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ABSTRACT
Energy demand is surging with the rise in population, economic development, and ever-increasing living standards. Due to sustainability and environmental issues, renewable energy sources have emerged as a credible option to meet this increased energy demand. However, it is plagued with the issue of variability and intermittency. Hybrid energy systems are proposed as a possible solution to this problem. The optimal sizing of hybrid energy systems ensures a reliable, efficient, and cost-effective power supply. Therefore, this paper discusses different hybrid energy systems in both on-grid and off-grid configurations, followed by the review of various sizing methodologies. The article also discusses various multi-criteria design indicators acting as decision variables, sensitivity variables, and constraints in different capacities while preparing the mathematical model of hybrid energy systems. As renewable resources and their based systems are inherently uncertain, it becomes imperative to characterize and model the uncertainty associated with such systems. Sincere efforts were made to understand various sources of uncertainty and how to characterize and model these uncertainties using different methodologies. The existing uncertainty modeling approaches were studied, compared, and analyzed. Further, the need for conducting sensitivity analysis and its usage in hybrid energy system design considering different sensitive parameters were also studied.
Abbreviations
| COVID | = | Coronavirus disease |
| GHG | = | Greenhouse gas |
| RES | = | Renewable energy system |
| HES | = | Hybrid energy system |
| HOMER | = | Hybrid Optimization of Multiple Electric Renewables |
| OOP | = | Object-Oriented Programming |
| MOPSO | = | Multi-objective particle swarm optimization |
| LCE/LCOE/COE | = | Levelized cost of energy/Cost of energy |
| LPSP | = | Loss of power supply probability |
| MES | = | Modified evolutionary strategy |
| GraSO | = | Gradient swarm optimization |
| TNPC/NPC | = | Total net present cost/Net present cost |
| PV | = | Photovoltaic |
| WT | = | Wind turbine |
| DG | = | Diesel generator |
| BES | = | Battery energy storage |
| PBP/SPBP | = | Payback period/Simple payback period |
| ROI | = | Rate of investment |
| IRR | = | Internal rate of return |
| NPV | = | Net present value |
| RE | = | Renewable energy |
| ES | = | Energy storage |
| NRE | = | Non-Renewable energy |
| MATLAB | = | Matrix Laboratory |
| SC | = | Supercapacitor |
| MOO | = | Multi-objective optimization |
| FC | = | Fuel cell |
| BG | = | Biogas |
| BM | = | Biomass |
| PHS | = | Pumped hydro storage |
| TAC/TACS: | = | Total annualized cost of system |
| HK | = | Hydrokinetic |
| LOLP | = | Loss of load probability |
| LOLE | = | Loss of Load Expectation |
| SOC | = | State of charge |
| LPS | = | Loss of power supply |
| ASC/ACS | = | Annualized system cost/Annualized cost of system |
| NSGA | = | Non-dominated sorting algorithm |
| PSO | = | Particle swarm optimization |
| RF | = | Renewable fraction |
| RER | = | Renewable energy resource |
| ArcGIS | = | Aeronautical reconnaissance coverage geographic information system |
| GOA | = | Grasshopper optimization algorithm |
| MILP | = | Mixed integer linear programming |
| GA | = | Genetic Algorithm |
| ICC | = | Initial capital cost |
| TRNSYS | = | Transient systems simulation program |
| TOPSIS | = | Technique for order preference by similarity to ideal solution |
| TOC/OC | = | Total Operating cost/Operating cost |
| EG | = | Energy generation |
| IR | = | Inflation rate |
| LeA | = | Lead acid |
| LI | = | Lithium-ion |
| SOFC | = | Solid oxide fuel cell |
| PrEM | = | Proton exchange membrane |
| EENS | = | Expected energy not supplied |
| ELF | = | Equivalent loss factor |
| TEL | = | Total energy loss |
| LA | = | Level of autonomy |
| LOEE | = | Loss of energy expectation |
| EIR | = | Energy index of reliability |
| LUEC/UEC | = | Levelized unit electricity cost/Unit electricity cost |
| EIU | = | Energy index of unreliability |
| LOLR | = | Loss of load risk |
| TED | = | Total energy deficit |
| AER | = | Annual energy recovery |
| DPSP | = | Deficiency of power supply probability |
| WRE | = | Wasted renewable energy |
| EE | = | Excess electricity |
| LCC | = | Life cycle cost |
| LCUC | = | Life cycle unit cost |
| CRF | = | Capital recovery factor |
| DPBP | = | Discounted payback period |
| CIC | = | Customer interruption costs |
| CF/CFOE | = | Carbon footprint of energy |
| CE | = | Carbon emissions |
| HDI | = | Human development index |
| EC | = | Employment creation |
| SCC | = | Social cost of carbon |
| SLLPR | = | Seasonal loss of load probability ratio |
| DC | = | Direct current |
| AI | = | Artificial Intelligence |
| MPSO | = | Modified Particle swarm optimization |
| PSO-RF | = | PSO based on repulsion factor |
| PSO-CF | = | PSO with constriction factor |
| PSO-W | = | PSO with adaptive inertia weight |
| ACO | = | Ant colony optimization |
| SA | = | Simulated annealing |
| ACOR | = | Ant colony optimization for continuous domains |
| ABC | = | Artificial bee colony |
| HS | = | Harmony search |
| MOCSA | = | Multi-objective crow search algorithm |
| DHS/DHS | = | Discrete harmony search |
| CS | = | Cuckoo search |
| GPAP | = | Grid power absorption probability |
| ALO | = | Ant lion optimization |
| GWO | = | Grey wolf algorithm |
| MDO-MOPSO | = | Multiple design option-Multi-objective particle swarm optimization |
| HCHSA | = | Hybrid chaotic search/harmony search/simulated annealing |
| TLBO | = | Teaching – learning-based optimization |
| MOL | = | Many optimizing liaisons |
| TS | = | Tabu search |
| GUI | = | Graphic user interface |
| AML | = | Algebraic modeling language |
| HSU | = | Hydrogen storage unit |
| BCA | = | Branch and cut algorithm |
| SSR | = | Self-sufficiency ratio |
| TCS/TSC/TC | = | Total cost of system |
| POO | = | Pareto optimal optimization |
| TDC | = | Total discounted cost |
| AOC | = | Average outage cost |
| LOLH | = | Loss of load hours |
| REGP | = | Renewable energy generation penetration |
| HPBS | = | Hybrid pumped battery storage |
| POE | = | Price of electricity |
| TIC | = | Total investment cost |
| SSO | = | Social spider algorithm |
| = | Probability density function | |
| LOLF | = | Loss of load frequency |
| FOSMM | = | First order second-moment method |
| PEM | = | Point estimate method |
| MAED | = | Multi-area economic dispatch |
| UT | = | Unscented transformation |
| DER | = | Distributed energy resources |
| IGDT | = | Information gap decision theory |
| RO | = | Robust optimization |
| AMFA | = | Adaptive modified firefly algorithm |
| OATSA | = | One-at-a-time sensitivity analysis |
| LSA | = | Local sensitivity analysis |
| GSA | = | Global sensitivity analysis |
Disclosure statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.