Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control
Abstract
:1. Introduction
- (1)
- We have adopted a fast response speed energy storage and a slow response speed energy storage to smooth the short term and the long term wind power fluctuations, respectively. Due to the two different response speeds, how to get the two energy storages to cooperate is a problem for real time scheduling. We have exploited the modularity of wind farms based on the MPC method with two types of storage to solve the problem.
- (2)
- We present that the MPC prediction horizon should be two hours according to the wind farm power spectrum density and prediction error.
- (3)
- Additionally, we have implemented a two-level MPC to reduce the optimal computation time. The experimental results are close to that of a single-level MPC. The single-level MPC has its own advantages in following the grid plan. The two-level MPC cannot be substituted for single-level MPC, and vice versa.
- (4)
- Two sound conclusions are drawn through theory analyses and simulations: One is that the decision values of the pumped storage are not sensitive to the flywheel capacity. The other is that in some situations, wind power generation being sent to the grid is sacrificed to reduce the wind curtailment.
2. Problem Description
2.1. Characteristics of Wind Power
2.2. Characteristics of Typical Energy Storage Systems
Type | Power (MW) | Energy (MWh) | Energy Density (Wh/kg) | Power Density (W/kg) | Efficiency (%) | Respond time (s) | Life (year or cycle) |
---|---|---|---|---|---|---|---|
Pumped hydro | 0–1800 | >200 | 0.5–1.5 | - | 75 | 10–600 | 50 y |
Compressed air | 0–300 | 0–105 | 30–60 | 10–100 | 64 | 1–600 | 30 y |
SMES | 0–10 | 0–1 | 30–100 | 104–105 | 95 | 0.005 | 30 y |
Fly wheel | 0–5 | 0–10 | 5–10 | 102–103 | 93 | 0.05 | 20 y |
Super cap | 0–0.3 | 0–10 | <50 | 0–4000 | 98 | 0.05 | 105 c |
Battery | 0–50 | 0–100 | 30–200 | 0–500 | 70 | 0.02 | 3000 c |
2.3. Relationship between Wind Power Fluctuation and Storage Systems
2.4. The Principle of Choosing Prediction Horizon
3. The System Configuration and Operating Process
3.1. Single-Level System Configuration and Operating Process
3.2. Two-Level System Configuration and Its Operating Process
4. System Model
4.1. Single-Level MPC Method
4.2. Single-Level MPC State-Space and Optimization Model
4.2.1. Single-Level MPC State-Space Model
4.2.2. Single-Level MPC Programming Model
- (a)
- Objective Function of the Model
- (b)
- Constraints of the model
- (1)
- The assignment process of wind power is described in Equation (2).
- (2)
- The composition process of the system total generation is described in Equation (3).
- (3)
- The energy balances of the pumped hydro storage and the fly wheel are presented in Equations (4) and (5), respectively.
- (4)
- The upper and lower bounds of pumped storage decision variable are:
- (5)
- The upper and lower bounds of the fly wheel decision variable are described as follows:
- (6)
- The pumped hydro storage occupies only one of three states at any given time: generation, pumping, or shut-down. The constraint is as follows:
- (7)
- The fly wheel storage occupies only one of three states at any given time: charge, discharge, or shut-down. The constraint is as follows:
- (8)
- Wind delivered directly to grid cannot exceed generation plan:
- (9)
- Constraints of Assumption 1:
4.3. Two-Level Rolling Optimization
4.4. Two-Level MPC State-Space and Optimization Model
4.4.1. Two-level MPC state-space model
- (a)
- State-space of MPC 2
- (b)
- State-space of MPC 1
4.4.2. Two-Level MPC Programming Model
- (a)
- Programming Model of MPC2
- (b)
- Programming Model of MPC1
- (1)
- MPC1 Objective Function of the Model
- (2)
- MPC1 Constraints
