Design and Feasibility Study of Biomass-Driven Combined Heat and Power Systems for Rural Communities

Over the past decades, there has been a significant increase in interest and research around the field of improving energy efficiency. This was driven by trying to address the large amounts of energy losses that occur in traditional power plants. It was observed that only about 30% of the available energy was converted into electric power [1]. The unconverted energy is lost during various processes, but the largest amount is lost due to waste heat in the system [2]. Additional heat losses are observed during the process of transporting electric energy to the end-user. With an ever-growing energy demand and a looming climate crisis, low efficiencies are wasteful and unnecessarily costly. The demand for increased energy efficiencies led to developments in “onsite” and “near-site” power generation. This is also referred to as combined heat and power (CHP) systems. In addition to energy generation, these onsite power generation systems collect waste heat to meet thermal demands [3–5]. By collecting waste heat during the power generation process, CHP systems efficiencies can rise to 80% [6]. Typical CHP systems are modeled and built consisting of a power generation unit (PGU) and boiler, working in tandem with thermally activated heating, ventilation, and air conditioning units (HVAC), as well as space and water heating components. A variety of PGUs are used commonly, such as internal combustion (IC) engines, microturbines, and fuel cells, all powered by various types of fuels. Further analysis and description of common CHP components and configurations can be found in Refs. [7−10].

A focus of current CHP system research has been on reducing operational costs although additional parameters such as primary energy consumption (PEC) and carbon dioxide emissions (CDE) are useful aspects to consider when evaluating their effectiveness. Most of the current literature focuses on natural gas-driven CHP systems. Those systems have seen success with CDE reductions of roughly 50–60% compared to a typical coal power plant [11]. Although there are worthwhile achievements, due to its nature as a fossil fuel, the environmental benefits of natural gas as a fuel source are limited. For that reason, biofuels have seen increased attention.

When discussing biofuels, it is important to clarify terms for mutual understanding. Biofuels can be categorized into three groups: First, second, and third-generation biofuels. The distinction between these groups is the feedstock they are produced from. Broadly speaking, first-generation biofuels are made from edible feedstock, whereas second and third-generation biofuels are developed from a non-food feedstock for humans. As such, many of the first-generation biofuels are ethanol-based in corn and grains. Second-generation biofuels are based on crop and wood residues such as wood pellets, lignocellulosic biomass, and energy crops [12]. Third-generation biofuels are based on algae and sewage sludge [13]. While first- and some second-generation biofuels compete with agricultural lands, third-generation biofuels do not and are thus seen as promising fuel sources. A detailed breakdown of the types of biofuels can be found in Refs. [14,15].

Researchers have been studying biofuels for some time, and bioenergy has been regarded as a possible alternative and additional source of green energy. In a 2020 International Energy Agency (IEA) report [16], it was estimated that global bioenergy capacity is expected to increase 32% to 171 gigawatts (GW) by 2024. Although this is only representative of approximately 3% of renewable capacity growth, the majority of this is attributed to biomass co-generation projects, also known as biomass-driven CHP (bCHP) systems.

Wood pellets (WP), in particular, have gained traction as a fuel source for bCHP systems, particularly for domestic systems. The EIA outlined in a 2016 report [17] that approximately 85% of the materials used to produce pellets are wood waste, and only the remaining 15% are raw materials. In comparison to the rest of the world, the implementation of bCHP systems in the United States can only be described as budding. A recent EIA report [18] highlights the quantity of CHP systems across the United States. Most of these systems are built near refineries or other steam-producing plants, with over 65% of the CHP systems operating in the United States, being powered by natural gas. The United States has, thus far, not seen many bCHP systems in use. Private companies, that offer installation of one-unit bCHP systems for homes exist but are mostly located outside of the United States. Currently, wood pellets are the only commercially available biofuel for already existing bCHP systems, making this the necessary fuel choice for bCHP system analysis. In this study, the viability of bCHP systems in rural communities is determined using operational cost, PEC, and CDE as comparative parameters.

