Collaborative Optimization of Multi-regional Integrated Energy System Based on Improved Beluga Whale Optimization Algorithm
DOI:
https://doi.org/10.13052/dgaej2156-3306.3942Keywords:
Multi-regional integrated energy system, wind power uncertainty, hybrid energy storage, collaborative optimization, improved beluga whale optimization algorithmAbstract
With the installed capacity of the renewable energy power generation is growing at a high speed, a large number of the renewable energy is connected to the grid, the energy problem has been solved to a large extent, but the ensuing problems can not be ignored, the first is the consumption of the renewable energy, which is followed by the stability problem of the power system, and a multi-regional integrated energy system (MRIES) is constructed to solve the problem. In view of the wind power uncertainty, the absorption treatment is carried out by the equipment layer and the optimization layer respectively. A hybrid energy storage system (HESS) is introduced in the equipment layer to suppress the influence of the wind power uncertainty on the system stability. A combination of the convolutional filtering algorithm, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm and the fuzzy control algorithm is introduced in the optimization layer to develop the charging and discharging strategy of the HESS. With the strategy, the impact of the electric power fluctuations on the power system during the optimization process can be reduced largely. Then, on the basis of fully considering the energy coupling in different equipments, a collaborative optimization model of the MRIES is constructed. And the integrated demand response model considering the time-of-use electric price is introduced in the load side. Finally, the improved Beluga optimization(IBWO) algorithm is developed to optimize the model. The optimization results show that the IBWO algorithm plays a good optimization effect both in the participation of energy supply equipments and the economy, plays a collaborative optimization role in the MRIES and ensures the stability of the whole power system.
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References
Atinkut B., Degarege A., Baseem K., et al. Grid Integration of Hybrid Energy System for Distribution Network[J]. Distributed Generation & Alternative Energy Journal, 2022, 37(3): 537–556.
Guo H., Ren Y., Zhao Q., et al. Techno-economic Analysis of Renewable-based Stand-alone Hybrid Energy Systems Considering Load Growth and Photovoltaic Depreciation Rates[J]. Distributed Generation & Alternative Energy Journal, 2020, 35(3): 209–236.
Tan J., Wu Q., Hu Q., et al. Adaptive robust energy and reserve co-optimization of integrated electricity and thermaling system considering wind uncertainty[J]. Applied Energy, 2020, 260: 114230. https://doi.org/10.1016/j.apenergy.2019.114230.
Duan J, Yang Y, Liu F, et al. Distributed optimization of integrated electricity-natural gas distribution networks considering wind power uncertainties[J]. International Journal of Electrical Power & Energy Systems, 2022, 135: 107460. https://doi.org/10.1016/j.ijepes.2021.107460.
Lu N, Su C, Guo C, et al. Stochastic optimal scheduling of wind power and pumped-storage hydropower complementary systems with multiple uncertainties[J]. Journal of Energy Storage, 2024, 78: 110060. https://doi.org/10.1016/j.est.2023.110060.
Liu X., LIU J., Yang Y., et al. Uncertainty collaborative planning method of integrated energy system considering low-carbon transformation of multi-function zone and multi-mode utilization of low-carbon characteristics of hydrogen energy [J/OL]. Power grid technology, 2024: 1–17. https://doi.org/10.13335/j.1000-3673.pst.2023.2054.
Wang L., Hou C., Ye B., et al. Optimal Operation Analysis of Integrated Community Energy System Considering the Uncertainty of Demand Response[J]. IEEE Transactions on Power Systems[J]. 2021, 36(4): 3681-3691. https://doi:10.1109/TPWRS.2021.3051720.
Zhang Z., Wang C., Wu Q., et al. Optimal dispatch for cross-regional integrated energy system with renewable energy uncertainties: A unified spatial-temporal cooperative framework[J]. Energy, 2024, 292: 130433. https://doi.org/10.1016/j.energy.2024.130433.
