A multi-objective optimization evaluation framework for integration of distributed energy resources

Ahmadi B., Ceylan O., Özdemir A.

JOURNAL OF ENERGY STORAGE, cilt.41, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 41
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.est.2021.103005
  • Dergi Adı: JOURNAL OF ENERGY STORAGE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Anahtar Kelimeler: Smart Grid, Distributed generation, Renewable energy, Energy storage, Multi-objective optimization, DISTRIBUTION-SYSTEMS, DISTRIBUTION NETWORK, FORECAST ENGINE, STORAGE SYSTEM, LOSS REDUCTION, WIND, ALLOCATION, REANALYSIS, OPERATION, MODELS
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Renewable distributed generation and energy storage systems (ESSs) have been a gamechanger for a reliable and sustainable energy supply. However, this new type of generation should be optimally planned and operated to maximize the expected benefits. In this regard, this paper presents a new formulation for optimal allocation and sizing of distributed energy resources and operation of ESSs to improve the voltage profiles and minimize the annual costs. The multi-objective multiverse optimization method (MOMVO) is used as a solution tool. Moreover, the resulting Pareto optimal solution set is minimized under economic concerns and cost sensitivity to provide a decision-support for the utilities. The proposed formulation and solution algorithm are tested for the revised 33-bus and 69-bus test systems where the load and renewable generation characteristics are taken from real Turkish data. When compared with the base case operating conditions, the proposed formulation eliminated all the voltage magnitude violations, and provided almost 50% loss reductions and 20% energy transfers to off-peak hours. Moreover, Pareto fronts of the proposed method are found to better than the ones provided by non dominated sorting genetic algorithm and multi-objective particle swarm optimization, according to two multi-objective optimization metrics.