Hybrid Energy Storage Systems Based on Redox-Flow Batteries: Recent Developments, Challenges, and Future Perspectives
Abstract
:1. Introduction
2. Evaluation of Key Performance Indicators
2.1. Classification of Single Storage Components
2.1.1. Redox-Flow Batteries (RFBs)
2.1.2. Lithium-Ion Batteries (LIBs)
2.1.3. Sodium–Sulfur Batteries (NaSs)
2.1.4. Lead–Acid Batteries (PbAs)
2.1.5. Supercapacitors (SCs)
2.1.6. Superconducting Magnetic Energy Storage (SMES)
2.1.7. Evaluation of Key Performance Indicators
2.2. Classification of HESSs
2.2.1. Definition of a HESS
- 1.
- Primarily ESS cluster: has to satisfy the requirements of higher peak power demand and has to handle the fast transient fluctuations, e.g., load or Renewable Energy Sources (RES) production. This cluster is marked by fast response time, high power peaks, high efficiency, and high cycle lifetime.
- 2.
- Secondary ESS cluster: has to comply with the requirement of high storage duration. This cluster is characterized by a low self-discharge rate and high efficiency.
2.2.2. Evaluation of Key Performance Indicators
3. Coupling Architecture Optimization Strategy
3.1. Coupling Architectures of Hybrid Storage Systems
3.2. HESS Optimization Strategy
4. Energy Management System (EMS) for HESS
4.1. Energy Management Structure for HESS
4.1.1. Application Scenarios
- 1.
- Transmission grid (T);
- 2.
- Distribution grid (D);
- 3.
- Behind the meter at end-user locations (E-U).
Source | Application | Purpose | Placement | Control | Duration | Control Parameter | Controller Rate |
---|---|---|---|---|---|---|---|
[38,46,47,48,49,50] | Momentary Reserve | S | T | P | t < msec | f_AC 1 | <20 ms |
[1,38,43,46,47,49,50,53,54] | Primary Control | S/G | T | P | t < msec | P_AC f_AC 1 | <30 s |
[10,38,42] | Secondary Control | S/G | T | P | s < t > 15 min | P_AC f_AC 1 | <5 min |
[10,38,42] | Tertiary Control | S/G | T | P | min < t > 60 min | P_AC f_AC 1 | <15 min |
[10,38,42] | Black Start | S | - | P | s <t > min | P 3 f_AC 1 U_AC | 1–10 s |
[1,10,38,42,44,55] | Island Grid 4 | S | - | E | s < t > days | P 3 | 1 s–1 min |
[1,38,42] | Transmission Support and Stability | S | T | E | t > h | P 3 | 1 s–1 min |
[10,38,42,49,56,57] | Voltage Support | G /S | T/D | P | 15 min < t >h | U 2 | 1–15 min |
[1,10,38,42,43,46,49,50,52] | Distribution Power Quality | G /S | D | P | s < t > min | P 3 | 1 s–1 min |
[10,38,43,44,52] | Peak Shaving (all time scales) | M /G | E-U | P | s < t > 15 min | P 3 | 30 s–1 min |
[38] | Uninterruptible Power Supply | M | E-U | P/E | s < t > h | P f_AC U | <20 ms |
[38,46,47,49,50,52,56,57] | Energy Time Shifting | M | E-U | E | 15 min < t > days | P 3 t | 1–15 min |
[1,38,43] | Energy Trading, Arbitrage | M | - | E | 15 min < t > h | P 3 EUR/kW EUR/kWh | 1–15 min |
4.1.2. Control and Optimization Parameters
4.2. Energy Management Optimization for HESS
4.2.1. Prediction
4.2.2. EMS Control Techniques
- 1.
- Low-level optimization functions control the AC/DC bus voltage and the electric current flow.
- 2.
- High-level optimization functions control many energy management strategies, among which are power performance, SoC monitoring, ESS charge/discharge cycles, and energy cost reduction.
- 1.
- Classical control techniques mainly include filtration-based control, dead beat control, droop control, and sliding mode control. These techniques are the most used in the literature, as demonstrated in Table 7, and are mainly applied for offline implementation independently of the filtration-based control technique.
- 2.
