IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i9p1646-d227179.html
   My bibliography  Save this article

Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response

Author

Listed:
  • Hyung-Chul Jo

    (Distributed Power System Research Center, Korea Electrotechnology Research Institute, Gyeongsangnam-do 51543, Korea)

  • Rakkyung Ko

    (The School of Electrical Engineering, Korea University, Seoul 02841, Korea)

  • Sung-Kwan Joo

    (The School of Electrical Engineering, Korea University, Seoul 02841, Korea)

Abstract

Periodic preventive maintenance of generators is required to maintain the reliable operation of a power system. However, generators under maintenance cannot supply electrical energy to the power system; therefore, it is important to determine an optimal generator maintenance schedule to facilitate efficient supply. The schedule should consider various constraints of the reliability-based demand response program, power system security, and restoration. Determining the optimal generator maintenance schedule is generally formulated as a non-linear optimization problem, which leads to difficulties in obtaining the optimal solution when the various power system constraints are considered. This study proposes a generator maintenance scheduling (GMS) method using transformation of mixed integer polynomial programming in a power system incorporating demand response. The GMS method is designed to deal with various system requirements and characteristics of demand response within a power system. A case study is conducted using data from the Korean power system to demonstrate the effectiveness of the proposed method for determining the optimal maintenance schedule. The results show that the proposed GMS method can be used to facilitate the efficient and reliable operation of a power system, by considering the applicable system constraints.

