An optimal power usage scheduling in smart grid integrated with renewable energy sources for energy management

Ateeq Ur Rehman, Zahid Wadud, Rajvikram Madurai Elavarasan, Ghulam Hafeez, Imran Khan, Zeeshan Shafiq, Hassan Haes Alhelou

Research output: Contribution to journalArticleResearchpeer-review

72 Citations (Scopus)

Abstract

Existing power grids (PGs) and in-home energy management controllers do not offer its users the choice to maintain comfort and provide a bearable solution in terms of low cost and reduced carbon emission. This work is based on energy usage scheduling and management under electric utility and renewable energy sources i.e., solar energy (SE), controllable heat and power (CHP) and wind energy (WE) together. Efficient integration of renewable energy sources (RES) and battery storage system (BSS) have been suggested to solve the energy management problem, reduce the bill cost, peak-to-average ratio (PAR) and carbon emission. User's electricity bill reduction have been achieved by proposed power usage scheduling method and integrating low cost RESs. PAR minimization have been achieved through shifting the demand in response to real time price from high-peak hours to low-peak hours. In this context, load scheduling and energy storage system management controller (LSEMC) is proposed which is based on heuristic algorithms i.e., genetic algorithm (GA), wind driven optimization (WDO), binary particle swarm optimization (BPSO), bacterial foraging optimization (BFO) and our suggested hybrid of GA, WDO and PSO (HGPDO) algorithm. The performance of the heuristic algorithms and proposed scheme is evaluated numerically. Results demonstrate that our proposed algorithm and the LSEMC reduces the electricity bill, PAR and CO2 in Case 1, by 58.69%, 52.78% and 72.40%, in Case 2, by 47.55%, 45.02% and 92.90% and in Case 3, by 33.6%, 54.35% and 91.64%, respectively as compared with unscheduled. Moreover, the user comfort by our proposed HGPDO algorithm in terms of delay, thermal, air quality and visual improves by 35.55%, 16.66%, 91.64% and 45%, respectively.

Original languageEnglish
Pages (from-to)84619-84638
Number of pages20
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 7 Jun 2021
Externally publishedYes

Keywords

  • battery energy storage systems
  • Energy management
  • hybrid heuristic algorithms
  • power usage scheduling
  • renewable
  • smart grid

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