Strategic Resource Planning for Sustainable Biogas Integration in Hybrid Renewable Energy Systems

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper deals with the logistic optimization of a small rural biogas power plant, intertwined with several other renewable energy sources, in the context of a quasi-autarky. Two scenarios of biomass delivery are studied, one with the other energy sources intermittently working, the other with these sources completely switched off.

The paper is quite clear and well written (no remark on English) and your intentions are explicit. In my opinion, it can be accepted after you take the following remarks into account:

1.- It is not clear what “t = 0” and “t = 1” represent in equations (2) and (3). t = 1 is the first days, which explains the “t-1” in the equations? Please specify.

2.- V storage is set to 400 m³ (line 390). However, it can be seen on figure 5 that there is a quite important gas loss (waste of valuable energy + greenhouse gas emission). This loss seems to be main drawback of this system: the low storage volume generates a high “tension” on the biogas feedstock when the demand is variable. Maybe the authors should consider increasing/optimizing V storage, in order to find a tradeoff between the addition construction cost and the saved gas? (loss of biomass + cost of greenhouse gas emissions) Maybe to be discussed in the “discussion” section?

3.- Simply a question: is it realistic, in eq. (14), to consider that the system is working constantly at its maximum capacity of 200 kW?

4.- Eq. (15) and (18): it sounds strange that “E electrical = itself divided by eta”. Except if eta=1, this makes no sense mathematically. These equations should be rewritten.

5.- Line 551: where does the demand value come from? (324 120 m³) It was not mentioned in section 2, while it is part of the scenario definition.

6.- Tables 1 and 2: the denominations in the column titles do not seem harmonized with the previous text. “Supply” (and “supply step” in line 559): what does it represent? The man.hours used to drive the trucks? It was not defined.

7.- Lines 660-661: “In Scenario 2 (…), the system generates and releases at a higher and more frequent rate than in Scenario 1”. Figure 6/2 suggests the opposite, scenario 2 releasing less biogas. This would sound logical, since the demand is more predictable in scenario 2. Or maybe you are talking about the time frequency of the release scenarios?

Author Response

We thank the reviewers and editors for our article's constructive comments and suggestions. They have helped us considerably improve the quality of the manuscript. We have carefully revised the article and have considered all the respected reviewers’ comments and suggestions. The revised document highlights the changes in yellow, and each point is specifically addressed below. We sincerely hope that the quality of this article is now suitable for publication in Applied Sciences.

Comment 1: It is not clear what “t = 0” and “t = 1” represent in equations (2) and (3). t = 1 is the first days, which explains the “t-1” in the equations? Please specify.

Response 1: Thank you for pointing out the confusion regarding the start time in our biogas production model. Based on your feedback, we have made the following clarifications:

Renamed Q₀​ to Q_max​: This new notation emphasizes that this value represents the maximum biogas production rate at the beginning of the process.

We now define t=1 as the first day of full-scale operation in the anaerobic digester. This removes any need to introduce a “day zero,” which can cause ambiguity in interpreting the model.

By adopting this approach, our model begins at t=1 (i.e., Day 1), and the production rate on this day is Q_max​. In such a setup, having t=0 or using Q₀​ would be confusing, as it does not correspond to an operational day within the current framework.

Please refer to the representation of Eq. (2) and Eq. (3) in the revised version to check the changes included (lines 307, 309, 314, Eq.2, Eq. 3).

Comment 2: V storage is set to 400 m³ (line 390). However, it can be seen on figure 5 that there is a quite important gas loss (waste of valuable energy + greenhouse gas emission). This loss seems to be main drawback of this system: the low storage volume generates a high “tension” on the biogas feedstock when the demand is variable. Maybe the authors should consider increasing/optimizing V storage, in order to find a tradeoff between the addition construction cost and the saved gas? (loss of biomass + cost of greenhouse gas emissions) Maybe to be discussed in the “discussion” section?

Response 2: Thank you for your constructive feedback regarding the storage volume (V_storage​) and its impact on biogas losses and greenhouse gas emissions. We acknowledge that a higher storage capacity could reduce gas venting and avoid the associated energy loss and emissions. However, the current study is based on the specific reactor, generator, and storage sizes established in a previous project (Reference 32). Our primary objective is to develop an optimized biomass logistics plan under the conditions defined by that reference design.

