Next Article in Journal
Improve Ship Propeller Efficiency via Optimum Design of Propeller Boss Cap Fins
Next Article in Special Issue
Resource and Energy Saving Control of the Steelmaking Converter Process, Taking into Account Waste Recycling
Previous Article in Journal
Model Predictive Control of DC–DC Boost Converter Based on Generalized Proportional Integral Observer
Previous Article in Special Issue
New Method of Degradation Process Identification for Reliability-Centered Maintenance of Energy Equipment
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Key Performance Indicators for Smart Energy Systems in Sustainable Universities

Research Center for Sustainable Products and Processes, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania
*
Author to whom correspondence should be addressed.
Energies 2023, 16(3), 1246; https://doi.org/10.3390/en16031246
Submission received: 23 December 2022 / Revised: 13 January 2023 / Accepted: 17 January 2023 / Published: 23 January 2023
(This article belongs to the Special Issue Smart Energy and Sustainable Environment)

Abstract

:
Sustainable campus management includes energy-saving measures and waste reduction and has become important to many universities, being part of the institution’s societal responsibility. Smart energy systems (SESs), as part of campus energy management, can bring many benefits, including increased efficiency, reduced energy consumption, reduced emissions, increased reliability, and real-time control, and facilitate the integration of the renewable energy systems (RES). Despite the growing interest in energy efficiency and for the initiatives and projects to implement SESs, there are no universally accepted standards for assessing the performance of SESs, with most techniques being dedicated to subsystems. A KPI (key performance indicator) framework for evaluating the SESs’ performance from university campuses is proposed, starting from the current findings and priorities from the scientific literature, energy standards, legislation, and university rankings. The framework can support the implementation, operation, and evaluation of the SESs from university campuses, based on SES requirements and the stakeholders’ goals. Unlike previously developed solutions, the framework is focused not only on the technical side of SESs but also on the role that education, research, and innovation should have in sustainable development, making universities key contributors to achieving these goals.

1. Introduction

Modern society is facing increasing problems of climate change, caused mainly by excessive pollution. Industry [1], internal combustion engines [2], and even buildings, mainly from the perspective of high energy consumption [3], are responsible for the environmental degradation.
If traditional studies were focused on the problem of internal combustion engines, recent studies have begun to address the issue of buildings in terms of operation and energy consumption [3,4,5]. Whether we are talking about a residential building, an office building, or a university, the mode of operation and energy performance are the main elements that influence energy consumption and implicitly the pollution generated by its operation [3].
The issue of better energy use, translated into reduced costs and CO2 emissions, is increasingly emphasized in the building sector, which is responsible for around 40% of total energy consumption and 36% of total CO2 emissions in the European Union (EU) [6,7].
Connolly, Lund, and Mathiesen, (2016) showed that in order to achieve the EU’s targets to reduce CO2 by 80% by 2050, compared to 1990 levels, the total annual cost of the energy system will be about 3% higher [8]. However, if the implementation of intelligent energy systems, based 100% on renewable energy, is moved forward, then the costs will be 12% higher compared to classic systems based on fossil fuels [8].
The recent COVID-19 pandemic and Russia’s war on Ukraine caused drastic disruptions in energy supply chains, and hampered investments, with a long-term impact on pollution and low-carbon energy transitions [9].
There is an increasing number of studies in the last years discussing the transition to 100% renewable energy [10,11,12,13] within smart energy systems [14,15,16,17] that combine smart electricity, heating, and gas networks [14,18,19]. Lund et al. (2018) considered that the integration between energy sectors can lead to the use of more efficient and low-cost storage systems, to balance fluctuating renewable energy generation [20]. Some authors highlighted global energy challenges such as: costs, effectiveness, environment protection, efficiency, resource use, sustainability, etc. [21].
Lund (2014) defined the Smart Energy System (SES) as “an approach in which smart electricity, thermal and gas grids are combined with storage technologies and coordinated to identify synergies between them in order to achieve an optimal solution for each individual sector as well as for the overall energy system” [22].
The SES can bring many benefits, including increased efficiency, reduced energy consumption, reduced emissions, increased reliability, real-time control, and facilitating the integration of the renewable energy systems (RES) and thus reduced costs. Basically, an SES can help provide a more sustainable mode of operation [23].
In this context, many authors have focused on energy efficiency to reduce both carbon emissions and maintenance [7,24,25,26] and operation costs [27,28,29,30].
On the other hand, several studies have indicated the importance and influence of stakeholder behavior on the energy efficiency of the buildings [31,32,33,34]. The real consumption of a building could become two or even three times higher when compared to the projected consumption, as a result of the stakeholder’s behavior [35,36]. This fact provides a perspective on the importance of user education and especially on the importance of a culture of sustainability among stakeholders.
Previous studies on sustainable universities were focused on energy efficiency in order to reduce both carbon emissions and maintenance and operating costs [27,28,29,30], and, on the other hand, on the introduction of sustainability in the curriculum to promote responsible behavior [37,38,39,40,41]. A number of universities have incorporated the SDGs (Sustainable Development Goals) into their institutional strategies [42].
Starting from such strategies, specific projects have been developed, for instance, the evaluation of the environmental impact of different hand-drying options in public restrooms from university campuses. Such a project can give consumers important information about which option is more eco-friendly and can be used to guide purchasing decisions and promote the use of more sustainable products [37].
Despite the growing interest in energy efficiency and for the initiatives and projects to implement SESs, there are no universally accepted KPIs for defining and measuring the performance of SESs, with most assessment techniques being dedicated to subsystems of SESs [43,44,45].
The main purpose of this paper is to define a KPI framework for evaluating SESs from university campuses, based on SES requirements and the stakeholders’ goals, starting from the current findings and priorities from the scientific literature, energy standards, legislation, and university rankings. Unlike previously developed solutions, our framework is focused not only on the technical side of SESs, but also on the role that education, research, and innovation should have in sustainable development, making universities key contributors to achieving the goals. Universities should adopt policies for clean energy and on separating investments from carbon-intensive energy industries, and the progress toward this goal needs to be continuously evaluated based on relevant KPIs.

2. Materials and Methods

A search strategy was defined, together with eligibility criteria, and a systematic review was performed based on the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) checklist. The results were further processed with the VOS Viewer software for the network and hierarchical clustering analysis.
The bibliographic data for this study were collected from the Scopus database in November 2022, within the indexed timespan from 1 January 2010 to 30 November 2022.
The initial search query for executing this systematic review was defined as the following combination of keywords: “smart energy” and “system” and “building” and “university” or “smart-university” or “E-university”, and it returned 665 publications.
Figure 1 shows the study selection diagram.
The citation criteria (at least 1 citation/article) removed 328 publications. Titles and abstracts that were not directly related to our research were also removed. Of the remaining 205 titles, 130 were removed after full-text screening, as we considered they were not directly related to our topic. When the 75 articles were reviewed, 71 further relevant titles (based on the established criteria) were identified in their reference lists, and included in our study. All these 146 articles were retained for the detailed review.

3. Results and Discussions

3.1. Results of the Bibliometric Analysis

Three main clusters can be observed in the VOS Viewer bibliometric mapping (Figure 2), with labels and circles representing the most frequently used keywords, the size of the label, and the circle being proportional to the number of occurrences for each keyword. The color of an item is determined by the group to which the item belongs. Lines between articles represent links. They appear between two circles, if the keywords associated with them appeared together, more than a predetermined number of times.
We call the red cluster smart energy system and its components, because here we can find labels specific to energy resources, conversion, storage, and demand, that will be considered parts of the SES.
The blue cluster is directly related to the sustainable university: its buildings that are the main energy consumers [6,7], on the one hand, and, on the other hand, the know how generated by universities for developing and adopting new SES solutions.
The green cluster seems to be the most complex, with labels associated with energy, smart energy systems, and sustainable smart energy systems, but also labels related to performance, efficiency, and stakeholder. This suggests, in our interpretation, the orientation of the recent studies on SES performance and, on the other hand, on policies and evaluations that should be focused on stakeholders’ needs.
It is not our intention to suggest a clear separation between the three clusters; it is merely a grouping of the topics of interest from specialized, recent literature, and also a structuration of our discussions to follow in the next chapter.

3.2. Smart Energy System and Its Components

Starting from the Lund’s model [14], we will consider that an SES has four basic components:
  • Resources—refer to all the resources that ensure the operation of the system, mainly RESs;
  • Conversion—includes the technologies that provide the conversion of the RESs to such energy as electricity, fuel, or heat;
  • Storage—refers to the storage of excess energy in various forms so that it can be consumed later;
  • Demand—refers to the balance between demand, production, and storage [18].

