Bioenergy Supply Chain Research Articles

A sustainable robust optimization model to design a sugarcane-based bioenergy supply network: A case study

Reasons such as volatile fossil fuel prices, environmental limitations, and swift economic growth have increased the demand for clean renewable energy. Bioenergy has been received much attention from researchers due to its various benefits compared to other renewable energy alternatives. In this paper, a three-stage approach is developed to design a sustainable sugarcane-based bioenergy supply chain network under an uncertain environment. The fuzzy data envelopment analysis method is employed in the first stage to determine the appropriate regions for opening the sugarcane fields according to climatic, ecological, and social criteria. The nominated regions are then integrated into a mathematical optimization model as candidate locations for sugarcane fields to configure the proposed supply chain design. This stage helps reduce the computational complexity and provides a reasonable solution by excluding the inappropriate regions. In the second stage, a robust mixed-integer linear programming model (MILP) is formulated to optimize a set of strategic and tactical decision variables. The enviromental impacts from CO2 equivalent emissions (CO2-eq emissions), water, and energy consumption in all echelons of the supply chain are incorporated in the developed model. This model is capable of providing a steady design against different expected scenarios. In the last stage, an experimental analysis is conducted, to examine the performance of the developed model in comparison with an expected scenario model. A real case study in Iraq is applied to check the applicability of the developed model. The results demonstrated the superiority of the proposed robust model over the expected scenario model in terms of the mean and standard deviation of their objective functions by 18% and 51% respectively.

Evolutionary game analysis for government regulations in a straw-based bioenergy supply chain

Many countries are devoting considerable efforts to replace fossil energy with renewable energy to achieve ‘Carbon Neutrality’. Straw-based bioenergy is considered as a potential substitute of fossil energy. This paper investigates the government regulations (i.e. penalty and subsidy policy) for a straw-based bioenergy supply chain consisting of power plants and farmers, in which the three parties are of bounded rationality. We develop a three-party evolutionary game model and obtain the evolutionary stable strategies of the three parties. We establish the dynamic penalty model and show that the dynamic penalty policy can encourage power plants to use bioenergy and farmers to collect bioenergy than the existing static penalty policy. Our results further demonstrate if the proportion of the farmers choosing the ‘Collecting bioenergy’ strategy is low, the government should choose the ‘Penalizing the farmer’ strategy; otherwise, the government should choose the ‘Penalizing the power plant’ strategy. We also extend our model to the government's subsidy policy and compare it with the penalty policy. Specially, when the proportion of the power plants (farmers) using (collecting) bioenergy is high, if the government's subsidies are lower than penalties, the government should choose the subsidy policy; otherwise, the government should choose the penalty policy.

Carbon Footprint and Feedstock Quality of a Real Biomass Power Plant Fed with Forestry and Agricultural Residues

Phasing out fossil fuels to renewables is currently a global priority due to the climate change threat. Advocacy for biomass use as an energy source requires assessing the quality biomass and ecological impacts of bioenergy supply chains. This study evaluated the quality of biomass residues from orchards and silviculture transported from different Northern and Central Italy locations and the carbon footprint of a biomass power plant. The total greenhouse emissions were calculated based on primary data for 2017 according to the ISO/TS 14067. All the residue samples showed their suitability for biofuel use. Ash content was relatively low on average (3–5% d.m.), except for grapevine residues (18% d.m.). The lower heating value was within the expected range of 15–21 MJ kg−1 for plant species. The average GHG emission from the power plant was 17.4 g CO2 eq./MJ of electrical energy, with the energy conversion (38%) and transportation of biomass (34%) phases being the main impact contributors. For this study, impacts of residual agricultural residue were about half that of residues from forest management, mainly due to chipping and greater transport distance. Results show that sourcing residual biomass materials for electricity generation close to power plants significantly reduce GHG emissions compared to conventional fossil fuels.

Exploring filamentous fungi depolymerization of corn stover in the context bioenergy queuing operations

