IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1999-d1071880.html
   My bibliography  Save this article

Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study

Author

Listed:
  • Donovin D. Lewis

    (SPARK Laboratory, ECE Department, University of Kentucky, Lexington, KY 40506, USA)

  • Aron Patrick

    (PPL Corporation, Allentown, PA 18101, USA
    Louisville Gas and Electric and Kentucky Utilities, Louisville, KY 40202, USA)

  • Evan S. Jones

    (SPARK Laboratory, ECE Department, University of Kentucky, Lexington, KY 40506, USA)

  • Rosemary E. Alden

    (SPARK Laboratory, ECE Department, University of Kentucky, Lexington, KY 40506, USA)

  • Abdullah Al Hadi

    (SPARK Laboratory, ECE Department, University of Kentucky, Lexington, KY 40506, USA)

  • Malcolm D. McCulloch

    (Department of Engineering Science, University of Oxford, Oxford OX13PJ, UK)

  • Dan M. Ionel

    (SPARK Laboratory, ECE Department, University of Kentucky, Lexington, KY 40506, USA)

Abstract

Decarbonization of existing electricity generation portfolios with large-scale renewable resources, such as wind and solar photo-voltaic (PV) facilities, is important for a transition to a sustainable energy future. This paper proposes an ultra-fast optimization method for economic dispatch of firm thermal generation using high granularity, one minute resolution load, wind, and solar PV data to more accurately capture the effects of variable renewable energy (VRE). Load-generation imbalance and operational cost are minimized in a multi-objective clustered economic dispatch problem with various generation portfolios, realistic generator flexibility, and increasing levels of VRE integration. The economic feasibility of thermal dispatch scenarios is evaluated through a proposed method of levelized cost of energy (LCOE) for clustered generation portfolios. Effective renewable economics is applied to assess resource adequacy, annual carbon emissions, renewable capacity factor, over generation, and cost to build between thermal dispatch scenarios with incremental increases in VRE penetration. Solar PV and wind generation temporally complement one another in the region studied, and the combination of the two is beneficial to renewable energy integration. Furthermore, replacing older coal units with cleaner and agile natural gas units increases renewable hosting capacity and provides further pathways to decarbonization. Minute-based chronological simulations enable the assessment of renewable effectiveness related to weather-related variability and of complementary technologies, including energy storage for which a sizing procedure is proposed. The generally applicable methods are regionally exemplified for Kentucky, USA, including eight scenarios with four major year-long simulated case studies and 176 subcases using high performance computing (HPC) systems.

Suggested Citation

  • Donovin D. Lewis & Aron Patrick & Evan S. Jones & Rosemary E. Alden & Abdullah Al Hadi & Malcolm D. McCulloch & Dan M. Ionel, 2023. "Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study," Energies, MDPI, vol. 16(4), pages 1-23, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1999-:d:1071880
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1999/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/4/1999/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Javed, Muhammad Shahzad & Ma, Tao & Jurasz, Jakub & Canales, Fausto A. & Lin, Shaoquan & Ahmed, Salman & Zhang, Yijie, 2021. "Economic analysis and optimization of a renewable energy based power supply system with different energy storages for a remote island," Renewable Energy, Elsevier, vol. 164(C), pages 1376-1394.
    2. van der Wiel, K. & Stoop, L.P. & van Zuijlen, B.R.H. & Blackport, R. & van den Broek, M.A. & Selten, F.M., 2019. "Meteorological conditions leading to extreme low variable renewable energy production and extreme high energy shortfall," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 261-275.
    3. John E. T. Bistline & David T. Young, 2022. "The role of natural gas in reaching net-zero emissions in the electric sector," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Lai, Chun Sing & McCulloch, Malcolm D., 2017. "Levelized cost of electricity for solar photovoltaic and electrical energy storage," Applied Energy, Elsevier, vol. 190(C), pages 191-203.
    5. van Zuijlen, Bas & Zappa, William & Turkenburg, Wim & van der Schrier, Gerard & van den Broek, Machteld, 2019. "Cost-optimal reliable power generation in a deep decarbonisation future," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Luz, Thiago & Moura, Pedro, 2019. "100% Renewable energy planning with complementarity and flexibility based on a multi-objective assessment," Applied Energy, Elsevier, vol. 255(C).
    7. Borasio, M. & Moret, S., 2022. "Deep decarbonisation of regional energy systems: A novel modelling approach and its application to the Italian energy transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    8. Li, Yang & Wang, Jinlong & Zhao, Dongbo & Li, Guoqing & Chen, Chen, 2018. "A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making," Energy, Elsevier, vol. 162(C), pages 237-254.
    9. 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.
    10. Copp, David A. & Nguyen, Tu A. & Byrne, Raymond H. & Chalamala, Babu R., 2022. "Optimal sizing of distributed energy resources for planning 100% renewable electric power systems," Energy, Elsevier, vol. 239(PE).
    11. Guerra, K. & Haro, P. & Gutiérrez, R.E. & Gómez-Barea, A., 2022. "Facing the high share of variable renewable energy in the power system: Flexibility and stability requirements," Applied Energy, Elsevier, vol. 310(C).
    12. Lunz, Benedikt & Stöcker, Philipp & Eckstein, Sascha & Nebel, Arjuna & Samadi, Sascha & Erlach, Berit & Fischedick, Manfred & Elsner, Peter & Sauer, Dirk Uwe, 2016. "Scenario-based comparative assessment of potential future electricity systems – A new methodological approach using Germany in 2050 as an example," Applied Energy, Elsevier, vol. 171(C), pages 555-580.
    13. Luis Montero & Antonio Bello & Javier Reneses, 2022. "A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods," Energies, MDPI, vol. 15(4), pages 1-40, February.
    14. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    Full references (including those not matched with items on IDEAS)

