Future Perspectives for Physics-Based Urban Building Energy Modelling Tools

Abstract

With the high concentration of people in urban areas and their significant contribution to greenhouse gas emissions, along with the unmet needs of rural settlements, it is crucial to place greater emphasis on designing more energy-efficient cities through the use of renewable energy sources. The present study reviews the advantages of using urban building energy models (UBEMs) to simulate and design urban energy systems, providing valuable insights to researchers and decision-makers working towards an energy transition to more efficient and cleaner forms of energy. This review presents the current state-of-the-art of physics-based UBEMs, including common approaches, necessary components, tools developed, and applications that have benefited from their use to date. Additionally, this study highlights current limitations and gaps, encouraging academics and developers to pursue future research and development opportunities. Finally, it proposes three topics of interest that can benefit from the implementation of building-to-grid urban energy system models, showing promising future applications. Results of this review have shown that further research on UBEM-oriented urban district Digital Twins, endowed with Energy Communities and Positive Energy District abilities, along with the use of mature open-access and user-friendly tools, can accelerate the design and planning of modern district energy systems.

1. Introduction

1.1. Background

Over the past century, the world’s population has been concentrated in urban settlements, resulting in excessive growth in these regions and a focus on supplying energy goods and services primarily in these areas [1]. Therefore, the application of energy-efficient methods in urban areas has great potential in terms of climate change mitigation [2]. Historically, energy markets have evolved in a unidirectional manner, with large generation plants providing products such as electricity and fuels to consumers. At the same time, the availability of these services has been limited by the technical constraints and economic interests of energy providers, resulting in some consumers being excluded from the market due to factors such as a great distance to distribution networks or fuel suppliers [3]. As there is a common agreement that urban areas are among the greatest consumers of energy and producers of greenhouse gases [4,5,6], ongoing efforts to transition towards clean energy sources have encouraged the development and implementation of renewable energy technologies at various scales, raising new implications when managing the traditional energy system. Despite this, these technologies can significantly reduce the dependence of cities on large-scale power generation plants (which typically use fossil fuels) and address the unmet needs of rural regions.

The implementation of building-scale renewable energy technologies is rapidly gaining acceptance in urban areas worldwide. Unfortunately, the integration of distributed energy sources at the urban scale has introduced new challenges in the electrical network [7] due to the interaction between the various generation devices available in the system. One example of this is the increasing concentration of rooftop photovoltaic technologies in low-voltage distribution grids, which, combined with a mismatch between the production curve and the typical demand load, can lead to potential stability disturbances and decreased power quality in the grid [8]. As the use of these technologies is expected to grow in most regions globally, it is crucial to expand the understanding of their behaviour by designing new tools with a high level of detail to aid in the planning and operation of urban energy systems.

Over the last decade, there has been a steady growth in grid-connected photovoltaic systems (GCPVSs). For this reason, their impact on the distribution network has been extensively evaluated and discussed in the existing literature [9]. The study conducted by Obi and Bass [10] reviewed the trends and challenges of GCPVSs, emphasising the importance of properly sizing the inverter and storage capacity to minimise interferences with the grid. Similarly, Kharrazi et al. [7] assessed the techniques used to measure the impact of these systems on the grid during high-penetration events, identifying Monte Carlo simulation as the most common method for representing the uncertainty of this equipment. The research concluded that the most common issues during grid operations are voltage regulation problems caused by generation excess, which can occur even during low penetration events. In addition, Tavakoli et al. [11] evaluated the impact of using PV panels in low-voltage grids, including electric vehicles, showing that strategic collaboration can support grid voltage and frequency while averting common issues caused by rooftop PV. With a similar methodology, Brinkel et al. [12] proposed three scenarios for the years 2017, 2030, and 2050, revealing similar advantages in future scenarios. Due to the significance of storage in these systems, Oliveira e Silva and Hendrick [13] evaluated the impact of PV combined with lithium-ion batteries on the public grid, achieving up to 40% self-sufficiency in Belgium. However, most of these studies have utilised historical consumption reports and surveys at various resolution levels and time scales to develop their models; data that is often unavailable in many locations worldwide, making it challenging to observe the behaviour of low-voltage distribution grids during unique urban district scenarios.

The construction of energy-efficient buildings and supply systems is fundamental to achieving a sustainable society [14]. However, since buildings within populated areas have unique energy demands and self-supply behaviours, accurately representing them individually and together in a computational model, as similar as possible to a real-life case study, can become a challenging task, especially in neighbourhoods with many households. This challenge can be eased by using Urban Building Energy Modelling (UBEM) tools, which, although advanced electricity sharing and trading models have not yet been fully integrated into mainstream software applications, have the potential to provide the level of detail needed to assess the impact of emerging distributed renewable energy systems on the grid, among other configurations [15]. Modelling the energy consumption of buildings can be used for purposes like identifying retrofit needs, evaluating energy management strategies, designing urban infrastructure and reducing carbon emissions [4]. For this reason, several UBEM tools have been developed during the last decade to support these applications. Unfortunately, most UBEM tools do not have the skills necessary to implement these types of configurations. By simulating the energy system of each individual building, UBEMs have already served as the foundation to build new research methods and models, considering the interaction of emerging technologies in the electric distribution system [16]. A UBEM tool with these capabilities can provide transparency, knowledge, and skills, among many other benefits for urban communities [17], supporting the planning and design of advanced user-friendly district energy system modelling tools.