The system components, comparative parameters, and operational strategy of a bCHP system are presented in this section. As mentioned in the introductory section of this paper, there are several key components of any CHP system, which also extend to bCHP systems, with slight modifications. To determine the associated benefits and costs with any given system certain measurable outputs need to be determined. For these models, the focus will be on operational cost (OC) savings, PEC savings, and CDE savings. The CHP system is compared to a traditional energy approach, the reference system, where the electricity demand is met with energy imported from the energy grid and the thermal demand is met by a natural gas-driven boiler. The individual components of a “regular” CHP system and a bCHP system are discussed next.

As described in the Introduction, a CHP system consists of certain key components. These vary slightly depending on applications, but in general they are very similar. Typical CHP systems consist of a PGU, usually a prime mover and generator to generate electricity as well as a boiler to generate additional heat, which varies in size depending on need. To recover usable heat from the PGU, a heat recovery system (HRS) is needed. Additionally, to convert the generated heat into usable heat, cooling and heating components are needed, depending on the configuration of the system. Figure 1 depicts a typical CHP system schematic, serving as a simplified visual representation of a CHP system. As shown in Fig. 1, fuel is supplied to both the PGU and boiler. The PGU produces electric energy which is used to power CHP components as well as meet the building’s electricity need. In the case that not enough electricity is generated by the PGU, the electricity demand can be met by importing electricity from the grid. When excess electricity is generated, it can be stored in a battery storage system or be sold to the electric grid. Additionally, the waste heat from the PGU is recovered and used for heating purposes. Similar to the generated electricity, if not enough heat is produced to meet the thermal demand for the building, additional heat can be produced using a boiler whose excess heat can be stored in thermal storage systems. There are several options when choosing a PGU while designing a CHP system, such as IC engines or microturbines, which use various fuel types such as natural gas or biofuels.

The design of a biofuel-driven CHP system mimics that of a typical CHP system closely. The main difference between the two is the fuel and PGU type. Different types of biofuels require certain treatment before they can be used for energy production. Thus, additional components need to be combined with the PGU to use and convert the biofuel for electricity conversion. Depending on the type of biofuel, a gasifier, an economizer, a condenser, a regenerator, and a heat consumer may have to be used. For further analysis and additional examples, see Refs. [19–21].

Across the world, many small-scale bCHP projects have seen success and are used to test and analyze the viability in micro-, small-, and large-scale applications. Salomón et al. [21] provided an in-depth analysis of the state of bCHP systems in Sweden and Finland. Their work also included a comprehensive review of currently available and under development small-scale CHP technologies. Patuzzi et al. [22] performed a comparative study of multiple small-scale (50 kWe) bCHP systems in Italy. Wright et al. [23] explored a failed bCHP project and analyzed potential mitigation methods for this and future projects. Strzalka et al. [24] studied a bCHP, located in Stuttgart, Germany over four years. Tańczuk and Ulbrich [25] analyzed and compared bCHP systems under multiple conditions in Germany and Poland. They examined the feasibility of such systems using a techno-economic analysis including cost analysis with different incentive structures in both countries. Obernberger et al. [26] examined a bCHP plant in Lienz, Austria for one year.

This research project focuses on the possible implementation of a biomass-driven CHP system in rural communities. The data for the bCHP model were obtained from a selected rural location. A combination of favorable conditions such as a variety of different building types, including a hospital, a school, various businesses, and restaurants as well as a population of approximately 1510 [27], are present in the selected location which makes this an ideal rural community for this project. To approximate the various building types and their floor area, google maps and google street view features were used as shown in Fig. 2. Building types and their respective floor area are listed in Table 1.

The U.S. Department of Energy (DOE) developed an open-source building energy simulation program called EnergyPlus,² which in conjunction with the DOE developed commercial reference buildings [28] and appropriate weather data [29] based on climate zones and ASHRAE standard 90.1 [30] was used to model the energy demand for the buildings in Ackerman, MS. EnergyPlus generated hourly electricity and heating demands for each building type which were summed for total hourly electricity and heating demands for the rural community. These values were imported into the model to evaluate the effectiveness of the bCHP system. They serve as benchmark values for the reference case which represents a typical system in which all the energy demand is met by purchasing electricity from the energy grid and heating demand is met through a traditional boiler. A diagram of the full process is displayed in Fig. 3. The methods diagram displays the order of steps taken during the calculations and analysis.

