Optimal energy management system for microgrids considering energy storage, demand response and renewable power generation

Erenoglu A. K., Sengor I., ERDİNÇ O., Tascikaraoglu A., Cataldo J. P. S.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, cilt.136, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 136
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.ijepes.2021.107714
  • Dergi Adı: INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Communication Abstracts, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Anahtar Kelimeler: Demand side management, Electric vehicle, Microgrids, Renewable generation, Shared energy storage, OPERATION
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

To ensure the autonomous power supply in microgrids (MGs) in stand-alone mode while also maintaining stability, energy storage systems (ESSs) and demand-side flexibility can be utilized together. Motivated by this fact, in this study, a scenario-based energy management system (EMS) modelled as a mixed-integer linear programming (MILP) problem is presented by taking the stochastic nature of wind and photovoltaic (PV) sources into account in order to analyze the operational behaviour of MGs and thereby to reduce the network energy losses. Direct load control (DLC) based demand response (DR) program is implemented to the system with the objective of exploiting the remarkable potential of thermostatically controllable appliances (TCAs) for energy reduction while satisfying comfort and operational constraints. Furthermore, a common ESS with a bi-directional power flow facility is incorporated in the proposed structure and electric vehicles (EVs) are employed as an additional flexible load in grid-to-vehicle (G2V) mode. To testify the effectiveness of the proposed optimization algorithm, different case studies are conducted considering diverse scenarios. Moreover, the performance is compared with a deterministic method from the perspective of achieving loss reduction and capturing the uncertainties.