Review of key issues in optimal planning of multi-time scale energy storage systems

FENG Jianzhou; HU Zechun; CUI Chang; WU Yinqiong

doi:10.19783/j.cnki.pspc.251172

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Review of key issues in optimal planning of multi-time scale energy storage systems

更新时间：2026-05-14

- Review of key issues in optimal planning of multi-time scale energy storage systems
- Power System Protection and Control Vol. 54, Issue 10, (2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.251172
  CLC：
- Published：2026
- 稿件说明：
移动端阅览
FENG Jianzhou, HU Zechun, CUI Chang, et al. Review of key issues in optimal planning of multi-time scale energy storage systems[J]. Power System Protection and Control, 2026, 54(10).
DOI：

FENG Jianzhou, HU Zechun, CUI Chang, et al. Review of key issues in optimal planning of multi-time scale energy storage systems[J]. Power System Protection and Control, 2026, 54(10). DOI： 10.19783/j.cnki.pspc.251172.

摘要

随着新能源在电力系统中占比的不断提升，储能作为保障系统安全性与灵活性的重要资源，其规划方法受到了广泛关注。多类型储能系统能够在多个时间尺度上为电力系统供需平衡提供支撑。基于此，首先聚焦于 3 个关键时间尺度的储能优化规划问题：在长时间尺度上，储能促进电力系统电力电量平衡；在中时间尺度上，储能用于应对极端天气的不确定性，提升系统韧性；在短时间尺度上，储能通过快速灵活的控制策略，保障电力系统安全稳定运行。随后，系统性地综述了近年来多类型储能系统在上述 3 个关键问题中的优化规划方法，总结了现有研究的典型建模思路与求解方法，并分析了其局限性。最后，进一步展望未来的研究方向，包括跨时间尺度与多功能一体化规划、多能源与灵活性资源的协同优化、极端天气时序特征的精细刻画、跟 / 构网型储能在暂态过程中的应用，以及市场机制下的成本收益分析。该研究旨在为新能源高占比电力系统背景下储能多时间尺度规划的后续研究与实际应用提供参考。

Abstract

With the increasing penetration of renewable energy in power systems

energy storage-serving as a critical resource for ensuring system security and flexibility-has attracted widespread attention in terms of its planning methods. Multi-type storage systems can support supply-demand balance across multiple time scales. In this context

this paper first focuses on three key time-scale related planning problems of energy storage: at the long-term scale

energy storage facilitates power and energy balance in the power system; at the medium-term scale

it is used to address uncertainties caused by extreme weather and enhance system resilience; and at the short-term scale

it ensures secure and stable system operation through fast and flexible control strategies. Subsequently

this paper systematically reviews recent optimization planning methods for multi-type energy storage systems in relation to these three key issues

summarizing typical modeling approaches and solution techniques

and analyzing their limitations. Finally

future research directions are highlighted

including cross multi-scale and multi-functional integrated planning

coordination optimization of multi-energy systems and flexibility resources

refined modeling of the temporal characteristics of extreme weather

applications of grid-forming and grid-following energy storage in transient stability

and cost-benefit analysis under market environments. This research aims to provide a reference for future research and practical applications of multi-time scale energy storage planning in power systems with high renewable energy penetration.

关键词

Keywords

references

United Nations. Paris Agreement[R]. United Nations, 2015.

International Energy Agency. World energy outlook 2024[R]. IEA, 2024.

Australia Energy Market Operator. Black system south Australia 28 September 2016 final report[R]. Australia Energy Market Operator Limited, 2017.

SHAIR J, XIE X, LIU W, et al. Modeling and stability analysis methods for investigating subsynchronous control interaction in large-scale wind power systems[J]. Renewable and Sustainable Energy Reviews, 2021, 135.

YANG D, WANG X, LIU F, et al. Symmetrical PLL for SISO impedance modeling and enhanced stability in weak grids[J]. IEEE Transactions on Power Electronics, 2020, 35(2): 1473-1483.

DONG W, XIN H, WU D, et al. Small signal stability analysis of multi-infeed power electronic systems based on grid strength assessment[J]. IEEE Transactions on Power Systems, 2019, 34(2): 1393-1403.

PULAZZA G, ZHANG N, KANG C, et al. Transmission planning with battery-based energy storage transportation for power systems with high penetration of renewable energy[J]. IEEE Transactions on Power Systems, 2021, 36(6): 4928-4940.

