Next Article in Journal
Looking for Environmental Scoring: A Case Study of a Portuguese Cotton White T-Shirt Made with Recycled Fiber
Next Article in Special Issue
Integrating Technological Environmental Design and Energy Interventions in the Residential Building Stock: The Pilot Case of the Small Island Procida
Previous Article in Journal
Analysis of Urban Expansion and Heat-Island Effect of Hefei Based on ENVI
Previous Article in Special Issue
Harnessing Curtailed Wind-Generated Electricity via Electrical Water Heating Aggregation to Alleviate Energy Poverty: A Use Case in Ireland
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Thermal Energy Storage in Energy Communities: A Perspective Overview through a Bibliometric Analysis

1
CIRIAF-Interuniversity Research Centre, University of Perugia, Via G. Duranti 63, 06125 Perugia, Italy
2
GREiA Research Group, Universitat de Lleida, Pere de Cabrera s/n, 25001 Lleida, Spain
3
EAPLAB, CIRIAF-Interuniversity Research Centre, University of Perugia, Via G. Duranti 63, 06125 Perugia, Italy
4
Department of Engineering, UNIPG—University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(14), 5895; https://doi.org/10.3390/su16145895
Submission received: 17 June 2024 / Revised: 3 July 2024 / Accepted: 8 July 2024 / Published: 10 July 2024
(This article belongs to the Special Issue Renewable Energies in the Built Environment)

Abstract

:
The climate and energy crisis requires immediate countermeasures. Renewable energy communities (RECs) are capable of enhancing the consumption of renewable energy, involving citizens with a leading role in the energy transition process. The main objective of a REC is to maximize the consumption of renewable energy by reducing the mismatch between energy supply and demand. This is possible through the use of strategies and technologies including energy storage systems. Among these, the use of thermal energy storage (TES) is an efficient strategy due to the lower investment required compared to other storage technologies, like electric batteries. This study aims to define the role of TES in RECs, through a bibliometric analysis, in order to highlight research trends and possible gaps. This study shows that the existing literature on TES does not present terms related to RECs, thus presenting a research gap. On the other hand, RESs address the topic of energy storage in the literature, without focusing on TES in particular but considering the general aspect of the topic. Therefore, this leaves open a possibility for the development of research on TES as a possible technology applied to a REC to maximize the renewable energy sharing.

1. Introduction

Year after year, the climate crisis represents an increasingly concrete threat against which it is now urgent to take countermeasures. In the stocktaking at the last UN Climate Change Conference (COP 28), the goal of peaking global greenhouse gas emissions by 2025 and reducing them by 43% by 2030 and 60% by 2035 compared to 2019 levels in order to limit global warming to 1.5 °C was confirmed [1].
In the context of climate change mitigation, energy is one of the sectors where emission reductions can have the greatest short-term effects. This is why there is an increasing need for a transition to renewable energy sources and a consequent shift away from fossil fuels.
The European Union manifested this objective in recent years through the “Clean Energy Package” [2] and the “Fit for 55” [3], measures promoted by the European Commission that set a perspective of climate neutrality from fossil fuels from 2050 onwards. The Clean Energy Package focus aims to transform the economy and society according to a new sustainability paradigm, in which citizens should be encouraged to actively participate in the energy market, even in aggregated forms, to foster their empowerment and make them responsible for their consumption. To achieve this objective, consumers should be able to directly manage their energy.
In this context, renewable energy communities (RECs) [4] represent a social innovation capable of advancing more equitable models for managing, consuming, and producing energy, enhancing democratic decision-making and control over renewable energy, and placing it in the hands of communities and individuals. This citizens’ empowerment further contributes to increasing social acceptance of renewable energy generation plants thanks to the underlying participatory approaches that provide citizens an active role in the decision-making process [5].
Introduced by the European Renewable Energy Directive (RED II), RECs are autonomous legal entities, in which the members and stakeholders controlling it can participate openly and voluntarily, staying close to renewable energy production plants. This participation must not generate financial gains, but can give economic, social, environmental, and energy benefits. Members of an energy community can be classified into energy producers, energy consumers, and energy prosumers, i.e., producers of energy that self-consume on site. The main objective of a REC is to maximize the sharing of energy produced through renewable energy sources (RESs) in a collective self-consumption scheme [6]. However, the use of RESs entails the usual problem of solving the time mismatch between production and user demand for energy.
In this context, the so-called power-to-X strategies [7] consist of energy carrier conversions in order to efficiently use the available energy storage technologies. Power-to-heat [8] is an example of a strategy for which thermal energy storage (TES) systems play an important role. With the use of a heat pump, it is possible to convert electrical energy into a heat carrier, storing it to meet the heat demand of a utility. In these systems, TES [9] consisting of hot water tanks is a simple and economical solution for storing excess renewable energy [10].
This can be a way of increasing the sharing of renewable energy within a REC. In fact, the use of TES allows for storing the excess electrical energy produced by the plants of the producers that are part of the REC. Thus, the use of energy from renewable sources is guaranteed to the different consumers of the REC, not only in the periods of electrical energy demand peaks, but also in the thermal energy demand peaks.
This solution proves to be more sustainable than the installation of an electric storage battery. This is evidenced by Fambri et al. [11], who in their study compare the use of an electric battery and a heat pump thermal energy storage system in a REC.
However, the use of TES also has additional benefits. For example, it can decrease the thermal energy demand of buildings; counteract the randomness of renewable energy production, both thermal and electrical; and constitute a source of heat recovery [12].
Furthermore, in a larger field than the individual building, e.g., in the case of districts [13,14,15], more relatable to a REC, they can foster the diffusion, development, and flexibility of district heating and cooling systems powered by renewables, providing thermal energy to all consumers in them.
This study shows that although there is an energy saving from increased self-consumption of renewables through electrical storage, the economic benefit is not such as to justify the investment. On the other hand, the use of TES, despite having lower results in terms of energy savings, proves to be more cost-effective. The use of TES in a REC is also taken up in other works as a strategy to utilize surplus energy [16,17,18].
The aim of this paper is to give a detailed overview and report on the state of the art in the role and involvement of TES within RECs. Thus, a bibliometric analysis, which is a recognized technique of interest used in order to study scientific advances in a given topic, is carried out. A quantitative analysis is conducted regarding the number of publications, authors, and institutions researching this topic.
The results show that studies about the integration of TES to RECs, two very recent topics that have grown considerably in recent years, are still only marginal, as evidenced by the small number of papers about it.
Furthermore, to the best of the authors’ knowledge, there are still no papers in the literature that fill this gap, nor are there any that do so through the bibliometric analysis approach.
The objective of this study is also to provide a guide for researchers wishing to investigate the main networks and institutions working in the field of TES as a tool for sharing renewable energy within RECs, and to use the main research gaps identified to inspire future research on this topic, filling this deficit.
This research can also be for industries, public administrations, and private citizens who intend to become part of a REC, seeking to deepen their understanding of TES as a tool for sharing renewable energy surplus.

2. Materials and Methods

This section reports the methodology applied to develop the bibliometric analysis shown in the following sections. For this work, Scopus was preferred as the reference database. Web of Science was not considered due to its low number of documents on technology topics [19], while ResearchGate and Google Scholar were also not considered due to their low reliability in the bibliometric field [20]. In order to identify research trends and gaps in the application of TES in energy communities, bibliometric data were collected using two queries (Table 1) on the database Scopus, with the last access on 12 January 2024. A first query was used to collect bibliometric data related to the studies on TES in buildings including districts [21]. Compared to the work by Borri et al. [17], the query used in this study includes both buildings and districts, whereas previously, these categories were analyzed separately. In addition, the category “roads and bridges” is not considered here. Moreover, in this case, the transport sector was excluded in the query. The analysis of TES was carried out regarding its application to buildings and districts, in order to consider not only the dimension of the individual building but also a broader one, as a community.
A second, more specific query, was then formulated to draw the state of the art in TES, specifically in energy communities. The concept of “energy community” was combined with the aspect of “renewable” sources. This is because renewable energy communities are often referred to simply as “energy communities”. However, the term “renewable” is added, as sometimes the term “energy community” may refer to a broader and diversified concept of community, understood as a group of researchers and scientists in the specific field of energy, which is different from the concept intended in this paper. Therefore, the query excludes the keywords “building energy community”, “solar renewable energy community”, “computational intelligence (CI) community”, “materials, chemistry, and renewable energy communities”, and “energy corridors”. Similarly, terms such as “nuclear”, “MEG”, “NGH”, “Cu2Se”, and “SDG 7” were excluded, as they are related to papers not relevant to the research in question.
In this study, bibliometric data were used to identify the main trends in the number of publications. In order to identify the research gaps, keywords were visually mapped and analyzed through the open-source software VOSviewer (version 1.6.20) [22]. Through the latter, it is possible to graphically visualize and analyze bibliometric networks based on the links between different items of the research publication. These links can be about co-authorship, co-occurrence, citation, bibliographic coupling, or co-citation. The elements available for generating maps from publications include authors, institutions, countries, and keywords. The practicality of these analyses conducted with VOSviewer is that the output data can be easily exported and displayed in other resources [23].
Furthermore, the great advantage of VOSviewer is that through its ease of use and accessibility, it enables accurate bibliometric analyses using clusters even for those without extensive knowledge of clustering or software skills [24].
For this reason, VOSviewer is considered a validated tool for investigating gaps and research opportunities in the scientific literature. In particular, it is interesting to see how this software is already used to support bibliometric analysis within studies concerning energy storage [25,26,27,28], and also those concerning prosumers and energy communities [29,30,31,32].
In this paper, the findings regarding the co-occurrence of author keywords are presented through a network visualization. Each element (author keyword) is represented by a colored circle, with its size reflecting its weight in terms of occurrences or link strength. The colors denote the clusters to which different elements belong, and the distance between circles indicates the relationship between the items. Finally, the two indicated queries were merged within VOSviewer and analyzed together in order to highlight possible points of contact between the two topics and possible research gaps.

