Energy Management System for a Residential Positive Energy District Based on Fuzzy Logic Approach (RESTORATIVE)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. while the introduction provides extensive background on energy consumption, renewable energy goals, and Positive Energy Districts (PEDs), it lacks a clear articulation of the specific research gap that this study aims to address. The transition from general energy issues to the specific focus on the RESTORATIVE system is not well-defined, leaving the reader uncertain about the unique contribution of this research. Explicitly stating the research gap and how the proposed study aims to fill this gap would improve the clarity and purpose of the introduction.

2. The introduction spends considerable effort detailing broad initiatives and general trends in energy management, such as European energy roadmaps and the role of PEDs, but does not adequately focus on the specifics of the RESTORATIVE system and its novel contributions until much later. This reduces the impact of the introduction and may cause readers to lose interest before reaching the key points. A more focused introduction that briefly presents the broader context before quickly zeroing in on the specific objectives, methods, and expected contributions of the study would be more effective.

3. I would suggest two articles relating to energy management that authors could also read and could be included in the literature https://doi.org/10.1016/j.energy.2022.124156  and   https://doi.org/10.3390/en16124835

4. The abstract briefly mentions fluctuations in electricity prices and extreme changes in energy carrier prices but does not delve into the economic feasibility or potential cost implications of implementing PEDs. Including some discussion on economic aspects would add depth.

5. The results of 75.6% self-sufficiency and 76.8% self-consumption are presented without context. It would be useful to compare these figures to benchmarks or targets to give readers a sense of the performance relative to expectations or goals. I would suggest that the comparison could be presented in tabular form.

Author Response

Reviewer 1:

Comments and Suggestions for Authors

 

1. While the introduction provides extensive background on energy consumption, renewable energy goals, and Positive Energy Districts (PEDs), it lacks a clear articulation of the specific research gap that this study aims to address. The transition from general energy issues to the specific focus on the RESTORATIVE system is not well-defined, leaving the reader uncertain about the unique contribution of this research. Explicitly stating the research gap and how the proposed study aims to fill this gap would improve the clarity and purpose of the introduction.

RESP:

Dear reviewer, the authors thank you for your recommendation. In the revised manuscript we have addressed this concern, building a streamline to easily identify the former concerns. We really hope these changes meet your expectations.

Energy is one of the key resources for human society [1], ensuring their comfort standards are met [2]. The traditional development of energy supplies has resulted in the massive use of fossil fuels, which has several harmful externalities [3]. In this regard, the energy roadmap for Europe aims at producing 75% of gross final energy consumption and 97% of the electricity consumption through Renewable Energy Sources (RES) by 2050 [4], [5].

As evidenced by, the International Energy Agency (IEA) stresses transport, industry, and buildings as key areas for reducing carbon emissions by 2050 [6]. Buildings are expected to reduce emissions by 40%, while transport applications (with greater technological constraints) by 10% [7].

With this in mind, European countries are developing a new concept: Positive Energy District (PED). The Joint Programming Initiative Urban Europe (JPI) defines PEDs as areas or groups of interconnected buildings [8], [9] that produce more energy on-site than is needed to meet their demand. PEDs may or may not comprise energy storage systems (ESS) and thermal and/or electric renewable systems. Currently, more than 100 cities in Europe are incorporating PED development into their decarbonization roadmaps [10].

Despite PEDs' advancements, there is a significant research gap in the effective integration of energy management systems (EMS) that optimise self-sufficiency and participation in flexibility markets. Existing studies have not comprehensively addressed the simultaneous management of e-mobility, home appliances, thermal demands (cooling, heating, and domestic hot water), and smart pole demands using a heuristic strategy considering battery State of Charge and electricity pricing conditions.

This research’s underlying motivation focuses on improving resilience values and enabling PEDs to participate in flexibility markets. By optimising self-consumption systems, the aim is to reduce the losses inherent in energy transport and decrease the need for investments in distribution network infrastructure. This approach increases energy efficiency and aligns sustainability objectives with economic viability.

Furthermore, enabling developing countries to participate in flexibility markets has the potential to diminish operating costs and generate new sources of revenue, which represents a significant step towards energy self-sufficiency and resilience of urban communities. These developments are fundamental to the transition towards a more sustainable and decentralised energy model, where consumers play an active role in the management and commercialisation of the energy they produce.

Regardless, the most common renewable energy source in PEDs is geothermal energy, which powers district heating [8], [11]. Nevertheless, the electrification of the transport sector would necessitate the massive deployment of batteries within the PED, as electric vehicles (EVs) can be considered mobile batteries. It is well-known that the most expensive item in EVs is its energy storage system (ESS), although technological progress is reducing its cost[12].

In this regard, this study proposes a communitarian on-site energy management system (ESS) for a residential PED based on the fuzzy logic approach. This setup relies on an ESS for a small residential PED, where electricity is generated through renewable systems. The efficacy of a heuristic approach for the ESS is tested.

