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Frontiers in Energy

Front. Energy    2018, Vol. 12 Issue (4) : 591-622     https://doi.org/10.1007/s11708-018-0567-x
REVIEW ARTICLE |
State-of-art review of the optimization methods to design the configuration of hybrid renewable energy systems (HRESs)
Maurizio FACCIO1, Mauro GAMBERI2, Marco BORTOLINI2, Mojtaba NEDAEI1()
1. Department of Management and Engineering, University of Padua, Stradella, San Nicola 3, 36100 Vicenza, Italy
2. Department of Industrial Engineering, University of Bologna, Viale del Risorgimento, 2, 40136 Bologna, Italy
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Abstract

The current research aims to present an inclusive review of latest research works performed with the aim of improving the efficiency of the hybrid renewable energy systems (HRESs) by employing diverse ranges of the optimization techniques, which aid the designers to achieve the minimum expected total cost, while satisfying the power demand and the reliability. For this purpose, a detailed analysis of the different classification drivers considering the design factors such as the optimization goals, utilized optimization methods, grid type as well as the investigated technology has been conducted. Initial results have indicated that among all optimization goals, load demand parameters including loss of power supply probability (LPSP) and loss of load probability (LLP), cost, sizing (configuration), energy production, and environmental emissions are the most frequent design variables which have been cited the most. Another result of this paper indicates that almost 70% of the research projects have been dedicated towards the optimization of the off-grid applications of the HRESs. Furthermore, it has been demonstrated that, integration of the PV, wind and battery is the most frequent configuration. In the next stage of the paper, a review concerning the sizing methods is also carried out to outline the most common techniques which are used to configure the components of the HRESs. In this regard, an analysis covering the optimized indicators such as the cost drivers, energy index parameters, load indicators, battery’s state of charge, PV generator area, design parameters such as the LPSP, and the wind power generation to load ratio, is also performed.