ZHANG N, KANG C, KIRSCHEN D S, et al. Planning pumped storage capacity for wind power integration[J]. IEEE Transactions on Sustainable Energy, 2013, 4(2): 393-401.

YAN Y, ZHANG C, LI K, et al. An integrated design for hybrid combined cooling, heating and power system with compressed air energy storage[J]. Applied Energy, 2018, 210: 1151-1166.

TAN J, WU Q, ZHANG X. Optimal planning of integrated electricity and heat system considering seasonal and short-term thermal energy storage[J]. IEEE Transactions on Smart Grid, 2023, 14(4): 2697-2708.

JIANG H, QI B, DU E, et al. Modeling hydrogen supply chain in renewable electric energy system planning[J]. IEEE Transactions on Industry Applications, 2022, 58(2): 2780-2791.

PAN G, GU W, LU Y, et al. Optimal planning for electricity-hydrogen integrated energy system considering power to hydrogen and heat and seasonal storage[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2662-2676.

ZHANG N, KANG C, KIRSCHEN D S, et al. Planning pumped storage capacity for wind power integration[J]. IEEE Transactions on Sustainable Energy, 2013, 4(2): 393-401.

HE G, MALLAPRAGADA D S, BOSE A, et al. Hydrogen supply chain planning with flexible transmission and storage scheduling[J]. IEEE Transactions on Sustainable Energy, 2021, 12(3): 1730-1740.

LU Z, LI H, QIAO Y. Probabilistic flexibility evaluation for power system planning considering its association with renewable power curtailment[J]. IEEE Transactions on Power Systems, 2018, 33(3): 3285-3295.

LI J, LIU X, ZHENG Z, et al. Optimal planning and operation of multi-type flexible resources based on differentiated feature matching in regional power grid with high proportion of clean energy[J]. Journal of Modern Power Systems and Clean Energy, 2024, 12(6): 1724-1736.

LI Y, WANG J, GU C, et al. Investment optimization of grid-scale energy storage for supporting different wind power utilization levels[J]. Journal of Modern Power Systems and Clean Energy, 2019, 7(6): 1721-1734.

HUO Y, YANG J, QU J, et al. Lightweight data-driven planning method of hybrid energy storage systems in the new power system[J]. IEEE Transactions on Industry Applications, 2025, 61(3): 4792-4800.

CHEN Z, LI Z, LIN D, et al. Multi-time-scale optimal scheduling of integrated energy system with electric-thermal-hydrogen hybrid energy storage under wind and solar uncertainties[J]. Journal of Modern Power Systems and Clean Energy, 2025, 13(3): 904-914.

CHEN B, LIU T, LIU X, et al. A Wasserstein distance-based distributionally robust chance-constrained clustered generation expansion planning considering flexible resource investments[J]. IEEE Transactions on Power Systems, 2023, 38(6): 5635-5647.

CRUZ M R M, FITIWI D Z, SANTOS S F, et al. Multi-flexibility option integration to cope with large-scale integration of renewables[J]. IEEE Transactions on Sustainable Energy, 2020, 11(1): 48-60.

ZHANG H, HU Z, XU Z, et al. Evaluation of achievable vehicle-to-grid capacity using aggregate PEV model[J]. IEEE Transactions on Power Systems, 2017, 32(1): 784-794.

LI H, ZHANG N, BAO W, et al. Modeling and planning of multi-timescale flexible resources in power systems[J]. CSEE Journal of Power and Energy Systems, 2022: 1-10.

YAN Q, MAR, DONG X, et al. Joint source-grid-storage planning method considering coupling relationship of water abandonment events[J]. Protection and Control of Modern Power Systems, 2024, 10(1): 121-131.

ZHANG N, JIANG H, DU E, et al. An efficient power system planning model considering year-round hourly operation simulation[J]. IEEE Transactions on Power Systems, 2022, 37(6): 4925-4935.

WU Y, FANG J, AI X, et al. Robust co-planning of AC/DC transmission network and energy storage considering uncertainty of renewable energy[J]. Applied Energy, 2023, 339.

QI W, LIANG Y, SHEN Z J M. Joint planning of energy storage and transmission for wind energy generation[J]. Operations Research, 2015, 63(6): 1280-1293.

BHATTACHARJEE S, SIOSHANSI R, ZAREIPOUR H. Benefits of strategically sizing wind-integrated energy storage and transmission[J]. IEEE Transactions on Power Systems, 2021, 36(2): 1141-1151.

PAUL S, NATH A P, RATHER Z H. A multi-objective planning framework for coordinated generation from offshore wind farm and battery energy storage system[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2087-2097.

ZHANG Z, ZHOU M, YUAN B, et al. Decarbonizing the power system by co-planning coal-fired power plant transformation and energy storage[J]. Journal of Energy Storage, 2023, 66.

JIANG H, DU E, ZHANG N, et al. Renewable electric energy system planning considering seasonal electricity imbalance risk[J]. IEEE Transactions on Power Systems, 2023, 38(6): 5432-5444.

YU Y, DU E, CHEN Z, et al. Optimal portfolio of a 100% renewable energy generation base supported by concentrating solar power[J]. Renewable and Sustainable Energy Reviews, 2022, 170.

FENG J, HU Z, DUAN X, et al. A multi stage DRO-SDDP approach for planning multi-type energy storage systems and flexible resources in high-penetration renewable power systems[J]. IEEE Transactions on Industry Applications, 2025, 61(4): 1-14.

YU Z, CHI Y, LIN J, et al. Joint multi-stage planning of renewable generation, HESS, and AESS for deeply decarbonizing power systems with high-penetration renewables[J]. IEEE Transactions on Sustainable Energy, 2024, 16(3): 1613-1628.

BIRGE J R, LOUVEAUX F. Introduction to stochastic programming[M]. New York: Springer, 2011.

HELSETH A, MO B. Hydropower aggregation by spatial decomposition—an SDDP approach[J]. IEEE Transactions on Sustainable Energy, 2023, 14(1): 381-392.

PEREIRA M V F, PINTO L M V G. Multi-stage stochastic optimization applied to energy planning[J]. Mathematical Programming, 1991, 52(1): 359-375.

DUQUE D, MORTON D P. Distributionally robust stochastic dual dynamic programming[J]. SIAM Journal on Optimization, 2020, 30(4): 2841-2865.

PHILPOTT A B, DE MATOS V L, KAPELEVICH L. Distributionally robust SDDP[J]. Computational Management Science, 2018, 15(3): 431-454.

BHATTACHARYA A, KHAROUFEH J P, ZENG B. Managing energy storage in microgrids: a multistage stochastic programming approach[J]. IEEE Transactions on Smart Grid, 2018, 9(1): 483-496.

HEMMATI R, SABOORI H, JIRDEHI M A. Multistage generation expansion planning incorporating large scale energy storage systems and environmental pollution[J]. Renewable Energy, 2016, 97: 636-645.

PADHEE M, PAL A, MISHRA C, et al. A fixed-flexible BESS allocation scheme for transmission networks considering uncertainties[J]. IEEE Transactions on Sustainable Energy, 2020, 11(3): 1883-1897.

LI J, FANG Z, WANG Q, et al. Optimal operation with dynamic partitioning strategy for centralized shared energy storage station with integration of large-scale renewable energy[J]. Journal of Modern Power Systems and Clean Energy, 2024, 12(2): 359-370.

HE Y, GUO S, ZHOU J, et al. Multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages[J]. Renewable Energy, 2022, 184: 776-790.

SUN M, CREMER J, STRBAC G. A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration[J]. Applied Energy, 2018, 228: 546-555.

JIN J, YE L, LI J, et al. Wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain[J]. CSEE Journal of Power and Energy Systems, 2022, 8(3): 757-768.

ONWUEGBUCHE F, KENYATTA A, AFFOGNON S, et al. Application of extreme value theory in predicting climate change induced extreme rainfall in Kenya[J]. International Journal of Statistics and Probability, 2019, 8(4).

SIGAUKE C, BERE A. Modelling non-stationary time series using a peaks over threshold distribution with time varying covariates and threshold: an application to peak electricity demand[J]. Energy, 2017, 119: 152-166.

ZATTI M, GABBA M, FRESCHINI M, et al. K-MILP: a novel clustering approach to select typical and extreme days for multi-energy systems design optimization[J]. Energy, 2019, 181: 1051-1063.

ZHU X, YU Z, LIU X. Security constrained unit commitment with extreme wind scenarios[J]. Journal of Modern Power Systems and Clean Energy, 2020, 8(3): 464-472.

YEGANEFAR A, AMIN-NASERI M R, SHEIKH-EL-ESLAMI M K. Improvement of representative days selection in power system planning by incorporating the extreme days of the net load to take account of the variability and intermittency of renewable resources[J]. Applied Energy, 2020, 272.

DU N, TANG F, LIAO Q, et al. Hosting capacity assessment in distribution networks considering wind-photovoltaic-load temporal characteristics[J]. Frontiers in Energy Research, 2021, 9.

WEI H, HONGXUAN Z, YU D, et al. Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks[J]. Applied Energy, 2019, 250: 389-403.

YUAN R, WANG B, SUN Y, et al. Conditional style-based generative adversarial networks for renewable scenario generation[J]. IEEE Transactions on Power Systems, 2023, 38(2): 1281-1296.

YAN J, HU B, XIE K, et al. Data-driven transmission defense planning against extreme weather events[J]. IEEE Transactions on Smart Grid, 2020, 11(3): 2257-2270.

ZHU Y, DAN Y, WANG L, et al. Inter-day energy storage expansion framework against extreme wind droughts based on extreme value theory and deep generation models[J]. IET Renewable Power Generation, 2024, 18(9-10): 1675-1690.

OIKONOMOU K, MALONEY P R, BHATTACHARYA S, et al. Energy storage planning for enhanced resilience of power systems against wildfires and heatwaves[J]. Journal of Energy Storage, 2025, 119.

WANG S, BO R. A resilience-oriented multi-stage adaptive distribution system planning considering multiple extreme weather events[J]. IEEE Transactions on Sustainable Energy, 2023, 14(2): 1193-1204.

MORADI-SEPAHVAND M, AMRAEE T, GOUGHERI S S. Deep learning based hurricane resilient coplanning of transmission lines, battery energy storages, and wind farms[J]. IEEE Transactions on Industrial Informatics, 2022, 18(3): 2120-2131.

WANG Q, ZHANG X, XU Y, et al. Planning of stationary-mobile integrated battery energy storage systems under severe convective weather[J]. IEEE Transactions on Sustainable Energy, 2025, 16(2): 1253-1268.

CHEN H, XIONG X, ZHU J, et al. A two-stage stochastic programming model for resilience enhancement of active distribution networks with mobile energy storage systems[J]. IEEE Transactions on Power Delivery, 2023, 39(4): 2001-2014.

SUN Q, WU Z, GU W, et al. Tri-level multi-energy system planning method for zero energy buildings considering long- and short-term uncertainties[J]. IEEE Transactions on Sustainable Energy, 2023, 14(1): 339-355.

WANG Y, HU S, SONG Y, et al. Robust co-planning of transmission network and B-SMES[J]. IEEE Transactions on Applied Superconductivity, 2021, 31(8): 1-4.

WANG Z, BIAN Q, XIN H, et al. A distributionally robust coordinated reserve scheduling model considering CVaR-based wind power reserve requirements[J]. IEEE Transactions on Sustainable Energy, 2016, 7(2): 625-636.

DA COSTA L C, THOMÉ F S, GARCIA J D, et al. Reliability-constrained power system expansion planning: a stochastic risk-averse optimization approach[J]. IEEE Transactions on Power Systems, 2021, 36(1): 97-106.

YANG X, HE H, LI J, et al. Toward optimal risk-averse configuration for HESS with CGANs-based PV scenario generation[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(3): 1779-1793.

BLANCHET J, HE F, MURTHY K. On distributionally robust extreme value analysis[J]. Extremes, 2020, 23(2): 317-347.

YANG H, ZHANG C, LI J, et al. A novel robust energy storage planning method for grids with wind power integration considering the impact of hurricanes[J]. IEEE Transactions on Sustainable Energy, 2025, 16(2): 1388-1400.

CHÁVEZ H, BALDICK R, SHARMA S. Governor rate-constrained OPF for primary frequency control adequacy[J]. IEEE Transactions on Power Systems, 2014, 29(3): 1473-1480.

SHI Q, LI F, CUI H. Analytical method to aggregate multi-machine SFR model with applications in power system dynamic studies[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6355-6367.

ZHANG Z, DU E, ZHU G, et al. Modeling frequency response dynamics in power system scheduling[J]. Electric Power Systems Research, 2020, 189.

ZHANG Z, DU E, TENG F, et al. Modeling frequency dynamics in unit commitment with a high share of renewable energy[J]. IEEE Transactions on Power Systems, 2020, 35(6): 4383-4395.

MARKOVIC U, CHU Z, ARISTIDOU P, et al. LQR-based adaptive virtual synchronous machine for power systems with high inverter penetration[J]. IEEE Transactions on Sustainable Energy, 2019, 10(3): 1501-1512.

CAI X, ZHANG N, DU E, et al. Low inertia power system planning considering frequency quality under high penetration of renewable energy[J]. IEEE Transactions on Power Systems, 2024, 39(2): 4537-4548.

GENC I, DIAO R, VITTAL V, et al. Decision tree-based preventive and corrective control applications for dynamic security enhancement in power systems[J]. IEEE Transactions on Power Systems, 2010, 25(3): 1611-1619.

LAGOS D T, HATZIARGYRIOU N D. Data-driven frequency dynamic unit commitment for island systems with high RES penetration[J]. IEEE Transactions on Power Systems, 2021, 36(5): 4699-4711.

ZHANG Y, CUI H, LIU J, et al. Encoding frequency constraints in preventive unit commitment using deep learning with region-of-interest active sampling[J]. IEEE Transactions on Power Systems, 2022, 37(3): 1942-1955.

DU W, DONG W, WANG H, et al. Dynamic aggregation of same wind turbine generators in parallel connection for studying oscillation stability of a wind farm[J]. IEEE Transactions on Power Systems, 2019, 34(6): 4694-4705.

EL-SHARKAWI M A. Dynamic equivalent models for wind power plants[C] // 2011 IEEE Power and Energy Society General Meeting, July 24-28, 2011, Detroit, MI, USA: 1-5.

ZHOU Y, XIN H, WU D, et al. Small-signal stability assessment of heterogeneous grid-following converter power systems based on grid strength analysis[J]. IEEE Transactions on Power Systems, 2023, 38(3): 2566-2579.

LIU C, XIN H, WU D, et al. Generalized operational short-circuit ratio for grid strength assessment in power systems with high renewable penetration[J]. IEEE Transactions on Power Systems, 2024, 39(4): 5479-5494.

LIU Dan, YANG Qiufan, CHEN Yin, et al. Optimal parameters and placement of hybrid energy storage systems for frequency stability improvement[J]. Protection and Control of Modern Power Systems, 2025, 10(2): 40-53.

GONG K, WANG X, JIANG C, et al. Security-constrained optimal sizing and siting of BESS in hybrid AC/DC microgrid considering post-contingency corrective rescheduling[J]. IEEE Transactions on Sustainable Energy, 2021, 12(4): 2110-2122.

WANG J, ZHANG J, YANG M, et al. Nodal frequency-constrained energy storage planning via hybrid data-model driven methods[J]. iEnergy, 2025, 4(1): 43-53.

ZHANG C, LIU L, CHENG H, et al. Frequency-constrained co-planning of generation and energy storage with high-penetration renewable energy[J]. Journal of Modern Power Systems and Clean Energy, 2021, 9(4): 760-775.

LIU Y, CHEN Y, XIN H, et al. System strength constrained grid-forming energy storage planning in renewable power systems[J]. IEEE Transactions on Sustainable Energy, 2024, 16(2): 981-994.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

考虑超分辨率风电不确定性的鲁棒安全经济调度

基于自适应准谐振控制的储能抑制广义强迫振荡传播方法

计及台风灾害全过程模拟的配电网差异化加固规划韧性提升方法

基于深度确定性策略梯度与模糊PID的直流微电网VRB储能系统就地层功率控制

Study on enhancing power supply capacity of active distribution networks considering transfer rate and capacity-to-load ratio

Related Author

DONG Moting

ZHENG Xuerui

CHEN Houhe

JIANG Tao

FAN Xin

LI Benxin

WU Jialong

YU Jianping

Related Institution

DONG Moting(1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University)

Jilin Power Supply Company, State Grid Jilin Electric Power Co., Ltd.

JU Ping(1. College of Energy and Electrical Engineering, Hohai University

Nanrui Group (State Grid Electric Power Research Institute) Co., Ltd.,,)

LU Wanjie(1. Faculty of Electrical and Control Engineering, Liaoning Technical University

AI问答

⁰