Benchmarking approach for empirical comparison of pricing models in DRMS. Issue 11 (4th November 2016)

• Record Type: Journal Article
• Authors: Azghandi, Seif; Hopkinson, Kenneth Mark; Laviers, Kennard Robert
• Is Part Of: Journal of engineering. Volume 2016:Issue 11(2016)
• Page Start: 394
• Page End: 401
• Publication Date: 2016-11-04
• Subjects: benchmark testing -- demand side management -- neural nets -- power engineering computing -- pricing -- proportional control -- regression analysis -- power system control; electric project -- pacific gas project -- Green Button project -- proportional controller -- sliding window‐multiple linear regression -- neural networks -- perfect control pricing -- empirical benchmark -- behavioural disparities -- times linear data pattern detection -- quasinonlinear data pattern detection -- electrical power pattern detection -- pricing schemes -- pricing models -- empirical comparison -- benchmarking approach -- DRMS -- demand response management systems; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2016.0223
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : 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.