Energy Optimisation Models for Self-Sufficiency of a Typical Turkish Residential Electricity Customer of the Future

Ayci D., Ogut F., Ozen U., Isgor B. B., Kufeoglu S.

ENERGIES, vol.14, no.19, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 19
  • Publication Date: 2021
  • Doi Number: 10.3390/en14196163
  • Journal Name: ENERGIES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, CAB Abstracts, Communication Abstracts, Compendex, INSPEC, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: energy management, electric vehicle (EV), energy storage, optimization, HEMS, demand response, solar, self-sufficient, DEMAND RESPONSE, MANAGEMENT, SYSTEM
  • Istanbul Technical University Affiliated: Yes


This paper utilises a two-stage demand response-enabled energy management algorithm for a typical Turkish self-sufficient living space. The proposed energy management model provides an additional gain in line with the goal of self-sufficiency by scheduling flexible loads and energy storage systems at home according to a static time of use tariff. The impact of load scheduling and battery optimisation were evaluated in the scope of self-sufficiency, economic gain and return on investment performances. According to the results, the proposed two-stage structure provided a net saving increase of 9.5% in the one-battery scenario, and it rises to 14% in the design with three batteries. On the other hand, when we inspect the energy management scenarios with the return on investment (ROI) calculations, we see that the single battery system has a higher ROI than the two or three battery systems due to the increased battery cost. Moreover, the ROI value, 13.9% without optimisation, increased to 15.3% in the proposed Home Energy Management System (HEMS) model. As can be seen from this calculation, intelligent management of batteries and flexible loads provided a 10% increase in ROI value.