3. Results and Discussion

3.1. TES in Buildings and Districts

This section reports the results of the bibliometric analysis carried out with the methodology described in Section 2. Figure 1 shows the number of documents published in the field of TES applied to buildings and districts. The trend indicates a rapid increase in the number of publications per year since 2009, with over 500 publications in the year 2023. This demonstrates that TES is gaining momentum in built environment applications, becoming a pivotal component of the energy transition.
Figure 2 illustrates the publication trend over the years for the leading territories that have published papers on TES for building and district applications. As already illustrated by Borri et al. [17], the data reveal that the United States (USA) began publishing documents related to TES before 1990, whereas Europe and China commenced their publications after 2008. In the USA, most cited publications are focused on phase change materials (PCMs) [33,34,35], optimizing the control of TES [36,37], and the use of TES in district heating and cooling systems [38].
Research on this topic in Europe has increased rapidly as a result of directives about the energy performance of buildings and energy efficiency introduced by the European Union [39,40]. The most cited papers from Europe on Scopus predominantly consist of review articles focusing on PCMs in buildings [41,42,43,44].
In addition to these, within the same scope lies the most cited article, published by Cabeza et al. [45] in 2011, which constitutes a review on the use of this technology, classification of materials, materials available, and problems and possible solutions on the application of such materials in buildings. Additional highly cited documents include general reviews on TES and the materials used in their application [9,46].
From Figure 2, it can be observed that China follows a similar trend to that of the European Union, albeit with a lower number of publications. This is because, like Europe, China has implemented a substantial plan to reduce emissions from fossil fuels by 2030 compared to those in 2005 [47].
Among the most cited articles in this field, there are general reviews on TES [48] and the application of latent heat thermal energy storage [49]. Additionally, numerous reviews regarding the study and use of PCMs [50,51,52,53] are available on Scopus. The most cited paper concerns precisely this topic and was published in 2012 by Zhou et al. [54]. In Figure 3, the main European countries publishing documents regarding the application of TES in the context of buildings and districts are shown.
It is possible to notice that Spain, Italy, and Germany have a similar trend of publications that started to rapidly increase after 2010. As of 2023, Italy is the country with the highest number of publications, among which the most cited ones focus on energy management strategies [55,56,57], cogeneration [58,59], district heating and cooling systems [60], and the use of thermal energy storage techniques in thermal insulation [61]. The second country with the highest number of publications is Spain, whose studies focus PCMs [62,63] and the application of TES in the domain of buildings [64,65].
Finally, the third country with the highest number of publications is Germany, which covers various topics including the application of TES to buildings to enhance their development [66,67], the study of PCMs [68,69], aquifer TES [70], TES for solar power plants [71,72], and district heating [73].
Figure 4 illustrates the journals with the most publications related to TES applied to buildings and districts. The best is Energy and Buildings, with 297 publications as of December 2023. It is interesting that the journal Energies appears in Figure 4, with 143 open-access publications. The most cited paper from this journal was published in 2020 by Enescu et al. [74]. The authors and the institutions with most publications on TES applied to buildings and districts are shown in Table 2 and Table 3, respectively. The author with the most publications is Luisa F. Cabeza, affiliated with the “Universitat de Lleida”, in Spain, with 121 publications as of December 2023. Her most cited article on TES applied to buildings and districts dates back to 2011 and it shows a review of the publications on the use of PCMs in buildings [45]. Some of his other most cited works concern the application of PCMs [75,76,77,78] and TES technologies integrated [79,80] to the field of buildings. This makes Universitat de Lleida the affiliation with the highest number of publications on this topic.
The second most published author is Ahmet Sari, affiliated with Karadeniz Technical University, Turkey, which is also among the affiliations with the highest number of publications on TES in buildings and districts. His most cited articles focus on the thermal properties of PCMs [81,82,83,84]. As China is the second most published territory, two Chinese affiliations are among those with the most published articles: the Ministry of Education of the People’s Republic of China, and Tsinghua University.
Figure 5 shows the co-occurrence of keywords obtained through the software VOSviewer for the first query related to TES in buildings and districts. As stated in the methodology, the keywords belonging to the same topic were grouped to represent the macro-areas of research. In order to obtain the results displayed in the figure, the keywords were filtered with a minimum occurrence of 10. In the right part of the figure (green cluster), it is possible to notice that a lot of studies are related to the development and the improvement of storage materials, in particular PCMs. On the left side of the figure, it is possible to notice that the bigger cluster (red) is related to TES applications, including keywords such as “control”, “optimization”, “demand side management”, “numerical model”, and “district heating”. The blue cluster is related to the applications of TES as a system in buildings, including keywords such as “heating”, “cooling”, “buildings”, and “energy saving”. The other main cluster (yellow) is related to methods to analyze improve the thermal parameters of storage materials.
Table 4 shows the author keywords with the highest number of occurrences on “TES on buildings and districts”. While the keyword with the most occurrences is obviously “TES”, it can be seen that the second is “PCM (phase change materials)”. This is evidenced by the large number of articles that were published on this topic in different territories and countries. A further keyword with a high occurrence is “numerical model”, due to the widespread use in the literature of numerical modelling techniques to simulate the behavior of TES, in particular PCMs [35,85,86,87].
The keyword “optimization” mainly refers to optimization studies for the integration of thermal storage in building systems, in order to reduce the mismatch between demand and supply of renewable energy and minimize costs [67,88,89,90,91]. The scope of the districts taken into consideration through the query is highlighted by the keyword “district heating”. Among the most frequently cited articles with this keyword are applications of TES to combined heat and power (CHP) systems for district heating [15,92,93], review articles [38,60,94], and studies on the thermal inertia of buildings as thermal storage in district heating and cooling systems [95,96].
A research gap emerges from Figure 5. In fact, there are no terms or keywords related to RECs. However, there are keywords such as “distributed energy system”, “smart grid”, “micro-grid”, and “sector coupling”, which are related to a broader concept than the single building and can be approached as energy sharing. Indeed, these terms are related to a collective dimension of energy consumption and management, attributable to the concept of RECs. The keyword “distributed energy system” refers to a system based on small-scale installations distributed across the territory, in stark contrast to the traditional model of centrally produced power plants. It is precisely on this basis that the REC is built, aiming to facilitate the spread of renewable energies in the territory, meeting both environmental and community needs [97]. The concept of a “microgrid” is instead linked to energy communities through the themes of energy self-sufficiency and energy sharing. The members of an energy community are no longer seen as mere consumers, but become “prosumers”, meaning they are energy producers who consume and are capable of exchanging energy with each other through “peer-to-peer” sharing mechanisms [98,99]. Furthermore, through energy efficiency systems and smart management energy systems, linked to the maximization of renewable energy sharing, “smart grids” become a fundamental tool for RECs [100,101,102]. Lastly, the possibility of having a highly integrated energy system, capable of maximizing the use of renewable energy produced either through storage [103] or across different consumption sectors [104,105], makes “sector coupling” a fundamental element in optimizing energy communities.
Figure 6 and Figure 7 show the co-occurrence of these keywords and their links. The figures highlight, given the small number of links, the low relevance of these terms in the literature. Figure 6 shows the co-occurrence of the keyword “distributed energy system”. The few links present all concern the red cluster, in the context of TES applications. The most cited paper on distributed energy systems applied in TES was published in 2020, from Wirtz et al. [73], and it is about a low-temperature network of buildings equipped with heat pumps, chillers, and heat exchangers for thermal energy storage, connected to minimize annual energy costs and to reduce emissions. This concept is very close to that of RECs and is also approached by other studies on this topic [106,107,108].
Figure 7 shows how microgrids and sector coupling have the lowest occurrences and thus represent research gaps in the applications of TES. In fact, the term microgrid has only four links besides TES: “demand side management”, “artificial intelligence”, “optimization”, and “renewables”. It is no coincidence that the main studies of TES on this topic concern optimal energy management models applied to microgrids [109,110,111]. Also, the term sector coupling has just four links besides TES, and they are “district heating”, “energy management”, “numerical model”, and “smart grid”. It is interesting to note that the most cited article related to this keyword in the context of TES, published by Bartolini et al. [112] in 2020, has as its subject the concept of an energy community based on a multi-energy system to which various storage technologies are applied.
The most cited article in the field of microgrids applied to TES dates back to 2021 and was published by Li et al. [113]. The most cited article on sector coupling applied to TES is by Zeyen et al. and it explores strategies to mitigate winter heating demand peaks in the European Union, crucial for cost and emissions reduction goals [114]. In the field of materials development, they are all related to the keyword “TES”, while in the field of TES applications, they are related to terms such as “demand side management”, “optimization”, and “numerical model”.