The design of the bCHP system used for modeling the energy and heating demand for a rural community is presented in this section. Although wood pellet-driven bCHP systems and traditional CHP systems are modeled identically, for application purposes, the difference in fuel, i.e., natural gas versus wood pellets, must be considered, and thus, a wood gasifier should be implemented.

For the design process of the bCHP system, the hourly electricity demand (ED) and gas demand (GD) played a key role. The minimum hourly ED value was used to size the PGU, e.g., a 600 kWh_el wood pellet fired PGU with a thermal output of approximately 1200 kWh_th.

Due to the lower annual GD compared to the annual ED for the selected building network in a rural community, illustrated in Fig. 2, a threshold to determine the system’s operational status (i.e., turn the PGU on and off) was implemented to ensure that the proposed bCHP system can remain high overall efficiency. The threshold value was determined to be 50% of the PGU thermal output. When the thermal load from the building network is lower than the determined threshold, the bCHP system is assumed to be turned off and the buildings are operated based on the grid electricity and natural gas-fired boiler. This threshold value should be adjusted and can be omitted depending on the location and overall ED and GD.

The effectiveness of any CHP system is important in deciding whether or not to implement a given system. To evaluate the benefits of this system, three parameters were chosen, i.e., OC savings, PEC savings, and CDE savings. Additional information such as local fuel costs and conversion factors are required to conduct this analysis. The prices for electricity, natural gas, and wood pellets in the selected location are presented in Table 2.

The U.S. DOE defines PEC as the amount of energy consumption summed up with the losses that arise during generation, transmission, and distribution of energy. The site-to-primary energy conversion factors are presented in Table 3.

The results obtained from the parametric calculations described in Sec. 4 are presented in this section. The impact of the threshold value, to determine the status of the bCHP system described in Sec. 4, on the number of operational hours based on the PGU size is presented in Table 5.

As the PGU size increases, the threshold value for the system status increases as well while the hours of operation decrease nonlinearly. With a PGU sized at 600 kWh_el, the bCHP system still runs approximately a quarter of the time. Larger than 600 kWh_el systems can be justified if the threshold is decreased. With a greater heating demand or a decrease in WP price, this should be considered. In the latter case, although energy is wasted, operational costs would still decrease significantly by selling excess electricity back to the grid. A graph depicting monthly electric and thermal loads is presented in Fig. 4.

As expected, the difference between electric and thermal loads, especially during the summer months, is stark. For most of the time during these months, the threshold value is not exceeded, setting the bCHP status to “Off” to reduce waste heat.

Hourly bCHP simulations for a representative winter and summer day were conducted. The bCHP feasibility on the representative days was evaluated based on OC, PEC, and CDE savings at all five PGU sizes (i.e., 300 kWh, 600 kWh, 1200 kWh, 1800 kWh, and 2400 kWh). A graph depicting hourly electric and thermal loads of a winter day is presented in Fig. 5.

The graphs clearly show that during a winter day, the thermal load exceeds the threshold value for the PGUs sized at 300 kWh, 600 kWh, and 1200 kWh most of the day. This can be easily seen as threshold values represent 50% of the thermal production which equal the PGU sizes. A comparison with Fig. 5 shows that the thermal load never exceeds 1800 kWh. Thus, PGUs sized over 1800 kWh do not turn on. The feasibility of this day, based on OC, PEC, and CDE savings, is presented in Fig. 6. The percent variation of the parameters is separate for the PGUs.

The bar graph above clearly shows that significant OC savings can be seen at PGU sizes 300, 600 kWh, and 1200 kWh. When the PGU is sized at 1800 kWh and 2400 kWh, none of the parameters display any savings, making those not viable options. At those PGU sizes, the threshold values exceed the thermal loads at all times during the day, meaning the bCHP system is turned off and therefore no savings exist. A graph depicting hourly electric and thermal loads of a representative summer day is presented in Fig. 7.