- Intelligent control techniques are classified into rule-based techniques and optimization-based techniques. Rule-based techniques are among the most widely adopted in previous work due to their simplicity in implementation (see Table 7). However, these techniques are still far from perfect, as they require deep knowledge of the domain and the definition of rules for a complex system is a challenging task. Recently, there has been considerable interest in real-time optimization techniques, with a rapid rise in the use of Deep Learning (DL) and Machine Learning (ML) algorithms, e.g., Neural Network (NN) and Reinforcement Learning (RL). ML techniques deliver accurate results in real time, but on the other hand, they require a lot of training data and suffer from high computational complexity.
5. Related Work
6. Conclusions
- The advance of real-time optimization of EMS came at a very high computational cost. One solution to address this issue is the use of the Digital Twin (DT) concept. DT uses real-world data to create a simulation that predicts system future performance [100]. DT has been recently adopted in many application fields due to several advantages, in particular energy management and operation optimization improvement.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AC | Alternating Current |
AFE | Active Front End |
ANN | Artificial Neural Network |
AORFB | Aqueous Organic Redox-Flow Battery |
ARIMA | Auto Regressive Integrated Moving Average |
BC | Battery Converter |
BMS | Battery Management System |
CNN | Convolution Neural Networks |
D | Distribution Grid |
DC | Direct Current |
DT | Digital Twin |
E | Energy |
EC | Energy Component |
EES | Electrical Energy Storage |
EMS | Energy Management System |
ESS | Energy Storage System |
E-U | Behind the Meter at End-User Locations |
FC | Fuel Cell |
GA | Gradient Descent |
GAN | Generative Adversarial Network |
HESS | Hybrid Energy Storage System |
ISC | Supercapacitor Current |
Isol | Isolated |
G | Grid |
KPI | Key Performance Indicator |
LIB | Lithium-Ion Battery |
LSTM | Long Short Term Memory |
M | Manage |
MAE | Mean Absolute Error |
MAPE | Mean Absolute Percentage Error |
MDPI | Multidisciplinary Digital Publishing Institute |
MILP | Mixed Integer Linear Programming |
MINLP | Mixed Integer Nonlinear Programming |
MLP | Mixed Linear Programming |
MPC | Model Predictive Control |
NaS | Sodium–Sulfur Battery |
NCM | Lithium–Nickel–Cobalt–Manganese Oxide |
NN | Neural Network |
Non Isol | Not Isolated |
NRMSE | Normalized Root-Mean-Square Error |
N/S | Not Specified |
OMEI | Open Mobility Electric Infrastructure |
P | Power |
PbA | Lead–Acid Battery |
PC | Power Component |
PSO | Particle Swarm Optimization |
RE | Renewable Energy |
RES | Renewable Energy Sources |
RFB | Redox-Flow Battery |
RL | Reinforcement Learning |
RMSE | Root-Mean-Square Error |
RNN | Recurrent Neural Networks |
S | System |
SC | Supercapacitor |
SCC | Supercapacitor Converter |
SCM | Supercapacitor Module |
SoC | State of Charge |
SMES | Superconducting Magnetic Energy Storage |
T | Transmission Grid |
UPS | Uninterruptible Power Supply |
VRFB | Vanadium Redox-Flow Battery |
VSC | Voltage at Supercapacitor Module |
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LIB | SC | NaS | PbA | RFB | |||
---|---|---|---|---|---|---|---|
Battery | oriented | Energy density in Wh/kg | |||||
Power density in W/kg | |||||||
Efficiency in % | |||||||
Self-discharge in %/day | |||||||
Reaction Time in s | |||||||
Application | oriented | Cost in EUR/kW | |||||
Cost in EUR/kWh | |||||||
Lifetime in cycles | |||||||
Shelf life in years | |||||||
Design Flexibility | |||||||
Ecologic impact | |||||||
Safety | |||||||
Storage duration | min-days | ms-hour | min-days | min-days | weeks |
