Multi-index Economical Peak Load Regulation Model Based on Improved Particle Swarm Optimization

DOI

10.2991/isaeece-17.2017.65How to use a DOI?

Keywords

virtual power plant; multi-objective optimization; peak load regulation; Grey Particle Swarm Optimization

Abstract

In recent years, new energy continued rapid growth in China. Installed capacity of wind power and solar power both surged the new highs. However, the problem of insufficient peak peaking capacity of power system of large-scale new energy grid becoming more and more serious due to the random and intermittent characteristics of wind power and photovoltaic. This paper put forward a strategy of virtual power plant, which consisting of uncontrollable renewable energy and controllable energy will participate peak load regulation. With goal of minimizing the load variance and operating cost in each period, a multi-objective peak load regulation modes of virtual power plant is established. Power output of each energy resource of virtual power plant is obtained with improved grey particle swarm algorithm. Take the IEEE33 node distribution system as an example to simulate. The results show that the peak load regulation model can be carried out economically and effectively peak-shaving.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaohong Hao
AU  - Jingyuan Yang
PY  - 2017/03
DA  - 2017/03
TI  - Multi-index Economical Peak Load Regulation Model Based on Improved Particle Swarm Optimization
BT  - Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017)
PB  - Atlantis Press
SP  - 338
EP  - 343
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-17.2017.65
DO  - 10.2991/isaeece-17.2017.65
ID  - Hao2017/03
ER  -