Optimal Coordinated Planning of Energy Storage and Tie-Lines to Boost Flexibility with High Wind Power Integration
Abstract
:1. Introduction
- Using the chance-constrained approach to reflect the uncertainty associated with the wind speed uncertainty.
- Identifying the optimal ESS sizes and positions.
- Identifying optimal tie-line options.
- Co-optimization of the design approach to determine the optimum size and position of the ESS along with the optimal location of the feeders to increase the flexibility of the networks.
- Taking into account uncertainties about wind levels.
- The approach suggested provides greater flexibility as opposed to other approaches.
2. Problem Formulation
2.1. Objective Function
- is the total cost which should be minimized.
- is the generation cost related to distributed generators.
- is the investment cost related to ESS.
- is the investment cost related to new transmission lines.
- is the cost or penalties related to load shedding and wind curtailment.
2.2. Generator Constraints
2.3. ESS Constraints
2.4. Power Flow Constraints
2.5. Power Loss Constraints
2.6. Transmission Constraints
2.7. Chance-Constrained Method
2.8. Reliability Indices
3. Proposed Solution Methodology
4. Case Study
5. Results and Discussions
- Calculation of net load ramping time series in both upward and downward (dn) directions for the entire planning horizon.
- Calculation of the versatile resources available up/dn within a defined time frame of interest (e.g., one hour), provided the availability and engagement status of each generation unit, start-up time, actual output level and total up- or down-ramping capabilities for the next cycle.
- To combine all time series to achieve the complete up/dn versatility time series available for all tools.
- Calculation of the empirical cumulative distribution function of up/dn available from the complete versatility time series available.
- Calculating the likelihood of inadequate ramping by removing the net load ramping needed in the obtained distribution function. The total of the time series up/down probabilities gives the IRRE.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
Investment decision of branch k from bus j | |
Time step | |
Voltage angle at bus i at hour t | |
Initial status of branch connecting bus i to bus j | |
Capacity of wind farm at bus i | |
Uncertain wind data | |
Parameters of cost functions of generators | |
Susceptance of branch connecting bus i to bus j | |
Cost of energy unit of ESS | |
Cost of power unit of ESS | |
Investment cost related to ESS | |
Generation cost related to distributed generators | |
Investment cost of branch connecting bus i to bus j | |
Cost or penalties related to load shedding and wind curtailment | |
Investment cost related to new transmission lines | |
Total cost | |
Energy size of ESS at bus i | |
Optimal energy size of ESS | |
Stored energy in ESS at bus i at hour t | |
M | Big number |
Power size of ESS at bus i | |
Optimal power size of ESS | |
Output power of generator g at hour t | |
Maximum limit of generator g | |
Minimum limit of generator g | |
Charging power of ESS at bus i at hour t | |
Discharging power of ESS at bus i at hour t | |
Load at bus i at hour t | |
Output power of ESS at bus i at hour t | |
Wind power at bus i at hour t | |
Load shedding at bus i at hour t | |
Wind curtailment at bus i at hour t | |
Power flow from bus i to bus j at hour t | |
Power flow in branch k from bus i to bus j at hour t | |
Maximum limit of branch connecting bus i to bus j | |
Ramp down limit of generator g | |
Ramp up limit of generator g | |
Value of loss of load | |
Value of wind curtailment | |
Normalized wind power at hour t | |
Reactance of branch connecting bus i to bus j | |
Loss of energy expectation at bus i | |
Loss of load probability at bus i | |
Expected demand not supplied at bus i |
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Bus | Bus 8 | Bus 19 | Bus 21 |
---|---|---|---|
Capacity | 200 MW | 150 MW | 100 MW |
Parameter | Value |
---|---|
1 h | |
$120/MW | |
$30/MWh | |
$10,000 | |
$50 |
Bus | Bus 8 | Bus 16 | Bus 17 | Bus 22 |
---|---|---|---|---|
Power (MW) | 27.7 | 3.5 | 14.0 | 42.5 |
Energy (MWh) | 50.3 | 8.4 | 19.8 | 84.7 |
Term | Base | Deterministic | Probablistic |
---|---|---|---|
$92,530 | $89,210 | $83,940 | |
78.23% | 71.18% | 66.47% | |
13.14 MWh | 11.36 MWh | 10.21 MWh | |
Availability | 97.9995% | 98.9395% | 99.9914% |
Unavailability | 2.0005% | 1.0605% | 0.0086% |
Term | Deterministic | Probabilistic |
---|---|---|
3.59% | 9.28% | |
9.01% | 15.03% | |
13.55% | 22.30% | |
Availability | 0.96% | 2.03% |
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Alismail, F.; Abdulgalil, M.A.; Khalid, M. Optimal Coordinated Planning of Energy Storage and Tie-Lines to Boost Flexibility with High Wind Power Integration. Sustainability 2021, 13, 2526. https://doi.org/10.3390/su13052526
Alismail F, Abdulgalil MA, Khalid M. Optimal Coordinated Planning of Energy Storage and Tie-Lines to Boost Flexibility with High Wind Power Integration. Sustainability. 2021; 13(5):2526. https://doi.org/10.3390/su13052526
Chicago/Turabian StyleAlismail, Fahad, Mohamed A. Abdulgalil, and Muhammad Khalid. 2021. "Optimal Coordinated Planning of Energy Storage and Tie-Lines to Boost Flexibility with High Wind Power Integration" Sustainability 13, no. 5: 2526. https://doi.org/10.3390/su13052526
APA StyleAlismail, F., Abdulgalil, M. A., & Khalid, M. (2021). Optimal Coordinated Planning of Energy Storage and Tie-Lines to Boost Flexibility with High Wind Power Integration. Sustainability, 13(5), 2526. https://doi.org/10.3390/su13052526