Review Special Issues

A review on peak shaving techniques for smart grids

  • Received: 20 July 2023 Revised: 16 August 2023 Accepted: 21 August 2023 Published: 08 September 2023
  • Peak shaving techniques have become increasingly important for managing peak demand and improving the reliability, efficiency, and resilience of modern power systems. In this review paper, we examine different peak shaving strategies for smart grids, including battery energy storage systems, nuclear and battery storage power plants, hybrid energy storage systems, photovoltaic system installations, the real-time scheduling of household appliances, repurposed electric vehicle batteries, uni- and bi-directional electric vehicle charging, the demand response, the time-of-use pricing, load shedding systems, distributed generation systems and energy-efficient management. We analyze the potential of each strategy to reduce peak demand and shift energy consumption to off-peak hours, as well as identify the key themes critical to the success of peak shaving for smart grids, including effective coordination and communication, data analytics and predictive modeling and clear policy and regulatory frameworks. Our review highlights the diverse range of innovative technologies and techniques available to utilities and power system operators and it emphasizes the need for continued research and development in emerging areas such as blockchain technology and artificial intelligence. Overall, the implementation of peak shaving strategies represents a significant step toward a more sustainable, reliable and efficient power system. By leveraging the latest technologies and techniques available, utilities and power system operators can better manage peak demand, integrate renewable energy sources, and create a more reliable and secure grid for the future. By discussing cutting-edge technologies and methods to effectively manage peak demand and incorporate renewable energy sources, this review paper emphasizes the significance of peak shaving strategies for smart grids as a crucial pathway towards realizing a more sustainable, dependable and efficient power system.

    Citation: Syed Sabir Hussain Rizvi, Krishna Teerth Chaturvedi, Mohan Lal Kolhe. A review on peak shaving techniques for smart grids[J]. AIMS Energy, 2023, 11(4): 723-752. doi: 10.3934/energy.2023036

    Related Papers:

  • Peak shaving techniques have become increasingly important for managing peak demand and improving the reliability, efficiency, and resilience of modern power systems. In this review paper, we examine different peak shaving strategies for smart grids, including battery energy storage systems, nuclear and battery storage power plants, hybrid energy storage systems, photovoltaic system installations, the real-time scheduling of household appliances, repurposed electric vehicle batteries, uni- and bi-directional electric vehicle charging, the demand response, the time-of-use pricing, load shedding systems, distributed generation systems and energy-efficient management. We analyze the potential of each strategy to reduce peak demand and shift energy consumption to off-peak hours, as well as identify the key themes critical to the success of peak shaving for smart grids, including effective coordination and communication, data analytics and predictive modeling and clear policy and regulatory frameworks. Our review highlights the diverse range of innovative technologies and techniques available to utilities and power system operators and it emphasizes the need for continued research and development in emerging areas such as blockchain technology and artificial intelligence. Overall, the implementation of peak shaving strategies represents a significant step toward a more sustainable, reliable and efficient power system. By leveraging the latest technologies and techniques available, utilities and power system operators can better manage peak demand, integrate renewable energy sources, and create a more reliable and secure grid for the future. By discussing cutting-edge technologies and methods to effectively manage peak demand and incorporate renewable energy sources, this review paper emphasizes the significance of peak shaving strategies for smart grids as a crucial pathway towards realizing a more sustainable, dependable and efficient power system.