3.2.1. Resources

Previous studies approaching SESs suggested that such systems should be based 100% on RESs: solar, wind, geothermal, biomass, hydro, hydrogen, waves, and tides [12,14,15].
However, of these types of energy, the solar energy [46,47,48] and wind energy [49,50,51,52] are mentioned most frequently in the category of the so-called fluctuating RESs.
Particular attention is given to the use of hydrogen [21,53,54,55], the integration of geothermal energy [56], or hybrid systems that use solar and geothermal energy [11,57].
Other studies suggested the need for power-to-gas to reduce the use of biomass to a sustainable level, using electrofuels [58,59,60,61]. Electrofuels are defined as fuels produced from electricity, water, and carbon or nitrogen, and are considered a useful option in all energy sectors to replace fossil fuels [16,62]. In 2014, Lund et al. described the process of obtaining electrofuels starting from the conversion of electricity through electrolysis into hydrogen. Subsequently, the produced hydrogen is used in two possible ways [13]:
  • in a process called hydrogenation which represents the stimulation of gasified or fermented biomass;
  • merged with CO2 emissions from such sources as energy or industrial facilities.
Depending on the chosen method of obtaining electrofuels, they are called bio-electrofuels or CO2-electrofuels [13].

3.2.2. Conversion

The flexibility of the SESs depends primarily on the conversion system, thus making the transition from simple approaches used today in energy systems, to interconnected approaches [8,14].
The main conversion systems found in the literature are as follows: photovoltaics [14,63,64,65]; solar panels [14,66,67,68]; solar plants [66,69]; wind turbine [70,71,72]; combined heat and power (CHP) [22,73,74,75]; heat pumps [66,76,77,78]; boilers [66,79,80]; and power plants [67].
There is a focus of the authors on fluctuating energy conversion systems, and almost every design of a SES involves photovoltaics [14,66] and wind turbines [64,81,82]. Less popular than photovoltaics, wind turbines are preferred in Nordic European countries such as Denmark (the leader in the implementation of these conversion systems). In 2020, wind turbines in Denmark supplied 50% of the final demand for electricity [83].
Lund et al. (2012) considered that heat pumps as well as additional heat storage capacities should be combined in SES together with cogeneration plants and operated in such a way that RES can be efficiently integrated, without losing the overall efficiency of the system [18].
Several studies showed that heat pumps represent the key conversion solution, connecting electrical and thermal sectors [14,84,85]. Buffa et al. (2018) considered that heat pumps should be integrated in low-temperature excess heat recovery installations [86]. Dincer and Acarac, (2017) emphasized that increasing the number of products from the same energy source will lead to a decrease in emissions per product unit and an increase in efficiency, referring to multigeneration systems such as the following [11]: cogeneration—heat and electricity production (CHP); trigeneration—heat, cooling, and electricity production (CCHP); and quadgeneration—heat, cooling, hydrogen, and electricity production (CCHP-H2).

3.2.3. Storage

Energy storage is another important element in assuring the flexibility of the SESs [12,87] permitting to integrate a higher share of fluctuating RES [12,18,19], and it facilitates the transition to 100% RES [14]. Several authors emphasized the need to use storage solutions for system reliability and flexibility [88,89]. Three main types of storage in the SES framework can be identified in the literature: electricity [21,52,66], thermal [10,72], and fuel [10,64,72].
Regarding electricity storage, the main storage systems identified in the literature are as follows: batteries [14,63,66,72] and electric vehicles (Evs) [4,79,90]. Some studies have shown that the electricity surplus should not be directly stored in the battery (off grid) [91], with other solutions being recommended: thermal storage by transforming electrical energy into thermal energy, storage in the heating networks, or chemical storage (hydrogen and methane) [92].
Electric energy storage systems are very expensive, especially because fluctuating RES require numerous conversion systems. The losses are very high, for example, electrical energy storage is approximately 100 times more expensive than thermal storage [14]. Thus, except for powering heat pumps, electric storage is not feasible to meet the flexible demands of consumers. Electricity storage systems should facilitate power saving or securing grid stability [76]. However, some authors indicated that electric vehicles offer the possibility of sources of electrical energy that can be programmed on demand, but also of storing the electrical energy itself that can later be reintroduced into the grid [93,94]. The solution of electric energy storage through EVs due to the flexibility advantages of the system they offer has become a topic increasingly researched by authors in recent years, and integrated in their models and scenarios [95,96,97,98].
Thermal storage is a much more efficient method of storage that involves lower costs compared to electrical storage [14,92]. The main thermal energy storage systems identified in the literature are as follows: water tank [21,64,67,90] and pit thermal energy storage [99,100,101].

3.2.4. Demand

In the case of a city, or, at a smaller scale, a university campus, there are three main types of useful energy demand [14,102]: electricity, thermal (heating and cooling), and fuel.
Lund et al. (2014) considered that the energy supply can be facilitated by using systems such as heat pumps and electric boilers [76]. Some authors believed that due to their high consumption and thermal inertia, buildings can ensure flexible demand [103].
Bačeković and Østergaard proposed a balance between demand, production, and storage [64], that should be updated, in our opinion, including the energy losses.
Regarding energy consumption in the building sector, one of the main concerns was related to the energy performance difference between the designed energy consumption of buildings and their actual operation consumption [25,27,28,104]. This difference is influenced by three factors: the performance of energy systems, the user behavior, and the insulation performance, especially in the case of buildings [105,106]. Numerous studies conducted in recent years have shown that one of the most influential factors affecting the energy consumption of buildings is the behavior of the occupants [31,107]. The difference between the estimated consumption when designing the building and the actual final consumption can be 200% higher [35], or even 300% higher [36], depending on user behavior. Electricity demand should be the main concern for energy conservation in universities, according to some authors who considered that the key factor involved in energy consumption is the cooling system [108]. Other authors emphasized the consumption of electricity for lighting; in this case, the estimated percentage of energy consumption varies between 20% [109] and 45% [110] of the building’s total energy consumption. In what concerns the educational institutions, classrooms are the biggest consumers of energy in terms of lighting, reaching up to 50% of the total building’s energy consumption [111].

3.3. SESs and Sustainable Universities

Sustainable campus management includes energy-saving measures, resource efficiency, and waste reduction, and has become important to many universities, and considered part of the institution’s societal responsibility [112].
The role of higher education in sustainable development has not always been very clear, because many institutions have not realized that integrating the Sustainable Development Goals (SDGs) [42] in their teaching and research programs could provide the opportunity to expand education for sustainable development [113], and, at the same time, it could serve as an important catalyst for student involvement [114].
Velazquez, Munguia, Platt, and Taddei (2006) proposed a model to assist universities to improve the effectiveness of their potential or current sustainability initiatives through the identification of strategies and opportunities for sustainability within universities. The model is based on four strategies delivered through a set of tailored initiatives: developing a sustainability vision for the university; the mission; a sustainability committee creating policies, targets, and objectives; and sustainability strategies [115].
Grecu (2012) showed also that a sustainable university should be based on six fundamental principles: leadership and vision, social network, participation, education and learning, research integration, and performance management [116].
Universities need to more closely assess the operation and management of the campus as well as the energy performance of buildings and their systems and facilities [117].
A university campus is very similar to a city, facing similar problems in general [118], from the problem of parking spaces to the energetics’ administrative problems such as electricity, hot water, or the gas grid. Most studies have focused on the concept of a smart city and less on smart universities [118]. However, both problems and solutions can be similar. Thus, thorough research should consider the SES whether it is a university building, an office building, or even a smart city.

3.4. KPI Framework for SESs in Sustainable Universities

Although many universities adopted sustainability-oriented policies and implemented SESs, there is a little evidence on how such systems are assessed for their performance and the results of these assessments.
Efkarpidis, et al. (2022) proposed a generic KPI framework for the evaluation of SESs installed in application areas of different scales. The framework is based on four main layers, which are required for the definition of the application area, the involved stakeholders, the SES requirements, and the stakeholders’ objectives, and the application area of each SES deployment is determined based on four levels of spatial aggregation varying from single buildings to communities, cities, or regions [45].
The only framework we could identify for university campuses was proposed by Saleh et al. (2015) for Malaysian universities to ensure sustainability, and consists of five clusters: top management support, comprehensive energy management team, stakeholders’ involvement, awareness, and risk management [119]. The framework is based on the Talloires Declaration ten-point action plan for incorporating sustainability and environmental literacy in teaching, research, operations, and outreach at colleges and universities [120].
The most relevant assessments of universities from the sustainability point of view come from ranking institutions who created in the last years specific indexes (for instance, QS World University Rankings: Sustainability, Times Higher Education—Impact Ranking), to show how universities are developing towards the UN Sustainable Development Goals [121], or how universities are taking actions to tackle the world’s most pressing environmental and social issues [122]. There are also rankings (Green Metrics, for instance) providing an overview of the sustainability policies [123], assessing the universities based on the environmental protection and ethical issues (People & Planet University League) [124], or highlighting the schools that have launched the most important initiatives to reduce campus waste and energy consumption, promote alternative modes of transport, fund environmentally friendly proposals from students and faculties, and take other measures for the benefit of the environment (Best Colleges) [125].
The KPI framework proposed in our study (Figure 3) is developed starting from the current findings and priorities from the scientific literature, energy standards, legislation, university rankings, and specialized websites.
The framework is based on a bottom-up approach and can support the implementation, operation, and evaluation of the SESs from university campuses, based on SES requirements and the stakeholders’ goals. Unlike previously developed solutions, our framework is focused not only on the technical side of SESs but also on the role that education, research, and innovation should have in sustainable development, making universities key contributors to achieving the goals. Universities should adopt policies for clean energy and on separating investments from carbon-intensive energy industries, and the progress toward this goal needs to be continuously evaluated based on relevant KPIs.
The KPIs were organized in 4 clusters (management and assessment, research and education, environmental impact and efficient operation, and SES components) and 10 categories, suggesting that the evaluation of the SES performance should be part of a holistic approach. The scoring for each indicator was adapted from the UI Green Metric and THE—impact ranking [121,123], and this can be adjusted based on the results of the evaluations and, respectively, on the new rankings (QS, for instance). The KPIs are discussed and presented below in Table 1, Table 2, Table 3 and Table 4.