This study aims to fill this gap by proposing a communitarian on-site energy management system (ESS) for a residential PED based on the fuzzy logic approach. This setup relies on an ESS for a small residential PED, where electricity is generated through renewable systems. The efficacy of a heuristic approach for the ESS is tested. This system, called RESTORATIVE, aims to guarantee a high level of self-sufficiency and self-consumption that can dramatically reduce the electricity bill for PED residents.

In addition, while technically viable, RESTORATIVE does not seek total energy independence due to the high investments required [13]. Instead, this rule-based Energy management system promotes the local use of electricity while optimising batteries’ State of Charge (SOC) so that electricity imports are synchronised (as much as possible) with low-cost tariff periods.

This study reveals that, optimally managed, it can ensure that no renewable electricity is wasted while using the utility grid for backup purposes. This would make the local ESS the main driver for cost-effectively meeting the residents’ energy demands. Similar set-ups have been deployed in places such as Sonderborg, Denmark, where a residential electricity storage system has been implemented coupled with photovoltaic (PV) systems [10] to provide some flexibility from the utility grid.

As a result, the unique contribution of this study lies in its holistic integration of local energy systems to optimise energy balance, profile smoothing, and electricity cost through a heuristic approach. Furthermore, it carries out a long-term evaluation considering climate change to evaluate the stability of the system under significant climate fluctuations, which has not been previously explored in the literature. This approach addresses the identified research gap and promotes the development of PEDs.

To sum up, the authors believe the current study presents relevant novelties. The study focuses on advancing the current state of the art in medium-sized PEDs, fully integrated into electric systems to attain comfort systems in buildings, and also include electromobility demands, which is a concern for energy producers and grid operators due to its future impact on the grid. The approach optimises local energy systems jointly for energy balance, energy profile smoothing, and electricity cost by using a heuristic approach. Furthermore, a long-term assessment in light of climate change is performed to evaluate system stability under important climatic fluctuations.

 

1. The introduction spends considerable effort detailing broad initiatives and general trends in energy management, such as European energy roadmaps and the role of PEDs, but does not adequately focus on the specifics of the RESTORATIVE system and its novel contributions until much later. This reduces the impact of the introduction and may cause readers to lose interest before reaching the key points. A more focused introduction that briefly presents the broader context before quickly zeroing in on the specific objectives, methods, and expected contributions of the study would be more effective.

RESP:

Dear reviewer, the authors thank you for your recommendation. In the revised manuscript we have enhanced the introduction, these enhancements entail making more concise, remarking objectives, methods, and expected contributions of the study. However, the methods are more detailed in the section disposed for this end, section 3, Methodology.  We kindly invite you to read this. We really hope these changes meet your expectations

F.i.

 … the aim is to reduce the losses inherent in energy …

This study aims to fill this gap by proposing a communitarian on-site energy management system

This system, called RESTORATIVE, aims to guarantee a high level of self-sufficiency and self-consumption that can dramatically reduce the electricity bill for PED residents.

 

1. I would suggest two articles relating to energy management that authors could also read and could be included in the literature https://doi.org/10.1016/j.energy.2022.124156  and   https://doi.org/10.3390/en16124835

RESP:

Dear reviewer, the authors thank you for your recommendation. The literature you had suggested to be included have been considered, and these two have been included in the paper. These now are part of the new section Related Work (section 2) In the revised manuscript.

 

1. The abstract briefly mentions fluctuations in electricity prices and extreme changes in energy carrier prices but does not delve into the economic feasibility or potential cost implications of implementing PEDs. Including some discussion on economic aspects would add depth.

RESP:

Dear reviewer, the authors thank you for your recommendation. In the revised manuscript we have addressed this concern. We really hope these changes meet your expectations.

Abstract: There is a clear European Strategy to transition by 2050 from a fossil fuel-based economy to a completely new system based on renewable energy resources, with electricity as a main energy carrier. Positive Energy Districts (PEDs) are urban areas that produce at least as much energy as yearly consumption. To meet this objective, they must incorporate distributed generation based on renewable systems within their boundaries. This article considers the fluctuations in electricity prices and local renewable availability and develops a PED model with a centralised energy storage system focused on electricity self-sufficiency and self-consumption. We present a fuzzy logic-based energy management system which optimises the state of charge of the energy storage solution considering the local electricity production and loads along with the contracted electric tariff. The methodology is tested in a PED comprising 360 households in Bilbao (a city in the North of Spain), setting various scenarios, including changes in the size of the electric storage, long-term climate change effects, and extreme changes in the price of energy carriers. The study revealed that the assessed PED could reach up to 75.6% of self-sufficiency and 76.8% of self-consumption, with climate change expected to improve these values. On economic aspects, the return over investment of the proposal ranges from 6 up to 12 years depending on the configuration choice. Also, the case that boosts the economic viability is tight to non-business as usual (BaU) whichever event spiked up the prices or climate change conditions shortens the economic variables. The average bill is around 12.89 €/month per house for scenario BaU, meanwhile, a catastrophic event increases the bill by as much as 76,7 %. On the other way round, the climate crisis events impact energy generation, strengthening this and as a consequence slightly reducing the bill up to 11.47 €/month.

