Due to the intermittent nature of renewable sources, the generation of power is varied which is the main problem in renewable energy system. Miss-matching between the power generation and load power causes a deviation from the desired voltage and frequency in power supply. Therefore, a new efficient smart grid system is required for an optimal power flow management. In this paper, a hybrid approach is presented for power flow management of HRES connected smart grid system. The novelty of the proposed approach is the combined execution of IHHO with SOA named as I2HOSOA technique. In the established work, the HHO is integrated by crossover and mutation function, it is known as IHHO. The main contribution of the proposed strategy is to control the power flow based on source and load side parameters variations. In the proposed approach, the control signals of the voltage source are developed by the IHHO based on the variety of power exchange between the source and load side. Similarly, the online control signals are located by the SOA procedure by utilizing the parallel execution against the active and reactive power varieties. The multi-objective function is shaped by the grid required active and reactive power varieties created based on accessible source power. Here, the control parameters of the power controller are enhanced by the proposed technique based control models in light of the power flow varieties. The comparison between established and existing methods is analyzed in terms of reactive current injection, grid code, current amplitude limitation control, active power control, zero active power oscillations, and injection of active and reactive power. Furthermore, the statistical evaluation of established, and existing methods of mean, median, and standard deviation, is evaluated. Finally, the proposed model is executed in MATLAB/Simulink working platform and the execution is compared with the existing techniques.
ArabaliA., GhofraniM., Etezadi-AmoliM., FadaliM. and BaghzouzY., Genetic-Algorithm-Based Optimization Approach for Energy Management, IEEE Transactions on Power Delivery28 (2013), 162–170. DOI: 10.1109/TPWRD.2012.2219598
2.
FarzanF., JafariM., MasielloR. and LuY., Toward Optimal Day-Ahead Scheduling and Operation Control of Microgrids Under Uncertainty, IEEE Transactions on Smart Grid6 (2015), 499–507. DOI: 10.1109/TSG.2014.2368077
3.
GabbarH. and AbdelsalamA., Microgrid energy management in grid-connected and islanding modes based on SVC, Energy Conversion and Management86 (2014), 964–972. https://doi.org/10.1016/j.enconman.2014.06.070
4.
GraditiG., Di SilvestreM., GalleaR. and Riva SanseverinoE., Heuristic-Based Shiftable Loads Optimal Management in Smart Micro-Grids, IEEE Transactions on Industrial Informatics11 (2015), 271–280. DOI: 10.1109/TII.2014.2331000
5.
HowladerA., UrasakiN., YonaA., SenjyuT. and SaberA., A review of output power smoothing methods for wind energy conversion systems, Renewable and Sustainable Energy Reviews26 (2013), 135–146. https://doi.org/10.1016/j.rser.2013.05.028
6.
JiangB. and FeiY., Smart Home in Smart Microgrid: A Cost-Effective Energy Ecosystem With Intelligent Hierarchical Agents, IEEE Transactions on Smart Grid6 (2015), 3–13. DOI: 10.1109/TSG.2014.2347043
7.
KaurA., KaushalJ. and BasakP., A review on microgrid central controller, Renewable and Sustainable Energy Reviews55 (2016), 338–345. https://doi.org/10.1016/j.rser.2015.10.141
8.
Ahmad KhanA., NaeemM., IqbalM., QaisarS. and AnpalaganA., A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids, Renewable and Sustainable Energy Reviews58 (2016), 1664–1683. https://doi.org/10.1016/j.rser.2015.12.259
9.
AbdelHadyR., Modeling and simulation of a micro grid-connected solar PV system, Water Science31 (2017), 1–10. https://doi.org/10.1016/j.wsj.2017.04.001-33
10.
LiuY., YuenC., Ul HassanN., HuangS., YuR. and XieS., Electricity Cost Minimization for a Microgrid With Distributed Energy Resource Under Different Information Availability, IEEE Transactions on Industrial Electronics62 (2015), 2571–2583. DOI: 10.1109/TIE.2014.2371780
11.
MaT., YangH., LuL. and PengJ., Technical feasibility study on a standalone hybrid solar-wind system with pumped hydro storage for a remote island in Hong Kong, Renewable Energy69 (2014), 7–15. https://doi.org/10.1016/j.renene.2014.03.028
12.
MarzbandM., AzarinejadianF., SavaghebiM. and GuerreroJ., An Optimal Energy Management System for Islanded Microgrids Based on Multiperiod Artificial Bee Colony Combined With Markov Chain, IEEE Systems Journal11 (2017), 1712–1722. DOI: 10.1109/JSYST.2015.2422253
13.
NikmehrN. and Najafi-RavadaneghS., Optimal operation of distributed generations in micro-grids under uncertainties in load and renewable power generation using heuristic algorithm, IET Renewable Power Generation9 (2015), 982–990. DOI: 10.1049/iet-rpg.2014.0357
14.
OuammiA., DagdouguiH., DessaintL. and SacileR., Coordinated Model Predictive-Based Power Flows Control in a Cooperative Network of Smart Microgrids, IEEE Transactions on Smart Grid6 (2015), 2233–2244. DOI: 10.1109/TSG.2015.2396294
15.
PascualJ., BarricarteJ., SanchisP. and MarroyoL., Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting, Applied Energy158 (2015), 12–25. https://doi.org/10.1016/j.apenergy.2015.08.040
16.
SmaouiM. and KrichenL., Control, energy management and performance evaluation of desalination unit based renewable energies using a graphical user interface, Energy114 (2016), 1187–1206. https://doi.org/10.1016/j.energy.2016.08.051-34
17.
MythiliS., ThiyagarajahK., RajeshP. and ShajinF.H., Ideal position and size selection of unified power flow controllers (UPFCs) to upgrade the dynamic stability of systems: An antlion optimiser and invasive weed optimisation Algorithm, HKIE Transactions27 (2020), 25–37. 10.33430/V27N1THIE-2018-0024.
18.
RahbarK., XuJ. and ZhangR., Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach, IEEE Transactions on Smart Grid6 (2015), 124–134. DOI: 10.1109/TSG.2014.2359004
19.
SandganiM. and SirouspourS., Coordinated Optimal Dispatch of Energy Storage in a Network of Grid-Connected Microgrids, IEEE Transactions on Sustainable Energy8 (2017), 1166–1176. DOI: 10.1109/TSTE.2017.2664666
20.
ShenJ., JiangC., LiuY. and WangX., A Microgrid Energy Management System and Risk Management Under an Electricity Market Environment, IEEE Access4 (2016), 2349–2356. DOI: 10.1109/ACCESS.2016.2555926
21.
ShiW., XieX., ChuC. and GadhR., Distributed Optimal Energy Management in Microgrids, IEEE Transactions on Smart Grid6 (2015), 1137–1146. DOI: 10.1109/TSG.2014.2373150
22.
SinghG., Solar power generation by PV (photovoltaic) technology: A review, Energy53 (2013), 1–13. https://doi.org/10.1016/j.energy.2013.02.057
23.
JiangQ., XueM. and GengG., Energy Management of Microgrid in Grid-Connected and Stand-Alone Modes, IEEE Transactions on Power Systems28 (2013), 3380–3389. DOI: 10.1109/TPWRS.2013.2244104
24.
ChenH., YangC., DengK., ZhouN. and WuH., Multi-objective optimization of the hybrid wind/solar/fuel cell distributed generation system using Hammersley Sequence Sampling, International Journal of Hydrogen Energy42 (2017), 7836–7846. https://doi.org/10.1016/j.ijhydene.2017.01.202-35
25.
Praveen KumarT., SubrahmanyamN. and SyduluM., CMBSNN for Power Flow Management of the Hybrid Renewable Energy – Storage System-Based Distribution Generation, IETE Technical Review36 (2018), 303–314. https://doi.org/10.1080/02564602.2018.1465860
26.
LingamuthuR. and MariappanR., Power flow control of grid connected hybrid renewable energy system using hybrid controller with pumped storage, International Journal of Hydrogen Energy44 (2019), 3790–3802. https://doi.org/10.1016/j.ijhydene.2018.12.092
27.
Et-TaoussiM., OuadiH. and ChakirH., Hybrid optimal management of active and reactive power flow in a smart microgrid with photovoltaic generation, Microsystem Technologies25 (2019), 4077–4090. https://doi.org/10.1007/s00542-019-04354-x
28.
MurtyV. and KumarA., Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems, Protection and Control of Modern Power Systems5 (2020). https://doi.org/10.1186/s41601-019-0147-z
29.
NagarajaY., DevarajuT. and VijaykumarM., Optimal distributed control of renewable energy-based microgrid – an energy management approach, International Journal of Ambient Energy (2019), 1–8. https://doi.org/10.1080/01430750.2019.1611660
30.
ÇelikD. and MeralM., Current control based power management strategy for distributed power generation system, Control Engineering Practice82 (2019), 72–85. https://doi.org/10.1016/j.conengprac.2018.09.025
31.
ChakirH., HamidO. and FouadG., Managing a hybrid energy smart grid with a renewable energy source, Asian Journal of Control21 (2019), 2060–2073. https://doi.org/10.1002/asjc.1995
32.
HumanG., van SchoorG. and UrenK., Power management and sizing optimisation of renewable energy hydrogen production systems, Sustainable Energy Technologies and Assessments31 (2019), 155–166.
33.
Praveen KumarT., SubrahmanyamN. and SyduluM., Power Flow Management of the Grid-Connected Hybrid Renewable Energy System: A PLSANN Control Approach, IETE Journal of Research 2019, 1–16.
34.
WangY., HanF., YangL., XuR. and Liu.R., A Three-Port Bidirectional Multi-Element Resonant Converter with Decoupled Power Flow Management for Hybrid Energy Storage Systems, IEEE Access 2018, 1–1.
35.
MohamedFaisal A. and KoivoHeikki N., System Modeling and Online Optimal Management of MicroGrid with Battery Storage, International Conference on Renewable Energies and Power Quality, pp.1–5, 2007.
36.
RoyK., MandalK. and MandalA., Modeling and managing of micro grid connected system using Improved Artificial Bee Colony algorithm, International Journal of Electrical Power & Energy Systems2016(75), 50–58.
37.
MohamadF. and TehJ., Impacts of Energy Storage System on Power System Reliability: A Systematic Review, Energies11 (2018), 1749. https://doi.org/10.3390/en11071749
38.
KanchevH., LuD., ColasF., LazarovV. and FrancoisB., Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications, IEEE Transactions on Industrial Electronics58 (2011), 4583–4592. DOI: 10.1109/TIE.2011.2119451
39.
MagroM., GiannettoniM., PincetiP. and VantiM., Real time simulator for microgrids, Electric Power Systems Research160 (2018), 381–396. https://doi.org/10.1016/j.epsr.2018.03.018
40.
TucciM., RiversoS., VasquezJ., GuerreroJ. and Ferrari-TrecateG., A Decentralized Scalable Approach to Voltage Control of DC Islanded Microgrids, IEEE Transactions on Control Systems Technology24 (2016), 1965–1979. DOI: 10.1109/TCST.2016.2525001
41.
MartiP., VelascoM., MartinE., Gracia de VicunaL., MiretJ. and CastillaM., Performance Evaluation of Secondary Control Policies with Respect to Digital Communications Properties in Inverter-based Islanded Microgrids, IEEE Transactions on Smart Grid (2016), 1–1. DOI: 10.1109/TSG.2016.2608323
42.
Al-SaediW., LachowiczS., HabibiD. and BassO., Power flow control in grid-connected microgrid operation using Particle Swarm Optimization under variable load conditions, International Journal of Electrical Power & Energy Systems49 (2013), 76–85. https://doi.org/10.1016/j.ijepes.2012.12.017
43.
HeidariA., MirjaliliS., FarisH., AljarahI., MafarjaM. and ChenH., Harris hawks optimization: Algorithm and applications, Future Generation Computer Systems97 (2019), 849–872. https://doi.org/10.1016/j.future.2019.02.028
44.
DhimanG. and KumarV., Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems, Knowledge-Based Systems165 (2019), 169–196. https://doi.org/10.1016/j.knosys.2018.11.024
KartciA., et al., Synthesis and Optimization of Fractional-Order Elements Using a Genetic Algorithm, IEEE Access7 (2019), 80233–80246. DOI: 10.1109/ACCESS.2019.2923166
48.
ChenghuiZ., QingshengS., NaxinC. and WuhuaLi, Particle Swarm Optimization for energy management fuzzy controller design in dual-source electric vehicle, 2007 IEEE Power Electronics Specialists Conference, 2007. Available: 10.1109/pesc.2007.4342200 [Accessed 4 May 2020].
49.
HakliH., BinEHO: a new binary variant based on elephant herding optimization algorithm, Neural Computing and Applications, 2020. Available: 10.1007/s00521-020-04917-4.
50.
BanatiH. and ChaudharyR., Weighted Multi-Modal Bat Algorithm with Improved Search (wMMBAIS), International Journal of Hybrid Intelligence1(1) (2020), 1. Available: 10.1504/ijhi.2020.10028083.
51.
RodriguezP., MedeirosG., LunaA., CavalcantiM. and TeodorescuR., Safe current injection strategies for a STATCOM under asymmetrical grid faults, 2010 IEEE Energy Conversion Congress and Exposition (2010), 3929–3935. DOI: 10.1109/ECCE.2010.5617794
52.
RodriguezP., LunaA., HermosoJ., Etxeberria-OtaduiI., TeodorescuR. and BlaabjergF., Current control method for distributed generation power generation plants under grid fault conditions, IECON 2011 – 37th Annual Conference of the IEEE Industrial Electronics Society, (2011), 1262–1269. DOI: 10.1109/IECON.2011.6119490
53.
LeeC., HsuC. and ChengP., A Low-Voltage Ride-Through Technique for Grid-Connected Converters of Distributed Energy Resources, IEEE Transactions on Industry Applications47 (2011), 1821–1832. DOI: 10.1109/TIA.2011.2155016
54.
CamachoA., CastillaM., MiretJ., VasquezJ. and Alarcon-GalloE., Flexible Voltage Support Control for Three-Phase Distributed Generation Inverters Under Grid Fault, IEEE Transactions on Industrial Electronics60 (2013), 1429–1441. DOI: 10.1109/TIE.2012.2185016
55.
MiretJ., CamachoA., CastillaM., de VicunaL. and MatasJ., Control Scheme With Voltage Support Capability for Distributed Generation Inverters Under Voltage Sags, IEEE Transactions on Power Electronics28 (2013), 5252–5262. DOI: 10.1109/TPEL.2013.2246190
56.
CamachoA., CastillaM., MiretJ., GuzmanR. and BorrellA., Reactive Power Control for Distributed Generation Power Plants to Comply With Voltage Limits During Grid Faults, IEEE Transactions on Power Electronics29 v(2014), 6224–6234. DOI: 10.1109/TPEL.2014.2301463
57.
CamachoA., CastillaM., MiretJ., BorrellA. and de VicunaL., Active and Reactive Power Strategies With Peak Current Limitation for Distributed Generation Inverters During Unbalanced Grid Faults, IEEE Transactions on Industrial Electronics62 (2015), 1515–1525. DOI: 10.1109/TIE.2014.2347266
58.
SosaJ., CastillaM., MiretJ., MatasJ. and Al-TurkiY., Control Strategy to Maximize the Power Capability of PV Three-Phase Inverters During Voltage Sags, IEEE Transactions on Power Electronics31 (2016), pp. 3314–3323. DOI: 10.1109/TPEL.2015.2451674
59.
ChenH., LeeC., ChengP., TeodorescuR. and BlaabjergF., A Low-Voltage Ride-Through Technique for Grid-connected Converters with Reduced Power Transistors Stress, IEEE Transactions on Power Electronics (2016), 1–1. DOI: 10.1109/TIA.2011.2155016