3.4.1. KPIs for Management and Assessment of the SESs

University policies should be adopted, ensuring that all renovations or new buildings are following energy efficiency standards and that the green building elements are implemented in the new projects [123,126]. The existing buildings need to be upgraded to higher energy efficiency and plan for carbon management and reducing the carbon dioxide emissions adopted. Universities should have an energy efficiency plan in place to reduce overall energy consumption, and a policy for clean energy and on divesting investments from carbon-intensive energy industries, notably coal and oil [121]. Once the SESs are implemented, they need to be continuously evaluated to ensure their conformance with standards (ISO 5001, for instance) [127] and the continuous improvement of the system. Table 1 shows the most relevant KPIs identified for this cluster.
Table 1. KPIs for management and assessment of the SESs.
Table 1. KPIs for management and assessment of the SESs.
ClusterCategoryKPIMeasurement Unit/CriteriaScoring (Based on: [121,123])
1. MANAGEMENT AND ASSESSMENT (10p)1.1. ManagementHave a policy for ensuring that all renovations or new buildings are following energy efficiency standards [121]Policy/institutionYes: 1
Yes/NoNo: 0
Implementing the green building elements in construction and renovation policies
For example [126]: energy efficiency and renewable energy; water efficiency; waste reduction; indoor air quality; smart growth, etc. [123]
Negbi (Number of elements of green building implementation)/institutionNone: 0
1 element: 0.25
2 elements: 0.5
3 elements: 0.75
>3 elements: 1
Have a plan to upgrade existing buildings to higher energy efficiency [121]Plan/institutionYes: 1
Yes/NoNo: 0
Have a process for carbon management and reducing carbon dioxide emissions [121]Process/institutionYes: 1
Yes/NoNo: 0
Have an energy efficiency plan in place to reduce overall energy consumption [121]Plan/institutionYes: 1
Yes/NoNo: 0
Have a policy for clean energy and on divesting investments from carbon-intensive energy industries, notably coal and oil [121]Policy/institutionYes: 1
Yes/NoNo: 0
Have a policy development for clean energy [121]Policy/institutionYes: 1
Yes/NoNo: 0
1.2 AssessmentHas an internal energy assessment been organized in the last five years?Internal assessments/last 5 yearsYes: 1
Yes/NoNo: 0
Have a certified energy management system (EMS) ISO 50001, for instanceEMS certificate/institutionYes: 1
Yes/NoNo: 0
Audits of the EMS organized in the last five years, other than certification audits [124]EMS audit/institutionYes: 1
Yes/NoNo: 0

3.4.2. KPIs for Research and Education in the Field of SESs

Education, research, and innovation are essential in sustainable development, making universities key contributors to achieving the goals [114]. The KPIs from this cluster (Table 2) are specific to universities and allow us to assess how devoted the universities are to effectively implement the green policies, and how the research and education processes are stimulated in this endeavor.
Table 2. KPIs for research and education in the field of SESs.
Table 2. KPIs for research and education in the field of SESs.
ClusterCategoryKPIMeasurement Unit/CriteriaScoring (Based on: [121,123])
2. RESEARCH AND EDUCATION2.1 ResearchFunding research on energy sustainability [123]Energy sustainability research funding/total research funding≤1%: 0
>1–8%: 0.25
>8–20%: 0.5
>20–40%: 0.75
>40%: 1
2.2 Education
-
Energy sustainability educational program for staff and students [121,128]
-
Increased environmental/sustainability education [22,107,129]
-
Involving the stakeholders [107,130]
Educational program/institutionYes: 1
Yes/NoNo: 0
-
A program for local community about the importance of energy efficiency and clean energy [121]
-
Increased environmental/sustainability education [22,107,129]
Local community program/institutionYes: 1
Yes/NoNo: 0
Promote a public pledge toward 100% renewable energy beyond the university [121]Public pledge/institutionYes: 1
Yes/NoNo: 0

3.4.3. KPIs for Infrastructure Assessment from the Environmental Impact Point of View

The KPIs from this category are dedicating to assessing areas with higher energy losses and also the usage of the equipment belonging to different energy efficiency categories. The total carbon footprint per total population of the institution is calculated, and greenhouse gas emission reduction programs are also evaluated. Table 3 shows the most relevant KPIs identified for this cluster.
Table 3. KPIs for infrastructure assessment from the environmental impact point of view.
Table 3. KPIs for infrastructure assessment from the environmental impact point of view.
ClusterCategoryKPIMeasurement Unit/Criteria Scoring (Based on: [121,123])
3. EFFICIENT INFRASTRUCTURE AND ENVIRONMENTAL IMPACT3.1 Efficient operation Energy report to assess areas with higher energy losses and optimal improvement measures [121]Energy report/institutionYes: 1
Yes/NoNo: 0
-
Renovation of the areas with significant energy losses [128,131]
-
Reduce heat demand (by 50%) [14,132]
Renovation areas/significant energy losses area identified<1%: 0
1–25%: 0.25
>25–50%: 0.5
>50–75%: 0.75
>75%: 1
Type I * energy efficient equipment ** [123,128]NeI/NI
  • NeI = Number of type I energy efficient equipment
  • NI = Number of all type I equipment
<1%: 0
1–25%: 0.25
>25–50%: 0.5
>50–75%: 0.75
>75%: 1
Type II *** energy efficient equipment ** [123,128]NeI/NII
  • NeII = Number of type II energy efficient equipment
  • NII = Number of all type II equipment
<1%: 0
1–25%: 0.25
>25–50%: 0.5
>50–75%: 0.75
>75%: 1
Equipment automation
Smart energy solutions’ implementation (automations) to reduce energy losses [123,128]
Tae/Te
  • Tae = Total automated equipment
  • Te = Total electrical equipment.
<1%: 0
1–25%: 0.25
>25–50%: 0.5
>50–75%: 0.75
>75%: 1
Ensuring thermal comfort for users in all classrooms and laboratories of a university [17,123,129,133]Mtu = 23 ± 3 °C
  • Mtu = The average temperature in all university spaces (classrooms, laboratories, etc.) [134]
Yes: 1
No: 0
3.2 Environmental impact
-
Greenhouse gas emission reduction program [123];
-
Reducing the CO2 emissions [12,86,129,135]
Program/institutionYes: 1
Yes/NoNo: 0
Total carbon footprint per total population of the institution (metric ton–mt) [53,92,123,129]TCf/Tp
  • TCf = Total carbon footprint
  • Tp = Total campus population
≥2.05 mt: 0
<2.05–1.11 mt: 0.25
<1.11–0.42 mt: 0.5
<0.42–0.10 mt: 0.75
<0.10 mt: 1
* For instance: washing machines; electronic displays; refrigerators; heaters; light sources; air conditioners and fans, etc. [136,137,138]. ** energy efficient equipment means: only A Class of energy efficiency for each specific equipment or appliance according to the EU energy efficient products [136]. *** For instance: pumps; transformers and converters; computers and servers; electric engines; welding equipment, etc. [136,137,138]

