Abstract
Combined heat and power (CHP) generation plants are an assessed valuable solution to significantly reduce primary energy consumption and carbon dioxide emissions. Nevertheless, the primary energy saving (PES) and CO2 reduction potentials of this solution are strictly related to the accurate definition and management of thermal and electric loads. Data-driven analysis could represent a significant contribution for optimizing the CHP plant design and operation and then to fully deploy this potential. In this paper, the use of a bi-level optimization approach for the design of a CHP is applied to a real application (a large Italian hospital in Rome). Based on historical data of the hospital thermal and electric demand, clustering analysis is applied to identify a limited number of load patterns representative of the annual load. These selected patterns are then used as input data in the design procedure. A Mixed Integer Linear Programming coupled with a Genetic Algorithm is implemented to optimize the energy dispatch and size of the CHP plant, respectively, with the aim of maximizing the PES while minimizing total costs and carbon emissions. Finally, the effects of integrating biogas from the Anaerobic Digestion (AD) of the Spent Coffee Ground (SCG) and Energy Storage (ES) technologies are investigated. The results achieved provide a benchmark for the application of these technologies in this specific field, highlighting performances and benefits with respect to traditional approaches. The effective design of the CHP unit allows for achieving CO2 reduction in the order of 10%, ensuring economic savings (up to 40%), when compared with a baseline configuration where no CHP is installed. Further environmental benefits can be achieved by means of the integration of AD and ES pushing the CO2 savings up to 20%, still keeping the economical convenience of the capital investment.
Highlights
Global warming, rapid population growth, and, more recently, the global COVID-19 pandemic are critical societal, economic, and engineering challenges
In the context previously defined, the main contribution of the present study is the application of the proposed methodology to simultaneously optimize the economic and environmental targets considering of a Combined heat and power (CHP) civil application integrating Anaerobic Digestion (AD) of the Spent Coffee Ground (SCG) and its value chain, as well as Energy Storage (ES) technologies
This application has been chosen as hospitals are ideal applications for CHP powerplants as they are able to successfully match electricity and heat demands during the whole year [37]; on the other hand, some economic advantages arise from the CHP installation: 1. The in situ cogeneration of electricity and heat is cheaper than the separated generation
Summary
Rapid population growth (the population is forecast to reach 9.7 billion people by the 2050 [1]), and, more recently, the global COVID-19 pandemic are critical societal, economic, and engineering challenges. As a matter of fact, the long-term strategic vision for a prosperous, modern, competitive, and climate-neutral economy [4] requires a severe change of paradigm in power generation, energy sources management, efficiency, and resiliency of the whole energy supply chain In this context, the waste management hierarchy guidelines [5] address the transition from a “Linear economy” model toward the “Circular Economy” one, requiring a systematic reduction of the amount of waste and a maximization of its value by an increase in the use of the secondary raw materials. In the context previously defined, the main contribution of the present study is the application of the proposed methodology to simultaneously optimize the economic and environmental targets considering of a CHP civil application integrating Anaerobic Digestion (AD) of the SCG and its value chain, as well as Energy Storage (ES) technologies. Provides energy and environmental KPIs as a benchmark for a real case study for a hospital building
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