1.2. Review Methodology

The methodology for the present literature review started with a mining search of three scientific web databases: ScienceDirect, Google Scholar and MDPI databases. These resources are among the biggest and most used databases in the engineering field, hosting over 12 million academic records [18]. The search involved two search steps: the first step focused on collecting review studies, and the second step tried to identify other related articles with important information for the study. The keyword search strings selected for the search were: “UBEM + TOOLS”, “UBEM + digital twins”, “UBEM + energy communities”, and “UBEM + positive”. The outcomes were limited to literature reviews published between 2000 and 2025. Results in these databases yielded a total of 140 reviews, starting from 2016, with a notable surge in research studies this year. The inclusion and exclusion criteria selected for these reviews considered all the review studies discussing new electrification models, gaps and opportunities, reducing the records to 19 review papers. Posteriorly, an abstract and intensive content revision was performed on the search results, focusing on papers that place more emphasis on Electricity Related Topics (ERT), such as building self-sufficiency, distributed renewable energy generation and storage, smart grids, community sharing and trading, among others. These papers served as search sources to identify experimental articles discussing the previously described search string. Figure 1 presents the final diagram of the literature review process workflow selected for the review process performed in the present research.

1.3. Previous Reviews

There is a significant growing interest in UBEM research and development, as evidenced by the numerous review studies conducted on this topic over the last years, a trend that was identified by Malhotra et al. [19]. Since 2016, when the topic of UBEM was first defined [20], its real-life application has evolved significantly. This year alone, 20 reviews have been published analysing different aspects of this subject.

After the application of the inclusion/exclusion criteria, 19 reviews were identified, providing an overview and discussion of the work performed on UBEM tools, particularly focusing on identifying applications, challenges, and proposing future research opportunities [21,22,23,24]. These are presented in Table 1. From these studies, Ang et al. [21] provided a detailed description of the four main applications of physics-based bottom-up UBEM and how they can assist in reducing the barriers and challenges faced by policymakers and urban planners, which was complemented by the work of Hong et al. [22]. Although all of these studies have discussed certain aspects of the topic of UBEM tools, some of them provide greater detail and analysis of the reach and limitations of the most used mainstream software tools available, such as the studies of Ferrando et al. [14,25] and Kamel [26]. In addition, the selected reviews showed concentration on topics oriented to development and calibration of UBEMs [2,27], occupant behaviour [4,28] and their integration with other urban-related models, such as transport [29], Sustainable development rating tools [5], and Urban Climate Models [6].

However, despite the growing interest in using UBEMs and tools, only four reviews were identified with similar interests to the present study; these are described next. In the study of Burovski et al. [17], existing models were critically assessed to determine the complexities and challenges inherent in modelling UBEM and their potential triggers towards energy communities. In addition, Pan et al. [30] aimed to provide a clear picture of how building energy modelling (BEM) performs in solving various building phase-related questions, such as building-to-grid integration. Manfren et al. [31] presented an in-depth review of data-driven methods, artificial intelligence tools, and their interpretation in the building environment. Most recently, Russo et al. [24] evaluated the current state of urban energy modelling, focusing on the interaction between end-users, buildings and districts.

Although these four reviews have envisioned the new paradigm that the current and future urban energy systems face, they have performed limited analysis of how UBEM tools can help achieve these new approaches, and therefore, justify the development of the present literature review.

Table 1. Review studies related to Urban Building Energy Modelling tools.

N.	Authors	Year	Scope	Approach	Tools
[28]	Happle et al.	2018	Occupant behaviour	B-U	12
[29]	Abbasabadi & Ashayeri	2019	Modelling methods, tools and techniques	B-U/T-D	13
[14]	Ferrando & Causone	2019	Appropriate UBEM tool selection	B-U/T-D	6
[5]	Yang & Jiang	2019	Sustainable Neighbourhood rating tools	-	8
[21]	Ang et al.	2020	Examples and case studies	-	12
[17]	Bukovzki et al.	2020	UBEM as a trigger for energy communities	B-U/T-D	-
[25]	Ferrando et al.	2020	Differences between tools	B-U	8
[22]	Hong et al.	2020	10 questions about UBEMs	B-U	18
[23]	Johari et al.	2020	Integration with other models	B-U	9
[32]	Ali et al.	2021	SWOT analysis of UBEMs	B-U/T-D	15
[4]	Doma & Ouf	2022	Occupant behaviour models and tools	B-U	6
[26]	Kamel, Ehsan	2022	Tools, data sources and challenges	B-U	-
[19]	Malhotra et al.	2022	Taxonomic review	B-U	6
[27]	Kong et al.	2023	Development and calibration	B-U/T-D	10
[30]	Pan et al.	2023	Future perspectives and challenges	B-U	-
[2]	Yakut & Esen	2023	Model creation process	B-U	20
[31]	Manfren et al.	2024	Data-driven methods	B-U	-
[24]	Russo et al.	2025	Modelling of interconnected energy systems	B-U	157
[6]	Yu et al.	2025	Integration with Urban Climate Model	B-U	6

Table 1. Review studies related to Urban Building Energy Modelling tools.

N.	Authors	Year	Scope	Approach	Tools
[28]	Happle et al.	2018	Occupant behaviour	B-U	12
[29]	Abbasabadi & Ashayeri	2019	Modelling methods, tools and techniques	B-U/T-D	13
[14]	Ferrando & Causone	2019	Appropriate UBEM tool selection	B-U/T-D	6
[5]	Yang & Jiang	2019	Sustainable Neighbourhood rating tools	-	8
[21]	Ang et al.	2020	Examples and case studies	-	12
[17]	Bukovzki et al.	2020	UBEM as a trigger for energy communities	B-U/T-D	-
[25]	Ferrando et al.	2020	Differences between tools	B-U	8
[22]	Hong et al.	2020	10 questions about UBEMs	B-U	18
[23]	Johari et al.	2020	Integration with other models	B-U	9
[32]	Ali et al.	2021	SWOT analysis of UBEMs	B-U/T-D	15
[4]	Doma & Ouf	2022	Occupant behaviour models and tools	B-U	6
[26]	Kamel, Ehsan	2022	Tools, data sources and challenges	B-U	-
[19]	Malhotra et al.	2022	Taxonomic review	B-U	6
[27]	Kong et al.	2023	Development and calibration	B-U/T-D	10
[30]	Pan et al.	2023	Future perspectives and challenges	B-U	-
[2]	Yakut & Esen	2023	Model creation process	B-U	20
[31]	Manfren et al.	2024	Data-driven methods	B-U	-
[24]	Russo et al.	2025	Modelling of interconnected energy systems	B-U	157
[6]	Yu et al.	2025	Integration with Urban Climate Model	B-U	6