3.4.4. KPIs for SES Components

The KPIs for SES components propose measures for evaluating each of the SES components: resources used, energy conversion and storage, and energy demand. Table 4 shows the most relevant KPIs identified for this category.
Table 4. KPIs for the SESs’ components.
Table 4. KPIs for the SESs’ components.
ClusterCategoryKPIMeasurement Unit/CriteriaScoring (Based on: [121,123])
4. SMART ENERGY SYSTEM4.1 ResourcesTotal of renewable energy purchased from grid (for example: electricity from RES with green tariffs and gas) [124,128];Tep/Tec
  • Tep = Total energy produced
  • Tec = Total energy consumed
>45%: 0
45–35%: 0.25
<35–25%: 0.5
<25–15%: 0.75
<15%: 1
The number of renewable energy sources in the institution (for example: solar; wind; geothermal; biomass, etc.) [121,128,139,140]nRES/institution
  • nRES = The number of renewable energy sources used
None: 0
1 RES: 0.25
2 RES: 0.5
3 RES: 0.75
>3 RES: 1
The share of fluctuating RES (for example: solar, wind, wave, etc.) [14,17,20,53]efRES/teRES
  • efRES = Electricity from fluctuating renewable energy sources
  • teRES = Total electricity renewable energy sources
<1%: 0
1–25%: 0.25
>25–50%: 0.5
>50–75%: 0.75
>75%: 1
4.2 ConversionThe share of renewable energy generated on site of the total energy used [123,139,140]Teg/Tec
  • Teg = Total energy generated on site
  • Tec = Total energy consumed
<1%: 0
1–25%: 0.25
>25–50%: 0.5
>50–75%: 0.75
>75%: 1
Report for assessing and reducing energy conversion losses [88,89,135]Report/institution
Yes/No
Yes: 1
No: 0
4.3 StorageStorage off grid for security:
-
The storage capacity of each type of energy must ensure the energy demand of the system in emergency situations (for example: breakdowns, blackout, etc.) [88,135,141,142]
-
Short differences between demand and production during certain hourly intervals (demand peak) [22,45,92,129]
-
Mitigation of maximum hourly surplus-deficit [129]
-
Secure and maintain voltage and frequency in the electricity supply [17,45,92,129]
Esong/Hd
  • Esong = Energy stored on grid
  • Hd = Hourly demand.
<12 h: 0
12–24 h: 0.25
>24–48 h: 0.5
>48–72 h: 0.75
>72 h: 1
Storage on grid for flexibility:
-
Ensuring system flexibility by storing the surplus energy production for times when demand is higher than production [11,12,13,64]
-
Balance between supply and demand [22,53,107]
-
Reducing the system average interruption frequency and duration [129]
Ei/Ep
  • Ei = Electricity injected on grid
  • Ep = Electricity purchased from grid
(Target: 100% energy from RES)
<1%: 0
1–25%: 0.25
>25–50%: 0.5
>50–75%: 0.75
>75%: 1
Report for assessing and reduction storage losses of the energy system [88,89,135]Report/institution
Yes/No
Yes: 1
No: 0
4.4 DemandSpecific requirements for universities:
-
Site energy use per unit of floor area;
-
The total electricity usage divided by total campus population (kWh/person);
-
Energy used/floor space;
-
Heating energy efficiency;
-
Cooling energy efficiency [121,123,128]
Criteria:
(a)
Energy consumption for class A 150 kWh/m2/year (100%) [14];
(b)
Primary energy for Nzeb (target for 2021) 92 kWh/m2/year [143];
(c)
Primary energy for Nzeb (target for 2050) 47.05 kWh/m2/year [144];
(d)
Passive building total energy 60–30 kWh/m2/year (total) [145]
>150 (kWh/m²/year): 0
150–100 (kWh/m²/year): 0.25
<100–75 (kWh/m²/year):0.5
<75–50 (kWh/m²/year): 0.75
<50 (kWh/m2/year): 1
General legal requirements:
-
Reduce energy consumption [146,147];
-
Increase the energy efficiency [14,131,143,148]
Fulfilment criteria from the total criteria [14] as follows:
  • ≤70 kWh/m2/year (heating);
  • ≤40 kWh/m2/year (lighting);
  • ≤15 kWh/m2/year (warm water);
  • ≤20 kWh/m2/year (cooling);
  • ≤5 kWh/m2/year (mechanical ventilation)
None: 0
1 fulfilled criterion: 0.25
2 fulfilled criteria: 0.5
3 fulfilled criteria: 0.75
>3 fulfilled criteria: 1
Proportion of energy from RES per total energy demand for system operation
Reduced/replaced fossil fuel consumption
[14,19,53,129];
Annual proportion of energy from RES used/total annual energy demand<25%: 0
25–50%: 0.25
>50–75%: 0.5
>75–99%: 0.75
>99%: 1

4. Conclusions

The SESs, as part of the EMSs, support the organizations to better manage energy use, by implementing new energy efficient technologies, reducing energy waste, and improving current processes to cut energy costs. This will be done by developing and implementing energy policies, setting achievable targets for energy use, and designing action plans to reach them and measure progress.
The development and implementation of SESs is a difficult task, especially if the goal is to achieve the goal of 100% use of energy from renewable sources [12,16]. These systems can be applied from the macro level (cities or neighborhoods) to the micro level (institutional buildings or homes) [4]. However, most of the reviewed studies are still in the research and testing stage, and only very few intelligent systems have been concretely applied.
Based on the bibliometric analysis, the relevant keywords most frequently used in the literature in the field of SESs were organized in three clusters in our study, specific to SES and its components, sustainable university, and the performance of the SES. Special attention was given to the last cluster, as the literature review also revealed that the assessment of the SESs for university campuses is an insufficiently approached subject [45].
In order to ensure a good implementation, operation, and assessment of the SESs in the institutional buildings, such as universities, KPIs need to be developed [45,129] and used as required by the energy management standards (ISO 50001, for instance), internal, and external assessment frameworks. Such indicators can help a university to prepare for evaluations in international rankings.
The study proposed a framework for evaluating SESs from university campuses, based not only on the technical side of SESs, but also on the role education, research, and innovation should have in sustainable development, making universities key contributors to achieving the goals.
The KPIs from this framework are structured in four clusters: management, education and research, infrastructure evaluation, and SES elements, and are dedicated to universities adopting policies for clean energy and interested in evaluating the progress toward this goal.
Future studies will aim to test the framework, analyzing how the KPI fits different types of universities, and how the KPI framework will support the university toward a green future. Testing the KPIs for different scenarios will allow an optimization of the KPI score, and also an estimation of the importance of each indicator relative to the other indicators.