Parameter | Case 0 | Case 1 | Case 2 | Case 3 | Case 4 | Case 5 | |
---|---|---|---|---|---|---|---|
N | 23 | 4 | 3 | 2 | 1 | 0 | |
t | 0–23 | 0–4 | 0–3 | 0–2 | 0–1 | 0 | |
variable | Constant | Constant | Constant | Constant | constant |
5. Simulation Results and Discussion
Type | Charging Power (MW) | Discharging Power (MW) | Initial Energy (MWh) | Minute Energy (MWh) | Maximum Energy (MWh) | Efficiency (η1, η2) | Efficiency (η3, η4) |
---|---|---|---|---|---|---|---|
Pumped storage | 100 | 100 | 100 | 20 | 200 | 0.87 | 0.85 |
Fly wheel | 10 | 10 | 2.5 | 0.0 | 5 | 0.95 | 0.95 |
5.1. Simulation Test for Three Sub-Objectives Based on Single-Level and Two-Level MPC
Values | Time(min) | ||||
---|---|---|---|---|---|
435 | 440 | 445 | 450 | 455 | |
/MW | 12.18 | 12.19 | 12.20 | 12.21 | 12.26 |
/MW | 20.15 | 20.16 | 16.26 | 18.45 | 17.39 |
/MW | 12.18 | 12.19 | 9.86 | 12.21 | 12.26 |
/MW | 6.39 | 6.39 | 6.39 | 4.51 | 4.51 |
/MW | 0.66 | 2.42 | 0 | 1.73 | 0.62 |
/MW·h | 98.58 | 99.03 | 99.48 | 99.80 | 100.1 |
/MW·h | 4.809 | 5.0 | 4.80 | 4.93 | 4.98 |
5.2. Simulation Test for Strengthening the Weighting Coefficients of the First Scheduling Interval
Values | Time (min) | ||||
---|---|---|---|---|---|
435 | 440 | 445 | 450 | 455 | |
/MW | 12.18 | 12.19 | 12.20 | 12.21 | 12.26 |
/MW | 20.15 | 20.16 | 16.26 | 18.45 | 17.39 |
/MW | 6.39 | 6.39 | 6.39 | 4.51 | 4.51 |
/MW | 1.581 | 1.507 | 0 | 1.729 | 0.619 |
/MWh | 0 | 0 | 2.34 | 0 | 0 |
/MW | 12.18 | 12.19 | 9.86 | 12.21 | 12.26 |
/MW·h | 98.58 | 99.03 | 99.48 | 99.80 | 100.1 |
/MW·h | 4.881 | 5.0 | 4.80 | 4.98 | 4.89 |
/MW | 0 | 0.917 | 0 | 0 | 0 |
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Nomenclature
Acronyms
FLC | fuzzy logic control |
GSM | global system for mobile communications |
MINLP | mixed-integer nonlinear programming |
MPC | model prediction control |
MPC 1 | short-term MPC |
MPC 2 | long-term MPC |
SMES | superconducting magnetic energy storage |
Variable of Model
available wind power [MW] | |
generated wind power sent to the grid [MW] | |
pumped power of the pumped hydro storage [MW] | |
charging power of the fly wheel storage [MW] | |
wind curtailment [MW] | |
total power generation of the wind power storage system [MW] | |
power generation of the pumped hydro storage [MW] | |
power generation of the fly wheel storage [MW] | |
energy of the pumped hydro storage at time t [MWh] | |
pumped hydro storage system pumped and generation efficiency ratios. | |
time period ( = 5/60 = 1/12) [h] | |
energy of the fly wheel system at time t [MWh] | |
fly wheel charging and discharging efficiency ratios | |
Case i | the No i initial condition of MPC |
ts | the serial number of the scheduling interval |
wi | The weight factor of the No. i sub-objective |
Ep | energy of the pumped hydro storage(used in Figure) [MWh] |
pa | pumped power of the pumped hydro storage(used in Figure) [MW] |
Ef | energy of the fly wheel system(used in Figure) [MWh] |
fa | charging power of the fly wheel storage(used in Figure) [MW] |
wd | generated wind power sent to the grid(used in Figure) [MW] |
wu | wind curtailment(used in Figure) [MW] |
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Xiong, M.; Gao, F.; Liu, K.; Chen, S.; Dong, J. Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control. Energies 2015, 8, 8020-8051. https://doi.org/10.3390/en8088020
Xiong M, Gao F, Liu K, Chen S, Dong J. Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control. Energies. 2015; 8(8):8020-8051. https://doi.org/10.3390/en8088020
Chicago/Turabian StyleXiong, Meng, Feng Gao, Kun Liu, Siyun Chen, and Jiaojiao Dong. 2015. "Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control" Energies 8, no. 8: 8020-8051. https://doi.org/10.3390/en8088020