3.2. Renewable Energy Communities

The number of documents published related to renewable energy communities (RECs) is shown in Figure 8. The trend shows a steady and progressive increase in publications since 2018. This shows how the energy community is considered a key tool in the energy transition process.
Figure 9 illustrates the trend in publications for the main territories that have published on renewable energy communities (RECs). The trend shows that, compared to the United States and China, the European Union has significantly increased the number of publications on RECs since 2018. This increase is due to the European Parliament approval of Directive 2018/2001/EU (RED II) [115] and Directive 2019/944/EU (IEM) [116]. These directives are part of the Clean Energy Package [2], a collection of regulations adopted by the European Union in 2019 to decarbonize the European energy system. Through the RED II and IEM directives, the concept of an energy community was introduced in Europe as well as the concept of renewable energy sharing.
The article with the highest number of citations in Europe was published by Lowitzsch et al. [117] in 2020 and contained a definition of the European regulatory context of the RED II and IEM directives, providing a preliminary practical interpretation while awaiting implementation by the member states of the European Union. Furthermore, the most cited papers focus on the innovations brought about by energy communities as a tool for transitioning towards renewable energies, from regulatory [118], economic [119], as well as environmental and social perspectives [120,121,122,123].
Despite significantly fewer publications, United States ranks second in terms of the highest number of citations. In the USA, there is a notable focus on research concerning net zero energy communities [98,124,125,126], where the primary goal is achieving zero emissions from fossil fuel combustion. Furthermore, several studies address the application in energy communities of optimization strategies [127,128] and of battery storage systems [98,129] for these communities. Regarding this topic, the most cited paper in the USA was published by Barbour et al. in 2018 [130].
Finally, China has started showing interest in RECs quite recently, since 2016, as seen in Figure 9. The most cited article is from 2020, authored by Feng et al. [127], and evaluates a coalitional game model to manage an energy community. The majority of the most cited articles in China also focus on net zero energy communities [131,132,133,134,135].
Figure 10 shows the main European countries by the number of publications between 1993 and 2023. Among these, Italy is the country that has increased its number of publications on this topic the most in recent years. In the figure, there is a noticeable positive deviation in the trend starting from 2018 and a further one starting from 2021. These two dates correspond to the implementation of the RED II and IEM directives in Italian legislation. In 2018, there was an early implementation through Law 8/2020 [136], while in 2021, there was the final implementation through Legislative Decree 199/2021 [137], which defined the characteristics of RECs in terms of energy and economics by establishing a shared energy incentive system [138].
The implementation of these directives in Italian legislation has provided a boost to research and development in energy communities as a new tool for energy management. The most cited Italian article is by Moroni et al. [139], dating back to 2019, and represents an introduction to energy communities through a taxonomic approach, attempting to explain what they are and their importance in the energy transition. Additional highly cited articles focus on the application of battery energy storage to RECs [112,129], smart energy systems in so-called “smart energy communities” [140,141], and the definition of optimization models for RECs [142,143].
In comparison to Italy, as can also be seen from Figure 10, in Germany, energy communities have represented a minor element of novelty, because they have already been established since the early 2000s in the German energy system, as “energy cooperatives” [144]. Furthermore, already in 2017, the Renewable Energy Sources Act 2017 (EEG) [145] defined self-supply and introduced the definition of an “energy community” into German legislation.
The most cited paper in Germany about RECs is from Lowitzsh et al. [117] and introduces the novelties of the Clean Energy Package in the European energy scenario. Further articles among the most cited focus on the importance of the new European directives [118,146]. In addition, as evidence of the established presence of energy communities in the German system, among the most cited articles is one by Schweizer-Ries et al. [147], dating back to 2008, which investigates the social dimension of the acceptability of renewable energies in the territory.
Spain represents the third European country in terms of the number of citations on the topic of energy communities. In 2019, Royal Decree 244/2019 [148] of April 5 established a legal definition of self-consumption and allowed for shared or collective self-consumption, both in the internal network and in nearby installations over the network. Indeed, Figure 10 shows an increase in the publication trend starting from 2019. The most cited article on energy communities in Spain was published in 2019 by Lezama et al. [119] and deals with the role of energy communities in local markets and how local markets can facilitate energy trading, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition. Among the most cited articles, there are also definitions of optimal REC models [149,150,151], as well as an analysis of sustainable energy communities, in a comparative analysis between Spain and Germany [151].
Figure 11 shows the main journals containing publications related to RECs. In this case, the top journal is Energies, with a total number of 124 publications updated to December 2023, all in open-access form. The authors and the institutions with the most publications on REC are shown in Table 5 and Table 6, respectively. Looking at Table 5, one can see that among the affiliations with the highest number of publications on RECs, the top three positions are occupied by Italian universities. This is mainly due to the recent transposition of European directives and the introduction of the REC as a model in the Italian energy system. For this reason, several studies have been published in recent years to illustrate what a REC is and what its benefits are, introducing reproducible models and case studies in the national energy context [139,142,152,153,154].
Furthermore, the Politecnico di Torino university supported the research activity that led to the creation of the first energy community in Italy [155,156], in Magliano Alpi, in the province of Cuneo. It is interesting to note that Delft University of Technology is among the affiliations with the highest number of publications. The Netherlands, in contrast to Italy, is a pioneer state on the subject of RECs, having already introduced them into its legislation in 1998 and counting over 500 projects in the country [157]. The most cited article from that university was published in 2018 by Koirala et al. [122] and emphasizes the importance of citizen involvement in order to create a low-carbon community.
Figure 12 shows the co-occurrence of keywords obtained through the software VOSviewer for the second query related to RECs. The results shown in the figure were obtained by filtering the keywords with a minimum occurrence of 7. From the figure, it can be observed that the keyword “energy community” is linked to various types of energy-sharing schemes. Indeed, there are connections with terms such as “renewable energy communities” and “citizen energy community” (green cluster), types introduced in Europe through the RED II and IEM directives of the Clean Energy Package [118,158], as well as local energy communities (yellow cluster), understood as energy communities where citizens are extensively involved in local renewable energy system projects [158]. Table 7 shows the author keywords with the highest number of occurrences on “renewable energy communities”.
Among the various items related to RECs, there is no specific reference to TES, but only the general term “energy storage” is mentioned. The subsequent Figure 13 illustrates the co-occurrence of the keyword “energy storage”. Through Figure 13, it is evident that the keyword “energy storage” has a high number of connections both within the general scope of “blue cluster” energy communities and within the more specific realms of renewable and citizen energy communities (green cluster), as well as within the energy market and relationships among consumers of the CER (yellow cluster). Currently, the figure shows that the implementation of energy storage in energy communities is linked with “PV”. Furthermore, there are some keywords that describe an optimization of the energy storage system as artificial intelligence and internet of things (red cluster).
The most cited article in the field of energy storage applied to RECs was published in 2018 by Barbour et al. [130] It highlights the necessity for the development of community-level storage technologies. While the literature predominantly features articles on battery energy storage [101,128,158,159,160,161], among the most cited articles is a study by Liu et al. [132], which applies a multi-energy storage system, both electrical and thermal, to achieve self-sufficiency in a net zero energy community. Additionally, Bartolini et al. [112] conduct a comparison between battery storage and TES to determine the optimal utilization of excess energy produced by a facility in a REC. The combination of battery and TES has also been taken up by further studies to create a hybrid system capable of maximizing the use of renewable energy [160,162,163].
In addition, Figure 13 indicates that energy storage applied to RECs is linked to the realm of smart grids and microgrids, corroborating what was previously analyzed in the co-occurrence analysis of TES. Finally, analyzing the latest publications on energy storage applied to RECs, it can be observed that solutions involving TES are increasingly prevalent, either in standalone systems or hybrid configurations with battery storage. For this reason, TES systems can represent a feasible possibility of application to RECs as a solution for maximizing renewable energy sharing.

3.3. Combined Results from Both Queries

Figure 14 shows the co-occurrence of keywords obtained by combining the queries concerning RECs and TES systems in buildings and districts into the VOSviewer software. The image shows how there is a clear separation between the two topics. It can be seen that the keywords related to the field of RECs are concentrated on the left-hand side, while those related to TES are on the right-hand side.
This gap of the implementation of TES in RECs is confirmed with Figure 15, highlighting the main links to the keywords “energy community” and “TES”. It can be seen that the two keywords are not related to each other. Furthermore, numerous keywords related to the sphere of TES are clearly separated from RECs. A clear example is “PCM”, located on the opposite side of the figure from “energy community”. However, it is possible to highlight several common points between TES and RECs that can be starting points for future research developments. For example, the term “TES” has links to the words “prosumer”, “smart grid”, “microgrid”, “pv”, and “demand side management”, which are related to the field of RECs and the collective sharing of renewables.
One of these is the keyword “energy storage”, the co-occurrence of which is shown in Figure 16a. As already shown in the previous section, most of the REC publications devoted to energy storage mostly concern electric storage batteries, as also shown by the terms “electric vehicle” and “electricity market”, so a further gap is evident. Furthermore, the presence of the term “seasonal tes” indicates that from the perspective of TES systems, the focus of research is more on large systems, in contrast to smaller, distributed technologies that are more suited to the scope of RECs. However, the keywords “heat pump”, “cogeneration”, “trigeneration”, and “district heating”, also present in the co-occurrence of another common keyword, “solar energy” (Figure 16b), open up future research possibilities not only on the electrical side, but also on the thermal side. For this reason, the topic of energy storage could represent a bridge between TES and RECs. “Heat pump” is an important common point, which means that electrification of heating systems in energy communities could be the main focus of TES implementation. The keyword “district heating” once again highlights the importance of TES in areas of application greater than the individual building, expanding the possibility of sharing renewable thermal energy between several consumers. Finally, the terms “cogeneration” and “trigeneration” refer to the already mentioned possible benefits of theses in terms of recovering surplus heat.
Figure 17, on the other hand, shows the co-occurrence of the keyword “numerical model”, for which there are several links to both RECs and TES. However, this shows a further gap, as it highlights the absence of the experimental field in the application of these two topics.
Finally, it is interesting to note that terms such as “building envelope”, “thermal comfort”, and “energy efficient buildings”, which can be traced back to the residential sector, are repeated in the occurrences of these keywords common to TES and RECs. This could represent a further possibility to research the use of TES as a domestic solution to achieve energy efficiency for consumers and members of a REC.
Figure 18 shows the overlay visualization created using the VOSviewer software, allowing for us to observe the trends of different research items. It is evident that both TES and RECs are relatively recent research topics. As we move from TES to RECs from right to left, RECs appear to be an increasingly recent topic. While terms related to materials in TESs, such as “thermal properties”, “fatty acids”, and “building material” are positioned in 2017 [164,165,166], the items in the field of RECs, including “prosumer”, “peer-to-peer trading”, “local energy community”, and “citizen energy community”, are very recent, dating back to 2021 [167,168,169,170]. It can be concluded that the application of TES in the field of RECs is still relatively unexplored at the research level. However, it represents a recently emerging topic, still in its initial phase, with several possibilities for expansion and exploration in the future.

4. Conclusions

Renewable energy communities (RECs) represent a fundamental tool for the energy transition to renewable energy sources. Thanks to the distributed generation of renewable energy and the involvement of community members, who are no longer mere consumers but active and aware participants, they can effectively constitute a new paradigm for energy production, management, and consumption. This paradigm starkly contrasts with the fossil fuel-dependent model, addressing not only environmental and energy needs but also social ones.
The main goal of a REC is to maximize the sharing of renewable energy among its members. To achieve this, it is necessary to align periods of energy demand and supply. In this regard, there are various strategies, including the use of energy storage systems. Among these, TES represents a solution. Thanks to the power-to-heat strategy, which involves converting excess electrical energy into heat; its versatility; and its lower cost compared to other types of storage such as battery storage, TES presents a tangible opportunity to maximize the utilization of renewable energy in a REC. Furthermore, in a collective dimension of self-consumption such as RECs, TES systems can represent an opportunity for the implementation of district heating and heating systems fueled by renewables, providing flexibility and the possibility of bridging the mismatch between demand and availability of energy.
This study aimed to define the perspective of TES in RECs with a detailed bibliometric analysis. An analysis of TES systems was carried out regarding their application to buildings and districts, in order to consider not only the dimension of the individual building but also a broader one, traceable to a community. An initial analysis of the results shows that these two topics are very recent, with a significant increase in the trend of publications only in recent years. TES research, for example, has seen a considerable increase in publications since 2010, especially in Europe, following the approval of energy efficiency targets. Even more recent is the development of REC studies, which have grown exponentially since 2018, also based on the approval of European directives.
For this reason, most of the publications on these topics are very recent.
This work showed that energy communities constitute a research gap in the TES literature. In the co-occurrence analysis of keywords referring to TES, no terms related to RECs emerged. However, the presence of some terms characteristic of a larger storage dimension than the individual user, like “district heating”, “smart grid”, and “micro grid”, leaves open the possibility of research in the area of collective self-consumption, not only from an electrical point of view, but also from that of thermal energy.
Furthermore, through bibliometric analysis, it became apparent that energy storage is applied in RECs, although it is referred to in the literature, through keywords, by this general definition, without focusing explicitly on thermal storage. However, among the most cited publications were several papers on the comparison between battery storage and TES. Moreover, more recent studies focus more on thermal or hybrid battery–TES systems. This allows for not only reducing the unreasonable investment costs of battery storage, but also for maximizing the sharing of renewable energies, through another energy carrier, towards increasing self-sufficiency.
Analyzing the merged keywords related to TES and RECs underscores the gap between these two topics. However, it also shows how the concepts of “energy storage”, “solar energy”, and “numerical model”, which are relevant to both, could be a starting point for future research. These common points, with the big novelty that characterizes the subject, give room for potential investigation of TES technologies applied into RECs, in particular in Europe, where the research of these two topics is focused.

