IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v223y2021ics0360544221002760.html
   My bibliography  Save this article

Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing

Author

Listed:
  • Zhang, Jingrui
  • Li, Zhuoyun
  • Wang, Beibei

Abstract

The prediction accuracy plays a very important role in the optimal dispatch problem of active distribution networks (ADN). Basing on the fact that the prediction accuracy will be greatly improved when approaching prediction domains and decreasing prediction time periods, rolling optimization provides an alternative approach to handle uncertainty of renewable sources in ADN. Considering the characteristics of power supplies and loads in ADN, a comprehensive model of rolling multi-objective optimal dispatch is established. Four objective functions of minimizing the ADN operation cost, minimizing adjustment of the active power outputs to the day-ahead plan, minimizing the total active power loss, and minimizing the total voltage deviation of the system are considered simultaneously. Then, the thought of simulated annealing is integrated into the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to change the process of generating new solutions and the neighborhood updating process. Therefore, the proposed MOEA/D with the thought of simulated annealing (MOEA/D-TSA) is adopted to solve this rolling multi-objective optimal dispatch problem. In the proposed approach, a dynamic probability value is employed to select which neighbor subproblem should be updated using the new generated solution in order to balance global and local searching ability of the algorithm. Finally, the performance of the proposed approach is tested and verified on an improved IEEE 33 node test system. The comparisons of the proposed method with the original MOEA/D and NSGA II on non-dominated solutions, extreme solutions, optimal compromise solution (OCS) and statistical indicators are well illustrated. The results show that MOEA/D-TSA algorithm has better convergence and comprehensive performance than other considered algorithms and its application in rolling dispatch problem is also presented through this test system.

Suggested Citation

  • Zhang, Jingrui & Li, Zhuoyun & Wang, Beibei, 2021. "Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing," Energy, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:energy:v:223:y:2021:i:c:s0360544221002760
    DOI: 10.1016/j.energy.2021.120027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221002760
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120027?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. Alves, M. & Segurado, R. & Costa, M., 2019. "Increasing the penetration of renewable energy sources in isolated islands through the interconnection of their power systems. The case of Pico and Faial islands, Azores," Energy, Elsevier, vol. 182(C), pages 502-510.
    2. Yuan, Xiaohui & Zhang, Binqiao & Wang, Pengtao & Liang, Ji & Yuan, Yanbin & Huang, Yuehua & Lei, Xiaohui, 2017. "Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm," Energy, Elsevier, vol. 122(C), pages 70-82.
    3. Mahmoud H. Alrefaei & Sigrún Andradóttir, 1999. "A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization," Management Science, INFORMS, vol. 45(5), pages 748-764, May.
    4. Zhang, Jingrui & Wang, Silu & Tang, Qinghui & Zhou, Yulu & Zeng, Tao, 2019. "An improved NSGA-III integrating adaptive elimination strategy to solution of many-objective optimal power flow problems," Energy, Elsevier, vol. 172(C), pages 945-957.
    5. Liu, Jia & Zeng, Peter Pingliang & Xing, Hao & Li, Yalou & Wu, Qiuwei, 2020. "Hierarchical duality-based planning of transmission networks coordinating active distribution network operation," Energy, Elsevier, vol. 213(C).
    6. Wang, Shouxiang & Wang, Kai & Teng, Fei & Strbac, Goran & Wu, Lei, 2018. "An affine arithmetic-based multi-objective optimization method for energy storage systems operating in active distribution networks with uncertainties," Applied Energy, Elsevier, vol. 223(C), pages 215-228.
    7. Zhang, Jingrui & Lin, Shuang & Liu, Houde & Chen, Yalin & Zhu, Mingcheng & Xu, Yinliang, 2017. "A small-population based parallel differential evolution algorithm for short-term hydrothermal scheduling problem considering power flow constraints," Energy, Elsevier, vol. 123(C), pages 538-554.
    8. Zhou, Yulu & Zhang, Jingrui, 2020. "Three-layer day-ahead scheduling for active distribution network by considering multiple stakeholders," Energy, Elsevier, vol. 207(C).
    9. 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).
    10. Altan, Aytaç & Karasu, Seçkin & Bekiros, Stelios, 2019. "Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 325-336.
    11. Lin, Zhenjia & Chen, Haoyong & Wu, Qiuwei & Li, Weiwei & Li, Mengshi & Ji, Tianyao, 2020. "Mean-tracking model based stochastic economic dispatch for power systems with high penetration of wind power," Energy, Elsevier, vol. 193(C).
    12. Zubo, Rana H.A. & Mokryani, Geev & Abd-Alhameed, Raed, 2018. "Optimal operation of distribution networks with high penetration of wind and solar power within a joint active and reactive distribution market environment," Applied Energy, Elsevier, vol. 220(C), pages 713-722.
    13. Gazijahani, Farhad Samadi & Salehi, Javad, 2018. "Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach," Energy, Elsevier, vol. 161(C), pages 999-1015.
    14. Tang, Chong & Liu, Mingbo & Xie, Min & Dong, Ping & Zhu, Jianquan & Lin, Shunjiang, 2021. "A single-leader and multiple-follower stackelberg model for the look-ahead dispatch of plug-in electric buses in multiple microgrids," Energy, Elsevier, vol. 214(C).
    15. Zhang, Jingrui & Wu, Yihong & Guo, Yiran & Wang, Bo & Wang, Hengyue & Liu, Houde, 2016. "A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints," Applied Energy, Elsevier, vol. 183(C), pages 791-804.
    16. Tang, Jia & Wang, Dan & Wang, Xuyang & Jia, Hongjie & Wang, Chengshan & Huang, Renle & Yang, Zhanyong & Fan, Menghua, 2017. "Study on day-ahead optimal economic operation of active distribution networks based on Kriging model assisted particle swarm optimization with constraint handling techniques," Applied Energy, Elsevier, vol. 204(C), pages 143-162.
    17. Kia, Mohsen & Setayesh Nazar, Mehrdad & Sepasian, Mohammad Sadegh & Heidari, Alireza & Siano, Pierluigi, 2017. "An efficient linear model for optimal day ahead scheduling of CHP units in active distribution networks considering load commitment programs," Energy, Elsevier, vol. 139(C), pages 798-817.
    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. Suryakiran, B.V. & Nizami, Sohrab & Verma, Ashu & Saha, Tapan Kumar & Mishra, Sukumar, 2023. "A DSO-based day-ahead market mechanism for optimal operational planning of active distribution network," Energy, Elsevier, vol. 282(C).
    2. Zakernezhad, Hamid & Setayesh Nazar, Mehrdad & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal scheduling of an active distribution system considering distributed energy resources, demand response aggregators and electrical energy storage," Applied Energy, Elsevier, vol. 314(C).
    3. Qingqing Ji & Shiyu Zhang & Qiao Duan & Yuhan Gong & Yaowei Li & Xintong Xie & Jikang Bai & Chunli Huang & Xu Zhao, 2022. "Short- and Medium-Term Power Demand Forecasting with Multiple Factors Based on Multi-Model Fusion," Mathematics, MDPI, vol. 10(12), pages 1-30, June.

