IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v131y2019icp956-965.html
   My bibliography  Save this article

Day-ahead thermal and renewable power generation scheduling considering uncertainty

Author

Listed:
  • Salkuti, Surender Reddy

Abstract

This paper proposes a multi-objective optimization (MOO) based optimal day-ahead scheduling of thermal and renewable (wind and solar photovoltaic) power generation problem considering the intermittent/uncertain nature of renewable energy sources, load demands and thermal generators. System operating cost (i.e., cost of thermal, wind, solar PV and battery powers), reliability and emission cost are considered to be optimized simultaneously. The uncertainties due to generator outages, wind, solar PV and load demand forecast errors are incorporated in the proposed optimization problem using Expected Unserved Energy (EUE) and Loss Of Load Probability (LOLP) reliability indices. In the proposed approach, the amount of spinning reserves (SRs) required are scheduled based on the desired level of system reliability. The proposed multi-objective optimization problem is solved using NSGA-II algorithm. Different case studies are performed considering two or three different objective functions that may be selected by the system operator (SO) based on his/her preference. The simulation results obtained on a sample test system validate the benefits of solving the hybrid power system scheduling problem as a transparent and realistic MOO problem considering the uncertainty.

Suggested Citation

  • Salkuti, Surender Reddy, 2019. "Day-ahead thermal and renewable power generation scheduling considering uncertainty," Renewable Energy, Elsevier, vol. 131(C), pages 956-965.
    • Handle: RePEc:eee:renene:v:131:y:2019:i:c:p:956-965
    DOI: 10.1016/j.renene.2018.07.106
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118309030
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.07.106?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. Shukla, Pradyumn Kumar & Deb, Kalyanmoy, 2007. "On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1630-1652, September.
    2. Zamani, Ali Ghahgharaee & Zakariazadeh, Alireza & Jadid, Shahram, 2016. "Day-ahead resource scheduling of a renewable energy based virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 324-340.
    3. Reddy, S. Surender & Abhyankar, A.R. & Bijwe, P.R., 2011. "Reactive power price clearing using multi-objective optimization," Energy, Elsevier, vol. 36(5), pages 3579-3589.
    4. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    5. Pousinho, H.M.I. & Esteves, J. & Mendes, V.M.F. & Collares-Pereira, M. & Pereira Cabrita, C., 2016. "Bilevel approach to wind-CSP day-ahead scheduling with spinning reserve under controllable degree of trust," Renewable Energy, Elsevier, vol. 85(C), pages 917-927.
    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. Xu, Jiuping & Liu, Liying & Wang, Fengjuan, 2022. "Equilibrium strategy-based economic-reliable approach for day-ahead scheduling towards solar-wind-gas hybrid power generation system: A case study from China," Energy, Elsevier, vol. 240(C).
    2. Yuwei Zhang & Wenying Liu & Yue Huan & Qiang Zhou & Ningbo Wang, 2020. "An Optimal Day-Ahead Thermal Generation Scheduling Method to Enhance Total Transfer Capability for the Sending-Side System with Large-Scale Wind Power Integration," Energies, MDPI, vol. 13(9), pages 1-19, May.
    3. Hong, Ying-Yi & Apolinario, Gerard Francesco DG. & Chung, Chen-Nien & Lu, Tai-Ken & Chu, Chia-Chi, 2020. "Effect of Taiwan's energy policy on unit commitment in 2025," Applied Energy, Elsevier, vol. 277(C).
    4. Li, Huanhuan & Zhang, Runfan & Mahmud, Md. Apel & Hredzak, Branislav, 2022. "A novel coordinated optimization strategy for high utilization of renewable energy sources and reduction of coal costs and emissions in hybrid hydro-thermal-wind power systems," Applied Energy, Elsevier, vol. 320(C).
    5. Zahid Ullah & Arshad & Hany Hassanin, 2022. "Modeling, Optimization, and Analysis of a Virtual Power Plant Demand Response Mechanism for the Internal Electricity Market Considering the Uncertainty of Renewable Energy Sources," Energies, MDPI, vol. 15(14), pages 1-16, July.
    6. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    7. You, Minglei & Wang, Qian & Sun, Hongjian & Castro, Iván & Jiang, Jing, 2022. "Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties," Applied Energy, Elsevier, vol. 305(C).
    8. Lin, Xiaojie & Mao, Yihui & Chen, Jiaying & Zhong, Wei, 2023. "Dynamic modeling and uncertainty quantification of district heating systems considering renewable energy access," Applied Energy, Elsevier, vol. 349(C).
    9. Das, Saborni & Basu, Mousumi, 2020. "Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources," Energy, Elsevier, vol. 190(C).
    10. Zhou, Yuekuan & Zheng, Siqian, 2020. "Multi-level uncertainty optimisation on phase change materials integrated renewable systems with hybrid ventilations and active cooling," Energy, Elsevier, vol. 202(C).
    11. Zhou, Yuekuan & Zheng, Siqian, 2020. "Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities," Applied Energy, Elsevier, vol. 262(C).
    12. Mehrjerdi, Hasan & Hemmati, Reza, 2020. "Energy and uncertainty management through domestic demand response in the residential building," Energy, Elsevier, vol. 192(C).
    13. Ying-Yi Hong & Gerard Francesco DG. Apolinario, 2021. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications," Energies, MDPI, vol. 14(20), pages 1-47, October.
    14. Amir Shahcheraghian & Hatef Madani & Adrian Ilinca, 2024. "From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices," Energies, MDPI, vol. 17(2), pages 1-45, January.