1.4. Aim of the Study

So far, developers of UBEM tools have been able to successfully represent most aspects of the heating side of the urban energy system, including assessments of heating and cooling needs in multiple buildings, urban heating energy systems and heating and cooling network design. Yet, the development of these tools toward the electrical side of the systems has been left behind. Although most UBEM tools could estimate and produce electricity consumption profiles effectively if their models are properly calibrated, only a few of them have considered modern aspects of the electric side, such as distributed renewable energy load impact, net zero energy buildings, decentralised energy storage systems, Peer-to-Peer sharing and trading in smart grids [17].

To achieve a rapid modernization of the existing energy system, it is imperative to provide to any interested everyday user multiple free-access friendly tools able to represent an energy system in an urban environment integrally, and allowing advanced users to extend new models and theories that can emerge during future research, thus motivating energy transition goals towards clean energies among worldwide decision-makers, urban designers, building constructors, and stakeholders. To accomplish this, decision-makers should be provided with evaluation tools allowing them to select the best options to decrease energy consumption in cities [14].

Similarly to the research of Burovski et al. [17], Pan et al. [30] and Russo et al. [24], this study focused on reviewing bottom-up physics-based approaches and tools, aiming to identify the limitations, gaps, and future research opportunities of using UBEM to develop tools capable of simulating novel energy sharing and management perspectives among urban districts.

1.5. Outline of the Document

The present review study has started by describing the background motivating the use of UBEM tools for the development of urban electric energy systems, it has presented a previous literature review related to the topic of physics-based UBEM tools and has justified the importance of this research through the identification of gaps in the literature of urban building energy models.

The rest of the document is organised as follows: Section 2 will describe in detail the state-of-the-art of UBEM, emphasising bottom-up physics-based models, Section 3 will outline a compilation of key limitations and gaps identified in the literature, and Section 4 will propose potential paths and identify future research opportunities in the field of UBEM. Finally, Section 5 presents the most important conclusions of the study.

2. Urban Building Energy Models

This section will present a detailed overview of the most important factors around the UBEM topic, including the data collection and processing, physics-based modelling approaches, existing tools and typical user applications. This modelling approach is considered a high computational tool, enabling the analysis of energy use and emissions of different building sectors of a district [4]. As it is believed that eventually every country will have to adapt to the use of UBEMs and tools [2], the development of a mature, robust and reliable software application can help standardise methods and results used during the planning and implementation of policies and technology, hence, easing efforts for decision-makers worldwide. Due to the vital role that buildings play in the construction of urban environments, accurately estimating building energy demand is essential [6], especially for the implementation of energy communities. UBEM can be used as an effective early-stage and cost-effective tool for energy planners and researchers during the design and construction of urban districts [30]. The reliability of a UBEM significantly depends on the expertise of the modeller to accurately represent physical processes of local conditions, carefully calibrate input parameters, and validate outputs against reported data [6].

In recent decades, the use of urban-scale simulation methods in urban energy planning has grown significantly. Among these methods, UBEM have become increasingly popular due to their ability to apply physics models of heat and mass flow [28]. UBEM refers to the computational simulation of the performance of a group of buildings, taking into account the dynamics of individual and inter-building effects in a microclimate [22]. They represent a relatively new, large-scale Building Energy Modelling approach, raising new challenges for their practical application [26]. As seen in Figure 2, these models can take two different approaches: top-down and bottom-up [25]. While top-down models treat a group of buildings as a single energy entity, bottom-up models simulate the individual energy system of each building and its end-use services, which can later be aggregated at an urban, regional or national level [33]. Notably, top-down approaches have been used at the national scale for long-term energy demand projections, while bottom-up techniques have focused on detailed granular-level energy demands [23]. However, the use of the former approach has gradually declined since 2012, whereas the latter approach has begun to gain acceptance [27]. At the moment of modelling behavioural and technological changes, bottom-up approaches are far superior to their counterparts [30]. Examples of bottom-up applications are mainly found in Europe and North America because of the easy access to urban macro-data sources. However, due to issues with data availability and standardisation, many regions worldwide are unable to implement these models effectively [2]. As seen in Figure 2, the study focused on physics-based UBEM methods and reduced-order R-C approaches, as they use simple input and output information to present the energy consumption of buildings rapidly [2]. Yet, other methods and tools, such as data-driven and multi-thermal-zone dynamic, have been revised and presented to allow readers to identify their potential capabilities; these are summarised in the following subsections.

2.1. Data Collection and Preprocessing

As shown in Figure 3, a typical bottom-up physics-based UBEM utilises input data, generates intermediate resources, and reports output results for subsequent analysis and applications. The most critical challenge in bottom-up UBEMs is providing appropriate input parameters to minimise uncertainties in the estimated results [34]. A reliable UBEM simulation depends on the quality and accuracy of the data inputs; however, the lack of available data and the difficulty in determining stochastic behaviours represent significant barriers to the development of these models [35]. Still, it is possible to improve their accuracy by uncertainty analysis and model calibration [30]. The most important input parameters to model a UBEM are location and weather data, building geometry and envelope, HVAC system technologies and occupancy-related information [6,13,16]. Here, construction materials comprising the building’s envelope (walls, floors, ceilings, and windows) are essential parameters to simulate energy demand, as they are mainly responsible for the building’s thermal performance [18]. As there is much public information available in geographical databases, the use of GIS-based models could help solve the problem of data collection and pre-processing during the initial stages of the process [27].

