Rolling optimization control method for hydro-photovoltaic-storage microgrid based on stochastic chance constraints

Qianjin Gui; Wenfa  Xu; Xiaoyang Li; Lirong  Luo; Haifeng Ye; Zhengfeng  Wang

doi:10.59400/adecp2799

Qianjin Gui State Grid Anhui Electric Power Co., Ltd. Anqing Power Supply Company, Anqing 246000, China
Wenfa Xu State Grid Anhui Electric Power Co., Ltd. Anqing Power Supply Company, Anqing 246000, China
Xiaoyang Li State Grid Anhui Electric Power Co., Ltd. Anqing Power Supply Company, Anqing 246000, China
Lirong Luo State Grid Anhui Electric Power Co., Ltd. Anqing Power Supply Company, Anqing 246000, China
Haifeng Ye State Grid Anhui Electric Power Co., Ltd., Hefei 230000, China
Zhengfeng Wang State Grid Anhui Electric Power Co., Ltd., Hefei 230000, China

Article ID: 2799

Keywords: hydro-photovoltaic-storage microgrid; stochastic optimal scheduling; correlated uncertainties; multivariate scenario reduction; model predictive control

Abstract

Hydro-photovoltaic-storage (HPS) microgrid has gradually become an important measure to optimize the energy structure and ensure the reliability of regional power supply. However, due to the strong randomness and spatiotemporal correlations of hydropower and photovoltaic (PV) output, traditional deterministic optimization methods are difficult to support the accurate regulation and reliable operation of microgrid with a high proportion of renewable energy integration. On this basis, a rolling optimization control method for HPS microgrid based on stochastic chance constraints is proposed. A novel multivariate scenario reduction method considering hydro-PV correlations is presented to characterize the uncertainty of renewable energy output, and a day-ahead stochastic optimal scheduling model based on chance-constrained programming is constructed. Combined with stochastic model predictive control strategies, the day-ahead scheduling plan can be adjusted at multiple time scales, both intraday power compensation and real-time adjustments, to suppress the intraday power fluctuations induced by day-ahead scenario errors and reduce the influence of the uncertainty of hydro-PV power output on microgrid operation. Experimental results show that compared with the traditional deterministic scheduling method, the proposed method can effectively improve the stability and economy of HPS microgrid operation under complex uncertain conditions.

References

[1]Olatunde O, Hassan MY, Abdullah MP, et al. Hybrid photovoltaic/small-hydropower microgrid in smart distribution network with grid isolated electric vehicle charging system. Journal of Energy Storage. 2020; 31: 101673. doi: 10.1016/j.est.2020.101673

[2]Guan Y, Vasquez JC, Guerrero JM, et al. Frequency Stability of Hierarchically Controlled Hybrid Photovoltaic-Battery-Hydropower Microgrids. IEEE Transactions on Industry Applications. 2015; 51(6): 4729–4742. doi: 10.1109/tia.2015.2458954

[3]Bagheri F, Dagdougui H, Gendreau M. Stochastic optimization and scenario generation for peak load shaving in Smart District microgrid: Sizing and operation. Energy and Buildings. 2022; 275: 112426. doi: 10.1016/j.enbuild.2022.112426

[4]Deng J, Li H, Hu J, et al. A new wind speed scenario generation method based on spatiotemporal dependency structure. Renewable Energy. 2021; 163: 1951–1962. doi: 10.1016/j.renene.2020.10.132

[5]Zhu X, Yu Z, Liu X. Security constrained unit commitment with extreme wind scenarios. Journal of Modern Power Systems and Clean Energy. 2020; 8(3): 464–472. doi: 10.35833/mpce.2018.000797

[6]Ma H, Zhang W, Wang A. A two-phase robust comprehensive optimal scheduling strategy for regional distribution network based on multiple scenarios. Frontiers in Energy Research. 2024; 12. doi: 10.3389/fenrg.2024.1496302

[7]Rezaee Jordehi A, Mansouri SA, Tostado-Véliz M, et al. A tri-level stochastic model for operational planning of microgrids with hydrogen refuelling station-integrated energy hubs. International Journal of Hydrogen Energy. 2024; 96: 1131–1145. doi: 10.1016/j.ijhydene.2024.11.401

[8]Azizivahed A, Gholami K, Arefi A, et al. Stochastic scheduling of energy sharing in reconfigurable multi-microgrid systems in the presence of vehicle-to-grid technology. Electric Power Systems Research. 2024; 231: 110285. doi: 10.1016/j.epsr.2024.110285

[9]Seyedeh-Barhagh S, Abapour M, Mohammadi-Ivatloo B, et al. Optimal scheduling of a microgrid based on renewable resources and demand response program using stochastic and IGDT-based approach. Journal of Energy Storage. 2024; 86: 111306. doi: 10.1016/j.est.2024.111306

[10]Shaillan HM, Tohidi S, Hagh MT, et al. Risk-aware two-stage stochastic short-term planning of a hybrid multi-microgrid integrated with an all-in-one vehicle station and end-user cooperation. Journal of Energy Storage. 2024; 78: 110083. doi: 10.1016/j.est.2023.110083

[11]Hu J, Li H. A transfer learning-based scenario generation method for stochastic optimal scheduling of microgrid with newly-built wind farm. Renewable Energy. 2022; 185: 1139–1151. doi: 10.1016/j.renene.2021.12.110

