Current Status, Sizing Methodologies, Optimization Techniques, and Energy Management and Control Strategies for Co-Located Utility-Scale Wind–Solar-Based Hybrid Power Plants: A Review
Abstract
:1. Introduction
Contribution of this Review Paper
2. Review Approach
3. Topologies and Configuration
4. Global Status of HPPs
Wind + Solar Project | |||||||
---|---|---|---|---|---|---|---|
Project | Location | Wind (MW) | Solar (MW) | Storage (MW/MWh) | Main Function | Status | Reference |
Cynog park | U.K. | 3.6 | 5 | Maximizing grid utilization | Operating (2016) | [47] | |
Minnesota Community Site | U.S. | 5 | 0.5 | Local municipality, but ensuring grid connection compliance | Operating (2018) | [12] | |
Kavithal Solar Wind Project | India | 50 | 28.8 | Enhanced and flatter power output, shared transmission infrastructure | Operating (2018) | [49] | |
Louzes | Greece | 24 | 1 | Operating (2012) | [12] | ||
Wind + Solar + Battery | |||||||
Haringvliet | Netherlands | 21 | 41 | 12 | Frequency containment reserve services and time-shifting services | Operating (2020) | [12,47] |
Kennedy Energy Park | Australia | 43.2 | 15 | 2/4 * | Meet local energy demand without excessive storage capacity | Operating (2017) | [12,47] |
La Plana | Spain | 0.85 | 0.245 | 0.4/0.5 * | Support remote areas without access to the gird and minimize diesel consumption | Operating (2017) | [50] |
Tilos Hybrid Plant | Greece | 0.8 | 0.16 | 0.8 | Power demand and enhanced stability | Operating (2018) | [51] |
Wheattridge Renewable Energy | USA | 300 | 50 | 30 | Contribution to GHG reduction | Operating (2020) | [52] |
Graciosa | Portugal | 4.5 | 1 | 6 | Meet power demand | Operating (2020) | |
Grand Ridge | USA | 210 | 20 | 36 | Enhanced energy supply stability | Operating (2020) | [53] |
Upcoming, under development, and approved [12] | |||||||
Kendinin | Australia | 120 | 50 | N/A | Enhanced and flatter power output | Under feasibility study | [12] |
Clarke Creek | Australia | 800 | N/A | N/A | Enhanced and flatter power output | Under feasibility study | |
Andra Pradesh hybrid project | India | 16 | 25 | 10 | Enhanced and flatter power output | Contracted | |
Tender Project | India | N/A | N/A | N/A | Enhanced and flatter power output | Approved | |
Three Gorges, Inner Mongolia | China | 2.7 GW | 300 | 880 | Enhanced and flatter power output | Under construction | |
Northwest Ohio | USA | 105 | 3.5 | 1 | Enhanced and flatter power output | Under development | |
Megisti hybrid project | Greece | 1 | 0.85 | 1.44 | Weak power grid | Under licensing | |
Angios Elestratios Green Island | Greece | 1 | 0.101 | 0.72 | Weak power grid | Under development | |
Endesa | Portugal | 264 | 365 | 168 | Enhanced and flatter power output | Under Planning |
5. Optimization Techniques
Summary and Evaluation
6. Sizing Methodologies
6.1. Sizing HPPs Using the Classical Approach
6.2. Modern Methods
6.3. Sizing HPPs Using Software Tools
Summary and Evaluation
7. Control and Energy Management Strategies
7.1. Control Strategies
Summary and Evaluation
7.2. Energy Management Strategies
Summary and Evaluation
8. Discussion
Control and Energy Management Strategies
9. Research Opportunities
10. Future Trends
11. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
HPP | hybrid power plant |
RES | renewable energy sources |
MW | mega watt |
PCC | point of common connection |
COE | cost of energy |
ASC | annualized system cost |
LCOE | levelized cost of energy |
LCC | life cycle cost |
LLP | loss of load probability |
LPSP | loss of power supply probability |
RF | renewable fraction |
REF | renewable energy factor |
AI | artificial intelligence |
GA | genetic algorithm |
PSO | particle swarm optimization |
HOMER | hybrid optimization of multiple energy resources |
EMSs | energy management strategies |
PV | photovoltaic |
WT | wind turbine |
HRES | hybrid renewable energy system |
WoS | Web of Science |
