Decision making mechanism for a smart neighborhood fed by multi-energy systems considering demand response

Cicek A., Sengor I., ERENOĞLU A. K., ERDİNÇ O.

ENERGY, vol.208, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 208
  • Publication Date: 2020
  • Doi Number: 10.1016/
  • Journal Name: ENERGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Yıldız Technical University Affiliated: Yes


This study covers a decision-making model in which a multi-energy system (MES) including heat pumps (HPs), combined heat and power (CHP), community energy storage (CES), air conditioners (ACs), and renewable energy sources (RESs) meets the electrical, cooling and heating demands of end-users in a smart neighborhood (SN). The thermostat set point control mechanism (TSCM) and direct compressor control mechanism (DCCM) based thermostatically controllable loads oriented demand response (DR) approaches are also considered in order to increase the effectiveness and economy of the MES operation. The SN, including houses with different types of residential end-users, has flexible and inelastic electricity, heating, and cooling power demands. CHPs, HPs, and ACs are operated optimally to keep the room temperatures between desired temperature limits; furthermore, some end-users have electric vehicles (EVs) assumed as flexible loads. Due to the intermittent nature of RESs, stochastic modeling is used to cope with uncertainties in their production pattern. In addition, time-of-use (TOU) electricity prices and real gas price data are used to handle the test system more realistically. Various comparative case studies have been conducted to prove the effectiveness of the proposed model. According to the obtained results, it can be stated that the DR strategies provide better results than the CES, and the most effective element in MES architecture is CHP for this study. Also, another striking finding is that the reduction in cost is experienced when RESs and EVs penetrate together. (C) 2020 Elsevier Ltd. All rights reserved.