Suggested Citation

  • Hyung-Chul Jo & Rakkyung Ko & Sung-Kwan Joo, 2019. "Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response," Energies, MDPI, vol. 12(9), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1646-:d:227179
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/9/1646/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/9/1646/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fred Glover, 1975. "Improved Linear Integer Programming Formulations of Nonlinear Integer Problems," Management Science, INFORMS, vol. 22(4), pages 455-460, December.
    2. Perez-Canto, Salvador & Rubio-Romero, Juan Carlos, 2013. "A model for the preventive maintenance scheduling of power plants including wind farms," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 67-75.
    3. Qian Liu & Rui Wang & Yan Zhang & Guohua Wu & Jianmai Shi, 2018. "An Optimal and Distributed Demand Response Strategy for Energy Internet Management," Energies, MDPI, vol. 11(1), pages 1-16, January.
    4. Ting-Chia Ou & Wei-Fu Su & Xian-Zong Liu & Shyh-Jier Huang & Te-Yu Tai, 2016. "A Modified Bird-Mating Optimization with Hill-Climbing for Connection Decisions of Transformers," Energies, MDPI, vol. 9(9), pages 1-12, August.
    5. Ou, Ting-Chia & Hong, Chih-Ming, 2014. "Dynamic operation and control of microgrid hybrid power systems," Energy, Elsevier, vol. 66(C), pages 314-323.
    6. Ting-Chia Ou & Kai-Hung Lu & Chiou-Jye Huang, 2017. "Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller)," Energies, MDPI, vol. 10(4), pages 1-16, April.
    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. Lee, Juyong & Cho, Youngsang, 2022. "Determinants of reserve margin volatility: A new approach toward managing energy supply and demand," Energy, Elsevier, vol. 252(C).
    2. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(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. Andrés Henao-Muñoz & Andrés Saavedra-Montes & Carlos Ramos-Paja, 2018. "Optimal Power Dispatch of Small-Scale Standalone Microgrid Located in Colombian Territory," Energies, MDPI, vol. 11(7), pages 1-20, July.
    2. Pengfei Wang & Jialiang Yi & Mansoureh Zangiabadi & Pádraig Lyons & Phil Taylor, 2017. "Evaluation of Voltage Control Approaches for Future Smart Distribution Networks," Energies, MDPI, vol. 10(8), pages 1-17, August.
    3. Yongsheng Cao & Guanglin Zhang & Demin Li & Lin Wang & Zongpeng Li, 2018. "Online Energy Management and Heterogeneous Task Scheduling for Smart Communities with Residential Cogeneration and Renewable Energy," Energies, MDPI, vol. 11(8), pages 1-20, August.
    4. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    5. Mohammed Elsayed Lotfy & Tomonobu Senjyu & Mohammed Abdel-Fattah Farahat & Amal Farouq Abdel-Gawad & Hidehito Matayoshi, 2017. "A Polar Fuzzy Control Scheme for Hybrid Power System Using Vehicle-To-Grid Technique," Energies, MDPI, vol. 10(8), pages 1-25, July.
    6. Hongyue Li & Xihuai Wang & Jianmei Xiao, 2018. "Differential Evolution-Based Load Frequency Robust Control for Micro-Grids with Energy Storage Systems," Energies, MDPI, vol. 11(7), pages 1-19, June.
    7. Chen, J.J. & Zhao, Y.L. & Peng, K. & Wu, P.Z., 2017. "Optimal trade-off planning for wind-solar power day-ahead scheduling under uncertainties," Energy, Elsevier, vol. 141(C), pages 1969-1981.
    8. Geng, Zhiqiang & Li, Yanan & Han, Yongming & Zhu, Qunxiong, 2018. "A novel self-organizing cosine similarity learning network: An application to production prediction of petrochemical systems," Energy, Elsevier, vol. 142(C), pages 400-410.
    9. Qian Liu & Rui Wang & Yan Zhang & Guohua Wu & Jianmai Shi, 2018. "An Optimal and Distributed Demand Response Strategy for Energy Internet Management," Energies, MDPI, vol. 11(1), pages 1-16, January.
    10. Kai Ma & Congshan Wang & Jie Yang & Qiuxia Yang & Yazhou Yuan, 2017. "Economic Dispatch with Demand Response in Smart Grid: Bargaining Model and Solutions," Energies, MDPI, vol. 10(8), pages 1-17, August.
    11. Jaewan Suh & Sungchul Hwang & Gilsoo Jang, 2017. "Development of a Transmission and Distribution Integrated Monitoring and Analysis System for High Distributed Generation Penetration," Energies, MDPI, vol. 10(9), pages 1-15, August.
    12. Qinliang Tan & Yihong Ding & Yimei Zhang, 2017. "Optimization Model of an Efficient Collaborative Power Dispatching System for Carbon Emissions Trading in China," Energies, MDPI, vol. 10(9), pages 1-19, September.
    13. Van-Hai Bui & Akhtar Hussain & Hak-Man Kim, 2017. "Optimal Operation of Microgrids Considering Auto-Configuration Function Using Multiagent System," Energies, MDPI, vol. 10(10), pages 1-16, September.
    14. Mohammed H. Alsharif, 2017. "Comparative Analysis of Solar-Powered Base Stations for Green Mobile Networks," Energies, MDPI, vol. 10(8), pages 1-25, August.
    15. Pouria Sheikhahmadi & Ramyar Mafakheri & Salah Bahramara & Maziar Yazdani Damavandi & João P. S. Catalão, 2018. "Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs," Energies, MDPI, vol. 11(3), pages 1-17, March.
    16. Changcheng Li & Jinghan He & Pei Zhang & Yin Xu, 2017. "A Novel Sectionalizing Method for Power System Parallel Restoration Based on Minimum Spanning Tree," Energies, MDPI, vol. 10(7), pages 1-21, July.
    17. Fei Wang & Lidong Zhou & Hui Ren & Xiaoli Liu, 2017. "Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimizat," Energies, MDPI, vol. 10(12), pages 1-23, November.
    18. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.
    19. Reza Sirjani, 2017. "Optimal Capacitor Placement in Wind Farms by Considering Harmonics Using Discrete Lightning Search Algorithm," Sustainability, MDPI, vol. 9(9), pages 1-20, September.
    20. Omid Hoseynpour & Behnam Mohammadi-ivatloo & Morteza Nazari-Heris & Somayeh Asadi, 2017. "Application of Dynamic Non-Linear Programming Technique to Non-Convex Short-Term Hydrothermal Scheduling Problem," Energies, MDPI, vol. 10(9), pages 1-17, September.

    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:12:y:2019:i:9:p:1646-:d:227179. 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.