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Recent Bibliography on the Optimization of Multi-source Energy Systems

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A Correction to this article was published on 15 October 2019

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Abstract

Due to fast increase in the world of the population and global warming, designing efficient and low-polluting or clean energy systems are one of the critical problems of the century. In the recent years, most researchers are focusing on wind and solar energy sources as they freely available, complementary, their costs are becoming competitive, and they are clean. Designing optimally hybrid energy is complex as involves various mixed-type variables, tight constraints, and other complexities. In the present paper, we summarize most important optimization works related to the design of hybrid PV–wind systems, and present findings on future trends.

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Change history

  • 15 October 2019

    This correction has been initiated as author names were incorrectly indicated and should be read as: GABOUR Amina instead of GAABOUR Amina.

Abbreviations

ACS:

Annual cost of system

HUS:

Hunting search

ACO:

Ant colony optimization

HS:

Harmony search

ABSO:

Artificial bee swarm optimization

IC:

Inequality coefficient

ABC:

Artificial bee colony

ICA:

Imperialist competitive algorithm

BA:

Bees algorithm

LA:

Level of autonomy batt battery

LOEE:

Loss of energy expected

BB:

Branch-and-bound

LOLE:

Loss of load expected

BESS:

Battery energy storage systems

LOLH:

Loss of load hours

CC:

Correlation coefficient

LOLP:

Loss of load probability

CAES:

Compressed air energy storage

LOLR:

Loss of load risk DIRECT dividing rectangles

LPSP:

Loss of power supply probability

DHS:

Discrete harmony search

LCOE:

Levelized cost of energy DSA discrete simulated annealing

ALO:

Ant lion optimizer

COE:

Cost of energy

MHP:

Micro hydro power

CS:

Cuckoo search

MOABC:

Multi-objective ABC algorithm

DG:

Diesel generator

NPC:

Net present cost

EDLC:

Electric double layer capacitors

PWGS:

Power generation system

EENS:

Expected energy not supplied

PUC:

Per unit cost

ELF:

Equivalent loss factor PSO particle swarm optimization

EMR:

Electricity match rate

PV:

Photovoltaic

ENS:

Energy not supplied

PHS:

Pumped hydro storage

FBESS:

Flow battery energy storage system

PWGS:

power generation system

FESS:

Flywheel energy storage system

REF:

Renewable energy fraction FC fuel cell

STC:

System total cost

GA:

Genetic algorithm

SMES:

Superconducting magnetic energy storage

GPAP:

Grid power absorption probability

TAC:

Total annual cost

GRG:

Generalized reduced gradient TEL total energy lost

GWO:

Grey wolf optimizer

TCD:

Transparent cost data base

HESS:

Hydrogen-based energy storage system

TS:

Tabu search

HMGS:

Hybrid micro-grid system

VSI:

Voltage stability index

HOMER:

Optimization model for electric renewable

WT:

Wind

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Authors and Affiliations

  1. LGMM Laboratory, Université 20 août 1955, Skikda, Algeria

    Amina Gaabour, Abderrezak Metatla, Ridha Kelaiaia & Adlen Kerboua

  2. Faculty of Computing and IT, University of Jeddah, Jeddah, Saudi Arabia

    Farid Bourennani

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  1. Amina Gaabour
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  2. Abderrezak Metatla
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  3. Ridha Kelaiaia
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Correspondence to Ridha Kelaiaia.

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Gaabour, A., Metatla, A., Kelaiaia, R. et al. Recent Bibliography on the Optimization of Multi-source Energy Systems. Arch Computat Methods Eng 26, 809–830 (2019). https://doi.org/10.1007/s11831-018-9271-6

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