Aikang Chen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1639/1/012060
Subject(s) - particle swarm optimization , computer science , cluster analysis , mathematical optimization , scheduling (production processes) , demand response , demand side , complementarity (molecular biology) , electricity , distributed computing , algorithm , engineering , mathematics , electrical engineering , machine learning , biology , economics , genetics , microeconomics
In recent years, the operation of power grid is under great pressure due to more and more distributed resources. This paper establishes three kinds of load models, namely the electric water heater(EWH), the electric vehicle(EV) and the energy storage(ES). The response cost, speed, capacity and duration are taken as the four characteristic elements to cluster the demand side loads. According to the scenario characteristics, the factor weights under different scenarios are determined. Furthermore, the particle swarm optimization (PSO) algorithm utilized to optimize the scheduling of demand-side resources selected by K-medoids clustering algorithm. Simulation results indicate that, by taking advantage of the complementarity of the time-domain and functional characteristics of multiple loads, the consumption needs of different scenarios can be satisfied.