Micro grids (MG) are seen as the future power system providing clear economic and environmental benefits. Most MG energy management solutions rely on centralized controller which is not suitable to guarantee the flexibility and the adaptability that modern electricity market needs. In other hand, multi objective optimization for fully decentralized system in MG environment is not realizable without certain level of coordination between agents. In this paper, we present an adaptive multi-agents system (AMAS) for MG power management based on enhanced fuzzy decision using multi swarm optimization (MS-PSO) algorithm. In the proposed architecture each agent presents a different MG unit. Fuzzy logic is used by each agent to estimate the amount of energy to be generated in order to cover the uncertainty and imprecision related to renewable energy sources and the MG constraints. For the MAS coordination, a MS-PSO algorithm is used by a coordinator agent to find the best compromised solution to satisfy economical/environmental objective based on agent proposals in order to improve them. Simulation results show the importance of the chosen optimization algorithm for the AMAS with MS-PSO algorithm which is compared to the basic particle swarm optimization for the same encapsulated knowledge.
ElamineD.O., SerrajiM., NfaouiE.H. and BoumhidiJ., Multi-agent architecture for optimal energy management of a smart micro-grid using a weighted hybrid BP-PSO algorithm for wind power prediction, International Journal of Technology Intelligence and Planning11(1) (2016), 20-35.
2.
WoodA.J. and WollenbergB.F., Power generation, operation, and control, John Wiley and Sons, 2012.
3.
MotevaselM. and SeifiA.R., Expert energy management of a micro-grid considering wind energy uncertainty, Energy Conversion and Management83 (2014), 58-72.
4.
NiknamT., Azizipanah-AbarghooeeR. and NarimaniM.R., An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation, Applied Energy99 (2012), 455-470.
5.
MoghaddamA.A., SeifiA., NiknamT. and PahlavaniM.R.A., Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source, Energy36 (2011), 6490-6507.
6.
OlivaresD.E., CañizaresC. and KazeraniM., A centralized optimal energy management system for microgrids, IEEE Power Energy Soc Gener Meet (2011), 1-6.
7.
AmbiaM.N., Al-DurraA., CaruanaC. and MuyeenS., Power management of hybridmicro-grid system by a generic centralized supervisory control scheme, Sustain Energy, Technol Assess8 (2014), 57-65.
8.
McArthurS.D., DavidsonE.M., CattersonV.M., DimeasA.L., HatziargyriouN.D., PonciF. and FunabashiT., Multi-agent systems for power engineering applications - Part I: Concepts, approaches, and technical challenges, Power Systems, IEEE Transactions on22 (2007), 1743-1752.
9.
McArthurS.D., DavidsonE.M., CattersonV.M., DimeasA.L., HatziargyriouN.D., PonciF. and FunabashiT., Multi-agent systems for power engineering applications - Part II: Technologies, standards, and tools for building multi-agent systems, Power Systems, IEEE Transactions on22 (2007), 1753-1759.
10.
SiljakD.D., Decentralized control of complex systems, Dover Publications, 2011.
11.
KantamneniA.B. and LauraE., ParkerG. et al., Survey of multi-agent systems for microgrid control, Engineering Applications of Artificial Intelligence45 (2015), 192-203.
12.
PohekarS.D. and RamachandranM., Application of multi-criteria decision making to sustainable energy planning - a review, Renewable and Sustainable Energy Reviews8 (2004), 365-381.
13.
BaziarA. and Kavousi-FardA., Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices, Renewable Energ59 (2013), 158-166.
14.
MohamedF.A. and KoivoH.N., System modelling and online optimal management of microgrid using mesh adaptive direct search, International Journal of Electrical Power and Energy Systems32 (2010), 398-407.
15.
KhazabM., TweedaleJ. and JainL., Web-based multi-agent system architecture in a dynamic environment, International Journal of Knowledge-based and Intelligent Engineering Systems14 (2010), 217-227.
16.
HoogendoornM. and TreurJ., An adaptive multi-agent organization model based on dynamic role allocation, International Journal of Knowledge-based and Intelligent Engineering Systems13 (2009), 119-139.
17.
RenX., ThompsonH.A. and FlemingP.J., An agent-based system for distributed fault diagnosis, International Journal of Knowledge-based and Intelligent Engineering Systems10 (2006), 319-335.
18.
WooldridgeM. and JenningsN.R., Intelligent agents: Theory and practice, The Knowledge Engineering Review10(2) (1995), 115-152.
19.
KimD., KimN.-H., SeoS.-J. et al., Use of fuzzy system in modeling of biped walking robot, International Journal of Knowledge-based and Intelligent Engineering Systems9 (2005), 341-349.
20.
YangM. and ShenQ., Fuzzy diagnosis of turbomachines, International Journal of Knowledge-based and Intelligent Engineering Systems12 (2008), 137-146.
21.
YangM. and ShenQ., Reinforcing fuzzy rule-based diagnosis of turbomachines with case-based reasoning, International Journal of Knowledge-based and Intelligent Engineering Systems12 (2008), 173-181.
22.
ParhiD.R. et al., Online fuzzy logic crack detection of a cantilever beam, International Journal of Knowledge-Based and Intelligent Engineering Systems12 (2008), 157-171.
23.
LiL., DingS.X., QiuJ., YangY. and ZhangY., Weighted fuzzy observer-based fault detection approach for discrete-time nonlinear systems via piecewise-fuzzy, Lyapunov Functions, 2016.
24.
LiL., DingS.X., QiuJ. and YangY., Real-time fault detection approach for nonlinear systems and its asynchronous TS fuzzy observer-based implementation, 2015.
25.
QiuJ., DingS.X., GaoH. and YinS., Fuzzy-model-based reliable static output feedback control of nonlinear hyperbolic PDE systems, IEEE Transactions on Fuzzy Systems24(2) (2016), 388-400.
26.
WeiY., QiuJ., LamH.K. and WuL., Approaches to TS Fuzzy-Affine-Model-Based Reliable Output Feedback Control for Nonlinear Ito Stochastic Systems, 2016.
27.
RanawanaR., PaladeV. and BandaraG., Automatic fuzzy rule base generation for on-line handwritten alphanumeric character recognition, International Journal of Knowledge-based and Intelligent Engineering Systems9 (2005), 327-339.
28.
SalehM.H. and MoniemT.A., A knowledge-based adaptive fuzzy controller for a two-area power system, International Journal of Knowledge-based and Intelligent Engineering Systems17 (2013), 219-222.
29.
ChauhanB.K. and HanmandluM., Load forecasting using wavelet fuzzy neural network, International Journal of Knowledge-Based and Intelligent Engineering Systems14 (2010), 57-71.
30.
GarcíaP., TorreglosaJ.P., FernándezL.M. and JuradoF., Optimal energy management system for stand-alone wind turbine/photovoltaic/hydrogen/battery hybrid system with supervisory control based on fuzzy logic, International Journal of Hydrogen Energy38 (2013), 14146-14158.
31.
LiangR.H. and LiaoJ.H., A fuzzy-optimization approach for generation scheduling with wind and solar energy systems, Power Systems, IEEE Transactions on22 (2007), 1665-1674.
32.
JouffeL., Fuzzy inference system learning by reinforcement methods, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on28(3) (1998), 338-355.
33.
WangT., ZhangY., QiuJ. et al., Adaptive fuzzy backstepping control for a class of nonlinear systems with sampled and delayed measurements, IEEE Transactions on Fuzzy Systems23(2) (2015), 302-312.
34.
WangT., GaoH. and QiuJ., A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control, IEEE Transactions on Neural Networks and Learning Systems27(2) (2016), 416-425.
35.
WangT., GaoH. and QiuJ., A combined fault-tolerant and predictive control for network-based industrial processes, IEEE Transactions on Industrial Electronics63(4) (2016), 2529-2536.
36.
CarseB. and FogartyT.C., A fuzzy classifier system using the Pittsburgh approach, in: Parallel Problem Solving From Nature - PPSN III Springer Berlin Heidelberg (1994), 259-269.
37.
ChenH., ZhuY. and HuK., Discrete and continuous optimization based on multi-swarm coevolution, Natural Computing9 (2010), 659-682.
38.
HuX. and EberhartR., Multiobjective optimization using dynamic neighborhood particle swarm optimization, in: Wcci IEEE (2002), 1677-1681.
39.
MostaghimS. and TeichJ., Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO), in: Swarm Intelligence Symposium, 2003 SIS'03 Proceedings of the 2003 IEEE, IEEE, (2003), 26-33.
40.
BritsR., EngelbrechtA.P. and van den BerghF., Locating multiple optima using particle swarm optimization, Applied Mathematics and Computation189 (2007), 1859-1883.
41.
TuppadungY. and KurutachW., Comparing nonlinear inertia weights and constriction factors in particle swarm optimization, International Journal of Knowledge-based and Intelligent Engineering Systems15 (2011), 65-70.
42.
NiuB., ZhuY., HeX. and WuH., MCPSO: A multi-swarm cooperative particle swarm optimizer, Applied Mathematics and Computation185 (2007), 1050-1062.
43.
LiJ. and XiaoX., Multi-swarm and multi-best particle swarm optimization algorithm, in: Intelligent Control and Automation, 2008 WCICA 2008 7th World Congress on IEEE (2008), 6281-6286.
44.
ZhangY. and TianY.P., Consentability and protocol design of multi-agent systems with stochastic switching topology, Automatica45(5) (2009), 1195-1201.
45.
MeskinN. and KhorasaniK., Fault detection and isolation of discrete-time Markovian jump linear systems with application to a network of multi-agent systems having imperfect communication channels, Automatica45(9) (2009), 2032-2040.
46.
WeiY., QiuJ. and KarimiH.R., Quantized H_∞ filtering for continuous-time markovian jump systems with deficient mode information, Asian Journal of Control17(5) (2015), 1914-1923.
47.
WeiY., QiuJ. and KarimiH.R. et al., Filtering design for two-dimensional Markovian jump systems with state-delays and deficient mode information, Information Sciences269 (2014), 316-331.
48.
WeiY., QiuJ., KarimiH.R. et al., Model approximation for two-dimensional Markovian jump systems with state-delays and imperfect mode information, Multidimensional Systems and Signal Processing26(3) (2015), 575-597.
49.
WeiY., QiuJ. and FuS., Mode-dependent nonrational output feedback control for continuous-time semi-Markovian jump systems with time-varying delay, Nonlinear Analysis: Hybrid Systems16 (2015), 52-71.