    加载中


    [1] Markovic DS, Zivkovic D, Branovic I, et al. (2013) Smart power grid and cloud computing. Renewable Sustainable Energy Rev 24: 566–577. https://doi.org/10.1016/j.rser.2013.03.068 doi: 10.1016/j.rser.2013.03.068
    [2] Ozdemir E, Ozdemir S, Erhan K, et al. (2016) Energy storage technologies opportunities and challenges in smart grids. International Smart Grid Workshop and Certificate Program (ISGWCP), Istanbul, Turkey, 1–6. https://doi.org/10.1109/ISGWCP.2016.7548263
    [3] Borlase S (2018) Smart grids: Advanced technologies and solutions, second edition (2nd Ed.). CRC Press, Taylor & Francis Group. https://doi.org/10.1201/9781351228480
    [4] Uddin M, Romlie MF, Abdullah MF, et al. (2017) A review on peak load shaving strategies. Renewable Sustainable Energy Rev 82: 3323–3332. https://doi.org/10.1016/j.rser.2017.10.056 doi: 10.1016/j.rser.2017.10.056
    [5] Yılmaz F, Eren Y (2023) A novel load profile generation method based on the estimation of regional usage habit parameters with genetic algorithm. Electric Power Syst Res 217: 1–13. https://doi.org/10.1016/j.epsr.2023.109165 doi: 10.1016/j.epsr.2023.109165
    [6] Ideal energy (2023) Peak shaving with solar and energy storage. Available from: https://www.idealenergysolar.com/peak-shaving-solar-storage.
    [7] Zhang TS, Sheng WX, Song XH, et al. (2013) Probabilistic modelling and simulation of stochastic load for power system studies. UKSim 15th International Conference on Computer Modelling and Simulation, Cambridge, UK, 519–524. https://doi.org/10.1109/UKSim.2013.23
    [8] Oudalov A, Cherkaoui R, Beguin A (2007) Sizing and optimal operation of battery energy storage system for peak shaving application. 2007 IEEE Lausanne Power Tech, Lausanne, Switzerland, 621–625. https://doi.org/10.1109/PCT.2007.4538388
    [9] Uddin M, Romlie MF, Abdullah MF, et al. (2020) A novel peak shaving algorithm for islanded microgrid using battery energy storage system. Energy 196: 117084. https://doi.org/10.1016/j.energy.2020.117084 doi: 10.1016/j.energy.2020.117084
    [10] Arcos-Vargas A, Lugo D, Nunez F (2018) Residential peak electricity management. A storage and control systems application taking advantages of smart meters. Int J Electr Power Energy Syst 102: 110–121. https://doi.org/10.1016/j.ijepes.2018.04.016 doi: 10.1016/j.ijepes.2018.04.016
    [11] Evans A, Strezov V, Evans TJ (2012) Assessment of utility energy storage options for increased renewable energy penetration. Renewable Sustainable Energy Rev 16: 4141–4147. https://doi.org/10.1016/j.rser.2012.03.048 doi: 10.1016/j.rser.2012.03.048
    [12] Song A, Zhou YK (2023) A hierarchical control with thermal and electrical synergies on battery cycling ageing and energy flexibility in a multi-energy sharing network. Renewable Energy 212: 1020–1037. https://doi.org/10.1016/j.renene.2023.05.050 doi: 10.1016/j.renene.2023.05.050
    [13] Chen X, Huang L, Liu J (2022) Peak shaving benefit assessment considering the joint operation of nuclear and battery energy storage power stations: Hainan case study. Energy 239: 21897. https://doi.org/10.1016/j.energy.2021.121897 doi: 10.1016/j.energy.2021.121897
    [14] Song D, Chang Q, Zheng S (2021) Adaptive model predictive control for Yaw system of variable-speed wind turbines. J Modern Power Syst Clean Energy 9: 219–224. https://doi.org/10.35833/MPCE.2019.000467 doi: 10.35833/MPCE.2019.000467
    [15] Yang J, Fang LQ, Song DR, et al. (2021) Review of control strategy of large horizontal-axis wind turbines yaw system. Wind Energy 24: 97–115. https://doi.org/10.1002/we.2564 doi: 10.1002/we.2564