SC+LIB | SC+NaS | SC+PbA | SC+RFB | LIB+RFB | |||
---|---|---|---|---|---|---|---|
Battery | oriented | Energy density in Wh/kg | |||||
Power density in W/kg | |||||||
Efficiency in % | |||||||
Self-discharge in %/day | |||||||
Reaction time in s | |||||||
Application | oriented | Cost in EUR/kW | |||||
Cost in EUR/kWh | |||||||
Lifetime in cycles | |||||||
Shelf life in years | |||||||
Design flexibility | |||||||
Ecologic impact | |||||||
Safety | |||||||
Storage duration | ms-days | ms-days | ms-days | ms-weeks | min-weeks |
Hybrid influence: | positive influence | no/medium influence | negative influence |
Legend: | = negative | = medium | = positive |
+ = | + ; + = | + ; + = |
Architecture Proposal | ||||
---|---|---|---|---|
Design Parameter | a | b | c | |
Power converted by SCC (kW) | 25 | 30 | 25 | |
Power converted by BC (kW) | 5 | 5 | 30 | |
Overall conversion power installed (kW) | 30 | c35 | 55 | |
Voltage ratio SCC | TBD | H | L | L |
Voltage ratio BC | H | L | H | H |
Maximum power processed with low voltage ratio (kW) | 25 | 5 | 30 | 25 |
Minimum power processed with high voltage ratio (kW) | 5 | 30 | 5 | 30 |
Application | Voltage Support | Distribution Power Quality | Peak Shaving | Energy Time Shifting | |
---|---|---|---|---|---|
Hybrid Component | PC | PC | PC | PC/EC | |
Island grids | EC | Improving transient response, increase efficiency/performance and lifetime of the EC, grid (voltage) quality, supply security [63,64,65] | Operational limits operation, self sufficiency, economic efficiency, efficiency, reduce energy costs [66,67] | ||
Uninterruptible Power Supply | EC | Utilization of UPS EC, economic efficiency, stability of power system [68] | |||
Peak Shaving | EC | Minimizing the power fluctuation, self-sufficiency, grid quality, optimizing the capacity ratio of EC, PC [69] | Dimensioning, efficiency, economic efficiency, lifetime, smoothing the current of EC [70] | ||
Energy time shifting | EC | Dimensioning, efficiency, economic efficiency, lifetime, smoothing the fluctuation of RE [71] | Self-sufficiency, reduce of max. power consumption/generation, utilization of RE, efficiency, dimensioning, lifetime [72] | ||
Energy Trading/Arbitrage | EC/PC | Economic efficiency (operational costs), efficiency, reduce energy costs [73] |
Predicted Data | Prediction Techniques | Evaluation Metrics |
---|---|---|
Charging demand [80,81,82] | CNN, LSTM, RNN | MAPE, MAE, NRMSE |
RE production [75,76,77,79] | CNN, MILP, NN, RNN, ARIMA, GAN, MLP, LSTM | MAPE, RMSE |
ESS Capacity [78,79] | MILP, MINLP, NN | RMSE |
Charging scheduling and pricing [83,84] | MILP, RL, ANN | N/S |
Charging station placement [85,86] | GA, RL, Linear Regression, Decision Trees | N/S |
Paper | Energy Storage System | Electric Topologies | Optimization | General Control Techniques | Used Data | |
---|---|---|---|---|---|---|
Optimization Function | Real Time | |||||
[9] | (H/Br) RFB, SC | DC coupled | Power | Yes | Mathematical model | Microgrids |
[87] | Battery, SC | DC coupled | Power allocation of different ESS | Yes | Classical Real-time optimization | Microgrids/Simulated |
[4] | Battery, SC | DC coupled | Reduces measurement inaccuracies | N/S | Classical | N/S |
[8] | VRFB, SC | Active topology | Current, SoC | No | Classical Fuzzy logic | EV charging park/Real |
[5] | Li-Ion battery, SC | DC coupled | N/S | N/S | Fuzzy logic | Ships |
[88] | VRFB, SC | Active topology | Power thresholds | No | Rule-based | Industrial grid—Real/Synthetic EV charging park |
[63] | Batteries, SC | DC coupled | Constant voltage to the DC bus | No | Classical | PV, AC- and DC Loads/Simulated |
[89] | Battery, SC | DC coupled | N/S | N/S | Global optimization Real-time optimization | Electric vehicle |
