Low-Carbon Control of Integrated Energy by Combining Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm
Abstract
:1. Introduction
2. Literature Review
3. Methods and Materials
3.1. Integrated Energy System Framework
3.2. Selection of Optimization Methods for CS Algorithm Based on Evolutionary Algorithm
3.3. Application of PSO-CS Algorithm in Low-Carbon Control of Integrated Energy
4. Results
4.1. Algorithm Validation Analysis
4.2. Comparative Analysis of Low-Carbon Control Effects of Algorithms
4.3. Simulation Analysis of Integrated Energy Low-Carbon Control System
5. Discussion and Conclusions
6. Limitations and Future Studies
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Numerical Value | Parameter | Numerical Value |
---|---|---|---|
Gas turbine power (MW) * | [5, 30] | Gas turbine climbing | [−20, 20] |
P2G electrolytic cell power (MW) | [0, 15] | P2G electrolytic cell climbing | [−5, 5] |
Hydrogen power input to methane generator (MW) | [0, 25] | Climbing of methane generator | [−5, 5] |
Hydrogen power input of hydrogen fuel cell (MW) | [0, 25] | Climbing of hydrogen fuel cells | [−5, 5] |
P2G operation and maintenance coefficient (RMB/MW) * | 22.98 | CCS operation and maintenance coefficient (RMB/MW) | 22.98 |
Abandoned wind cost coefficient (RMB/MW) | 313.82 | Abandoned light cost coefficient (RMB/MW) | 313.82 |
Cost coefficient for CO2 consumption (RMB/MW) | 52.36 | Cost coefficient for CO2 sequestration (RMB/MW) | 31.42 |
Population Size | Discovery Probability | Convergence Iteration Number | Fitness Value |
---|---|---|---|
40 | 0.15 | 130 | 0.87 |
0.25 | 140 | 0.88 | |
0.35 | 135 | 0.91 | |
60 | 0.15 | 115 | 0.90 |
0.25 | 100 | 0.95 | |
0.35 | 120 | 0.91 | |
80 | 0.15 | 125 | 0.89 |
0.25 | 110 | 0.90 | |
0.35 | 130 | 0.92 |
Environment | Equipment and Parameters |
---|---|
Processor | Intel(R) Core(TM) i7-8565U CPU @1.80 GHz (Intel, Santa Clara, CA, USA) |
GPU | NVIDIA GeForce RTX 2060 8 GB (NVIDIA, Santa Clara, CA, USA) |
Memory | DDR 4 3200 MHz 8G2 (Crucial, Boise, Idaho) |
Operating system | Windows 10 (64 bit) |
Software | Matlab 2020 |
GPU parallel computing architecture | CUDA 10.1 |
Programming language | Java SE 17 |
Integrated development environment | (JDK 1.8 version, ×64) |
Model | Mean Squared Error (MSE) | Root Mean Squared Error (RMSE) | Mean Absolute Error (MAE) |
---|---|---|---|
Electric load | 0.41 | 0.002 | 0.0022 |
Gas load | 0.55 | 0.003 | 0.0069 |
Heat load | 0.50 | 0.007 | 0.0044 |
Photovoltaic energy | 0.46 | 0.011 | 0.0010 |
Wind energy | 0.32 | 0.023 | 0.0061 |
P2G power | 0.21 | 0.024 | 0.0056 |
CCS power consumption | 0.65 | 0.017 | 0.0048 |
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Share and Cite
Wang, D.; Guan, J.; Liu, H.; Zhang, H.; Wang, Q.; Zhang, L.; Dong, J. Low-Carbon Control of Integrated Energy by Combining Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm. Sustainability 2025, 17, 3206. https://doi.org/10.3390/su17073206
Wang D, Guan J, Liu H, Zhang H, Wang Q, Zhang L, Dong J. Low-Carbon Control of Integrated Energy by Combining Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm. Sustainability. 2025; 17(7):3206. https://doi.org/10.3390/su17073206
Chicago/Turabian StyleWang, Dandan, Jian Guan, Hongyan Liu, Hanwen Zhang, Qi Wang, Lijian Zhang, and Jingzheng Dong. 2025. "Low-Carbon Control of Integrated Energy by Combining Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm" Sustainability 17, no. 7: 3206. https://doi.org/10.3390/su17073206
APA StyleWang, D., Guan, J., Liu, H., Zhang, H., Wang, Q., Zhang, L., & Dong, J. (2025). Low-Carbon Control of Integrated Energy by Combining Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm. Sustainability, 17(7), 3206. https://doi.org/10.3390/su17073206