Author Contributions

Conceptualization, L.F.C. and E.B.; methodology, E.B.; formal analysis, C.M. and A.L.P.; investigation, L.B. and E.B.; resources, A.L.P. and L.F.C.; data curation, L.F.C.; writing—original draft preparation, L.B.; writing—review and editing, E.B., A.L.P., A.N., C.M. and L.F.C.; visualization, L.B.; supervision, A.N. and L.F.C.; project administration, A.L.P. and L.F.C.; funding acquisition, A.L.P. and L.F.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was partially funded by the Ministerio de Ciencia e Innovación—Agencia Estatal de Investigación (AEI) (PID2021-123511OB-C31—MCIN/AEI/10.13039/501100011033/FEDER, UE) and by the Ministerio de Ciencia e Innovación—Agencia Estatal de Investigación (AEI) (RED2022-134219-T). L. Brunelli acknowledges Fondazione Perugia for the support given through the “EDUCER” project (21082, 2022.0406).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data available upon request to the corresponding author.

Acknowledgments

The authors at the University of Lleida thank the Generalitat de Catalunya for the quality accreditation granted to the GREiA research group (2021 SGR 01615). GREiA is a TECNIO-certified agent in the category of technological development of the Gobierno de Cataluña. This work is partially supported by ICREA inside the program ICREA Academia. L. Brunelli aknowledgments are due to the PhD school in Energy and Sustainable Development at CIRIAF from the University of Perugia.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Smeeth, L.; Haines, A. COP 28: Ambitious climate action is needed to protect health. BMJ 2023, 383, p2938. [Google Scholar] [CrossRef] [PubMed]
  2. Nouicer, A.; Meeus, L. European University Institute. Robert Schuman Centre for Advanced Studies. The EU Clean Energy Package. Available online: https://energy.ec.europa.eu/topics/energy-strategy/clean-energy-all-europeans-package_en (accessed on 14 March 2024).
  3. Schlacke, S.; Wentzien, H.; Thierjung, E.M.; Köster, M. Implementing the EU Climate Law via the ‘Fit for 55’ package. Oxf. Open Energy 2022, 1, oiab002. [Google Scholar] [CrossRef]
  4. Walker, G.; Devine-Wright, P. Community renewable energy: What should it mean? Energy Policy 2008, 36, 497–500. [Google Scholar] [CrossRef]
  5. Ferreira, E.; Sequeira, M.M.; Gouveia, J.P. Sharing Is Caring: Exploring Distributed Solar Photovoltaics and Local Electricity Consumption through a Renewable Energy Community. Sustainability 2024, 16, 2777. [Google Scholar] [CrossRef]
  6. Giarmanà, E. Managing renewable electricity within collective self-consumption schemes: A systematic private law approach. Renew. Sustain. Energy Rev. 2023, 188, 113896. [Google Scholar] [CrossRef]
  7. Sternberg, A.; Bardow, A. Power-to-What?-Environmental assessment of energy storage systems. Energy Environ. Sci. 2015, 8, 389–400. [Google Scholar] [CrossRef]
  8. Pastore, L.M.; Basso, G.L.; Ricciardi, G.; de Santoli, L. Smart energy systems for renewable energy communities: A comparative analysis of power-to-X strategies for improving energy self-consumption. Energy 2023, 280, 128205. [Google Scholar] [CrossRef]
  9. Sarbu, I.; Sebarchievici, C. A Comprehensive Review of Thermal Energy Storage. Sustainability 2018, 10, 191. [Google Scholar] [CrossRef]
  10. Pastore, L.M.; Basso, G.L.; Ricciardi, G.; de Santoli, L. Synergies between Power-to-Heat and Power-to-Gas in renewable energy communities. Renew. Energy 2022, 198, 1383–1397. [Google Scholar] [CrossRef]
  11. Fambri, G.; Marocco, P.; Badami, M.; Tsagkrasoulis, D. The flexibility of virtual energy storage based on the thermal inertia of buildings in renewable energy communities: A techno-economic analysis and comparison with the electric battery solution. J. Energy Storage 2023, 73, 109083. [Google Scholar] [CrossRef]
  12. Arce, P.; Medrano, M.; Gil, A.; Oró, E.; Cabeza, L.F. Overview of thermal energy storage (TES) potential energy savings and climate change mitigation in Spain and Europe. Appl. Energy 2011, 88, 2764–2774. [Google Scholar] [CrossRef]
  13. Verrilli, F.; Srinivasan, S.; Gambino, G.; Canelli, M.; Himanka, M.; Del Vecchio, C.; Sasso, M.; Glielmo, L. Model Predictive Control-Based Optimal Operations of District Heating System With Thermal Energy Storage and Flexible Loads. IEEE Trans. Autom. Sci. Eng. 2017, 14, 547–557. [Google Scholar] [CrossRef]
  14. Dahash, A.; Ochs, F.; Janetti, M.B.; Streicher, W. Advances in seasonal thermal energy storage for solar district heating applications: A critical review on large-scale hot-water tank and pit thermal energy storage systems. Appl. Energy 2019, 239, 296–315. [Google Scholar] [CrossRef]
  15. Wang, H.; Yin, W.; Abdollahi, E.; Lahdelma, R.; Jiao, W. Modelling and optimization of CHP based district heating system with renewable energy production and energy storage. Appl. Energy 2015, 159, 401–421. [Google Scholar] [CrossRef]
  16. Fouladvand, J.; Rojas, M.A.; Hoppe, T.; Ghorbani, A. Simulating thermal energy community formation: Institutional enablers outplaying technological choice. Appl. Energy 2022, 306, 117897. [Google Scholar] [CrossRef]
  17. Pasqui, M.; Vaccaro, G.; Lubello, P.; Milazzo, A.; Carcasci, C. Heat pumps and thermal energy storages centralised management in a Renewable Energy Community. Int. J. Sustain. Energy Plan. Manag. 2023, 38, 65–82. [Google Scholar] [CrossRef]
  18. Mohiti, M.; Mazidi, M.; Oggioni, N.; Steen, D.; Tuan, L.A. An IGDT-Based Energy Management System for Local Energy Communities Considering Phase-Change Thermal Energy Storage. IEEE Trans. Ind. Appl. 2024, 60, 4470–4481. [Google Scholar] [CrossRef]
  19. Cabeza, L.F.; Chàfer, M.; Mata, É. Comparative analysis of web of science and scopus on the energy efficiency and climate impact of buildings. Energies 2020, 13, 409. [Google Scholar] [CrossRef]
  20. Falagas, M.E.; Pitsouni, E.I.; Malietzis, G.A.; Pappas, G. Comparison of PubMed, Scopus, web of science, and Google scholar: Strengths and weaknesses. FASEB J. 2008, 22, 338–342. [Google Scholar] [CrossRef]
  21. Borri, E.; Zsembinszki, G.; Cabeza, L.F. Recent developments of thermal energy storage applications in the built environment: A bibliometric analysis and systematic review. Appl. Therm. Eng. 2021, 189, 116666. [Google Scholar] [CrossRef]
  22. van Eck, N.J.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef] [PubMed]
  23. Moral-Muñoz, J.A.; Herrera-Viedma, E.; Santisteban-Espejo, A.; Cobo, M.J. Software tools for conducting bibliometric analysis in science: An up-to-date review. Prof. Inf. 2020, 29, 1–20. [Google Scholar] [CrossRef]
  24. van Eck, N.J.; Waltman, L. Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics 2017, 111, 1053–1070. [Google Scholar] [CrossRef] [PubMed]
  25. Ogarek, P.; Wojtoń, M.; Słyś, D. Hydrogen as a Renewable Energy Carrier in a Hybrid Configuration of Distributed Energy Systems: Bibliometric Mapping of Current Knowledge and Strategies. Energies 2023, 16, 5495. [Google Scholar] [CrossRef]
  26. Paliwal, M.K.; Jakhar, S.; Sharma, V. Nano-enhanced phase change materials for energy storage in photovoltaic thermal management systems: A bibliometric and thematic analysis. Int. J. Thermofluids 2023, 17, 100310. [Google Scholar] [CrossRef]
  27. Liu, J.; Li, J.; Wang, J. In-depth analysis on thermal hazards related research trends about lithium-ion batteries: A bibliometric study. J. Energy Storage 2021, 35, 102253. [Google Scholar] [CrossRef]
  28. Omrany, H.; Chang, R.; Soebarto, V.; Zhang, Y.; Ghaffarianhoseini, A.; Zuo, J. A bibliometric review of net zero energy building research 1995–2022. Energy Build. 2022, 262, 111996. [Google Scholar] [CrossRef]
  29. Parra-Domínguez, J.; Sánchez, E.; Ordóñez, Á. The Prosumer: A Systematic Review of the New Paradigm in Energy and Sustainable Development. Sustainability 2023, 15, 10552. [Google Scholar] [CrossRef]
  30. Gough, M.; Santos, S.F.; Javadi, M.; Castro, R.; Catalão, J.P.S. Prosumer Flexibility: A Comprehensive State-of-the-Art Review and Scientometric Analysis. Energies 2020, 13, 2710. [Google Scholar] [CrossRef]
  31. Papatsounis, A.G.; Botsaris, P.N.; Katsavounis, S. Thermal/Cooling Energy on Local Energy Communities: A Critical Review. Energies 2022, 15, 1117. [Google Scholar] [CrossRef]
  32. Shah, S.H.H.; Lei, S.; Ali, M.; Doronin, D.; Hussain, S.T. Prosumption: Bibliometric analysis using HistCite and VOSviewer. Kybernetes, 2019; ahead-of-print. [Google Scholar] [CrossRef]
  33. Ji, H.; Sellan, D.P.; Pettes, M.T.; Kong, X.; Ji, J.; Shi, L.; Ruoff, R.S. Enhanced thermal conductivity of phase change materials with ultrathin-graphite foams for thermal energy storage. Energy Environ. Sci. 2014, 7, 1185–1192. [Google Scholar] [CrossRef]
  34. Song, M.; Niu, F.; Mao, N.; Hu, Y.; Deng, S. Review on building energy performance improvement using phase change materials. Energy Build. 2018, 158, 776–793. [Google Scholar] [CrossRef]
  35. Al-Saadi, S.N.; Zhai, Z. Modeling phase change materials embedded in building enclosure: A review. Renew. Sustain. Energy Rev. 2013, 21, 659–673. [Google Scholar] [CrossRef]
  36. Braun, J.E. Reducing energy costs and peak electrical demand through optimal control of building thermal storage. ASHRAE Trans. 1990, 96, 876–888. [Google Scholar]
  37. Ma, Y.; Borrelli, F.; Hencey, B.; Coffey, B.; Bengea, S.; Haves, P. Model Predictive Control for the Operation of Building Cooling Systems. IEEE Trans. Control Syst. Technol. 2012, 20, 796–803. [Google Scholar] [CrossRef]
  38. Lake, A.; Rezaie, B.; Beyerlein, S. Review of district heating and cooling systems for a sustainable future. Renew. Sustain. Energy Rev. 2017, 67, 417–425. [Google Scholar] [CrossRef]
