IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v237y2019icp304-313.html
   My bibliography  Save this article

A regional power grid operation and planning method considering renewable energy generation and load control

Author

Listed:
  • Zeng, Yuan
  • Zhang, Ruiwen
  • Wang, Dong
  • Mu, Yunfei
  • Jia, Hongjie

Abstract

The active development and application of renewable energy, such as wind power and solar power, has become an inevitable tendency, which will promote the increase of the proportion of renewable energy generation accordingly in regional power grids. The intermission and randomness of renewable energy generation lead to enormous challenges in the operation and planning of power systems. One of the current methods to accommodate and reduce renewable energy fluctuation is conventional storage devices, e.g. battery energy storage devices, but this approach has the limitation of a high operating cost, which hinders the technology from popularizing. We present a novel load control algorithm employing the technology of residential thermostatically controlled appliances to maximize renewable energy usage in regional power grids. The thermal dynamics of the thermostatically controlled loads are modeled as a simplified first-order equivalent thermal parameter model. The new load control algorithm, based on the state-queueing model with modified colored power algorithm, is developed to manage activities of residential thermostatically controlled appliances, which allows customers to set different colors as the response levels of their appliances. Such an algorithm can ensure both the comfort level and the fairness of customers. Both a practical case and a real case are applied to demonstrate the effectiveness and practicability of the proposed algorithm.

Suggested Citation

  • Zeng, Yuan & Zhang, Ruiwen & Wang, Dong & Mu, Yunfei & Jia, Hongjie, 2019. "A regional power grid operation and planning method considering renewable energy generation and load control," Applied Energy, Elsevier, vol. 237(C), pages 304-313.
    • Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:304-313
    DOI: 10.1016/j.apenergy.2019.01.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919300169
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.01.016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Chengshan & Zhou, Yue & Wang, Jidong & Peng, Peiyuan, 2013. "A novel Traversal-and-Pruning algorithm for household load scheduling," Applied Energy, Elsevier, vol. 102(C), pages 1430-1438.
    2. Garðarsdóttir, Stefanía Ó. & Göransson, Lisa & Normann, Fredrik & Johnsson, Filip, 2018. "Improving the flexibility of coal-fired power generators: Impact on the composition of a cost-optimal electricity system," Applied Energy, Elsevier, vol. 209(C), pages 277-289.
    3. Zhang, S. & Mishra, Y. & Shahidehpour, M., 2017. "Utilizing distributed energy resources to support frequency regulation services," Applied Energy, Elsevier, vol. 206(C), pages 1484-1494.
    4. Shariatzadeh, Farshid & Mandal, Paras & Srivastava, Anurag K., 2015. "Demand response for sustainable energy systems: A review, application and implementation strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 343-350.
    5. Stenner, Karen & Frederiks, Elisha R. & Hobman, Elizabeth V. & Cook, Stephanie, 2017. "Willingness to participate in direct load control: The role of consumer distrust," Applied Energy, Elsevier, vol. 189(C), pages 76-88.
    6. Adhikari, Rajendra & Pipattanasomporn, M. & Rahman, S., 2018. "An algorithm for optimal management of aggregated HVAC power demand using smart thermostats," Applied Energy, Elsevier, vol. 217(C), pages 166-177.
    7. Zeng, Bo & Wu, Geng & Wang, Jianhui & Zhang, Jianhua & Zeng, Ming, 2017. "Impact of behavior-driven demand response on supply adequacy in smart distribution systems," Applied Energy, Elsevier, vol. 202(C), pages 125-137.
    8. Malik, Anam & Ravishankar, Jayashri, 2018. "A hybrid control approach for regulating frequency through demand response," Applied Energy, Elsevier, vol. 210(C), pages 1347-1362.
    9. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    10. Vithayasrichareon, Peerapat & Riesz, Jenny & MacGill, Iain, 2017. "Operational flexibility of future generation portfolios with high renewables," Applied Energy, Elsevier, vol. 206(C), pages 32-41.
    11. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    12. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    13. Yin, Rongxin & Kara, Emre C. & Li, Yaping & DeForest, Nicholas & Wang, Ke & Yong, Taiyou & Stadler, Michael, 2016. "Quantifying flexibility of commercial and residential loads for demand response using setpoint changes," Applied Energy, Elsevier, vol. 177(C), pages 149-164.
    14. Bomela, Walter & Zlotnik, Anatoly & Li, Jr-Shin, 2018. "A phase model approach for thermostatically controlled load demand response," Applied Energy, Elsevier, vol. 228(C), pages 667-680.
    15. Lakshmanan, Venkatachalam & Marinelli, Mattia & Hu, Junjie & Bindner, Henrik W., 2016. "Provision of secondary frequency control via demand response activation on thermostatically controlled loads: Solutions and experiences from Denmark," Applied Energy, Elsevier, vol. 173(C), pages 470-480.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. O., Yugeswar Reddy & J., Jithendranath & Chakraborty, Ajoy Kumar & Guerrero, Josep M., 2022. "Stochastic optimal power flow in islanded DC microgrids with correlated load and solar PV uncertainties," Applied Energy, Elsevier, vol. 307(C).
    2. Weijiang Liu & Min Liu & Tingting Liu & Yangyang Li & Yizhe Hao, 2022. "Does a Recycling Carbon Tax with Technological Progress in Clean Electricity Drive the Green Economy?," IJERPH, MDPI, vol. 19(3), pages 1-18, February.
    3. Xiaoyu Xu & Chun Chang & Xinxin Guo & Mingzhi Zhao, 2023. "Experimental and Numerical Study of the Ice Storage Process and Material Properties of Ice Storage Coils," Energies, MDPI, vol. 16(14), pages 1-18, July.
    4. Feng, Penghui & Liu, Yang & Ayub, Iqra & Wu, Zhen & Yang, Fusheng & Zhang, Zaoxiao, 2019. "Techno-economic analysis of screening metal hydride pairs for a 910 MWhth thermal energy storage system," Applied Energy, Elsevier, vol. 242(C), pages 148-156.
    5. Song, Yuguang & Chen, Fangjian & Xia, Mingchao & Chen, Qifang, 2022. "The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution," Applied Energy, Elsevier, vol. 309(C).
    6. Wadim Strielkowski & Dalia Streimikiene & Alena Fomina & Elena Semenova, 2019. "Internet of Energy (IoE) and High-Renewables Electricity System Market Design," Energies, MDPI, vol. 12(24), pages 1-17, December.
    7. Heiko Dunkelberg & Maximilian Sondermann & Henning Meschede & Jens Hesselbach, 2019. "Assessment of Flexibilisation Potential by Changing Energy Sources Using Monte Carlo Simulation," Energies, MDPI, vol. 12(4), pages 1-24, February.
    8. Chunyang Hao & Yibo Wang & Chuang Liu & Guanglie Zhang & Hao Yu & Dongzhe Wang & Jingru Shang, 2023. "Research on Two-Stage Regulation Method for Source–Load Flexibility Transformation in Power Systems," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    9. Daiva Stanelyte & Virginijus Radziukynas, 2019. "Review of Voltage and Reactive Power Control Algorithms in Electrical Distribution Networks," Energies, MDPI, vol. 13(1), pages 1-26, December.
    10. Morteza Zare Oskouei & Ayşe Aybike Şeker & Süleyman Tunçel & Emin Demirbaş & Tuba Gözel & Mehmet Hakan Hocaoğlu & Mehdi Abapour & Behnam Mohammadi-Ivatloo, 2022. "A Critical Review on the Impacts of Energy Storage Systems and Demand-Side Management Strategies in the Economic Operation of Renewable-Based Distribution Network," Sustainability, MDPI, vol. 14(4), pages 1-34, February.
    11. Chen, C.Q. & Diao, Y.H. & Zhao, Y.H. & Ji, W.H. & Wang, Z.Y. & Liang, L., 2019. "Thermal performance of a thermal-storage unit by using a multichannel flat tube and rectangular fins," Applied Energy, Elsevier, vol. 250(C), pages 1280-1291.
    12. Hosseinnia, Hamed & Modarresi, Javad & Nazarpour, Daryoush, 2020. "Optimal eco-emission scheduling of distribution network operator and distributed generator owner under employing demand response program," Energy, Elsevier, vol. 191(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tang, Yi & Li, Feng & Chen, Qian & Li, Mengya & Wang, Qi & Ni, Ming & Chen, Gang, 2018. "Frequency prediction method considering demand response aggregate characteristics and control effects," Applied Energy, Elsevier, vol. 229(C), pages 936-944.
    2. Nizami, Sohrab & Tushar, Wayes & Hossain, M.J. & Yuen, Chau & Saha, Tapan & Poor, H. Vincent, 2022. "Transactive energy for low voltage residential networks: A review," Applied Energy, Elsevier, vol. 323(C).
    3. Xu, Fangyuan & Wu, Wanli & Zhao, Fei & Zhou, Ya & Wang, Yongjian & Wu, Runji & Zhang, Tao & Wen, Yongchen & Fan, Yiliang & Jiang, Shengli, 2019. "A micro-market module design for university demand-side management using self-crossover genetic algorithms," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    4. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    5. Ding, Yi & Cui, Wenqi & Zhang, Shujun & Hui, Hongxun & Qiu, Yiwei & Song, Yonghua, 2019. "Multi-state operating reserve model of aggregate thermostatically-controlled-loads for power system short-term reliability evaluation," Applied Energy, Elsevier, vol. 241(C), pages 46-58.
    6. Francesco Mancini & Sabrina Romano & Gianluigi Lo Basso & Jacopo Cimaglia & Livio de Santoli, 2020. "How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy," Energies, MDPI, vol. 13(13), pages 1-25, July.
    7. Okur, Özge & Heijnen, Petra & Lukszo, Zofia, 2021. "Aggregator’s business models in residential and service sectors: A review of operational and financial aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    8. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    9. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    10. Kazmi, Hussain & Suykens, Johan & Balint, Attila & Driesen, Johan, 2019. "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads," Applied Energy, Elsevier, vol. 238(C), pages 1022-1035.
    11. Lu, Qing & Yu, Hao & Zhao, Kangli & Leng, Yajun & Hou, Jianchao & Xie, Pinjie, 2019. "Residential demand response considering distributed PV consumption: A model based on China's PV policy," Energy, Elsevier, vol. 172(C), pages 443-456.
    12. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    13. Dong, Zihang & Zhang, Xi & Li, Yijun & Strbac, Goran, 2023. "Values of coordinated residential space heating in demand response provision," Applied Energy, Elsevier, vol. 330(PB).
    14. Ray, Manojit & Chakraborty, Basab, 2019. "Impact of evolving technology on collaborative energy access scaling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 13-27.
    15. Sampath Kumar & M Sushama, 2020. "Strategic demand response framework for energy management in distribution system based on network loss sensitivity," Energy & Environment, , vol. 31(8), pages 1385-1402, December.
    16. Kai Ma & Chenliang Yuan & Jie Yang & Zhixin Liu & Xinping Guan, 2017. "Switched Control Strategies of Aggregated Commercial HVAC Systems for Demand Response in Smart Grids," Energies, MDPI, vol. 10(7), pages 1-18, July.
    17. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    18. Li, Pei-Hao & Pye, Steve, 2018. "Assessing the benefits of demand-side flexibility in residential and transport sectors from an integrated energy systems perspective," Applied Energy, Elsevier, vol. 228(C), pages 965-979.
    19. Simone Ferrari & Milad Zoghi & Giancarlo Paganin & Giuliano Dall’O’, 2023. "A Practical Review to Support the Implementation of Smart Solutions within Neighbourhood Building Stock," Energies, MDPI, vol. 16(15), pages 1-35, July.
    20. Dranka, Géremi Gilson & Ferreira, Paula, 2019. "Review and assessment of the different categories of demand response potentials," Energy, Elsevier, vol. 179(C), pages 280-294.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:237:y:2019:i:c:p:304-313. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.