AbstractRecalcitrance of lignocellulosic feedstocks to depolymerization is a significant barrier for bioenergy production approaches that require conversion of monomeric carbohydrates to renewable energy sources. This study assesses how low‐cost modifications in the supply chain can be transformed into targeted pretreatments in the context of the entire bioenergy supply chain. This research aims to overcome the physiochemical barriers in corn stover that necessitate increased severity in downstream conversion in terms of chemical loading, temperature, and residence time. Corn stover samples were inoculated with a selective (Ceriporiopsis subvermispora) and nonselective (Phanerochaete chrysosporium) lignin‐degrading filamentous fungal strains, then stored aerobically to determine the working envelope for fungal pretreatment to achieve lignin degradation. Dry matter loss and gross chemical makeup of corn stover varied by the length of treatment (2 and 4 weeks) and by the moisture content of the treated corn stover samples (40% and 60%, wet basis). Dry matter loss in P. chrysosporium inoculated biomass was elevated compared to C. subvermispora inoculated biomass; however, treatment also induced additional chemical composition changes suggestive of depolymerization. These results highlight that fungal treatment approaches must balance the loss of convertible material with the potential for reduction in recalcitrance. Techno‐economic assessment (TEA) of fungal pretreatment in a short‐term queuing system indicated the viability of this approach compared to conventional queuing operations. Total queuing system cost was estimated at $1.65/ton of biomass stored. After applying the credit of $1.48/ton from energy savings in the conversion phase using fungal pretreated biomass, the total system cost was $0.80 lower than traditional biomass queuing approach. While the TEA results suggested that treating biomass with C. subvermispora is the most economically viable storage method in the designed fungal‐assisted queuing system, future research should focus on additional fungal depolymerization such as those observed in the P. chrysosporium inoculated biomass.

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.

Sustainability performance of lignocellulosic biomass-to-bioenergy supply chains for Rural Growth Centres in Zambia

This study provides insight into the sustainability performance of bioenergy systems for providing cooking and electricity-enabled energy services in the rural growth centres of Zambia. This is achieved by modeling and optimizing a bioenergy supply chain for the provision of these services, and then comparing its sustainability performance to that of existing energy systems. The findings indicate that using biomass-based gasifier-internal combustion engines instead of diesel generators for the provision of electricity-enabled services could result in greenhouse gas, NOX, and SO2 emission reductions of 36, 8, and 97%, respectively. When pellet-gasifier cookstoves are used instead of firewood/three-stone cookstoves for providing cooking services, greenhouse gas and PM2.5 emissions are reduced by 57 and 94%, respectively. Furthermore, households that use bioenergy systems instead of traditional energy systems to deliver cooking and electricity-enabled services can save approximately 275 and 11 US dollars per year, respectively. In general, bioenergy systems outperform existing systems in providing the two energy services in question from a sustainability standpoint. Hence, policymakers must consider their use as alternative energy systems to the existing ones in order to foster sustainable development in rural areas.

Screening of alkali‐assisted storage conditions to define the operational window of deacetylation within storage systems in the bioenergy supply chain

AbstractOvercoming biomass heterogeneity and associated recalcitrance to thermal, chemical, and enzymatic depolymerization is a necessary but challenging aspect of valorizing lignocellulosic biomass to fuels and chemicals. This study explores how this recalcitrance can be reduced during the supply chain unit operation of storage, which is required for seasonally harvested agricultural residues to maintain constant throughput at a biorefinery. In this work, partial alkali pretreatment was performed just prior to corn stover entering storage, which had the benefit of providing a high pH environment entering storage such that soluble sugars were preserved and saponified acetyl groups in hemicellulose. This work investigated a range of viable conditions where saponification and preservation occur simultaneously by varying the moisture content (40% and 60%) and concentrations of sodium hydroxide (low and high) during aerobic and anaerobic storage. Anaerobic conditions preserved overall dry matter below 5% in the three scenarios evaluated, and the highest alkali loading solubilized up to 15% lignin, 18% xylan, and 50% of acetate, meanwhile doubling the extractable components. Scanning electron microscopy images highlighted potential physical impacts including cell‐wall disruption near vascular bundles, pitting within parenchyma cells, and cell‐wall distortion. Techno‐economic assessment indicated that this storage approach and associated logistics system is economically competitive with a conventional approach using low‐moisture bales. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd

Government penalty provision and contracting with asymmetric quality information in a bioenergy supply chain

Bioenergy, which is viewed as a renewable and clean energy, has been receiving great attention in recent years. To protect the environment and raise the share of bioenergy, the government usually offers penalties to the power plant if the ratio of bioenergy in electricity is below a certain target level. This paper studies the government’s penalty provision in a bioenergy supply chain consisting of a power plant and farmers who privately know their quality information of the bioenergy. We consider two scenarios, i.e., symmetric information and asymmetric information, under which the power plant can offer a single wholesale price contract or a menu of quantity-payment contracts. We conduct some game-theoretical analysis and obtain the optimal decision of the government and the optimal contracts for the power plant under different scenarios. Some non-trivial results are obtained. When the target level is low, the social welfare is higher with symmetric information than that with asymmetric information. However, when the target level is high, the social welfare is hurt by the improvement of information transparency. Moreover, the government will set a higher target level under the wholesale price contract than that under the menu of quantity-bundle contracts. The profit for the power plant is higher with the menu of quantity-bundle contracts than that with the wholesale price contract. We also find that when the target level is either low or high, the farmers should make the bioenergy quality information transparent to maximize their own expected payoffs. However, when the target level is medium, the farmers should withhold the relevant information.