 

1. The results of 75.6% self-sufficiency and 76.8% self-consumption are presented without context. It would be useful to compare these figures to benchmarks or targets to give readers a sense of the performance relative to expectations or goals. I would suggest that the comparison could be presented in tabular form.

RESP:

Dear reviewer, the authors thank you for your recommendation. Unfortunately, we did find a very few information for PEDs’, however, we conducted one table with studies with some potential of similarity. We really hope these changes meet your expectations.

 

Table 1. Benchmark of different studies that perform a SS and/or SC analysis.

Publication title	Self-sufficiency	Self-consumption	Method or technique	Reference
Energy communities to advance towards positive energy districts as a strategy towards decarbonisation	NA	77% foresee	Not Provided	[32]
Energy Self-Sufficiency Urban Module (ESSUM): GIS-LCA-based multi-criteria methodology to analyze the urban potential of solar energy generation and its environmental implications	70%	NA	GIS-LCA-based multi-criteria methodology	[33]
Towards energy self-consumption and self-sufficiency in urban energy communities	53% -- 67%	52%	A metaheuristic   PSO technique	[34]
A techno-economic analysis of an optimal self-sufficient district	76%	NA	Two methods, a rule-based control method (EnFloMatch tool) and a deterministic optimization method	[35]
Agent based modelling of a local energy market: A study of the economic interactions between autonomous pv owners within a micro-grid	24% (no-ESS)	NA	Agent-based modelling	[36]
Potential for exploiting the synergies between buildings through DSMapproaches. Case study: La Graciosa Island	64%	42%	Rule-based control strategy	[37]
Optimal Simulation of Three Peer to Peer (P2P) Business Models for Individual PV Prosumers in a Local Electricity Market Using Agent-Based Modelling	28.4%	85%	Agent-based modelling	[38]
Combining Power-to-Heat and Power-to-Vehicle strategies to provide system flexibility in smart urban energy districts	30%--52%	64%--78%	An economic based approach using MaT4EnergyPLAN	[39]
Renewable Energy Communities as Modes of Collective Prosumership: A Multi-Disciplinary Assessment Part II—Case Study	35%	61%	Not Provided	[40]
Energy Community Measures Evaluation via Differential Evolution Optimization	29.74%	NA	Pareto-optimal solutions	[41]
Evaluation of load matching indicators in residential PV systems-the case of Cyprus	31.17%	48.17%	Generation-to-demand ratio (GTDR)	[15]
Energetic performance of a smart neighborhood ofexisting multifamily buildings with heat pumps, PVand CHP focusing on energy balance and CO2emissions	55%	52%	Not Provided	[42]

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper is well written and structured, in addition to covering an interesting topic. Its standout feature is the meticulous modeling of the PED, supported by thorough references. However, I didn't observe any significant contributions pertaining to the Management Strategy. Given this observation, I recommend a major revision of the paper, incorporating the raised comments. This step is essential for enhancing the paper's quality and comprehensiveness, thus making it suitable for publication.

 

1 - What are the distinct advantages of the proposed battery management strategy compared to other strategies documented in the literature?

 

2 - Many articles consider using the electric vehicle to realize vehicle-to-grid (V2G) to promote even more flexibility for the Management Strategy. Integrating this aspect could elevate both the novelty and quality of the paper.

 

3 - Update the references with up-to-date work.

 

4 - In my view, the justification for the research gap requires more robust arguments to strengthen its validity.

Author Response

Reviewer 2:

 

Comments and Suggestions for Authors

 

The paper is well written and structured, in addition to covering an interesting topic. Its standout feature is the meticulous modeling of the PED, supported by thorough references. However, I didn't observe any significant contributions pertaining to the Management Strategy. Given this observation, I recommend a major revision of the paper, incorporating the raised comments. This step is essential for enhancing the paper's quality and comprehensiveness, thus making it suitable for publication.

 

1 - What are the distinct advantages of the proposed battery management strategy compared to other strategies documented in the literature?

RESP:

Dear reviewer, the authors thank you for your question. The literature has been revised and as a consequence, We can answer your question as follows, the advantages are:

