Integrated demand response considering substitute effect and time-varying response characteristics under incomplete information

Abstract

Integrated demand response (IDR) has been recognized as an effective approach to alleviate supply–demand imbalance in integrated energy systems (IESs). However, complex response characteristics in demand side, including substitute effect, time-varying characteristics, and uncertainties, coupled with incomplete information, have been main obstacles to predict consumer’s response behavior accurately and map out effective incentive strategies. This paper proposes an improved incentive-based IDR model based on cross-elasticity theory, behavioral economics, and multi-stage rebound theory to deal with the complex characteristics, with a recursive moving window linear regression algorithm based on maximum likelihood estimation to cope with incomplete information. Our IDR model is mathematically expressed as a bi-level stochastic optimization problem, which is transformed into an equivalent nonlinear convex optimization problem to solve it efficiently. Simulation results verify merits of our model in enhancing effectiveness of incentive strategies, accuracy of consumer behavior estimation, and capability of risk management of multi-energy aggregators (MEAs), which is conducive to decreasing total cost as well as power deviation and increasing consumer’s utility.