The achievement of optimal energetic and economic results through combined heat and power plants is a complex problem. In fact, it is not possible to quantify the benefits even with an accurate knowledge of the user load profiles. The number of variables involved in the problem could also completely change energetic and cost savings with changes in regulation, tariffs or reference energetic scenarios. Equally significant is the dependence on the plant configuration and its management strategy. The study, for example, has revealed that the overall energy savings can vary in the range of 0-19% for a specific CHP gas engine.
Therefore, a predictive analysis such as the one proposed in this study is important in determining a plant solution (engine size, plant configuration, management logic, possible absorption chiller size, engines number, etc. – as shown in the figure) that approaches the best energetic solution while ensuring a reasonable profit. For this reason, the determination of the plant optimal configuration has been pursued through a multi-objective approach, specifically through the genetic evolutionary algorithm MOGA II implemented in the optimization software mode FRONTIER®. The study shows how the search for configurations aimed at maximizing the global energy saving leads to the simple payback worsening results (as shown in the figure). The configuration of a trade-off between Primary Energy Saving and Simple Payback confirms the inability to conduct any predictive analysis that disregards the use of vector optimization.
The potential energy savings of combined heat and power justifies the attention given to the cogeneration technology based on reciprocating internal combustion gas engines. The plant configurations and management strategies analyzed in this work, which require further optimization and refinement, indicate primary energy savings approaching 20% for hospital facilities. The result is even more interesting when you consider that the load variability in the civil sector often affects, at least partially, the potential benefits of combined heat and power.
With regard to the S. Paolo Hospital in Naples, for example, solutions that use three gas engines are particularly interesting and are characterized by energy savings of approximately 18%, SPB of approximately 4 years and electric power output in the range 225-240 kW for each engine. These solutions seem to represent a good compromise of operational flexibility, plant simplification and power reliability in the case of short or long power outages.
Applied Energy, Volume 104, 2013, Pages 910-923.
Alfredo Gimelli, Massimiliano Muccillo.
Department of Mechanical and Energy Engineering, University of Naples Federico II, Via Claudio, 21, 80125 Napoli, Italy
Cogeneration is commonly recognized as one of the most effective solutions to achieve the increasingly stringent requirements in primary energy consumption reduction and greenhouse emissions reduction. The potential of cogeneration led to the adoption of specific directives promoting this technique. In addition, distributed generation plays a strategic role in power reliability. The study and prototyping of cogeneration plants has thus involved many research centers. Similar activities were carried out by DiME (University of Naples). These activities highlighted the need to study the cogeneration system-user interaction to estimate the real energetic and economic benefits.
The paper develops a specific methodology that is used to conduct an analysis on the loads of a specific hospital facility. The energetic and economic benefits generated by CHP plants depend on plant and user characteristics, plant layout, management strategies, and economic variables. Therefore, the potential benefits that have attracted the attention of the scientific community are not always granted. For this reason, a predictive analysis is needed to find the optimum configuration of the plant (i.e., engine size, plant configuration, management strategies, absorption chiller size, engine number) that approaches the best energetic solution while ensuring a reasonable profit. Therefore, this study attempts to determine the optimal configuration through a multi-objective approach.