A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations
Abstract
:1. Introduction
2. Formulation of Economic Load Dispatch
2.1. Economic Dispatch with Thermal Units
2.2. Economic Dispatch Integrating Solar
2.3. Integrated Cost Function Incorporating Solar PV
2.3.1. Power Balance
2.3.2. Ramping Rate Constraint
2.3.3. Prohibited Operating Zones
2.3.4. Combatting the Intermittency and Uncertainties
Load Demand Uncertainty
PV Generation Uncertainty
- Probabilistic forecasting: Based on historical data and current weather conditions, the ELD algorithm can use probabilistic forecasting techniques to predict the expected output of renewable energy sources. The algorithm can also account for forecast uncertainty by modeling forecast error as a probability distribution.
- Metaheuristic optimization: In the presence of uncertainty from photovoltaic (PV) generation, metaheuristics can be crucial in the solution of the economic load dispatch (ELD) problem. The ELD problem entails determining the ideal generator output combination to satisfy the power demand while reducing the overall cost of generation. Traditional deterministic optimization techniques may not be suitable in the presence of PV generating uncertainty because they do not account for the variability and unpredictability of the PV generation. By including the uncertainty of PV generation in the optimization problem and locating close to optimal solutions, metaheuristics can be applied to solve the ELD problem. They are particularly suited for ELD problems in the presence of PV generation uncertainty because they can manage big, complicated problems and handle uncertainty effectively.
3. Optimization Algorithms for Economic Power Dispatch
4. Recent Progress on Power Dispatch with PV
5. Current Practices in ELD
5.1. Large-Scale PV System Integration
- Automatic Generation Control;
- Scheduling considering unit commitments;
- Regulation of Generation;
- Emphasis on the combined cycle generation where the response time of turning the units on and off is quite shorter.
5.2. Grid Code and Large-Scale PV System
- Size of the system;
- Voltage and current levels;
- Transmission and distribution;
- Characteristics of the generating units;
- Energy policy.
5.3. Economic Feasibility of Large-Scale Photo Voltaic Systems
5.4. Economic Dispatch of Large-Scale Solar PV System
- Optimal Power Flow (OPF);
- AGC;
- Dynamic Economic Dispatch;
- Economic Dispatch with RES.
6. Challenges and Technical Solution
6.1. Overview of the Technical Solution
6.2. Spinning Reserve Requirement for Generation Dispatch
- Sudden loss in transmission line;
- Change in load demand;
- Change in power generation from PV panel due to the cloud cover.
6.2.1. Regulating Reserve
6.2.2. Load following Reserve
6.2.3. Contingency Reserves
6.3. Generation Scheduling with PV
6.4. Solar Plant’s Power Prediction
6.5. Generation Units’ Dispatch Selection
6.6. Operating Limits of Generator
6.7. Planning for Short Term Dispatch of Power System
6.7.1. Monitoring of Load, Tie Line and Generation
6.7.2. Transmission Line Flow Monitoring
Transmission Voltage Stability
Rotor Angle Stability of the Transmission System
Transmission System Frequency Stability
6.8. Factors for Effective Economic Dispatch
6.8.1. Generation Resources
6.8.2. Geographic Area
6.8.3. Transmission Resources
- Introduction of new terminology from the DG site to the connection point;
- Considering the geographic data and distribution infrastructure;
- Proper evaluation of all the network points;
- The original structure of the distribution business market.
6.9. Solar Tracker
6.10. Adequate Protection of Solar PV System
6.11. Voltage Stability in Large-Scale PV Integrated Systems
7. Discussion
8. Recommendation
- Global search capability: Unlike traditional approaches, which are prone to becoming stuck in local minima, hybrid metaheuristic algorithms are able to traverse the whole search space.
- Flexibility: Hybrid metaheuristic algorithms can be quickly adjusted to add new constraints or objectives and can be easily adaptable to other sorts of economic load dispatch situations.
- Robustness: Large-scale, non-convex, and non-smooth optimization problems can be handled by hybrid metaheuristic algorithms.
- Solutions of high quality: Hybrid metaheuristic algorithms are capable of finding solutions of high quality with a high level of accuracy and precision.
- Efficiency: Hybrid metaheuristic algorithms are capable of solving complex problems quickly and are computationally efficient.