AC | alternate current |
DC | direct current |
LPM | linear programming model |
MOEA | multi-objective evolutionary algorithms |
MILP | multi-integer linear programming |
NLP | non-linear programming |
HSA | harmony search algorithm |
SA | simulated annealing |
ACA | ant colony algorithm |
BFO | bacterial foraging algorithm |
ABC | artificial bee colony algorithm |
CS | cuckoo search |
ANN | artificial neural network |
SOC | state of charge |
FLC | fuzzy logic control |
FL | fuzzy logic |
ANFIS | adaptive neuro-fuzzy inference system |
FAHP | fuzzy analytic hierarchy process |
GAPSO | generic algorithm particle swarm optimization |
SOO | single-objective optimization |
MOO | multi-objective optimization |
NPV | net present value |
IPSO | improved particle swarm optimization |
GWO | grey wolf optimization |
NREL | National Renewable Energy Laboratory |
HOGA | hybrid optimization of generic algorithm |
TRNSYS | transient system simulation software |
RETSCreen | renewable-energy and energy-efficiency technology screening software |
FC | fuel cell |
PGUs | power generating units |
STATCOM | static synchronous compensator |
MPC | model predictive control |
TLBO | teaching learning-based optimization |
EDE | enhanced differential evolution |
SSA | slap swarm algorithm |
FPA | flower pollination algorithm |
GSA | gravitation search algorithm |
CSA | crow search algorithm |
Appendix A
Assessment | Preferred Indicators | Functions |
---|---|---|
Technical indicator | LPSP | |
Economic indicator | NPC | |
ACS | + | |
LCOE | ||
Social political indicator | HDI | |
JC | ||
Energy Efficiency indicator | ECR | |
Environmental indicator | Ecarbon | |
LCCF | ||
LEOE |
Reference/Year of Study | System Studied | Topic Covered | Highlights |
---|---|---|---|
Chauhan and Saini [109] | HRES |
|
|
Siddaiah and Saini [25] | HRES |
|
|
Al-falahi et al. [55] | HRES |
|
|
Khan et al. [28] | Solar photovoltaic and wind hybrid energy systems |
|
|
Anoune et al. [17] | PV–wind based HRES |
|
|
Lian et al. [83] | HRES |
|
|
Lindberg et al. [13] | Wind–solar battery HPP |
|
|
Ammari et al. [34] | HRES |
|
|
Emad et al. [91] | Wind–solar battery |
|
|
Thirunavukkarasu et al. [81] | HRES |
|
|
Iweh et al. [35] | HRES |
|
|
Optimization Method | System Configurations | Optimization Function | Constraints | Reference |
---|---|---|---|---|
GA FL | Wind/PV/battery | Minimize total cost | Power balance State of charge | Adbelhak et al. [155] |
ACA, Integer LPM | Wind/PV | Minimize total cost | Number of PV panels, wind turbines, and batteries | Fetanat et al. [156] |
PSO SA | Wind/PV/battery | Minimize total present cost | Number of hybrid components, energy not supplied, battery SOC | Ahmadi et al. [157] |
CS | Wind/PV/battery | Minimize total cost | Seasonal variation in the load | Sanajaoba and Fernandez, [158] |
SA, CS, Improved HS | PV–wind–reverse osmosis–battery | Minimize total LCC | Surface area of PV arrays, wind turbine blades, quantity of batteries, LPSP, and SOC | Peng et al. [159] |
TLBO, EDE, and SSA | Wind–PV | Minimize TAC, reliability | Number of hybrid system components, LPSP, DOD | Khan et al. [20] |
Jaya, TLBO | Wind/PV/battery | Minimize TAC | Number of hybrid system components, LPSP, SOC | Khan et al. [160] |
AC, firefly algorithm, PSO, GA | Wind/PV/battery | NPC | Number of hybrid system components, SOC | Javed et al. [161] |
Crow and PSO | Wind/PV/battery | Minimize COE | Distribution of power supply and demand planning | Guneser et al. [162] |
GWO Sine cosine Algorithm | Wind/PV/H2 | Minimize LCC | Number of hybrid system components | Jahannoosh et al. [163] |
Optimization Method | System Configurations | Optimization Function | Constraints | Reference |
---|---|---|---|---|
FPA SA | Wind/PV/battery | Minimize LPSP Maximize cumulative saving | PV panel tilt angle, number of PVs, wind turbines, and batteries | Tahani et al. [164] |
PSO GA | Wind/PV/battery | Minimize LPSP Minimize LCC Minimize fluctuation rate Minimize loss of energy probability | Numbers of PVs, wind turbines, and batteries | Ma et al. [165] |
PSO Nelder Mead Algorithm | PV/wind/fuel cell | Minimize power loss | Power balance, bus voltage | Senthil et al. [166] |
PSO GSA | PV/wind | Minimize total energy loss Maximize voltage profit | Power flow m bus voltage, load constraints, PV/wind capacity | Radosavljevic et al. [167] |
Biogeography-based optimization, PSO | Wind/PV/battery | Minimize cost Minimize system index reliability | Power balance between supply and demand | Abuelrub et al. [168] |
CSA, PSO | Wind/PV/battery | Minimize cost Minimize loss Minimize voltage profile | Number of hybrid system components, size of batteries, network bus voltage constraint, allowable current constraint, peak capacity of each renewable DG constraint, and power balance constraint | Aliabadi et al. [169] |
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Configuration | Advantage | Disadvantage | Application |
---|---|---|---|
DC-coupled |
|
|
|
AC-coupled |
|
|
|
Hybrid |
|
|
|
Techniques | Advantage | Disadvantage |
---|---|---|
Classical |
|
|
Artificial |
|
|
Hybrid |
|
|
Software | Input | Output | Limitations | Availability |
---|---|---|---|---|
HOMER |
|
|
| Free access www.homerenergy.com (accessed on 15 Decmeber 2023) |
HYBRID2 |
|
|
| Free access https://www.umass.edu/windenergy/research/topics/tools/software/hybrid2 (accessed on 7 Februaryr2024) |
HYBRIDS |
|
| - | |
IHOGA |
|
|
| The EDU version is free, while the PRO version is priced www.ihoga.unizar.es/en/ (accessed on 7 February 2024) |
RETScreen |
|
|
| Free access www.retscreen.net (accessed on 22 Decmeber 2023) |
TRNSYS |
|
|
| Priced www.trnsys.com (accessed on 20 Decmeber 2023) |
Software | Energy Resources | Objective of the Study | Reference | |||
---|---|---|---|---|---|---|
Wind | Solar | Battery | Other | |||
HOMER | ✓ | ✓ | ✓ | Cost-effective configuration of HRES | Muller et al. [92] | |
✓ | ✓ | FC | Evaluate technical and financial performance | Al-Badi et al. [100] | ||
✓ | ✓ | ✓ | Sizing design of HRES | Hoarca et al. [101] | ||
HYBRID2 | ✓ | ✓ | ✓ | Sizing method of standalone RES based on techno-economic analysis and object-oriented programming | Belmili et al. [102] | |
IHOGA | ✓ | ✓ | Optimal sizing of RES | Fadaeenejad et al. [103] | ||
✓ | ✓ | ✓ | Sizing design of HRES | Hoarca et al. [101] | ||
HOMER PRO | ✓ | ✓ | Minimize LCOE, life cycle cost | Ranaboldo et al. [104] | ||
HOMER | ✓ | ✓ | ✓ | Energy production, net present cost, and levelized cost of electricity | Baker [105] | |
HOMER | ✓ | ✓ | ✓ | Hydrogen | Total net present cost | Babatunde et al. [106] |
RETScreen | ✓ | ✓ | ✓ | Biomass | Feasibility study based on economics and the environment | Hossen and Shezan [107] |
TRNSYS | ✓ | ✓ | Optimal sizing of wind–PV-based hybrid system | Anoune et al. [17] | ||
✓ | ✓ | ✓ | Energy performance of the system | Mazzeo et al. [108] |
Techniques/Tools | Advantage | Disadvantage | Reference |
---|---|---|---|
Iterative |
|
| Chauhan and Saini [109] |
Probabilistic |
|
| Ganguly et al. [84]; Lian et al. [83] |
Analytical |
|
| Lian et al. [83] |
Graphical |
|
| Rathore and Patidar [110] |
LP |
|
| Saiprasad et al. [111] |
GA |
|
| Iweh et al. [35]; Riaz et al. [88] |
PSO |
|
| Dubey et al. [112]; Gad et al. [113]; Wang et al. [114]; Gupta and Srivastava [115] |
ACO |
|
| Gupta and Srivastava [115] |
CS |
|
| Shen et al. [116] |
SA |
|
| Iweh et al. [35] |
HS |
|
| Dubey et al. [112] |
GWO |
|
| Wang et al. [114] |
HOMER |
|
| Saiprasad et al. [111]; Kavadias et al. [117] |
iHOGA |
|
| Saiprasad et al. [111] |
RETScreen |
|
| Ramli et al. [118] |
Control Method | Advantage | Disadvantage |
---|---|---|
Centralized |
|
|
Distributed |
|
|
Hybrid |
|
|
Management Strategy | Design Constraint | Control Algorithm | Advantages | Disadvantages |
