Self-scheduling model for home energy management systems considering the end-users discomfort index within price-based demand response programs. (May 2021)

• Record Type: Journal Article
• Authors: Javadi, Mohammad Sadegh; Nezhad, Ali Esmaeel; Nardelli, Pedro H.J.; Gough, Matthew; Lotfi, Mohamed; Santos, Sérgio; Catalão, João P.S.
• Is Part Of: Sustainable cities and society. Volume 68(2021)
• Publication Date: 2021-05
• Subjects: Home energy management system -- Discomfort index -- Self-scheduling -- MILP -- Price-based demand response; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.102792
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Proposing a linear penalizing mechanism to calculate the discomfort index. Presenting a MILP multi-objective model for the HEMS self-scheduling problem. Evaluating different time-based DR programs in the self-scheduling problem. Assessing the impacts of the EES device on the self-scheduling problem. Developing MO-MILP for HEMS and applying VIKOR for the decision-making process. Abstract: This paper presents a self-scheduling model for home energy management systems (HEMS) in which a novel formulation of a linear discomfort index (DI) is proposed, incorporating the preferences of end-users in the daily operation of home appliances. The HEMS self-scheduling problem is modelled as a mixed-integer linear programming (MILP) multi-objective problem, aimed at minimizing the energy bill and DI. In this framework, the proposed DI determines the optimal time slots for the operation of home appliances while minimizing end-users' bills. The resulting multi-objective optimization problem has then been solved by using the epsilon-constraint technique and the VIKOR decision maker has been employed to select the most desired Pareto solution. The proposed model is tested considering tariffs in the presence of various price-based demand response programs (DRP), namely time-of-use (TOU) and real-time pricing (RTP). In addition, different scenarios considering the presence of electrical energy storage (EES) are investigated to study their impact on the optimal operation of HEMS. The simulation results show that the self-scheduling approach proposed in this paper yields significant reductions in the electricity bills for different electricity tariffs.