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comparison, ACM Computing Surveys (CSUR), 35(3), 268–308, 2003.
51. Das, B.K., Al-Abdeli, Y.M., Kothapalli, G., Effect of load following strategies, hardware, and thermal load distribution on standalone hybrid CCHP systems, Appl. Energy, 2018.
52. Luu, N.A., Tran, Q.T., Bacha, S., Optimal energy management for an island microgrid by using dynamic programming method, In Proceedings of the 2015 IEEE Eindhoven PowerTech, Eindhoven, The Netherlands, 29 June–2 July 2015.
53. Neves, D., Pina, A., Silva, C.A., Comparison of different demand response optimization goals on an isolated microgrid, Sustain. Energy Technol. Assess., 2018.
54. Yu, J., Dou, C., & Li, X., MAS-based energy management strategies for a hybrid energy generation system, IEEE Transactions on Industrial Electronics, 63(6), 3756–3764, 2016.
55. Ju, C., Wang, P., Goel, L., Xu, Y., A two-layer energy management system for microgrids with hybrid energy storage considering degradation costs, IEEE Trans. Smart Grid, 2018.
56. Wang, Z., Zhu, Q., Huang, M., & Yang, B., Optimization of economic/environmental operation management for microgrids by using hybrid fireworks algorithm, International Transactions on Electrical Energy Systems, 27(12), 2017.
57. Roy, K., Mandal, K.K., & Mandal, A.C., Modeling and managing of micro grid connected system using improved artificial bee colony algorithm, International Journal of Electrical Power & Energy Systems, 75, 50–58, 2016.
58. Lin, W.M., Tu, C.S., & Tsai, M.T., Energy management strategy for microgrids by using enhanced bee colony optimization, Energies, 9(1), 5, 2015.
59. Marzband, M., Azarinejadian, F., Savaghebi, M., Guerrero, J.M., An optimal energy management system for islanded microgrids based on multiperiod artificial bee colony combined with markov chain, IEEE Syst. J., 2017.
60. Prathyush, M., Jasmin, E.A., Fuzzy Logic Based Energy Management System Design for AC Microgrid, In Proceedings of the International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 20 April 2018.
61. De Santis, E., Rizzi, A., Sadeghian, A., Hierarchical genetic optimization of a fuzzy logic system for energy flows management in microgrids, Appl. Soft Comput. J., 2017.
62. Ma, L., Liu, N., Zhang, J., Tushar, W., Yuen, C., Energy Management for Joint Operation of CHP and PV Prosumers Inside a Grid-Connected Microgrid: A Game Theoretic Approach, IEEE Trans. Ind. Inform., 2016.
63. Leonori, S., Rizzi, A., Paschero, M., Mascioli, F.M.F., Microgrid Energy Management by ANFIS Supported by an ESN Based Prediction Algorithm, In Proceedings of the International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 8–13 July 2018.
64. Liu, N., Yu, X., Wang, C., Wang, J., Energy Sharing Management for Microgrids with PV Prosumers: A Stackelberg Game Approach, IEEE Trans. Ind. Inform., 2017.
65. Mondal, A., Misra, S., Patel, L.S., Pal, S.K., Obaidat, M.S., DEMANDS: Distributed energy management using noncooperative scheduling in smart grid, IEEE Syst. J., 2018.
1 *Corresponding author: [email protected]
2
Power and Energy Management in Microgrid
Jayesh J. Joglekar
MIT World Peace University, Pune, India
Abstract
The microgrid voltage management has a significant concern during unstable system condition due to limited power to frequency ratio (MW/Hz). The selection of sources for microgrid would play an essential role and power management techniques could save the microgrid from the complete blackout. The modification in the power flow controller could achieve desirable results with an appropriate position of the power flow controller.
Keywords: BESS, fuel cell, energy storage, microgrid, renewable source
2.1 Introduction
The power system in the modern world is restructured based on source, nature of load and geographical space availability. The grid size also depends on the need of society and consumers. The concept of microgrid (MG) emerges from the traditional grid. But like the traditional grid, MG has a limited area to serve and hence the transmission lines could be replaced by underground cables. Depending on the application at the consumer end, MG could be with Alternating Current or with Direct Current or mixed one. Due to limited size, capacity and consumer base, power flow in MG could be a critical issue. For the interconnected AC transmission line network, transfer capacity is an economical operational constraint. It forces to use the available infrastructure to its maximum limit. The increased usability of the transmission limit handled by the FACTS controllers. These controllers are known for their applications in improving power transfer capacity as well as stability using the existing infrastructure of a transmission utility. In addition to transmission capacity enhancement and power flow control, FACTS controllers have other advantages like transient stability improvement, power oscillation damping, voltage stability and control. The transmission line capacity is enhanced by around 40 to 50% by installing a FACTS controller in comparison to conventional mechanically-driven devices, as FACTS controllers are not subject to wear and tear and require a lower maintenance [1].
2.2 Microgrid Structure
The Microgrid (MG) broadly comprises of source, load and controller, as shown in Figure 2.1. The choice of the source depends on the geographical location of MG and the type as well as the demand of the load. The emerging technologies such as fuel cell could be suitable for supplying the base load and proves advantageous over batteries. The working of a fuel cell is similar to the battery. The batteries contain the limited capacity of support chemicals. They have a fixed life cycle, but a fuel cell is supplied with fuel externally and operates continuously as long as fuel supplied. An electrolyte separates an anode and a cathode in the fuel cell. The type of fuel cell decided by electrolyte used. A fuel cell is a static energy converter from chemical to electrical energy. It is a modular, efficient and very low emission power source for a distributed system. It is clear that fuel cell is an upcoming option for conventional power generation resources [2–5]. Fuel cell produces electricity from external supplies of hydrogen fuel (on the anode side) and oxidant (on the cathode side) in the presence of an electrolyte. Generally, the reactants flow in and reaction products flow out while the electrolyte remains in the cell. Fuel cells can operate almost continuously as long as the necessary fuel flows are maintained. There are different types of fuel cell based on base chemical or membrane used. Those are Phosphoric Acid Fuel Cell (PAFC), Solid Oxide Fuel Cell (SOFC), Molten Carbonate Fuel Cell (MCFC) Proton Exchange Membrane Fuel Cell (PEMFC), etc.
Figure 2.1 Basic structure of microgrid (MG).
2.2.1 Selection of Source for DG
With multiple options available based on technology used, fuel cells became a well-known source of energy in recent years. The selection of fuel cell is broadly based on the type of application it performs. Table 2.1 shows comparison of present fuel cell technologies and suitable applications. The fuel cell is an upcoming option for power generation and hybrid power system. It ensures a reliable