# Data-Driven Optimal Design of a CHP Plant for a Hospital Building: Highlights on the Role of Biogas and Energy Storages on the Performance

^{*}

## Abstract

**:**

_{2}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 CO

_{2}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 CO

_{2}savings up to 20%, still keeping the economical convenience of the capital investment.

## 1. Introduction

_{2}” [6]. This definition underlines how the effectiveness of biorefinery processes strongly depends on the biomass input.

- Proposes a bi-level optimal design for the integration of Biogas from Anaerobic Digestion (AD) and Energy Storage (ES) technologies (Thermal Energy Storage—TES and Battery Energy Storage System (BESS) for CHP applications;
- Uses the developed design and control algorithm on a real case study (the energy system of a hospital facility located in Rome) to evaluate the potential benefits arising from the innovative approach. Hospitals have in fact often used CHP power systems due to the relevant electric and thermal power consumed and the demands’ contemporaneity;
- Provides energy and environmental KPIs as a benchmark for a real case study for a hospital building.

## 2. Materials and Methods

#### 2.1. System Layout

- The in situ cogeneration of electricity and heat is cheaper than the separated generation.
- Financial incentives are available in Italy (as in the rest of the EU) for the CHP units that achieve specific performance goals [37].

^{2}with a central structure of four horizontal levels, intended for diagnosis and treatment, and two eleven-level building units with vertical development, designed for hospital stays (500 beds).

_{2}emitted by the truck is computed as a GHG emission of the ES, but the CO

_{2}emitted from the combustion of biogas is not taken into account, as it is a part of the carbon cycle of the biomass itself. The data on coffee to SCG and the characteristics of SCG were measured in the laboratory of the University of Rome Tor Vergata through several experimental tests (Mass conversion coffee—SCG = 2.27 and Volatile Solids/SCG mass = 31.5%). Further details about the whole process modeling, from transportation to biogas production, are reported in the Appendix A for the sake of clarity.

#### 2.2. Modeling of Thermal and Electric Load

#### 2.3. Optimal Design Method

#### Optimal Solution through the Pareto Plot

## 3. Results

- SCENARIO 1—Optimal design of the CHP plant;
- SCENARIO 2—Optimal design of the CHP plant integrated with the AD reactor;
- SCENARIO 3—Optimal design of the CHP plant and the AD reactor, integrating TES and BESS.

#### 3.1. Scenario 1—CHP Optimal Design

_{2}emissions are reduced between 8 to 10% at maximum, and cost benefits range from 37 to 40%. As a matter of fact, positive effects are observed both on the environmental and economic objectives in the optimal design range; however, limits are encountered due to the intrinsic characteristics of the thermal and electric demands.

- The loss of efficiency due to the waste heat occurring when electric and heat demands are not matched (Figure 5).
- The investment and maintenance costs that increase with the CHP size.

_{2}emissions. Increasing the weight of the environmental objective, the optimal CHP size progressively decreases, saturating at a size of 3.5 MW. Indeed, a CHP plant with a smaller size would lead to a reduction in PES as well.

#### 3.2. Scenario 2—CHP and AD Optimal Design

_{2}emissions. Considerations about the two latter aspects can be made looking at the results of the Pareto front and the sizing results as a function of the weighting factor (Figure 4—blue stars, and Table 3).

_{2}emissions can be obtained by increasing the weight of the environmental factor in the objective function. It is worth recalling that the analysis also accounts for CO

_{2}emissions related to feedstock transportation. However, due to the high cost of the SCG transportation system, the economic target results are negatively affected by this design solution, although savings up to 20% are achieved with respect to the reference case. In fact, if the weight of the economic target is increased, the optimization algorithm leads back to a design solution without the AD integration (Table 3).

#### 3.3. Scenario 3—CHP, TES, BESS and AD Optimal Design

- The extension of the maximum benefits achievable (up to 42% and 22%, respectively, for costs and emissions reductions);
- The reduction in the carbon emissions at a given economic target.

_{2}emissions since it allows for achieving high PES values also at a smaller CHP size.

## 4. Discussion of the Results

_{eco}= α

_{env}= 0.5) to thoroughly understand the differences among the performances of the scenarios.