[12]Wang Z, Hu J, Liu B. Stochastic optimal dispatching strategy of electricity-hydrogen-gas-heat integrated energy system based on improved spectral clustering method. International Journal of Electrical Power & Energy Systems. 2021; 126: 106495. doi: 10.1016/j.ijepes.2020.106495

[13]Abunima H, Park WH, Glick MB, et al. Two-Stage stochastic optimization for operating a Renewable-Based Microgrid. Applied Energy. 2022; 325: 119848. doi: 10.1016/j.apenergy.2022.119848

[14]Li K, Yang F, Wang L, et al. A scenario-based two-stage stochastic optimization approach for multi-energy microgrids. Applied Energy. 2022; 322: 119388. doi: 10.1016/j.apenergy.2022.119388

[15]Jani A, Karimi H, Jadid S. Two-layer stochastic day-ahead and real-time energy management of networked microgrids considering integration of renewable energy resources. Applied Energy. 2022; 323: 119630. doi: 10.1016/j.apenergy.2022.119630

[16]Aaslid P, Korpås M, Belsnes MM, et al. Stochastic operation of energy constrained microgrids considering battery degradation. Electric Power Systems Research. 2022; 212: 108462. doi: 10.1016/j.epsr.2022.108462

[17]Kizito R, Liu Z, Li X, et al. Multi-stage stochastic optimization of islanded utility-microgrids design after natural disasters. Operations Research Perspectives. 2022; 9: 100235. doi: 10.1016/j.orp.2022.100235

[18]Heitsch H, Römisch W. Scenario reduction algorithms in stochastic programming. Computational Optimization and Applications. 2003; 24: 187–206.

[19]Xu J, Yi X, Sun Y, et al. Stochastic Optimal Scheduling Based on Scenario Analysis for Wind Farms. IEEE Transactions on Sustainable Energy. 2017; 8(4): 1548–1559. doi: 10.1109/tste.2017.2694882

[20]Pandzic H, Dvorkin Y, Qiu T, et al. Toward Cost-Efficient and Reliable Unit Commitment Under Uncertainty. IEEE Transactions on Power Systems. 2016; 31(2): 970–982. doi: 10.1109/tpwrs.2015.2434848

[21]Park S, Xu Q, Hobbs BF. Comparing scenario reduction methods for stochastic transmission planning. IET Generation, Transmission & Distribution. 2019; 13(7): 1005–1013. doi: 10.1049/iet-gtd.2018.6362

[22]Beltran F, de Oliveira W, Finardi EC. Application of Scenario Tree Reduction Via Quadratic Process to Medium-Term Hydrothermal Scheduling Problem. IEEE Transactions on Power Systems. 2017; 32(6): 4351–4361. doi: 10.1109/tpwrs.2017.2658444

[23]Zhan J, Chung CY, Zare A. A fast solution method for stochastic transmission expansion planning. IEEE Transactions on Power Systems. 2017; 32(6): 4684–4695. doi: 10.1109/tpwrs.2017.2665695

[24]Hu J, Li H. A new clustering approach for scenario reduction in multi-stochastic variable programming. IEEE Transactions on Power Systems. 2019; 34(5): 3813–3825. doi: 10.1109/tpwrs.2019.2901545

[25]Hu J, Li H, Liu Z. Scenario reduction based on correlation sensitivity and its application in microgrid optimization. International Transactions on Electrical Energy Systems. 2021; 31(3). doi: 10.1002/2050-7038.12747

[26]Zhang Y, Meng F, Wang R, et al. Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid. Energy. 2019; 179: 1265–1278. doi: 10.1016/j.energy.2019.04.151

[27]Bazmohammadi N, Tahsiri A, Anvari-Moghaddam A, et al. Stochastic predictive control of multi-microgrid systems. IEEE Transactions on Industry Applications. 2019; 55(5): 5311–5319. doi: 10.1109/tia.2019.2918051

[28]Jiao F, Zou Y, Zhang X, et al. Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station. Energy. 2022; 247: 123220. doi: 10.1016/j.energy.2022.123220

[29]Lin Y, Li L, Zhang J, et al. A scenario-based stochastic model predictive control approach for microgrid operation at an Australian cotton farm under uncertainties. International Journal of Electrical Power & Energy Systems. 2024; 159: 110025. doi: 10.1016/j.ijepes.2024.110025

[30]Wei S, Gao X, Zhang Y, et al. An improved stochastic model predictive control operation strategy of integrated energy system based on a single-layer multi-timescale framework. Energy. 2021; 235: 121320. doi: 10.1016/j.energy.2021.121320

[31]Hu J, Yan P, Tan G. A two-layer optimal scheduling method for microgrids based on adaptive stochastic model predictive control. Measurement Science and Technology. 2025; 36(2): 026208. doi: 10.1088/1361-6501/ada39b

[32]Hu J, Yan P, Tan G. Spatiotemporal dependence modeling of wind speeds via adaptive-selected mixture pair copulas for scenario-based applications. Renewable Energy. 2025; 244: 122650. doi: 10.1016/j.renene.2025.122650

[33]Qin Z, Li W, Xiong X. Incorporating multiple correlations among wind speeds, photovoltaic powers and bus loads in composite system reliability evaluation. Applied Energy. 2013; 110: 285–294. doi: 10.1016/j.apenergy.2013.04.045

[34]Ju C, Wang P, Goel L, Xu Y. A two-layer energy management system for microgrids with hybrid energy storage considering degradation costs. IEEE Transactions on Smart Grid. 2018; 9(6): 6047–6057. doi: 10.1109/tsg.2017.2703126