Given that bottom-up methods require more parameters than top-down approaches, they are more challenging to apply to entire regions or cities. This is why the archetype method, which involves a group of analogous structures, has increased in popularity [27]. Since gathering data on each building in a district is a difficult task, simplifying the building stock into representative archetypes is often a valuable and necessary approach [23]. Nowadays, the use of archetypes to classify building stock is a common practice; however, their classification can still vary in complexity depending on the size of the building stock [36]. Studies focused on archetype development have shown that the most common variables used for this process are construction typology, construction year, end-use, envelope dimensions and HVAC systems [26]; Yet, occupant behaviour archetypes should also be identified and modelled [4]. The approaches and applications of UBEM demonstrate that building archetype modelling is essential for predicting urban energy profiles [31]. Generally, the process to identify building archetypes can be performed in three steps: First, the stock of buildings is classified, according to their characteristics and energy demand; Then, representative archetypes are classified, into sub-groups of buildings with identical typologies, and characterised, by their non-geometrical parameters; Finally, the archetypes are calibrated using measured energy data in different spatiotemporal levels [23]. Nevertheless, Abbasabadi and Ashayeri [29] proposed that utilising advanced machine learning techniques could aid in determining localised archetypes, thereby increasing the accuracy of models. UBEM tool developers have smoothed the definition of archetypes with an identified methodology as long as data is available [4]. Therefore, it is advisable to promote the publication of standardised information needed to build building archetypes.

Part of the characterisation of a UBEM includes the description of Occupancy Behaviour (OB), which refers to the interaction between building users and existing technologies, such as appliances, lighting, heaters, coolers, thermostats, and any other energy-consuming devices. Since OB can significantly influence the results of the individual and total energy use of buildings, its correct selection is essential to avoid misleading results [34]. Previous studies have quantified the impact of OB on energy use using top-down approaches at individual, city or national scales, suggesting alternative models including techno-economic conventions and cultural adaptations [29]. However, few of them have accounted for identifying OB at the urban level [15]. To help during OB estimation, Happle et al. [28] reviewed three approaches to this matter: deterministic space-based, stochastic space-based, and stochastic person-based approaches. Additionally, Dahlstrom et al. [36] recommended that data-driven or probabilistic occupant approaches should be used instead. Probabilistic methods have been studied to consider the intrinsic nature of users and their movement through the different thermal zones of an urban district [25]. Nevertheless, focusing on the development of buildings-to-grid integration models, OB has been useful in representing the diverse activities of residential neighbourhoods to simulate appropriate peak demand curves and energy use [22]. However, given that currently OB is considered to use fixed schedules and behaviour patterns, the applicability of OB models integrated in UBEM tools needs to be evaluated [4].

Another basic set of information used by bottom-up UBEM is weather and environmental data, which are used to simulate the building performance based on environmental conditions. This information typically includes ambient temperature, solar radiation, wind speed, and air pressure [26]. Although this data is mainly used by bottom-up approaches, physical climatic factors have also been incorporated into some top-down models [33]. These climate datasets, usually based on a typical meteorological year, are essential for building thermal simulations of buildings [32] and their microclimate, as well as for considering events such as heat island effects, local wind patterns, and climate change predictions [21]. Fortunately, climate data for building simulation has been available for many years [20], considering different periods and locations. However, typical meteorological year data are compiled from historical datasets or measured statistics in remote, open-space areas, failing to represent the actual microclimate or boundary conditions of the district [22] and extreme conditions [36]. Since weather data is characterised by a high level of uncertainty and change over time [27], it is crucial to carefully select the origin of this resource to reduce the uncertainty of the model.

2.2. Bottom-Up Methods

Unlike top-down models, bottom-up approaches estimate the energy use of an area considering the attributes of individual units and at the urban microscale [29]. Usually, when physics-based notes are used, UBEMs are commonly utilised [2]. These approaches are generally better than top-down models at scenario analysis, which are capable of considering OB during hourly resolutions to reduce the ambiguity of results [17]. They can be categorised into two types: statistical and physics-based methods. The main difference between them lies in how they acquire and manage data while building the model. While statistical methods use real-life historical reports from the area of study, physics-based approaches require specific information on the features of the buildings in the area to generate individual and total energy demand [33]. Although both approaches may face setbacks in obtaining the necessary information to build a UBEM model, bottom-up models can be used to generate the level of granularity required to simulate more complex analysis models [28]. Bottom-up approaches can model multiple energy services and generate a wide range of outputs, including rebound effect, market transaction and cost optimisation, identifying appropriate networks, among others [17]. The results of these models, regardless of the approach used, can provide the initial information needed to develop strong and reliable tools to simulate building technologies in a low-voltage distribution network and propose novel scenarios using UBEM.

2.2.1. Statistical Methods

They are also known as data-driven methods as they rely on existing available information, such as energy billing data, socioeconomic variables, surveys and public records. These approaches utilise long-term historical data, such as energy consumption and GDP, to simulate district energy demand. These can be classified into three main groups: regression analysis, conditional demand analysis, and machine learning analysis [33]. Examples of models using regression methods are linear, multiple linear and non-linear regressions [32]. Regression methods can use inverse statistical models to infer building design or operational parameters from known outputs [22]. Unlike the previous method, CDA performs regression based on the end-use appliances available in the building stock, requiring detailed data related to equipment ownership and behaviour [33]. Machine Learning techniques are artificial intelligence models used to represent urban energy use and have been widely adopted compared to traditional statistical procedures because they can provide more accurate predictions [32]. The most significant strengths of this approach are its accuracy in representing urban energy use, its computational and temporal efficiency, and the incorporation of occupancy and socio-economic factors. However, they are limited by the availability of historical data, which increases research costs if the data is not publicly available, and the rigidity of design modifications [29].