1. Heuristic and Fuzzy Logic-Based Approach: The proposed strategy uses a heuristic approach combined with fuzzy logic (FL) to manage the energy surplus efficiently. This method is designed to optimise the renewable energy systems (RES), economic operations, and battery health of an energy storage system (ESS) within the Positive Energy District (PED) framework. This approach allows for better management of energy resources by taking into account various factors such as state of charge (SOC), time of day, and power error (PE)​.
2. Enhanced Self-Sufficiency and Self-Consumption: The strategy aims to maximise self-sufficiency (SS) and self-consumption (SC) within the PED. By optimising the local use of electricity and synchronising imports with low-cost tariff periods, the strategy helps reduce the reliance on the utility grid and lowers electricity bills for residents​​.
3. Economic and Environmental Considerations: The proposed strategy includes considerations for economic aspects such as electricity tariffs and the impact of crises (f.i.., wars or lockdowns) on energy prices. This ensures that the system remains cost-effective even under fluctuating economic conditions. Additionally, the strategy evaluates the potential impact of climate change on the PED, ensuring long-term system stability and sustainability.
4. Integration of Mobility and Thermal Demands: Unlike other strategies that may focus solely on electrical demands, the proposed strategy integrates mobility, home appliances, thermal demands (heating, cooling, domestic hot water), and smart poles. This comprehensive approach ensures a holistic management of all energy demands within the PED​​.
5. Community-Based Energy Storage System: The strategy employs a community-based ESS to support energy demands, smooth peak loads, and prevent potential curtailment of RES availability. This communal approach allows for a more efficient and resilient energy management system compared to individual household systems​.
6. Technical and Operational Robustness: The strategy's robustness is enhanced through continuous monitoring and advanced control mechanisms, ensuring optimal performance under varying load, weather, and climate conditions. This adaptability is critical for maintaining system efficiency and reliability over time​​.

These advantages demonstrate that the proposed energy management strategy not only optimises energy use and reduces costs but also enhances the overall sustainability and resilience of the PED.

In addition, we build a table in order to benchmark our study to others in literature.

Table 1. Benchmark of different studies that perform a SS and/or SC analysis.

Publication title	Self-sufficiency	Self-consumption	Method or technique	Reference
Energy communities to advance towards positive energy districts as a strategy towards decarbonisation	NA	77% foresee	Not Provided	[32]
Energy Self-Sufficiency Urban Module (ESSUM): GIS-LCA-based multi-criteria methodology to analyze the urban potential of solar energy generation and its environmental implications	70%	NA	GIS-LCA-based multi-criteria methodology	[33]
Towards energy self-consumption and self-sufficiency in urban energy communities	53% -- 67%	52%	A metaheuristic   PSO technique	[34]
A techno-economic analysis of an optimal self-sufficient district	76%	NA	Two methods, a rule-based control method (EnFloMatch tool) and a deterministic optimization method	[35]
Agent based modelling of a local energy market: A study of the economic interactions between autonomous pv owners within a micro-grid	24% (no-ESS)	NA	Agent-based modelling	[36]
Potential for exploiting the synergies between buildings through DS Mapproaches. Case study: La Graciosa Island	64%	42%	Rule-based control strategy	[37]
Optimal Simulation of Three Peer to Peer (P2P) Business Models for Individual PV Prosumers in a Local Electricity Market Using Agent-Based Modelling	28.4%	85%	Agent-based modelling	[38]
Combining Power-to-Heat and Power-to-Vehicle strategies to provide system flexibility in smart urban energy districts	30%--52%	64%--78%	An economic based approach using MaT4EnergyPLAN	[39]
Renewable Energy Communities as Modes of Collective Prosumership: A Multi-Disciplinary Assessment Part II—Case Study	35%	61%	Not Provided	[40]
Energy Community Measures Evaluation via Differential Evolution Optimization	29.74%	NA	Pareto-optimal solutions	[41]
Evaluation of load matching indicators in residential PV systems-the case of Cyprus	31.17%	48.17%	Generation-to-demand ratio (GTDR)	[15]
Energetic performance of a smart neighborhood of existing multifamily buildings with heat pumps, PV and CHP focusing on energy balance and CO2emissions	55%	52%	Not Provided	[42]

 

We really hope the answer meets and clarifies your doubts about the EMS implemented in this analysis.

 

2 - Many articles consider using the electric vehicle to realize vehicle-to-grid (V2G) to promote even more flexibility for the Management Strategy. Integrating this aspect could elevate both the novelty and quality of the paper.

            RESP:

Dear reviewer, the authors thank you for your observation. However, this inclusion is not possible to be included now because it represents to perform new experiments that cannot be done at this moment. Additionally, we think it worth highlighting that V2G was not included in this experimentation set-up since these models are thought to be very similar to what is going to be the final PED built. We really hope you can understand our decision to not include the V2G approach.

 

On this topic, the definition of mobility demands is based on the arbitrary requirement of 100 EVs throughout the year. Spain has one of the lowest mobility electrification rates in the European Union, with only around 5% of the fleet being electric, followed only by Italy, Poland, and the Czech Republic [28]. However, Spain is promoting politics to boost EV adoption. We did not include Vehicle-to-Grid (V2G) in this set-up since we do not have data, and also it is not part of the envisions of the project.

 

3 - Update the references with up-to-date work.

RESP:

Dear reviewer, the authors thank you for your recommendation. The literature has been revised as well as the rest of the references and as a consequence, some new (up to date) references have been included in the new version of the paper. We really hope these inclusions meet your expectations.

 

4 - In my view, the justification for the research gap requires more robust arguments to strengthen its validity.