    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. Changyu Zhou & Guohe Huang & Jiapei Chen, 2019. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks," Energies, MDPI, vol. 12(13), pages 1-21, June.
    2. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    3. Kumar Jadoun, Vinay & Rahul Prashanth, G & Suhas Joshi, Siddharth & Narayanan, K. & Malik, Hasmat & García Márquez, Fausto Pedro, 2022. "Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm," Applied Energy, Elsevier, vol. 315(C).
    4. Cao, K.H. & Qi, H.S. & Tsai, C.H. & Woo, C.K. & Zarnikau, J., 2021. "Energy trading efficiency in the US Midcontinent electricity markets," Applied Energy, Elsevier, vol. 302(C).
    5. Zhou, Xu & Ma, Zhongjing & Zou, Suli & Zhang, Jinhui, 2022. "Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions," Applied Energy, Elsevier, vol. 324(C).
    6. Wang, Jiawei & You, Shi & Zong, Yi & Træholt, Chresten & Dong, Zhao Yang & Zhou, You, 2019. "Flexibility of combined heat and power plants: A review of technologies and operation strategies," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    7. Li, Yang & Yang, Zhen & Li, Guoqing & Mu, Yunfei & Zhao, Dongbo & Chen, Chen & Shen, Bo, 2018. "Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing," Applied Energy, Elsevier, vol. 232(C), pages 54-68.
    8. Xu Chen & Shuai Fang & Kangji Li, 2023. "Reinforcement-Learning-Based Multi-Objective Differential Evolution Algorithm for Large-Scale Combined Heat and Power Economic Emission Dispatch," Energies, MDPI, vol. 16(9), pages 1-23, April.
    9. Gronier, Timothé & Fitó, Jaume & Franquet, Erwin & Gibout, Stéphane & Ramousse, Julien, 2022. "Iterative sizing of solar-assisted mixed district heating network and local electrical grid integrating demand-side management," Energy, Elsevier, vol. 238(PA).
    10. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Elattar, Ehab & Ginidi, Ahmed R., 2022. "An Amalgamated Heap and Jellyfish Optimizer for economic dispatch in Combined heat and power systems including N-1 Unit outages," Energy, Elsevier, vol. 246(C).
    11. Henrique Pires Corrêa & Rafael Ribeiro de Carvalho Vaz & Flávio Henrique Teles Vieira & Sérgio Granato de Araújo, 2019. "Reliability Based Genetic Algorithm Applied to Allocation of Fiber Optics Links for Power Grid Automation," Energies, MDPI, vol. 12(11), pages 1-26, May.
    12. Muhammad Faisal Shehzad & Mainak Dan & Valerio Mariani & Seshadhri Srinivasan & Davide Liuzza & Carmine Mongiello & Roberto Saraceno & Luigi Glielmo, 2021. "A Heuristic Algorithm for Combined Heat and Power System Operation Management," Energies, MDPI, vol. 14(6), pages 1-22, March.
    13. Simon Pezzutto & Giulio Quaglini & Andrea Zambito & Antonio Novelli & Philippe Riviere & Lukas Kranzl & Eric Wilczynski, 2022. "Potential Evolution of the Cooling Market in the EU27+UK: An Outlook until 2030," Sustainability, MDPI, vol. 14(8), pages 1-25, April.
    14. Li, Yang & Wang, Ruinong & Li, Yuanzheng & Zhang, Meng & Long, Chao, 2023. "Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach," Applied Energy, Elsevier, vol. 329(C).
    15. Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
    16. Ali Sulaiman Alsagri & Abdulrahman A. Alrobaian, 2022. "Optimization of Combined Heat and Power Systems by Meta-Heuristic Algorithms: An Overview," Energies, MDPI, vol. 15(16), pages 1-34, August.
    17. Jamin Koo & Soung-Ryong Oh & Yeo-Ul Choi & Jae-Hoon Jung & Kyungtae Park, 2019. "Optimization of an Organic Rankine Cycle System for an LNG-Powered Ship," Energies, MDPI, vol. 12(10), pages 1-17, May.
    18. Plaga, Leonie Sara & Bertsch, Valentin, 2023. "Methods for assessing climate uncertainty in energy system models — A systematic literature review," Applied Energy, Elsevier, vol. 331(C).
    19. Lugovoy, Oleg & Gao, Shuo & Gao, Ji & Jiang, Kejun, 2021. "Feasibility study of China's electric power sector transition to zero emissions by 2050," Energy Economics, Elsevier, vol. 96(C).
    20. Sen Liu & Wei Yu & Ling Liu & Yanan Hu, 2019. "Variable weights theory and its application to multi-attribute group decision making with intuitionistic fuzzy numbers on determining decision maker’s weights," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-21, March.

    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:16:y:2023:i:4:p:1999-:d:1071880. 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.