2.2.2. Physics-Based Methods

These approaches estimate energy consumption in a district based on the architectural characteristics of the buildings and the thermodynamic interaction between the interior of the buildings and the environment [29,33]. They are also known as simulation or engineering approaches, as they utilise simulation techniques to incorporate the characteristics of a group of buildings, technologies, and the microclimate of its surroundings to estimate the end-use energy demand of a district [32]. They enable users to evaluate strategies and energy supply options concretely, contributing to the elaboration of effective policies and energy management [2]. The main strengths of this approach are its non-reliance on historical data, adaptability to building information models and tools, and ability to use renewable energy models. Although their most apparent drawback relates to the availability of physical and technical information about the buildings [37], they are also limited by the simplification of local and district factors, as well as the intensive demand for computational and time-consuming efforts [29].

Depending on their simulation method, these models can be further categorised into reduced-order resistance-capacitance and multi-thermal-zone dynamic simulation methods [25]. The main difference between these two methods lies in the level of detail in simulating the participating buildings, where the first method simulates the entire building as a single area. In contrast, the second method executes balance equations between the different spaces of the building. Additionally, Physics-based reduced-order methods require fewer inputs than the second approach [22], as they estimate specific parameter values using standard calculations developed by the European Committee for Standardisation and the International Organisation for Standardisation [32]. These models offer computational efficiency at the expense of some modelling accuracy [6], but they should not be overly simplified if reducing computational time is the only goal [30]. Yet, although they have accuracy drawbacks, particularly in terms of computing efficiency and input requirements [6], their advantages have contributed to the growth of UBEM-based studies. UBEM tools with hybrid or physics-based reduced-order approaches are the most capable of calculating, iterating quickly and working in data-scarce environments [17].

2.3. Software Modelling Tools

For efficient low-energy urban design and management, developing efficient tools for modelling urban building energy is essential [5]. These tools can be used to assess carbon emissions reduction in future perspectives [14]. Therefore, many UBEM tools have been developed to ease the creation of models based on the data available in each region and the required input of each modelling approach, where almost 40 tools have been identified and categorised [4]. These tools play an essential role in integrating building clusters considering multiple approaches according to their scale [24]. They aim to describe building characteristics and energy systems at different scales, ranging from neighbourhood to city level analysis [6]. However, most UBEM tools have been developed for specific objectives [4], since the urban energy analysis involves different levels of detail and scales. Therefore, one tool could facilitate larger analysis concerning another depending on its primary focus [14].

To sum up, many literature reviews have been conducted to discuss certain aspects of each tool in detail, according to the scope of the review. For this reason, rather than describing each software application in detail, this review will guide readers to locate articles that mention the tools available for urban modelling, saving time when researching a specific UBEM tool. It will also highlight the physics-based tools that could achieve the research goal. First, Abbasabadi and Ashayeri [29] reviewed key tools used for urban energy modelling to identify their limitations and strengths, aiming to envision future simulation tools. Similarly, Ferrando et al. [25] provided an overview of eight bottom-up physics-based applications available for users interested in urban scale models, comparing them to identify their strengths and weaknesses according to their specific purpose and approach to the UBEM field. However, the best description of physics-based tools used for UBEM modelling was presented by Hong et al. [22], who presented 20 tools and summarised their use according to their approach. For statistical data-driven applications, the review presenting the most detailed description was given by Ali et al. [32], which summarised 16 models used for mapping, benchmarking, classification, energy analysis, and forecasting. Although most of these tools are stand-alone applications, there are also examples of web-based and extension-based tools available. Moreover, the research performed by Malhotra et al. [19] further classified the UBEM applications into two categories (independent simulation tools and auxiliary tools) according to their dependence on existing frameworks to perform simulations, i.e., Simulation tools are self-contained applications, and auxiliary tools connect to the main framework to extend features and usability. Finally, Kong et al. [27] provided an extensive description of eight mainstream UBEM simulation tools, including their approaches, algorithms, and simulation engines used in various studies between 2001 and 2023.

In recent years, the development of tools oriented towards UBEM analysis has been growing. Many studies reviewed focused on describing physics-based and reduced-order tools, while only a few tried to evaluate statistical (data-driven) approaches.

Table 2. List of mainstream software tools used for UBEM modelling.

Software	Type	Developer	Study
			Abbasabadi	Hong	Ferrando	Ali	Kamel	Malhotra	Dabirian	Kong
Physics-based
CESAR	Auxiliary	ETH	x					x (a)	x
CityBES	Web-based	LBNL	x	x	x	x		x (a)	x	x
CitySIM	Stand-alone	EPFL	x	x	x	x	x	x	x	x
HUES	GIS-based	ETH	x						x
UBEM	Web-based	MIT	x	x		x			x
SEMANCO	Web-based	FUNITEC	x (S)	x		x
UMI	Auxiliary	MIT	x	x	x	x	x	x (a)	x	x
URBANopt	Stand-alone	NREL		x	x	x	x			x
Reduced-order
CEA	Stand-alone	ETH		x	x			x	x	x
EnergyATLAS	Auxiliary	MIT	x	x
OpenIDEAS	Auxiliary	KUL		x	x					x
SimStadt	Stand-alone	HFTS	x	x	x		x	x	x	x
TEASER	Auxiliary	RWTH	x	x	x			x (a)	x	x
Statistical (Data-driven)
CoBAM	Stand-alone	ANL		x		x
Energy Proforma	Web-based	MIT	x			x
Urban Footprint	Web-based	Calthorpe Analytics		x		x

