A review of hybrid renewable energies optimisation: design, methodologies, and criteria

Figures & data

Figure 1. Outline of the paper review.

Figure 2. Classifications of optimisation techniques.

Figure 3. Renewable energy system sizing methodology/optimum sizing criteria.

Table 1. Off-grid HRES configurations using HOMER Pro: a summary.

Study	System configuration	Computing tool	Application	Reference location
(Baker 2018) (Peffley and Pearce 2019) (Ma et al. 2015) (Delano et al. 2020) (Bekele and Tadesse 2012) (Kalinci, Hepbasli, and Dincer 2015) (Hossen and Shezan 2019) (Adaramola, Paul, and Oyewola 2014) (Abdilahi et al. 2014) (Rajkumar et al. 2011). (Kanase-Patil, Saini, and Sharma 2011)	PV-wind-battery PV-battery/Generator Hydro/pumped storage/PV PV/Diesel Generator/Battery Wind/PV/battery/diesel Hydro/PV/Wind/battery Wind/PV/hydrogen storage & wind only PV/wind/Biomass/ battery PV/diesel/battery PV-Wind-diesel-battery PV/Wind/battery Hydro/biogas: wood-based biomass gasifier/Rice husk-based gasifier/wind/PV Biogas/wood-based biomass gasifier/Rice husk-based gasifier/wind-PV	HOMER HOMER Single & double objective optimisation Genetic Algorithm HOMER HOMER HOMER HOMER RET screen & HOMER HOMER HOMER Artificial neural fuzzy inference system (ANFIS) Integrated RE optimisation model (IREOM)	Off-grid Off-grid Off-grid Off-grid Off-grid Off-grid Off-grid Off-grid Off-grid	Northern Cyprus Mediterranean Island United States China Benin Saudi Arabia Ethiopia Turkey Nigeria Somaliland Malaysia India

Table 2. Summary of key optimisation objective functions, constraints, methods, and findings in the HRES.

Objective functions	Constraints	System configurations	Finding	Optimisation method	References
$minCT=\sum _{k=1}^3{I}_k+P_{pwk}+O{M}_{pwk}+S_{pwk}$	LPSP	Solar(PV)/Wind	PSO was used to reduce the iterative complexity that rises in GA. PSO iteration proved better than GA operation.	AI	(Sasidhar and Kumar 2015)
$F=mim\sum _{i=1}^{n}RI+ACS+COE$	POWER	Solar(PV)/WIND/BATT/GEN	The COE can be reduced with an increase in RERs integration.	P	(Adefarati, Bansal, and Justo 2017)
$F^{obj}=max(F_{WDG}^{obj}-F_{DG}^{obj})$	LOAD SOC POWER BALANCE	Solar(PV)/WIND/BATT/GRID	Integration of RERs-DG into the conventional system will improve reliability and reduce cost.	P	(Adefarati and Bansal 2017)
$mmCC$ $mimEE$	System Capacity	Solar (PV)/ Solar thermal/ PHES/AA-CAES/MTS Storage	A stochastic/Multi-objective might be needed to correct identified trade-offs between cost and reliability.	A	(Amusat, Shearing, and Fraga 2016)
$F_1=mimLCOE$ $F_2=MIMLPSP$	Capacity	Hydro/PHS-PV	Effect of net head variation on the system	L	(Chaima et al. 2021)
$F=mimUEC$	LPSP	PV/Wind/Batteries (LA, Li-ion & Ni-Cd)	Comparative studies of ALO, JAYA, KH, and GWO were presented. The result thereby proved JAYA to be superior to others in terms of converging and cost reduction.	AI	(Kaabeche and Bakelli 2019)
$F=mim{C}^{tot}$	SOC SOH	PV/Batteries	Three steps in solving BESS optimal-capacity configuration with multiple batteries using MINLP models were presented.	L/NL	(Jiang, Kang, and Liu 2020)
$F=mim{C}^{tot}$	LPSP	PV/Wind/DG	ACO presents a relatively lower system annual cost of energy in the study.	AI	(Suhane et al. 2016)
$F=mim(TAC+LPSP+FuelCost)$	Generation Battery	PV/Wind/DG/Batt.	The optimal result was obtained when the penalty function was 1, LPSP$<4\text{\%}$ with the structure of $N_{WIND}=215$, $N_{batt}=1$, and $N_{DG}=8.$	H	(Cho, Chun, and Hong 2016)
$F=mim(cost+Polutiondemages)$	Fuel delivery Capacity. Power balance.	Gas engine	SA presents the least emission value of 1757.39$/hr while DE gives the least cost of installation of 1545.61$.	AI	(Júnior et al. 2018)
$F=mimTNPC$	LOLP Capacity	PV/Biogas/PH/Battery	The water cycle, month flame, and Genetic algorithms were used and compared. The result proved WCA to be a more effective intern of convergence, cost, and reliability.	AI	(Das, Singh, and Biswas 2019)
$F=TNPC=\sum NP{C}_K\ast N_K$	Capacity Battery	PV/Wind/ Battery	MILP, GAM, CPLEX Solver, and Homer Pro were combined as Hybrid optimisation methods for HRES optimal Sizing while Demand response programmes are utilised for optimal management & cost reduction	H	(Amrollahi and Bathaee 2017)
$F=mimTNPC$	LPSP	PV/Wind/battery	GS-PSO, MOPSO, and HOMER were used for optimal sizing and comparative study. GA-PSO result proved to be cost-efficient compared to the others at 2%, 5%, and 10% LPSP with LCOE of 0.508kWh at 7 & 10% deviation from other methods.	AI	(Ghorbani et al. 2018)

