Stochastic interval-based optimal offering model for residential energy management systems by household owners
Journal article, 2019

This paper proposes an optimal bidding strategy for autonomous residential energy management systems. This strategy enables the system to manage its domestic energy production and consumption autonomously, and trade energy with the local market through a novel hybrid interval-stochastic optimization method. This work poses a residential energy management problem which consists of two stages: day-ahead and real-time. The uncertainty in electricity price and PV power generation is modeled by interval-based and stochastic scenarios in the day-ahead and real-time transactions between the smart home and local electricity market. Moreover, the implementation of a battery included to provide energy flexibility in the residential system. In this paper, the smart home acts as a price-taker agent in the local market, and it submits its optimal offering and bidding curves to the local market based on the uncertainties of the system. Finally, the performance of the proposed residential energy management system is evaluated according to the impacts of interval optimistic and flexibility coefficients, optimal bidding strategy, and uncertainty modeling. The evaluation has shown that the proposed optimal offering model is effective in making the home system robust and achieves optimal energy transaction. Thus, the results prove that the proposed optimal offering model for the domestic energy management system is more robust than its non-optimal offering model. Moreover, battery flexibility has a positive effect on the system's total expected profit. With regarding to the bidding strategy, it is not able to impact the smart home's behavior (as a consumer or producer) in the day-ahead local electricity market.

Smart home

Stochastic programming

Bidding strategy

Energy management

Interval optimization

Author

Amin Shokri Gazafroudi

University of Salamanca

João Soares

Instituto Politecnico do Porto

Mohammad Ali Fotouhi Ghazvini

Chalmers, Electrical Engineering, Electric Power Engineering, Power grids and Components

Tiago Pinto

University of Salamanca

Zita Vale

Instituto Politecnico do Porto

Juan Manuel Corchado

University of Salamanca

Osaka Institute of Technology

International Journal of Electrical Power and Energy Systems

0142-0615 (ISSN)

Vol. 105 201-219

Subject Categories

Embedded Systems

Energy Systems

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1016/j.ijepes.2018.08.019

More information

Latest update

12/10/2018