(a)—Auxiliary tool; (S)—Classified as a statistical approach.

presents, in alphabetical order, a description of the software applications discussed at least twice in the ten reviews that describe UBEM tools. Well-known tools include CitySim, City Building Energy Saver (CityBES), City Energy Analyst (CEA), Tool for Energy Analysis and Simulation for Efficient Retrofit (TEASER), Urban Modelling Interface (UMI), and URBANopt [6]. Of these tools, only UMI requires a commercial licence [24]. Tools (like CityBES and UMI) are connected to existing computer-based simulation engines, such as EnergyPlus, or (CitySim, SimStadt, CEA and TEASER) have developed their own physics-based thermal RC model to simulate the thermal behaviour of buildings, adopting simplified calculation approaches [4,14]. The two software applications that were mentioned in every review considered were CitySIM and UMI, followed by CityBES and SimStadt.

The tools identified as having the most potential to develop building-to-grid integrated models are the CEA, CityBES, and SimStadt [14]. These tools are frequently applied to evaluate strategies for integrating RES and hybrid systems in urban environments [24]. These three tools can generate load profiles of electric and heat demand, as well as calculate the potential production of solar PV panels in a district of buildings. Additionally, CitySim, SimStadt and CEA run faster simulations for large-scale geometries; therefore, they are perfect for early design analysis, where the last seems to be the most versatile one, allowing simulations from neighbourhood to city scales [14]. CEA features a distinctive 4D user interface to illustrate time-dependent energy flows between buildings and infrastructure [6]. TEASER is well integrated with the design of urban energy systems and could be used by designers, system managers, and distribution and transmission operators, but is recommended for advanced users with good knowledge of urban energy systems [14].

Several tools have already been considered to incorporate electric storage capabilities, including CEA, TEASER, CityBES, OpenIDEAS, and URBANopt [25]. However, a detailed evaluation of the documentation of each tool revealed that only URBANopt include this ability in its models (or the full skills of the tool are not available in public literature). The unique advantages of URBANopt have already been tested in the application of PV systems in energy communities and net-zero energy in multi-building scenarios [6]. Additionally, CitySim has partially integrated networks and storage, and tools like SynCity, iTeam, and reMAC are attempting to incorporate the level of detail into their models. Additionally, CityBES was identified as the most suitable tool for social upscaling in energy communities [17]. So far, this is the UBEM tool with the friendliest web-based user interface [14]. Unfortunately, the creation of new models requires previous approval from the website platform’s managers. Other tools demonstrating the ability to perform complex energy models in urban grids include INSEL, OpenIDEAS, and the Commercial Building Agent-based Model (CoBAM).

Finally, a notable result of the analysis revealed a discrepancy between the studies of Hong et al. [22] and Ali et al. [32], particularly in their consideration of SEMANCO as a physics-based tool, whereas research by Abbasabadi and Ashayeri [29] identified it as a data-driven method.

2.4. UBEM Applications

The four main applications of UBEMs identified by Ang et al. [21] are urban planning and new neighbourhood design, stock-level carbon reduction strategies, individual building-level recommendations, and building-to-grid integration, with the latter being the primary goal of the present review. Additionally, energy benchmarking, urban management, and urban microclimate seem to be other major applications of UBEM tools [5,30]. In recent years, the goal of achieving low-carbon power networks in many regions of the world has transformed the operation of the grid, thanks to the inclusion of decentralised energy resources [21]. Since the decentralised generation of electricity has increased considerably over the last decades, thanks to the integration of multigeneration systems and renewable energy sources (RES), the modern distribution system has become more flexible [38]. This is why it is demonstrated that there is a need to design and optimise district energy systems using dynamic UBEMs [39]. For this reason, to enhance the knowledge and evaluation of district energy networks, simulation models should incorporate variables such as energy storage and on-site renewable energy systems connected to demand loads [25]. Unfortunately, although the benefits of integrating energy system simulations have already been identified, they have not been fully integrated with UBEM tools [21], leaving room for future research and technology development.

Urban energy systems can be improved in many ways if they are designed to incorporate integrated model systems [35]. Implementing UBEMs with these capabilities can benefit several stakeholders, including decision-makers, industry investors, city users, urban energy planners, and researchers [22]. As highlighted by Ferrando et al. [25], if these models are designed with the proper spatiotemporal resolution, bottom-up models could predict current and future energy demands. Similarly, the identified applications of building-to-grid integration include examples such as building energy use, energy conservation maximising and savings, demand response and flexibility, predictive control, reducing peak demand, load-shedding potential of HVAC systems, electricity supply costs for price-based load control, implementation of renewable energy generation, electric transportation, large-scale storage, and urban resiliency under climate change effects [21,22,35]. Furthermore, since there is a critical need to estimate the energy demand and flexibility of buildings at a district or city scale [40], there is enormous potential for reducing grid operation costs by manipulating the load curve [21].

3. Research Limitations and Gaps

Currently, research related to UBEM has reached a stage where existing methods are maturing and their limitations are being identified [27]. This section presents the limitations faced by current models and categorises the gaps in the literature (

Table 3. Identified gaps in the Literature Review.

