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