    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. Zhang, Jingrui & Zhou, Yulu & Li, Zhuoyun & Cai, Junfeng, 2021. "Three-level day-ahead optimal scheduling framework considering multi-stakeholders in active distribution networks: Up-to-down approach," Energy, Elsevier, vol. 219(C).
    2. Zhang, Jingrui & Zhu, Xiaoqing & Chen, Tengpeng & Yu, Yanlin & Xue, Wendong, 2020. "Improved MOEA/D approach to many-objective day-ahead scheduling with consideration of adjustable outputs of renewable units and load reduction in active distribution networks," Energy, Elsevier, vol. 210(C).
    3. Li, Shuijia & Gong, Wenyin & Wang, Ling & Yan, Xuesong & Hu, Chengyu, 2020. "Optimal power flow by means of improved adaptive differential evolution," Energy, Elsevier, vol. 198(C).
    4. Jiao, P.H. & Chen, J.J. & Peng, K. & Zhao, Y.L. & Xin, K.F., 2020. "Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resources," Energy, Elsevier, vol. 191(C).
    5. Khojasteh, Meysam & Faria, Pedro & Lezama, Fernando & Vale, Zita, 2023. "A hierarchy model to use local resources by DSO and TSO in the balancing market," Energy, Elsevier, vol. 267(C).
    6. Sherif S. M. Ghoneim & Mohamed F. Kotb & Hany M. Hasanien & Mosleh M. Alharthi & Attia A. El-Fergany, 2021. "Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis," Sustainability, MDPI, vol. 13(14), pages 1-15, July.
    7. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Hasanien, Hany M. & Ginidi, Ahmed R., 2022. "An improved heap optimization algorithm for efficient energy management based optimal power flow model," Energy, Elsevier, vol. 250(C).
    8. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Alharthi, Mosleh M. & Ghoneim, Sherif S.M. & Ginidi, Ahmed R., 2021. "Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework," Energy, Elsevier, vol. 237(C).
    9. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    10. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    11. Chen, Houhe & Wang, Di & Zhang, Rufeng & Jiang, Tao & Li, Xue, 2022. "Optimal participation of ADN in energy and reserve markets considering TSO-DSO interface and DERs uncertainties," Applied Energy, Elsevier, vol. 308(C).
    12. Jeddi, Babak & Mishra, Yateendra & Ledwich, Gerard, 2021. "Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems," Applied Energy, Elsevier, vol. 300(C).
    13. Dimou, Andreas & Vakalis, Stergios, 2022. "Technoeconomic analysis of green energy transitions in isolated grids: The case of Ai Stratis – Green Island," Renewable Energy, Elsevier, vol. 195(C), pages 66-75.
    14. Weihong Cai & Fengxi Duan, 2023. "Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm," Future Internet, MDPI, vol. 15(11), pages 1-23, October.
    15. Rajpal, Sheetal & Lakhyani, Navin & Singh, Ayush Kumar & Kohli, Rishav & Kumar, Naveen, 2021. "Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    16. Misaghian, M.S. & Saffari, M. & Kia, M. & Heidari, A. & Shafie-khah, M. & Catalão, J.P.S., 2018. "Tri-level optimization of industrial microgrids considering renewable energy sources, combined heat and power units, thermal and electrical storage systems," Energy, Elsevier, vol. 161(C), pages 396-411.
    17. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    18. Mohammad Zohrul Islam & Noor Izzri Abdul Wahab & Veerapandiyan Veerasamy & Hashim Hizam & Nashiren Farzilah Mailah & Josep M. Guerrero & Mohamad Nasrun Mohd Nasir, 2020. "A Harris Hawks Optimization Based Single- and Multi-Objective Optimal Power Flow Considering Environmental Emission," Sustainability, MDPI, vol. 12(13), pages 1-25, June.
    19. Khasanzoda, Nasrullo & Safaraliev, Murodbek & Zicmane, Inga & Beryozkina, Svetlana & Rahimov, Jamshed & Ahyoev, Javod, 2022. "Use of smart grid based wind resources in isolated power systems," Energy, Elsevier, vol. 253(C).
    20. Valdes, Javier & Poque González, Axel Bastián & Masip Macia, Yunesky & Dorner, Wolfgang & Ramirez Camargo, Luis, 2020. "Unveiling the potential for combined heat and power in Chilean industry - A policy perspective," Energy Policy, Elsevier, vol. 140(C).

    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:energy:v:223:y:2021:i:c:s0360544221002760. 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.journals.elsevier.com/energy .

    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.