IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v256y2019ics0306261919316605.html
   My bibliography  Save this article

Optimization of a multiple-scale renewable energy-based virtual power plant in the UK

Author

Listed:
  • Hany Elgamal, Ahmed
  • Kocher-Oberlehner, Gudrun
  • Robu, Valentin
  • Andoni, Merlinda

Abstract

Commercial Virtual Power Plants (CVPPs) have recently emerged as one of the most promising solutions for enabling intermittent renewable energy generation sources to efficiently trade the energy they generate in the electricity market. In this study, we develop several optimization and forecasting methods, and apply them to model the operation of multiple renewable generators across Scotland, trading energy as a single CVPP. The aim of the techniques developed is to optimize the scheduling of the CVPP, such as to maximize revenues and reduce the penalties resulting from forecasting errors, while considering operational and market constraints, such as variable costs, ramping rates, start-up costs, day-ahead and imbalance prices. The practical application is based on a case study of operational renewable energy plants in Scotland, and optimizes the CVPP operation for 3 months in winter and summer of 2017, respectively. Renewable generation output, day-ahead prices and imbalance prices are obtained from historical data for the same year. The numerical results show a profit increase of around 12% for the CVPP compared to standalone operation of renewable plants. This increase is observed for different market and imbalance settlement strategies.

