Benchmarking approach for empirical comparison of pricing models in DRMS

The Journal of Engineering

Seif Azghandi¹  Kennard Robert Laviers¹  Kenneth Mark Hopkinson¹ 

[1] Department of Electrical and Computer Engineering, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USA

关键词: benchmarking approach;    dem;    response management systems;    Green Button project;    pacific gas project;    proportional controller;    perfect control pricing;    sliding window-multiple linear regression;    quasinonlinear data pattern detection;    DRMS;    times linear data pattern detection;    electrical power pattern detection;    pricing schemes;    behavioural disparities;    empirical benchmark;    neural networks;    empirical comparison;    pricing models;    electric project;   

DOI  :  10.1049/joe.2016.0223

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

Demand response management systems often involve the use of pricing schemes to motivate the efficient use of electrical power. Achieving this efficiency requires the detection of electrical power patterns. The detection of these patterns normally involves use of non-linear, quasi-non-linear, and at times linear data pattern detection models. The behavioural disparities of these models and specifically when used for a specific set of data make it hard to select the most efficient model. The contribution of this study is devising an empirical benchmark (reference) (perfect) control pricing (PCP) model through which various models are compared in order to select the most efficient model. In this study, the authors elect neural networks, sliding window–multiple linear regression, and a proportional controller models to be representative of non-linear, quasi-non-linear, and linear models, respectively, in order to demonstrate the effectiveness of PCP. The dataset used for demonstrating both the operation of PCP and the elected models for comparisons is collected from Green Button project and Pacific Gas and Electric.

【 授权许可】

CC BY