Nevertheless, we agree that determining the optimal storage size (as well as reactor and generator capacities) is crucial when designing a full-scale system from the ground up. In such a case, the precise sizing of all components should be based on the demand and the potential for other valuable outputs, ensuring a balance between construction costs, reduced gas venting, lower greenhouse gas emissions, and improved operational flexibility.

Please refer to the discussion section. We have added some sentences to accentuate this critical point. (lines 831-838).

Comment 3: Simply a question: is it realistic, in eq. (14), to consider that the system is working constantly at its maximum capacity of 200 kW?

Response 3: Thank you for your inquiry about whether it is realistic for the generator to operate constantly at its maximum capacity of 200 kW in Eq. (14). We acknowledge that this assumption represents an ideal or upper-bound scenario rather than a day-to-day operational reality. In practice, generator output typically varies due to several factors, including:

Fluctuations in Biomass Supply and Biogas Production: Biogas production may not be perfectly steady, and the generator may not always have sufficient fuel to run at 200 kW continuously.

Maintenance and Operational Downtime: Regular maintenance intervals, startup and shutdown cycles, and unplanned downtimes mean the generator is not always at peak output.

Partial-Load Efficiencies: Generators often operate at lower loads with reduced efficiency. The 200‑kW assumption omits these partial-load scenarios.

Integration with Other Energy Sources: Depending on solar or wind availability, the load on the biogas generator may be adjusted to complement other resources.

Despite these realities, we use the “maximum capacity for 12 hours per day” assumption to establish a clear energy requirements baseline and simplify the calculation. This approach provides an upper bound on the energy the biogas generator can supply during the specified period (when the solar PV system cannot operate).

In future work or more detailed modeling, it would be beneficial to account for variable generator output over time. Refining the results could involve using load profiles, partial-load efficiencies, or accurate biogas production data. Nevertheless, for the scope of this scenario, assuming continuous full-capacity operation allows us to highlight the maximum potential energy supply from biogas within 12 hours.

Comment 4: Eq. (15) and (18): it sounds strange that “E electrical = itself divided by eta”. Except if eta=1, this makes no sense mathematically. These equations should be rewritten.

Response 4: Thank you for pointing out the inconsistency in Equations (15) and (18) regarding the expression Eelec=Eelec/ηengine. We acknowledge that this was indeed an error in the manuscript. The correct equation should read: Ebiogas=Eelec/ηengine

where

• Ebiogas is the total energy required from biogas to produce the desired electrical output,
• Eelec is the final electrical energy requirement, and
• ηengine is the efficiency of the biogas engine/generator.

The mismatch occurred because different manuscript drafts contained slightly different formulations. During editing, the symbol on the left side of the equation was accidentally not updated when the denominator on the right side changed. We agree that the erroneous version made little mathematical sense unless ηengine​=1. We corrected this in the revised manuscript to reflect the physically meaningful relationship above.

Please refer to the Eq. (15) and (18). Now, they have been written correctly.

Comment 5: Line 551: where does the demand value come from? (324 120 m³) It was not mentioned in section 2, while it is part of the scenario definition.

Response 5: Thank you for pointing out that the annual demand value of 324,120 m³ was not referenced in Section 2. This figure originates from our daily biogas requirement, which we estimated in Equation (16) and then extrapolated to an annual basis. Specifically, Equation (16) calculates the daily volume of biogas needed (approximately 887 Nm³/day) to meet the system’s energy demand, and multiplying by 365 yields about 324,120 Nm³ per year:

Annual Biogas Demand = 887 Nm3/day * 365 day = 324,000 Nm3/year

Because Section 2 primarily focused on describing the system setup rather than on annualized demand scenarios, or as mentioned in Eq. (7), Q_Total, we did not explicitly reference the yearly requirement in that section. We agree that it would improve clarity to introduce or cross-reference the annual demand in the scenario definition. Consequently, we will revise the text so readers can see how the daily requirement translates into a yearly demand value.

Please refer to Sce.1 results clarification. Now, it is mentioned that both conditions have been met (lines 573-575).

