An Improved Artificial Ecosystem Algorithm for Economic Dispatch with Combined Heat and Power Units

• Published in: Applied sciences 2022-11, Vol.12 (22), p.11773
• Main Authors: Mahdy, Araby, El-Sehiemy, Ragab, Shaheen, Abdullah, Ginidi, Ahmed, Elbarbary, Zakaria M. S.
• Format: Article
• Language: English
• Subjects: Algorithms; Alternative energy sources; artificial ecosystem optimizer; Cogeneration; combined heat and power units; Convergence; Cost control; economic dispatch; Ecosystems; Efficiency; Energy resources; Energy transfer; Environmental conditions; fitness-distance-based; Heat; Industrial plant emissions; Optimization; Power plants; Transmission loss; valve-point loading effect
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app122211773

The most effective use of numerous Combined Heat and Power Units (CHPUs) is a challenging issue that requires strong approaches to handle the Economic Dispatch (ED) with CHPUs. It aims at minimizing the fuel costs by managing the Power-Only Units (POUs), CHPUs, and Heat-Only Units (HOUs). The transmission losses are also integrated, which increases the non-convexity of the ED problem. This paper proposes a Modified Artificial Ecosystem Algorithm (MAEA) motivated by three energy transfer processes in an ecosystem: production, consumption, and decomposition. The MAEA incorporates a Fitness Distance Balance Model (FDBM) with the basic AEA to improve the quality of the solution in non-linear and multivariate optimization environments. The FDBM is a selection approach meant to find individuals which will provide the most to the searching pathways within a population as part of a reliable and productive approach. Consequently, the diversity and intensification processes are carried out in a balanced manner. The basic AEA and the proposed MAEA are performed, in a comparative manner considering the 7-unit and 48-unit test systems. According to numerical data, the proposed MAEA shows a robustness improvement of 97.31% and 96.63% for the 7-unit system and 46.03% and 60.57% for the 48-unit system, with and without the power losses, respectively. On the side of convergence, based on the average statistics, the proposed MAEA shows a considerable improvement of 47% and 43% of the total number of iterations for the 7-unit system and 13% and 20% of the total number of iterations for the 48-unit system, with and without the power losses, respectively. Thus, the suggested MAEA provides significant improvements in the robustness and convergence properties. The proposed MAEA also provides superior performance compared with different reported results, which indicates a promising solution methodology based on the proposed MAEA.