_{2}emissions per bed for all the simulated scenarios evaluated using either the real or the clustered load data. Results are always close each other (max deviation of about 2%), confirming that the synthesized load is representative of the dynamic behavior of the hospital electric and thermal demands. Moreover, the analysis offers significant benchmark parameters. In particular, it can be observed that the energy cost per bed in a standard configuration (CASE 0) is slightly below kEUR 12, and that the CHP unit can allow, if properly designed, to reduce this value up to a minimum of about 7 kEUR. The introduction of the AD and ESs leads to a slight increase in the total energy cost per bed (5.07% and 8.94%, respectively, for Scenario 2 and 3). However, the cost increase is counterbalanced by the reduction in the CO

_{2}emissions that decrease by about 7.62% and 10.82% with respect to Scenario 1, respectively, for Scenarios 2 and 3.

## 5. Conclusions

_{2}emissions and total costs (capital expenditures and operating expenses). Clustering analyses have been performed to carry out the design optimization with representative annual thermal and electric load profiles. The effects of the integration of different energy storage (ES) technologies and biogas produced by the anaerobic digestion (AD) of spent coffee grounds (SCG) into the combined heat and power (CHP) plant installed at the Tor Vergata Hospital (PTV) are evaluated in terms of primary energy savings, GHG emissions, and economic convenience of the overall energy system.

- Compared with a static design, a dynamic procedure would allow for achieving better performance in terms of both economic and environmental perspectives.
- The minimum total energy cost per bed is achieved for the optimized CHP plant at about 7 kEUR/per bed, whereas to achieve the best performance in terms of CO
_{2}emissions, the integration of the AD process and ES technologies is needed, allowing to reduce the carbon foot print up to about 38 tons/per bed. - The introduction of biogas from SCG AD helps to extend the positive influence on CO
_{2}emissions (saving up to 20% with respect to the reference case), but it negatively affects the economic performance due to the high costs of transportation. - Further benefits both in terms of economic and environmental targets can be achieved through a proper design of the Thermal and Battery Energy Storages—with maximum obtainable savings up to 42% and 22%, respectively, increasing costs and the emission weighing factor.

## Author Contributions

## Funding

## Conflicts of Interest

## Nomenclature

Element | Description |

AD | Anaerobic Digestion |

BESS | Battery Energy Storage System |

CHP | Combined Heat and Power |

CRF | Actualization Factor |

ES | Energy Storage |

FPEC | Fossil Primary Energy Consumption |

GA | Genetic Algorithm |

GHG | Greenhouse Gas |

HE-CHP | high-efficiency incentives |

ICE | Internal Combustion Engine |

MESs | Multi Energy Systems |

PES | Primary Energy Saving |

PHR | Power to Heat Ratio |

PV | Photovoltaic |

PTV | Policlinico Tor Vergata |

SCG | Spent Coffee Ground |

TES | Thermal Energy Storage |

$${\alpha}_{eco}$$
| Economic weight |

$${\alpha}_{env}$$
| Environmental weight |

$$Ob{j}_{eco}$$
| Economic Objective |

$$Ob{j}_{env}$$
| Environmental Objective |

$${\eta}_{elchp}$$
| Electric efficiency of CHP unit |

$${\eta}_{thchp}$$
| Thermal efficiency of CHP unit |

$${\eta}_{elrif}$$
| Reference efficiencies for standalone electric energy conversion |

$${\eta}_{thrif}$$
| Reference efficiencies for standalone thermal energy conversion |

## Appendix A

_{HRT}) during the Hydraulic Retention Time (HRT) of the SCG.

- HRT: 28 days
- CMP
_{HRT}: 0.314 lCH_{4}/gVS; - T
_{REACTOR}: 35 °C.

_{AD}) is calculated as 9.32% of the power obtainable by the electric conversion of the biogas into electricity, as reported in [44], whereas the thermal power needed for the AD process (Q

_{AD}) is the heat flow required to keep constant the temperature of the reactor. It is, therefore, equal to the heat dispersion due to the heat flow along the reactor walls and the sensible heat losses in the daily charge/discharge of reactor for heating the SCG in input and exit of the warm digestate and it has been estimated by numerical simulation equal to 28.9% of the power obtainable by the electric conversion of the biogas into electricity.