Author Contributions

Conceptualization, C.V.K.; methodology, C.V.K. and A.O.; validation, A.O., M.Z.; formal analysis, A.O. and M.Z.; data collecting and analysis, A.O.; writing—original draft preparation, C.V.K. and A.O.; writing—review and editing, C.V.K. and M.Z.; visualization, A.O and M.Z.; supervision, C.V.K.; funding acquisition, C.V.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a Hasso Plattner Excellence Research Grant (LBUS-HPI-ERG2020–02), financed by the Knowledge Transfer Center of the Lucian Blaga University of Sibiu.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this paper are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gamper-Rabindran, S.; Finger, S.R. Industry, Internal Combustion Engines and More Recently Buildings Are Mainly Responsible for Environmental Degradation; Springer Science+Business Media: Berlin/Heidelberg, Germany, 2012. [Google Scholar]
  2. Mansha, M.; Saleemi, A.; Javed, S.; Ghauri, B.M. Prediction and measurement of pollutant emissions in CNG fired internal combustion engine. J. Nat. Gas Chem. 2010, 19, 539–547. [Google Scholar] [CrossRef]
  3. Frilingou, N.; Demetri, B. Effects of Building Energy Efficiency Measures on. Energies 2020, 13, 5689. [Google Scholar] [CrossRef]
  4. Grimm, C.; Molina, J.M.; Mahlknecht, S.; Damm, M.; Lukasch, F. Infrastructures and Platforms. Int. J. Energy A Clean Environ. 2013, 14, 21–39. [Google Scholar] [CrossRef]
  5. Shaikh, P.H.; Nor NB, M.; Nallagownden, P.; Elamvazuthi, I.; Ibrahim, T. A review on optimized control systems for building energy and comfort management of smart sustainable buildings. Renew. Sustain. Energy Rev. 2014, 34, 409–429. [Google Scholar] [CrossRef]
  6. Comisia Europeana. Energy Performance of Buildings Directive. 16 May 2019. Available online: https://ec.europa.eu/energy/topics/energy-efficiency/energy-efficient-buildings/energy-performance-buildings-directive_en (accessed on 14 August 2020).
  7. Lu, M.; Lai, J. Review on carbon emissions of commercial buildings. Renew. Sustain. Energy Rev. 2020, 119, 109545. [Google Scholar] [CrossRef]
  8. Connolly, D.; Lund, H.; Mathiesen, B. Smart Energy Europe: The technical and economic impact of one potential 100% renewable energy scenario for the European Union. Renew. Sustain. Energy Rev. 2016, 60, 1634–1653. [Google Scholar] [CrossRef]
  9. Zakeri, B.; Paulavets, K.; Barreto-Gomez, L.; Echeverri, L.G.; Pachauri, S.; Boza-Kiss, B.; Zimm, C.; Rogelj, J.; Creutzig, F. Pandemic, War, and Global Energy Transitions. Energies 2022, 15, 6114. [Google Scholar] [CrossRef]
  10. Lund, H. Renewable Heating Strategies and their Consequences for Storage and Grid Infrastructures Comparing a Smart Grid to a Smart Energy Systems Approach. Energy 2018, 151, 94–102. [Google Scholar] [CrossRef]
  11. Dincer, I.; Acarac, C. Smart energy systems for a sustainable future. Appl. Energy 2017, 194, 225–235. [Google Scholar] [CrossRef]
  12. Hansen, K.; Breyer, C.; Lund, H. Status and perspectives on 100% renewable energy systems. Energy 2019, 175, 471–480. [Google Scholar] [CrossRef]
  13. Lund, H.; Østergaard, P.A.; Connolly, D.; Mathiesen, B.V. Smart Energy and Smart Energy Systems. Energy 2017, 137, 556–565. [Google Scholar] [CrossRef]
  14. Lund, H.; Østergaard, P.A.; Connolly, D.; Ridjan, I.; Mathiesen, B.V.; Hvelplund, F.; Thellufsen, J.Z.; Sorknæs, P. Energy Storage and Smart Energy Systems. Int. J. Sustain. Energy Plan. Manag. 2016, 11, 3–14. [Google Scholar]
  15. Lund, H.; Thellufsen, J.Z.; Aggerholm, S.; Wittchen, K.B.; Nielsen, S.; Mathiesen, B.V.; Möller, B. Heat saving strategies in sustainable smart energy systems. Int. J. Sustain. Energy Plan. Manag. 2014, 4, 3–16. [Google Scholar]
  16. Connolly, D.; Mathiesen, B.V.; Ridjan, I. A comparison between renewable transport fuels that can supplement or replace biofuels in a 100% renewable energy system. Energy 2014, 73, 110–125. [Google Scholar] [CrossRef]
  17. Xu, Y.; Yan, C.; Liu, H.; Wang, J.; Yang, Z.; Jiang, Y. Smart Energy Systems: A Critical Review on Design and Operation. Sustain. Cities Soc. 2020, 62, 102369. [Google Scholar] [CrossRef]
  18. Lund, H.; Andersen, A.N.; Østergaard, P.; Mathiesen, B.; Connolly, D. From electricity smart grids to smart energy systems–A market operation based approach and understanding. Energy 2012, 42, 96–102. [Google Scholar] [CrossRef]
  19. Mathiesen, B.V.; Lund, H.; Hansen, K.; Ridjan, I.; Djørup, S.R.; Nielsen, S.; Thellufsen, J.Z.; Grundahl, L.; Lund, R.S.; Drysdale, D.; et al. DA’s Energy Vision 2050: A Smart Energy System Strategy for 100% Renewable Denmark; Department of Development and Planning, Aalborg University: Aalborg, Denmark, 2015. [Google Scholar]
  20. Lund, H.; Duic, N.; Østergaard, P.A.; Mathiesen, B.V. Future District Heating Systems and Technologies: On the role of Smart Energy. Energy 2018, 165, 614–619. [Google Scholar] [CrossRef]
  21. Dincer, I.; Acar, C. Smart energy solutions with hydrogen options. Int. J. Hydrog. Energy 2018, 43, 8579–8599. [Google Scholar] [CrossRef]
  22. Lund, H. Renewable Energy Systems—A Smart Energy Systems Approach to the Choice and Modeling of 100% Renewable Solutions, 2nd ed.; Academic Press: Cambridge, MA, USA, 2014. [Google Scholar]
  23. Smart Energy Networks. Vision for Smart Energy in Denmark. Available online: https://ens.dk/sites/ens.dk/files/Forskning_og_udvikling/vision_for_smart_energy_in_denmark_2015.pdf (accessed on 15 August 2022).
  24. Salvia, M.; Simoes, S.G.; Herrando, M.; Čavar, M.; Cosmi, C.; Pietrapertosa, F.; Gouveia, J.P.; Fueyo, N.; Gómez, A.; Papadopoulou, K.; et al. Improving policy making and strategic planning competencies of public authorities in the energy management of municipal public buildings: The PrioritEE toolbox and its application in five mediterranean areas. Renew. Sustain. Energy Rev. 2021, 135, 110106. [Google Scholar] [CrossRef]
  25. Turner, C.; Frankel, M. Energy Performance of LEED for New Construction Buildings; U.S. Green Building Council: Washington, DC, USA, 2008. [Google Scholar]
  26. Hub, Z.C. A Review of the Modelling Tools and Assumptions: Topic 4, Closing the Gap between Designed and Built Performance; Zero Carbon Hub: London, UK, 2010. [Google Scholar]
  27. Meneze, A.C.; Cripps, A.; Bouchlaghem, D.; Buswell, R. Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap. Appl. Energy 2012, 97, 355–364. [Google Scholar] [CrossRef] [Green Version]
  28. Korjenic, A.; Bednar, T. Validation and evaluation of total energy use in office buildings: A case study. Autom. Constr. 2012, 23, 64–70. [Google Scholar] [CrossRef]
  29. Dasgupta, A.; Prodromou, A.; Mumovic, D. Operational versus designed performance of low carbon schools in England: Bridging a credibility gap. HVACR Res. 2012, 18, 37–50. [Google Scholar]
  30. Newsham, G.; Birt, B.; Arsenault, C.; Thompson, L.; Veitch, J.; Mancini, S.; Galasiu, A.; Gover, B.; Macdonald, I.; Burns, G. Do green buildings outperform conventional buildings? In Indoor Environment and Energy Performance in North American Offices; National Research Council of Canada: Ottawa, ON, Canada, 2012. [Google Scholar]
  31. Pozas, B.M.; Gamero, I.A.; Domínguez, A.S.; De Castro, P.B.G. A methodology to improve energy efficiency and comfort conditions with low-cost ICTs in rural public buildings. Sustain. Cities Soc. 2020, 60, 102156. [Google Scholar] [CrossRef]
  32. Ahn, K.-U.; Park, C.-s. Correlation between occupants and energy consumption. Energy Build. 2016, 116, 420–433. [Google Scholar] [CrossRef]
  33. Zhang, Y.; Bai, X.; Mills, F.P.; Pezzey, J.C. Rethinking the role of occupant behavior in building energy performance: A review. Energy Build. 2018, 172, 279–294. [Google Scholar] [CrossRef]
  34. Langevin, J.; Gurian, P.L.; Wen, J. Tracking the human-building interaction: A longitudinal field study of occupant behavior in air-conditioned offices. J. Environ. Psychol. 2015, 42, 94–115. [Google Scholar] [CrossRef]
  35. Tam, V.W.Y.; Almeida, L.; Le, K. Energy-Related Occupant Behaviour and Its Implications in Energy Use: A Chronological Review. Sustainability 2018, 10, 2635. [Google Scholar] [CrossRef] [Green Version]
  36. Delzendeh, E.; Wu, S.; Lee, A.; Zhou, Y. The impact of occupants’ behaviours on building energy analysis: A research review. Renew. Sustain. Energy Rev. 2017, 80, 1061–1071. [Google Scholar] [CrossRef]
  37. Carvalho, M.; Abrahão, R. Environmental and Economic Perspectives in the Analysis of Two Options for Hand Drying At an University Campus. Int. J. Emerg. Res. Manag. Technol. 2017, 6, 24–35. [Google Scholar] [CrossRef]
  38. Karatzoglou, B. An in-depth literature review of the evolving roles and contributions of universities to Education for Sustainable Development. J. Clean. Prod. 2013, 49, 44–53. [Google Scholar] [CrossRef]
  39. Figueiró, P.S.; Rauffletb, E. Sustainability in higher education: A systematic review with focus on management education. J. Clean. Prod. 2015, 106, 22–33. [Google Scholar] [CrossRef]
  40. Sady, M.; Żak, A.; Rzepka, K. The Role of Universities in Sustainability-Oriented Competencies Development: Insights from an Empirical Study on Polish Universities. Adm. Sci. 2019, 9, 62. [Google Scholar] [CrossRef] [Green Version]
  41. Gatti, L.; Ulrich, M.; Seele, P. Education for sustainable development through business simulation games: An exploratory study of sustainability gamification and its effects on students’ learning outcomes. J. Clean. Prod. 2019, 207, 667–678. [Google Scholar] [CrossRef]
  42. ONU. Agenda 2030 Pentru Dezvoltare Durabilă. 2015. Available online: https://www.mae.ro/node/35919 (accessed on 7 October 2022).
  43. Yavor, K.M.; Bach, V.; Finkbeiner, M. Resource Assessment of Renewable Energy Systems—A Review. Sustainability 2021, 13, 6107. [Google Scholar] [CrossRef]
  44. Francis, C.; Costa, A.; Thomson, R.C.; Ingram, D.M. Developing a Multi-Criteria Assessment Framework for Smart Coal Energy Systems: EnergyREV Technical Report; EnergyREV, University of Strathclyde: Glasgow, UK, 2020. [Google Scholar]
  45. Efkarpidis, N.; Goranović, A.; Yang, C.-W.; Geidl, M.; Herbst, I.; Wilker, S.; Sauter, T. A Generic Framework for the Definition of Key Performance Indicators for Smart Energy Systems at Different Scales. Energies 2022, 15, 1289. [Google Scholar] [CrossRef]
  46. Neupane, D.; Kafle, S.; Karki, K.R.; Kim, D.H.; Pradhan, P. Solar and wind energy potential assessment at provincial level in Nepal: Geospatial and economic analysis. Renew. Energy 2022, 181, 278–291. [Google Scholar] [CrossRef]
  47. Fittrin, D.W. Determining Optimal Schedule and Load Capacity in the Utilization of Solar and Wind Energy in the Microgrid Scheme: A Case Study. Energy Procedia 2015, 65, 48–57. [Google Scholar] [CrossRef] [Green Version]
  48. Askarzadeh, A. Optimisation of solar and wind energy systems: A survey. Int. J. Ambient. Energy 2017, 38, 653–662. [Google Scholar] [CrossRef]
  49. Giordano, A.; Mastroianni, C.; Sorrentino, N.; Menniti, D.; Pinnarelli, A. An energy community implementation: The unical energy cloud. Electronics 2019, 8, 1517. [Google Scholar] [CrossRef] [Green Version]
  50. Rynska, E.; Klimowicz, J.; Kowal, S.; Lyzwa, K.; Pierzchalski, M.; Rekosz, W. Smart energy solutions as an indispensable multi-criteria input for a coherent urban planning and building design process two case studies for smart office buildings in warsaw downtown area. Energies 2020, 13, 3757. [Google Scholar] [CrossRef]
  51. Naji, N.; Abid, M.R.; Benhaddou, D.; Krami, N. Context-aware wireless sensor networks for smart building energy management system. Information 2020, 11, 530. [Google Scholar] [CrossRef]
  52. Tucci, F.; Santucci, D.; Endres, E.; Hausladen, G. Smart urban districts: Dynamic energy systems for synergic interactions between building and city. Techne 2018, 1, 92–102. [Google Scholar]
  53. Nastasi, B.; Basso, G.L. Hydrogen to link heat and electricity in the transition towards future Smart Energy Systems. Energy 2016, 110, 5–22. [Google Scholar] [CrossRef]
  54. You, C.; Kim, J. Optimal design and global sensitivity analysis of a 100% renewable energy sources based smart energy network for electrified and hydrogen cities. Energy Convers. Manag. 2020, 223, 113252. [Google Scholar] [CrossRef]
  55. Gerboni, R.; Grosso, D. Testing future hydrogen penetration at local scale through an optimisation tool. Int. J. Hydrogen Energy 2016, 41, 22626–22634. [Google Scholar] [CrossRef]
  56. Dumas, P. A European perspective of the development of deep geothermal in urban areas: Smart thermal grids, geothermal integration into smart cities. Geomech. Tunn. 2016, 9, 447–450. [Google Scholar] [CrossRef]
  57. Cao, Y.; Dhahad, H.A.; Togun, H.; El-Shafay, A.S.; Alamri, S.; Rajhi, A.A.; Anqi, A.E.; Ibrahim, B.F. Development and transient performance analysis of a decentralized grid-connected smart energy system based on hybrid solar-geothermal resources; Techno-economic evaluation. Sustain. Cities Soc. 2021, 76, 103425. [Google Scholar] [CrossRef]
  58. Ridjan, I. Integrated Electrofuels and Renewable Energy Systems; Department of Development and Planning, Aalborg University: Aalborg, Denmark, 2015. [Google Scholar]
  59. Lester, M.S.; Bramstoft, R.; Münster, M. Analysis on Electrofuels in Future Energy Systems: A 2050 Case Study. Energy 2020, 199, 117408. [Google Scholar] [CrossRef]
  60. Rixhon, X.; Limpens, G.; Coppitters, D.; Jeanmart, H.; Contino, F. The Role of Electrofuels under Uncertainties for the Belgian Energy Transition. Energies 2021, 14, 4027. [Google Scholar]
  61. Conrado, R.J.; Haynes, C.A.; Haendler, B.E.; Toone, E.J. Electrofuels: A new paradigm for renewable fuels. In Advanced Biofuels and Bioproducts; Springer: New York, NY, USA, 2013; pp. 1037–1064. [Google Scholar]
  62. Grahn, M.; Malmgren, E.; Korberg, A.D.; Taljegard, M.; Anderson, J.E.; Brynolf, S.; Hansson, J.; Skov, I.R.; Wallington, T. J Review of electrofuel feasibility—cost and environmental impact. Prog. Energy 2022, 4, 32010. [Google Scholar] [CrossRef]
  63. Hassan, N.U.; Yuen, C.; Niyato, D. Blockchain technologies for smart energy systems: Fundamentals, challenges, and solutions. IEEE Ind. Electron. Mag. 2019, 13, 106–118. [Google Scholar] [CrossRef] [Green Version]
  64. Bačeković, I.; Østergaard, P.A. A smart energy system approach vs a non-integrated renewable energy system. Energy 2018, 155, 824–837. [Google Scholar] [CrossRef]
  65. Mura, P.G.; Baccoli, R.; Innamorati, R.; Mariotti, S. Solar energy system in a small town constituted of a network of photovoltaic collectors to produce Electricity for homes and hydrogen for transport services of municipality. In Proceedings of the 6th International Building Physics Conference, IBPC 2015, Torino, Italy, 14–17 June 2015. [Google Scholar]
  66. Marczinkowski, H.M.; Østergaard, P.A. Residential versus communal combination of photovoltaic and battery in smart energy systems. Energy 2018, 152, 466–475. [Google Scholar] [CrossRef] [Green Version]
  67. Blaauwbroek, N.; Nguyen, P.H.; Konsman, M.J.; Shi, H.; Kamphuis, R.I.G.; Kling, W.L. Decentralized resource allocation and load scheduling for multicommodity smart energy systems. IEEE Trans. Sustain. Energy 2015, 6, 1506–1514. [Google Scholar] [CrossRef] [Green Version]
  68. Bao, Q.; Honda, T.; El Ferik, S.; Shaukat, M.M.; Yang, M.C. Understanding the role of visual appeal in consumer preference for residential solar panels. Renew. Energy 2017, 113, 1569–1579. [Google Scholar] [CrossRef]
  69. Soydan, O. Solar power plants site selection for sustainable ecological development in Nigde, Turkey. SN Appl. Sci. 2021, 3, 41. [Google Scholar] [CrossRef]
  70. Prieto-Araujo, E.; Gomis-Bellmunt, O. Wind turbine technologies. In HVDC Grids for Transmission of Electrical Energy: Offshore Grids and a Future Supergrid; Wiley: Hoboken, NJ, USA, 2016; pp. 97–108. [Google Scholar]
  71. Chen, J.; Chen, G.; Wang, Z.; Song, H.; Zhou, J. Simulator design of variable pitch wind turbine. In Proceedings of the 2011 7th International Conference on MEMS, NANO and Smart Systems, ICMENS 2011, Kuala Lumpur, Malaysia, 4–6 November 2011. Code 87709, 2012. [Google Scholar]
  72. Dominković, D.F.; Dobravec, V.; Jiang, Y.; Nielsen, P.S.; Krajačić, G. Modelling smart energy systems in tropical regions. Energy 2018, 155, 592–609. [Google Scholar] [CrossRef]
  73. Erixno, O.; Rahim, N.A.; Ramadhani, F.; Adzman, N.N. Energy management of renewable energy-based combined heat and power systems: A review. Sustain. Energy Technol. Assess. 2022, 51, 101944. [Google Scholar] [CrossRef]
  74. De Rosa, M.; Carragher, M.; Finn, D.P. Flexibility assessment of a combined heat-power system (CHP) with energy storage under real-time energy price market framework. Therm. Sci. Eng. Prog. 2018, 8, 426–438. [Google Scholar] [CrossRef]
  75. Yi, Z.; Li, Z. Combined Heat and Power Dispatching Strategy Considering Heat Storage Characteristics of Heating Network and Thermal Inertia in Heating Area. Dianwang Jishu/Power Syst. Technol. 2018, 42, 1378–1384. [Google Scholar]
  76. Lund, H.; Wernerb, S.; Wiltshirec, R.; Svendsend, S.; Thorsene, J.E.; Hvelplunda, F.; Mathiesenf, B.V. 4th Generation District Heating (4GDH): Integrating smart thermal grids into future sustainable energy systems. Energy 2014, 68, 1–11. [Google Scholar] [CrossRef]
  77. Lundqvist, P. The role of heat pumps in the smart energy systems. In Proceedings of the 24th IIR International Congress of Refrigeration, ICR 2015, Yokohama, Japan, 16–22 August 2015. [Google Scholar]