Keywords hybrid renewable energy systems (HRESs)      design and optimization      environmental pollutions      PV array      wind turbines (WTs)      inverter      diesel generator (DG)     
Corresponding Authors: Mojtaba NEDAEI   
Just Accepted Date: 03 May 2018   Online First Date: 11 June 2018    Issue Date: 21 December 2018
 Cite this article:   
Maurizio FACCIO,Mauro GAMBERI,Marco BORTOLINI, et al. State-of-art review of the optimization methods to design the configuration of hybrid renewable energy systems (HRESs)[J]. Front. Energy, 2018, 12(4): 591-622.
 URL:  
http://journal.hep.com.cn/fie/EN/10.1007/s11708-018-0567-x
http://journal.hep.com.cn/fie/EN/Y2018/V12/I4/591
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Maurizio FACCIO
Mauro GAMBERI
Marco BORTOLINI
Mojtaba NEDAEI
Advantages Disadvantages
Utilization of the natural and renewable sources Dependency on the natural cycles
Low level of O&M costs Initial costs of these systems are higher than comparably sized traditional generators
No pollution or wastes produced by the natural sources Relatively high costs
Minimizing the intermittency The peak-loads cannot be managed well without energy storage
Lower atmosphere contamination Complexity of the design procedure
Fuel saving Monthly fee charge
Tab.1  General advantages and disadvantages of the HRESs as demonstrated by previous research
Type Size Typical load
Small-scale Lower than 5 kW Suitable for remote homes or telecommunication systems
Medium-scale Between 5 to 100 kW Remotely located communities
Large-scale Upper than 100 kW Regional loads
Tab.2  Classification of the HRESs on the basis of their size
Source type Frequency of the journals Impact factor (IF) categorization of the journals (based on 2017 IFs)
Top-ranked energy journals Other sources
Journal-cases Conference-cases Applied Energy Energy Solar Energy Renewable Energy ECM IEEETSE EPSR IJEP&S IJHE SEC JCP RSER With no IF IF≤1 1≤IF≤2 2≤IF≤3 3≤IF
93 5 14 9 9 20 7 2 1 2 7 2 2 12 11 11 0 2 3 82
Tab.3  Categorization of the extracted research papers in terms of the source type
Fig.1  The contribution of each scientific journal in the advancements of the design and optimization of the HRESs based on current survey report from 2010 till 2017
Tab.4  A comprehensive review of the classifications drivers for the design of the HRESs
Fig.5  The optimization and design process of the HRES by considering different classification drivers
Fig.6  Frequency of the common optimization goals
Tab.5  Classification of the sizing methods, which are used in the design and optimization the HRESs
Fig.8  Contribution of the commonly used algorithms and methods
Fig.9  The contribution of different HRESs configurations
Fig.10  The I-V characteristics of a typical solar PV array [98]
Fig.11  A typical PCU system for utilization in the configuration of the hybrid power system
No. MPPT method The involved parameters The type of implementation Dependency on the PV module parameters Reference
1 Artificial intelligence Reliant on the adopted method Digital Yes [118]
2 Short circuit current Current Analog, and digital Yes [118]
3 Open circuit voltage Voltage Analog and digital Yes [119]
4 Incremental conductance (INC) Voltage and current Digital No [119]
5 Perturb and Observe (P&O) Voltage and current Analog and digital No [120]
6 Hill climbing Voltage and current Analog and digital No [121]
Tab.6  The latest MPPT methods and their characteristics
Fig.12  The series and parallel configuration of batteries in the circuit
EIR Energy index ratio
SOC Battery state of charge
O&M Operation and management
PVEC PV electricity cost
EE Embodied energy
LA Level of autonomy
LST Load shifting technique
IF Impact factor
SEMA Smart energy management algorithm
BBO Biogeography-based optimization
PPOA Power pinch optimization analysis
P&O Perturb and observe algorithm
GSM Global system for mobile communications
WT Wind turbine
HPL High priority load
PDF Probability density function
TMC Total manufacturing cost/€
RA Resource availability
GC Generator’s capacity/ kW
PGA PV generator area/m2
UAC Useful accumulator capacity/ kW
IRR Internal rate of return/%
DPSP Deficiency of the power supply probability/%
DG Diesel generator
UC Ultra capacitor
WGG Wood gas generator
GA Genetic algorithm
BA Bees algorithm
SA Simulated annealing
PA Pinch analysis
PSO Particle swarm optimization
ACO Ant colony algorithm
ABSO Artificial bee swarm optimization
SFL Shuffled frog leap
IGP Internal grid point
GGP Grid generation point
BGED Break-grid extension distance
VSI Voltage stability index
P-ICA Preference-inspired co-evolutionary algorithm
MOABC Multi-objective artificial bee colony
HSS Hammersley sequence sampling
MPC Model predictive control
GRG Generalized reduced gradient
MSDO Matlab Simulink design and optimization
MCDM Multi-criteria decision making
P&O Perturb and observe
B&B Branch and bound
MILP Mixed integer linear programming
LP Linear programming
MPPT Maximum power point tracking
SEMA Smart energy management algorithm
ESPCs Energy savings performance contracts
UESCs Utility energy service contracts
WLR Wind power generation to load ratio
FLMPVS Fraction of load met by a PV system
GDB Generation demand balance
LCE Levelized cost of energy/€
RA Resource availability
NPV Net present value/€
LCC Life cycle cost/€
EAC Equivalent annualized costs/€
AE Applied Energy
IJHE International Journal of Hydrogen Energy
E Energy
RE Renewable Energy
RSER Renewable and Sustainable Energy Reviews
SE Solar Energy
ECM Energy Conversion and Management
JCP Journal of Cleaner Production
IJEP&ES International Journal of Electrical Power, and Energy Systems
Dtot Total hours of operation/h
Dlol Loss of load/%
ERj Emission rates of the jth technology of DG/%
It Investment expenditures in year t (including financing)/€
Mt Operations and maintenance expenditures in year t/€
Ft Fuel expenditures in year t/€
Et Electricity generation in year t/kW
At,r Loan repayment factor/%
μinv Efficiency of inverter/%
Sj Total amount of the electricity energy shortage/kW
E0 Total energy demand of the system/kWh
Pw(t) Electric power from the wind turbine/kW
Pspv(t) Solar PV output/kW
H Hour/h
Lps(t) Loss of power supply during hour
Ncells Number of battery’s cells
Plosd Load demand of the HRESs/kW
Gref Solar radiation at standard condition/(W·m2)
PPV Energy production from the PV array/kW
PWind Power from the wind turbine/kW
Pout Output power from the PV array/kW
Pr Rated power of the PV array/kW
PRenewable Power production from the renewable energy/kW
DPSP(t) Deficiency of power supply probability at time t/%
DPS(t) Deficiency power supply (DPS) at hour t/kW
Egen Energy production of the HRES at time t/kWh
μinv Inverter efficiency of the HRESs/%
Pc Production cost/€
GWP Global warming potential/Million Metric Tons CO2 Eq.
Fs Required capacity for the battery’s cells/(A·h)
Psto Pressure of stored hydrogen gas/Pa
Vsto Storage tank volume/m3
Tsto Gas temperature/°C
Euse Overall disposable energy from a UC/kW
vi Terminal voltage of the capacitor bank at the rated SOC/V
vf Minimum voltage obtainable/V
  
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