RESP:

Dear reviewer, the authors thank you for your recommendation. In the revised manuscript we have addressed this concern, building a streamline to easily identify your former concern. We have reorganised and rewritten part of the introduction in order to clearly show which is the gap we attempt to fulfil.  We really hope these changes meet your expectations.

Energy is one of the key resources for human society [1], ensuring their comfort standards are met [2]. The traditional development of energy supplies has resulted in the massive use of fossil fuels, which has several harmful externalities [3]. In this regard, the energy roadmap for Europe aims at producing 75% of gross final energy consumption and 97% of the electricity consumption through Renewable Energy Sources (RES) by 2050 [4], [5].

As evidenced by, the International Energy Agency (IEA) stresses transport, industry, and buildings as key areas for reducing carbon emissions by 2050 [6]. Buildings are expected to reduce emissions by 40%, while transport applications (with greater technological constraints) by 10% [7].

With this in mind, European countries are developing a new concept: Positive Energy District (PED). The Joint Programming Initiative Urban Europe (JPI) defines PEDs as areas or groups of interconnected buildings [8], [9] that produce more energy on-site than is needed to meet their demand. PEDs may or may not comprise energy storage systems (ESS) and thermal and/or electric renewable systems. Currently, more than 100 cities in Europe are incorporating PED development into their decarbonization roadmaps [10].

Despite PEDs' advancements, there is a significant research gap in the effective integration of energy management systems (EMS) that optimise self-sufficiency and participation in flexibility markets. Existing studies have not comprehensively addressed the simultaneous management of e-mobility, home appliances, thermal demands (cooling, heating, and domestic hot water), and smart pole demands using a heuristic strategy considering battery State of Charge and electricity pricing conditions.

This research’s underlying motivation focuses on improving resilience values and enabling PEDs to participate in flexibility markets. By optimising self-consumption systems, the aim is to reduce the losses inherent in energy transport and decrease the need for investments in distribution network infrastructure. This approach increases energy efficiency and aligns sustainability objectives with economic viability.

Furthermore, enabling developing countries to participate in flexibility markets has the potential to diminish operating costs and generate new sources of revenue, which represents a significant step towards energy self-sufficiency and resilience of urban communities. These developments are fundamental to the transition towards a more sustainable and decentralised energy model, where consumers play an active role in the management and commercialisation of the energy they produce.

Regardless, the most common renewable energy source in PEDs is geothermal energy, which powers district heating [8], [11]. Nevertheless, the electrification of the transport sector would necessitate the massive deployment of batteries within the PED, as electric vehicles (EVs) can be considered mobile batteries. It is well-known that the most expensive item in EVs is its energy storage system (ESS), although technological progress is reducing its cost[12].

In this regard, this study proposes a communitarian on-site energy management system (ESS) for a residential PED based on the fuzzy logic approach. This setup relies on an ESS for a small residential PED, where electricity is generated through renewable systems. The efficacy of a heuristic approach for the ESS is tested.

This study aims to fill this gap by proposing a communitarian on-site energy management system (ESS) for a residential PED based on the fuzzy logic approach. This setup relies on an ESS for a small residential PED, where electricity is generated through renewable systems. The efficacy of a heuristic approach for the ESS is tested. This system, called RESTORATIVE, aims to guarantee a high level of self-sufficiency and self-consumption that can dramatically reduce the electricity bill for PED residents.

In addition, while technically viable, RESTORATIVE does not seek total energy independence due to the high investments required [13]. Instead, this rule-based Energy management system promotes the local use of electricity while optimising batteries’ State of Charge (SOC) so that electricity imports are synchronised (as much as possible) with low-cost tariff periods.

This study reveals that, optimally managed, it can ensure that no renewable electricity is wasted while using the utility grid for backup purposes. This would make the local ESS the main driver for cost-effectively meeting the residents’ energy demands. Similar set-ups have been deployed in places such as Sonderborg, Denmark, where a residential electricity storage system has been implemented coupled with photovoltaic (PV) systems [10] to provide some flexibility from the utility grid.

As a result, the unique contribution of this study lies in its holistic integration of local energy systems to optimise energy balance, profile smoothing, and electricity cost through a heuristic approach. Furthermore, it carries out a long-term evaluation considering climate change to evaluate the stability of the system under significant climate fluctuations, which has not been previously explored in the literature. This approach addresses the identified research gap and promotes the development of PEDs.

To sum up, the authors believe the current study presents relevant novelties. The study focuses on advancing the current state of the art in medium-sized PEDs, fully integrated into electric systems to attain comfort systems in buildings, and also include electromobility demands, which is a concern for energy producers and grid operators due to its future impact on the grid. The approach optimises local energy systems jointly for energy balance, energy profile smoothing, and electricity cost by using a heuristic approach. Furthermore, a long-term assessment in light of climate change is performed to evaluate system stability under important climatic fluctuations

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

- 1) The authors should justify the choice of Fuzzy Logic instead of other techniques. Fuzzy Logic Type 1 is a classical approach that has been used in several similar studies. What specific advantages does it offer for your application compared to alternative methods?