As already shown in Fig. 4, very little thermal demand exist during a representative summer day. At no time does the thermal demand exceed any of the threshold values, meaning the bCHP does not run at all during this day. Therefore, no matter the PGU size, no OC, PEC, or CDE savings exist. The results for a representative transitional day mirrored the results from Fig. 7; thus, a separate graph is omitted. Due to the chosen location, transitional days are rare and as seen in Fig. 4, comparable to summer days. A system in a different climate zone would show results for a transitional day. These findings are directly related to the introduced threshold value. Without the threshold, the system would run even though there is very little thermal need. This would result in large amounts of heat waste, making the system run inefficiently.

In addition to the representative summer and winter days, an annual evaluation of the bCHP system was conducted based on OC, PEC, and CDE savings. An annual analysis helps identify the trends of the system, which are presented in Fig. 8. The percent variation of the three parameters is separate for each PGU size.

The bar graph in Fig. 8 displays that annual savings exist at all PGU sizes. All savings (i.e., OC, PEC, and CDE) are largest with a PGU sized at 600 kWh and decrease as the PGU size increases (as well as for 300 kWh). This can be explained due to the decreasing number of operational hours as the PGU size decreases, as given in Table 5. The second-largest savings can be seen for a system sized at 300 kWh. A smaller system necessarily has a lower threshold value, and thus, operational hours increase. As shown in Table 5, this system runs approximately 40% of the year during which savings are accumulated. In addition to percent variations for OC, PEC, and CDE savings, absolute savings values are presented in Table 6.

The table displays annual values for OC, PEC, and CDE savings which can help determine the system’s viability. As shown in Fig. 4, a system sized at 600 kWh has the largest amount of savings in all three categories. The annual savings for systems sized over 1200 kWh are very small which may not make them viable options. This is supported in Fig. 6 where those systems display no savings. Although creating significant waste heat, it is worth revisiting the threshold value of 50% of the thermal output of the PGU. A threshold value of 25% of the thermal output of the PGU shows promise, which is presented in Fig. 9.

Lowering the threshold to 25% of the thermal output of the PGU drastically increases OC, PEC, and CDE savings. The lowering of the threshold allows for greater operational hours with lower operational costs. Even as some of the heat generated by PGU is wasted, increased savings can justify this kind of configuration.

Operational cost savings are highly dependent on wood pellet costs and threshold values. The 600-kWh PGU size is used for comparison.

For neither the 50% nor 25% threshold values, as shown in Table 7, the operational cost savings at a wood pellet price of $200/ton are positive. At that price point, it is not feasible to operate the system. For that reason, this price point is excluded from the payback period analysis.

Importantly, larger PGUs meet greater energy demands and make the system less dependable on the electric grid. Although larger PGU sizes display fewer savings and greater investment costs, additional advantages of those systems exist. The greatest advantage of a larger system is electric grid independence. If all large portions of the annual energy demand are provided through onsite energy generation, the community protects itself from electric grid fluctuations. Additionally, during natural disasters, places that need energy the most are often without energy supply due to damage to the grid. Onsite energy production alleviates this problem. In this case, although little OC, PEC, or CDE savings can be recorded, providing essential services, such as hospitals, with energy can save lives.

The payback period (PBP) is a great indicator of the feasibility of a system as it shows when a system reaches profitability. To determine which PGU sizes are worth investing in, the payback period was calculated based on PGU sizes. The payback period for the bCHP system with original (50%) and 25% threshold values is presented in this section. For this calculation, initial costs of approximately $3300/kWh are used which was obtained from various manufactures with working systems. Additionally, material and construction costs for the approximately 9000 ft of piping are included in the PBP analysis. A nominal pipe size of DN150 (6 in.) was selected at a price point of 410€/m [31]. At an equivalent cost of $149/ft, additional costs of approximately $1.3 million are included.

The payback period calculation demonstrates that at the regular threshold value, only PGUs sized at 300 kWh and 600 kWh make financial sense. For a system with reduced threshold values, certain stakeholders such as governments or energy companies can justify a PGU sized larger than that at 600 kWh due to their additional benefits. Although the payback period is very large, depending on the stakeholder, grid independence and increased resilience may be more important than profits.

The PBP analysis is vastly impacted by the wood pellet price. The results from Table 8 were calculated at a price point of $100/ton. Figure 10 highlights the changes in PBP at two WP price points and a constant PGU size of 600 kWh for various threshold values.

The payback period is naturally shortest at the lowest WP price point. As the WP price increases, so does the PBP. Unless various costs, such as the material, construction, or WP costs, can be reduced, only WP prices of $50/ton and $100/ton are economically feasible since the PBP at the price point exceeds 100 years for 50% and 25% thermal threshold. At 10% thermal threshold, operational losses occur, and therefore, no PBP exists. At lower WP price points and low threshold values, the shortest PBP can be found. Since the threshold values were primarily implemented to ensure efficient operation, an optimized thermal threshold is most beneficial. High operational cost savings, relatively low PBP, and reasonable WP cost can be identified at a WP price of $100/ton and a 25% thermal threshold value, resulting in a PBP of 30.3 years. The PBP continues to decrease as the WP price decreases. Additionally, in some instances, PEC and CDE savings may outweigh long payback periods, depending on the stakeholders and their intentions. In those cases, higher thresholds resulting in greater efficiencies may be desirable.

The vast majority of current operational bCHP systems are studied in Europe. In general, the conclusions of various researchers on analyzed bCHP systems align in that for systems to be feasible, a combination of factors needs to be achieved. Although OCs play an important role, there are scenarios in which OC savings can be secondary to PEC and CDE savings. This paper discussed the simulation and performance of a biomass-driven CHP system in a rural community. The effectiveness of the bCHP system was determined by comparing it to the reference case based on three parameters, the operational cost, primary energy consumption, and carbon dioxide emission savings. To ensure a cost-effective usage of the bCHP system in a rural community located in a warm climate zone, a threshold determining the operating status of the system should be determined in consideration of low thermal demand. For a system in a cold climate zone with relatively high thermal demand, this threshold might not be necessary and significantly increase the system's effectiveness, thus making it more viable. The results showed that the major factor to make the system viable is the appropriate sizing of bCHP systems and the purchase price of wood pellets. The closer this price is to the price of natural gas, the smaller the payback period. A reduced threshold value equally increases savings since the operational hours of the system are increased. Both PEC and CDE savings can be significant even when operating at a loss. They should be important factors to consider when deciding to invest in a system. Each parameter should be weighed based on individual goals and needs. The results demonstrate that bCHP systems are a viable alternative to the reference system as well as traditional CHP systems due to their significant energy and environmental upsides, especially as the costs of biofuels, particularly wood pellets, continue to decrease. Furthermore, biomass-driven combined cooling, heating, and power systems may serve as a way to implement systems without threshold values in warm climate zones as those systems can also meet cooling loads.

This work is supported by Bioenergy Feedstock Logistics Program (Grant No. 2020-67019-30772/Project accession no. 1022075) from the United States Department of Agriculture (USDA) National Institute of Food and Agriculture.

There are no conflicts of interest.

The data sets generated and supporting the findings of this article are obtainable from the corresponding author upon reasonable request. The authors attest that all data for this study are included in the paper. Data provided by a third party are listed in Acknowledgment.

E_base =

base loaded PGU size

E_cost =

electricity price

F_Boiler =

fuel needed by the boiler

F_PGU =

fuel needed by the PGU

F_{PGU_loss} =

PGU Energy loss factor before the HRS

Q_PGU =

heat produced by PGU

Q_{Rec_PGU} =

heat recovered from the PGU

Q_Req =

heat required by the building

AHU =

air handling unit

Boiler_eff =

boiler efficiency

ECF_PEC/CDE =

electricity conversion factors for PEC/CDE

HRS_eff =

heat Recovery System efficiency

NG_cost =

natural gas price

PGU_eff =

PGU efficiency

WP_cost =

wood pellet price

WPCF =

wood pellet conversion factors for PEC/CDE