  39. European Commission. Energy Performance of Buildings Directive. 2018. Available online: https://ec.europa.eu/energy/topics/energy-efficiency/energy-efficient-buildings/energy-performance-buildings-directive_en (accessed on 14 March 2024).
  40. Horizon. The EU Framework Programme for Research and Innovation. 2020. Available online: https://ec.europa.eu/programmes/horizon2020/en/ (accessed on 12 February 2019).
  41. Cabeza, L.F.; Castello, C.; Nogués, M.; Medrano, M.; Leppers, R.; Zubillaga, O. Use of microencapsulated PCM in concrete walls for energy savings. Energy Build. 2007, 39, 113–119. [Google Scholar] [CrossRef]
  42. Kuznik, F.; David, D.; Johannes, K.; Roux, J.-J. A review on phase change materials integrated in building walls. Renew. Sustain. Energy Rev. 2011, 15, 379–391. [Google Scholar] [CrossRef]
  43. Soares, N.; Costa, J.J.; Gaspar, A.R.; Santos, P. Review of passive PCM latent heat thermal energy storage systems towards buildings’ energy efficiency. Energy Build. 2013, 59, 82–103. [Google Scholar] [CrossRef]
  44. de Gracia, A.; Cabeza, L.F. Phase change materials and thermal energy storage for buildings. Energy Build. 2015, 103, 414–419. [Google Scholar] [CrossRef]
  45. Cabeza, L.F.; Castell, A.; Barreneche, C.; De Gracia, A.; Fernández, A.I. Materials used as PCM in thermal energy storage in buildings: A review. Renew. Sustain. Energy Rev. 2011, 15, 1675–1695. [Google Scholar] [CrossRef]
  46. Tatsidjodoung, P.; Le Pierrès, N.; Luo, L. A review of potential materials for thermal energy storage in building applications. Renew. Sustain. Energy Rev. 2013, 18, 327–349. [Google Scholar] [CrossRef]
  47. Li, Y.; Wei, Y.; Dong, Z. Will China Achieve Its Ambitious Goal?—Forecasting the CO2 Emission Intensity of China towards 2030. Energies 2020, 13, 2924. [Google Scholar] [CrossRef]
  48. Alva, G.; Lin, Y.; Fang, G. An overview of thermal energy storage systems. Energy 2018, 144, 341–378. [Google Scholar] [CrossRef]
  49. Zhang, Y.; Zhou, G.; Lin, K.; Zhang, Q.; Di, H. Application of latent heat thermal energy storage in buildings: State-of-the-art and outlook. Build. Environ. 2007, 42, 2197–2209. [Google Scholar] [CrossRef]
  50. Lin, Y.; Jia, Y.; Alva, G.; Fang, G. Review on thermal conductivity enhancement, thermal properties and applications of phase change materials in thermal energy storage. Renew. Sustain. Energy Rev. 2018, 82, 2730–2742. [Google Scholar] [CrossRef]
  51. Huang, X.; Chen, X.; Li, A.; Atinafu, D.; Gao, H.; Dong, W.; Wang, G. Shape-stabilized phase change materials based on porous supports for thermal energy storage applications. Chem. Eng. J. 2019, 356, 641–661. [Google Scholar] [CrossRef]
  52. Zhao, C.Y.; Zhang, G.H. Review on microencapsulated phase change materials (MEPCMs): Fabrication, characterization and applications. Renew. Sustain. Energy Rev. 2011, 15, 3813–3832. [Google Scholar] [CrossRef]
  53. Pomianowski, M.; Heiselberg, P.; Zhang, Y. Review of thermal energy storage technologies based on PCM application in buildings. Energy Build. 2013, 67, 56–69. [Google Scholar] [CrossRef]
  54. Zhou, D.; Zhao, C.-Y.; Tian, Y. Review on thermal energy storage with phase change materials (PCMs) in building applications. Appl. Energy 2012, 92, 593–605. [Google Scholar] [CrossRef]
  55. Arteconi, A.; Hewitt, N.J.; Polonara, F. State of the art of thermal storage for demand-side management. Appl. Energy 2012, 93, 371–389. [Google Scholar] [CrossRef]
  56. Arteconi, A.; Hewitt, N.J.; Polonara, F. Domestic demand-side management (DSM): Role of heat pumps and thermal energy storage (TES) systems. Appl. Therm. Eng. 2013, 51, 155–165. [Google Scholar] [CrossRef]
  57. Comodi, G.; Giantomassi, A.; Severini, M.; Squartini, S.; Ferracuti, F.; Fonti, A.; Cesarini, D.N.; Morodo, M.; Polonara, F. Multi-apartment residential microgrid with electrical and thermal storage devices: Experimental analysis and simulation of energy management strategies. Appl. Energy 2015, 137, 854–866. [Google Scholar] [CrossRef]
  58. Barbieri, E.S.; Melino, F.; Morini, M. Influence of the thermal energy storage on the profitability of micro-CHP systems for residential building applications. Appl. Energy 2012, 97, 714–722. [Google Scholar] [CrossRef]
  59. Barbieri, E.S.; Spina, P.R.; Venturini, M. Analysis of innovative micro-CHP systems to meet household energy demands. Appl. Energy 2012, 97, 723–733. [Google Scholar] [CrossRef]
  60. Guelpa, E.; Verda, V. Thermal energy storage in district heating and cooling systems: A review. Appl. Energy 2019, 252, 113474. [Google Scholar] [CrossRef]
  61. Pisello, A.L. State of the art on the development of cool coatings for buildings and cities. Sol. Energy 2017, 144, 660–680. [Google Scholar] [CrossRef]
  62. Cao, V.D.; Pilehvar, S.; Salas-Bringas, C.; Szczotok, A.M.; Rodriguez, J.F.; Carmona, M.; Al-Manasir, N.; Kjøniksen, A.L. Microencapsulated phase change materials for enhancing the thermal performance of Portland cement concrete and geopolymer concrete for passive building applications. Energy Convers. Manag. 2017, 133, 56–66. [Google Scholar] [CrossRef]
  63. Saffari, M.; de Gracia, A.; Fernández, C.; Cabeza, L.F. Simulation-based optimization of PCM melting temperature to improve the energy performance in buildings. Appl. Energy 2017, 202, 420–434. [Google Scholar] [CrossRef]
  64. Lizana, J.; Chacartegui, R.; Barrios-Padura, A.; Valverde, J.M. Advances in thermal energy storage materials and their applications towards zero energy buildings: A critical review. Appl. Energy 2017, 203, 219–239. [Google Scholar] [CrossRef]
  65. Navarro, L.; de Gracia, A.; Colclough, S.; Browne, M.; McCormack, S.J.; Griffiths, P.; Cabeza, L.F. Thermal energy storage in building integrated thermal systems: A review. Part 1. Active storage systems. Renew. Energy 2016, 88, 526–547. [Google Scholar] [CrossRef]
  66. Stinner, S.; Huchtemann, K.; Müller, D. Quantifying the operational flexibility of building energy systems with thermal energy storages. Appl. Energy 2016, 181, 140–154. [Google Scholar] [CrossRef]
  67. Henze, G.P.; Felsmann, C.; Knabe, G. Evaluation of optimal control for active and passive building thermal storage. Int. J. Therm. Sci. 2004, 43, 173–183. [Google Scholar] [CrossRef]
  68. Lazaro, A.; Peñalosa, C.; Solé, A.; Diarce, G.; Haussmann, T.; Fois, M.; Zalba, B.; Gshwander, S.; Cabeza, L.F. Intercomparative tests on phase change materials characterisation with differential scanning calorimeter. Appl. Energy 2013, 109, 415–420. [Google Scholar] [CrossRef]
  69. Youssef, Z.; Delahaye, A.; Huang, L.; Trinquet, F.; Fournaison, L.; Pollerberg, C.; Doetsch, C. State of the art on phase change material slurries. Energy Convers. Manag. 2013, 65, 120–132. [Google Scholar] [CrossRef]
  70. Fleuchaus, P.; Godschalk, B.; Stober, I.; Blum, P. Worldwide application of aquifer thermal energy storage—A review. Renew. Sustain. Energy Rev. 2018, 94, 861–876. [Google Scholar] [CrossRef]
  71. Stein, W.H.; Buck, R. Advanced power cycles for concentrated solar power. Sol. Energy 2017, 152, 91–105. [Google Scholar] [CrossRef]
  72. Laing, D.; Lehmann, D.; Fi, M.; Bahl, C. Test results of concrete thermal energy storage for parabolic trough power plants, Journal of Solar Energy Engineering. Trans. ASME 2009, 131, 0410071–0410076. [Google Scholar] [CrossRef]
  73. Wirtz, M.; Kivilip, L.; Remmen, P.; Müller, D. 5th Generation District Heating: A novel design approach based on mathematical optimization. Appl. Energy 2020, 260, 114158. [Google Scholar] [CrossRef]
  74. Enescu, D.; Chicco, G.; Porumb, R.; Seritan, G. Thermal Energy Storage for Grid Applications: Current Status and Emerging Trends. Energies 2020, 13, 340. [Google Scholar] [CrossRef]
  75. Marin, P.; Saffari, M.; de Gracia, A.; Zhu, X.; Farid, M.M.; Cabeza, L.F.; Ushak, S. Energy savings due to the use of PCM for relocatable lightweight buildings passive heating and cooling in different weather conditions. Energy Build. 2016, 129, 274–283. [Google Scholar] [CrossRef]
  76. Moreno, P.; Solé, C.; Castell, A.; Cabeza, L.F. The use of phase change materials in domestic heat pump and air-conditioning systems for short term storage: A review. Renew. Sustain. Energy Rev. 2014, 39, 1–13. [Google Scholar] [CrossRef]
  77. Ibanez, M.; Lázaro, A.; Zalba, B.; Cabeza, L.F. An approach to the simulation of PCMs in building applications using TRNSYS. Appl. Therm. Eng. 2005, 25, 1796–1807. [Google Scholar] [CrossRef]
  78. Ferrer, G.; Solé, A.; Barreneche, C.; Martorell, I.; Cabeza, L.F. Corrosion of metal containers for use in PCM energy storage. Renew. Energy 2015, 76, 465–469. [Google Scholar] [CrossRef]
  79. Navarro, L.; de Gracia, A.; Niall, D.; Castell, A.; Browne, M.; McCormack, S.J.; Griffiths, P.; Cabeza, L.F. Thermal energy storage in building integrated thermal systems: A review. Part 2. Integration as passive system. Renew. Energy 2016, 85, 1334–1356. [Google Scholar] [CrossRef]
  80. Solé, A.; Martorell, I.; Cabeza, L.F. State of the art on gas–solid thermochemical energy storage systems and reactors for building applications. Renew. Sustain. Energy Rev. 2015, 47, 386–398. [Google Scholar] [CrossRef]
  81. Karaipekli, A.; Sarı, A. Capric–myristic acid/vermiculite composite as form-stable phase change material for thermal energy storage. Sol. Energy 2009, 83, 323–332. [Google Scholar] [CrossRef]
  82. Sarı, A.; Karaipekli, A. Preparation, thermal properties and thermal reliability of palmitic acid/expanded graphite composite as form-stable PCM for thermal energy storage. Sol. Energy Mater. Sol. Cells 2009, 93, 571–576. [Google Scholar] [CrossRef]