    [16] Atawi IE, Al-Shetwi AQ, Magableh AM, et al. (2023) Recent advances in hybrid energy storage system integrated renewable power generation: Configuration, control, applications, and future directions. Batteries 9: 1–35. https://doi.org/10.3390/batteries9010029 doi: 10.3390/batteries9010029
    [17] Mohd A, Ortjohann E, Schmelter A, et al. (2008) Challenges in integrating distributed energy storage systems into future smart grid. 2008 IEEE International Symposium on Industrial Electronics, Cambridge, UK, 1627–1632. https://doi.org/10.1109/isie.2008.4676896
    [18] Zhao P, Wang JF, Dai YP (2015) Capacity allocation of a hybrid energy storage system for power system peak shaving at high wind power penetration level. Renewable Energy 75: 541–549. https://doi.org/10.1016/j.renene.2014.10.040 doi: 10.1016/j.renene.2014.10.040
    [19] Zhou YK (2022) Transition towards carbon-neutral districts based on storage techniques and spatiotemporal energy sharing with electrification and hydrogenation. Renewable Sustainable Energy Rev 162: 112444. https://doi.org/10.1016/j.rser.2022.112444 doi: 10.1016/j.rser.2022.112444
    [20] Korpaas M, Holen AT, Hildrum R (2003) Operation and sizing of energy storage for wind power plants in a market system. Int J Electric Power Energy Syst 25: 599–606. https://doi.org/10.1016/s0142-0615(03)00016-4 doi: 10.1016/s0142-0615(03)00016-4
    [21] Müller M, Viernstein L, Truong CN, et al. (2017) Evaluation of grid-level adaptability for stationary battery energy storage system applications. Europe J Energy Storage 9: 1–11. https://doi.org/10.1016/j.est.2016.11.005 doi: 10.1016/j.est.2016.11.005
    [22] Ceran B, Jurasz J, Mielcarek A, et al. (2021) PV systems integrated with commercial buildings for local and national peak load shaving. Poland J Cleaner Product 322: 129076. https://doi.org/10.1016/j.jclepro.2021.129076 doi: 10.1016/j.jclepro.2021.129076
    [23] Shu S, Mo L, Wang Y (2019) Peak shaving strategy of wind-solar-hydro hybrid generation system based on modified differential evolution algorithm. Energy Proc 158: 3500–3505. https://doi.org/10.1016/j.egypro.2019.01.920 doi: 10.1016/j.egypro.2019.01.920
    [24] Syafii, Zaini, Juliandri D, et al. (2018) Design of PV system for electricity peak-shaving: A case study of faculty of engineering, Andalas University. 2018 International Conference on Computing, Power and Communication Technologies (GUCON), Greater Noida, India, 294–298. https://doi.org/10.1109/gucon.2018.8675096
    [25] Zheng X, Zhou Y (2023) A three-dimensional unsteady numerical model on a novel aerogel-based PV/T-PCM system with dynamic heat-transfer mechanism and solar energy harvesting analysis. Appl Energy 338: 120899. https://doi.org/10.1016/j.apenergy.2023.120899 doi: 10.1016/j.apenergy.2023.120899
    [26] Jurasz JK, Bronisław P, Campana PE (2019) Can a city reach energy self-sufficiency by means of rooftop photovoltaics? Case study from Poland. J Cleaner Product 245: 118813. https://doi.org/10.1016/j.jclepro.2019.118813 doi: 10.1016/j.jclepro.2019.118813
    [27] Uddin M, Romlie MF, Abdullah MF, et al. (2018) A review on peak load shaving strategies. Renewable Sustainable Energy Rev 82: 3323–3332. https://doi.org/10.1016/j.rser.2017.10.056 doi: 10.1016/j.rser.2017.10.056
    [28] Caprino D, Della Vedova ML, Facchinetti T (2014) Peak shaving through real-time scheduling of household appliances. Energy Build 75: 133–148. https://doi.org/10.1016/j.enbuild.2014.02.013 doi: 10.1016/j.enbuild.2014.02.013