_{2}emissions related to the transportation of SCG ($C{O}_{2,AD})$have been accounted as described in Equation (A5) [45].

## Appendix B

_{2}as objective functions. They are described in Equations (A6) and (A7), respectively:

# | Lower Limit | Upper Limit | ||
---|---|---|---|---|

1 | ${P}_{max,chp}$ | 0 | 7000 | (kW) |

2 | ${C}_{TES}$ | 0 | 45,000 | (kWh) |

3 | ${C}_{BEES}$ | 0 | 6000 | (kWh) |

4 | $C{H}_{4}{}_{AD}$ | 0 | 2000 | (kWh) |

Element | Description |
---|---|

cc | High Efficiency incentives (EUR/kWh) |

${c}_{el}$ | Electricity cost (EUR/kWhel) |

${c}_{NG}$ | Natural Gas cost (EUR/kWhth) |

${C}_{BESS}$ | Battery Energy Storage System Capacity (kWh) |

${C}_{TES}$ | Thermal Energy Storage Capacity (kWh) |

${C}_{inv}$ | Capital cost of ES (EUR) |

${C}_{inv,AD}$ | Capital cost of Thermal Anaerobic Digestion System (EUR) |

${C}_{inv,BESS}$ | Capital cost of Battery Energy Storage System (EUR/kWh) |

${C}_{inv,chp}$ | Annual Capital cost of CHP unit (EUR/kWh) |

${C}_{inv,TES}$ | Capital cost of Thermal Energy Storage (EUR/kWh) |

${c}_{m,chp}$ | Maintenance cost of CHP unit (EUR/kWhth) |

${C}_{o\&m}$ | Operation and Maintenance cost of the system (EUR) |

$Cos{t}_{o\&m,AD}$ | Total Transport, Operation and Maintenance cost of the Anaerobic Digestion System (EUR) |

${C}_{o\&m,BESS}$ | Operation and Maintenance cost of the Battery Energy Storage System (EUR/kWh) |

${C}_{o\&m,pv}$ | Operation and Maintenance cost of PV (EUR/kWh) |

${C}_{o\&m,TES}$ | Operation and Maintenance cost of the Thermal Energy Storage (EUR/kWh) |

$C{H}_{4}{}_{AD}$ | Hourly production of methane form Anaerobic Digestion (kWh) |

$C{O}_{2,AD}$ | Carbon Footprint due to the SCG transportation (tCO_{2}) |

$C{O}_{2,grid}$ | Carbon Footprint due to the grid (tCO_{2}) |

$C{O}_{2,NG}$ | Carbon Footprint due to the NG consumption (tCO_{2}) |

${e}_{C{O}_{2,grid}}$ | Emission factor of the electric grid (gCO_{2}/kWhel) |

${e}_{C{O}_{2,NG}}$ | Emission factor of the natural gas (gCO_{2}/kWhth) |

$Ec{h}_{k,BESS}^{i}$ | Charging energy of the Battery Energy Storage System at time step k of week i (kWh) |

${E}_{k,boiler}^{i}$ | Thermal energy of the boiler at time step k of week i (kWh) |

${E}_{k,chp}^{i}$ | Electric energy of the CHP at time step k of week i (kWh) |

${E}_{k,Diss}^{i}$ | Dissipated Thermal Energy of the CHP unit (thermal energy not used in CHP mode) at time step k of week i (kWh) |

${E}_{k,grid}^{i}$ | Electric energy from the grid at time step k of week i (kWh) |

${E}_{k,PV}^{i}$ | Electric energy produced by PV at time step k of week i (kWh) |

${E}_{k,TES}^{i}$ | Thermal energy from the TES at time step k of week i (kWh) |

${h}_{week}$ | number of hours in a week (168) |

${n}_{week}$ | number of weeks in a year (52) |

${P}_{max,chp}$ | Maximum Power of the CHP Unit |

${\delta}_{sdch,BEES}$ | Self-discharge index of the Electric Energy Storage |

${\delta}_{sdch,TES}$ | Self-discharge index of the Thermal Energy Storage |

${\eta}_{BESS}$ | Round-trip efficiency of Battery Energy Storage System |

${\eta}_{boiler}$ | Boiler efficiency |

${\eta}_{el,chp}$ | Electric conversion efficiency of the CHP Unit |

${\eta}_{th,chp}$ | Thermal conversion efficiency of the CHP unit |

${\eta}_{el,rif}$ | Reference electric efficiency |

${\eta}_{th,rif}$ | Reference thermal efficiency |

${\eta}_{TES}$ | Round-trip efficiency of Thermal Energy Storage |

## References

- Available online: https://www.un.org/development/desa/en/news/population/world-population-prospects-2019.html (accessed on 13 December 2021).
- Available online: https://unfccc.int/process-and-meetings/the-paris-agreement/the-glasgow-climate-pact-key-outcomes-from-cop26 (accessed on 13 December 2021).
- Jiang, P.; Van Fan, Y.; Klemeš, J.J. Impacts of COVID-19 on energy demand and consumption: Challenges, lessons and emerging opportunities. Appl. Energy
**2021**, 285, 116441. [Google Scholar] [CrossRef] [PubMed] - European Commission. A Clean Planet for All. A European Long-Term Strategic Vision for a Prosperous, Modern, Competitive and Climate Neutral Economy. 2018. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52018DC0773&from=EN (accessed on 15 December 2021).
- European Parliament and the Council. Directive 2008/98/EC of the European Parliament and of the Council. In Fundamental Texts On European Private Law; European Parliament and The Council: Strasbourg, France, 2020; pp. 3–30. [Google Scholar]
- Available online: https://www.ieabioenergy.com/blog/task/biorefining-sustainable-processing-of-biomass-into-a-spectrum-of-marketable-biobased-products-and-bioenergy/ (accessed on 14 December 2021).
- Kirk, N.K.; Navarrete, C.; Juhl, J.E.; Martínez, J.L.; Procentese, A. The “zero miles product” concept applied to biofuel production: A case study. Energies
**2021**, 14, 565. [Google Scholar] [CrossRef] - Park, S.; Jeong, H.-R.; Shin, Y.-A.; Kim, S.-J.; Ju, Y.-M.; Oh, K.-C.; Cho, L.-H.; Kim, D. Performance optimisation of fuel pellets comprising pepper stem and coffee grounds through mixing ratios and torrefaction. Energies
**2021**, 14, 4667. [Google Scholar] [CrossRef] - Brunerová, A.; Roubik, H.; Brožek, M.; Haryanto, A.; Hasanudin, U.; Iryani, D.A.; Herak, D. Valorization of bio-briquette fuel by using spent coffee ground as an external additive. Energies
**2019**, 13, 54. [Google Scholar] [CrossRef] [Green Version] - Available online: https://www.bio-bean.com/ (accessed on 10 December 2021).
- Massaya, J.; Pereira, A.P.; Mills-Lamptey, B.; Benjamin, J.; Chuck, C.J. Conceptualization of a spent coffee grounds biorefinery: A review of existing valorisation approaches. Food Bioprod. Process.
**2019**, 118, 149–166. [Google Scholar] [CrossRef] - Battista, F.; Zanzoni, S.; Strazzera, G.; Andreolli, M.; Bolzonella, D. The cascade biorefinery approach for the valorization of the spent coffee grounds. Renew. Energy
**2020**, 157, 1203–1211. [Google Scholar] [CrossRef] - Atabani, A.E.; Al-Muhtaseb, A.H.; Kumar, G.; Saratale, G.D.; Aslam, M.; Khan, H.A.; Sid, Z.; Mahmoud, E. Valorization of spent coffee grounds into biofuels and value-added products: Pathway towards integrated bio-refinery. Fuel
**2019**, 254, 115640. [Google Scholar] [CrossRef] - Rajesh Banu, J.; Kavitha, S.; Kannah, R.Y.; Kumar, M.D.; Preethi Atabani, A.E.; Kumar, G. Biorefinery of spent coffee grounds waste: Viable pathway towards circular bioeconomy. Bioresour. Technol.
**2020**, 302, 122821. [Google Scholar] [CrossRef] - Mayson, S.; Williams, I.D. Applying a circular economy approach to valorize spent coffee grounds. Resour. Conserv. Recycl.
**2021**, 172, 105659. [Google Scholar] [CrossRef] - Kim, J.; Kim, H.; Baek, G.; Lee, C. Anaerobic co-digestion of spent coffee grounds with different waste feedstocks for biogas production. Waste Manag.
**2017**, 60, 322–328. [Google Scholar] [CrossRef] - Rivera, X.C.S.; Gallego-Schmid, A.; Najdanovic-Visak, V.; Azapagic, A. Life cycle environmental sustainability of valorisation routes for spent coffee grounds: From waste to resources. Resour. Conserv. Recycl.
**2020**, 157, 104751. [Google Scholar] [CrossRef] - Van Keulen, M.; Kirchherr, J. The implementation of the circular economy: Barriers and enablers in the coffee value chain. J. Clean. Prod.
**2021**, 281, 125033. [Google Scholar] [CrossRef] - Matrapazi, V.K.; Zabaniotou, A. Experimental and feasibility study of spent coffee grounds upscaling via pyrolysis towards proposing an eco-social innovation circular economy solution. Sci. Total Environ.
**2020**, 718, 137316. [Google Scholar] [CrossRef] [PubMed] - Vakalis, S.; Moustakas, K.; Benedetti, V.; Cordioli, E.; Patuzzi, F.; Loizidou, M.; Beratieri, M. The “COFFEE BIN” concept: Centralized collection and torrefaction of spent coffee grounds. Environ. Sci. Pollut. Res
**2019**, 26, 35473–35481. [Google Scholar] [CrossRef] [PubMed] - Vicidomini, M.; Wang, Q.; Chu, W.; Calise, F.; Duić, N. Recent Advances in technology, strategy and application of sustainable energy systems. Energies
**2020**, 13, 5229. [Google Scholar] [CrossRef] - The European Parliament and the Council of the European Union. Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 on Energy Efficiency. 2012. Available online: https://www.legislation.gov.uk/eudr/2012/27/contents (accessed on 3 December 2021).
- Calise, F.; Vicidomini, M.; Costa, M.; Wang, Q.; Østergaard, P.A.; Duić, N. Toward an efficient and sustainable use of energy in industries and cities. Energies
**2019**, 12, 3150. [Google Scholar] [CrossRef] [Green Version] - Paine, S.; James, P.; Bahaj, A.B. Evaluating CHP management and outputs using simple operational data. Int. J. Low-Carbon Technol.
**2018**, 13, 109–115. [Google Scholar] [CrossRef] [Green Version] - Vialetto, G.; Noro, M. An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods. Energy Convers. Manag.
**2020**, 214, 112901. [Google Scholar] [CrossRef] - Testi, D.; Conti, P.; Schito, E.; Urbanucci, L.; D’Ettorre, F. Synthesis and optimal operation of smart microgrids serving a cluster of buildings on a campus with centralized and distributed hybrid renewable energy units. Energies
**2019**, 12, 745. [Google Scholar] [CrossRef] [Green Version] - Wang, Y.; Yu, H.; Yong, M.; Huang, Y.; Zhang, F.; Wang, X. Optimal scheduling of integrated energy systems with combined heat and power generation, photovoltaic and energy storage considering battery lifetime loss. Energies
**2018**, 11, 1360. [Google Scholar] [CrossRef] [Green Version] - Kaffash, M.; Ceusters, G.; Deconinck, G. Interval optimization to schedule a multi-energy system with data-driven PV uncertainty representation†. Energies
**2021**, 14, 2739. [Google Scholar] [CrossRef] - Alavijeh, N.M.; Steen, D.; Norwood, Z.; Tuan, L.A.; Agathokleous, C. Cost-effectiveness of carbon emission abatement strategies for a local multi-energy system—A case study of chalmers university of technology campus. Energies
**2020**, 13, 1626. [Google Scholar] [CrossRef] [Green Version] - Wang, Y.; Lu, Y.; Ju, L.; Wang, T.; Tan, Q.; Wang, J.; Tan, Z. A Multi-objective scheduling optimization model for hybrid energy system connected with wind-photovoltaic-conventional gas turbines, CHP Considering heating storage mechanism. Energies
**2019**, 12, 425. [Google Scholar] [CrossRef] [Green Version] - Ghiasi, M.; Niknam, T.; Dehghani, M.; Siano, P.; Alhelou, H.H.; Al-Hinai, A. Optimal multi-operation energy management in smart microgrids in the presence of ress based on multi-objective improved de algorithm: Cost-emission based optimization. Appl. Sci.
**2021**, 11, 3661. [Google Scholar] [CrossRef] - Bartolucci, L.; Cordiner, S.; Mulone, V.; Santarelli, M.; Lombardi, P.; Arendarski, B. Towards net zero energy factory: A multi-objective approach to optimally size and operate industrial flexibility solutions. Int. J. Electr. Power Energy Syst.
**2021**, 137, 107796. [Google Scholar] [CrossRef] - Bartolucci, L.; Cordiner, S.; Mulone, V.; Pasquale, S.; Sbarra, A. Design and management strategies for low emission building-scale Multi Energy Systems. Energy
**2022**, 239, 122160. [Google Scholar] [CrossRef] - Wu, C.; Gu, W.; Xu, Y.; Jiang, P.; Lu, S.; Zhao, B. Bi-level optimization model for integrated energy system considering the thermal comfort of heat customers. Appl. Energy
**2018**, 232, 607–616. [Google Scholar] [CrossRef] - Carrasqueira, P.; Alves, M.J.; Antunes, C.H. Bi-level particle swarm optimization and evolutionary algorithm approaches for residential demand response with different user profiles. Inf. Sci.
**2017**, 418, 405–420. [Google Scholar] [CrossRef] [Green Version] - Morvaj, B.; Evins, R.; Carmeliet, J. Bi-level optimisation of distributed energy systems incorporating non-linear power flow constraints. In Proceedings of the International Conference CISBAT 2015 Future Buildings and Districts Sustainability from Nano to Urban Scale, Lausanne, Switzerland, 9–11 September 2015; pp. 859–864. [Google Scholar]
- Gestore dei Servizi Energetici (GSE). Guida alla cogenerazione ad alto rendimento. Aggiorn. Ed.
**2018**, 6, 1–18. [Google Scholar] - Available online: https://aprireunbar.com/2017/03/13/quante-tazzine-prepara-in-media-un-bar-e-quanto-costa-una-tazzina-di-caffe-in-italia/ (accessed on 3 December 2021).
- Bischi, A.; Taccari, L.; Martelli, E.; Amaldi, E.; Manzolini, G.; Silva, P.; Campanari, S.; Macchi, E. A rolling-horizon optimization algorithm for the long term operational scheduling of cogeneration systems. Energy
**2019**, 184, 73–90. [Google Scholar] [CrossRef] - Alhuyi Nazari, M.; Maleki, A.; El Haj Assad, M.; Rosen, M.A.; Haghighu, A.; Sharabaty, H.; Chen, L. A review of nanomaterial incorporated phase change materials for solar thermal energy storage. Sol. Energy
**2020**, 228, 725–743. [Google Scholar] [CrossRef] - Gazzetta Ufficiale dell’Unione Europea. Direttiva (UE) 2018/2002 del Parlamento Europeo e Del Consiglio. Available online: https://eur-lex.europa.eu/legal-content/IT/TXT/PDF/?uri=CELEX:32018L2002&from=EN (accessed on 13 December 2021).
- Atelge, M.R.; Atabani, A.E.; Abut, S.; Kaya, M.; Eskicioglu, C.; Semaan, G.; Lee, C.; Yildiz, Y.S.; Unalan, S.; Mohanasundraram, R.; et al. Anaerobic co-digestion of oil-extracted spent coffee grounds with various wastes: Experimental and kinetic modeling studies. Bioresour. Technol.
**2020**, 322, 124470. [Google Scholar] [CrossRef] [PubMed] - Vítěz, T.; Koutný, T.; Šotnar, M.; Chovanec, J. On the spent coffee grounds biogas production. Acta Univ. Agric. Silvic. Mendel. Brun.
**2016**, 64, 1279–1282. [Google Scholar] [CrossRef] [Green Version] - Zepter, J.M.; Engelhardt, J.; Gabderakhmanova, T.; Marinelli, M. Empirical validation of a biogas plant simulation model and analysis of biogas upgrading potentials. Energies
**2021**, 14, 2424. [Google Scholar] [CrossRef] - Bortolini, M.; Faccio, M.; Ferrari, E.; Gamberi, M.; Pilati, F. Fresh food sustainable distribution: Cost, delivery time and carbon footprint three-objective optimization. J. Food Eng.
**2016**, 174, 56–67. [Google Scholar] [CrossRef] - EPA. U.S. Environmental Protection Agency Combined Heat and Power Partnership. 2017. Available online: https://www.epa.gov/chp (accessed on 3 December 2021).
- Banzato, D. Analisi Economica Degli Impianti di Digestione Anaerobica. 2016. Available online: http://levicases.unipd.it/wp-content/uploads/2016/11/Banzato-modalità-compatibilità.pdf (accessed on 3 December 2021).

#Combination | α_{eco} | α_{env} |
---|---|---|

1 | 1 | 0 |

2 | 0.75 | 0.25 |

3 | 0.5 | 0.5 |

4 | 0.25 | 0.75 |

5 | 0 | 1 |

# | α_{eco} | α_{env} | Size_{CHP} | Distance from Ideal |
---|---|---|---|---|

1 | 1 | 0 | 5000 | 0.606715 |

2 | 0.75 | 0.25 | 4750 | 0.696809 |

3 | 0.5 | 0.5 | 3750 | 0.773042 |

4 | 0.25 | 0.75 | 3500 | 0.839668 |

5 | 0 | 1 | 3500 | 0.90013 |

# | α_{eco} | α_{env} | Size_{CHP} (kW_{el}) | Size_{AD}(kW _{th}) | Distance from Ideal |
---|---|---|---|---|---|

1 | 1 | 0 | 5000 | 0 | 0.606715 |

2 | 0.75 | 0.25 | 5000 | 250 | 0.696498604 |

3 | 0.5 | 0.5 | 3750 | 850 | 0.768223141 |

4 | 0.25 | 0.75 | 3750 | 1950 | 0.800594604 |

5 | 0 | 1 | 3500 | 2000 | 0.804014218 |

# | α_{eco} | α_{env} | Size_{CHP} (kW_{el}) | Size_{AD} (kW_{th}) | Size_{TES} (kWh_{th}) | Size_{BESS} (kWh_{el}) | Distance from Ideal |
---|---|---|---|---|---|---|---|

1 | 1 | 0 | 5750 | 0 | 35,000 | 0 | 0.588610647 |

2 | 0.75 | 0.25 | 5750 | 0 | 35,000 | 0 | 0.692419719 |

3 | 0.5 | 0.5 | 3500 | 900 | 30,000 | 3000 | 0.760299511 |

4 | 0.25 | 0.75 | 4000 | 2000 | 35,000 | 3000 | 0.784488196 |

5 | 0 | 1 | 3500 | 2000 | 25,000 | 6000 | 0.78281029 |

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**MDPI and ACS Style**

Bartolucci, L.; Cordiner, S.; De Maina, E.; Mulone, V.
Data-Driven Optimal Design of a CHP Plant for a Hospital Building: Highlights on the Role of Biogas and Energy Storages on the Performance. *Energies* **2022**, *15*, 858.
https://doi.org/10.3390/en15030858

**AMA Style**

Bartolucci L, Cordiner S, De Maina E, Mulone V.
Data-Driven Optimal Design of a CHP Plant for a Hospital Building: Highlights on the Role of Biogas and Energy Storages on the Performance. *Energies*. 2022; 15(3):858.
https://doi.org/10.3390/en15030858

**Chicago/Turabian Style**

Bartolucci, Lorenzo, Stefano Cordiner, Emanuele De Maina, and Vincenzo Mulone.
2022. "Data-Driven Optimal Design of a CHP Plant for a Hospital Building: Highlights on the Role of Biogas and Energy Storages on the Performance" *Energies* 15, no. 3: 858.
https://doi.org/10.3390/en15030858