State-of-art review of the optimization methods to design the configuration of hybrid renewable energy systems (HRESs)

doi:10.1007/s11708-018-0567-x

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 Author(s): 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:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-018-0567-x
https://academic.hep.com.cn/fie/EN/Y2018/V12/I4/591

Tab.1 General advantages and disadvantages of the HRESs as demonstrated by previous research

Tab.2 Classification of the HRESs on the basis of their size

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

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
D_tot	Total hours of operation/h
D_lol	Loss of load/%
ER_j	Emission rates of the jth technology of DG/%
I_t	Investment expenditures in year t (including financing)/€
M_t	Operations and maintenance expenditures in year t/€
F_t	Fuel expenditures in year t/€
E_t	Electricity generation in year t/kW
A_t,r	Loan repayment factor/%
$μ$ _inv	Efficiency of inverter/%
S_j	Total amount of the electricity energy shortage/kW
E₀	Total energy demand of the system/kWh
P_w(t)	Electric power from the wind turbine/kW
P_spv(t)	Solar PV output/kW
H	Hour/h
Lps(t)	Loss of power supply during hour
N_cells	Number of battery’s cells
P_losd	Load demand of the HRESs/kW
G_ref	Solar radiation at standard condition/(W·m²)
P_PV	Energy production from the PV array/kW
P_Wind	Power from the wind turbine/kW
P_out	Output power from the PV array/kW
P_r	Rated power of the PV array/kW
P_Renewable	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
E_gen	Energy production of the HRES at time t/kWh
$μ$ _inv	Inverter efficiency of the HRESs/%
P_c	Production cost/€
GWP	Global warming potential/Million Metric Tons CO₂ Eq.
F_s	Required capacity for the battery’s cells/(A·h)
P_sto	Pressure of stored hydrogen gas/Pa
V_sto	Storage tank volume/m³
T_sto	Gas temperature/°C
E_use	Overall disposable energy from a UC/kW
v_i	Terminal voltage of the capacitor bank at the rated SOC/V
v_f	Minimum voltage obtainable/V

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