1. Risk assessment of demand response considering wind power generation
- Author
-
Bolun Zhang, Shuangye Mo, Yifei Hua, Min Lei, and Wuqing Wei
- Subjects
peak time ,risk analysis ,wind power plants ,Monte Carlo simulation method ,Energy Engineering and Power Technology ,valley load ,Demand response ,risk model ,pricing ,risk distribution ,load risk index ,peak load ,power generation economics ,power grid loss risk calculation model ,incentive load curve ,Wind power generation ,demand side management ,system risk reduction ,General Engineering ,demand response load curve ,risk value calculation ,risk assessment ,Monte Carlo methods ,incentive measure ,Environmental economics ,load point fault rate ,power grids ,time-of-use price ,demand response measures ,risk ratio ,risk changes evaluation ,lcsh:TA1-2040 ,partial high load ,TOU ,load point fault time ,Business ,wind power generation ,Risk assessment ,lcsh:Engineering (General). Civil engineering (General) ,Software - Abstract
Demand response can reduce peak load, transfer part of peak load to valley load, and reduce system risk at peak time. In this study, a calculation model of power grid loss risk is proposed, which is suitable for evaluating the risk changes caused by the demand response, and calculating the corresponding risk value under the demand response load curve. In the calculation of demand response load, considering the impact of two measures of time-of-use (TOU) price and incentive measure, the TOU and incentive load curve are obtained based on the user's maximum benefit. In order to gain the corresponding load risk index, Monte Carlo simulation method is used, demand response load curve is taken as input, and load point fault time, load point fault rate and expected energy not serve indexes are introduced. Moreover, compared with the risk ratio of the original load curve, the simulation results verify the effectiveness of the proposed risk model, and prove that the demand response measures can transfer the risk of partial high load and balance the risk distribution.
- Published
- 2018
- Full Text
- View/download PDF