Skip to content

This study is using distributionally robust optimization (DRO) algorithm with conditional value-at-risk (CVaR) to solve self-scheduling problem to obtain a suitable and adjustable self-scheduling strategy

Notifications You must be signed in to change notification settings

dim666dim/A-novel-DRO-model-for-self-scheduling-problem

 
 

Repository files navigation

A novel DRO model for self-scheduling problem

What about this project/study?

  This study proposes a novel moment-based distributionally robust optimization (DRO) model with 
conditional value-at-risk (CVaR) for self-scheduling problem under elecyticity price uncertainty.
This model comprehensively considers electricity price fluctuations, unit parameters and load re-
quirement, and it can be adjusted by adjusting the size of the ambiguity set, so it provides a 
suitable and adjustable self-scheduling strategy for generation companies (GENCOs), decision makers
can adjust the strategy according to the actual scenarios for the scheme to be accepted by independents
system operators (ISOs) while maxmizing the generation profit. Such DRO models are usually translated 
into semi-definite pro-gramming (SDP) for solution, however, solving large-scale SDP needs a lot of com-
putational time and resources. For this short-coming, two effective approximate models are proposed: 
one ap-proximate model based on vector splitting and another based on alternate direction multiplier me-
thod (ADMM) algorithm, both can greatly reduce the calculation time and resources. In order to verify the
correctness and effectiveness of our proposed model, we apply them on a various of numerical case studies,
and compare with the model in [1].Simulations of three IEEE test systems (6-bus system, 30-bus system and 
118-bus system) are conducted.

[1]	R. A. Jabr, "Robust self-scheduling under price uncertainty using conditional value-at-risk," 
IEEE Trans. Power Syst., vol. 20, no. 4, pp. 1852-1858, Nov. 2005.

User Guide

The description of implement code files (函数文件说明)

DRO_CVaR_Alg1 : The DRO model.  

RO_CVaR : The RO model.

Approximation1_of_DRO_CVaR : The APP1 model.

Approximation2_of_DRO_CVaR : The APP1 mode2.

ReadDataSCUC :  Read the data.

SCUC_nodeY :  Construct network admittance matrix.

acquire_lamda : Calculate price of electricity.

MonteCarlo_Price : Generating fluctuating electricity price.

portion : Divide the area.

SCUC_X_Y :IEEE X-bus Y-periods test system.

Prerequisite:

Matlab R2018a
Cplex 12.7.1
Mosek 9.2

Publication:

If you use our study in academic work then please consider citing our papers.

About Us

Authors:Lingfeng Yang ([email protected]),Ying Yang ([email protected]),Guo Chen,Zhaoyang Dong
Team:www.scholat.com/team/eidp
Webpage: http://jians.gxu.edu.cn/default.do

About

This study is using distributionally robust optimization (DRO) algorithm with conditional value-at-risk (CVaR) to solve self-scheduling problem to obtain a suitable and adjustable self-scheduling strategy

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • MATLAB 100.0%