Mu Y., Wang C., Cao Y., et al. A CVaR-based risk assessment method for park-level integrated energy system considering the uncertainties and correlation of energy prices[J]. Energy, 2022, 247: 123549. https://doi.org/10.1016/j.energy.2022.123549.
Fahim M., Anwar A., Shoaib A., et al. Hybrid Energy System Modelling for Oil & Gas Fields: A Case Study of Pasakhi Satellite Oil & Gas Complex[J]. Distributed Generation & Alternative Energy Journal, 2022, 37(2): 281–310.
Shang W, LI G, Ding Y, et al. A collaborative scheduling method for integrated energy system considering the uncertainty of source load and new energy consumption[J]. Power grid technology, 2024, 48(02): 517–532. https://doi:10.13335/j.1000-3673.pst.2023.0577.
Li P, Wang J, Li C, et al. A collaborative optimization operation method of integrated energy system in Park considering the uncertainty of source load and the characteristics of equipment operating conditions [J]. Proceedings of the CSEE, 2023, 43(20): 7802–7812. https://doi:10.13334/j.0258-8013.pcsee.222753.
Li Y., Han M., Han Z., et al. Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems With an Electric Vehicle Charging Station: A Bi-Level Approach[J]. IEEE Transactions on Sustainable Energy, 2021, 12(4): 2321–2331. https://doi:10.1109/TSTE.2021.3090463.
Dong Y., Zhang H., Ma P., et al. A hybrid robust-interval optimization approach for integrated energy systems planning under uncertainties[J]. Energy, 2023, 274: 127267. https://doi.org/10.1016/j.energy.2023.127267.
Lin J., Gao C., Zeng J., et al. Stackelberg-Nash asymmetric bargaining-based scheduling optimization and revenue-allocation for multi-operator regional integrated energy system considering competition-cooperation relationship and source-load uncertainties[J]. Energy, 2024, 291: 130262. https://doi.org/10.1016/j.energy.2024.130262.
Wang Y., Zheng Y., Yang Q., et al. Day-ahead bidding strategy of regional integrated energy systems considering multiple uncertainties in electricity markets[J]. Applied Energy, 2023, 348: 121511. https://doi.org/10.1016/j.apenergy.2023.121511.
Liu Z., Cui Y., Wang J., et al. Multi-objective optimization of multi-energy complementary integrated energy systems considering load prediction and renewable energy production uncertainties[J]. Energy, 2022, 254: 124399. https://doi.org/10.1016/j.energy.2022.124399.
Zhou G., Bai M., Li H., et al. Multi-objective station-network synergy planning for regional integrated energy system considering energy cascade utilization and uncertainty[J]. Energy Conversion and Management, 2024, 301: 118073. https://doi.org/10.1016/j.enconman.2024.118073.
Huang S., Lu H., Chen M., et al. Integrated energy system scheduling considering the correlation of uncertainties[J]. Energy, 2023, 283: 129011. https://doi.org/10.1016/j.energy.2023.129011.
Zhou Y., Ma Z., Shi X., et al. Multi-agent optimal scheduling for integrated energy system considering the global carbon emission constraint[J]. Energy, 2024, 288: 129732. https://doi.org/10.1016/j.energy.2023.129732.
Wang Q., Wang Y., Chen Z., et al. Multi-agent system consistency-based cooperative scheduling strategy of regional integrated energy system[J]. Energy, 2024, 295: 130904. https://doi.org/10.1016/j.energy.2024.130904.
Liu J., Li Y., Ma Y., et al. Coordinated energy management for integrated energy system incorporating multiple flexibility measures of supply and demand sides: A deep reinforcement learning approach[J]. Energy Conversion and Management, 2023, 297: 117728. https://doi.org/10.1016/j.enconman.2023.117728.
Li K., Ye N., Li S., et al. Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory[J]. Energy, 2023, 273: 127137. https://doi.org/10.1016/j.energy.2023.127137.
Liang Z., Mu L. Multi-agent low-carbon optimal dispatch of regional integrated energy system based on mixed game theory[J]. Energy, 2024, 295: 130953. https://doi.org/10.1016/j.energy.2024.130953.
Li Z., Xia Y, Bo Y., et al. Optimal planning for electricity-hydrogen integrated energy system considering multiple timescale operations and representative time-period selection[J]. Applied Energy, 2024, 362: 122965. https://doi.org/10.1016/j.apenergy.2024.122965.
Liu S., Song L., Wang T., et al. Negative carbon optimal scheduling of integrated energy system using a non-dominant sorting genetic algorithm[J]. Energy Conversion and Management, 2023, 291: 117345. https://doi.org/10.1016/j.enconman.2023.117345.
Pan C., Fan H., Zhang R., et al. An improved multi-timescale coordinated control strategy for an integrated energy system with a hybrid energy storage system[J]. Applied Energy, 2023, 343: 121137. https://doi.org/10.1016/j.apenergy.2023.121137.
Xu Z., Han G., Han L., et al. Multi-Energy Scheduling of an Industrial Integrated Energy System by Reinforcement Learning-Based Differential Evolution[J]. IEEE Transactions on Green Communications and Networking, 2021, 5(3): 1077–1090. https://doi:10.1109/TGCN.2021.3061789.
Guo J., Wu D., Wang Y., et al. Co-optimization method research and comprehensive benefits analysis of regional integrated energy system[J]. Applied Energy, 2023, 340: 121034. https://doi.org/10.1016/j.apenergy.2023.121034.
Yang W., Huang Y., Huang T., et al. Hybrid continuous-discrete time control strategy to optimize thermal network dynamic storage and thermal-electricity integrated energy system dynamic operation[J]. Energy Conversion and Management, 2024, 301: 118035.
Wang X., Han Li., Wang C., et al. A time-scale adaptive dispatching strategy considering the matching of time characteristics and dispatching periods of the integrated energy system[J]. Energy, 2023, 267: 126584. https://doi.org/10.1016/j.energy.2022.126584.
Ma X., Peng B., Ma X., et al. Multi-timescale optimization scheduling of regional integrated energy system based on source-load joint forecasting[J]. Energy, 2023, 283: 129186. https://doi.org/10.1016/j.energy.2023.129186.
Wang L., Lin J., Dong H., et al. Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system[J]. Energy, 2023, 270: 126893. https://doi.org/10.1016/j.energy.2023.126893.
Cai P., Mi Y., Ma S., et al. Hierarchical game for integrated energy system and electricity-hydrogen hybrid charging station under distributionally robust optimization[J]. Energy, 2023, 283: 128471. https://doi.org/10.1016/j.energy.2023.128471.
Zhang Z., Wang C., Wu Q., et al. Optimal dispatch for cross-regional integrated energy system with renewable energy uncertainties: A unified spatial-temporal cooperative framework[J]. Energy, 2024, 292: 130433. https://doi.org/10.1016/j.energy.2024.130433.
Liu J., Li Yao., Ma Y., et al. Two-layer multiple scenario optimization framework for integrated energy system based on optimal energy contribution ratio strategy[J]. Energy, 2023, 285: 128673. https://doi.org/10.1016/j.energy.2023.128673.
Yang X., Zhang Z., Mei L., et al. Optimal configuration of improved integrated energy system based on stepped carbon penalty response and improved power to gas[J]. Energy, 2023, 263: 125985. https://doi.org/10.1016/j.energy.2022.125985.
Duan P., Zhao B., Zhang X., et al. A day-ahead optimal operation strategy for integrated energy systems in multi-public buildings based on cooperative game[J]. Energy, 2023, 275: 127395. https://doi.org/10.1016/j.energy.2023.127395.
Zheng L., Wu H., Guo S., et al. Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy[J]. Energy, 2023, 277: 127636. https://doi.org/10.1016/j.energy.2023.127636.
