The provincial power grid monthly purchasing risk management model based on Monte-Carlo stochastic simulation technology and wind power uncertainty

DOI

10.2991/mmebc-16.2016.323How to use a DOI?

Keywords

electricity market, monthly power-purchasing plan, risk management, uncertainty of wind power generation, provincial power grid, Genetic Algorithm.

Abstract

Considering the uncertainty of wind power generation in the context of the regional electricity market, the risk quantitative index and risk management model for the monthly electricity purchasing plan considering the uncertainty of wind power generation is proposed. First, based on the risk measure index of the monthly peak and light load with the wind power output uncertainty, monthly peak load and light load state branch power flow is limited, and upper and lower bounds on the monthly electricity balance, which established provincial grid monthly electricity purchasing optimization model of purchase the smallest electricity cost and risk value. Secondly, aiming at the characteristics of the built model for multi-objective model and the index of risk measurement, the model is solved by the hybrid genetic algorithm which is based on the relative dominance of the embedded object and the Monte-Carlo stochastic simulation technology. Finally, a numerical example shows the effectiveness of the above mentioned work.

Copyright

© 2016, 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  - Junmei Wang
AU  - Lin Guo
AU  - Chao Ma
AU  - Chuncheng Gao
AU  - Dunnan Liu
AU  - Mo Yang
PY  - 2016/06
DA  - 2016/06
TI  - The provincial power grid monthly purchasing risk management model based on Monte-Carlo stochastic simulation technology and wind power uncertainty
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 1591
EP  - 1596
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.323
DO  - 10.2991/mmebc-16.2016.323
ID  - Wang2016/06
ER  -