    [29] Lin JT, Chen CM (2015) Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing. Simul Model Practice Theory 51: 100–114. https://doi.org/10.1016/j.simpat.2014.10.008 doi: 10.1016/j.simpat.2014.10.008
    [30] Vinyals M, Bistaffa F, Farinelli A, et al. (2012) Coalitional energy purchasing in the smart grid. Energy Conference and Exhibition (ENERGYCON), 2012 IEEE International, 848–853. https://doi.org/10.1109/EnergyCon.2012.6348270
    [31] Zhou YK (2022) Low-carbon transition in smart city with sustainable airport energy ecosystems and hydrogen-based renewable-grid-storage-flexibility. Energy Rev 1: 100001. https://doi.org/10.1016/j.enrev.2022.100001 doi: 10.1016/j.enrev.2022.100001
    [32] Zhao L, Aravinthan V (2013) Strategies of residential peak shaving with integration of demand response and V2H. 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). https://doi.org/10.1109/appeec.2013.6837260
    [33] Lee JW, Haram MHSM, Ramasamy G (2021) Technical feasibility and economics of repurposed electric vehicles batteries for power peak shaving. J Energy Storage 40: 102752. https://doi.org/10.1016/j.est.2021.102752 doi: 10.1016/j.est.2021.102752
    [34] Laserna EM, Gandiaga I, Zabala ES, et al. (2018). Battery second life: Hype, hope or reality? A critical review of the state of the art. Renewable Sustainable Energy Rev 93: 701–718. https://doi.org/10.1016/j.rser.2018.04.035 doi: 10.1016/j.rser.2018.04.035
    [35] Giorgio AD, Liberati F, Canale S (2014). Electric vehicles charging control in a smart grid: A model predictive control approach. Control Eng Pract 22: 147–162. https://doi.org/10.1016/j.conengprac.2013.10.005 doi: 10.1016/j.conengprac.2013.10.005
    [36] Kriekinge GV, Cauwer CD, Sapountzoglou N, et al. (2021) Peak shaving and cost minimization using model predictive control for uni- and bi-directional charging of electric vehicles. Energy Rep 7: 8760–8771. https://doi.org/10.1016/j.egyr.2021.11.207 doi: 10.1016/j.egyr.2021.11.207
    [37] Ito A, Kawashima A, Suzuki T (2018) Model predictive charging control of in-vehicle batteries for home energy management based on vehicle state prediction. IEEE Transactions on Control Systems Technology, 26: 51–64. https://doi.org/10.1109/tcst.2017.2664727 doi: 10.1109/tcst.2017.2664727
    [38] Zhaoxi L, Qiuwei W, Kang M, et al. (2019) Two-stage optimal scheduling of electric vehicle charging based on transactive control. IEEE Trans Smart Grid 10: 2948–2958. https://doi.org/10.1109/TSG.2018.2815593 doi: 10.1109/TSG.2018.2815593
    [39] Song A, Zhou Y (2023) Advanced cycling ageing-driven circular economy with E-mobility-based energy sharing and lithium battery cascade utilisation in a district community. J Cleaner Prod 415: 137797. https://doi.org/10.1016/j.jclepro.2023.137797 doi: 10.1016/j.jclepro.2023.137797
    [40] Lai J, Zhou H, Hu W, et al. (2015) Smart demand response based on smart homes. Math Probl 2015: 912535. Eng https://doi.org/10.1155/2015/912535 doi: 10.1155/2015/912535
    [41] Shen J, Jiang C, Liu Y, et al. (2016) A microgrid energy management system with demand response for providing grid peak shaving. Electr Power Compon Syst 44: 843–852. https://doi.org/10.1080/15325008.2016.1138344 doi: 10.1080/15325008.2016.1138344
    [42] Zhong H, Xie L, Xia Q (2013) Coupon incentive-based demand response: Theory and case study. IEEE Trans Power Syst 28: 1266–1276. https://doi.org/10.1109/TPWRS.2012.2218665 doi: 10.1109/TPWRS.2012.2218665
    [43] Zhou Y (2022) Incentivising multi-stakeholders' proactivity and market vitality for spatiotemporal microgrids in Guangzhou-Shenzhen-Hong Kong Bay Area. Appl Energy 328: 120196. https://doi.org/10.1016/j.apenergy.2022.120196 doi: 10.1016/j.apenergy.2022.120196