  83. Sarı, A. Thermal energy storage characteristics of bentonite-based composite PCMs with enhanced thermal conductivity as novel thermal storage building materials. Energy Convers. Manag. 2016, 117, 132–141. [Google Scholar] [CrossRef]
  84. Karaipekli, A.; Sarı, A. Capric–myristic acid/expanded perlite composite as form-stable phase change material for latent heat thermal energy storage. Renew. Energy 2008, 33, 2599–2605. [Google Scholar] [CrossRef]
  85. Arkar, C.; Medved, S. Free cooling of a building using PCM heat storage integrated into the ventilation system. Sol. Energy 2007, 81, 1078–1087. [Google Scholar] [CrossRef]
  86. Guarino, F.; Athienitis, A.; Cellura, M.; Bastien, D. PCM thermal storage design in buildings: Experimental studies and applications to solaria in cold climates. Appl. Energy 2017, 185, 95–106. [Google Scholar] [CrossRef]
  87. Kuznik, F.; Virgone, J. Experimental investigation of wallboard containing phase change material: Data for validation of numerical modeling. Energy Build. 2009, 41, 561–570. [Google Scholar] [CrossRef]
  88. Wang, Z.; Hong, T.; Piette, M.A. Building thermal load prediction through shallow machine learning and deep learning. Appl. Energy 2020, 263, 114683. [Google Scholar] [CrossRef]
  89. Finck, C.; Li, R.; Kramer, R.; Zeiler, W. Quantifying demand flexibility of power-to-heat and thermal energy storage in the control of building heating systems. Appl. Energy 2018, 209, 409–425. [Google Scholar] [CrossRef]
  90. Good, N.; Karangelos, E.; Navarro-Espinosa, A.; Mancarella, P. Optimization under Uncertainty of Thermal Storage-Based Flexible Demand Response with Quantification of Residential Users’ Discomfort. IEEE Trans. Smart Grid 2015, 6, 2333–2342. [Google Scholar] [CrossRef]
  91. Lu, Y.; Wang, S.; Sun, Y.; Yan, C. Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming. Appl. Energy 2015, 147, 49–58. [Google Scholar] [CrossRef]
  92. Dai, Y.; Chen, L.; Min, Y.; Chen, Q.; Hao, J.; Hu, K.; Xu, F. Dispatch Model for CHP with Pipeline and Building Thermal Energy Storage Considering Heat Transfer Process. IEEE Trans. Sustain. Energy 2019, 10, 192–203. [Google Scholar] [CrossRef]
  93. Nuytten, T.; Claessens, B.; Paredis, K.; Van Bael, J.; Six, D. Flexibility of a combined heat and power system with thermal energy storage for district heating. Appl. Energy 2013, 104, 583–591. [Google Scholar] [CrossRef]
  94. Vandermeulen, A.; van der Heijde, B.; Helsen, L. Controlling district heating and cooling networks to unlock flexibility: A review. Energy 2018, 151, 103–115. [Google Scholar] [CrossRef]
  95. Kensby, J.; Trüschel, A.; Dalenbäck, J.-O. Potential of residential buildings as thermal energy storage in district heating systems—Results from a pilot test. Appl. Energy 2015, 137, 773–781. [Google Scholar] [CrossRef]
  96. Romanchenko, D.; Kensby, J.; Odenberger, M.; Johnsson, F. Thermal energy storage in district heating: Centralised storage vs. storage in thermal inertia of buildings. Energy Convers. Manag. 2018, 162, 26–38. [Google Scholar] [CrossRef]
  97. Musolino, M.; Maggio, G.; D’Aleo, E.; Nicita, A. Three case studies to explore relevant features of emerging renewable energy communities in Italy. Renew. Energy 2023, 210, 540–555. [Google Scholar] [CrossRef]
  98. Novoa, L.; Flores, R.; Brouwer, J. Optimal renewable generation and battery storage sizing and siting considering local transformer limits. Appl. Energy 2019, 256, 113926. [Google Scholar] [CrossRef]
  99. Spertino, F.; Ciocia, A.; Di Leo, P.; Fichera, S.; Malgaroli, G.; Ratclif, A. Toward the Complete Self-Sufficiency of an nZEBs Microgrid by Photovoltaic Generators and Heat Pumps: Methods and Applications. IEEE Trans. Ind. Appl. 2019, 55, 7028–7040. [Google Scholar] [CrossRef]
  100. Kloppenburg, S.; Boekelo, M. Digital platforms and the future of energy provisioning: Promises and perils for the next phase of the energy transition. Energy Res. Soc. Sci. 2019, 49, 68–73. [Google Scholar] [CrossRef]
  101. Liu, J.; Yang, H.; Zhou, Y. Peer-to-peer trading optimizations on net-zero energy communities with energy storage of hydrogen and battery vehicles. Appl. Energy 2021, 302, 117578. [Google Scholar] [CrossRef]
  102. Ghiani, E.; Giordano, A.; Nieddu, A.; Rosetti, L.; Pilo, F. Planning of a Smart Local Energy Community: The Case of Berchidda Municipality (Italy). Energies 2019, 12, 4629. [Google Scholar] [CrossRef]
  103. Radl, J.; Fleischhacker, A.; Revheim, F.H.; Lettner, G.; Auer, H. Comparison of Profitability of PV Electricity Sharing in Renewable Energy Communities in Selected European Countries. Energies 2020, 13, 5007. [Google Scholar] [CrossRef]
  104. Pastore, L.M. Combining Power-to-Heat and Power-to-Vehicle strategies to provide system flexibility in smart urban energy districts. Sustain. Cities Soc. 2023, 94, 104548. [Google Scholar] [CrossRef]
  105. Backe, S.; Korpås, M.; Tomasgard, A. Heat and electric vehicle flexibility in the European power system: A case study of Norwegian energy communities. Int. J. Electr. Power Energy Syst. 2021, 125, 106479. [Google Scholar] [CrossRef]
  106. Zhang, J.; Cho, H.; Mago, P.J.; Zhang, H.; Yang, F. Multi-Objective Particle Swarm Optimization (MOPSO) for a Distributed Energy System Integrated with Energy Storage. J. Therm. Sci. 2019, 28, 1221–1235. [Google Scholar] [CrossRef]
  107. Li, Z.; Zhi, X.; Wu, Z.; Qian, G.; Jiang, R.; Wang, B.; Huang, R.; Yu, X. Role of different energy storage methods in decarbonizing urban distributed energy systems: A case study of thermal and electricity storage. J. Energy Storage 2023, 73, 108931. [Google Scholar] [CrossRef]
  108. Powell, K.M.; Kim, J.S.; Cole, W.J.; Kapoor, K.; Mojica, J.L.; Hedengren, J.D.; Edgar, T.F. Thermal energy storage to minimize cost and improve efficiency of a polygeneration district energy system in a real-time electricity market. Energy 2016, 113, 52–63. [Google Scholar] [CrossRef]
  109. Hussain, A.; Bui, V.-H.; Kim, H.-M.; Im, Y.-H.; Lee, J.-Y. Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations. Energies 2017, 10, 789. [Google Scholar] [CrossRef]
  110. Tooryan, F.; HassanzadehFard, H.; Dargahi, V.; Jin, S. A cost-effective approach for optimal energy management of a hybrid CCHP microgrid with different hydrogen production considering load growth analysis. Int. J. Hydrog. Energy 2022, 47, 6569–6585. [Google Scholar] [CrossRef]
  111. Martirano, L.; Habib, E.; Parise, G.; Greco, G.; Manganelli, M.; Massarella, F.; Parise, L. Demand Side Management in Microgrids for Load Control in Nearly Zero Energy Buildings. IEEE Trans. Ind. Appl. 2017, 53, 1769–1779. [Google Scholar] [CrossRef]
  112. Bartolini, A.; Carducci, F.; Muñoz, C.B.; Comodi, G. Energy storage and multi energy systems in local energy communities with high renewable energy penetration. Renew. Energy 2020, 159, 595–609. [Google Scholar] [CrossRef]
  113. Li, Z.; Xu, Y.; Feng, X.; Wu, Q. Optimal Stochastic Deployment of Heterogeneous Energy Storage in a Residential Multienergy Microgrid with Demand-Side Management. IEEE Trans. Ind. Inf. 2021, 17, 991–1004. [Google Scholar] [CrossRef]
  114. Zeyen, E.; Hagenmeyer, V.; Brown, T. Mitigating heat demand peaks in buildings in a highly renewable European energy system. Energy 2021, 231, 120784. [Google Scholar] [CrossRef]
  115. Directive (EU) 2018/2001. Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the Promotion of the Use of Energy from Renewable Sources (Recast). Available online: https://joint-research-centre.ec.europa.eu/welcome-jec-website/reference-regulatory-framework/renewable-energy-recast-2030-red-ii_en (accessed on 3 April 2024).
  116. Directive (EU) 2019/944. Directive (EU) 2019/944 of the European Parliament and of the Council of 5 June 2019 on Common Rules for the Internal Market for Electricity and Amending Directive 2012/27/EU. Available online: https://eur-lex.europa.eu/legal-content/IT/LSU/?uri=CELEX:32019L0944 (accessed on 14 November 2023).
  117. Lowitzsch, J.; Hoicka, C.E.; van Tulder, F.J. Renewable energy communities under the 2019 European Clean Energy Package—Governance model for the energy clusters of the future? Renew. Sustain. Energy Rev. 2020, 122, 109489. [Google Scholar] [CrossRef]
  118. Inês, C.; Guilherme, P.L.; Esther, M.-G.; Swantje, G.; Stephen, H.; Lars, H. Regulatory challenges and opportunities for collective renewable energy prosumers in the EU. Energy Policy 2020, 138, 111212. [Google Scholar] [CrossRef]
  119. Lezama, F.; Soares, J.; Hernandez-Leal, P.; Kaisers, M.; Pinto, T.; Vale, Z. Local Energy Markets: Paving the Path toward Fully Transactive Energy Systems. IEEE Trans. Power Syst. 2019, 34, 4081–4088. [Google Scholar] [CrossRef]
  120. Dóci, G.; Vasileiadou, E.; Petersen, A.C. Exploring the transition potential of renewable energy communities. Futures 2015, 66, 85–95. [Google Scholar] [CrossRef]
  121. Dóci, G.; Vasileiadou, E. “Let’s do it ourselves” Individual motivations for investing in renewables at community level. Renew. Sustain. Energy Rev. 2015, 49, 41–50. [Google Scholar] [CrossRef]
  122. Koirala, B.P.; Araghi, Y.; Kroesen, M.; Ghorbani, A.; Hakvoort, R.A.; Herder, P.M. Trust, awareness, and independence: Insights from a socio-psychological factor analysis of citizen knowledge and participation in community energy systems. Energy Res. Soc. Sci. 2018, 38, 33–40. [Google Scholar] [CrossRef]
  123. Gjorgievski, V.Z.; Cundeva, S.; Georghiou, G.E. Social arrangements, technical designs and impacts of energy communities: A review. Renew. Energy 2021, 169, 1138–1156. [Google Scholar] [CrossRef]
