Agent-Based System for Microgrid Power Real-Time Pricing with Client Preferences
(*) Corresponding author
Microgrid technology is actually the most practical way for Distributed Energy Resources (DERs) deployment in urban and rural communities. The factors that motivate reliance to the microgrid technology include energy security, economy and environment. The economical sustainability of microgrids depends on the energy market within a community. This can be challenged by issues related to the scalability of the microgrid and the setting up of a pricing system. Various energy management systems and energy pricing methods developed so far offer little or no option to the end user/client to accept or reject the proposed price. Thus, this paper presents a client focused agent that automatically negotiates various available economical options in real-time pricing context within a microgrid. The proposed agent collects and analyses the prices as communicated by the microgrid network operator. Moreover, based on client predefined preferences, it regulates the client load demand in a predefined sequence with the aim of minimizing the power consumption during high price period. Therefore, the proposed system will reduce the client overall energy cost and contributes towards demand side management from the network operator perspective. The agent mathematical model and algorithm are simulated in MATLAB environment. The findings of the study demonstrate the effectiveness of the proposed agent approach in offering the client a real-time control over the energy cost control in a microgrid context.
Copyright © 2022 Praise Worthy Prize - All rights reserved.
N. Voropai, Electric power system transformations: A review of main prospects and challenges, Energies, vol. 13, no. 21. 2020.
T. Adefarati and R. C. Bansal, Energizing Renewable Energy Systems and Distribution Generation, in Pathways to a Smarter Power System, 2019, pp. 29-65.
O. Azeem et al., A comprehensive review on integration challenges, optimization techniques and control strategies of hybrid ac/dc microgrid, Appl. Sci., vol. 11, no. 14, 2021.
L. Yan, Z. Changhong, J. Huaiguang, Z. Yingchen, and M. Eduard, Distributed energy resource units, in New Technologies for Power System Operation and Analysis, 2021, pp. 1-21.
A. Hirscha, Y. Parag, and J. Guerrero, Microgrids: A review of technologies, key drivers, and outstanding issues, Renew. Sustain. Energy Rev., vol. 90, pp. 402-411, 2018.
I. Vokony, B. Hartmann, C. Farkas, J. Kiss, and P. Sores, Future level of distribution system operator's involvement in spread of microgrid technologies: A review, in 7th International Youth Conference on Energy, IYCE 2019, 2019.
M. Z. Zizoui, B. Tabbache, M. F. Zia, and M. Benbouzid, Control of isolated photovoltaic-battery-ultracapacitor microgrid for remote areas, Int. J. Energy Convers., vol. 8, no. 2, pp. 38-44, 2020.
C. Liu, A Review of Microgrid Development and Technology," IOP Conf. Ser. Earth Environ. Sci., vol. 300, no. 4, p. 42048, Aug. 2019.
F. Gao, R. Kang, J. Cao, and T. Yang, Primary and secondary control in DC microgrids: a review, J. Mod. Power Syst. Clean Energy, vol. 7, no. 2, pp. 227-242, Mar. 2019.
Thararak, P., Jirapong, P., Quaternary Protection Scheme with Optimal Dual-Directional Overcurrent Relay Setting for Smart Microgrids, (2020) International Review of Electrical Engineering (IREE), 15 (2), pp. 174-187.
Albatran, S., Salameh, O., Harasis, S., Modified Transient Stability Evaluation Methodology of a Multi-Machine Power System Incorporating Renewable Energy Resources, (2021) International Review of Electrical Engineering (IREE), 16 (6), pp. 566-577.
A. Hirsch, Y. Parag, and J. Guerrero, Microgrids: A review of technologies, key drivers, and outstanding issues, Renewable and Sustainable Energy Reviews. 2018.
G. Shahgholian, A brief review on microgrids: Operation, applications, modeling, and control, Int. Trans. Electr. Energy Syst., vol. 31, no. 6, Jun. 2021.
Z. A. Arfeen, A. B. Khairuddin, R. M. Larik, and M. S. Saeed, Control of distributed generation systems for microgrid applications: A technological review, Int. Trans. Electr. Energy Syst., vol. 29, no. 9, Sep. 2019.
Prompinit, K., Khomfoi, S., A Battery Energy Storage System Control Technique with Ramp Rate and C-Rate Parameter Consideration for AC Microgrid Applications, (2018) International Review of Electrical Engineering (IREE), 13 (2), pp. 137-148.
L. Ahmethodzic and M. Music, Comprehensive review of trends in microgrid control, Renewable Energy Focus, vol. 38. pp. 84-96, 2021.
F. Mohammadi et al., Robust Control Strategies for Microgrids: A Review, IEEE Syst. J., pp. 1-12, 2021.
M. Ahmed, L. Meegahapola, A. Vahidnia, and M. Datta, Stability and Control Aspects of Microgrid Architectures-A Comprehensive Review, IEEE Access, vol. 8. pp. 144730-144766, 2020.
R. Kamdar, P. Paliwal, and Y. Kumar, A State of Art Review on Various Aspects of Multi-Agent System, J. Circuits, Syst. Comput., vol. 27, no. 11, p. 1830006, Oct. 2018.
T. Nagata, A multiagent‐based microgrid operation method considering charging and discharging strategies of electric vehicles, Electr. Eng. Japan, vol. 208, no. 1-2, pp. 35-42, Jul. 2019.
K. M. Bhargavi, N. S. Jayalakshmi, D. N. Gaonkar, A. Shrivastava, and V. K. Jadoun, A Comprehensive Review on Control Techniques for Power Management of Isolated DC Microgrid System Operation, IEEE Access, vol. 9, pp. 32196-32228, 2021.
Q. Zhang, K. Dehghanpour, Z. Wang, F. Qiu, and D. Zhao, Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids, IEEE Trans. Smart Grid, vol. 12, no. 2, pp. 1048-1062, 2021.
Singsanga, S., Usaha, W., Packet Forwarding in Common Sink Multi-Domain Wireless Sensor Networks Using Non-Cooperative Game, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (2), pp. 153-164.
H. Zhou and M. Erol-Kantarci, Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach, in 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2020, pp. 1-7.
J. G. Kim and B. Lee, Automatic P2P energy trading model based on reinforcement learning using long short-term delayed reward, Energies, vol. 13, no. 20, 2020.
N. Fawzy, H. F. Habib, O. Mohammed, and S. Brahma, Protection of Microgrids with Distributed Generation based on Multiagent System, in Proceedings - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020, 2020.
A. A. Shobole and M. Wadi, Multiagent systems application for the smart grid protection, Renewable and Sustainable Energy Reviews, vol. 149. 2021.
A. S. Nair et al., Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid, Technol. Econ. Smart Grids Sustain. Energy, vol. 3, no. 15, 2018.
T. Zhao, Z. Li, and Z. Ding, Consensus-Based Distributed Optimal Energy Management with Less Communication in a Microgrid, IEEE Trans. Ind. Informatics, vol. 15, no. 6, pp. 3356-3367, 2019.
M. S. Jonban et al., Autonomous energy management system with self-healing capabilities for green buildings (microgrids), J. Build. Eng., vol. 34, 2021.
Boudiaf, B., Zebirate, S., Aissani, N., Chaker, A., Isolated Microgrid Management Using a Multi-Agent System, (2021) International Review on Modelling and Simulations (IREMOS), 14 (1), pp. 1-9.
Benalia, N., Diabi, R., Maun, J., Moussaoui, A., Optimal Reactive Power Planning Technique Using Multi-Agent System, (2018) International Journal on Energy Conversion (IRECON), 6 (3), pp. 106-110.
Min Zhang ; Frank Eliassen ; Amir Taherkordi ; Hans-Arno Jacobsen ; Hwei-Ming Chung ; Yan Zhang, Energy Trading with Demand Response in a Community-based P2P Energy Market, in 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2019.
Z. Ma, M. Varbak, and B. Norregaard Jorgensen, Multi-agent Simulation of Households' Behaviors Towards Hourly Electricity Price Scheme in Denmark, in Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020, 2020, pp. 1081-1087.
H. S. V. S. K. Nunna, A. Sesetti, A. K. Rathore, and S. Doolla, Multiagent-Based Energy Trading Platform for Energy Storage Systems in Distribution Systems with Interconnected Microgrids, IEEE Trans. Ind. Appl., vol. 56, no. 3, pp. 3207-3217, 2020.
L. Gomes, Z. A. Vale, and J. M. Corchado, Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading, IEEE Access, vol. 8, pp. 64169-64183, 2020.
A. Paudel, M. Khorasany, and H. B. Gooi, Decentralized Local Energy Trading in Microgrids with Voltage Management, IEEE Trans. Ind. Informatics, vol. 17, no. 2, pp. 1111-1121, 2021.
M. S. H. Nizami, M. J. Hossain, and E. Fernandez, Multiagent-Based Transactive Energy Management Systems for Residential Buildings with Distributed Energy Resources, IEEE Trans. Ind. Informatics, vol. 16, no. 3, pp. 1836-1847, 2020.
S. Mohammadi, F. Eliassen, and Y. Zhang, Effects of false data injection attacks on a local P2P energy trading market with prosumers, in IEEE PES Innovative Smart Grid Technologies Conference Europe, 2020, vol. 2020-Octob, pp. 31-35.
W. Y. Chiu, C. W. Hu, and K. Y. Chiu, Renewable Energy Bidding Strategies Using Multiagent Q-Learning in Double-Sided Auctions, IEEE Syst. J., 2021.
A. Afzaal, F. Kanwal, A. H. Ali, K. Bashir, and F. Anjum, Agent-Based Energy Consumption Scheduling for Smart Grids: An Auction-Theoretic Approach, IEEE Access, vol. 8, pp. 73780-73790, 2020.
M. B. Rasheed, M. A. Qureshi, N. Javaid, and T. Alquthami, Dynamic Pricing Mechanism with the Integration of Renewable Energy Source in Smart Grid, IEEE Access, vol. 8, pp. 16876-16892, 2020.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2023 Praise Worthy Prize