The quantitative techno-economic comparisons and multi-objective capacity optimization of wind-photovoltaic hybrid power system considering different energy storage technologies

•Quantitative techno-economic comparisons of energy storages are conducted.•Operation characteristics of devices are considered in capacity optimization.•Comprehensive performance metrics of multi-objective algorithms are proposed.•Sensibility analysis of different load profile and resources level a...

Full description

Saved in:
Bibliographic Details
Published in:Energy conversion and management Vol. 229; p. 113779
Main Authors: He, Yi, Guo, Su, Zhou, Jianxu, Wu, Feng, Huang, Jing, Pei, Huanjin
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.02.2021
Elsevier Science Ltd
Subjects:
accuracy
administrative management
Algorithms
batteries
Capacity optimization
Cost analysis
cost effectiveness
degradation
Economics
energy conversion
Energy management
Energy storage
Evolutionary algorithms
Genetic algorithms
Hybrid systems
hybrids
hydrogen
Hydrogen storage
Hydrogen-based energy
Model accuracy
Multi-objective evolutionary algorithm
Multiple objective analysis
Operation characteristics
Optimization
Particle swarm optimization
Performance assessment
Photovoltaics
probability
Quantitative techno-economic comparison
Reliability
sorting
Sorting algorithms
storage
supply
Thermal energy
Wind
Capacity optimization
Operation characteristics
Quantitative techno-economic comparison
Multi-objective evolutionary algorithm
Energy storage
ISSN:0196-8904, 1879-2227
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Quantitative techno-economic comparisons of energy storages are conducted.•Operation characteristics of devices are considered in capacity optimization.•Comprehensive performance metrics of multi-objective algorithms are proposed.•Sensibility analysis of different load profile and resources level are conducted.•Thermal energy storage is the most cost-effective energy storage alternative. To provide investors with a selection method of energy storage technology, this paper proposes a quantitative techno-economic comparison method of battery, thermal energy storage, pumped hydro storage and hydrogen storage in wind-photovoltaic hybrid power system from the perspective of multi-objective capacity optimization. The multi-objective capacity optimization models are developed based on minimizing the levelized cost of energy (economy) and loss of power supply probability (reliability) simultaneously. Comprehensive metrics based on hypervolume are proposed to compare the performance of four multi-objective evolutionary algorithms. Moreover, the operation characteristics of devices is considered in the model to improve the simulation accuracy. The performance comparisons of algorithms show that the average rank of nondominated sorting genetic algorithm, multi-objective evolutionary algorithm based on decomposition, multi-objective particle swarm optimization and strength Pareto evolutionary algorithm are 2.8, 3.6, 1.8 and 1.8 respectively, which demonstrates that multi-objective particle swarm optimization and strength Pareto evolutionary algorithm have relatively better overall performance when applied in capacity optimization problems. The quantitative techno-economic comparisons of energy storage show that the levelized cost of energy of thermal energy storage, battery, hydrogen storage and pumped hydro storage under the same reliability are 0.1224 $/kWh, 0.1812 $/kWh, 0.1863 $/kWh and 0.2225 $/kWh respectively, which demonstrates that thermal energy storage is the most cost-effective alternative. Furthermore, the sensibility analysis demonstrates that thermal energy storage is always the most cost-effective alternative for different load profile, different resources level and different energy storage cost. Finally, the conclusions can help investors to select a cost-effective and reliable energy storage technology.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2020.113779