  124. Mittal, A.; Krejci, C.C.; Dorneich, M.C.; Fickes, D. An agent-based approach to modeling zero energy communities. Sol. Energy 2019, 191, 193–204. [Google Scholar] [CrossRef]
  125. Gaiser, K.; Stroeve, P. The impact of scheduling appliances and rate structure on bill savings for net-zero energy communities: Application to West Village. Appl. Energy 2014, 113, 1586–1595. [Google Scholar] [CrossRef]
  126. Lopes, R.A.; Martins, J.; Aelenei, D.; Lima, C.P. A cooperative net zero energy community to improve load matching. Renew. Energy 2016, 93, 1–13. [Google Scholar] [CrossRef]
  127. Feng, C.; Wen, F.; You, S.; Li, Z.; Shahnia, F.; Shahidehpour, M. Coalitional Game-Based Transactive Energy Management in Local Energy Communities. IEEE Trans. Power Syst. 2020, 35, 1729–1740. [Google Scholar] [CrossRef]
  128. Cosic, A.; Stadler, M.; Mansoor, M.; Zellinger, M. Mixed-integer linear programming based optimization strategies for renewable energy communities. Energy 2021, 237, 121559. [Google Scholar] [CrossRef]
  129. Campana, P.E.; Cioccolanti, L.; François, B.; Jurasz, J.; Zhang, Y.; Varini, M.; Stridh, B.; Yan, J. Li-ion batteries for peak shaving, price arbitrage, and photovoltaic self-consumption in commercial buildings: A Monte Carlo Analysis. Energy Convers. Manag. 2021, 234, 113889. [Google Scholar] [CrossRef]
  130. Barbour, E.; Parra, D.; Awwad, Z.; González, M.C. Community energy storage: A smart choice for the smart grid? Appl. Energy 2018, 212, 489–497. [Google Scholar] [CrossRef]
  131. Liu, Z.; Fan, G.; Sun, D.; Wu, D.; Guo, J.; Zhang, S.; Yang, X.; Lin, X.; Ai, L. A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero-energy communities and buildings. Energy 2022, 239, 122577. [Google Scholar] [CrossRef]
  132. Liu, Z.; Guo, J.; Wu, D.; Fan, G.; Zhang, S.; Yang, X.; Ge, H. Two-phase collaborative optimization and operation strategy for a new distributed energy system that combines multi-energy storage for a nearly zero energy community. Energy Convers. Manag. 2021, 230, 113800. [Google Scholar] [CrossRef]
  133. Fan, G.; Liu, Z.; Liu, X.; Shi, Y.; Wu, D.; Guo, J.; Zhang, S.; Yang, X.; Zhang, Y. Energy management strategies and multi-objective optimization of a near-zero energy community energy supply system combined with hybrid energy storage. Sustain. Cities Soc. 2022, 83, 103970. [Google Scholar] [CrossRef]
  134. Liu, J.; Zhou, Y.; Yang, H.; Wu, H. Uncertainty energy planning of net-zero energy communities with peer-to-peer energy trading and green vehicle storage considering climate changes by 2050 with machine learning methods. Appl. Energy 2022, 321, 119394. [Google Scholar] [CrossRef]
  135. Alabi, T.M.; Lu, L.; Yang, Z.; Zhou, Y. A novel optimal configuration model for a zero-carbon multi-energy system (ZC-MES) integrated with financial constraints. Sustain. Energy Grids Netw. 2020, 23, 100381. [Google Scholar] [CrossRef]
  136. Legge del 28 Febbraio 2020, n. 8. Available online: https://www.gazzettaufficiale.it/eli/id/2020/02/29/20G00021/sg (accessed on 9 April 2024).
  137. Decreto Legislativo 199/2021. DECRETO-LEGISLATIVO-8-novembre-2021-n.-199. 2021. Available online: https://www.gazzettaufficiale.it/atto/serie_generale/caricaDettaglioAtto/originario?atto.dataPubblicazioneGazzetta=2021-11-30&atto.codiceRedazionale=21G00214&elenco30giorni=true (accessed on 14 November 2023).
  138. Tatti, A.; Ferroni, S.; Ferrando, M.; Motta, M.; Causone, F. The Emerging Trends of Renewable Energy Communities’ Development in Italy. Sustainability 2023, 15, 6792. [Google Scholar] [CrossRef]
  139. Moroni, S.; Alberti, V.; Antoniucci, V.; Bisello, A. Energy communities in the transition to a low-carbon future: A taxonomical approach and some policy dilemmas. J. Environ. Manag. 2019, 236, 45–53. [Google Scholar] [CrossRef] [PubMed]
  140. Ceglia, F.; Esposito, P.; Marrasso, E.; Sasso, M. From smart energy community to smart energy municipalities: Literature review, agendas and pathways. J. Clean. Prod. 2020, 254, 120118. [Google Scholar] [CrossRef]
  141. Moreno-Munoz, A.; Bellido-Outeirino, F.J.; Siano, P.; Gomez-Nieto, M.A. Mobile social media for smart grids customer engagement: Emerging trends and challenges. Renew. Sustain. Energy Rev. 2016, 53, 1611–1616. [Google Scholar] [CrossRef]
  142. Martirano, L.; Rotondo, S.; Kermani, M.; Massarella, F.; Gravina, R. Power Sharing Model for Energy Communities of Buildings. IEEE Trans. Ind. Appl. 2021, 57, 170–178. [Google Scholar] [CrossRef]
  143. Fioriti, D.; Frangioni, A.; Poli, D. Optimal sizing of energy communities with fair revenue sharing and exit clauses: Value, role and business model of aggregators and users. Appl. Energy 2021, 299, 117328. [Google Scholar] [CrossRef]
  144. Krug, M.; Di Nucci, M.R.; Caldera, M.; De Luca, E. Mainstreaming Community Energy: Is the Renewable Energy Directive a Driver for Renewable Energy Communities in Germany and Italy? Sustainability 2022, 14, 7181. [Google Scholar] [CrossRef]
  145. The 2017 Renewable Energy Sources Act. Available online: https://www.bmwk.de/Redaktion/EN/Artikel/Energy/res-2017.html (accessed on 9 April 2024).
  146. Hanke, F.; Lowitzsch, J. Empowering Vulnerable Consumers to Join Renewable Energy Communities—Towards an Inclusive Design of the Clean Energy Package. Energies 2020, 13, 1615. [Google Scholar] [CrossRef]
  147. Schweizer-Ries, P. Energy sustainable communities: Environmental psychological investigations. Energy Policy 2008, 36, 4126–4135. [Google Scholar] [CrossRef]
  148. Real Decreto 244/2019, de 5 de Abril, por el que se Regulan las Condiciones Administrativas, Técnicas y Económicas del Autoconsumo de Energía Eléctrica. Available online: https://www.boe.es/buscar/doc.php?id=BOE-A-2019-5089 (accessed on 9 April 2024).
  149. Faia, R.; Soares, J.; Pinto, T.; Lezama, F.; Vale, Z.; Corchado, J.M. Optimal Model for Local Energy Community Scheduling Considering Peer to Peer Electricity Transactions. IEEE Access 2021, 9, 12420–12430. [Google Scholar] [CrossRef]
  150. Tostado-Véliz, M.; Kamel, S.; Hasanien, H.M.; Turky, R.A.; Jurado, F. Optimal energy management of cooperative energy communities considering flexible demand, storage and vehicle-to-grid under uncertainties. Sustain. Cities Soc. 2022, 84, 104019. [Google Scholar] [CrossRef]
  151. Romero-Rubio, C.; de Andrés Díaz, J.R. Sustainable energy communities: A study contrasting Spain and Germany. Energy Policy 2015, 85, 397–409. [Google Scholar] [CrossRef]
  152. Minuto, F.D.; Lanzini, A. Energy-sharing mechanisms for energy community members under different asset ownership schemes and user demand profiles. Renew. Sustain. Energy Rev. 2022, 168, 112859. [Google Scholar] [CrossRef]
  153. D’Adamo, I.; Mammetti, M.; Ottaviani, D.; Ozturk, I. Photovoltaic systems and sustainable communities: New social models for ecological transition. The impact of incentive policies in profitability analyses. Renew. Energy 2023, 202, 1291–1304. [Google Scholar] [CrossRef]
  154. Moncecchi, M.; Meneghello, S.; Merlo, M. A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities. Appl. Sci. 2020, 10, 8166. [Google Scholar] [CrossRef]
  155. Ghiani, E.; Trevisan, R.; Rosetti, G.L.; Olivero, S.; Barbero, L. Energetic and Economic Performances of the Energy Community of Magliano Alpi after One Year of Piloting. Energies 2022, 15, 7439. [Google Scholar] [CrossRef]
  156. Olivero, S.; Ghiani, E.; Rosetti, G.L. The first Italian Renewable Energy Community of Magliano Alpi. In Proceedings of the 2021 IEEE 15th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), Florence, Italy, 14–16 July 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 1–6. [Google Scholar] [CrossRef]
  157. Tarpani, E.; Piselli, C.; Fabiani, C.; Pigliautile, I.; Kingma, E.J.; Pioppi, B.; Pisello, A.L. Energy Communities Implementation in the European Union: Case Studies from Pioneer and Laggard Countries. Sustainability 2022, 14, 12528. [Google Scholar] [CrossRef]
  158. Azarova, V.; Cohen, J.; Friedl, C.; Reichl, J. Designing local renewable energy communities to increase social acceptance: Evidence from a choice experiment in Austria, Germany, Italy, and Switzerland. Energy Policy 2019, 132, 1176–1183. [Google Scholar] [CrossRef]
  159. Hahnel, U.J.J.; Herberz, M.; Pena-Bello, A.; Parra, D.; Brosch, T. Becoming prosumer: Revealing trading preferences and decision-making strategies in peer-to-peer energy communities. Energy Policy 2020, 137, 111098. [Google Scholar] [CrossRef]
  160. Kim, M.-H.; Kim, D.; Heo, J.; Lee, D.-W. Techno-economic analysis of hybrid renewable energy system with solar district heating for net zero energy community. Energy 2019, 187, 115916. [Google Scholar] [CrossRef]
  161. Cielo, A.; Margiaria, P.; Lazzeroni, P.; Mariuzzo, I.; Repetto, M. Renewable Energy Communities business models under the 2020 Italian regulation. J. Clean. Prod. 2021, 316, 128217. [Google Scholar] [CrossRef]
  162. Zheng, S.; Jin, X.; Huang, G.; Lai, A.C.K. Coordination of commercial prosumers with distributed demand-side flexibility in energy sharing and management system. Energy 2022, 248, 123634. [Google Scholar] [CrossRef]
  163. Ding, X.; Guo, Q.; Qiannan, T.; Jermsittiparsert, K. Economic and environmental assessment of multi-energy microgrids under a hybrid optimization technique. Sustain. Cities Soc. 2021, 65, 102630. [Google Scholar] [CrossRef]
  164. Ke, H. Phase diagrams, eutectic mass ratios and thermal energy storage properties of multiple fatty acid eutectics as novel solid-liquid phase change materials for storage and retrieval of thermal energy. Appl. Therm. Eng. 2017, 113, 1319–1331. [Google Scholar] [CrossRef]