-    2) The membership functions of the output are not presented; only the inputs are shown (Fig. 6). Please include and discuss the output membership functions. How do these functions influence the overall system behavior?

- 3) The authors should specify the number of rules used to design the controller. I recommend summarizing some of these rules in a table to provide greater clarity for the paper. How were these rules derived, and how do they contribute to the control strategy?

-4) Could the authors provide a comparative analysis of the Fuzzy Logic controller against other control techniques in terms of performance metrics such as accuracy, stability, and computational efficiency?

-5) How does the proposed system handle variations and uncertainties in the input data? Are there any mechanisms in place to manage these issues?

- It would be beneficial to include a discussion on the scalability of the proposed approach. Can the system be easily adapted or extended to other similar applications?

Comments on the Quality of English Language

This is a moderate edition of the English language used to write the article. Overall, it's understandable.

Author Response

Reviewer 3:

Comments and Suggestions for Authors

• The authors should justify the choice of Fuzzy Logic instead of other techniques. Fuzzy Logic Type 1 is a classical approach that has been used in several similar studies. What specific advantages does it offer for your application compared to alternative methods?

RESP:

Dear reviewer, the authors thank you for your question. The literature has been revised and as a consequence, We can answer your question as follows, the advantages are:

1. Heuristic and Fuzzy Logic-Based Approach: The proposed strategy uses a heuristic approach combined with fuzzy logic (FL) to manage the energy surplus efficiently. This method is designed to optimise the renewable energy systems (RES), economic operations, and battery health of an energy storage system (ESS) within the Positive Energy District (PED) framework. This approach allows for better management of energy resources by taking into account various factors such as state of charge (SOC), time of day, and power error (PE)​.
2. Enhanced Self-Sufficiency and Self-Consumption: The strategy aims to maximise self-sufficiency (SS) and self-consumption (SC) within the PED. By optimising the local use of electricity and synchronising imports with low-cost tariff periods, the strategy helps reduce the reliance on the utility grid and lowers electricity bills for residents​​.
3. Economic and Environmental Considerations: The proposed strategy includes considerations for economic aspects such as electricity tariffs and the impact of crises (for example wars or lockdowns) on energy prices. This ensures that the system remains cost-effective even under fluctuating economic conditions. Additionally, the strategy evaluates the potential impact of climate change on the PED, ensuring long-term system stability and sustainability.
4. Integration of Mobility and Thermal Demands: Unlike other strategies that may focus solely on electrical demands, the proposed strategy integrates mobility, home appliances, thermal demands (heating, cooling, domestic hot water), and smart poles. This comprehensive approach ensures a holistic management of all energy demands within the PED​​.
5. Community-Based Energy Storage System: The strategy employs a community-based ESS to support energy demands, smooth peak loads, and prevent potential curtailment of RES availability. This communal approach allows for a more efficient and resilient energy management system compared to individual household systems​.
6. Technical and Operational Robustness: The strategy's robustness is enhanced through continuous monitoring and advanced management mechanisms, ensuring optimal performance under varying load, weather, and climate conditions. This adaptability is critical for maintaining system efficiency and reliability over time​​.

These advantages demonstrate that the proposed energy management strategy not only optimises energy use and reduces costs but also enhances the overall sustainability and resilience of the PED.

In addition, we build a table in order to benchmark our study to others in literature.

Table 1. Benchmark of different studies that perform a SS and/or SC analysis.

Publication title	Self-sufficiency	Self-consumption	Method or technique	Reference
Energy communities to advance towards positive energy districts as a strategy towards decarbonisation	NA	77% foresee	Not Provided	[32]
Energy Self-Sufficiency Urban Module (ESSUM): GIS-LCA-based multi-criteria methodology to analyze the urban potential of solar energy generation and its environmental implications	70%	NA	GIS-LCA-based multi-criteria methodology	[33]
Towards energy self-consumption and self-sufficiency in urban energy communities	53% -- 67%	52%	A metaheuristic   PSO technique	[34]
A techno-economic analysis of an optimal self-sufficient district	76%	NA	Two methods, a rule-based control method (EnFloMatch tool) and a deterministic optimization method	[35]
Agent based modelling of a local energy market: A study of the economic interactions between autonomous pv owners within a micro-grid	24% (no-ESS)	NA	Agent-based modelling	[36]
Potential for exploiting the synergies between buildings through DS Mapproaches. Case study: La Graciosa Island	64%	42%	Rule-based control strategy	[37]
Optimal Simulation of Three Peer to Peer (P2P) Business Models for Individual PV Prosumers in a Local Electricity Market Using Agent-Based Modelling	28.4%	85%	Agent-based modelling	[38]
Combining Power-to-Heat and Power-to-Vehicle strategies to provide system flexibility in smart urban energy districts	30%--52%	64%--78%	An economic based approach using MaT4EnergyPLAN	[39]
Renewable Energy Communities as Modes of Collective Prosumership: A Multi-Disciplinary Assessment Part II—Case Study	35%	61%	Not Provided	[40]
Energy Community Measures Evaluation via Differential Evolution Optimization	29.74%	NA	Pareto-optimal solutions	[41]
Evaluation of load matching indicators in residential PV systems-the case of Cyprus	31.17%	48.17%	Generation-to-demand ratio (GTDR)	[15]
Energetic performance of a smart neighborhood of existing multifamily buildings with heat pumps, PV and CHP focusing on energy balance and CO2emissions	55%	52%	Not Provided	[42]

 

We really hope the answer meets and clarifies your doubts about the EMS implemented in this analysis.

 

• The membership functions of the output are not presented; only the inputs are shown (Fig. 6). Please include and discuss the output membership functions. How do these functions influence the overall system behavior?

RESP:

Dear reviewer, the authors thank you for your question. The following text and image have been included in the paper.

 

Figure 6. RESTORATIVE membership functions.

TOP identifies the time of the day according to electricity tariff profiles. Here, it is specified for Spain, with six separate periods: Supervalley (considered the cheapest period), Flat (three separate periods along the day: Flat_1, Flat_2, and Flat_3), and Peak (two separate periods along the day: Peak_1 and Peak_2).

SOC defines the level of electricity stored in the batteries. It is divided into “Extreme low” (<30%), “low”, “safe”, “high”, and “extreme high” (>95%). The ESS should avoid the two extreme scenarios to avoid deteriorating the ESS system. The FL algorithm cuts off the ESS charge when it gets extremely high SOC levels and redirects excess energy to the PED or UG to protect the ESS. For extremely low SOC situations, the electricity drain from the ESS is interrupted.

PE aims to check the PV generation capacity. It flags to enable or disable the connec-tion with UG. Error_POS_deficit reflects periods with demands larger than RES production, while error_NEG_surplus represents periods with surplus energy production. The interaction map between PE and the time of day used to manage the ESS is presented in Figure 7, where the yellow area represents the section where ESS charging from PV tends to be prioritised over UG because of the RES availability and electricity price. This means charging from the RES surplus is enabled while charging from UG is disabled. The Supervalley period from 00:00 to 08:00 hours (dark blue) is preferred in terms of ESS charging from the UG because of the price and the absence of PV generation.

The RESTORATIVE approach is based on fuzzy logic, it has two outputs, each with its respective membership functions. The first Output "SOC_CC/Bat_control" is responsible for caring for the state of charge (SOC) of the battery. These membership functions in-dicate whether the state of charge is low or high and enable the controller to make management strategy decisions based on the current SOC.

The second output "Bat_charge" is related to the charge control of the energy storage system. This maintains the state of charge (SOC) of the battery within defined limits based on information from the manufacturer, always avoiding SOC > 98.5% and SOC <20% so as not to rapidly degrade the cells that make up the batteries that are part of the battery of the ESS.

We really hope these changes meet your expectations

 

• The authors should specify the number of rules used to design the controller. I recommend summarizing some of these rules in a table to provide greater clarity for the paper. How were these rules derived, and how do they contribute to the control strategy?

RESP:

Dear reviewer, the authors thank you for your question. We have included the whole table with the total of rules used for the analysis, rather than summarising this. You can find this table in an annex created for such commitment. We really hope these changes meet your expectations.

This annex gathers all rules that were derived based on expert knowledge and the dynamics of the system to be controlled. The process for generation of the rules includes several steps. First, relevant variables were identified, such as Timetable, SOC (State of Charge), and POWER_error, due to their significance in the system's behaviour. Each input variable was then categorised into discrete states like Supervalley, Flat_1, Flat_2, Peak_1 and Peak_2. Finally, "IF-THEN" rules were formulated to describe the relationship between the states of the input variables and the output actions (SOC_CC/Bat_control and Bat_charge).

Also, these rules were categorised by its level of importance, until the current 58 rules that it might be shortened even more depending on the approach. In this case, the prioritisation of renewable over grid, and health of batteries were the focus. This decision was made after performing a pre-test and post-test strategy in order to make sure the rules have the behaviour expected by researchers during its implementation in the PED archetype during simulation.

In this regard, these rules contribute to the management strategy in several ways. They enable the controller to make decisions based on the current states of Timetable, SOC, and POWER_error. Each rule defines specific actions (SOC_CC/Bat_control and Bat_charge) to be taken for every combination of input states, ensuring an appropriate system response.

Overall, the set of rules provides a flexible and robust management strategy. It is capable of handling various operational scenarios and system conditions. This flexibility and robustness are essential for maintaining optimal performance and reliability in dynamic environments.

• Could the authors provide a comparative analysis of the Fuzzy Logic controller against other control techniques in terms of performance metrics such as accuracy, stability, and computational efficiency?

