Balancing Curtailment and Economy in Source-Load Dispatch and Operation Management Systems Using DC-OPF and NSGA-II

Yang Zhang; Weinan Zheng; Han Zheng; Zhuang Dou; Jun Shi

doi:10.46604/ijeti.2026.16343

Authors

Yang Zhang Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid, Guangdong, Shenzhen, 518001, China
Weinan Zheng Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid, Guangdong, Shenzhen, 518001, China
Han Zheng Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid, Guangdong, Shenzhen, 518001, China
Zhuang Dou Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid, Guangdong, Shenzhen, 518001, China
Jun Shi Shenzhen Power Supply Bureau Co., Ltd. of China Southern Power Grid, Guangdong, Shenzhen, 518001, China

DOI:

https://doi.org/10.46604/ijeti.2026.16343

Keywords:

DC optimal power flow, NSGA-II, renewable curtailment, economic dispatch, new energy management system

Abstract

Integrating high shares of renewable energy requires dispatch tools that minimize operating cost and renewable curtailment while respecting network constraints. This study develops a transmission-aware source-load dispatch framework based on lossless DC optimal power flow (DC-OPF). A deterministic 24-hour scheduling case is constructed using a modified IEEE 30-bus system. Renewable utilization and curtailment are explicitly incorporated as decision variables. The model minimizes operating cost and renewable curtailment subject to network and operating constraints. Under the same input data and renewable accounting scheme, its performance is compared with a network-unconstrained economic dispatch baseline and a bi-objective NSGA-II approach. Results show that DC-OPF limits renewable curtailment to 0.03 MWh while increasing operating cost by only 0.41% relative to the unconstrained baseline. The selected NSGA-II solution remains approximately 4% more expensive. The results indicate that transmission-aware dispatch can achieve near-complete renewable utilization with a marginal economic penalty under the tested operating conditions.

References

H.-Q. Deng, H. Wu, Z.-W. Liang, P.-R. Zheng, and J.-F. Shen, “Proactive Prevention Strategies for Grid Risk Scenarios Based on Pumped Storage Participation,” International Journal of Engineering and Technology Innovation, vol. 14, no. 4, pp. 389-406, 2024.

M. M. Rahman, S. H. Dadon, M. He, M. Giesselmann, and M. M. Hasan, “An Overview of Power System Flexibility: High Renewable Energy Penetration Scenarios,” Energies, vol. 17, no. 24, article no. 6393, 2024.

Y. Sharifian and H. Abdi, “Multi-Area Economic Dispatch Problem: Methods, Uncertainties, and Future Directions,” Renewable and Sustainable Energy Reviews, vol. 191, article no. 114093, 2024.

G. Papazoglou and P. Biskas, “Review and Comparison of Genetic Algorithm and Particle Swarm Optimization in the Optimal Power Flow Problem,” Energies, vol. 16, no. 3, article no. 1152, 2023.

S. Park and P. Van Hentenryck, “Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow,” IEEE Transactions on Power Systems, vol. 40, no. 3, pp. 2205-2216, 2024.

B. Taheri and D. K. Molzahn, “Optimizing Parameters of the DC Power Flow,” Electric Power Systems Research, vol. 235, article no. 110719, 2024.

M. A. Abido, “Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 315-329, 2006.

H. Nourianfar and H. Abdi, “Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique,” Sustainability, vol. 14, no. 6, article no. 3173, 2022.

H. Y. Shahri, S. A. Hosseini, and J. Pourhossein, “Multi-Objective Evolutionary Method for Multi-Area Dynamic Emission/Economic Dispatch Considering Energy Storage and Renewable Energy Units,” Heliyon, vol. 10, no. 18, article no. e37476, 2024.

D. Zou, L. Ma, and C. Li, “An Enhanced NSGA-II and a Fast Constraint Repairing Method for Combined Heat and Power Dynamic Economic Emission Dispatch,” Information Sciences, vol. 678, article no. 120915, 2024.

S. Bhavsar, R. Pitchumani, J. Maack, I. Satkauskas, M. Reynolds, and W. Jones, “Stochastic Economic Dispatch of Wind Power under Uncertainty Using Clustering-Based Extreme Scenarios,” Electric Power Systems Research, vol. 229, article no. 110158, 2024.

A. Ali, G. Abbas, M. U. Keerio, M. A. Koondhar, K. Chandni, and S. Mirsaeidi, “Solution of Constrained Mixed‐Integer Multi‐Objective Optimal Power Flow Problem Considering the Hybrid Multi‐Objective Evolutionary Algorithm,” IET Generation, Transmission & Distribution, vol. 17, no. 1, pp. 66-90, 2023.

S. Li, W. Gong, L. Wang, and Q. Gu, “Multi-Objective Optimal Power Flow with Stochastic Wind and Solar Power,” Applied Soft Computing, vol. 114, article no. 108045, 2022.

A. Maheshwari, Y. R. Sood, and S. Jaiswal, “Flow Direction Algorithm-Based Optimal Power Flow Analysis in the Presence of Stochastic Renewable Energy Sources,” Electric Power Systems Research, vol. 216, article no. 109087, 2023.

L. Bird, D. Lew, M. Milligan, E. M. Carlini, A. Estanqueiro, D. Flynn, et al., “Wind and Solar Energy Curtailment: A Review of International Experience,” Renewable and Sustainable Energy Reviews, vol. 65, pp. 577-586, 2016.

P. Pijarski, C. Saigustia, P. Kacejko, A. Belowski, and P. Miller, “Optimal Network Reconfiguration and Power Curtailment of Renewable Energy Sources to Eliminate Overloads of Power Lines,” Energies, vol. 17, no. 12, article no. 2965, 2024.

M. Laimon, “Renewable Energy Curtailment: A Problem or an Opportunity?,” Results in Engineering, vol. 26, article no. 104925, 2025.

L. Ren, D. Peng, D. Wang, J. Li, and H. Zhao, “Multi-Objective Optimal Dispatching of Virtual Power Plants Considering Source-Load Uncertainty in V2g Mode,” Frontiers in Energy Research, vol. 10, article no. 983743, 2023.

J. Li, J. Guo, and Y. Deng, “Multi-Objective Optimization for Economic Load Distribution and Emission Reduction with Wind Energy Integration,” International Journal of Electrical Power & Energy Systems, vol. 161, article no. 110175, 2024.

J. Nie, L. Zhou, M. F. Kaye, C. C. Silveira, A. Nwokolo, X. Jiang, et al., “High-Performance Optimal Power Flow Estimation for EV-Interfaced Microgrids with Standardized Grid Services,” IEEE Transactions on Industry Applications, vol. 59, no. 1, pp. 1199-1211, 2022.

M. Abid, M. Belazzoug, S. Mouassa, A. Chanane, and F. Jurado, “Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and Facts Devices Using Non-Dominated Sorting Kepler Optimization Algorithm,” Sustainability, vol. 16, no. 21, article no. 9599, 2024.

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002.

P. P. Biswas, P. N. Suganthan, R. Mallipeddi, and G. A. J. Amaratunga, “Multi-Objective Optimal Power Flow Solutions Using a Constraint Handling Technique of Evolutionary Algorithms,” Soft Computing, vol. 24, no. 4, pp. 2999-3023, 2020.

Z. Wang, A. Younesi, M. V. Liu, G. C. Guo, and C. L. Anderson, “AC Optimal Power Flow in Power Systems with Renewable Energy Integration: A Review of Formulations and Case Studies,” IEEE Access, vol. 11, pp. 102681-102712, 2023.

M. M.-U.-T. Chowdhury, K. Murari, M. S. Hasan, and S. Kamalasadan, “Optimal Power Flow (OPF) Analysis for AC–DC Active Distribution Networks Utilizing Second-Order Cone Programming (SOCP) Approach,” IEEE Transactions on Industrial Informatics, vol. 21, no. 5, pp. 3555-3564, 2025.

E. Obio, S. Ali, I. Oyebanjo, D. Abara, F. Suleiman, P. Ohiero, et al., “Comparison of Economic Dispatch, OPF and Security Constrained-OPF in Power System Studies RN,” Journal of Power and Energy Engineering, vol. 10, no. 8, pp. 54-74, 2022.

S. A. Mohamed, N. Anwer, and M. M. Mahmoud, “Solving Optimal Power Flow Problem for IEEE-30 Bus System Using a Developed Particle Swarm Optimization Method: Towards Fuel Cost Minimization,” International Journal of Modelling and Simulation, vol. 45, no. 1, pp. 307-320, 2025.