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A two-stage stochastic framework for effective management of multiple energy carriers

Author

Listed:
  • Mohamed, Mohamed A.
  • Tajik, Elham
  • Awwad, Emad Mahrous
  • El-Sherbeeny, Ahmed M.
  • Elmeligy, Mohammed A.
  • Ali, Ziad M.

Abstract

This paper suggests optimal scheduling of Energy Hub (EH) considering water to maximize its profit in a pool-based day-ahead electricity market where the EH including electrical-thermal-water demands. The proposed model is expressed as a bi-level problem and allows the EH acts as an independent price maker considering uncertain parameters. The market settlement mechanism is based on the pay-at-MCP where every market participant is paid at the Market Clearing Price (MCP). Maximizing the profit of the recommended strategic producer and minimizing dispatch cost within the power grid are represented as the upper level and lower level of the bi-level stochastic optimization problem, respectively. The problem is formulated with Mathematical Program with Equilibrium Constraints (MPEC) and is converted into a new Mixed-Integer Linear Program (MILP) based on Karush-Kuhn-Tucker (KKT) conditions. To model the high uncertainties associated with water, heat and electricity demand within the EH as well as the uncertainties in the generators and loads submitted a price to the market, a stochastic framework based on Point Estimate Method (PEM) is developed. Due to the complex and nonlinear nature of the proposed problem, a new optimization algorithm based on a modified Bat Algorithm (BA) is applied to solve the problem optimally. To confirm the strong performance of the proposed strategic EH producer in the electricity market as well as its effect on the buses’ Locational Marginal Prices (LMPs) in a transmission-constrained market, authors applied the presented approach on the 24-bus IEEE system. So overall, the presence of line congestions may increase the opportunity of the proposed producers to make a profit. Depending on the lines’ connection points, the effects of the congestion lines on profit varies in lines. The effective performance of the proposed model is demonstrated in the results.

Suggested Citation

  • Mohamed, Mohamed A. & Tajik, Elham & Awwad, Emad Mahrous & El-Sherbeeny, Ahmed M. & Elmeligy, Mohammed A. & Ali, Ziad M., 2020. "A two-stage stochastic framework for effective management of multiple energy carriers," Energy, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220302772
    DOI: 10.1016/j.energy.2020.117170
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    References listed on IDEAS

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    1. Mohamed A Mohamed & Ali M Eltamaly & Abdulrahman I Alolah, 2016. "PSO-Based Smart Grid Application for Sizing and Optimization of Hybrid Renewable Energy Systems," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-22, August.
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    Cited by:

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    2. Tan, Hong & Yan, Wei & Ren, Zhouyang & Wang, Qiujie & Mohamed, Mohamed A., 2022. "A robust dispatch model for integrated electricity and heat networks considering price-based integrated demand response," Energy, Elsevier, vol. 239(PA).
    3. Tianze Lan & Kittisak Jermsittiparsert & Sara T. Alrashood & Mostafa Rezaei & Loiy Al-Ghussain & Mohamed A. Mohamed, 2021. "An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand," Energies, MDPI, vol. 14(3), pages 1-25, January.
    4. Mohamed, Mohamed A. & Jin, Tao & Su, Wencong, 2020. "Multi-agent energy management of smart islands using primal-dual method of multipliers," Energy, Elsevier, vol. 208(C).
    5. Mohamed, Mohamed A. & Jin, Tao & Su, Wencong, 2020. "An effective stochastic framework for smart coordinated operation of wind park and energy storage unit," Applied Energy, Elsevier, vol. 272(C).
    6. Bao, Shiyuan & Yang, Zhifang & Yu, Juan, 2021. "Decomposition and analysis of marginal prices in multi-energy systems," Energy, Elsevier, vol. 221(C).
    7. Tan, Hong & Li, Zhenxing & Wang, Qiujie & Mohamed, Mohamed A., 2023. "A novel forecast scenario-based robust energy management method for integrated rural energy systems with greenhouses," Applied Energy, Elsevier, vol. 330(PB).
    8. Yan Xiong & Jiakun Fang, 2022. "Co-Operative Optimization Framework for Energy Management Considering CVaR Assessment and Game Theory," Energies, MDPI, vol. 15(24), pages 1-17, December.
    9. Martinho, V.J.P.D., 2020. "Relationships between agricultural energy and farming indicators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).

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