    [44] Zhou Y (2022) Demand response flexibility with synergies on passive PCM walls, BIPVs, and active air-conditioning system in a subtropical climate. Renewable Energy 199: 204–225. https://doi.org/10.1016/j.renene.2022.08.128 doi: 10.1016/j.renene.2022.08.128
    [45] Papadopoulos V, Delerue T, Ryckeghem JV, et al. (2017) Assessing the impact of load forecasting accuracy on battery dispatching strategies with respect to Peak Shaving and Time-of-Use (TOU) applications for industrial consumers. 52nd International Universities Power Engineering Conference (UPEC), Heraklion, Greece, 1–5. https://doi.org/10.1109/UPEC.2017.8231939
    [46] Manoochehri H, Fereidunian A (2016) Peak-shaving using time-of-use pricing and market coordination. 4th Regional Conference on Electricity Distribution (CIRED), Tehran, Iran. https://doi.org/10.1109/ISTEL.2016.7881915
    [47] Hassan R, M. Abdallah M, Radman G (2012) Load shedding in smart grid: A reliable efficient Ad-Hoc broadcast algorithm for smart house. Proceedings of IEEE Southeastcon, Orlando, FL, USA, 1–5. https://doi.org/10.1109/SECon.2012.6196919
    [48] Mishra S, Palanisamy P (2018) Efficient power flow management and peak shaving in a microgrid-PV system. IEEE Energy Conversion Congress and Exposition (ECCE), Portland, OR, USA, 3792–3798. https://doi.org/10.1109/ECCE.2018.8558312
    [49] Hassan R, Abdallah M, Radman G, et al. (2011) Under-Frequency Load Shedding: Towards a smarter smart house with a consumer level controller. Proc IEEE, 73–78. https://doi.org/10.1109/SECON.2011.5752909
    [50] Panda S, Mohanty S, Rout PK, et al. (2022) Residential demand side management model, optimization and future perspective: A review. Energy Rep 8: 3727–3766. https://doi.org/10.1016/j.egyr.2022.02.300 doi: 10.1016/j.egyr.2022.02.300
    [51] Zhou L, Zhou Y (2023) Study on thermo-electric-hydrogen conversion mechanisms and synergistic operation on hydrogen fuel cell and electrochemical battery in energy flexible buildings. Energy Conver Manage 277: 116610. https://doi.org/10.1016/j.enconman.2022.116610 doi: 10.1016/j.enconman.2022.116610
    [52] Pudjianto D, Ramsay C, Strbac G (2007) Virtual power plant and system integration of distributed energy resources. Renewable Power Gener 10–16. https://doi.org/10.1049/iet-rpg: 20060023
    [53] Behera S, Misra R (2018) SmartPeak: Peak shaving and ambient analysis for energy efficiency in electrical smart grid. Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference, 157–165. https://doi.org/10.1145/3299819.3299833
    [54] Zhou Y, Lund PD (2023) Peer-to-peer energy sharing and trading of renewable energy in smart communities—Trading pricing models, decision-making and agent-based collaboration. Renewable Energy 207: 177–193. https://doi.org/10.1016/j.renene.2023.02.125 doi: 10.1016/j.renene.2023.02.125
    [55] Molderink A, Bakker V, Bosman MGC, et al. (2010) Management and control of domestic smart grid technology. IEEE Trans Smart Grid 1: 109–119. https://doi.org/10.1109/TSG.2010.2055904 doi: 10.1109/TSG.2010.2055904
    [56] Holcomb D, Li W, Seshia SA (2009) Algorithms for green buildings: Learning-based techniques for energy prediction and fault diagnosis. Technical Report No: UCB/EECS-2009-138. Available from: https://digitalassets.lib.berkeley.edu/techreports/ucb/text/EECS-2009-138.pdf.
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1339) PDF downloads(141) Cited by(0)

Article outline

Figures and Tables

Figures(14)  /  Tables(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog