IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i7p1806-d523483.html
   My bibliography  Save this article

Penalty Electricity Price-Based Optimal Control for Distribution Networks

Author

Listed:
  • Qingle Pang

    (School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Lin Ye

    (School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Houlei Gao

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Xinian Li

    (School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China)

  • Yang Zheng

    (School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Chenbin He

    (School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China)

Abstract

With the integration of large-scale renewable energy and the implementation of demand response, the complexity and volatility of distribution network operations are increasing. This has led to the inconsistency between the actual net power consumption of power users and their optimal dispatching orders. As a result, the distribution networks cannot operate according to their optimization strategy. The study proposed a penalty electricity price mechanism and the optimal control method based on this electricity price mechanism for distribution networks. First, we established the structure of the distribution network optimal control system. Second, aiming at the actual net power consumption (including power generation and consumption) of power users tracking their dispatching orders, we established a penalty electricity price mechanism. Third, we designed an optimal control strategy and process of distribution networks based on the penalty electricity price. Finally, we verified the proposed method by taking the IEEE-33 node system as an example. The verification results showed that the penalty electricity price could effectively limit the net power consumption fluctuations of power users to achieve optimal control of distribution networks.

Suggested Citation

  • Qingle Pang & Lin Ye & Houlei Gao & Xinian Li & Yang Zheng & Chenbin He, 2021. "Penalty Electricity Price-Based Optimal Control for Distribution Networks," Energies, MDPI, vol. 14(7), pages 1-16, March.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1806-:d:523483
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/7/1806/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/7/1806/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Akhil Joseph & Patil Balachandra, 2020. "Energy Internet, the Future Electricity System: Overview, Concept, Model Structure, and Mechanism," Energies, MDPI, vol. 13(16), pages 1-26, August.
    2. Xin Zhang & Jianhua Yang & Weizhou Wang & Man Zhang & Tianjun Jing, 2018. "Integrated Optimal Dispatch of a Rural Micro-Energy-Grid with Multi-Energy Stream Based on Model Predictive Control," Energies, MDPI, vol. 11(12), pages 1-23, December.
    3. Yuyan Sun & Zexiang Cai & Ziyi Zhang & Caishan Guo & Guolong Ma & Yongxia Han, 2020. "Coordinated Energy Scheduling of a Distributed Multi-Microgrid System Based on Multi-Agent Decisions," Energies, MDPI, vol. 13(16), pages 1-20, August.
    4. Sun, Mei & Ji, Jian & Ampimah, Benjamin Chris, 2018. "How to implement real-time pricing in China? A solution based on power credit mechanism," Applied Energy, Elsevier, vol. 231(C), pages 1007-1018.
    5. Martin Zapf & Tobias Blenk & Ann-Catrin Müller & Hermann Pengg & Ivana Mladenovic & Christian Weindl, 2021. "Lifetime Assessment of PILC Cables with Regard to Thermal Aging Based on a Medium Voltage Distribution Network Benchmark and Representative Load Scenarios in the Course of the Expansion of Distributed," Energies, MDPI, vol. 14(2), pages 1-25, January.
    6. Junwei Cao & Wanlu Zhang & Zeqing Xiao & Haochen Hua, 2019. "Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach," Energies, MDPI, vol. 12(8), pages 1-17, April.
    7. Ariel Villalón & Marco Rivera & Yamisleydi Salgueiro & Javier Muñoz & Tomislav Dragičević & Frede Blaabjerg, 2020. "Predictive Control for Microgrid Applications: A Review Study," Energies, MDPI, vol. 13(10), pages 1-32, May.
    8. Leehter Yao & Fazida Hanim Hashim & Chien-Chi Lai, 2020. "Dynamic Residential Energy Management for Real-Time Pricing," Energies, MDPI, vol. 13(10), pages 1-15, May.
    9. Zhou, Kaile & Wei, Shuyu & Yang, Shanlin, 2019. "Time-of-use pricing model based on power supply chain for user-side microgrid," Applied Energy, Elsevier, vol. 248(C), pages 35-43.
    10. Wang, Zhengchao & Perera, A.T.D., 2020. "Integrated platform to design robust energy internet," Applied Energy, Elsevier, vol. 269(C).
    11. Gokturk Poyrazoglu, 2021. "Determination of Price Zones during Transition from Uniform to Zonal Electricity Market: A Case Study for Turkey," Energies, MDPI, vol. 14(4), pages 1-13, February.
    12. Tianze Lan & Kittisak Jermsittiparsert & Sara T. Alrashood & Mostafa Rezaei & Loiy Al-Ghussain & Mohamed A. Mohamed, 2021. "An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand," Energies, MDPI, vol. 14(3), pages 1-25, January.
    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. Yizheng Li & Yuan Zeng & Zhidong Wang & Lang Zhao & Yao Wang, 2023. "Optimal Economic Scheduling Method for Power Systems Based on Whole-System-Cost Electricity Price," Energies, MDPI, vol. 16(24), pages 1-17, December.

    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. Kaiye Gao & Tianshi Wang & Chenjing Han & Jinhao Xie & Ye Ma & Rui Peng, 2021. "A Review of Optimization of Microgrid Operation," Energies, MDPI, vol. 14(10), pages 1-39, May.
    2. Ariel Villalón & Carlos Muñoz & Javier Muñoz & Marco Rivera, 2023. "Fixed-Switching-Frequency Modulated Model Predictive Control for Islanded AC Microgrid Applications," Mathematics, MDPI, vol. 11(3), pages 1-27, January.
    3. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    4. Yi, Zonggen & Luo, Yusheng & Westover, Tyler & Katikaneni, Sravya & Ponkiya, Binaka & Sah, Suba & Mahmud, Sadab & Raker, David & Javaid, Ahmad & Heben, Michael J. & Khanna, Raghav, 2022. "Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system," Applied Energy, Elsevier, vol. 328(C).
    5. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    6. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    7. Li Zeng & Tian Xia & Salah K. Elsayed & Mahrous Ahmed & Mostafa Rezaei & Kittisak Jermsittiparsert & Udaya Dampage & Mohamed A. Mohamed, 2021. "A Novel Machine Learning-Based Framework for Optimal and Secure Operation of Static VAR Compensators in EAFs," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    8. Cao, GangCheng & Fang, Debin & Wang, Pengyu, 2021. "The impacts of social learning on a real-time pricing scheme in the electricity market," Applied Energy, Elsevier, vol. 291(C).
    9. Julia Morgan & Casey Canfield, 2021. "Comparing Behavioral Theories to Predict Consumer Interest to Participate in Energy Sharing," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    10. Xuhong Yang & Hui Li & Wei Jia & Zhongxin Liu & Yu Pan & Fengwei Qian, 2022. "Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability," Energies, MDPI, vol. 15(22), pages 1-13, November.
    11. Luís Caseiro & André Mendes, 2021. "Fault Analysis and Non-Redundant Fault Tolerance in 3-Level Double Conversion UPS Systems Using Finite-Control-Set Model Predictive Control," Energies, MDPI, vol. 14(8), pages 1-39, April.
    12. Dongdong Zhang & Jun Tian & Hui-Hwang Goh & Hui Liu & Xiang Li & Hongyu Zhu & Xinzhang Wu, 2022. "The Key Technology of Smart Energy System and Its Disciplinary Teaching Reform Measures," Sustainability, MDPI, vol. 14(21), pages 1-29, October.
    13. Marek Stawowy & Adam Rosiński & Jacek Paś & Stanisław Duer & Marta Harničárová & Krzysztof Perlicki, 2023. "The Reliability and Exploitation Analysis Method of the ICT System Power Supply with the Use of Modelling Based on Rough Sets," Energies, MDPI, vol. 16(12), pages 1-18, June.
    14. Nor Liza Tumeran & Siti Hajar Yusoff & Teddy Surya Gunawan & Mohd Shahrin Abu Hanifah & Suriza Ahmad Zabidi & Bernardi Pranggono & Muhammad Sharir Fathullah Mohd Yunus & Siti Nadiah Mohd Sapihie & Asm, 2023. "Model Predictive Control Based Energy Management System Literature Assessment for RES Integration," Energies, MDPI, vol. 16(8), pages 1-27, April.
    15. Samar Fatima & Verner Püvi & Ammar Arshad & Mahdi Pourakbari-Kasmaei & Matti Lehtonen, 2021. "Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks," Energies, MDPI, vol. 14(9), pages 1-23, April.
    16. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2022. "Efficiency of resilient three-part tariff pricing schemes in residential power markets," Energy, Elsevier, vol. 239(PD).
    17. Muhammad Anique Aslam & Syed Abdul Rahman Kashif & Muhammad Majid Gulzar & Mohammed Alqahtani & Muhammad Khalid, 2023. "A Novel Multi Level Dynamic Decomposition Based Coordinated Control of Electric Vehicles in Multimicrogrids," Sustainability, MDPI, vol. 15(16), pages 1-29, August.
    18. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).
    19. Yang, Shenbo & Tan, Zhongfu & Lin, Hongyu & Li, Peng & De, Gejirifu & Zhou, Feng’ao & Ju, Liwei, 2020. "A two-stage optimization model for Park Integrated Energy System operation and benefit allocation considering the effect of Time-Of-Use energy price," Energy, Elsevier, vol. 195(C).
    20. Yang, Kang & Li, Chunhua & Jing, Xu & Zhu, Zhiyu & Wang, Yuting & Ma, Haodong & Zhang, Yu, 2022. "Energy dispatch optimization of islanded multi-microgrids based on symbiotic organisms search and improved multi-agent consensus algorithm," Energy, Elsevier, vol. 239(PC).

    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:gam:jeners:v:14:y:2021:i:7:p:1806-:d:523483. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.