    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. 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.
    2. Tong Xing & Hongyu Lin & Zhongfu Tan & Liwei Ju, 2019. "Coordinated Energy Management for Micro Energy Systems Considering Carbon Emissions Using Multi-Objective Optimization," Energies, MDPI, vol. 12(23), pages 1-27, November.
    3. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    4. Yu, Songyuan & Fang, Fang & Liu, Yajuan & Liu, Jizhen, 2019. "Uncertainties of virtual power plant: Problems and countermeasures," Applied Energy, Elsevier, vol. 239(C), pages 454-470.
    5. Mohammad Mohammadi Roozbehani & Ehsan Heydarian-Forushani & Saeed Hasanzadeh & Seifeddine Ben Elghali, 2022. "Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    6. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    7. Soares, João & Ghazvini, Mohammad Ali Fotouhi & Borges, Nuno & Vale, Zita, 2017. "Dynamic electricity pricing for electric vehicles using stochastic programming," Energy, Elsevier, vol. 122(C), pages 111-127.
    8. Bianca Goia & Tudor Cioara & Ionut Anghel, 2022. "Virtual Power Plant Optimization in Smart Grids: A Narrative Review," Future Internet, MDPI, vol. 14(5), pages 1-22, April.
    9. Amit Kumer Podder & Sayemul Islam & Nallapaneni Manoj Kumar & Aneesh A. Chand & Pulivarthi Nageswara Rao & Kushal A. Prasad & T. Logeswaran & Kabir A. Mamun, 2020. "Systematic Categorization of Optimization Strategies for Virtual Power Plants," Energies, MDPI, vol. 13(23), pages 1-46, November.
    10. Jingjing Luo & Yajing Gao & Wenhai Yang & Yongchun Yang & Zheng Zhao & Shiyu Tian, 2018. "Optimal Operation Modes of Virtual Power Plants Based on Typical Scenarios Considering Output Evaluation Criteria," Energies, MDPI, vol. 11(10), pages 1-22, October.
    11. Liwei Ju & Peng Li & Qinliang Tan & Zhongfu Tan & GejiriFu De, 2018. "A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses," Energies, MDPI, vol. 11(11), pages 1-28, October.
    12. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
    13. Pedro Faria & Zita Vale, 2019. "A Demand Response Approach to Scheduling Constrained Load Shifting," Energies, MDPI, vol. 12(9), pages 1-16, May.
    14. Reihani, Ehsan & Motalleb, Mahdi & Thornton, Matsu & Ghorbani, Reza, 2016. "A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture," Applied Energy, Elsevier, vol. 183(C), pages 445-455.
    15. Daeho Kim & Hyungkyu Cheon & Dong Gu Choi & Seongbin Im, 2022. "Operations Research Helps the Optimal Bidding of Virtual Power Plants," Interfaces, INFORMS, vol. 52(4), pages 344-362, July.
    16. Sampath Kumar Venkatachary & Jagdish Prasad & Ravi Samikannu & Annamalai Alagappan & Leo John Baptist & Raymon Antony Raj, 2020. "Macro Economics of Virtual Power Plant for Rural Areas of Botswana," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 196-207.
    17. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    18. Ameknassi, Lhoussaine & Aït-Kadi, Daoud & Rezg, Nidhal, 2016. "Integration of logistics outsourcing decisions in a green supply chain design: A stochastic multi-objective multi-period multi-product programming model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 165-184.
    19. Pan, Jeng-Shyang & Hu, Pei & Chu, Shu-Chuan, 2021. "Binary fish migration optimization for solving unit commitment," Energy, Elsevier, vol. 226(C).
    20. Yan, Rujing & Wang, Jiangjiang & Wang, Jiahao & Tian, Lei & Tang, Saiqiu & Wang, Yuwei & Zhang, Jing & Cheng, Youliang & Li, Yuan, 2022. "A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties," Energy, Elsevier, vol. 247(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:renene:v:131:y:2019:i:c:p:956-965. 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/renewable-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.