A cogeneration technique such as the combined heat and power generation offers prospects for emission of non-toxic gases which are in serious need for the energy demand world. Hence several research efforts have been conducted to obtain a desirable balance between energy supply and economic objectives.
Researchers from University of Naples Federico II in Italy proposed a new methodology which involves a coupled calculation algorithm to genetic optimization algorithm MOGA II and a multi-objective robust design optimization approach in order to determine the capabilities of an optimized combined heat and power plants in hospital facilities. The research work is now published in Energy Conversion and Management.
The calculation algorithm developed to the genetic optimization algorithm compares the specific load profiles of two Italian hospital facilities while considering the combined heat and power system-user interaction with the sole objective of increasing the total primary energy conversion and reducing the simple payback period. The multi-objective optimization approach which also involves robust design optimization involved a sensitivity analysis which accommodates certain uncertainties economic-wise and energy as well.
The authors implemented two management strategies in the calculation algorithm; maximum primary energy savings management MPESM logic and maximum profitability management MPM logic. They investigated load profiles of the two hospital facilities were the S. Paolo hospital in Naples and the second, Oncological Reference Center of Basilicata CROB.
Pareto optimal front solutions derived from the multi-objective optimization approach when using the MPSEM strategy in a hospital facility of S. Paolo showed that plant configurations which aid the overall energy savings favors the simple payback period. A multiple gas engine of two and three, gave an optimum relation between the energy and economic results. A reasonable Pareto optimal front solutions were observed in the total primary energy savings at a value greater than 16.5%, the simple payback period between 2.9- 4.6 years and engines between one to three with an electrical power range between 260-570KW for each. The MPM logic had a decreased efficiency in designing an optimized plant configuration.
The Pareto optimal front solutions when considering a hospital facility of CROB indicated a higher value of total primary energy savings at 18.2%, while the simple payback period is a little above 3 years with three combined heat and power engines of 440KW. With the use of MPM logic strategy, a decrease in total primary energy savings of 0.5% was discovered. Compared to that of S. Paolo hospital facility, that of CROB had a higher total primary savings value in all cases.
Results from the Pareto optimal solutions for the first multi-objective optimization approach used in the S. Paolo hospital indicated a higher economic sensitivity compared to the energetic sensitivity as standard deviation accounted up to 7% of its mean value ratios under 3% for total primary energy savings. The most stable plant design for the two hospital facilities was also provided.
However, the multi-objective robust design optimization in order to derive a last-longing solution economically and energetically, gave Pareto optimal solutions with standard deviation for a simple payback period less than 3.5% of its mean value, which reaches 7% of the total primary savings in hospital facility of S. Paolo. Pareto optimal solutions for the hospital facility in the CROB had a standard deviation of simple payback less than 2.5% of its mean value while reaching 6% of the total primary energy savings.
The optimization tool proposed in this study provides a reasonable approach for determining long-lasting performance for the combined heat and power plant while considering its effect on the economy and energy supply.
Gimelli, A., Muccillo, M., Sannino, R. Optimal Design of Modular Cogeneration Plants for Hospital Facilities and Robustness Evaluation of the Results, Energy Conversion and Management 134 (2017) 20–31.
DII – Department of Industrial Engineering, University of Naples Federico II, Via Claudio 21, 80125 Napoli, Italy.Go To Energy Conversion and Management