RESP:

Dear reviewer, the authors thank you for your question. Unfortunately, conducting a comparative analysis of the Fuzzy Logic controller against other control techniques is beyond the scope of our current research. Our decision to use a Fuzzy Logic controller is based on its unique advantages, including its status as an AI method that can be trained with data while remaining a white-box model. This transparency makes it easier to tune and faster to compute compared to other AI methods such as Bayesian networks or artificial neural networks.

Due to resource constraints, we were unable to train and test alternative controllers. Including such comparisons would have significantly increased the complexity and resource requirements of our study. We focused on leveraging the strengths of the Fuzzy Logic controller to address our specific research goals effectively.

We really hope you understand our current situation and the impossibility of perform this task.

 

• How does the proposed system handle variations and uncertainties in the input data? Are there any mechanisms in place to manage these issues?

RESP:

Dear reviewer, the authors thank you for your question. We have carried on a small sensitivity analysis to the framework disturbing the input parameters using Additive white Gaussian noise with 1% variance. The output does not change significatively (all differences are of the same order of magnitude of the perturbation carried out) so we consider the model to be robust. We have added the following text to the article. We really hope these changes meet your expectations

A small sensitivity analysis has been performed to the RESTORATIVE EMS. To this end we have included an additive white Gaussian noise with 1% variance component to each one of the input variables (self-consumption and self-sufficiency) and rerun the simulations. The results could be seen in Table 10. As can be seen in all diff columns from Table 10, the outputs do not differ significantly from the original run of the simulations. In particular, it is below the perturbation included so we conclude that the control seems to be robust. 

 

Table 10. Results of the sensitivity analysis of RESTORATIVE.

	Self-consumption			Self-sufficiency
Scenario	Original	Perturbed	Diff	Original	Perturbed	Diff
Scen_1	63.7	65.24	-1.54	50.1	53.09	-2.99
Scen_2	63.5	65.06	-1.56	49.8	52.53	-2.73
Scen_3	63.4	64.83	-1.43	49.4	52.14	-2.74
Scen_4	63.3	64.68	-1.38	49.2	51.54	-2.34
Scen_5	-	-	-	-	-	-
Scen_6	45.1	46.48	-1.38	57.3	59.69	-2.39
Scen_7	45.0	46.38	-1.38	57.0	58.9	-1.9
Scen_8	44.8	46.19	-1.39	56.7	58.45	-1.75
Scen_9	44.7	46.85	-2.15	56.4	58.04	-1.64
Scen_10	-	-	-	-	-	-

 

 

• It would be beneficial to include a discussion on the scalability of the proposed approach. Can the system be easily adapted or extended to other similar applications?

RESP:

Dear reviewer, the authors thank you for your question. We have included a small section that deals with the scalability of the RESTORATIVE before conclusions of the manuscript. We really hope these changes meet your expectations.

The RESTORATIVE energy management system (EMS) for Positive Energy Districts (PEDs) presents a promising approach towards achieving high levels of self-sufficiency and self-consumption. Its design, based on fuzzy logic, allows for optimization of energy storage systems (ESS) and can be adapted to various scenarios, including changes in energy prices and climatic conditions. The scalability of this system to other similar applications is a critical factor in determining its broader applicability and potential for widespread adoption.

Considering the need of making this tool feasible to other environments, RESTORATIVE system is built upon a modular design that makes it adaptable to different PED configurations. The key components, such as the ESS, fuzzy logic controller, and photovoltaic (PV) systems, can be scaled up or down depending on the size and energy requirements of the PED. In this line, the fuzzy logic approach used in RESTORATIVE can be fine-tuned to accommodate various climatic conditions and energy consumption patterns, making it applicable to diverse geographical locations.

Additionally, the system is designed to integrate seamlessly with multiple renewable energy sources, primarily PV systems, geothermal energy but it can also incorporate wind turbines and other renewable technologies. This flexibility ensures that the system can be tailored to leverage the most abundant local renewable resources. The system also contributes to substantial reductions in carbon emissions, aligning with global sustainability goals and enhancing its appeal for broader application. The advanced control and monitoring strategies used in RESTORATIVE, such as load/discharge optimization and innovative technologies for extending the lifetime of storage devices, can be applied to other PEDs to improve performance and sustainability. The capability to interact dynamically with the utility grid (UG) ensures that the system can maintain optimal operation even under varying grid conditions. This interaction allows for cost-effective energy management, which can be replicated in other contexts. This application can be replicated and/or scaled to similar systems, such as urban and suburban residential areas, commercial and industrial zones, regardless if the climate differs from the both analyses in this study.

The RESTORATIVE energy management system’s design, economic viability, and environmental benefits demonstrate its potential for scalability to other similar applications. Its modularity, adaptability, and advanced management strategies make it a versatile solution for various residential, commercial, and industrial energy management needs. The system’s success in different climatic and economic scenarios further underscores its broad applicability and potential for widespread adoption in the transition towards sustainable and resilient energy systems.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors' review greatly enhanced the paper's quality, and I have no further comments to add.