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Recent developments in PV/wind hybrid renewable energy systems: a review

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Abstract

Electrical energy plays a vital role in every individual, and also it is crucial for the economy of any country. Looking into the sustainability and environment factors, it is evident that the renewable energy sources are certainly the way forward for the future electricity needs. But, due to the dependency on climatic conditions, these sources are not entirely reliable when utilized individually. A Hybrid Renewable Energy System (HRES) is a combination of two or more resources that will improve reliability and reduce the cost of the system. Hence, sizing of HRES for a particular area becomes an important research topic in this field. In this paper, a detailed and up-to-date review of research that has been carried out in the area of HRES primarily focusing on solar PV and wind energy systems in terms of technical, economic, and environmental aspects, is presented. At first, various configurations of HRES in both stand-alone and grid-connected mode were discussed. An effort has been made to highlight the different storage systems along with their benefits and drawbacks in this paper. Further various optimization methods used by the researchers were also summarized in the text and tabular forms. More importantly, this paper also includes a review of demand response methodologies that are used by the researchers when sizing the system and methodologies that involve the Electric Vehicles (EVs) as an additional battery storage/controllable load. The mathematical modelling of PEVs, various constraints used for optimization were also described in this paper with relevant explanation. Hence, this paper gives an overall perspective about various sizing methods of the HRES including DRM and EVs to the reader. Finally, the overall observations and few recommendations were presented in this paper, which will be helpful for the researchers, policy makers working in this field.

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Abbreviations

\(\eta _{i,t}\) :

EV battery efficiency

\(\eta _{REVB}\) :

Efficiency of retired electric vehicles battery

\(C_m(t)\) :

Maximum remaining capacity of retired electric vehicle batteries

\(C_{loss}\) :

The capacity loss of Li-ion battery

\(E_{t,s}^{arr}\) :

Calculated EV energy at arrival time

\(E_{t,s}^{dep}\) :

Calculated EV energy at departure time

\(I_{m}(t)\) :

Module current of retired electric vehicle battery

\(P^{EV}\) :

EV Charging power or discharging power

\(SOC^{PEV}\) :

Plug in electrical vehicle state of charge

\(T_{arriv}^{PEV}\) :

Plug in electric vehicle arrival time

\(T_{dep}^{PEV}\) :

Plug in electric vehicle departure time

\(V_{m}(t)\) :

Module voltage of retired electric vehicle battery

ABC:

Artificial Bee-Colony

AT:

Arrival Time

BESS:

Battery energy storage systems

CHEED:

Combined heat emission and economic dispatch

CS:

Cuckoo-Search

DD:

Distance driven

DG:

Diesel generator

DRM:

Demand Response Methods

DRP:

Demand response program

DT:

Departure Time

ECES:

Electro-chemical energy storage

EES:

Electrical energy storage

EMS:

Energy management systems

ESS:

Energy storage systems

EV:

Electric vehicles

EVs:

Electric vehicles

FC:

Fuel cell

FPA:

Flower pollination algorithm

G2V:

Grid to vehicle

GA:

Genetic Algorithm

GAMS:

General Algebraic Modelling System

GC:

Grid-Connected

GWO:

Grey Wolf Optimizer

HBB-BC:

Hybrid Big Bang and Big Crunch

HESS:

Hybrid Energy Storage Systems

HOMER:

Hybrid Optimization Model for Electric Renewables

HRES:

Hybrid renewable energy systems

HS:

Harmony Search

IEA:

International Energy Agency

IRENA:

International renewable energy agency

LCE:

Levilized cost of energy

LCOE:

Levelized Cost of Energy

LPSP:

Loss of Power Supply Probability

LPSP:

Loss of power supply probability

MCSA:

Modified Crow Search Algorithm

MES:

Mechanical Energy Storage

MOEA:

Multi-Objective Evolutionary Algorithm

MOEA/D:

Multi-Objective Evolutionary Algorithm based Decomposition

MOGA:

Multi-Objective Genetic Algorithm

MSFLA:

Modified Shuffled Frog Lea** Algorithm

NPC:

Net present cost

NSGA-II:

Non-dominated Sorted Genetic Algorithm-II

PEVs:

Plug-in Electric Vehicles

PSHP:

Pumped storage hydro-power plant

PSO:

Particle Swarm Optimization

PV:

Photovoltaic

REVB:

Retired electric vehicle batteries

RHO:

Receding Horizon Optimization

SA:

Stand-alone

SC:

Super-capacitor

SCES:

Super-conducting energy storage

SOC:

State of charge

SSR:

Search space reduction algorithm

TC:

Total cost

TLBO:

Teaching Learning based optimization

UC:

Ultra-capacitor

V2G:

Vehicle to grid

WDO:

Wind Driven Optimization

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  1. Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India

    Ambati Bhimaraju & Aeidapu Mahesh

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Bhimaraju, A., Mahesh, A. Recent developments in PV/wind hybrid renewable energy systems: a review. Energy Syst (2024). https://doi.org/10.1007/s12667-024-00679-3

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