Contracting with asymmetric information under government subsidy programmes in a bioenergy supply chain

This paper investigates the government subsidy programmes for a bioenergy supply chain composed of a power plant and farmers who privately know their quality information of bioenergy. The government offers two types of subsidy programmes to increase the supply of bioenergy: Agriculture Quantity Coverage (AQC) programme that pays the farmers subsidies based on the quantity of bioenergy, and Price Loss Coverage (PLC) programme that is triggered when the market price of bioenergy falls below a reference price. With AQC programme, when the quantity of bioenergy is large, farmers may get more subsidies but the wholesale price of bioenergy would decrease, thus hurting the farmers' payoffs. By contrast, with PLC programme, the farmers can get more subsidies with a lower wholesale price, which also undermines the farmers' profitability. In equilibrium, the government prefers the AQC programme when the cost of the non-bioenergy is high, or the cost of the non-bioenergy is low and the government's subsidy payment coefficient in the PLC programme is high. Moreover, social welfare is higher with the PLC programme than that with the AQC programme when the cost of the non-bioenergy is high and the subsidy payment coefficient is low, and is lower otherwise.

Using systems thinking and causal loop diagrams to identify cascading climate change impacts on bioenergy supply systems

Increased use of bioenergy, driven by ambitious climate and energy policies, has led to an upsurge in international bioenergy trade. Simultaneously, it is evident that every node of the bioenergy supply chain, from cultivation of energy crops to production of electricity and heat, is vulnerable to climate change impacts. However, climate change assessments of bioenergy supply chains neither account for the global nature of the bioenergy market, nor the complexity and dynamic interconnectivity between and within different sub-systems in which the bioenergy supply chain is embedded, thereby neglecting potential compounding and cascading impacts of climate change. In this paper, systems thinking is utilised to develop an analytical framework to address this gap, and aided by causal loop diagrams, cascading impacts of climate change are identified for a case study concerning imports of wood pellets from the United States to the European Union. The findings illustrate how the complexity and interconnectivity of the wood pellet supply system predispose the supply chain to various cascading climate change impacts stemming from environmental, social, political and economic domains, and highlight the value of using system-based analysis tools for studying such complex and dynamic systems.

Resilience Lessons From the Southeast United States Woody Pellet Supply Chain Response to the COVID-19 Pandemic†

The Coronavirus Disease 2019 (COVID-19) instigated a pandemic that impacted economies, employment, and shipping worldwide. This paper reviews how one international supply chain performed and identifies lessons that may be helpful to improve future resilience. Economic and employment data through November 2020 are used to review the effects of COVID-19 on operations of the bioenergy supply chain in the southeastern United States (SE United States) that utilizes wood fiber to fabricate pellets. Conditions associated with the production of pellets in the SE United States changed with the outbreak of COVID-19. Federal and state government programs and classification of workers in this sector as “essential” during the pandemic helped maintain the woody pellet supply chain and other industries during a period of general shut down in 2020. The availability of personal protective equipment, long-term supply contracts, and established safety cultures are among the factors that enhance supply chain resilience while limited availability of skilled workers, inadequate stakeholder engagement, and dependence on external policies are among factors that reduce resilience. The analysis concludes with recommendations for the SE pellet supply chain, and other biomass supply chains, to improve their resilience to future disturbances. When best practices are implemented, SE United States biomass offers opportunities to contribute to post-pandemic economic recovery while incentivizing better forest resource management.

Bioenergy supply chain optimization for addressing energy deficiency: A dynamic model for large-scale network designs

This study is motivated by the challenges faced by weak economies in addressing their energy deficiency. A gradual and strategic development of a sustainable bioenergy system is proposed as an opportunity to alleviate the connected energy and economy issues through a cleaner energy-mix. The optimization model presented in this study designs a bioenergy supply chain over multiple periods with the objective to minimize the energy gap under budget and biomass availability constraints. The dynamic elements of the model capture the interplay between population size, energy demand, biomass availability, energy consumption, and the Gross Domestic Product. A salient feature of the model is the consideration of an end-to-end bioenergy supply network from biomass reserves to the electricity demand zones. Besides facility setup and operating costs, the model considers biofuel transportation costs and transmission yield losses. The model is extensively analyzed through a case study. Practical insights about budget allocation and system expansion are discussed based on the case study. The analysis highlights that the cost for the optimum development of a bioenergy system can significantly vary over the planning horizon. The optimum setup starts as a highly centralized system and evolves into a largely decentralized system in which powerplants source biofuel and supply power locally.

A flexible biofuel and bioenergy production system with transportation disruption under a sustainable supply chain network

Increasing energy demand and the rising levels of greenhouse gas emissions have increased the necessity of bioenergy and sustainable energy generation. The sustainable bioenergy supply chain is the key to produce sustainable biofuel. This paper points to render an optimization model for planning a versatile and dependable biomass-to-bioenergy sustainable supply chain network in which crop residuals from several agricultural sectors, multi-transportation disruption modes, multi-biorefineries, multi-biogas plants, and multi-market centers are investigated for two bioenergy sources, namely biofuel and biogas. For this reason, supply chain cost optimization, emissions of feedstock transport, processing, and distributing end biofuel to respective markets are utilized. The multi-setup-multi-delivery (MSMD) concept is incorporated and lead time crashing cost is applied to minimize the lead time in this model. The carbon emission costs at all steps and the variable production rate for the production of bioenergy are included within the model. Finally, one small-scale data and another large-scale data of six agricultural areas, four biorefineries, four biogas plants, and six markets are considered to validate the model's usefulness. The model's goal is to show bioenergy's effect to make a sustainable supply chain of biofuel and biogas. The numerical results reveal that the cost of bioenergy production contributed 54.12%, a major extent of the whole cost in biorefinery and biogas plants. The transportation segment is the preeminent initiator of carbon emissions with 83.33% in the entire carbon emission within the whole supply chain. Although, transportation cost contributed 33.09% of the total cost, the multi setup-delivery-multi-delivery policy minimized the whole supply chain cost. The results provide a systematic guideline for developing a sustainable biofuel supply-chain by minimizing cost, various aspects of transportation logistics, and multi-model alternatives under reduced energy effects.

Forest Biomass and Bioenergy Supply Chain Resilience: A Systematic Literature Review on the Barriers and Enablers

This research aimed to systematically review the development studies pertaining to forest biomass and bioenergy supply chain resilience (SCR). In this regard, a mixed procedure was implemented in order to explore and analyze the relevant publications, and to answer the research questions. First, the databases and journals working on forest biomass and bioenergy supply chains (SCs) were identified based on the indices of the review process and the indices of the barriers and enablers. Next, data refinement was employed to filter the publications into four levels and determine the semifinal cases. Moreover, the references of the semifinal publications were tracked in order to achieve the final cases. Consequently, 88 papers were determined as the final cases through which the barriers and enablers were explored and analyzed. Furthermore, in order to meet the research gap in this area and prove the connections of those barriers and enablers with the resilience capability, their relationships with the main resilience factors were investigated. According to the assessment, the findings of this research on the definition, barriers and enablers of forest biomass and bioenergy SCR can be applied as a basis for the comprehension and optimization of the structure of SCs in the forest biomass and bioenergy industries.

The choice of cooperation mode in the bioenergy supply chain with random biomass feedstock yield

A successful cooperation mode between bioenergy producers and farmers can effectively promote the supply of biomass feedstock, which plays an important role in the bioenergy industry. In this study, we examine two prevailing cooperation modes in bioenergy supply chain, namely contract farming (CF) and land as shares (LS). This study assesses how each cooperation mode influences the planting acreage, the feedstock quality and the profits of supply chain participants. Under CF, the farmer and the bioenergy producer sign a contract in which the bioenergy producer purchases all feedstock produced by the farmer. Under LS, the farmer converts their land use rights into company shares, so that the bioenergy producer will share part of sales revenue with the farmer. First, we find that the optimal planting scale of biomass feedstock under LS is larger than that under CF when the bioenergy market size is sufficiently large. If the market size is relatively small, the supply quantity of biomass feedstock under LS depends on the marginal value of feedstock quality. Second, when the bioenergy market is sufficiently large, the farmer and the bioenergy producer under LS can achieve a win-win situation, which improves the reliability of the bioenergy supply chain. Third, we extend our model to the case where the government implements subsidies for biomass feedstock. We find that when the subsidy is high enough, the biomass feedstock quantity under LS will be larger. In addition, government subsidy does not necessarily improve the profit of all supply chain participants and excessive government subsidy may adversely affect the reliability of the bioenergy supply chain.

A System Dynamics Approach to Comparative Analysis of Biomass Supply Chain Coordination Strategies

Biomass is an abundant energy source, particularly in Canada, as an alternative or primary source for electricity generation. However, low economy of scale could cause a loss of efficiency for bioenergy adoption in small remote communities. In this sense, coordination among the players could promote the efficiency and profitability of bioenergy supply chains for these communities. There are different coordination strategies with varying impacts on supply chain players’ profit or cost. Therefore, analyzing and comparing them could provide insights on how to decide about the choice of coordination strategy. In doing so, this study considers the coordination strategies of quantity discounts and cost-sharing. The study adopts a system dynamics approach for simulating these coordination scenarios, obtaining their corresponding optimal supply chain decisions, followed by a comparative analysis. For a case study, the study considers multiple suppliers providing biomass for electricity generation in three communities in northern Quebec.

De-Risking Wood-Based Bioenergy Development in Remote and Indigenous Communities in Canada

Remote and Indigenous communities in Canada have a unique opportunity to mobilize the vast amount of wood-based biomass to meet their energy needs, while supporting a local economy, and reducing greenhouse gas (GHG) emissions. This study realized in collaboration with five remote and Indigenous communities across Canada investigates the main barriers and potential solutions to developing stable and sustainable wood-based bioenergy systems. Our results highlight that despite the differences in available biomass and geographical context, these communities face common policy, economic, operational, cultural, social, and environmental risks and barriers to developing bioenergy. The communities identified and ranked the biggest barriers as follows; the high initial investment of bioenergy projects, the logistical and operational challenges of developing a sustainable wood supply chain in remote locations, and the limited opportunities for community leadership of bioenergy projects. Environmental risks have been ranked as the least important by all the communities, except for the communities in Manitoba, which ranked it as the second most important risk. However, all the communities agreed that climate change is the main environmental driver disturbing the wood-based bioenergy supply chain. To de-risk the wood-based bioenergy system, we suggest that stable and sustainable supply chains can be implemented by restoring community-based resources management supported by local knowledge and workforce. Using local knowledge can also help reduce the impacts caused by biomass harvesting on the ecosystem and avoid competition with traditional land uses. Including positive externalities to cost benefit analysis, when comparing bioenergy systems to existing energy installation, will likely make bioenergy projects more attractive for the community financially. Alternatively, supporting co-learning between partners and among communities can improve knowledge and innovation sharing.

A mixed uncertainty approach to design a bioenergy network considering sustainability and efficiency measures

Environmental concerns such as water pollution and global warming resulted in considerable attention being paid to sustainable bioenergy. Due to the eco-friendly nature of sustainable bioenergy, we investigated an integrated bioenergy supply chain network design. In the proposed network, different energy crops, Azadirachta indica and Eruca sativa are taken into account, which leads to different sources for bioenergy production. Furthermore, to prevent further environmental pollution, the wastewater produced at biorefinery is used to generate bioelectricity. This paper addresses a sustainable-efficient bioenergy supply chain network design under uncertainty. A multi-period, multi-product mathematical model is proposed to minimize inefficiency, environmental impacts, and supply chain costs. Furthermore, to cope with uncertainty, a mixed robust-possibilistic programming approach is applied to the model. Finally, computational results based on Iran's real-world case demonstrate the improved performance of the designed bioenergy network by considering three measures of cost, efficiency, and environmental impacts.

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

AbstractBioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

Biomass supply chain coordination for remote communities: A game-theoretic modeling and analysis approach

Biomass, as one of the most available renewable energies, could reduce dependency on fossil fuels and the consequent environmental impacts. There is a need for biomass supply chain management, which is managing bioenergy production from harvesting feedstock to energy conversion facilities. In case of remote communities, bioenergy adoption requires dealing with dispersed geographies of suppliers and places of consumption with small scales of energy demand. As such, coordination plays a key role in increasing the efficiency of the biomass supply chain network through bundling of demand and thus improving the economy of scale. This paper employs a game-theoretic approach to formulate a coordinated biomass supply chain with three echelons including suppliers, hubs, and energy convertors. To investigate the strategic interactions of participants, three decision making structure scenarios have been considered under Stackelberg game providing insights into the impact of power distribution, the role of side payments in enforcing the flow of decisions, and the resulting efficiency and performance improvements. In doing so, a case study bioenergy supply chain for three northern Canadian communities is explored to demonstrate the application of the proposed formulation, solution methods, and the practicality and significance of the adopted approach and outcomes for remote communities.