EPE  Vol.12 No.8 , August 2020
Agents for Smart Power Grids
Abstract: The future of electricity systems will compose of small-scale generation and distribution where end-users will be active participants with localized energy management systems that are able to interact on a free energy market. Software agents will most likely control power assets and interact together to decide the best and safest configuration of the power grid system. This paper presents a design of agents that can be deployed in real-time with capabilities that include optimization of resources, intensive computation, and appropriate decision-making. Jordan 51-bus system has been used for simulation with a total generation capacity of 4050 MW of which 230 MW represents renewable energy. The economic analyses demonstrated the use of smart grid technologies with 2016 generationload profiles for nominal liquified gas (NLG) prices and ±20% sensitivity analysis. The results have shown variations in the range of 1% in the price of MWh with smart grid technologies. These variations are mainly driven by the fact that agents shift power generation to renewable power plants to produce maximum power at peak hours. As a result, there is a positive economic impact in both NLG ± 20% sensitivity analysis, due to the fact that agents coordinate to better displace expensive thermal generation with renewable generation. It is evident that renewable resources compensate for power at peak times and provide economic benefits and savings.
Cite this paper: Al-Agtash, S. , Hafez, H. (2020) Agents for Smart Power Grids. Energy and Power Engineering, 12, 477-489. doi: 10.4236/epe.2020.128029.

[1]   Al-Agtash, S. (2012) Electricity Agents in Smart Grid Markets. Computers in Industry, 64, 235-241.

[2]   Khazaei, J. and Nguyen, D. (2019) Multi-Agent Consensus Design for Heterogeneous Energy Storage Devices with Droop Control in Smart Grids. IEEE Transactions on Smart Grid, 10, 1395-1404.

[3]   Lau, J., Huang, G., Mak, K. and Liang, L. (2006) Agent-Based Modeling of Supply Chains for Distributed Scheduling. IEEE Transactions on Systems, Man, and Cybernetics, 36, 847-861.

[4]   Bose, A. (2010) Smart Transmission Grid Applications and Their Supporting Infrastructure. IEEE Transactions on Smart Grid, 1, 11-19.

[5]   Bintoudi, A., Zyglakis, L., Tsolakis, A., Ioannidis, D., Hadjidemetriou, L., Zacharia, L., Mutlaq, N., Hashem, M., Al-Agtash, S., Kyriakides, E., Demoulias, C. and Tzovaras, C. (2020) Hybrid Multi-Agent-Based Adaptive Control Scheme for AC Microgrids with Increased Fault-Tolerance Needs. IET Renewable Power Generation, 14, 1-6.

[6]   Lopez-Rodriguez, I., Hernandez-Tejera, M. and Lopez, A. (2016) Methods for the Management of Distributed Electricity Networks Using Software Agents and Market Mechanisms: A Survey. Electric Power Systems Research, 136, 362-369.

[7]   Molderink, A., Bakker, V., Bosman, M., Hurink, J. and Smit (2010) Management and Control of Domestic Smart Grid Technology. IEEE Transactions on Smart Grid, 1, 109-119.

[8]   Rohbogner, G., Fey, S., Hahnel, U., Benoit, P. and Wille-Haussmann, B. (2012) What the Term Agent Stands for in the Smart Grid Definition of Agents and Multi-Agent Systems from an Engineer’s Perspective. Proceedings of the 2012 Federated Conference on Computer Science and Information Systems, Wroclaw, 9-12 September 2012, 1301-1305.

[9]   National Institute of Standards and Technology (2010) NIST Framework and Roadmap for Smart Grid Interoperability Standards Release 1.0.

[10]   Spataru, C. and Barrett, M. (2013) Smart Consumers, Smart Controls, Smart Grid. In: Hakansson, A., Hojer, M., Howlett, R. and Jain, L., Eds., Sustainability in Energy and Buildings, Smart Innovation, Systems and Technologies, Vol. 22, Springer, Berlin, Heidelberg, 381-389.

[11]   Li, F., Qiao, W., Sun, H., Wan, H., Wang, J., Xia, Y., Xu, Z. and Zhang, P. (2010) Smart Transmission Grid: Vision and Framework. IEEE Transactions on Smart Grid, 1, 168-177.

[12]   Shawon, M., Muyeen, S., Ghosh, A., Islam, S. and Baptista, M. (2019) Multi-Agent Systems in ICT Enabled Smart Grid: A Status Update on Technology Framework and Applications. IEEE Access, 7, 97959-97973.

[13]   Rahman, S., Pipattanasomporn, M. and Teklu, Y. (2007) Intelligent Distributed Autonomous Power Systems. Proceedings of 2007 IEEE Power Engineering Society General Meeting, Tampa, 24-28 June 2007, 1-8.

[14]   Bellifemine, F., Caire, G. and Greenwood, D. (2008) Developing Multi-Agent Systems with JADE. John Wiley & Sons, Hoboken.

[15]   Wooldridge, W. (2009) An Introduction to Multiagent Systems. John Wiley & Sons, Hoboken.

[16]   Jain, M., Gupta, S., Masand, D., Agnihotri, G. and Jain, S. (2016) Real-Time Implementation of Islanded Microgrid for Remote Areas. Journal of Control Science and Engineering, 2016, Article ID: 5710950.

[17]   Brown, R. (2008) Impact of Smart Grid on Distribution System Design. Proceedings of 2008 IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, 20-24 July 2008, 1-4.

[18]   AbdelHafez, H. (2017) Multi Agent Systems for Smart Power Grids. Master Thesis, German Jordanian University, Amman.

[19]   Elsied, M., Oukaour, A., Gualous, A. and Ottavio, B. (2016) Optimal Economic and Environment Operation of Micro-Grid Power Systems. Energy Conversion and Management, 122, 182-194.

[20]   Alzyoud, F., Alsharman, N. and Almofleh, A. (2019) Best Practice Fundamentals in Smart Grids For a Modern Energy System Development in Jordan. Proceedings of the 9th International Conference on Advances in Computing, Communication and Information Technology, Rome, 7-8 December 2019, 80-86.

[21]   Jordan National Electric Power Company NEPCO (2015) The 2015 Annual Report.