- Handling uncertainty: Hybrid metaheuristics can handle the system’s uncertainty, such as the output of the random generator, the speed of the wind, etc.
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Refs | Optimization Algorithm | Survey | Research | Pros | Cons |
---|---|---|---|---|---|
[23,24] | PSO | √ |
|
| |
[25] | ACO | √ |
|
| |
[26,27] | GA | √ |
|
| |
[28] | Simulated Annealing | √ |
|
| |
[29] | Linear Programming | √ |
|
| |
[30] | DE | √ |
|
| |
[31,32] | ABC | √ |
|
| |
[33,34,35] | GWO | √ |
|
| |
[36,37] | Jaya | √ |
|
| |
[38] | Rao-1, Rao-2, MRao-2 | √ |
|
| |
[39,40] | SMO | √ |
|
| |
[41,42,43] | MFO | √ |
|
| |
[44,45,46] | PSO-ACO | √ |
|
|
Deterministic Methods | Stochastic Methods |
---|---|
Deterministic methods employ mathematical models to determine the best solution to the ELD problem. | Stochastic approaches use probabilistic models and algorithms to find the solution. |
These methods offer a one-of-a-kind solution to the problem. | These methods offer a variety of potential solutions to the problem, allowing for more exploration of the solution space. |
The solution obtained by these methods is deterministic and does not change when the algorithm is run multiple times. | These methods produce random solutions that change with each run of the algorithm, resulting in better results in optimization problems with complex, non-linear objectives. |
These methods are computationally fast, but they can become stuck in local optima, resulting in suboptimal solutions. | These methods are slower in terms of computation, but they are better at escaping local optima and converge to near-optimal solutions for complex, large-scale problems. |
Although deterministic methods are more precise, they may not always find the best solution to complex problems. | Stochastic methods are less precise but more robust and flexible in solving complex problems. |
Reference | Type of Paper | Methods | Test Unit | Optimization Objective | Constraints | ||||
---|---|---|---|---|---|---|---|---|---|
Thermal | Solar | Wind | Cost | Emission | CEED | ||||
[57] | Research | MRFO | I and E | ||||||
[58] | Research | Hybrid Bat-Crow | I and E | ||||||
[59] | Research | PSO | I and E | ||||||
[60] | Research | BPSO-QP | I and E | ||||||
[61] | Research | EPFA | I | ||||||
[62] | Research | NSGA-II and RNSGA-II | I | ||||||
[44] | Research | Rao-1,2,3 | I and E | ||||||
[63] | Review | CMOPEO | I, E and S | ||||||
[64] | Research | ISPSO | I | ||||||
[65] | Research | PSO-SSA | I | ||||||
[66] | Research | GWO-PSO | I | ||||||
[67] | Research | IJAYA | I and E | ||||||
[61] | Research | SSA | I and E | ||||||
[62] | Research | Firefly | I and E | ||||||
[36] | Research | MP-CJAYA | I and E | ||||||
[67] | Research | JAYA-TLBO | I and E |
Very Short Term | Short Term | Long Term or Medium Term | |
---|---|---|---|
Time horizon | 5 min to 6 h | Up to 3 days | Up to number of months or years |
Models Used | ANN,SVR,ARMA,ARIMA.LSTM | GFS,ECMWF,LSTM | Statistical models with processed data |
Criteria | Metaheuristics [145] | Mathematical Optimization [146] |
---|---|---|
Global optimality | No guarantee | Guaranteed |
Complexity | High | Low |
Convergence speed | Slow | Fast |
Handling constraints | May require modification | Directly handled |
Handling non-linearity | Good | Good |
Handling discrete variables | Good | Not always efficient |
Handling multi-modal problems | Good | Not always efficient |
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Fahim, K.E.; Silva, L.C.D.; Hussain, F.; Yassin, H. A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations. Sustainability 2023, 15, 11837. https://doi.org/10.3390/su151511837
Fahim KE, Silva LCD, Hussain F, Yassin H. A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations. Sustainability. 2023; 15(15):11837. https://doi.org/10.3390/su151511837
Chicago/Turabian StyleFahim, Khairul Eahsun, Liyanage C. De Silva, Fayaz Hussain, and Hayati Yassin. 2023. "A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations" Sustainability 15, no. 15: 11837. https://doi.org/10.3390/su151511837