  165. Alva, G.; Lin, Y.; Liu, L.; Fang, G. Synthesis, characterization and applications of microencapsulated phase change materials in thermal energy storage: A review. Energy Build. 2017, 144, 276–294. [Google Scholar] [CrossRef]
  166. Wahid, M.A.; Hosseini, S.E.; Hussen, H.M.; Akeiber, H.J.; Saud, S.N.; Mohammad, A.T. An overview of phase change materials for construction architecture thermal management in hot and dry climate region. Appl. Therm. Eng. 2017, 112, 1240–1259. [Google Scholar] [CrossRef]
  167. Horstink, L.; Wittmayer, J.M.; Ng, K. Pluralising the European energy landscape: Collective renewable energy prosumers and the EU’s clean energy vision. Energy Policy 2021, 153, 112262. [Google Scholar] [CrossRef]
  168. Hashemipour, N.; del Granado, P.C.; Aghaei, J. Dynamic allocation of peer-to-peer clusters in virtual local electricity markets: A marketplace for EV flexibility. Energy 2021, 236, 121428. [Google Scholar] [CrossRef]
  169. Jasiński, J.; Kozakiewicz, M.; Sołtysik, M. Determinants of Energy Cooperatives’ Development in Rural Areas—Evidence from Poland. Energies 2021, 14, 319. [Google Scholar] [CrossRef]
  170. Frieden, D.; Tuerk, A.; Antunes, A.R.; Athanasios, V.; Chronis, A.-G.; d’Herbemont, S.; Kirac, M.; Marouço, R.; Neumann, C.; Catalayud, E.P.; et al. Are We on the Right Track? Collective Self-Consumption and Energy Communities in the European Union. Sustainability 2021, 13, 12494. [Google Scholar] [CrossRef]
Figure 1. Number of documents published per year in thermal energy storage.
Figure 1. Number of documents published per year in thermal energy storage.
Sustainability 16 05895 g001
Figure 2. Number of publications over the years on TES in buildings and districts for the top 3 territories between 1993 and 2023.
Figure 2. Number of publications over the years on TES in buildings and districts for the top 3 territories between 1993 and 2023.
Sustainability 16 05895 g002
Figure 3. Number of publications over the years on TES in buildings and districts for the top EU27 countries between 1993 and 2023.
Figure 3. Number of publications over the years on TES in buildings and districts for the top EU27 countries between 1993 and 2023.
Sustainability 16 05895 g003
Figure 4. Top 5 journals publishing on TES applied to buildings and districts.
Figure 4. Top 5 journals publishing on TES applied to buildings and districts.
Sustainability 16 05895 g004
Figure 5. Network visualization of author keywords on “TES on buildings and districts” elaborated with VOSviewer.
Figure 5. Network visualization of author keywords on “TES on buildings and districts” elaborated with VOSviewer.
Sustainability 16 05895 g005
Figure 6. Co-occurrence and links concerning the keywords (a) “distributed energy system” and (b) “smart grid”.
Figure 6. Co-occurrence and links concerning the keywords (a) “distributed energy system” and (b) “smart grid”.
Sustainability 16 05895 g006
Figure 7. Co-occurrence and links regarding the keywords (a) “micro-grid”, and (b) “sector coupling”.
Figure 7. Co-occurrence and links regarding the keywords (a) “micro-grid”, and (b) “sector coupling”.
Sustainability 16 05895 g007
Figure 8. Number of publications per year in RECs.
Figure 8. Number of publications per year in RECs.
Sustainability 16 05895 g008
Figure 9. Number of publications over the years on RECs for the top 3 territories between 1993 and 2023.
Figure 9. Number of publications over the years on RECs for the top 3 territories between 1993 and 2023.
Sustainability 16 05895 g009
Figure 10. Number of publications during the year in RECs for the top 3 EU27 countries between 1993 and 2023.
Figure 10. Number of publications during the year in RECs for the top 3 EU27 countries between 1993 and 2023.
Sustainability 16 05895 g010
Figure 11. Top 5 journals publishing on RECs.
Figure 11. Top 5 journals publishing on RECs.
Sustainability 16 05895 g011
Figure 12. Co-occurrence of keywords related to RECs.
Figure 12. Co-occurrence of keywords related to RECs.
Sustainability 16 05895 g012
Figure 13. Co-occurrences and links with the keyword “energy storage”.
Figure 13. Co-occurrences and links with the keyword “energy storage”.
Sustainability 16 05895 g013
Figure 14. Co-occurrence of the keywords related to the merged queries.
Figure 14. Co-occurrence of the keywords related to the merged queries.
Sustainability 16 05895 g014
Figure 15. Co-occurrence of the keywords (a) “energy community” and (b) “TES”.
Figure 15. Co-occurrence of the keywords (a) “energy community” and (b) “TES”.
Sustainability 16 05895 g015aSustainability 16 05895 g015b
Figure 16. Co-occurrence of the keywords (a) “energy storage” and (b) “solar energy”.
Figure 16. Co-occurrence of the keywords (a) “energy storage” and (b) “solar energy”.
Sustainability 16 05895 g016aSustainability 16 05895 g016b
Figure 17. Co-occurrence of the keywords “numerical model”.
Figure 17. Co-occurrence of the keywords “numerical model”.
Sustainability 16 05895 g017
Figure 18. Overlay visualization for merged queries.
Figure 18. Overlay visualization for merged queries.
Sustainability 16 05895 g018
Table 1. Queries used for the Scopus database.
Table 1. Queries used for the Scopus database.
TopicQuery
TES in buildings and districtsTITLE-ABS-KEY (“thermal energy storage*” AND (“building*” OR “built environment” OR “district*” OR “municipal*” OR “neighbo$rhood”) AND NOT (“aircraft” OR “transport* application” OR “transport sector”))
Renewable energy communities“energy communit*” AND “renewable*” AND NOT (“renewable energy forecasting” OR “building energy community” OR “energy corridors” OR “ solar renewable energy community” OR “NGH” OR “Cu2Se”OR “SDG 7”OR “computational intelligence (CI) community” OR “MEG” OR “nuclear” OR “materials, chemistry, and renewable energy communities”)
Table 2. Top 5 institutions with documents published on TES in buildings and districts.
Table 2. Top 5 institutions with documents published on TES in buildings and districts.
AffiliationPublicationCountry
Universitat de Lleida123Spain
Ministry of Education of the People’s Republic of China108China
Tsinghua University71China
Karadeniz Technical University59Turkey
King Fahd University of Petroleum and Minerals58Saudi Arabia
Table 3. Top 10 authors on documents published on TES in buildings and districts.
Table 3. Top 10 authors on documents published on TES in buildings and districts.
Author Name# Publications AffiliationCountry
Cabeza, L.F.121Universitat de LleidaSpain
Sari, A.84Karadeniz Technical UniversitySaudi Arabia
Barreneche, C.40Universitat de BarcelonaSpain
Tyagi, V.V.37Shri Mata Vashini Devi UniversityIndia
Fernández, A.I.34Universitat de BarcelonaSpain
Hekimoğlu, G.34Karadeniz Technical UniversityTurkey
Kim, S.33Yonsei UniversitySouth Korea
de Gracia, A.28Universitat de LleidaSpain
Gencel, O.25Bartin ÜniversitesiTurkey
Pisello, A.L.25Università degli Studi di PerugiaItaly
Table 4. Highest number of occurrences on “TES on buildings and districts”.
Table 4. Highest number of occurrences on “TES on buildings and districts”.
KeywordNumber of Occurrences
TES (thermal energy storage)1609
PCM (phase change material)1257
numerical model230
optimization204
energy storage184
district heating168
latent heat TES164
solar energy155
heat pump138
phase change enthalpy124
Table 5. Top 5 institutions with documents published on RECs.
Table 5. Top 5 institutions with documents published on RECs.
AffiliationPublicationsCountry
Politecnico di Torino47Italy
Sapienza Università di Roma46Italy
Politecnico di Milano27Italy
Delft University of Technology26The Netherlands
Technische Universität Wien26Austria
Table 6. Top 10 authors with documents published on RECs.
Table 6. Top 10 authors with documents published on RECs.
Author Name# PublicationsAffiliationCountry
Vale, Z.20Instituto Politécnico do PortoPortugal
Ghiani, E.16Università degli Studi di CagliariItaly
Martirano, L.15Sapienza University of RomeItaly
Menniti, D.12Università della CalabriaItaly
Pinnarelli, A.12Università della CalabriaItaly
Table 7. Author keywords with the highest number of occurrences on “renewable energy communities”.
Table 7. Author keywords with the highest number of occurrences on “renewable energy communities”.
KeywordNumber of Occurrences
energy community401
renewables258
renewable energy communities219
energy storage63
prosumers62
smart grid58
microgrid57
optimization55
energy management43
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Brunelli, L.; Borri, E.; Pisello, A.L.; Nicolini, A.; Mateu, C.; Cabeza, L.F. Thermal Energy Storage in Energy Communities: A Perspective Overview through a Bibliometric Analysis. Sustainability 2024, 16, 5895. https://doi.org/10.3390/su16145895

AMA Style

Brunelli L, Borri E, Pisello AL, Nicolini A, Mateu C, Cabeza LF. Thermal Energy Storage in Energy Communities: A Perspective Overview through a Bibliometric Analysis. Sustainability. 2024; 16(14):5895. https://doi.org/10.3390/su16145895

Chicago/Turabian Style

Brunelli, Luca, Emiliano Borri, Anna Laura Pisello, Andrea Nicolini, Carles Mateu, and Luisa F. Cabeza. 2024. "Thermal Energy Storage in Energy Communities: A Perspective Overview through a Bibliometric Analysis" Sustainability 16, no. 14: 5895. https://doi.org/10.3390/su16145895

APA Style

Brunelli, L., Borri, E., Pisello, A. L., Nicolini, A., Mateu, C., & Cabeza, L. F. (2024). Thermal Energy Storage in Energy Communities: A Perspective Overview through a Bibliometric Analysis. Sustainability, 16(14), 5895. https://doi.org/10.3390/su16145895

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop