Abstract
Micro grids are drawing in more consideration since they can mitigate the worry of primary transmission systems, reduce feeder losses, enhance system power quality (PQ), and power management. Power management in MG is challengeable utilizing energy storage and generation components like PV, diesel generator, micro-hydro generator, battery bank with a bidirectional inverter. In this paper, the power management of the MG is investigated with the proposed droop controller. The droop controller is intended to get the optimal energy management of MG. The proposed method is the combination of Ant Lion Optimization (ALO) with a Bat algorithm to enhance the power management of MG. The ALO calculation is a nature-inspired algorithm. It imitates the chasing system of ant lions in nature. The Bat algorithm is utilized to refresh the insect lion position of the ALO algorithm. The droop control is sharing the power in the generation side depends upon the load demand of the grid side through the control action of the real and reactive power. The goal of the paper is used to enhance the real and reactive power of MG. With the assistance of the proposed technique, the real and reactive power is enhanced and the voltage is controlled. Based on the droop characteristics, power management is accomplished continuously by way of the upgraded proposed approach. The proposed strategy is executed in the MATLAB/Simulink platform and also analysis of the PV power, wind power, power demand, real power, reactive power, voltage, current, demand values, and grid power. The upgrading proposed framework is compared to current techniques such as CSO and PSO algorithms. Hence, the overall performance of the proposed approach will be effective.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Abbreviations
- \(V_{m}\) :
-
The maximum voltage of V
- \(\omega\) :
-
The angular frequency
- \(I_{m}\) :
-
The maximum current
- \(V_{\alpha }\) and \(V_{\beta }\) :
-
The \(\alpha - \beta\) coordinate system
- \(P_{ref}\) :
-
The reference real power
- \(Q_{ref}\) :
-
The reference reactive power
- P and Q :
-
The contrasted and the values of \(P_{ref}\) and \(Q_{ref}\)
- E :
-
The referred voltage amplitude
- \(\omega^{*}\) :
-
The nominal frequency
- \(E^{*}\) :
-
The nominal voltage amplitude
- \(p\) and \(q\) :
-
The calculated real and reactive power
- \(p^{*}\) and \(q^{*}\) :
-
The preferred value of real and reactive power
- \(s\) :
-
The real power coefficient
- \(t\) :
-
The reactive power coefficient
- \(\Delta \omega_{{\rm max}}\) :
-
The maximum allowed voltage amplitude droop
- \(P_{{\rm max}}\) :
-
The maximum allowed real power
- \(\Delta E_{{\rm max}}\) :
-
The maximum allowed voltage frequency
- \(Q_{{\rm max}}\) :
-
The maximum allowed reactive power
- \(K_{p}\) :
-
The proportional constant
- \(K_{i}\) :
-
The integration constant
- \(E(t)\) :
-
The error value
- \(E_{p} (t)\) :
-
The error value in real power or active power
- \(E_{q} (t)\) :
-
The error value in the reactive power or active power
- \(N_{A}^{t}\) :
-
The random walk around the ant lion chosen by the roulette wheel at \(t\text{th}\) iteration
- \(N_{E}^{t}\) :
-
The random walk around the elite at tth iteration
- \(ANT_{i}^{t}\) :
-
The position of ith ant at tth iteration
- \(G^{t}\) :
-
The minimum of all variables at tth iteration
- \(H^{t}\) :
-
The vector admitting the maximum of all variables at tth iteration
- Fk :
-
The pulse frequency
- Pk :
-
The pulse rate
- Bk :
-
The loudness parameters
- \(\Delta E_{p}\) :
-
Represent the change in error value of real power
- \(\Delta E_{q}\) :
-
Represent the change in error value of reactive power
- RE:
-
Renewable energy
- DG:
-
Distributed generation
- MG:
-
Micro grid
- AE:
-
Alternative energy
- RES:
-
Renewable energy sources
- SOC:
-
State of charge
- WT:
-
Wind turbine
- PV:
-
Photovoltaic cell
- FC:
-
Fuel cell
- PQ:
-
Power quality
- SMC:
-
Sliding mode control
- ALO:
-
Ant Lion Optimization algorithm
- PSO:
-
Particle Swarm Optimization algorithm
- ANNs:
-
Artificial neural networks
- FLC:
-
Fuzzy logic control
- BDPCs:
-
Bi-directional power converters
- CSO:
-
Cuckoo-Search Optimization algorithm
- FA:
-
Firefly algorithm
- MO:
-
Multi-objective
- MOPM:
-
Multi-objective power management
- VSI:
-
Voltage source inverter
- PWM:
-
Pulse width modulation
- AC:
-
Alternating current
- DC:
-
Direct current
References
Abd-Elazim SM, Ali ES (2016) Load frequency controller design via BAT algorithm for nonlinear interconnected power system. Int J Electr Power Energy Syst 77:166–177
Abd-Elazim SM, Ali ES (2018) Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm. Neural Comput Appl 30(2):607–616
Abedini M, Moradi HM, Hosseinian SM (2016) Optimal management of microgrids including renewable energy scources using GPSO-GM algorithm. Renew Energy 90:430–439
Ali ES, Abd Elazim SM, Abdelaziz AY (2016) Ant lion optimization algorithm for renewable distributed generations. Energy 116:445–458
Barklund E, Pogaku N, Prodanovic M, Hernandez-Aramburo C, Green TC (2008) Energy management in autonomous microgrid using stability-constrained droop control of inverters. IEEE Trans Power Electron 23(5):2346–2352
Belvedere B, Michele B, Alberto B, Alberto NC, Mario P, Antonio P (2012) A microcontroller-based power management system for standalone microgrids with hybrid power supply. IEEE Trans Sustain Energy 3(3):422–431
Dehghanpour K, Nehrir H (2017) Real-time multiobjective microgrid power management using distributed optimization in an agent-based bargaining framework. IEEE Trans Smart Grid 9(6):1–10
Dubey HM, Pandit M, Panigrahi BK (2016) Ant lion optimization for short-term wind integrated hydrothermal power generation scheduling. Int J Electr Power Energy Syst 31(83):158–174
Erkan D, 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(1):81–89
Falahi A, Monaaf DA, Jayasinghe SDG, Enshaei HJEC (2017) A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system. Energy Convers Manag 143:252–274
Ghazanfari A, Mohsen H, Mokhtari H, Karimi H (2012) Active power management of multi hybrid fuel cell/supercapacitor power conversion system in a medium voltage microgrid. IEEE Trans Smart Grid 3(4):1903–1910
Guerrero JM, Vasquez CJ, Matas J, De Vicuna LG, Castilla M (2011) Hierarchical control of droop-controlled AC and DC microgrids—a general approach toward standardization. IEEE Trans Ind Electron 58(1):158–172
Guerrero JM, Chandorkar M, Lee TL, Loh PC (2013) Advanced control architectures for intelligent microgrids part I: decentralized and hierarchical control. IEEE Trans Ind Electron 60(4):1254–1262
Hisham M, Michaelson D, Jiang J (2015) Decentralized power management of a PV/battery hybrid unit in a droop-controlled islanded microgrid. IEEE Trans Power Electron 30(12):7215–7229
Hong M, Yu X, Yu NP, Loparo KA (2016) An energy scheduling algorithm supporting power quality management in commercial building microgrids. IEEE Trans Smart Grid 7(2):1044–1056
Hooshmand A, Malki AH, Javad M (2012) Power flow management of microgrid networks using model predictive control. Comput Math Appl 64(5):869–876
Hosseinzadeh M, Salmasi FR (2015) Robust optimal power management system for a hybrid AC/DC micro-grid. IEEE Trans Sustain Energy 6(3):675–687
Jayachandran M, Ravi G (2019) Predictive power management strategy for PV/battery hybrid unit based islanded AC microgrid. Int J Electr Power Energy Syst 110:487–496
Karimi Y, Oraee H, Guerrero JM (2017) Decentralized method for load sharing and power management in a hybrid single/three-phase-islanded microgrid consisting of hybrid source pv/battery units. IEEE Trans Power Electron 32(8):6135–6144
Khan MW, Wang J, Xiong L, Ma M (2018) Modelling and optimal management of distributed microgrid using multi-agent systems. Sustain Cities Soc 41:154–169
Khooban MH, Niknam T (2015) A new intelligent online fuzzy tuning approach for multi-area load frequency control: self adaptive modified bat algorithm. Int J Electr Power Energy Syst 71:254–261
Kim J, Guerrero JM, Rodriguez P, Teodorescu R, Nam K (2011) Mode adaptive droop control with virtual output impedances for an inverter-based flexible ac microgrid. IEEE Trans Power Electron 26(3):689–701
Koohi K, Rahim NA, Mokhlis H (2014) Smart power management algorithm in microgrid consisting of photovoltaic, diesel, and battery storage plants considering variations in sunlight, temperature, and load. Energy Convers Manag 84:562–582
Kumar SA, Rajasekar S, Raj PADV (2015) Power quality profile enhancement of utility connected microgrid system using ANFIS-UPQC. Procedia Technol 21:112–119
Li X, Deb K, Fang Y (2017) A derived heuristics based multi-objective optimization procedure for micro-grid scheduling. Eng Optim 49(6):1078–1096
Lu X, Guerrero JM, Sun K, Vasquez JC (2014) An improved droop control method for dc microgrids based on low bandwidth communication with dc bus voltage restoration and enhanced current sharing accuracy. IEEE Trans Power Electron 29(4):1800–1812
Majumder R, Ghosh A, Ledwich G, Zare F (2010a) Power management and power flow control with back-to-back converters in a utility connected microgrid. IEEE Trans Power Syst 25(2):821–834
Majumder R, Chaudhuri B, Ghosh A, Majumder R, Ledwich G, Zare F (2010b) Improvement of stability and load sharing in an autonomous microgrid using supplementary droop control loop. IEEE Trans Power Syst 25(2):796–808
Milczarek A, Mariusz M, Guerrero JM (2015) Reactive power management in islanded microgrid—proportional power sharing in hierarchical droop control. IEEE Trans Smart Grid 6(4):1631–1638
Moghaddam AA, Seifi A, Niknam T, Pahlavani MRA (2011) Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source. Energy 36(11):6490–6507
Mohamed FA, Heikki NK (2010) System modelling and online optimal management of microgrid using mesh adaptive direct search. Int J Electr Power Energy Syst 32(5):398–407
Nejabatkhah F, Li WY (2015) Overview of power management strategies of hybrid AC/DC microgrid. IEEE Trans Power Electron 30(12):7072–7089
Saikia LC, Sinha N (2016) Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller. Int J Electr Power Energy Syst 80:52–63
Saxena P, Kothari A (2016) Ant Lion Optimization algorithm to control side lobe level and null depths in linear antenna arrays. AEU Int J Electron Commun 70(9):1339–1349
Senthilkumar A, Poongothai K, Selvakumar S, Silambarasan M, Raj PDAV (2015) Mitigation of harmonic distortion in microgrid system using adaptive neural learning algorithm based shunt active power filter. Procedia Technol 21:147–154
Tidjani FS, Hamadi A, Chandra A, Pillay P, Ndtoungou A (2017) Optimization of standalone microgrid considering active damping technique and smart power management using fuzzy logic supervisor. IEEE Trans Smart Grid 8(1):475–484
Xunwei Y, She X, Ni X, Alex HQ (2014) System integration and hierarchical power management strategy for a solid-state transformer interfaced microgrid system. IEEE Trans Power Electron 29(8):4414–4425
Yang X, He X (2013) Bat algorithm: literature review and applications. Int J Bioinspired Comput 5(3):141–149
Yi Z, Dong W, Etemadi AH (2017) 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(6):5975–5985
Yunjie G, Li W, He X (2015) Frequency-coordinating virtual impedance for autonomous power management of DC microgrid. IEEE Trans Power Electron 30(4):2328–2337
Author information
Authors and Affiliations
Corresponding author
Additional information
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
Sridhar, N., Kowsalya, M. Enhancement of power management in micro grid system using adaptive ALO technique. J Ambient Intell Human Comput 12, 2163–2182 (2021). https://doi.org/10.1007/s12652-020-02313-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02313-3