Journal of Energy Management and Technology

Journal of Energy Management and Technology

Investigating the Impacts of Pollution and Electric Vehicle Charging Station on Energy Management in Multi-Agent-Microgrids

Document Type : Original Article

Authors
Farhood Ghalkhani
Mohsen Hayati
Hamdi Abdi
Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran
10.22109/jemt.2022.345453.1390
Abstract
Increasing the energy consumption, greenhouse gas emission, the need to improve reliability and sustainable supply of electricity, are some of the most challenging issues in modern power systems. To tackle these challenges, using renewable-energy based sources to reduce dependence on fuel-based energy sources is focused. For this purpose, using the electric vehicles, in the form of distributed generation, as an appropriate solution to replace combustion vehicles is strongly considered. In this paper, the energy management in multi-agent microgrids in an integrated framework including the electric vehicle charging stations and reducing pollution is suggested. In the proposed strategy, to manage the energy optimally, two stages are implemented. First, in each microgrid, local energy management is performed, pollution of diesel generation sources is considered, and the hourly amounts of surplus/shortage powers are determined. At the second stage, the microgrid is connected to the upstream network, and the impacts of electric vehicle charging stations, and also the sale/buy of power are modeled. To improve the power quality and optimize the net power, energy storage systems are used. The results of simulation studies using General Algebraic Modeling System software confirm that by applying the proposed technique the operating costs are optimized. They confirm that the total operation costs of microgrids will be increased by considering the fuel cost and produced pollution by diesel generators. Also, by using the electric vehicles charging stations, the overall costs over 24 hours will be reduced, up to $792.
Keywords
Energy management
Multiagent MGs
Energy storage
Electric vehicles
Pollution

Subjects

1. Farhangi, H., The path of the smart grid. IEEE power and energy magazine, 2009. 8(1): p. 18-28.
2. Iqbal, M., et al., Optimization classification, algorithms and tools for renewable energy: A review. Renewable and Sustainable Energy Reviews, 2014. 39: p. 640-654.
3. Anvari-Moghaddam, A., et al., Microgrids: Advances in Operation, Control, and Protection. 2021: Springer Nature.
4. Wang, Z., B. Chen, and J. Wang, Decentralized energy management system for networked microgrids in grid-connected and islanded modes. IEEE Transactions on Smart Grid, 2015. 7(2): p. 1097-1105.
5. Morais, H., et al., Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming. Renewable Energy, 2010. 35(1): p. 151-156.
6. Parol, M. and T. Wójtowicz. Optimization of exchange of electrical energy between microgrid and electricity utility distribution network. in 2010 Modern Electric Power Systems. 2010. IEEE.
7. Hawkes, A. and M. Leach, Modelling high level system design and unit commitment for a microgrid. Applied energy, 2009. 86(7-8): p. 1253-1265.
8. Olivares, D.E., C.A. Cañizares, and M. Kazerani, A centralized energy management system for isolated microgrids. IEEE Transactions on smart grid, 2014. 5(4): p. 1864-1875.
9. Masoudi, K. and H. Abdi, Scenario-Based Two-Stage Stochastic Scheduling of Microgrid Considered as the Responsible Load. Electric Power Components and Systems, 2020. 48(14-15): p. 1614-1631.
10. Shayeghi, H., B. Sobhany, and M. Moradzadeh, Management of Autonomous Microgrids Using Multi-Agent Based Online Optimized NFPID Controller. Journal of Energy Management and Technology, 2017. 1(1): p. 79-87.
11. Masoudi, K. and H. Abdi, Multi-objective stochastic programming in microgrids considering environmental emissions. Journal of Operation and Automation in Power Engineering, 2020. 8(2): p. 141-151.
12. Areekkara, S., R. Kumar, and R.C. Bansal, An intelligent multi agent based approach for autonomous energy management in a Microgrid. Electric Power Components and Systems, 2021. 49(1-2): p. 18-31.
13. Fang, X., et al., Multi-agent Deep Reinforcement Learning for Distributed Energy Management and Strategy Optimization of Microgrid Market. Sustainable Cities and Society, 2021. 74: p. 103163.
14. Shahooei, Z., et al., A Novel Agent-Based Power Management Scheme for Smart Multiple-Microgrid Distribution Systems. Energies, 2022. 15(5): p. 1774.
15. Zhong, X., et al., Optimal energy management for multi-energy multimicrogrid networks considering carbon emission limitations. Energy, 2022. 246: p. 123428.
16. Khan, M.W., et al., Optimal energy management and control aspects of distributed microgrid using multi-agent systems. Sustainable Cities and Society, 2019. 44: p. 855-870.
17. Boglou, V., et al., An intelligent decentralized energy management strategy for the optimal electric vehicles’ charging in low-voltage islanded microgrids. International Journal of Energy Research, 2022. 46(3): p. 2988-3016.
18. Khan, M.W., J.Wang, and L. Xiong, Optimal energy scheduling strategy for multi-energy generation grid using multi-agent systems. International Journal of Electrical Power Energy Systems, 2021. 124: p. 106400.
19. Chen, T., et al., Peer-to-peer energy trading and energy conversion in interconnected multi-energy microgrids using multi-agent deep reinforcement learning. IEEE Transactions on Smart Grid, 2021. 13(1): p. 715-727.
20. Vuddanti, S. and S.R. Salkuti, Review of energy management system approaches in microgrids. Energies, 2021. 14(17): p. 5459.
21. Finn, P., M. O’connell, and C. Fitzpatrick, Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reduction. Applied energy, 2013. 101: p. 678-685.
22. Jiang, W., et al., A multiagent-based hierarchical energy management strategy for maximization of renewable energy consumption in interconnected multi-microgrids. IEEE Access, 2019. 7: p. 169931-169945." Jiang, W., et al., A multiagent-based hierarchical energy management strategy for maximization of renewable energy consumption in interconnected
multi-microgrids. IEEE Access, 2019. 7: p. 169931-169945.
23. Bui, V.-H., A. Hussain, and H.-M. Kim, A multiagent-based hierarchical energy management strategy for multi-microgrids considering adjustable power and demand response. IEEE Transactions on Smart Grid, 2016. 9(2): p. 1323-1333.
24. Afzalan, E. and M. Joorabian, Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using epsilon-multi-objective genetic algorithm variable. International Journal of Electrical Power Energy Systems, 2013. 52: p. 55-67.
25. Abdi, H., M. Moradi, and R. Rashidi, Hybrid transmission expansion planning and reactive power planning considering the real network uncertainties. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2022. 35(1): p. e2937.
26. Li, N., L. Chen, and S.H. Low. Optimal demand response based on utility maximization in power networks. in 2011 IEEE power and energy society general meeting. 2011. IEEE.
Journal of Energy Management and Technology
Volume 7, Issue 1
Winter 2023
Pages 49-58

  • Receive Date 03 June 2022
  • Revise Date 01 August 2022
  • Accept Date 14 August 2022