Abstract
In this paper, a novel hybrid algorithm is implemented for the system modelling and the optimal management of the micro-grid (MG)-connected systems with low cost. The increasing number of renewable energy sources and distributed generators requires new strategies for their operations in order to maintain the energy balance between the renewable sources and MG. Therefore, an efficient hybrid technique is proposed in the paper. The main objective of the process was the optimum operation of micro-sources for decreasing the electricity production cost by hourly day-ahead and real-time scheduling. The proposed hybrid technique is to manage the power flows between the energy sources and the grid. To achieve this point, demand response and minimum cost of energy are determined. The proposed hybrid technique is the combined performance of both the gravitational search algorithm (GSA)-based artificial neural network (ANN) and squirrel search algorithm (SSA), and it is named as SOGSNN. This technique is involved with the mathematical optimization problems that necessitate more than one fitness function to be optimized simultaneously. By using the inputs of MG-like wind turbine, photovoltaic array, fuel cell, micro-turbine, diesel generator and battery storage with corresponding cost functions, the GSA-based ANN learning phase is employed to predict the load demand. SSA clarifies the squirrel in optimizing the configuration of MG based on the load demand. The proposed hybrid technique is implemented in MATLAB/Simulink working platform and compared with other solution techniques like ANFASO method. The comparison result reveals that the superiority of the proposed technique confirms its ability to solve the problem.
Similar content being viewed by others
References
Aghajani G, Ghadimi N (2018) Multi-objective energy management in a micro-grid. Energy Rep 4:218–225. https://doi.org/10.1016/j.egyr.2017.10.002
Ahmed N, Miyatake M, Al-Othman A (2008) Power fluctuations suppression of stand-alone hybrid generation combining solar photovoltaic/wind turbine and fuel cell systems. Energy Convers Manag 49:2711–2719. https://doi.org/10.1016/j.enconman.2008.04.005
Aktas A, Erhan K, Özdemir S, Özdemir E (2018) Dynamic energy management for photovoltaic power system including hybrid energy storage in smart grid applications. Energy 162(72–82):2018. https://doi.org/10.1016/j.energy.2018.08.016
Aktas A, Erhan K, Ozdemir S, Ozdemir E (2019) Experimental investigation of a new smart energy management algorithm for a hybrid energy storage system in smart grid applications. Electr Power Syst Res 144:185–196. https://doi.org/10.1016/j.epsr.2016.11.022
Bajpai P, Dash V (2012) Hybrid renewable energy systems for power generation in stand-alone applications: a review. Renew Sustain Energy Rev 16:2926–2939. https://doi.org/10.1016/j.rser.2012.02.009
Dali M, Belhadj J, Roboam X (2010) Hybrid solar–wind system with battery storage operating in grid-connected and standalone mode: control and energy management – Experimental investigation. Energy 35:2587–2595. https://doi.org/10.1016/j.energy.2010.03.005
Deshmukh M, Deshmukh S (2008) Modeling of hybrid renewable energy systems. Renew Sustain Energy Rev 12:235–249. https://doi.org/10.1016/j.rser.2006.07.011
Dursun E, Kilic O (2012) Comparative evaluation of different power management strategies of a stand-alone PV/Wind/PEMFC hybrid power system. Int J Electr Power Energy Syst 34:81–89. https://doi.org/10.1016/j.ijepes.2011.08.025
Elsied M, Oukaour A, Gualous H, Lo Brutto O (2016) Optimal economic and environment operation of micro-grid power systems. Energy Convers Manag 122:182–194. https://doi.org/10.1016/j.enconman.2016.05.074
Figueiredo J, Martins J (2010) Energy production system management—renewable energy power supply integration with building automation system. Energy Convers Manag 51:1120–1126. https://doi.org/10.1016/j.enconman.2009.12.020
Golsorkhi M, Lu D (2015) A control method for inverter-based islanded microgrids based on V–I droop characteristics. IEEE Trans Power Deliv 30:1196–1204. https://doi.org/10.1109/tpwrd.2014.2357471
Goroohi Sardou I, Zare M, Azad-Farsani E (2018) Robust energy management of a microgrid with photovoltaic inverters in VAR compensation mode. Int J Electr Power Energy Syst 98:118–132. https://doi.org/10.1016/j.ijepes.2017.11.037
Gu W, Wu Z, Bo R et al (2014) Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: a review. Int J Electr Power Energy Syst 54:26–37. https://doi.org/10.1016/j.ijepes.2013.06.028
Hajizadeh A, Golkar M (2007) Intelligent power management strategy of hybrid distributed generation system. Int J Electr Power Energy Syst 29:783–795. https://doi.org/10.1016/j.ijepes.2007.06.025
Indragandhi V, Logesh R, Subramaniyaswamy V, Vijayakumar V, Siarry P, Uden L (2018) Multi-objective optimization and energy management in renewable based AC/DC microgrid. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2018.01.023
Jain M, Singh V, Rani A (2018) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evolut Comput. https://doi.org/10.1016/j.swevo.2018.02.013
Kaundinya D, Balachandra P, Ravindranath N (2009) Grid-connected versus stand-alone energy systems for decentralized power—a review of literature. Renew Sustain Energy Rev 13:2041–2050. https://doi.org/10.1016/j.rser.2009.02.002
Luna A, Meng L, Diaz N et al (2018) Online energy management systems for microgrids: experimental validation and assessment framework. IEEE Trans Power Electron 33:2201–2215. https://doi.org/10.1109/tpel.2017.2700083
Mohamed F, Koivo H (2007) System modelling and online optimal management of microgrid with battery storage. Renew Energy Power Qual J 1:74–78. https://doi.org/10.24084/repqj05.220
Moradi M, Foroutan V, Abedini M (2017) Power flow analysis in islanded micro-grids via modeling different operational modes of DGs: a review and a new approach. Renew Sustain Energy Rev 69:248–262. https://doi.org/10.1016/j.rser.2016.11.156
Moradi H, Esfahanian M, Abtahi A, Zilouchian A (2018) Optimization and energy management of a standalone hybrid microgrid in the presence of battery storage system. Energy 147:226–238. https://doi.org/10.1016/j.energy.2018.01.016
Morstyn T, Hredzak B, Aguilera R, Agelidis V (2018) Model predictive control for distributed microgrid battery energy storage systems. IEEE Trans Control Syst Technol 26:1107–1114. https://doi.org/10.1109/tcst.2017.2699159
Muralitharan K, Sakthivel R, Vishnuvarthan R (2018) Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing 273:199–208. https://doi.org/10.1016/j.neucom.2017.08.017
Najafzadeh K, Heydari H (2012) New inverter fault current limiting method by considering microgrid control strategy. Adv Mater Res 463–464:1647–1653. https://doi.org/10.4028/www.scientific.net/amr.463-464.1647
Palizban O, Kauhaniemi K, Guerrero J (2014) Microgrids in active network management—part I: hierarchical control, energy storage, virtual power plants, and market participation. Renew Sustain Energy Rev 36:428–439. https://doi.org/10.1016/j.rser.2014.01.016
Pavan Kumar Y, Ravikumar B (2016) A simple modular multilevel inverter topology for the power quality improvement in renewable energy based green building microgrids. Electr Power Syst Res 140:147–161. https://doi.org/10.1016/j.epsr.2016.06.027
Prakash S, Sinha S (2014) Simulation based neuro-fuzzy hybrid intelligent PI control approach in four-area load frequency control of interconnected power system. Appl Soft Comput 23:152–164. https://doi.org/10.1016/j.asoc.2014.05.020
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
Roy K, Mandal K (2014) Hybrid optimization algorithm for modeling and management of micro grid connected system. Front Energy 8:305–314. https://doi.org/10.1007/s11708-014-0308-8
Roy K, Mandal K, Mandal A (2016) Modeling and managing of micro grid connected system using improved artificial bee colony algorithm. Int J Electr Power Energy Syst 75:50–58. https://doi.org/10.1016/j.ijepes.2015.08.003
Roy K, Krishna Mandal K, Chandra Mandal A, Narayan Patra S (2018) Analysis of energy management in micro grid—a hybrid BFOA and ANN approach. Renew Sustain Energy Rev 82:4296–4308. https://doi.org/10.1016/j.rser.2017.07.037
Sharma S, Bhattacharjee S, Bhattacharya A (2018) Probabilistic operation cost minimization of micro-grid. Energy 148:1116–1139. https://doi.org/10.1016/j.energy.2018.01.164
Su W, Wang J, Roh J (2014) Stochastic energy scheduling in microgrids with intermittent renewable energy resources. IEEE Trans Smart Grid 5:1876–1883. https://doi.org/10.1109/tsg.2013.2280645
Thao N, Uchida K (2016) A control strategy based on fuzzy logic for three-phase grid-connected photovoltaic system with supporting grid-frequency regulation. J Autom Control Eng 4:96–103. https://doi.org/10.12720/joace.4.2.96-103
Vasquez JC, Guerrero JM, Miret J, Castilla M, De Vicuna LG (2010) Hierarchical control of intelligent microgrids. IEEE Ind Electron Mag 4:23–29. https://doi.org/10.1109/mie.2010.938720
Yi Z, Dong W, Etemadi A (2018) A unified control and power management scheme for PV-battery-based hybrid microgrids for both grid-connected and islanded modes. IEEE Trans Smart Grid 9:5975–5985. https://doi.org/10.1109/tsg.2017.2700332
Funding
No funding has been received.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants performed by any of the authors.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Roy, K., Mandal, K.K. & Mandal, A.C. Energy management of the energy storage-based micro-grid-connected system: an SOGSNN strategy. Soft Comput 24, 8481–8494 (2020). https://doi.org/10.1007/s00500-019-04412-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-019-04412-6