Gaps	Topic	Research Opportunities
Model integration	District Energy Systems Electric power networks Urban mobility models Sensitivity analysis Climate change scenarios Micro-climate and mutual shading	Coupling multi-physics urban systems Estimate mobility energy needs Identify the most influential parameters. Quantification of environmental and inter-building influences
Model standardisation	Data format standardisation Develop a model standard approach Develop generalised solutions Compare calibration methods	Apply specific models in specific scenarios. Scaling up the level of detail Strengthen the advantages of occupant behaviour Assess the impact of Bayesian approaches
Data collection	High-density weather data Large-scale energy modelling data Socio-economic parameters Non-geometric data Absence of information from previous studies	Taking account of spatial patterns in cities. Cloud-based computing for data acquisition and preprocessing. Identify household income, demographics, construction assemblies, OB and HVAC systems. Improve data sources and simulation workflow.
Process automation	Archetype development Implement artificial intelligence and hybrid models. Auto-calibration routines Application of remote sensing	Data mining and machine learning techniques. Mechanisation of the modelling of existing or new building stock. Use Bayesian inference and Monte-Carlo to calibrate input variables.
Occupant behaviour	Improve stochastic occupancy OB of uncommon urban sectors Generic OB in statistical methods	Improve non-residential and non-commercial archetypes. Evaluate the benefits of deterministic, stochastic and probabilistic approaches. Identify the uncertainty of using different approaches. Extract the OB dynamically from the collected data.

) that could stimulate future research, with a focus on developing integrated electric urban energy system models and tools. Despite their importance having been highlighted a long time ago, the microsimulation of electric demand in urban districts remains a significant issue [40]. The modelling of urban energy use is essential to understand and manage the energy performance of cities. Unfortunately, UBEM has failed to represent some aspects of a large-scale energy system, such as potential PV generation, electric mobility, and load-matching strategies at a district scale [36]. Other technical limitations of urban systems models include data exchange mechanisms, coupling methods, and synchronisation control [22]. Although there are many ways to leverage building-to-grid integration models, it remains unclear how well UBEM can accurately predict building loads at the sub-hourly level. For this reason, it has been recommended to include practices to improve the fidelity of these models to collect metered data from a representative group of buildings, and, if possible, high spatial resolutions at the apartment scale [21]. Additionally, future research is recommended, discussing the drivers, project funding and barriers of novel use-cases, especially when it comes to hybrid use-cases and multi-purpose energy communities, and the role of technological affordances in fostering sustainable transition [17].

Stakeholders and researchers face various challenges when implementing energy modelling, including data availability, data inconsistencies, scalability, integration, geospatial analysis, privacy issues, and computational resources [32]. One example is that top-down approaches are incapable of representing the impact of new constructions on existing buildings, e.g., high-rise buildings can shade low-rise existing buildings. In addition, they can only simulate energy consumption at the aggregated level, and therefore, for more detailed results, these approaches may not be the best [33]. Similarly, bottom-up statistical or data-driven methods also demonstrate some weaknesses. Unlike physics-based approaches, they are not suitable for identifying opportunities to increase building energy efficiency, as they do not allow for high-temporal energy consumption data [33]. Some of the challenges of empirical data-driven methods include the requirement for training in model development, the limited applicability of the models to specific locations and building types, and the lack of a physical explanation for specific building performance parameters [22].

Among the main issues outlined in the literature are the use of mixed databases, which can delay the rapid creation of large models, and the use of divergent nomenclature between tools [25]. This includes the development of archetypes, which is one of the biggest challenges while setting up these models, as there is considerable uncertainty about the building stock in each country or region [23]. Because physics-based approaches use variables that are not always accounted for in energy surveys and statistics, they are built using assumptions of occupancy, materials and technology that increase the uncertainty of UBEM purposes [29]. In addition, even though occupancy has a vital impact on the energy consumption of buildings, how it affects the optimisation of the building-to-grid operation remains unaddressed [30].

The most common deficiencies in related literature were the lack of measured energy consumption data and the absence of calibration and validation methods, making it difficult to evaluate the accuracy of models [23,36], due to the often-overlooked challenge of data privacy protection and security [6]. Therefore, as most of this information has been estimated or assumed from the researcher’s perspective [18], the discrepancy between the predicted results of the models and the actual building performance undermines the effectiveness and reliability of these models and tools [4]. Like UBEMs, the developed tools do not use a standardised terminology, as they use different nomenclatures, hindering the interpretation and comparison of results [14]. Encouraging communication efforts between researchers and tool developers is essential to establishing shared baseline concepts among different tools, guiding future research in a common direction. Another critical limitation of these models is the time required to build and run large-scale models. Computation time is one of the biggest obstacles for large-scale energy simulations in urban buildings. Developing a UBEM requires a significant amount of initial work and time to build a model, which can be a challenging issue for individual research teams during this stage [18]. However, while most studies present no validation of their models against measured data, others lack estimations of the required computation time. Similarly, models requiring higher levels of detail and inputs increase computational complexity and time costs significantly, especially in large-scale simulations [6]. Yet, the rapid evolution of computing power offers the possibility of faster simulations, even for more complex models [23]. This obstacle may resolve on its own over time, even for large global metropolises. Still, free-access UBEM tools must be able to run on computers of users with regular hardware and minimise the processing time of models at least up to the neighbourhood scale.

Finally, current methods and tools often reduce the context of operational energy use in buildings, barely discussing urban mobility [23], embodied energy during the infrastructure life cycle [29], urban climate models [6], and environmental impact assessment [41]. Some challenges during the integration of UBEM with other urban models include differences in data structure, mismatch of spatial resolution, computational requirements, and model validation [6].