  78. Barberis, S.; Rivarolo, M.; Bellotti, D.; Magistri, L. Heat pump integration in a real poly-generative energy district: A techno-economic analysis. Energy Convers. Manag. 2022, 10, 15. [Google Scholar] [CrossRef]
  79. Bracco, S.; Delfino, F.; Laiolo, P.; Rossi, M. The Smart City Energy infrastructures at the Savona Campus of the University of Genoa. In Proceedings of the 2016 AEIT International Annual Conference (AEIT), Capri, Italy, 5–7 October 2016. [Google Scholar]
  80. Rey-Hernández, J.M.; González, S.L.; José-Al, J.F.S.; Tejero-González, A.; Velasco-Gómez, E.; Rey-Martínez, F.J. Smart energy management of combined ventilation systems in a nZEB. E3S Web Conf. 2019, 111, 01050. [Google Scholar] [CrossRef] [Green Version]
  81. Leire, G.-A. The benefits of local cross-sector consumer ownership models for the transition to a renewable smart energy system in Denmark. An exploratory study. Energies 2020, 13, 1508. [Google Scholar]
  82. Rizwan, M.; Hong, L.; Waseem, M.; Ahmad, S.; Sharaf, M.; Shafiq, M. A robust adaptive overcurrent relay coordination scheme for wind-farm-integrated power systems based on forecasting the wind dynamics for smart energy systems. Appl. Sci. 2020, 10, 6318. [Google Scholar] [CrossRef]
  83. Energywatch. Danish Wind Turbines Curtailed Like Never Before. Available online: https://energywatch.com/EnergyNews/Policy___Trading/article12666098.ece (accessed on 12 November 2021).
  84. Hedegaard, K.; Münster, M. Influence of individual heat pumps on–Energy system investments and operation. Energy Convers Manag. 2013, 75, 673–684. [Google Scholar] [CrossRef] [Green Version]
  85. Østergaard, P. Wind power integration in Aalborg Municipality using compression heat pumps and geothermal absorption heat pumps. Energy 2013, 49, 502–508. [Google Scholar] [CrossRef]
  86. Buffa, S.; Marco, C.; Matteo, D.; Marco, B.; Roberto, F. 5th generation district heating and cooling systems: A review of existing. Renew. Sustain. Energy Rev. 2019, 104, 504–522. [Google Scholar] [CrossRef]
  87. Breyer, C.; Bogdanov, D.; Gulagi, A.; Aghahosseini, A.; Barbosa, I.; Koskinen, O.; Barasa, M.; Caldera, U.; Afanasyeva, S.; Child, M.; et al. On the role of solar photovoltaics in global energy. Prog. Photovolt. Res. Appl. 2017, 25, 727–745. [Google Scholar] [CrossRef]
  88. Locatelli, G.; Invernizzi, D.; Mancini, M. Investment and risk. Energy 2016, 104, 114–131. [Google Scholar] [CrossRef]
  89. Tan, X.; Li, Q.; Wang, H. Advances and trends of energy storage. Int. J. Electr. Power Energy Syst. 2013, 44, 179–191. [Google Scholar] [CrossRef]
  90. Gonzalez, R.M.; Van Goch, T.A.J.; Aslam, M.F.; Blanch, A.; Ribeiro, P.F. Microgrid design considerations for a smart-energy university campus. In Proceedings of the 2014 5th IEEE PES International Conference on Innovative Smart Grid Technologies (ISGT Europe), Istanbul, Turkey, 12–15 October 2014. [Google Scholar]
  91. Lund, P.D.; Lindgren, J.; Mikkola, J.; Salpakari, J. Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renewable and Sustainable Energy Rev. 2015, 45, 785–807. [Google Scholar] [CrossRef] [Green Version]
  92. Ajanovic, A.; Hiesl, A.; Haas, R. On the role of storage for electricity in smart energy systems. Energy 2020, 200, 117473. [Google Scholar] [CrossRef]
  93. Lund, H.; Kempton, W. Integration of renewable energy into the transport and electricity sectors through V2G. Energy Policy 2008, 36, 3578–3587. [Google Scholar] [CrossRef]
  94. Prebeg, P.; Gasparovic, G.; Krajacic, G.; Duic, N. Long-term energy planning of Croatian power system using multi-objective optimization with focus on renewable energy and integration of electric vehicles. Appl. Energy 2016, 184, 1493–1507. [Google Scholar] [CrossRef]
  95. Bellocchi, S.; Manno, M.; Noussan, M.; Vellini, M. Impact of grid-scale electricity storage and electric vehicles on renewable energy penetration: A case study for Italy. Energies 2019, 12, 1303. [Google Scholar] [CrossRef] [Green Version]
  96. Skarvelis-Kazakos, S.; Daniel, S.; Buckley, S. Distributed energy storage using second-life electric vehicle batteries. In Proceedings of the IET Power in Unity: A Whole System Approach, London, UK, 16–17 October 2013. [Google Scholar]
  97. Bellocchi, S.; Gambini, M.; Manno, M.; Stilo, T.; Vellini, M. Positive interactions between electric vehicles and renewable energy sources in CO2-reduced energy scenarios: The Italian case. Energy 2018, 161, 172–182. [Google Scholar] [CrossRef]
  98. Chitsuphaphan, T.; Yang, X.; Dai, H. Stochastic Programming for Residential Energy Management with Electric Vehicle under Photovoltaic Power Generation Uncertainty. In Proceedings of the 2020 International Conference on Probabilistic Methods Applied to Power Systems, Liege, Belgium, 18–21 August 2020. [Google Scholar]
  99. Dominković, D. Zero carbon energy system of South East Europe in 2050. Appl. Energy 2016, 184, 1517–1528. [Google Scholar] [CrossRef] [Green Version]
  100. Schmid, T.; Pauschinger, T.; Sørensen, P.; Snijders, A.; Djebbar, R.; Boulter, R.; Thornton, J. Design Aspects for Large-scale Pit and Aquifer Thermal Energy Storage for District Heating and Cooling. Energy Procedia 2018, 149, 585–594. [Google Scholar] [CrossRef]
  101. Sifnaios, I.; Jensen, A.; Furbo, S.; Fan, J. Performance comparison of two water pit thermal energy storage (PTES) systems using energy, exergy, and stratification indicators. J. Energy Storage 2022, 52, 104947. [Google Scholar] [CrossRef]
  102. Grahn, M.; Anderson, J.E.; Wallington, T.J. Chapter Four–Cost-Effective Vehicle and Fuel Technology Choices in a Carbon-Constrained World: Insights from Global Energy Systems Modeling. In Electric and Hybrid Vehicles; Elsevier: Amsterdam, The Netherlands, 2010; pp. 91–111. [Google Scholar]
  103. Hanif, S.; Massier, T.; Gooi, H.B.; Hamacher, T.; Reindl, T. Cost Optimal Integration of Flexible Buildings. IEEE Trans. Power Syst. 2017, 32, 2254–2266. [Google Scholar] [CrossRef]
  104. Carbon Trust. Closing the Gap: Lessons Learned on Realising the Potential of Low Carbon Building Design; Carbon Trust: London, UK, 2011. [Google Scholar]
  105. Uriarte, I.; Erkoreka, A.; Giraldo-Soto, C.; Martin, K.; Uriarte, A.; Eguia, P. Mathematical development of an average method for estimating the reduction of the Heat Loss Coefficient of an energetically retrofitted occupied office building. Energy Build. 2019, 192, 101–122. [Google Scholar] [CrossRef]
  106. Wilde, P. The gap between predicted and measured energy performance of buildings: A framework for investigation. Autom. Constr. 2014, 41, 40–49. [Google Scholar] [CrossRef]
  107. Van der Werff, E.; Steg, L. The psychology of participation and interest in smart energy systems. Energy Res. Soc. Sci. 2016, 22, 107–114. [Google Scholar] [CrossRef] [Green Version]
  108. Wang, J.C. A study on the energy performance of school buildings in Taiwan. Energy Build. 2016, 133, 810–822. [Google Scholar] [CrossRef]
  109. Doulos, L.; Kontadakis, A.; Madias, E.; Sinou, M.; Tsangrassoulis, A. Minimizing energy consumption for artificial lighting in a typical classroom of a Hellenic public school aiming for near Zero Energy Building using LED DC luminaires and daylight harvesting systems. Energy Build. 2019, 194, 201–217. [Google Scholar] [CrossRef]
  110. Dubois, M.C.; Blomsterberg, Å. Energy saving potential and strategies for electric lighting in future North European, low energy office buildings: A literature review. Energy Build. 2011, 43, 2572–2582. [Google Scholar] [CrossRef]
  111. Bernardo, H.; Antunes, C.H.; Gaspar, A.; Pereira, L.D.; Silva, M.G. An approach for energy performance and indoor climate assessment in a Portuguese school building. Sustain. Cities Soc. 2017, 30, 184–194. [Google Scholar] [CrossRef]
  112. European University Association. Universities and Sustainable Development Towards the Global Goals; European University Association: Geneva, Switzerland, 2018. [Google Scholar]
  113. Shiel, C.; Smith, N.; Cantarello, E. Aligning Campus Strategy with the SDGs: An Institutional Case Study. In Universities as Living Labs for Sustainable Development; Springer: Berlin/Heidelberg, Germany, 2019; pp. 11–27. [Google Scholar]
  114. Filho, W.L. Sustainable Development Goals and sustainability teaching at universities: Falling behind or getting ahead of the pack? J. Clean. Prod. 2019, 232, 285–294. [Google Scholar] [CrossRef] [Green Version]
  115. Velazquez, L.; Munguia, N.; Platt, A.; Taddei, J. Sustainable university: What can be the matter? J. Clean. Prod. 2006, 14, 810–819. [Google Scholar]
  116. Grecu, V. Contribuții Privind Sustenabilitatea în Universități; ULBS: Sibiu, Romania, 2012. [Google Scholar]
  117. Alsharif, M.A.; Peters, M.D.; Dixon, T.J. Designing and Implementing Effective Campus. Sustainability 2020, 12, 5096. [Google Scholar] [CrossRef]
  118. Nagowah, S.; Sta, H.B.; Gobin, B.A. An Ontology for an IoT-enabled Smart Parking in a University Campus. In Proceedings of the 2019 IEEE International Smart Cities Conference (ISC2), Casablanca, Morocco, 14–17 October 2019. [Google Scholar]
  119. Saleh, A.A.; Mohammed, A.H.; Abdullah, M.N. Critical success factors for sustainable university: A framework from the energy management view. Procedia Soc. Behav. Sci. 2015, 172, 503–510. [Google Scholar] [CrossRef] [Green Version]
  120. ULFS. ulsf.org. 2015. Available online: http://ulsf.org/talloires-declaration/ (accessed on 11 November 2022).
  121. THE World University Ranking. THE Impact Rankings 2020: Methodology. 2020. Available online: https://www.timeshighereducation.com/impact-rankings-2020-methodology (accessed on 12 November 2021).
  122. Craig, O. Introducing the QS World University Rankings: Sustainability 2023. 2022. Available online: https://www.topuniversities.com/university-rankings-articles/qs-sustainability-ranking/introducing-qs-world-university-rankings-sustainability-2023 (accessed on 10 November 2022).
  123. UI Green Metric. Criteria & Indicators. 2020. Available online: http://greenmetric.ui.ac.id/criteria-indicator/ (accessed on 12 January 2022).
  124. People & Planet University League. Methodology. 2019. Available online: https://peopleandplanet.org/university-league-methodology (accessed on 12 January 2022).
  125. Best Colleges. Greenest Universities. Bestcolleges 2020. Available online: https://www.bestcolleges.com/features/greenest-universities/ (accessed on 15 January 2022).
  126. EPA. 2016. Available online: https://archive.epa.gov/greenbuilding/web/html/components.html (accessed on 5 January 2022).
  127. ISO. Energy Management System. Guidance for Implementing a Common Energy Management System in Multiple Organizations. Available online: https://www.iso.org/publication/PUB100400.html (accessed on 11 March 2022).
  128. STARS. The Sustainability Tracking. Technical Manual. 17 May 2019. Available online: https://stars.aashe.org/resources-support/technical-manual/ (accessed on 12 January 2022).
  129. Pramangioulis, D.; Atsonios, K.; Nikolopoulos, N.; Rakopoulos, D.; Grammelis, P.; Kakaras, E. A Methodology for Determination and Definition of Key Performance Indicators for Smart Grids Development in Island Energy Systems. Energies 2019, 12, 242. [Google Scholar] [CrossRef] [Green Version]
  130. Lund, H.; Thellufse, J.Z.; Østergaard, P.; Sorknæs, P.; Skov, I.R.; Mathiesen, B. EnergyPLAN—Advanced analysis of smart energy systems. Smart Energy 2021, 1, 100007. [Google Scholar] [CrossRef]
  131. Guvernul României. Strategia Națională Din 27 Noiembrie 2020 de Renovare pe Termen Lung Pentru Sprijinirea Renovării Parcului Național de Clădiri Rezidențiale și Nerezidențiale, Atât Publice, cât și Private, și Transformarea sa Treptată într-un Parc Imobiliar cu un Nivel Ridicat de Eficiență Energetică și Decarbonat. Bucuresti, 2020. Available online: https://aaecr.ro/wp-content/uploads/2020/01/Strategia-Nationala-Renovare.pdf (accessed on 11 May 2022).
  132. Lund, H.; Østergaard, P.A.; Chang, M.; Werner, S.; Svendsen, S.; Sorknæs, P.; Thorsen, J.E.; Hvelplund, F.; Mortensen, B.O.G.; Mathiesen, B.V.; et al. The Status of 4th Generation District Heating: Research and Results. Energy 2018, 164, 147–159. [Google Scholar] [CrossRef]
  133. Attia, S.; Alphonsine, P.; Amer, M.; Ruellan, G. Towards a European rating system for sustainable student housing: Key performance indicators (KPIs) and a multi-criteria assessment approach. Environ. Sustain. Indic. 2020, 7, 100052. [Google Scholar] [CrossRef]
  134. World Health Organization. School Environment. Available online: https://www.euro.who.int/__data/assets/pdf_file/0009/276624/School-environment-Policies-current-status-en.pdf (accessed on 28 March 2021).
  135. Kourkoumpas, D.S.; Benekos, G.; Nikolopoulos, N.; Karellas, S.; Grammeli, P.; Kakaras, E. A review of key environmental and energy performance indicators for the case of renewable energy systems when integrated with storage solutions. Applied Energy 2018, 231, 380–398. [Google Scholar] [CrossRef]
  136. European Commission. Questions and Answers about the Rescaled EU Energy Labels and Ecodesign Measures. Available online: https://ec.europa.eu/commission/presscorner/detail/en/qanda_21_819 (accessed on 9 March 2022).
  137. European Commission. Energy-Efficient Products. Available online: https://ec.europa.eu/info/energy-climate-change-environment/standards-tools-and-labels/products-labelling-rules-and-requirements/energy-label-and-ecodesign/energy-efficient-products_en (accessed on 11 March 2022).
  138. nZEB Criteria. Ernational Report: NZEB Criteria for Typical Single-Family Home Renovations in Cohereno. 2013. Available online: http://bpie.eu/wp-content/uploads/2015/11/nZEB_Criteria_for_renovation_COHERENO.pdf (accessed on 12 March 2021).
  139. European Parliament. EU Climate Target Plan: Raising the Level of Ambition for 2030. 8 December 2020. Available online: https://www.europarl.europa.eu/thinktank/en/document.html?reference=EPRS_BRI(2020)659370 (accessed on 11 March 2022).
  140. ANRE. Standard de Performanţă Pentru Serviciul de Distribuţie a energiei Electrice. Available online: https://www.anre.ro/ro/legislatie/norme-tehnice/standarde-de-performanta1387201290 (accessed on 12 June 2021).
  141. E-distribuție. Compensații Pentru Neasigurarea Continuității în Alimentarea cu Energie Electrică. Available online: https://www.e-distributie.com/ro/energia-electrica-in-casa-ta/compensatii-intreruperi.html (accessed on 12 August 2021).
  142. Ministerul Dezvoltării Regionale și Administraţiei. Metodologie de Calcul al Performanței Energetice a Cladirilor. Available online: https://www.mdlpa.ro/pages/metodologie (accessed on 11 July 2021).
  143. Ministry of Regional Development and Public Administration. Plan for Increasing the Number of Nearly Zero Energy Buildings. 2013. Available online: https://ec.europa.eu/energy/sites/ener/files/documents/romania_en_version.pdf (accessed on 15 July 2021).
  144. Passive House. 2015. Available online: https://passivehouse.com/03_certification/02_certification_buildings/08_energy_standards/08_energy_standards.html (accessed on 11 May 2022).
  145. Guvernul României. Plan Național din 3 aprilie 2019 de Acțiune în Domeniul Eficienței Energetice IV. Available online: https://legislatie.just.ro/Public/DetaliiDocumentAfis/216833 (accessed on 23 July 2021).
  146. European Parliament. Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 on Energy Efficiency, Amending Directives 2009/125/EC and 2010/30/EU and Repealing Directives 2004/8/EC and 2006/32/EC; Publication Office of the European Union: Brussels, Belgium, 2012. [Google Scholar]
  147. European Parliament. Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the Energy PERFORMANCE of Buildings; Publication Office of the European Union: Brussels, Belgium, 2010. [Google Scholar]
  148. Ministerul Dezvoltării Regionale și Locuinței. Anexă din 16 Decembrie 2009 la Ordinul Ministrului Dezvoltării Regionale și Locuinței nr. 1.071/2009 Privind Modificarea și Completarea Ordinului Ministrului Transporturilor, Construcțiilor și Turismului nr. 157/2007 Pentru Aprobarea Reglementării Tehnice. Available online: https://legislatie.just.ro/Public/DetaliiDocumentAfis/190993 (accessed on 11 August 2021).
Figure 1. Study selection chart.
Figure 1. Study selection chart.
Energies 16 01246 g001
Figure 2. The VOS Viewer map of research trends in the selected papers from Scopus database.
Figure 2. The VOS Viewer map of research trends in the selected papers from Scopus database.
Energies 16 01246 g002
Figure 3. KPI framework for SESs in sustainable universities.
Figure 3. KPI framework for SESs in sustainable universities.
Energies 16 01246 g003
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

Kifor, C.V.; Olteanu, A.; Zerbes, M. Key Performance Indicators for Smart Energy Systems in Sustainable Universities. Energies 2023, 16, 1246. https://doi.org/10.3390/en16031246

AMA Style

Kifor CV, Olteanu A, Zerbes M. Key Performance Indicators for Smart Energy Systems in Sustainable Universities. Energies. 2023; 16(3):1246. https://doi.org/10.3390/en16031246

Chicago/Turabian Style

Kifor, Claudiu Vasile, Alexandru Olteanu, and Mihai Zerbes. 2023. "Key Performance Indicators for Smart Energy Systems in Sustainable Universities" Energies 16, no. 3: 1246. https://doi.org/10.3390/en16031246

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