Optimal Power Flow Scheduling Strategy for Multi-Microgrids with Multi-Time Scale Method | IEEE Conference Publication | IEEE Xplore

Optimal Power Flow Scheduling Strategy for Multi-Microgrids with Multi-Time Scale Method


Abstract:

This paper proposes an optimal power flow scheduling strategy for the energy management of multi-MG systems. At the multi-MG level, the global central controller (GCC) is...Show More

Abstract:

This paper proposes an optimal power flow scheduling strategy for the energy management of multi-MG systems. At the multi-MG level, the global central controller (GCC) is responsible for managing the MGs. The GCC calculates the amount of power exchanged within the MGs by using a novel optimal energy allocation policy. Based on the energy supply and demand mismatch, MGs are classified as providers and consumers. The GCC collects information, then distributes energy among the consumers and divides benefits to the providers. Each consumer determines the price of the purchased energy from other MGs based on a priority parameter, in which the local load demand and renewable energy penetration rate are considered as important factors. At the MG level, with the goal of minimizing the operating cost of the MG, the energy is controlled from two time scales, namely day-ahead and intraday, to optimize the output power of generators and energy storage devices. Finally, a simulation of a multi-MG system with three MGs demonstrate the effectiveness of the proposed optimal method.
Date of Conference: 18-21 July 2021
Date Added to IEEE Xplore: 06 August 2021
ISBN Information:
Conference Location: Arlington, VA, USA

Funding Agency:

References is not available for this document.

I. Introduction

As the energy demand and environmental issues increase worldwide, a large amount of renewable energy is flooding into the electricity market. To meet the challenges brought by the random and intermittent renewable energy generations (RGs) to the main grid, multi-microgrids (MGs) emerge as one of the promising solutions to integrate the REGs effectively [1 – 6]. MGs usually consist of distributed generations, loads, energy storage systems, etc. They can solve the connection problem between the RGs and the main grid by integrating coordinated control and energy management systems [7]. Generally, a single MG has a limited capability to maintain a stable and economic operation, while a combination of MGs, i.e. a multi-MG system, guarantees an improved ability. Compared with a single MG, multi-MG systems can ensure normal operation of the internal single MGs and can simultaneously balance the energy flows among the MGs in the system to improve the power quality and reliability, and reduce the distribution power losses [8 – 12]. Therefore, the coordinated operation control of multi-MGs is essential to realize an autonomous operation of the system.

Select All
1.
J. M. Guerrero, P. C. Loh, T. Lee and M. Chandorkar, "Advanced Control Architectures for Intelligent Microgrids—Part II: Power Quality Energy Storage and AC/DC Microgrids", IEEE Transactions on Industrial Electronics, vol. 60, no. 4, pp. 1263-1270, April 2013.
2.
P. J. d. S. Neto, T. A. d. S. Barros, J. P. C. Silveira, E. R. Filho, J. C. Vasquez and J. M. Guerrero, "Power Management Strategy Based on Virtual Inertia for DC Microgrids", IEEE Transactions on Power Electronics, vol. 35, no. 11, pp. 12472-12485, Nov. 2020.
3.
D. Yao, K. Cui, H. Li, C. Yang and B. Liu, "Power Flow Calculation of Shipboard DC Microgrid Power System", 2019 IEEE Third International Conference on DC Microgrids (ICDCM), pp. 1-6, 2019.
4.
P. Rahimzadehkivi, G. Kadkhodaei and M. Hamzeh, "Control and Operation of the Proposed Interlinking Converter in a DC-AC-DC Hybrid Microgrid Based on the Proposed Modes of Operation", 2019 27th Iranian Conference on Electrical Engineering (ICEE), pp. 819-824, 2019.
5.
N. Nasser and M. Fazeli, "Buffered-Microgrid Structure for Future Power Networks; a Seamless Microgrid Control", IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 131-140, Jan. 2021.
6.
Q. Li, Z. Xu and L. Yang, "Recent advancements on the development of microgrids", Journal of Modern Power Systems and Clean Energy, vol. 2, no. 3, pp. 206-211, September 2014.
7.
Xingyou Zhang, Bo Chen, Yan Cheng et al., "A Multi-microgrids System Model Considering Stochastic Correlations Among Microgrids", Energy Procedia, vol. 145, pp. 3-8, 2018.
8.
Jun-Sung Kim, Seong Min So, Joong-Tae Kim et al., "Microgrids platform: A design and implementation of common platform for seamless microgrids operation", Electric Power Systems Research, vol. 167, pp. 21-38, 2019.
9.
Z. Xu, P. Yang, Y. Zhang et al., "Control devices development of multi-microgrids based on hierarchical structure", IET Generation Transmission Distribution, vol. 10, no. 16, pp. 4249-4256, 2016.
10.
Shouxiang Wang, Xingyou Zhang, Lei Wu et al., "New metrics for assessing the performance of multi-microgrid systems in stand-alone mode", International Journal of Electrical Power Energy Systems, vol. 98, pp. 382-388, 2018.
11.
Ali Abdali, Reza Noroozian and Kazem Mazlumi, "Simultaneous control and protection schemes for DC multi microgrids systems", International Journal of Electrical Power Energy Systems, vol. 104, pp. 230-245, 2019.
12.
Yahya Naderi, Seyed Hossein Hosseini, Saeid Ghassem Zadeh et al., "An optimized direct control method applied to multilevel inverter for microgrid power quality enhancement", International Journal of Electrical Power Energy Systems, vol. 107, pp. 496-506, 2019.
13.
A. Ouammi, H. Dagdougui, L. Dessaint and R. Sacile, "Coordinated Model Predictive-Based Power Flows Control in a Cooperative Network of Smart Microgrids", IEEE Transactions on Smart Grid, vol. 6, no. 5, pp. 2233-2244, Sept. 2015.
14.
A. K. Marvasti, Y. Fu, S. DorMohammadi and M. Rais-Rohani, "Optimal Operation of Active Distribution Grids: A System of Systems Framework", IEEE Transactions on Smart Grid, vol. 5, no. 3, pp. 1228-1237, May 2014.
15.
J. Lee, J. Guo, J. K. Choi and M. Zukerman, "Distributed Energy Trading in Microgrids: A Game-Theoretic Model and Its Equilibrium Analysis", IEEE Transactions on Industrial Electronics, vol. 62, no. 6, pp. 3524-3533.
16.
S. Park, J. Lee, G. Hwang and J. K. Choi, "Event-Driven Energy Trading System in Microgrids: Aperiodic Market Model Analysis With a Game Theoretic Approach", IEEE Access, vol. 5, pp. 26291-26302, 2017.
17.
M. Khalid and A. V. Savkin, "Closure to discussion on “A method for short-term wind power prediction with multiple observation points", IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1898-1899, May 2013.
Contact IEEE to Subscribe

References

References is not available for this document.