Where $CT$= $C^{tot}$-Total Cost $F_{WDG}^{obj}$ – Total cost of a power outage without RERs-DG

$CC$ – Capital cost $I_{}$-Investment cost

$EE$ – External energy $F_{DG}^{obj}$ – Total cost of power outages with RERs-DG

R-Replacement cost RI – Reliability index

UEC-unit electricity cos O&M – Operation & Maintenance Cost ACS – The annualised cost of System L – Linear programming $S_{pwk}$-Salvage $COE$-Cost of Electricity NL – Non-linear programing k – individual system $CC$ – Capital cost

$TNPC$ – Total Net Present Cost H – Hybrid Technique MINLP – Mixed integer Non-linear programming AI – Artificial Intelligent Technique CT-Computing tools

P – Probability Technique A – Analytical Technique

Table 3. Comparison of HRES optimisation tools/techniques.

Techniques/Tools	Strengths	Weakness	Ref
HOMER Pro Software	A quick way to get an objective solution from a single target objective	The initial properties of the linear equation model cannot be specified. The system is assumed to remain stationary throughout the study. It does not allow the user to naturally select the appropriate device. The effect of possible future battery charging/DOD growth is not taken into account. The solution is limited by the range of input parameters and cannot provide key value metrics if there are missed like modern optimisation algorithms. It cannot accept a control strategy like iHOGA.**It is less superior to Nature-inspired algorithms. Experienced criterion is required for the optimisation to converge to a good solution.	(Atallah et al. 2020; Chauhan and Saini 2014; Clack, Xie, and Macdonald 2015; Erdinc and Uzunoglu 2012; Kavadias and Triantafyllou 2021; Khare, Nema, and Baredar 2017; Okedu and Uhunmwangho 2014; Ramli et al. 2017; Saiprasad, Kalam, and Zayegh 2018; Zhou et al. 2010)
iHOGA	Ability to explicitly apply control strategy in HRES sizing.	Considered only single objective and Non-linear problems	(Saiprasad, Kalam, and Zayegh 2018)
Retscreen	Significantly reduces the costs associated with identifying and assessing potential energy projects compared to Homer Pro.	Compile manually in a workbook/Excel sheet	(Ramli et al. 2017)
Probabilistic	It is stochastic and can randomly determine the best solution from the available data.	It cannot adequately represent the dynamic nature of performance changes within a hybrid system. It is less efficient than the analytical method	(Chauhan and Saini 2014; Clack, Xie, and Macdonald 2015; Ganguly, Kalam, and Zayegh 2018; Kaabeche, Belhamel, and Ibtiouen 2011; Lian et al. 2019; Zhou et al. 2010)
Iterative	It is effective in obtaining minimised costs through linear variable parameters or linear programming techniques.	Considered inefficient for optimising swept area of the turbine, turbine height, the area of PV installations, and PV module angle, which have a more significant effect on cost.	(Chauhan and Saini 2014)
Analytical	It can accept long-term climatology data with short-time consumption and provides selective comparative information for decision-making for the best viable systems. The analytical method is more efficient than the Monte Carlos Method.	The model cannot estimate the position-related mathematical equation coefficient.	(Khatib, Ibrahim, and Mohamed 2016; Lian et al. 2019; Luna-Rubio et al. 2012)
Graphical	The simplest method of representing complex problems/situations involving too many mathematics equations.	It cannot accommodate more than two-dimensional problems. Due to the variable scale and difficulty in graphical interpretation, the accuracy of the solution is questionable. Difficulty in graphing non-linear, exponential, logarithmic, and trigonometric expressions. It is characterised by an unrealistic assumption that deterred its suitability except combining with other methods.	(Erdinc and Uzunoglu 2012; Rathore and Patidar 2019)
Linear Programming (LP)	It exhibits linearity between variables. It is characterised by good convergence and is less rigorous	It can be stuck in local search. It loses accuracy and certainty over time. It also failed to consider evolution and changes in variables.	(Birge and Louveaux 2011; Kavadias and Triantafyllou 2021; Khare, Nema, and Baredar 2017; Okedu and Uhunmwangho 2014; Ramli et al. 2017; Saiprasad, Kalam, and Zayegh 2018; Sinha and Chandel 2015)
Genetic algorithm (GA)	Ability to handle several parameters (integer, discrete, non-differential, and so on) simultaneously as multiple targets/objectives at the same time. The parallelism properties of GA allow multiple schemes to be implicitly evaluated at the same time, giving a greater chance of obtaining the optimal solution at each iteration. It can yield good results from several/different problem formations. GA outperformed HOMER Pro software and SA in terms of cost and environmental analysis.	Consume more time with an increase in the length of chromosomes. It takes more time and less computation effort than the Analytical method. It is only meant for local searching unlike other natured-inspired algorithms with both local and global search space.	(Birge and Louveaux 2011; Craenen et al. 2000; Hobson 1978; Iweh, Clarence, and Roger 2022; Momoh, El-Hawary, and Adapa 1999; Raju 2014; Riaz et al. 2022; Saiprasad, Kalam, and Zayegh 2018; Shorman and Pitchay 2015; Sultana et al. 2016)
Tabu Search (TS)	Ability to seek efficient and cost-effective solutions. It provides better local search capabilities than GA. Better than SA to get neighbourhood solutions.	Exploration can be trapped in local search due to its fixed size and static nature.	(Couman, de Klerk, and Jansen 2014; Qu 1986; Suresh, Muralidhar, and Kiranmayi 2020; Zuhaimy 2009)
Firefly Algorithm (FA)	Ability to deal with the multimodal problem. It performs more brilliantly than PSO.	It can be stuck in local search and has a relatively long convergence time.	(Hashmi et al. 2013; Lohrer 2013; Yang and Xingshi )
Simulated Annealing (SA)	Achieving optimal results is systematic and deterministic. Easy to programme. It is robust enough to handle the non-linear problem and various constraints. Flexibility and ability to switch from local search to global search.	Low performance with relatively long computational time.	(Busetti 2003; Skoonpong and Sirisumrannukul 2008)
Gravitation Search Algorithm (GSA)	It has two variable adjustable parameter settings (speed and mass). It has less memory than other nature-inspired algorithms. The algorithm is more suitable for non-linear problems and is more efficient than HS in analysing database functions.	It is complicated (complex). Gravitation constant (G) is difficult to appropriate. Convergence time is longer and if the initial population is not created correctly, there may be a convergence problem.	(Rather 2019; Sabri 2013; Systems and Sahoo 2014; Enikeeva et al.; Narimani et al. 2014)
Grey Wolf Optimiser (GWO)	Fewer parameters like GSA above and easy to implement.	Inability to exist local search due to slow convergence speed at the later end of the iteration. It is characterised by low precision/accuracy.	(Grey and Optimization 2022; Wang et al. 2020)
Harmony Search (HS)	Fewer turn-able control parameters are needed. An initial decision variable setting is not required. Require no derivative information. Better than K-means and GS in cluster problem analysis.	Slow and premature convergence. Poor limitation and adaptability search range.	(Dubey et al. 2021; Kumar, Chhabra, and Kumar 2012; Yusup et al. 2018)
Particle Swarm Algorithm (PSO)	Require fewer turn-able control parameters. Ability to perform parallel computation with high convergence speed. Ability to find the best global solution with high efficiency. Ability to solve complex problems.	Exhibit poor local search when dealing with the high-dimensional problem. It’s characterised by premature convergence.	(Systems and Sahoo 2014; Enikeeva et al.; Dubey et al. 2021; Gad 2022; Grey and Optimization 2022; Gupta and Srivastava 2020; Kumar, Chhabra, and Kumar 2012; Li, Du, and Nian 2014; Narimani et al. 2014; Search, Zhang, and Zhang 2020; Wang et al. 2020; Yusup et al. 2018)
Ant Colony Optimization (ACO)	Rapid discovery of good solutions because of positive feedback mechanisms. It exhibits parallelism. It can be easily customized and has the great advantage of being combined with another algorithm to get a powerful and highly reliable algorithm. It returns the global minimum with fewer iterations compared to PSO.	Improper selection of initial parameter values might lead to local search stagnation immature convergence and inability to reach an optimum solution. It can only handle discrete problems while continuous problems might pose a problem	(Abreu et al. 2011; Gupta and Srivastava 2020; Selvi and Tamilnadu 2010; Shehab, Khader, and Al-Betar 2017)
Cuckoo Search	Fewer turn-able control parameters are needed. It enhances by Levi's flight trait/character, which helps to converge at global optima solution. Strong random searching path and optimisation capacities. Strong affinity to form a hybridising with other algorithms.	It can be stuck in local search if initial parameters are not properly set. Slow convergence time	(Babukartik 2012; Liu et al. 2018; Shehab, Khader, and Al-Betar 2017; Shen et al. 2021)

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