This paper proposes a fuzzy expert system for demand-side management, management of renewable energy sources, and electrical energy storage for smart households and microgrids. The proposed fuzzy expert system is used for automatic decision making regarding energy management in smart microgrids containing renewable sources, storage systems, and controllable loads. The fuzzy expert system optimizes energy consumption and storage in order to utilize renewable energy and maximize the financial gain of a microgrid. In order to enable energy management, the fuzzy expert system uses insolation, price of electrical energy, temperature, wind speed, and power of the controllable and uncontrollable loads as input variables. These input data can be directly measured, imported from grid measurements, or predicted using any data prediction method. This paper presents fuzzification of input variables, defines a set of rules of the expert system, and presents defuzzification of outputs. The outputs of the expert system are decisions, i.e., answers to the question of how to manage energy production and consumption in a microgrid. Three outputs are defined to decide about produced energy, controllable loads, and own consumption. The first output is used to store, sell, or consume produced energy. The second output is used to manage the controllable load. The third output shows how to supply own consumption of the prosumer. The expert system is tested on hourly values of input variables in a single day in Serbia. The proposed approach is compared with other available approaches in order to validate the results.
References
1.
Y.
Tiejiang
, D.
Xiaoshun
, C.
Xiangping
, C.
Wenping
, H.
Juan
, and L.
Chuang
, “Energetic macroscopic representation control method for a hybrid-source energy system including wind, hydrogen, and fuel cell
,” J. Renewable Sustainable Energy
10
, 1
–17
(2018
).2.
S.
Mukesh
, K.
Praveen
, and K.
Indrani
, “Implementation of vehicle to grid infrastructure using fuzzy logic controller
,” IEEE Trans. Smart Grid
3
, 565
–577
(2012
).3.
J.
Wenzhao
, K.
Chongqing
, and C.
Qixin
, “Analysis on demand-side interactive response capability for power system dispatch in a smart grid framework
,” Electr. Power Syst. Res.
90
, 11
–17
(2012
).4.
L.
Thillainathan
, S.
Dipti
, and K. W. M.
Vanessa
, “Demand side management of smart grid: Load shifting and incentives
,” J. Renewable Sustainable Energy
6
, 01
–18
(2014
).5.
Y.
Zhou
, “The optimal home energy management strategy in smart grid
,” J. Renewable Sustainable Energy
8
, 045101
(2016
).6.
K.
Konstantinos
, A.
Fabio
, S.
Enric
, G. E.
Antonio
, and R.
Luis
, “Multiobjective optimization of multi-carrier energy system using a combination of ANFIS and genetic algorithms
,” IEEE Trans. Smart Grid
9
, 2276
–2283
(2018
).7.
P.
Mehdi
, F.
Farid
, A.
Rahimi-Kian
, and M.
Hassan
, “A multi-objective optimization for energy management in a renewable micro-grid system: A data mining approach
,” J. Renewable Sustainable Energy
6
, 023127
(2014
).8.
K.
Derck
, S.
Navid
, and K.
Wolfgang
, “Machine learning for identifying demand patterns of home energy management systems with dynamic electricity pricing
,” Appl. Sci.
7
, 1160
–1178
(2017
).9.
F.
Mohammad
and B.
Hassan
, “Regulating power management in interconnected microgrids
,” J. Renewable Sustainable Energy
9
, 055502
(2017
).10.
W.
Erin
and P.
Christopher
, “Alaska case study: Solar photovoltaic technology in remote microgrids
,” J. Renewable Sustainable Energy
9
, 061704
(2017
).11.
E. U.
Emrah
and K.
Gülgün
, “Mathematical model for a microgrid consisting of wind turbine, PV panels, and energy storage unit
,” J. Renewable Sustainable Energy
8
, 054101
(2016
).12.
K.
Jasmine
, R. S.
Yog
, and S.
Rajnish
, “A two-layer optimization approach for renewable energy management of green microgrid in deregulated power sector
,” J. Renewable Sustainable Energy
9
, 065905
(2017
).13.
K.
Reza
, B.
Bahman
, and S.
Mohsen
, “Optimal switching in reconfigurable microgrids considering electric vehicles and renewable energy sources
,” J. Renewable Sustainable Energy
10
, 045905
(2018
).14.
M.
Ebrahim
and V.
Jorge
, “Microgrid energy scheduling using storage from electric vehicles
,” Electr. Power Syst. Res.
143
, 554
–562
(2017
).15.
G.
Liu
, M.
Starke
, B.
Xiao
, X.
Zhang
, and K.
Tomsovic
, “Microgrid optimal scheduling with chance-constrained islanding capability
,” Electr. Power Syst. Res.
145
, 197
–206
(2017
).16.
S.
Julia
and S.
Oliver
, “Multi-objective three stage design optimization for island microgrids
,” Appl. Energy
165
, 789
–800
(2016
).17.
Z.
Yan
, F.
Lijun
, Z.
