Biomass-Driven Polygeneration Coupled to Power-to-X: An Energy and Economic Comparison Between On-Site Electric Vehicle Charging and Hydrogen Production

Abstract

The power-to-X strategy for passenger car applications offers a viable solution for using the surplus electrical power from renewable energy sources instead of exporting it to the grid. The innovative system proposed in this study allocates surplus electrical power from a building-integrated biomass-based Combined Cooling Heating and Power (CCHP) system to on-site applications and evaluates the energetic and economic benefits. The system comprises two key components: a 50 kW electric vehicle (EV) charging station for EVs and a 50 kW alkaline electrolyzer system for on-site hydrogen production, which is later dispensed to fuel cell electric vehicles (FCEVs). The primary goal is to decrease the surplus of electricity exports while simultaneously encouraging sustainable transportation. The system’s economic viability is assessed through two scenarios of fuel (e.g., biomass) supply costs (e.g., with and without fuel market costs) and compared to the conventional approach of exporting the excess power. The key findings of this work include a substantial reduction in surplus electricity exports, with only 3.7% allocated for EV charging and 31.5% for hydrogen production. The simple payback period (SPB) is notably reduced, enhancing economic viability. Sensitivity analysis identifies the optimal hydrogen system, featuring a 120 kW electrolyzer and a 37 kg daily hydrogen demand. The results underscore the importance of prioritizing self-consumed energy over exports to the national grid, thereby supporting integrated renewable energy solutions that enhance local energy utilization and promote sustainable transportation initiatives.

1. Introduction

In recent years, awareness regarding climate change is becoming more relevant. It has been noticed that the European State Member communities are making efforts to reduce the trend of greenhouse gas (GHG) emissions and mitigate the causes of global warming in the forthcoming decades [1,2]. Among GHGs, carbon dioxide (CO₂) is considered the main constituent and the prominent reason for global warming [3]. The transport sector alone is responsible for 25% of global CO₂ emissions [4]. Among other things, road transport is responsible for 83% share of the total energy consumption in the transportation sector [5]. Moreover, the road transport sector holds a share of 93% of CO₂ emissions in the combined portion of transport sector emissions [6].

Conventional road transport vehicles use internal combustion engines (ICEs) [7] and are the leading cause of GHG emissions by the transport sector. In this framework, replacing such conventional ICE-based vehicles with electric vehicles and hydrogen-based fuel cell electric vehicles, which are responsible for zero direct emissions of GHGs, can be an attractive solution in the transportation sector [8]. However, when such vehicles are charged with energy obtained from a conventional fossil fuel-based power grid, the emissions of GHGs and pollutants are still released to the environment but only shifted from the vehicle to the power station [9]. Therefore, to achieve a significant reduction in GHG emissions, it is advised to charge the EVs with renewable energy systems [10,11].

In this context, this paper aims to develop a dynamic model of a sustainable and renewable mobility system based on the surplus electrical power of a building-integrated biomass-based CCHP system [12] coupled with electric vehicles (EVs) and electrolyzer systems to produce hydrogen, which can later be used as fuel for FCEVs.

Several research articles have been published in the past few years on the design, analysis, and optimization of different polygeneration systems composed of EV charging and/or hydrogen production systems based on conventional fossil fuels or renewables. For example, Ribberink et al. [13] analyzed the potential synergy between a 2 kWe ICE-based micro-combined heat and power (CHP) system fueled with natural gas and EV charging for a single isolated house in Ottawa, Canada. The study revealed that using micro-CHP systems to create domestic hot water (DHW) for space heating throughout the night will also produce a significant amount of electricity. This is advantageous because the power demand of the house is often low during nighttime hours. Exporting the excess electricity to the grid throughout the night is not advisable because of the low feed-in tariff. Integrating EVs with buildings might result in a decrease in the ratio of exported power to the grid, from 60% to 54%. Additionally, this integration can provide an extra annual revenue of CAD 200–300. Moreover, the optimal time to charge the electric vehicle battery is when electricity is abundant from the micro-CHP during its peak period. Also, Angrisani et al. [14] examined a micro-CHP system powered by natural gas integrated with an EV in a multi-family house. The system meets thermal and electrical needs and also charges EVs. A dynamic simulation is performed using TRNSYS 18 software for two locations (Naples and Turin) with EV travel lengths of 0–120 km/day. Results showed that EV and CHP integration reduced excess electricity delivered to the grid. The system achieved a 19% primary energy savings in Turin with 120 km/day travel. Onovwiona et al. [15] present a study where a micro trigeneration system of 6 kWe, fueled with natural gas, is used to serve an Italian residential house located in Naples and charge an EV. The work analyzes several charging profiles to optimize the use of power generated by CHP. It also demonstrates that the coupling of EV and CHP can reduce the excess electricity delivered to the national grid. Zafarani et al. [16] proposed a nonlinear programming approach to minimize the operational and energy costs of a multicarrier energy system. The input is electric and gas energy, while the output is electric and heating energy. The paper shows that the coupling of CHP and EV can reduce the peak demand for EV electric energy.

As mentioned above, another promising strategy to reduce the environmental impact of the transportation sector is represented by the use of hydrogen [17,18,19]. For instance, Apostolou et al. [20] in their work predicted exponential growth in hydrogen fuel stations in the near future. Data on refueling profiles of car-sharing vehicles have been provided by Gruger et al. [21]. Another important factor to be considered is the hydrogen cost. Riedl [22] suggested a model that can result in reducing the design cost and the development time of hydrogen systems. To have profitable system Apostolou et al. [23] estimated a retail price of hydrogen produced of 50.2 EUR/kg, considering a small-scale on-site autonomous hydrogen production facility using an alkaline electrolyzer powered by renewable energy produced from a small wind turbine. Li et al. [24] proposed the use of wind energy, integrating a turbine with a natural gas CCHP system, a wind turbine, and a proton exchange membrane (PEM) electrolyzer system for hydrogen production. The proposed energy system fulfills the building energy requirements as well as meets the daily demand of hydrogen refueling stations in winter, transition seasons, and summer of 500 kg, 500 kg, and 266 kg, respectively. Also, the effect of the system capacity on the hydrogen cost has been investigated in the literature. As an example, Squadrito et al. [25] performed a size-dependent economic analysis of a hydrogen and oxygen production system. The study revealed that for small-sized electrolyzers (<300 kW), the investment is economically sustainable if the market price of oxygen and hydrogen is 4 EUR/kg and 10 EUR/kg, respectively.

When multi-energy systems are considered, handling uncertainties from renewable energy and load and coordinating multiple energy carriers are very challenging. In this context, a two-stage robust operation method has been proposed to consider heterogeneous uncertainties in modelling electricity, gas, and heat networks, integrating power-to-hydrogen-and-heat units, and implementing a ladder-type carbon trading mechanism in order to reduce the multi-energy supply cost while also significantly reducing carbon emissions [26]. Uncertainties may also be related to refuelling demand. To take into account this aspect, a probabilistic model has been proposed to evaluate the maximum number of FCEVs that can be integrated into a power distribution network coupled with a hydrogen distribution system [27].

In order to increase the feasibility, numerous techno-economic studies have explored the benefits of integrating hydrogen technologies (e.g., electrolyzers and hydrogen storage tanks), along with battery storage systems integrated with renewable energy systems. Kalinci et al. [28] performed a techno-economic analysis of two hybrid systems (only a wind turbine and a wind turbine/PV hybrid system) in order to investigate the suitability of the hydrogen system and the effects of some parameters on the production capacity and cost of hydrogen. Abdin et al. [29] performed a detailed techno-economic analysis of nine different configurations of renewable energy systems for off-grid scenarios using HOMER Pro software. The studied systems considered both electric battery storage and hydrogen technologies. The results revealed that hydrogen storage technologies are more economical than battery storage systems.

According to the research article reviewed above and the best of the author’s knowledge, it is concluded that there are few works that deal with the analysis of a CCHP system based on renewables integrated with a building, EVs, and on-site hydrogen production systems. As a matter of fact, none of the work presented in the previous literature investigates the energy and economic benefits of such an energy system that focuses on the production of energy from renewable sources (e.g., biomass) integrated with building and on-site energy production technologies for the transport sector such as EVcharging and hydrogen production for FCEVs.

Therefore, to overcome the above-mentioned gap in the literature, the authors have developed a research study that investigates the use of surplus electricity derived from biomass energy conversion for EV charging and hydrogen production by using a dynamic simulation model developed in TRNSYS [30]. Through this model, a comprehensive energy and economic analysis of the simultaneously operating EV charging and hydrogen production system is developed.

In particular, the novelty of the present work can be summarized as follows:

• The development of an innovative EV fast charging model integrated with a biomass-based CCHP system that fulfills the electric energy demand of different fleets of EVs;
• A charging model that considers a state of charge (SOC)-based realistic power withdrawal profiles of a case study EV (e.g., Renault Zoe EV model);
• the development of a transient simulation model for hydrogen production using water electrolysis.
• An analysis of the effect of adopting the EV charging and hydrogen production system on the energy and economic performance of a biomass-based CCHP system.

Furthermore, the work presents a comprehensive sensitivity analysis of the proposed hydrogen production system to investigate the effect of various parameters (e.g., EV charging cost and hydrogen selling cost) on the economic indices of the system and a detailed parametric analysis to investigate the effect of the electrolyzer capacity and the daily hydrogen demand on the economic indices of the proposed system. Moreover, the outcomes of this study can be extended to other renewable energy systems with surplus electrical power available, encouraging the scope re-applicability of the similar approach. Future research could explore more sophisticated multi-agent energy systems that include multiple renewable energy sources. For example, the work of Ding et al. [31] presents a distributed cooperative operation strategy for multi-agent energy systems, integrating wind, solar, and buildings based on chance-constrained programming (CCP). This strategy leverages bidirectional interactions between renewable energy sources and buildings to enhance operational flexibility and economic benefits while preserving privacy and managing uncertainties in renewable generation and outdoor temperature. In addition, a novel approach of the integration of energy storage sharing (ESS) can offer promising opportunities for optimizing the energy system [32].

The proposed study will encourage the promotion of private sustainable mobility solutions for building integrated renewable energy systems. The results of the study can be used as guidelines by designers and policymakers to address efficient on-site energy utilization and management for small-scale energy systems. The methodology used can also be applied to other small-scale decentralized renewable energy systems. In general, the paper contributes to the literature by widening the knowledge regarding the use of surplus electrical energy from renewable energy systems for power-to-x applications.

The remainder of this paper is structured as follows: Section 2 describes the layout of the proposed system by comparing it to the conventional system by describing the on-site applications along with their respective control strategies. Section 3 presents the simulation models of EV charging systems and hydrogen production systems, along with their respective economic models. Section 4 elaborates on the design strategies of the present case study. Section 5 explains in detail the results and discussions of the study. Finally, Section 6 concludes with insights on the broader applicability of proposed systems and recommendations for future research.

2. System Layout

This section presents the operation and sequence of the activities of the system from power generation to utilization. Figure 1 displays the layout of the system. As can be seen from the scheme, the system can be divided into two parts: the conventional system and the proposed system. In the conventional system, a biomass-based CHP system generates electrical and thermal energy and utilizes it to fulfil the electrical and thermal energy requirements of the case study building and sells the extra surplus electricity to the grid [12]. Conversely, the proposed system in the present layout is based on the idea of using a portion of the surplus electrical power for several on-site applications. A portion of the generated power is allocated to charging electric vehicles (EVs) owned by building employees, utilizing a 50 kW fast charger. This allows an EV to be fully charged within an hour, ensuring availability for other users [33]. Another fraction of the power is directed to an alkaline electrolyzer for hydrogen production, while any surplus electricity is sold to the national grid.

The choice of electrolyzer technology is critical to both the energy efficiency and economic viability of the system. Given the variable nature of power from renewable sources, the electrolyzer must operate efficiently under fluctuating conditions. Among available options, the alkaline electrolyzer stands out as the most established and commercially viable technology. It offers lower specific capital and maintenance costs compared to alternatives, making it well suited for integration into systems like ours, which require reliable operation and cost-effectiveness [34].

The sequence of activities of the proposed system is prioritized and decided based on the amount of net surplus electrical power available, the magnitude of power required for EV charging, and the demand for hydrogen production. For instance, the surplus power in the proposed system is first used to charge the EVs connected to the charging station. The charging station withdraws the actual power (P_el,ch,actual) from the bus bar based on the instantaneous state of charge of the EVs (e.g., SOC_EV). The residual power after this is diverted to the controller unit of the hydrogen production unit. Note that the maximum power allowed to the hydrogen unit depends on the nominal capacity of the electrolyzer installed. The power conditioner converts the electrolyzer power (e.g., $P_{el,ELY}$) from the controller to the desirable voltage and sends it to the alkaline electrolyzer. The electrolyzer produces hydrogen and oxygen, which are collected at their respective electrodes. Hydrogen is compressed through a multi-stage compressor and stored in a pressurized vessel, which can later be sold to fuel cell-based cars through a dispenser, whereas oxygen is collected and stored at the electrolyzer’s pressure, which can later to be sold to an industrial application. In the end, the remaining electrical power (e.g., P_el,grid) is delivered to the electric national grid.

Control Strategy

The study opts a novel control strategy in a way that it utilizes a maximum amount of surplus electrical power for EV charging and hydrogen production. The system also sizes the components in a way that it does not need any auxiliary energy source at any timestep. The strategies of the system are designed such that it prioritizes the events in the sequence of EV charging > hydrogen production system > electrical power for the grid. Figure 2 shows the operating strategy of EV recharging stations in the proposed scheme, showing how the electrical surplus power from the CHP system is utilized by the charger. In particular, the system control logic explains the stepwise approach of the charging station. When an EV with an SOC of less than 95% is connected to the charger, the controller ensures the supply of guaranteed power (e.g., $P_{el,ch,fix}$ = 50 kW) based on the charger capacity, whereas the remaining power (e.g., $P_{el,residue}$ = $P_{el,surplus}$ − $P_{el,ch,actual}$) is made available for imminent applications. Similarly, Figure 3 shows the operating strategy of the hydrogen system. The residual power from the EV charger (see Figure 2) is only delivered to the electrolyzer when the available residue power is higher than the minimum threshold (e.g., $P_{el,ELY,min})$; see Figure 2. Similarly, when the available residue power is higher than the maximum threshold (e.g., $P_{el,ELY,max})$, the exceeding power is delivered to the grid.

3. Simulation Model

The dynamic simulation model of the energy system presented in Section 2 was developed in the TRNSYS 18 environment [30]. This software has the privilege to integrate a large number of experimentally validated built-in components in its library and to allow one to expand the software functionality with user-developed models.

The simulation environment allows evaluating components-based dynamic outputs (e.g., temperatures, flow rates, and powers) of the energy system, as well as their integration for a selected time period. In the frame of this paper, it is not possible to present all the models of the components used; thus the main components of the system taken from the software library are only presented in brief, while the model developed by the authors has been described in detail.

The main components taken from the TRNSYS library to develop the model of the system in the present work are the following [30]:

• Type 48: This component from the library simulates the electrical behavior of the inverter/regulator. It directs and distributes the power to the storage systems. It also regulates the power based on the state of charge of the storage system. If the storage system is fully charged, the excess power is then delivered to the grid.
• Type 549: This component from the library models a lithium-ion battery using the voltage model proposed by Tremblay [35]. In addition, it also includes a simple energy balance to estimate the energy exchanged with the surroundings. To operate this component as a lithium-ion battery of the EVs, the parameters of type 549 are set such that it behaves as a commercial lithium-ion battery installed in the EVs chosen in the present case studies. In particular, the lithium-ion battery specifications of the Renault Zoe 50 [35] are considered.
• Type 100a: This component implements a control function for the electrolyzer system. This controller defines the balance between power usage, production, and storage of hydrogen. In the present study, this component operates on variable power input [36].
• Type 175a: This is a power conditioning component based on a mathematical model [36]. It converts the available input power into an output current for a predefined voltage value.
• Type 160a: This component refers to a mathematical model of an alkaline water electrolyzer [36]. The mathematical model is composed of fundamental electrochemical, thermodynamic, and heat transfer relationships.
• Type 167: This component refers to a multi-stage polytropic compressor. The model calculates the work input required based on input pressure and desired output pressure [36].
• Type 164b: This refers to a real gas compressed storage tank that calculates the inside pressure using the van der Waals equation of state for real gases [37]. The model simply performs a mass balance of the gas entering and leaving the storage tank without considering the thermal effects of the gases.

3.1. Electric Vehicle Charging Model

The surplus power from the CCHP system (e.g., $P_{el,surplus}=P_{el,generated}-P_{el,load})$ is diverted to the electric vehicles connected to the charging station. $P_{el,generated}$ Is the total power generated by the system (e.g., a wood-biomass-combustion-based gas turbine along with an ORC system) equal to 169 kWe_, whereas $P_{el,load}$ is the power consumed by the electric appliances of the building along with the electric and hydronic components of the heating and cooling system. $P_{el,surplus}$ is divided among the vehicles connected to the charging stations as follows:

$$P_{el,ch}={\displaystyle \frac{P_{el,surplus}}{N_{EV}}}$$

(1)

where $P_{el,ch}$ is the charging power delivered to the single EVand N_EV is the number of EVs connected to the charging station at that instant.

When the EV charger is connected and the state of charge (SOC) of the EV battery is less than 95%, a fixed power of $P_{el,ch,fix}$ is ensured to each EV. The actual power withdrawn (e.g., $P_{el,ch,actual}$) from the charging station is a function of the SOC of the connected EV battery.

3.2. Electrolyzer System Model

The residue power from the CCHP system and EV charging (e.g., $P_{el,residue}$) is utilized to produce on-site hydrogen using an alkaline electrolyzer. Moreover, when the electrolyzer system is active, the electrical power demand of the compression system is also considered. Therefore, the electrical power available for hydrogen production is

$$P_{el,residue}=P_{el,surplus}-P_{el,ch,actual}-P_{el,\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{p}}$$

(2)

where $P_{el,ch,actual}$ is the actual instantaneous power withdrawn from the EV charger, $P_{el,comp}$ is the electrical power consumed by the hydrogen compression system. The electrolyzer controller regulates the power by using the following algorithm:

$$P_{el,ELY}=\left\{\begin{array}{c}{P}_{el,ELY,max}if{P}_{el,residue}>P_{el,ELY,max}\\ P_{el,residue}if{P}_{el,ELY,min}<P_{el,residue}\le P_{el,ELY,max}\\ P_{el,ELY,idle}if{P}_{el,residue}<P_{el,ELY,min}\end{array}\right.$$

(3)

where $P_{el,ELY}$ is the actual power delivered to the electrolyzer. The alkaline electrolyzer operates between a maximum and minimum allowable power range (e.g., $P_{el,ELY,max}$ and $P_{el,ELY,min}$). $P_{el,ELY,idle}$ represents the minimum power that must be powered all the time to avoid a complete shutdown of the electrolyzer and to keep it running. This strategy is adopted to limit the power directed to the power conditioner of the electrolyzer according to the nominal set value. This limit is selected to avoid the maximum allowable current density of the stack limit.

3.3. Energy and Economical Model

A detailed energy and economic model was developed for the newly developed system and is compared with the reference system (RS). In RS, the total surplus electrical power was sold to the national grid. Conversely, in the proposed system, several on-site options for electrical power utilization are considered (e.g., EV charging and hydrogen production) to analyze the effects of such decisions on the overall economic performance of the system. The analysis is carried out by evaluating economic indexes such as Simple Pay Back (SPB), the net present value (NPV), and the profitability index (PI).

The achievable potential economic savings in terms of electrical power for on-site utilization (e.g., proposed system) vs. the conventional RS is estimated on a yearly basis. The economic models of the RS components are reported in [12]. Conversely, the costs of the new components considered in the proposed system are taken into account in this paper. The capital expenditure (CAPEX) of the proposed system can be calculated as follows:

$$J_{CAPEX,new}=J_{total,RS}+J_{CAPEX,PS}$$

(4)

where $J_{total,RS}$ represents the total CAPEX of the reference layout of the system, whereas $J_{CAPEX,PS}$ refers to the additional capital expenditure of the new constituent components of the proposed system layout. The capital costs can be evaluated as follows:

$$J_{CAPEX,PS}=J_{EV,charger}+J_{ELY}+J_{H2,comp}+J_{H2,TK}+J_{O2,TK}+J_{H2,dispenser}$$

(5)

Some generalized individual cost functions of the constituent components of the proposed system are mentioned below. This approach allows us to estimate the cost of the components, which varies with the size of the plant. The specific cost of the alkaline electrolyzer (e.g., $J_{ELY}$ [€/kW]) [38] as a function of the nominal power of the electrolyzer can be determined using Equation (6). This logarithmic relationship uses the approach of the “economies of scale” approach to reflect how the specific costs evolve with the size of the system.

$$J_{ELY}=1200\times ({P_{el,ELY,max})}^{SF}$$

(6)

where $P_{el,ELY,max}$ is the alkaline electrolyzer nominal power (in MW) and SF refers to a scale factor. In the present case, the cost of the electrolyzer is estimated by assuming a reference-specific cost of 1200 EUR/kW for a 1 MW capacity electrolyzer plant (with a global cost of 1200 kEUR), and a scale factor of −0.2 corresponding to the specific cost of the electrolyzer [38].

Choosing cost functions for a hydrogen compression system is more complex compared to the other components due to the highly varying cost functions in the literature, which makes the choice of appropriate cost function difficult. However, Equation (7) is used in the present case to estimate the capital cost of the compression system as it lies in the closest range with the three different manufacturer quotations received from the Italian market survey [25].

$$J_{H2,comp}=1000\times {\left(P_{el,ELY,max}\right)}^{SF}$$

(7)

where $J_{H2,compressor}$ is the specific cost of the compressor calculated in kEUR/kW. $P_{el,ELY,max}$ is the electrolyzer capacity in MW, and SF is the scale factor of 0.9 in the present case.

The capital cost estimation for high-pressure storage tanks was also considered in this study. In agreement with the literature [39], a storage system for a refueling station with a nominal storage pressure of 350 bar (required for dispensing) can be assumed to be 195 EUR/Nm³.

The annual revenue of the proposed layout from the electrical energy exchange with the grid is also considered. The proposed system considers utilizing the surplus electrical power for on-site EV charging, hydrogen production, and the net electrical power sold to the grid. The revenues from the thermal model remain unchanged as no changes were made to the thermal model adopted in RS [12]. The annual revenues (e.g., $R_{el,CHP,new}$) generated from the electrical power with the novel proposed layout can be calculated as

$$\begin{gathered} R_{el,CHP,new}=\left(E_{el,BOP}+E_{el,building}\right)\times J_{buy}+\left(E_{EV,charging}\right)\times J_{EV,charging}+{(\sum \dot{m}}_{H2})\times J_{H2}+{(\sum \dot{m}}_{O2})\times J_{O2} \\ +\left(E_{el,Grid,new}\right)J_{sell} \end{gathered}$$

(8)

where $E_{el,BOP}$ and $E_{el,building}$ are the avoided annual electrical energies, which would be purchased from the grid at standard electrical energy prices according to the Italian market rates (e.g., $J_{buy}$). $E_{EV,charging}$ is the annual electrical energy consumed by the EV charger with a selling price of $J_{EV,charging}$ [EUR/kWh]. The revenues also comprise the returns generated from the sum of hydrogen [kg/year] dispensed throughout the year at a selling cost of $J_{H2}$. Note that the savings due to the export of oxygen [kg/year] are also considered. Unfortunately, the present analysis does not include the cost estimation of oxygen based on its purity. However, the oxygen produced from the present layout can be sold to industries at a selling price of $J_{O2}$. Lastly, $E_{el,Grid,new}$ is the new remaining electrical energy sold to the grid at a selling price $J_{sell}$.

Once all the revised system layout assumptions and the revenues associated with the novel proposed model are considered, the annual economic savings (e.g., ${∆CF}_{new}$) of the system also changes and can be estimated as

$${∆CF}_{new}=R_{el,CHP,new}+R_{th,CHP}-C_{fuel}-M_{new}$$

(9)

where $R_{el,CHP,new}$ represents the updated revenue associated with electrical power utilization and $R_{th,CHP}$ is the revenue associated with the avoided cost of fuel, which would be used for thermal energy generation. $C_{fuel}$ is the annual cost of fuel and $M_{new}$ is the maintenance cost of the system. The $R_{th,CHP}$ and $C_{fuel}$ are the same for both the RS and PS, whereas the $M_{new}$ includes the maintenance costs associated with the new constituent components. Table 1 lists the economic assumptions of the remaining system components opted for in this study.

The economic indexes for the novel layout of the system can be calculated using the updated parameters shown in Equations (10)–(12) [40].

$${SPB}_{new}={\displaystyle \frac{J_{CAPEX,new}}{{∆CF}_{new}}}$$

(10)

$${NPV}_{new}=-\left(J_{CAPEX,new}\right)+\sum _{i=1}^N{\displaystyle \frac{{∆CF}_{new}}{{\left(1+a\right)}^i}}$$

(11)

$${PI}_{new}={\displaystyle \frac{{NPV}_{new}}{J_{CAPEX,new}}}$$

(12)

where ${SPB}_{new}$, ${NPV}_{new}$ and ${PI}_{new}$ are the revised economic indexes considered in the study. In order to take into account the effect of the variation of certain design parameters (e.g., electrolyzer capacity, storage capacity, and selling prices of various products) on the above-mentioned economic indexes, a suitable sensitivity analysis is performed and presented in the results section.

Table 1. Energy and economic analysis assumptions.

Component	Parameter	Value	Unit
$J_{EV,charger}$	The capital cost of an EV charger	51 [41]	kEUR
$J_{H2,dispenser}$	The capital cost of the hydrogen dispenser	100 [42]	kEUR
$J_{ELY,OPEX}$	Electrolyzer operational expenditure	$4\%\mathrm{of}{J}_{ELY}$ [39]	kEUR
$J_{H2,comp,OPEX}$	Compressor operational expenditure	$3\%\mathrm{of}{J}_{H2,comp}$ [39]	kEUR
$J_{H2,TK,OPEX}$	Hydrogen tank operational expenditure	$1.5\%\mathrm{of}{J}_{H2,TK}$ [39]	kEUR
$J_{EV,charging}$	Cost of EV fast charging	0.6 [43]	EUR/kWh
$J_{H2}$	Hydrogen selling price	10 [25]	EUR/kg
$J_{O2}$	Oxygen selling price	2 [44]	EUR/kg
$J_{buy}$	Electric energy purchasing unit cost	0.35 [12]	EUR/kWh
$J_{sell}$	Electric energy selling unit cost	0.1 [12]	EUR/kWh
AF	Annuity factor	12.5 [40]	years

Table 1. Energy and economic analysis assumptions.

Component	Parameter	Value	Unit
$J_{EV,charger}$	The capital cost of an EV charger	51 [41]	kEUR
$J_{H2,dispenser}$	The capital cost of the hydrogen dispenser	100 [42]	kEUR
$J_{ELY,OPEX}$	Electrolyzer operational expenditure	$4\%\mathrm{of}{J}_{ELY}$ [39]	kEUR
$J_{H2,comp,OPEX}$	Compressor operational expenditure	$3\%\mathrm{of}{J}_{H2,comp}$ [39]	kEUR
$J_{H2,TK,OPEX}$	Hydrogen tank operational expenditure	$1.5\%\mathrm{of}{J}_{H2,TK}$ [39]	kEUR
$J_{EV,charging}$	Cost of EV fast charging	0.6 [43]	EUR/kWh
$J_{H2}$	Hydrogen selling price	10 [25]	EUR/kg
$J_{O2}$	Oxygen selling price	2 [44]	EUR/kg
$J_{buy}$	Electric energy purchasing unit cost	0.35 [12]	EUR/kWh
$J_{sell}$	Electric energy selling unit cost	0.1 [12]	EUR/kWh
AF	Annuity factor	12.5 [40]	years

4. Case Study

In order to evaluate the possibilities of using the surplus electrical power from a renewable energy-based polygeneration system, a suitable comprehensive case study analysis was considered. The scope of the case study adopted in the present scenario is to analyze the effect of utilizing the surplus power of the reference model [12] for on-site EV charging and hydrogen production instead of selling it to the national grid. The operating and control strategies of the energy generation part (e.g., reference model) of the system were previously discussed in detail [12]. However, in this work, the strategy of electrical energy utilization has been discussed in detail. Figure 4 shows the annual surplus electrical power from the reference model. The study also aims to analyze the energy and economic performance of the proposed system. The energy consumption of the system includes power flows from the EV charging station correlated to different electric vehicles with distinct mobility patterns and the energy consumed by an alkaline electrolyzer to produce hydrogen and oxygen. The system was modeled and sized based on the lowest instantaneous surplus electrical power so that the system can operate completely in an autonomous way, independent of the input electrical power from the grid and/or any other auxiliary energy source.

To simulate an EV model, the commercially available models of Renault Zoe [45] EVs’ battery specifications shown in

Table 2. Main design parameters of the study.

Component	Parameter	Value	Unit
Inverter [9]	Efficiency (AC to DC)	0.98
	Efficiency (DC to AC)	0.96
	Regulator efficiency	0.95
	High and low limits on the fractional state of charge (SOC)	0.95–0.10
EVs [45]	Cell energy capacity	78	Ah
	Battery voltage	400	V
	Available capacity	52	kWh
	Max charging power allowed	50	kW
	Max discharging power allowed	50	kW
	Weight of the battery	326	kg
Electrolyzer [44]	$\mathrm{Maximum}\mathrm{power}\mathrm{allowed}(P_{el,ELY,max}$)	50	kW
	$\mathrm{Minimum}\mathrm{power}\mathrm{allowed}\left(P_{el,ELY,min}\right)$	13	kW
	$\mathrm{Idling}\mathrm{Power}(P_{el,ELY,idle}$)	5	kW
	Electrode surface area	0.25	m2
	Number of cells in series	21	-
	Number of stacks in parallel	1	-
	Maximum allowable current density	1000	mA/cm2
	Minimum permissible voltage	1.4	V
	Maximum allowable operating temperature	80	°C
	Electrolyzer pressure	7	bar
H2 storage	Tank Volume	10	m3
	Maximum tank pressure	350	bar

were assumed.

Table 3. EVs summary.

Type of Vehicle	NEV [-]		Lday [km/day]	Vmean [km/h]	Scheduling
	Weekdays	Weekends
A	2	1	70	50	09:00–10:00
B	2	1	90	60	10:00–11:00
C	2	1	60	90	11:00–12:00
D	2	1	30	50	12:00–13:00
E	2	1	50	50	13:00–14:00

lists the assumptions regarding the charging schedules, average velocities, and daily trips covered by each group of EVs. The minimum instantaneous surplus power available throughout the year is 115 kW. Therefore, a maximum of 2 EVs can be charged simultaneously using fast chargers (e.g., 50 kW each) to avoid any power taken from the grid or other storage materials. The EV charger is modeled such that it withdraws the actual power (P_el,ch,actual) as a function of the SOC of the respective EV. The power profile of the Renault Zoe car as a function of the SOC is reported in Figure 5 [45]. The charging of a total (N_EV) of 10 EVs is considered during the study, which is subdivided into five groups (e.g., A–E) with each group having two cars on weekdays and one car on weekends, charged together at the same time. All 10 EVs are personal cars owned by the employees who work in the Sant’ Apollinare building [46]. This means that the EV battery consumption is estimated for limited use only. Every group of EVs is plugged in for 1 h for charging from 9:00 a.m. till 2:00 p.m. The power consumption of each EV is referred from a power consumption profile of Renault Zoe cars concerning their average velocity as shown in Figure 6 [47]. According to the Italian EV market, the fast charging (e.g., DC charging) cost ranges from approximately 0.24 to −79 EUR/kWh with an average value of 0.6 EUR/kWh [43].

The case study also considers the installation of a 50 kW capacity alkaline electrolyzer in the Sant’ Apollinare vicinity. The analysis was conducted with the perspective to install such a system that will use the surplus electrical power and produce on-site hydrogen and oxygen. The produced gases would be sold to generate revenue. One of the attractive hypotheses was to sell the produced hydrogen in a fuel station to fuel-cell-based cars and oxygen to industrial applications. Figure 7 shows a profile of the daily percent demand for hydrogen on an hourly basis for a typical hydrogen fuel station [48]. The model assumes a single dispenser with a single hose that dispenses 15 kg/day of hydrogen to FCEVs. According to the Italian market scenario, it is assumed that compressed hydrogen can be sold at 10 EUR/kg [25]. However, the selling cost of oxygen varies in a range of 1 to 7 EUR/kg based on a series of possible market applications. For instance, in the present study, it was assumed that oxygen is sold to industrial applications at 2 EUR/kg [49].

The proposed system layout analyzes the system performance as a function of constituent component design parameters (e.g., capacities, scheduling, and economic indexes). These parameters directly affect the electrical energy flows in the system. In particular, these parameters define the amount of electrical energy that would be used for EV charging by the electrolyzer or will be sold to the grid. Therefore, in order to make this study more valuable, a sensitivity analysis is also reported as a function of various design and economic parameters (e.g., Electrolyzer capacity, storage tank size, EV charging costs, and selling prices of produced gases).

5. Results and Discussions

In this section, the main results assessed by this work are discussed, mainly focusing on the analysis of the electrical energy performance of the whole system by comparing the proposed system layout with the conventional system. The analysis is carried out by means of dynamic simulation software, i.e., TRNSYS. The tool allows us to simulate the real-time operation of the system as well as to integrate energies on any time bases (e.g., hours, weeks, months, and years) The main goal of this work is to evaluate the performance of the polygeneration system coupled with EV charging stations and hydrogen production systems. The energy and economic details of conventional system layout are provided in [12], whereas the options for the electric power utilization are widely discussed here. Moreover, a parametric analysis is carried out to evaluate the optimum configuration and provide suitable guidelines for these types of systems.

5.1. Daily Results

In this section, to analyze the EV charging system and hydrogen production system on an hourly basis, two random days (e.g., one on weekdays and one on weekend) are selected. The reason for selecting these days is the uneven number of EVs during the selected days

5.1.1. EV Charging System

Figure 8 and Figure 9 show the hourly results of the EV charging system for a typical weekday (e.g., 30th January) and weekend (e.g., 3rd February), respectively. In particular, such figures show the surplus power ($P_{el,surplus}$) from the conventional system, the actual power ($P_{actual})$ withdrawn from the bus bar by the charging station for the charging of different sets of EVs (e.g., A–E), the power consumed by these sets of EVs during their daily trips, and the SoC of LiB of the respective sets of EVs.

In particular, the surplus electrical power, previously defined as $P_{el,surplus}=P_{el,generated}-P_{el,load}$, is greater than the maximum power ($P_{el,ch,fix}$) required to charge a set of two EVs simultaneously with fast charging (e.g., 50 kW for each EV). Figure 8 shows that all sets of EVs reach the Sant’ Apollinare location at 08:00 with a certain distance and velocity; see Table 3. Note that each set of EVs completes its round trip against a distinct drop in SoC value based on the journey distance and average velocity of the EVs. It is evident from the figure that set B of EVs covers the longest daily round trip with a moderate average velocity with a reasonable peak and widest power consumption profile. One side journey of set B of EVs results in lowering SoC from 0.78 to 0.62. However, on the other hand, set C of EVs travels with the highest average velocity, which results in the highest peak value of consumption profile and eventually the lowest SoC (e.g., 0.48) of batteries among the groups.

As discussed in the previous sections, the EV charger operates from 09:00 till 14:00. During this period of time, one hour each is dedicated to charging every set of EVs; see Table 3. From Figure 8, it is evident that at 09:00, the charging of the set A of EVs starts and the actual charging power (P_actual_A) continues to charge a set of two EVs, which results in the increase of the SOC_A of their respective batteries until it reaches a set point of 0.95. The results shows similar pattern as shown in the literature [50,51]. It is worth noting that, although a complete hour is dedicated to charging each set of EVs, the first set reaches the set point in only 20 min. The same pattern goes on for every set of EVs. It is also worth noting that the instantaneous actual charging power and the time duration to reach an SOC_set is a function of the respective group’s batteries’ SOC. This is because a different peak and charging duration is observed for each set of EVs. At 17:00, when the employee has to return home, each set of EVs consumes a similar amount of energy as consumed in the morning while reaching the office.

Figure 9 illustrates the hourly results of the EV charging station’s parameters during the weekend. According to Table 3, there is only one EV unit per set on weekends, compared to the weekdays when each set contains two units. The figure clearly demonstrates that the power consumption of each unit remains constant, as their trip journeys remain unchanged. Subsequently, the SOC profiles and the width of the power profiles for each unit of the set remains unchanged (e.g., similar to the weekdays, Figure 8). However, since there is half the number of units per set on weekends, the power profile for each unit is also halved during this time.

5.1.2. Hydrogen System

In this section, the hourly results of the hydrogen production system are discussed in order to analyze the system’s behavior. Figure 10a shows the electrical power associated with the hydrogen system along with the pressure level of the storage tank and the time-dependent efficiency of the electrolyzer for a typical weekday (e.g., 30th January). In particular, the figure includes the residue electrical power (P_el,residue) after the EV charging station, the actual electrical power consumed by the electrolyzer (P_el,ELY), and the net electrical power sent to the grid (P_el,grid = P_el,residue − P_el,ELY). The figure clearly demonstrates that a steady power equal to P_el,ELY,max (e.g., 50 kW) is supplied to the electrolyzer when the residue power is greater than the electrolyzer’s nominal power. During this time period, the electrolyzer works at full capacity, and all the excess power is exported to the grid. However, at 09:00, when the first set of EVs are connected to the charging station, a considerable amount of electrical power is being withdrawn by the charging station, thereby dropping the residue power lower than the nominal power of the electrolyzer. Therefore, a substantial surge in the power profiles can be observed during all of the times when the EVs need to be charged.

Similarly, Figure 10b illustrates the storage tank inside pressure and the mass flow rate of the gases (e.g., hydrogen and oxygen) produced in the electrolyzer for a typical weekday (e.g., 30th January). It is evident that for a nominal steady electrolyzer power, gases are produced at a constant rate. The pattern of the hydrogen tank inside pressure depends on the mass balance of the tank. For instance, if the amount of hydrogen produced is greater than the amount of hydrogen dispensed, the inside pressure tends to increase. However, at 09:30, the pressure inside the tank starts decreasing because of a higher demand for hydrogen until 20:30. The results are in great agreement with the literature [44].

Figure 11a,b displays the hourly analysis of various parameters of the hydrogen system for a typical weekend day (e.g., 3rd February). It is evident from the figures that the electrolyzer operates at nominal capacity all day, which results in higher production of the gases each day. In particular, the system produces 0.21 kg/day of hydrogen and 1.7 kg/day of oxygen more on weekends than weekdays if the system operates all day.

5.2. Annual Results

The energy and economic performance of the proposed system is also analyzed with an annual integration period. This analysis considers the idea of utilizing the surplus electrical power from the conventional reference system for several on-site applications (e.g., EV charging and hydrogen fueling stations). In

Table 4. Energies, annual results.

Parameter	${\mathbf{E}}_{\mathbf{e}\mathbf{l},\mathbf{s}\mathbf{u}\mathbf{r}\mathbf{p}\mathbf{l}\mathbf{u}\mathbf{s}}$ (MWh/Year)	${\mathbf{E}}_{\mathbf{e}\mathbf{l},\mathbf{c}\mathbf{h},\mathbf{E}\mathbf{V}}$ (MWh/Year)	${\mathbf{E}}_{\mathbf{e}\mathbf{l},\mathbf{c}\mathbf{o}\mathbf{n}\mathbf{s},\mathbf{E}\mathbf{V}}$ (MWh/Year)	${\mathbf{E}}_{\mathbf{e}\mathbf{l},\mathbf{E}\mathbf{L}\mathbf{Y}}$ (MWh/Year)	${\mathbf{E}}_{\mathbf{e}\mathbf{l},\mathbf{g}\mathbf{r}\mathbf{i}\mathbf{d}}$ (MWh/Year)	Hydrogen Produced (t/Year)	Oxygen Produced (t/Year)
Value	1130	42.47	24.21	356.0	707.6	5.514	44.11

, the main energies associated with the system are listed. $E_{el,surplus}$ refers to the excess electrical energy from the conventional system, previously sold completely to the national grid. However, in the proposed system, a share of the total surplus energy is used for EV charging ($E_{el,EV}$) and on-site hydrogen production ($E_{el,ELY}$). In particular, 3.7% of the total surplus energy is used for EV charging, and 31.5% by the electrolyzer to produce hydrogen and oxygen all year around, whereas 62.6% of the surplus energy still goes to the national grid.

The analysis of the annual economic parameters of the proposed system follows the same approach as the reference system. It considers two scenarios; in the first case, it does not consider the cost of the fuel used for power generation, while in the second case, it does consider the fuel cost.

Table 5. Main results of the economic analysis.

Parameter	Scenario 1: Fuel Cost Excluded	Scenario 2: Fuel Cost Included	Unit
${∆CF}_{new}$	266.4	179.6	k€/year
${SPB}_{new}$	2.860	4.240	Years
${NPV}_{new}$	2567	1483	k€
${PI}_{new}$	3.370	1.950	-

lists all the key economic parameters. The comparison of the proposed system economic indexes with the conventional system economic indexes reveals that the annual revenue of the proposed system hikes up around 81.19% for scenario 1 and 197.6% for scenario 2. This drastic increase in the annual revenue of the proposed system also affects other economic parameters. The results are similar to the one achieved in [29], as it uses hydrogen storage system. For instance, the SPB period is shortened by 0.070 years and 2.910 years for scenarios 1 and 2, respectively. Correspondingly, the NPV of the system increases by a percentage of 82.44% and 358.9% for scenarios 1 and 2, respectively. The comparison also determines that the proposed system designed for scenario 1 is 3.37% more profitable, whereas scenario 2 records a 160% higher profitability index than the conventional system.

5.3. Parametric Analysis

A parametric analysis is carried out to detect the best possible parameters that will result in the optimum economic performance of the proposed system. For simplicity purposes, only the hydrogen production system is considered for the parametric runs. The electrolyzer capacity and the hydrogen storage tank capacity are varied from 50 kW to 140 kW and 10 m³ to 40 m³, respectively. To analyze the effect of the unit cost of EV charging and hydrogen gas on the economic performance of the proposed system, a sensitivity analysis is carried out in the range of market value costs of the Italian market survey.

5.3.1. Sensitivity Analysis: Selling Unit Cost of EV Charging and Hydrogen

Figure 12, Figure 13 and Figure 14 display the effect of sensitivity analysis of the unit selling cost of EV charging and hydrogen gas for both scenarios on the economic parameters of the proposed system. Since the revenue of the proposed system is based on the unit cost of the selling entities, the increase in the selling prices to the highest market values (e.g., J_EV charging to 0.8 EUR/kWh and J_H2 to 11 EUR/kg) thereby enhances the economic performance of the proposed system. For instance, Figure 12 shows that for the highest market selling prices, the SPB period of the proposed system drops to 2.72 years and 3.94 years for scenarios 1 and 2, respectively. Note that if the selling prices are discounted to the lowest Italian market available rates (e.g., J_EV = 0.2 EUR/kWh and J_H2 = 5 EUR/kg), the SPB rises to only 3.43 years and 5.62 years for scenarios 1 and 2, respectively.

Similarly, Figure 13 shows the variation of the NPV versus the charging price of EV chargers (NPV vs. J_EV) as a function of the selling price of hydrogen. It is evident that the NPV of both scenarios has a direct relation with the selling prices. The NPV value for the lowest market price is 2.015 MEUR and 0.931 MEUR for scenarios 1 and 2, respectively. However, when the commodities are sold for the highest market value, the sensitivity analysis trend reveals a rise of 36% and 77.98% of NPV for scenarios 1 and 2, respectively.

Correspondingly, the PI of the proposed system can reach a maximum of 3.60 and 2.18 for scenarios 1 and 2, respectively, for the highest market selling prices; see Figure 14. On the contrary, when the trend lines are extrapolated to the y-intercept of the figure, the PI remains in the positive range. This implies that the proposed system will be profitable even if the EV charging is made free for the Sant’ Apollinare employees.

5.3.2. Parametric Analysis: Electrolyzer Capacity and Hydrogen Daily Demand

The proposed system’s economic performance was also analyzed as a function of the electrolyzer’s nominal capacity and the daily demand for hydrogen in the fuel station. The variation of the electrolyzer capacity significantly affects the magnitude of the electrical power sold to the grid. With an increasing value of the nominal capacity of the electrolyzer, the system results in a reduced amount of net electrical energy sent to the grid. The range of the parametric parameters was selected based on the peak electrical power available throughout the year and the maximum possible amount of hydrogen that can be dispensed for the corresponding capacities of the system. Note that the hydrogen storage tank capacity for this analysis was increased to a maximum of 50 m³ to increase its compatibility with large electrolyzers.

Figure 15 displays the trends of SPB curves as a function of electrolyzer nominal capacity and the hydrogen daily demand for both scenarios. The most feasible economic results refer to the case when the economic parameters are better than the base case. For instance, it is observed that the increase in electrolyzer capacities is viable only with the increase in hydrogen demand. Otherwise, the capital cost of an oversized electrolyzer capacity and lower hydrogen demand will adversely affect the economic parameters. Based on the trends, the electrolyzer system with 120 kW capacity and a daily demand of 37 kg/day has the lowest SPB period of 2.39 and 3.04 for scenarios 1 and 2, respectively. The obvious reason for this is that the 120 kW capacity electrolyzer is in the nearest possible combination among the others to fulfill the corresponding daily demand of 32 kg of hydrogen. In other words, a 120 kW capacity electrolyzer is the least oversized system to fulfill the daily demand, which reduces the capital investment of the system and results in the most optimum combination of the parameters.

Similarly, Figure 16 displays the trends of the NPV from varying the size of the electrolyzer system and hydrogen demand. Correspondingly, the PI of the proposed system also has the same trends; see Figure 17. The highest value of the PI appears to be 4.23 and 3.11 for an electrolyzer capacity of 120 kW and a daily hydrogen demand of 37 kg in both scenarios.

6. Conclusions

This study proposes a novel model that uses the surplus electrical power from a biomass-based CCHP system (e.g., conventional system) for several on-site applications (power-to-X) instead of selling it to the national grid. For instance, a portion of the surplus electrical power is delivered to a 50 kW capacity charger used for charging EVs owned by the Sant’ Apollinare building employees, and a portion of the electrical power is delivered to a 50 kW alkaline electrolyzer system to produce on-site hydrogen, which is compressed and stored in a 10 m³ tank. Note that the simulated model in the case study adopted the EV charging of commercially available models of the Renault Zoe. The produced hydrogen is later dispensed to FCEVs at a rate of 15 kg/day. The proposed system aims to limit the excess electrical power exported to the national grid.

The system performance is analyzed by comparing the economic indexes of the proposed system, consisting of EVs charging and hydrogen production system with a reference conventional system, comprising only selling the surplus electrical power to the national grid. The economic analysis of the system considers realistic cost assumptions based on Italian market rates for the per unit costs of the EVs charging system and hydrogen system (e.g., 0.6 EUR/kWh and 10 EUR/kg, respectively). The study also performs a sensitivity analysis for a range of economic and design parameters to achieve the optimum design parameters of the system.

The main results of the proposed system shows a significant reduction in the surplus electrical power export to the national grid. In particular, around 3.7% of the surplus electrical power is used for EV charging and 31.5% for producing hydrogen, which results in the annual hydrogen production of 5.514 tons and oxygen production of 44.11 tons.

The economic analysis of the system records a hike up in the annual revenue of the proposed system around 81.19% and 197.6% for scenarios 1 and 2, respectively, which also results in the shortening of the SPB period by 0.070 years and 2.910 years for scenarios 1 and 2, respectively. Moreover, for the same system configuration, if the unit selling prices are discounted to the lowest Italian market rates, the SPB rises only to 3.43 years and 5.62 years for scenarios 1 and 2, respectively.

Furthermore, the sensitivity analysis highlights that the optimal hydrogen system consists of a 120 kW capacity electrolyzer with 37 kg daily demand, with a corresponding SPB period of 2.39 years and 3.04 years for scenarios 1 and 2, respectively.

In conclusion, the proposed system layout, integrating on-site EV charging and hydrogen production, has demonstrated promising results. The system not only promotes the adoption of EVs and FCEVs but also highlights the importance of maximizing self-consumed energy over exporting excess power to the national grid. Prioritizing local energy utilization supports the development of integrated renewable energy solutions, enhancing energy efficiency and contributing to emission reduction in urban areas. This approach aligns with broader sustainability goals and paves the way for more efficient, decentralized energy systems that support sustainable transportation initiatives. Policymakers should promote decentralized renewable energy systems by integrating EV charging and hydrogen production with building energy systems. To achieve this, they should offer financial incentives for on-site energy storage and streamline regulations for small-scale energy installations. Further research should explore the scalability of the system for larger communities or industrial applications. Additionally, expanding the scope to include a broader range of renewable sources and storage technologies could enhance the robustness of the system.