Comment 6: Tables 1 and 2: the denominations in the column titles do not seem harmonized with the previous text. “Supply” (and “supply step” in line 559): what does it represent? The man.hours used to drive the trucks? It was not defined.

Response 6: Thank you for pointing out the lack of definition for the “Supply Step” column in Tables 1 and 2. The “Supply Step” represents the time interval (in hours) between successive biomass feedings into the reactor. For example, a value of 90 hours means that new biomass is supplied to the reactor every 90 hours. These intervals are carefully chosen to ensure the reactor’s retention time is respected and the system does not need to be emptied during the study period. They also help manage continuous production by controlling when and how much biomass is delivered.

Please refer to Tables 1 and 2 and their clarification (lines 582-583).

Comment 7: Lines 660-661: “In Scenario 2 (…), the system generates and releases at a higher and more frequent rate than in Scenario 1”. Figure 6/2 suggests the opposite, scenario 2 releasing less biogas. This would sound logical, since the demand is more predictable in scenario 2. Or maybe you are talking about the time frequency of the release scenarios?

Response 7:  You are absolutely correct. The original statement suggesting that Scenario 2 has a higher biogas release was inaccurate. We have removed that reference to “release” in the earlier paragraph, as it conflicted with the subsequent explanation (around line 703), which correctly indicates that Scenario 2 releases less biogas overall. The revision clarifies that the system in Scenario 2 uses biogas more frequently (due to 24-hour demand) rather than releasing more of it.

Please refer to line 696.

We appreciate your comment and believe these changes address your concerns, making the time referencing and notation more straightforward. If you have any further questions or suggestions, please let us know.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This paper is a valuable contribution to renewable energy systems, particularly in its innovative approach to integrating biogas with hybrid setups. While some areas could benefit from additional depth and broader applicability, it sets a solid foundation for future research and practical applications.

all remarks, questions, and recommendations are detailed in the attached file.

Comments for author File: Comments.pdf

Author Response

We genuinely thank the reviewers and editors for their insightful feedback and suggestions, which significantly strengthened our manuscript. We have thoroughly revised the paper, incorporating all points raised, and have highlighted the changes in yellow throughout the updated version. Each comment has been addressed in detail below. We sincerely hope this improved submission meets the standards for publication in Applied Sciences.

Remarks

Remark 1: The introduction effectively sets the context but could benefit from a more precise statement of the research gap.

Response 1: Thank you for your constructive feedback. The research gap lies in the lack of comprehensive models that integrate the optimization of biomass logistics and storage with the technical and economic aspects of energy systems, using real-world data to address practical constraints like fluctuating biomass supply, transportation challenges, and storage limitations.

The research gap identified in the introduction has been studied and addressed in the manuscript. The authors have developed a comprehensive model that integrates biomass logistics, storage management, and technical optimization within the context of a hybrid renewable energy system, using real-world data from Aras de los Olmos. This approach includes:

• Biomass Logistics Optimization: Incorporating transportation and storage considerations with actual costs, capacities, and demand data.
• Practical Constraints: Addressing real-life challenges like fluctuating biomass availability, generator outages, and logistical inefficiencies.
• Hybrid Energy System Integration: Designing a system that optimizes biogas production while balancing solar and wind energy intermittency.

The study's results and discussion demonstrate how the proposed solutions effectively enhance logistical efficiency, reduce biogas loss, and improve energy reliability, thereby bridging the gaps identified in prior research.

Please refer to the introduction (lines 134-175).

Remark 2: The equations and constraints are well-defined but assume fixed conditions for biomass characteristics. Variability in feedstock quality should be considered.

Response 2: Thank you for highlighting our model's assumption of fixed biomass characteristics. We agree that feedstock quality can fluctuate significantly due to moisture content, volatile solids composition, and seasonal substrate type variations. Indeed, feedstock diversity and quality improvement is a substantial area of research—spanning pre-treatment methods, co-digestion strategies, and other approaches that can enhance biogas yields and conversion efficiency as the different sectors of the M4B project try to improve it.

We utilized average (fixed) values for simplicity and model manageability in the current study. This choice is akin to the simplifying assumption we used for the maximum 200 kW generator capacity, an upper-bound constraint from a prior project design. While these assumptions help isolate the effects of supply logistics within a stable system configuration, they do not capture the full complexity introduced by real-world fluctuations in feedstock properties and operational conditions.

Moving forward, we plan to:

• Include statistical distributions for key parameters (e.g., volatile solids content, moisture level) to capture a range of possible feedstock qualities.
• Conduct scenario analyses where each scenario represents a “worst case,” “best case,” or “typical” feedstock condition.
• Investigate pre-treatment or co-digestion methods within the model to reflect potential improvements in feedstock quality.
• Perform sensitivity analyses on scheduling and storage strategies to evaluate how different feedstock conditions—and potential upgrading techniques—affect the system's overall performance and energy output.

By incorporating these elements, we aim to provide a more robust and realistic depiction of biogas systems' behavior under varying biomass characteristics. We appreciate your suggestion and will consider these approaches in future work to more accurately reflect feedstock quality's inherent variability and impact on system operation.

Remark 3: Results are detailed, especially regarding cost breakdowns and biogas release rates. However, visualizations like Figure 5 could better emphasize system efficiency differences between scenarios.

Response 3: We appreciate your suggestion to add more visual elements (e.g., additional figures) to emphasize system efficiency differences between scenarios. However, due to some limitations, we cannot add new statistics at this stage. Instead, we have added further descriptive paragraphs in the Results and Discussion sections to provide deeper insight into the cost breakdowns, release rates, and overall system performance. These textual enhancements clarify the differences in efficiency and operation across scenarios, serving the same purpose you recommended—namely, to highlight how each scenario behaves in terms of cost, energy supply, and biogas release.

Please refer to the results (lines 675-685).

Remark 4: The discussion touches on seasonal and logistical challenges but does not address potential technological interventions to mitigate these issues.

Response 4: Thank you for your valuable feedback. You are correct that the discussion highlights seasonal and logistical challenges but does not sufficiently delve into potential technological interventions to mitigate these issues. We appreciate the opportunity to address this aspect more comprehensively.

In response, we have added a section to the discussion where we explore technological interventions that could be integrated into the system to address these challenges, including:

• Real-time Monitoring and Predictive Analytics: Implementing IoT sensors and machine learning algorithms for real-time monitoring of biomass supply and demand fluctuations. These technologies can provide actionable insights for optimizing storage use and scheduling transportation efficiently.
• Advanced Pre-Treatment Techniques: Pre-treatment technologies, such as enzymatic or thermal methods, are utilized to enhance the degradability of biomass and reduce the retention time required in the reactor. This could mitigate the effects of seasonal variations in biomass quality.
• Adaptive Energy Management Systems: Incorporating adaptive control systems that dynamically adjust biogas production based on real-time data from other renewable energy sources (e.g., solar and wind) and demand patterns. This integration would ensure a seamless energy supply during seasonal peaks and troughs.
• Logistics Optimization Tools: Using geographic information systems (GIS) and optimization algorithms to plan transportation routes and schedules, minimizing costs and emissions while ensuring timely biomass delivery.

We have updated the discussion section to include these technological approaches and their potential to enhance system efficiency and resilience. We hope this additional information addresses your concern and strengthens the manuscript.

Please refer to the discussion (lines 744-759).

Remark 5: The conclusion summarizes findings but could be more assertive in highlighting contributions to policy-making and future research directions.

Response 5: Thank you for your constructive feedback. We agree that the conclusion could benefit from a stronger emphasis on the manuscript’s contributions to policy-making and future research directions. In response, we have revised the conclusion to address these aspects explicitly.

Specifically, we have added the following points:

1. Policy Contributions: The study’s findings demonstrate the critical role of integrating logistical optimization with energy production in rural hybrid renewable systems. This insight supports policymakers in designing frameworks that promote the sustainable development of energy infrastructure, particularly in regions with significant biomass resources. Policies could subsidize advanced logistical technologies and encourage the co-development of renewable energy systems with local agricultural sectors.
2. Future Research Directions: We suggest further exploring adaptive control systems that utilize real-time data to enhance biogas production and storage management. Additionally, future studies could investigate the scalability of our model in more extensive or dynamic energy networks, as well as its integration with other emerging technologies like blockchain for supply chain transparency.

These additions highlight the broader implications of our research, aligning it with practical applications and paving the way for subsequent studies. We hope these revisions address your concern and clarify the manuscript’s contributions.

Please refer to the conclusion (lines 898-911).

Questions

Question 1: How does this research compare to recent advancements in hybrid systems, particularly regarding scalability and adaptability?

Response 1: Thank you for your insightful question. We recognize the importance of comparing our research with recent advancements in hybrid systems, particularly regarding scalability and adaptability. While a thorough review of the literature revealed no directly comparable studies using real-world data like ours, this distinction highlights the novelty and significance of our work.

Specifically, our research addresses critical gaps in the field by:

1. Utilizing Real Data: Unlike many studies that rely on simulations or controlled environments, our work is grounded in actual Aras de los Olmos data. This allows for a more accurate representation of the practical challenges and solutions associated with hybrid renewable systems.
2. Solving Identified Research Gaps: Our study bridges gaps by integrating biomass logistics and storage optimization into hybrid energy systems' technical and economic modeling. This holistic approach enhances scalability by providing a framework that can adapt to different regional conditions and biomass availability scenarios.
3. Scalability and Adaptability: While our study focuses on a specific rural setting, the methodology developed is highly adaptable. By incorporating flexible constraints, such as biomass supply limits, reactor capacity, and logistical costs, the model can be adjusted to accommodate varying scales and conditions in other regions.

While direct comparisons with recent advancements were not feasible due to the lack of studies with similar real-world data, we believe our work sets a benchmark for future research. It provides a scalable and adaptable framework that other researchers and practitioners can build upon. We hope this response clarifies the unique contributions of our study to the field.

Question 2: Why was the biomass-to-biogas conversion efficiency (75%) fixed, and how does this assumption affect the outcomes under varying feedstock conditions?

Response 2: Thank you for this insightful question. We fixed the biomass-to-biogas conversion efficiency at 75% based on commonly cited industry data and reliable literature covering anaerobic digestion under stable operating conditions. While this assumption simplifies the model and allows for more direct comparisons across scenarios, it inevitably introduces certain limitations. In practice, feedstock quality and composition can vary substantially—for example, in moisture content, carbon-to-nitrogen ratio, or the presence of inhibitory substances. Such variations could either raise or lower the actual conversion efficiency. Consequently, if real-world feedstock properties deviate from our baseline assumption, the overall biogas yield, system performance, and cost-effectiveness could differ from the modeled outcomes. In future work, incorporating dynamic efficiency values or feedstock-specific parameters would capture these variations more accurately and provide an even more robust representation of operational conditions.

Question 3: What factors could influence the higher release rates in larger biomass inputs (as observed in Case 4 of Scenario 1)?

Response 3: Thank you for your question. Several key factors can lead to higher biogas release rates when larger biomass inputs are used (as in Case 4 of Scenario 1):

1. Storage Limitations – Restricted or rigid storage capacity may force excess gas to be vented whenever production temporarily exceeds the tank’s threshold.
2. Digestive Dynamics – Higher biomass loading can accelerate digestion, potentially boosting gas output beyond what can be utilized or stored at any given time.
3. Supply Timing and Intervals – Large, infrequent feed inputs often create production spikes that surpass short-term demand, leading to surplus gas release.
4. Energy Demand Alignment – If demand does not match the elevated production levels, surplus gas accumulates quickly and must be released once storage is full.
5. Transportation and Logistical Constraints – Delays or rigid delivery schedules can generate short-term oversupply, making it challenging to distribute biomass feeding evenly and avoid production surges.

Question 4: Could integrating advanced storage solutions or predictive analytics further optimize the system's reliability and cost-effectiveness?

Response 4: Thank you for this excellent suggestion. Integrating advanced storage solutions—such as high-capacity or flexible on-site storage tanks—or implementing predictive analytics based on real-time monitoring could enhance reliability and cost-effectiveness. For instance, predictive models and IoT-based sensors can anticipate fluctuations in biogas production and demand, allowing operators to optimize when and how much to store or release, thus minimizing waste and reducing greenhouse gas emissions. We also discuss the importance of storage optimization in the revised Discussion section, emphasizing that tailoring storage capacity to variable demand patterns and upgrading logistical and technological resources can significantly improve overall system performance.

Question 5: How can this model be adapted for larger communities or regions with more diverse energy demands?

Response 5: Thank you for your question. The core modeling framework can extend to larger communities or regions with diverse energy demands. While the underlying methodology—integrating biomass logistics, storage management, and energy system optimization—remains the same, specific parameters would require adjustment. For instance, larger-scale scenarios may involve multiple feedstock sources, more complex transportation networks, and differing energy consumption profiles. These factors could affect the logistics (e.g., additional routes, storage facilities) and the optimization algorithm (e.g., computational complexity, variable constraints). Nevertheless, the fundamental approach of aligning supply, demand, and storage capacity in a dynamic, cost-effective manner still applies, making the model suitable for adaptation to broader contexts.

Recommendations

Recommendation 1: Include a section or paragraph discussing the scalability and adaptability, expanding on how the proposed model can be adapted or scaled for different regions and larger communities.

Response 1: Thank you for recommending a dedicated section on scalability and adaptability. While we briefly addressed these topics in the Discussion, we have now expanded that part of the text to explain how the proposed model can be adapted to different regions and scaled for larger communities. Specifically, we outline how modifying reactor and storage capacities, optimizing transportation routes, and integrating additional renewables can ensure the model remains effective across varying geographical and operational contexts. This additional discussion also highlights potential computational challenges and considerations when extending the model to larger or more dynamic systems, reinforcing its practical relevance and broad applicability.

Please refer to the discussion (lines 767-785).

Recommendation 2: Include a sensitivity analysis of biomass quality and other input variables to enhance robustness to test the impact of variable changes on outcomes.

Response 2: Thank you for this valuable recommendation. While our study relies heavily on real-world data from an actual project—thus limiting our ability to introduce extensive variations in specific parameters—we acknowledge the importance of evaluating how changes in biomass quality and other key input variables could affect the system’s outcomes. In the revised discussion, we have highlighted factors such as seasonal fluctuations in feedstock composition, moisture content differences, and generator efficiency variations that could be explored in a more comprehensive sensitivity analysis. Considering these factors in future work would enhance the model’s robustness and practical applicability, especially for scenarios with different local conditions.

Please refer to the discussion (lines 815-830).

Recommendation 3: Provide insights into policy recommendations and integration with existing renewable energy programs.

Response 3: Thank you for this recommendation. We have expanded the Discussion to include a dedicated subsection on policy implications, highlighting how the proposed biomass logistics and hybrid energy framework could align with existing renewable energy programs. This section outlines how incentives, subsidies, and collaborative initiatives between local agriculture and energy sectors could encourage more widespread adoption of biogas technology. Integrating our model with policy and regulatory support can accelerate the transition toward sustainable energy systems, especially in areas with significant biomass resources.

Please refer to the discussion (lines 786-797).

Recommendation 4: Enhance the discussion of policy relevance and economic implications to appeal to a broader audience.

Thank you for this insightful recommendation. We have strengthened our discussion by emphasizing policy relevance and economic implications, ensuring that our findings resonate with a broader audience—including policymakers, industry stakeholders, and researchers. Specifically, we have added details on financial viability, subsidy programs, and regulatory frameworks that could support biogas integration and encourage its adoption on a broader scale. This expanded coverage clarifies the potential socioeconomic benefits of our approach and its alignment with long-term energy policies.

Please refer to the discussion (lines 798-808).

Recommendation 5: Refine visuals to make the data easier to interpret and highlight critical insights.

Response 5: Thank you for your valuable suggestion to refine our figures for better clarity and emphasis on the key findings. While we fully acknowledge the importance of clear and informative visuals, we are currently constrained in layout and the number of figures we can incorporate into the manuscript. To address this recommendation within our limitations, we have enhanced the accompanying descriptions and captions so readers can interpret the presented data more efficiently. By providing additional context and highlighting critical insights in the text, we aim to convey the same clarity and depth you have proposed without substantially revising or adding new figures. Additionally, please note that the final version of the figures, with the best possible resolution, will be submitted to the editor in the final version of the article once the editors accept it.

We sincerely appreciate your comments and believe our revisions effectively address your concerns, providing greater clarity and consistency in time referencing and notation. Should you have any additional questions or suggestions, please don’t hesitate to reach out.

 

Author Response File: Author Response.pdf