Wanlu
, B.
Xianqiang
, and L.
Cang
, “Robust model predictive control for optimal energy management of island microgrids with uncertainties
,” Energy
164
, 1229
–1241
(2018
).18.
S.
Julia
and S.
Oliver
, “Study on the operation optimization of an isolated island microgrid with renewable energy layout planning
,” Energy
161
, 1211
–1225
(2018
).19.
A. P.
Carboni de Mello
, L. L.
Pfitscher
, and D. P.
Bernardon
, “Coordinated Volt/VAr control for real-time operation of smart distribution grids
,” Electr. Power Syst. Res.
151
, 233
–242
(2017
).20.
C. S.
Chang
, Z.
Wang
, F.
Yang
, and W. W.
Tan
, “Hierarchical fuzzy logic system for implementing maintenance schedules of offshore power systems
,” IEEE Trans. Smart Grid
3
, 3
–11
(2012
).21.
D. Q.
Oliveira
, A. C.
Zambroni de Souza
, M. V.
Santos
, A. B.
Almeida
, B. I. L.
Lopes
, and O. R.
Saavedra
, “A fuzzy-based approach for microgrids islanded operation
,” Electr. Power Syst. Res.
149
, 178
–189
(2017
).22.
B.
Qela
and H. T.
Mouftah
, “Peak load curtailment in a smart grid via fuzzy system approach
,” IEEE Trans. Smart Grid
5
, 761
–768
(2014
).23.
IRENA, IEA, and REN21
, Renewable Energy Policies in a Time of Transition
(IRENA, OECD/IEA and REN21
, 2018
).24.
W.
Bingheng
, S.
Mengxuan
, C.
Kai
, H.
Zhongyang
, and Z.
Xing
, “Wind power prediction system for wind farm based on auto regressive statistical model and physical model
,” J. Renewable Sustainable Energy
6
, 013101
(2014
).25.
L.
Fang-Fang
, W.
Si-Ya
, and W.
Jia-Hua
, “Long term rolling prediction model for solar radiation combining empirical mode decomposition (EMD) and artificial neural network (ANN) techniques
,” J. Renewable Sustainable Energy
10
, 013704
(2018
).26.
G. O.
Chigbogu
, “New temperature-based models for reliable prediction of monthly mean daily global solar radiation
,” J. Renewable Sustainable Energy
10
, 023706
(2018
).27.
S.
Anbazhagan
and N.
Kumarappanm
, “A neural network approach to day–ahead deregulated electricity market prices classification
,” Electr. Power Syst. Res.
86
, 140
–150
(2012
).28.
R. C.
Garcia
, J.
Contreras
, M.
van Akkeren
, and J. B. C.
Garcia
, “A GARCH forecasting model to predic day–ahead electricity prices
,” IEEE Trans. Power Syst.
20
(2
), 867
–874
(2005
).29.
C.
Yanhua
, L.
Min
, Y.
Yi
, L.
Caihong
, L.
Yafei
, and L.
Lian
, “A hybrid model for electricity price forecasting based on least square support vector machines with combined kernel
,” J. Renewable Sustainable Energy
10
, 055502
(2018
).30.
D.
Kotur
and M.
Žarković
, “Neural network models for electricity prices and loads short and long–term prediction
,” in 2016 4th International Symposium on Environmental Friendly Energies and Applications (EFEA)
, Belgrade, Serbia, September 2016
.31.
I.
Babić
, Ž.
Đurišić
, and M.
Žarković
, “Analysis of impact of building integrated photovoltaic systems on distribution network losses
,” Renewable and Sustainable Energy
7
, 043119
(2015
).32.
M.
Forcan
, Ž.
Đurišić
, and J.
Mikulović
, “An algorithm for elimination of partial shading effect based on a theory of reference PV string
,” Sol. Energy
132
, 51
–63
(2016
).33.
A.
Đorđević
and Ž.
Đurišić
, “General mathematical model for the calculation of economic cross sections of cables for wind farms collector systems
,” IET Renewable Power Gener.
12
, 901
–909
(2018
).34.
Ž.
Đurišić
and J.
Mikulović
, “A model for vertical wind speed data extrapolation for improving wind resource assessment using WAsP
,” Renewable Energy
41
, 407
–411
(2012
).35.
M.
Žarković
and J.
Mikulović
, “Dimensioning of the battery in an isolated photovoltaic system
,” in International Scientific Symposium INFOTEH-JAHORINA 2012
, March 2012
, Vol. 11
.36.
M. M.
Gilbery
, Renewable and Efficient Electric Power Systems
(Wiley-IEEE
, Hoboken, United States
, 2013
).37.
M.
Žarković
and Z.
Stojković
, “Fuzzy logic and artificial neural network based thermography approach for monitoring of high voltage equipment
,” Int. J. Electr. Eng. Educ.
52
, 81
–96
(2015
).© 2019 Author(s).
2019
Author(s)
You do not currently have access to this content.