Suggested Citation

  • Hany Elgamal, Ahmed & Kocher-Oberlehner, Gudrun & Robu, Valentin & Andoni, Merlinda, 2019. "Optimization of a multiple-scale renewable energy-based virtual power plant in the UK," Applied Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:appene:v:256:y:2019:i:c:s0306261919316605
    DOI: 10.1016/j.apenergy.2019.113973
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919316605
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113973?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. Cheng, William Y.Y. & Liu, Yubao & Bourgeois, Alfred J. & Wu, Yonghui & Haupt, Sue Ellen, 2017. "Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation," Renewable Energy, Elsevier, vol. 107(C), pages 340-351.
    2. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
    3. Robu, Valentin & Chalkiadakis, Georgios & Kota, Ramachandra & Rogers, Alex & Jennings, Nicholas R., 2016. "Rewarding cooperative virtual power plant formation using scoring rules," Energy, Elsevier, vol. 117(P1), pages 19-28.
    4. McIlveen-Wright, David R. & Huang, Ye & Rezvani, Sina & Redpath, David & Anderson, Mark & Dave, Ashok & Hewitt, Neil J., 2013. "A technical and economic analysis of three large scale biomass combustion plants in the UK," Applied Energy, Elsevier, vol. 112(C), pages 396-404.
    5. 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.
    6. Cristina Ballester & Dolores Furió, 2017. "Impact of Wind Electricity Forecasts on Bidding Strategies," Sustainability, MDPI, vol. 9(8), pages 1-17, August.
    7. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    8. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Energy Economics, Elsevier, vol. 51(C), pages 430-444.
    9. Kirby, Brendan & Milligan, Michael, 2008. "Facilitating Wind Development: The Importance of Electric Industry Structure," The Electricity Journal, Elsevier, vol. 21(3), pages 40-54, April.
    10. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    11. Sahin, Cem & Shahidehpour, Mohammad & Erkmen, Ismet, 2012. "Generation risk assessment in volatile conditions with wind, hydro, and natural gas units," Applied Energy, Elsevier, vol. 96(C), pages 4-11.
    12. Andoni, Merlinda & Robu, Valentin & Früh, Wolf-Gerrit & Flynn, David, 2017. "Game-theoretic modeling of curtailment rules and network investments with distributed generation," Applied Energy, Elsevier, vol. 201(C), pages 174-187.
    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. Behnaz Behi & Ali Baniasadi & Ali Arefi & Arian Gorjy & Philip Jennings & Almantas Pivrikas, 2020. "Cost–Benefit Analysis of a Virtual Power Plant Including Solar PV, Flow Battery, Heat Pump, and Demand Management: A Western Australian Case Study," Energies, MDPI, vol. 13(10), pages 1-24, May.
    2. Songkai Wang & Rong Jia & Xiaoyu Shi & Chang Luo & Yuan An & Qiang Huang & Pengcheng Guo & Xueyan Wang & Xuewen Lei, 2022. "Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)," Energies, MDPI, vol. 15(4), pages 1-18, February.
    3. Behnaz Behi & Ali Arefi & Philip Jennings & Arian Gorjy & Almantas Pivrikas, 2021. "Advanced Monitoring and Control System for Virtual Power Plants for Enabling Customer Engagement and Market Participation," Energies, MDPI, vol. 14(4), pages 1-19, February.
    4. Khalil Gholami & Behnaz Behi & Ali Arefi & Philip Jennings, 2022. "Grid-Forming Virtual Power Plants: Concepts, Technologies and Advantages," Energies, MDPI, vol. 15(23), pages 1-26, November.
    5. Dong, Lianxin & Fan, Shuai & Wang, Zhihua & Xiao, Jucheng & Zhou, Huan & Li, Zuyi & He, Guangyu, 2021. "An adaptive decentralized economic dispatch method for virtual power plant," Applied Energy, Elsevier, vol. 300(C).
    6. 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.
    7. Kong, Xiangyu & Lu, Wenqi & Wu, Jianzhong & Wang, Chengshan & Zhao, Xv & Hu, Wei & Shen, Yu, 2023. "Real-time pricing method for VPP demand response based on PER-DDPG algorithm," Energy, Elsevier, vol. 271(C).
    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. Guo, Hongye & Chen, Qixin & Shahidehpour, Mohammad & Xia, Qing & Kang, Chongqing, 2022. "Bidding behaviors of GENCOs under bounded rationality with renewable energy," Energy, Elsevier, vol. 250(C).
    10. Hengyu Liu & Qingqi Zhao & Yang Liu & Zuoxia Xing & Dawei Hu & Pengfei Zhang & Zhi Zhang & Jiazheng Sun, 2023. "A Multi-Subject Game-Based Operation Strategy for VPPs Integrating Wind-Solar-Storage," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    11. Bhuiyan, Erphan A. & Hossain, Md. Zahid & Muyeen, S.M. & Fahim, Shahriar Rahman & Sarker, Subrata K. & Das, Sajal K., 2021. "Towards next generation virtual power plant: Technology review and frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    12. Park, Sung-Won & Son, Sung-Yong, 2020. "Interaction-based virtual power plant operation methodology for distribution system operator’s voltage management," Applied Energy, Elsevier, vol. 271(C).
    13. Kirli, Desen & Couraud, Benoit & Robu, Valentin & Salgado-Bravo, Marcelo & Norbu, Sonam & Andoni, Merlinda & Antonopoulos, Ioannis & Negrete-Pincetic, Matias & Flynn, David & Kiprakis, Aristides, 2022. "Smart contracts in energy systems: A systematic review of fundamental approaches and implementations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    14. Hsu, Ching-Chi, 2023. "Influence of climate finance and natural resource consumption on the mitigation of climate change in developed countries in the Pre-COP26 era," Resources Policy, Elsevier, vol. 83(C).
    15. Li, Dongsen & Gao, Ciwei & Chen, Tao & Guo, Xiaoxuan & Han, Shuai, 2021. "Planning strategies of power-to-gas based on cooperative game and symbiosis cooperation," Applied Energy, Elsevier, vol. 288(C).
    16. Jiajia Li & Jinfu Liu & Peigang Yan & Xingshuo Li & Guowen Zhou & Daren Yu, 2021. "Operation Optimization of Integrated Energy System under a Renewable Energy Dominated Future Scene Considering Both Independence and Benefit: A Review," Energies, MDPI, vol. 14(4), pages 1-36, February.
    17. Chen, Yongbao & Xu, Peng & Chen, Zhe & Wang, Hongxin & Sha, Huajing & Ji, Ying & Zhang, Yongming & Dou, Qiang & Wang, Sheng, 2020. "Experimental investigation of demand response potential of buildings: Combined passive thermal mass and active storage," Applied Energy, Elsevier, vol. 280(C).
    18. Hosseini, Seyyed Ahmad & Toubeau, Jean-François & De Grève, Zacharie & Vallée, François, 2020. "An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision," Applied Energy, Elsevier, vol. 280(C).
    19. Chen, Yongbao & Zhang, Lixin & Xu, Peng & Di Gangi, Alessandra, 2021. "Electricity demand response schemes in China: Pilot study and future outlook," Energy, Elsevier, vol. 224(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. 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.
    2. 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.
    3. Hadayeghparast, Shahrzad & SoltaniNejad Farsangi, Alireza & Shayanfar, Heidarali, 2019. "Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant," Energy, Elsevier, vol. 172(C), pages 630-646.
    4. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Mahmud, Khizir & Khan, Behram & Ravishankar, Jayashri & Ahmadi, Abdollah & Siano, Pierluigi, 2020. "An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    6. Moreno, Blanca & Díaz, Guzmán, 2019. "The impact of virtual power plant technology composition on wholesale electricity prices: A comparative study of some European Union electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 100-108.
    7. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    8. Galarneau-Vincent, Rémi & Gauthier, Geneviève & Godin, Frédéric, 2023. "Foreseeing the worst: Forecasting electricity DART spikes," Energy Economics, Elsevier, vol. 119(C).
    9. Kath, Christopher & Ziel, Florian, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Energy Economics, Elsevier, vol. 76(C), pages 411-423.
    10. Narajewski, Michał & Ziel, Florian, 2020. "Econometric modelling and forecasting of intraday electricity prices," Journal of Commodity Markets, Elsevier, vol. 19(C).
    11. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    12. Sergei Kulakov, 2020. "X-Model: Further Development and Possible Modifications," Forecasting, MDPI, vol. 2(1), pages 1-16, February.
    13. 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.
    14. Bhatia, Kushagra & Mittal, Rajat & Varanasi, Jyothi & Tripathi, M.M., 2021. "An ensemble approach for electricity price forecasting in markets with renewable energy resources," Utilities Policy, Elsevier, vol. 70(C).
    15. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    16. 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).
    17. Zorana Božić & Dušan Dobromirov & Jovana Arsić & Mladen Radišić & Beata Ślusarczyk, 2020. "Power Exchange Prices: Comparison of Volatility in European Markets," Energies, MDPI, vol. 13(21), pages 1-15, October.
    18. Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Post-Print hal-03621974, HAL.
    19. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    20. 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.

    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:256:y:2019:i:c:s0306261919316605. 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.