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Optimization of a CHP system using a forecasting dispatch and teaching-learning-based optimization algorithm

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  • Toopshekan, Ashkan
  • Abedian, Ali
  • Azizi, Arian
  • Ahmadi, Esmaeil
  • Vaziri Rad, Mohammad Amin

Abstract

Using optimization algorithms and developing dispatch strategies are essential in sizing renewable energy systems to ensure optimal performance, cost-effectiveness, and sustainability. This study employs the Teaching-Learning-based Optimization (TLBO) algorithm to determine the optimal size of a Combined Heat and Power (CHP) system. The optimization results are validated using the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Furthermore, a novel dispatch strategy is developed to make an informed decision when using different energy sources. The strategy considers a 24-h foresight of upcoming electrical demand, solar irradiation, temperature, and wind speed. The developed dispatch strategy has led to a reduction in cost and excess electricity compared to the pre-prepared strategies. The energy sources employed include Photovoltaic panels (PV), Wind Turbines (WT), Diesel Generators (DG) with heat recovery capability, battery banks, and boilers to supply electrical and thermal demand. A Levelized cost of energy (LCOE) of 0.142 $/kWh is obtained for the PV/WT/DG/Battery/Boiler system. Although the three algorithms find almost similar optimal solutions, TLBO exhibits better convergence speed than PSO and GA. A comparison with HOMER software control strategies shows the developed dispatch strategy is 3.4% and 15.5% more efficient than Cycle Charging and Load Following strategies, respectively. Lastly, a comprehensive economic sensitivity analysis is performed to investigate the effect of inflation and discount rates on the size of components and final objective functions.

Suggested Citation

  • Toopshekan, Ashkan & Abedian, Ali & Azizi, Arian & Ahmadi, Esmaeil & Vaziri Rad, Mohammad Amin, 2023. "Optimization of a CHP system using a forecasting dispatch and teaching-learning-based optimization algorithm," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223020650
    DOI: 10.1016/j.energy.2023.128671
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    2. Toopshekan, Ashkan & Ahmadi, Esmaeil & Abedian, Ali & Vaziri Rad, Mohammad Amin, 2024. "Techno-economic analysis, optimization, and dispatch strategy development for renewable energy systems equipped with Internet of Things technology," Energy, Elsevier, vol. 296(C).

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