Inverse optimization approach to the identification of electricity consumer models

András Kovács

doi:10.1007/s10100-020-00699-1

Abstract

Stackelberg game models for demand response management in smart electricity grids have been studied extensively in the scientific literature. Still, a barrier to their practical applicability is the assumption that the retailer (leader in the game) has perfect knowledge about the consumers’ (followers’) decision model. This paper investigates the possibilities of reconstructing the consumers’ decision model from historic tariff and consumption data. For this purpose, it introduces an inverse optimization approach to eliciting the parameters of electricity consumer models formulated as linear programs from the historic samples. The inverse problem is first transformed into a quadratically constrained quadratic program, and then solved using successive linear programming techniques. The approach is demonstrated on a common consumer model with multiple types of deferrable loads behind a single smart meter. Experimental results are presented, and directions for future research are proposed.

Highlights

An utmost challenge in the operation of future smart electricity grids is developing effective practices for demand response management (DRM): with the increasing share of renewables in the electricity mix, power generation is becoming less and less flexible
The MRSE does not converge to zero as the number of samples K increases. These negative results indicate that further research should be invested into developing more efficient algorithms for solving the inverse optimization problem
This paper introduced a novel approach to elicit the parameters of electricity consumer models from historic time series based on inverse optimization for the purpose of DRM

Summary

Introduction

An utmost challenge in the operation of future smart electricity grids is developing effective practices for demand response management (DRM): with the increasing share of renewables in the electricity mix, power generation is becoming less and less flexible. This implies that the traditional supply follows demand approach, i.e., power plants at any point in time generating exactly as much electricity as required by consumers, seems less and less feasible. The collective name of the practices for adjusting consumer behavior to better match available supply is demand response management (DRM, with a focus on short-term, e.g., intra-day behavior) or demand-side management (DSM, allowing a longer time horizon). Designing a suitable electricity tariff requires a deep understanding of consumer behavior

Results

Discussion

Conclusion