[90] | Li-Ion battery, SC | DC coupled | Meet power demand Reduce the cost of energy storage device | Yes | Classical Real-time optimization | Ship load |
[91] | Battery, SC | DC coupled | Power allocation | Yes | Classical Rule-based | EV application |
[64] | Fuel cell, Battery, SC | DC coupled | Provide power for load in time Good tracking performance of HESS current Obtain a stable voltage of the dc bus | Yes | Projection operator adaptive law | N/S |
[69] | Battery, SC | DC coupled | Minimizing the power fluctuation Optimizing the capacity ratio of each ESS | Yes | Real-time optimization | N/S |
[92] | Battery, SC | N/S | N/S | Yes | Rule-based Global optimization Real-time optimization | Electric vehicle |
[93] | battery, SC | DC coupled | Power Charge/Discharge cycle | Yes | Real-time optimization | PV power generation |
[67] | Li-Ion battery, SC | AC coupled | Optimize the cycle life of the HESS | Yes | Mathematic model | Microgrids |
[72] | Battery, SMES | DC coupled | Control charge/discharge prioritization | No | Classical | Off-grid load profile/Simulated Sea wave energy conversion/Simulated |
[68] | Battery, fuel cell, | AC coupled, On grid | Power | N/S | N/S | Grid data/Real |
[94] | Battery, SC | Three-level NPC Converter | N/S | N/S | Classical | Electric vehicle |
[71] | Battery Superconducting magnetic ESS | One DC/AC converter Two DC/ DC converters | Smoothing the fluctuations of the wind power output | N/S | Device/system-level control strategies | Wind power generation |
[95] | Battery, SC | DC coupled | N/S | N/S | Rule-based | Electric vehicle |
[73] | Battery, fuel cell, electrolyzer | DC coupled, On grid AC | Energy costs, power | N/S | Rule-based | Predicted daily data |
[66] | Fuel cell, battery, SC | DC coupled, Off grid | Power | Yes | Real-time optimization | Grid data/Real |
[65] | Battery, SC | DC coupled | N/S | N/S | Microgrid | |
[96] | PbA and Li-Ion battery, SC | Three different architectures | Maintain the grid power and voltage | No | Classical | Residential load/Literature data |
[97] | battery, SC | DC coupled | Current, voltage | Yes | Real-time optimization | N/S |
[98] | Fuel cell, SC | DC converters | Voltage | No | Classical | Electric vehicle/Simulated |
[70] | Battery, SMES | DC coupled, On grid | Current | N/S | N/S | Grid data/Real |
[99] | Fuel cell, Battery, Electrolyzer | AC bus and DC bus considered | N/S | Yes | Real-time optimization | Residential load |
Real-Time EMS Control Techniques | Advantages | Limitations | ||
---|---|---|---|---|
Classical | Filtration [91] |
|
| |
Intelligent | Rule-based | Fuzzy logic [88,91,92,95] |
|
|
Real-time optimization | MPC [7,92,97] |
|
| |
NN [7,77] |
|
| ||
RL [85] |
|
| ||
PSO [69,89] |
|
|
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Share and Cite
Schubert, C.; Hassen, W.F.; Poisl, B.; Seitz, S.; Schubert, J.; Oyarbide Usabiaga, E.; Gaudo, P.M.; Pettinger, K.-H. Hybrid Energy Storage Systems Based on Redox-Flow Batteries: Recent Developments, Challenges, and Future Perspectives. Batteries 2023, 9, 211. https://doi.org/10.3390/batteries9040211
Schubert C, Hassen WF, Poisl B, Seitz S, Schubert J, Oyarbide Usabiaga E, Gaudo PM, Pettinger K-H. Hybrid Energy Storage Systems Based on Redox-Flow Batteries: Recent Developments, Challenges, and Future Perspectives. Batteries. 2023; 9(4):211. https://doi.org/10.3390/batteries9040211
Chicago/Turabian StyleSchubert, Christina, Wiem Fekih Hassen, Barbara Poisl, Stephanie Seitz, Jonathan Schubert, Estanis Oyarbide Usabiaga, Pilar Molina Gaudo, and Karl-Heinz Pettinger. 2023. "Hybrid Energy Storage Systems Based on Redox-Flow Batteries: Recent Developments, Challenges, and Future Perspectives" Batteries 9, no. 4: 211. https://doi.org/10.3390/batteries9040211