---|---|---|---|---|
Power requirements |
|
|
|
|
Technical-oriented |
|
|
|
|
Economic-oriented |
|
|
|
|
Techno-economic-oriented |
|
|
|
|
Anagement Strategies | Control Algorithm/ Approach | Energy System | Design Constraints | Objectives | Reference |
---|---|---|---|---|---|
Power requirement | Flowchart | Wind/solar/FC | Power balance, SOC, H2 stock | Ensure demand sizing | Cozzolino et al. [143] |
Flowchart | Wind/solar/H2 | Power balance, SOC, H2 stock | Ensure demand | Zhang et al. [144] | |
Flowchart | Wind/solar/battery | Power balance, SOC | Ensure demand | Bade et al. [40] | |
Technical | ANN | Wind/solar/battery | Power balance, SOC, battery degradation | Redue LPSP, ensure demand | Q. Li et al. [145] |
Flowchart | Wind/solar/battery | Power balance, SOC | Ensure demand, quality of service | Long et al. [39] | |
PMC/multi-objective approach | Wind/solar/battery/FC | Power balance, SOC, battery degradation, H2 | Increase reliability, ensure demand | Eriksson and Gray [135] | |
PSO Recurrent neural network | Wind/solar/battery/FC | Power balance, SOC, battery degradation, H2 | Reduce LPSP, ensure demand | Yan et al. [137] | |
Flowchart Multi-stage machine learning | Wind/solar/battery/FC | Power balance, SOC, battery degradation, H2 | Ensure demand, reliability | Shibl et al. [128] | |
Economical | Linear programing and simulation | Wind/solar/battery | Power demand, SOC, cost | Reduce system cost | Nogueira et al. [146] |
MLIP | Wind/solar | Power demand, SOC, cost | Reduce total operating cost | Lamedica et al. [147] | |
FL | Wind/solar/battery/FC | Power demand, SOC, cost, H2 | Ensure demand | Rouholamini and Mohammadian [148] | |
Flowchart, supervisory hierarchical control system | Wind/solar/battery/auxiliary | Power demand, SOC, cost | Ensure demand, increase revenue | Long et al. [39] | |
Techno-economic | FL | Wind/solar | Power balance, cost | Increase reliability and reduced loss | García-Triviño et al. [149] |
PSO | Wind/solar/battery/FC | Power demand, SOC, cost, H2, battery and electrolyzer degradation | Ensure demand, minimize operating and maintenance cost, increase reliability and performance | Valverde et al. [150] | |
Lyapunov technique, simultaneous perturbation stochastic approximation | Wind/solar/battery | Power demand, SOC, cost, battery degradation | Increase reliability and performance | Ciupageanu et al. [151] | |
GA | Wind/solar/battery/thermal load | Cost | Cost reduction and sustainability | Das et al. [152] | |
ANN, MATLAB | Wind/solar/battery/electrolyzer/ FC/ | LCOE | Reduce LCOE, reduce power curtail | Hamdi et al. [142] | |
HOMER | Wind/solar/battery/electrolyzer/ FC/thermal load | Power demand, SOC, cost, battery/FC/electrolyzer degradation, | Ensure demand, cost of energy | Priyanka et al. [153] | |
Improve search space reduction | Wind/solar/battery | power demand, SOC, cost | Ensure demand, reduce levelized cost of energy | Nirbheram et al. [154] |
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Bade, S.O.; Meenakshisundaram, A.; Tomomewo, O.S. Current Status, Sizing Methodologies, Optimization Techniques, and Energy Management and Control Strategies for Co-Located Utility-Scale Wind–Solar-Based Hybrid Power Plants: A Review. Eng 2024, 5, 677-719. https://doi.org/10.3390/eng5020038
Bade SO, Meenakshisundaram A, Tomomewo OS. Current Status, Sizing Methodologies, Optimization Techniques, and Energy Management and Control Strategies for Co-Located Utility-Scale Wind–Solar-Based Hybrid Power Plants: A Review. Eng. 2024; 5(2):677-719. https://doi.org/10.3390/eng5020038
Chicago/Turabian StyleBade, Shree O., Ajan Meenakshisundaram, and Olusegun S. Tomomewo. 2024. "Current Status, Sizing Methodologies, Optimization Techniques, and Energy Management and Control Strategies for Co-Located Utility-Scale Wind–Solar-Based Hybrid Power Plants: A Review" Eng 5, no. 2: 677-719. https://doi.org/10.3390/eng5020038