Greedy Strategy and Self-Adaptive Crossover Operator Based Monarch Butterfly Optimization for Simultaneous Integration of Renewables and Battery Energy Storage in Distribution Systems

P. Singh, B. Singh, S.k. Bishnoi, N.k. Meena, Jin Yang

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

The article presents, a bi-level optimization framework for optimally deploying and managing solar and wind power base DG (Distribution Generator). An energy storage system (BESS) is also implemented in a power distribution network (PDN) to maximize the renewable hosting capacity of distribution networks. A new objective function is broached with the consideration of annual energy losses, reverse power flow into the grid, node voltage deviation, non-utilised BESS capacities and round-trip conversion losses of BESS. As a high installation and maintenance cost is associated with BESS it's observed to install one BESS in the system which is to be placed at the nodes of DG. Artificial intelligence based optimal control and management system is projected to adequately manage the high renewable power generation. Greedy strategy and self-adaptive crossover operator base monarch butterfly optimization (GCMBO) had been applied as an optimization tool. in order to ensure the efficacy of the presented model, it is tested and implemented on a 3 3-bus benchmark test distribution system under various test cases. Various simulation studies have been carried out which depicts the usefulness of the proposed methodology.
Original languageEnglish
Title of host publication8th Renewable Power Generation Conference (RPG 2019)
PublisherIEEE
Pages35 (8 pp.)-35 (8 pp.)
ISBN (Electronic)978-1-83953-124-8
ISBN (Print)978-1-83953-125-5
DOIs
Publication statusPublished - 19 Mar 2020

Keywords

  • Battery energy storage
  • Distributed generation
  • GCMBO
  • Renewable energy

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