4. Future Development Opportunities

This literature review has demonstrated that the UBEM field remains a fertile area for future research and development. However, there is still a need to address the current challenges (identified in Section 3) to enhance the reliability of these models and tools. This section outlines three areas of interest that future research could improve the implementation of urban energy networks using physics-based UBEMs, such as Urban Digital Twins, Smart Communities, and Positive Energy Districts. Although legislation is still catching up, these topics are trending in Western developed countries [17]. Focusing on the aim of this study, it is essential to accurately simulate energy use at the urban scale to develop models able to include other components of the urban environment, such as building embodied energy, road infrastructure, and transportation energy use at its various scales [29], contributing to local energy self-sufficiency, emissions reduction, and community empowerment [24]. Future studies are needed considering hybrid-resolution models and cloud-based computing to improve computational efficiency and time [6]. Furthermore, integrating UBEM with urban systems energy models can support the design and optimisation of smart networks [25], enabling the efficient use of energy. For this, new technologies like machine learning algorithms and cloud computing could be promising approaches to help accelerate the development of UBEMs, even in their most complex forms [23,32]. Unfortunately, despite the growing policy interest, only a few modelling tools capture the full range of collective dynamics [24]. It is then of critical importance to evaluate, design and disseminate the necessary knowledge and tools to accelerate their evolution in Society.

4.1. Urban Digital Twins

Digital Twins (DTs) is an emerging technology that enables communication between physical entities and a virtual environment, improving the accuracy of energy predictions at various levels within a district [42]. DT is the digital counterpart of a physical object in the real world, existing in a virtual platform. It enables the simulation of dynamic interactions within urban environments and offers insights into the temporal and spatial impacts of energy use and environmental factors [41]. They are particularly notable for their ability to compare measured and simulated data, enabling continuous monitoring of buildings and evaluating their real-time performance [31]. In addition, physics-based DT could help incorporate virtual technologies to assess their performance within existing infrastructure, including electric energy storage systems, solar-heated water tanks, hydrogen tanks, and electric mobility. However, characterising a DT as a digital replica version of physical assets that is connected and synchronised is challenging to achieve in practice in the built environment, encompassing the elements and dynamics of system and device operations within their environment and lifecycle [31]. Thanks to the rapid advancement of sensing technology, the concept of the digital twin is now becoming a reality through the integration of virtual building models and real-time data from cutting-edge measurement technologies [43].

Most of the studies in Digital Twins have focused on two key aspects: data interactions, with the help of the Internet of Things (IoT), and building simulation and modelling, having a comprehensive perception of the physical system of the buildings [43]. Implementing a successful DT model requires the development of a reliable process using trustworthy data and methods [28]. The emergence of the IoT and advanced metering infrastructure enables the real-time interaction between virtual models and actual buildings, and the timeliness and rationality of operation decisions and fault diagnosis, easing the process of retrieving real data of buildings [30]. In addition, IoT methods can collect and record energy footprints to integrate not only the consumption data of each system element, but also renewable energy, into urban energy network models, utilising a standardised data interface [27]. Future advancements should prioritise improving data integration and standardisation in Digital Twin infrastructure and using real-time data from IoT to increase assessment precision [41].

Smart cities will need urban digital twins to support decarbonization policies, using advanced control and management systems for emissions accounting. Likewise, load matching and on-site energy storage are aspects of interest in the modelling of urban energy systems that could be faced with the implementation of urban digital twins, as they can show the ability of the area under study by reducing the amount of electricity imported from the distribution grid to take advantage of the energy available in the region [36]. However, while they are promising technologies, they require substantial investment and infrastructure as they rely on large datasets that may not be available in all regions [41].

Digital twin models of buildings in an urban region can serve as the interaction nodes to design future EC scenarios. Under this vision, Hong et al. [22] believed that UBEM will be a key component for developing digital twins in smart cities, providing innovative value during the design and operation of low-energy buildings and communities. Creating DT models of urban areas using UBEM can help examine how various technologies and practices are adopted across building stocks at multiple scales. With the proper technology, UBEM could provide mass data inputs to feed future scenarios during the simulation of these distribution systems [36]. So far, traditional UBEM tools have not considered integration models with sensing technologies. Despite this, using fixed data values could be enough to design and build district energy systems with UBEMs [44] that could be later integrated with these technologies once they are globally implemented. The knowledge acquired by these models could provide guidelines for stronger and more reliable energy system models that can include off-grid consumers, eventually allowing them to request and deliver electricity from the public grid. In addition, modelling with UBEM using multi-objective optimisation can help city planners and policymakers analyse mixes of renewable energy systems better to understand their needs and trade-offs [45].

To ensure access to consistent data, future work could apply big data analysis and cloud computing to acquire, preprocess, and simulate large-scale energy data models [32]. Additionally, Machine Learning (ML) model refinement and the application of advanced techniques, like fuzzy logic and MCDA, will improve predictive capabilities [41]. Furthermore, digital twins enhanced with ML techniques could provide valuable insights into the performance of districts throughout their various stages of life cycle [31]. However, ML models rely on large, high-quality datasets, which may not be available in all regions. Furthermore, applying fuzzy logic and MCDA requires sophisticated algorithms and expert knowledge, which may not be universally accessible [41].

4.2. Energy Communities

Energy communities (EC) are clusters of buildings that utilise local RE production and employ efficient consumption techniques to minimise the need for electricity and heat [43]. They have been steadily attracting attention as social innovations that could drive the decarbonisation of energy systems through the democratisation of resources [17]. Yet, the potential of energy communities remains theoretical, and ECs themselves are confined to specific niches in a few developed nations [46]. The primary objective of these communities is to provide sustainable and economically viable top-quality energy to meet the end-use and mobility needs of users [47]. The significance of ECs lies in their potential to drive the decarbonisation of cities, promote investment in and access to clean, affordable energy, and respond to sustainable development goals [17]. Urban communities play a significant role in the design and implementation of energy-efficient policies and low-carbon distribution systems [48]. Therefore, EC can be proposed as a transversal solution to ensure a sustainable transition to decarbonise urban areas within a renewable and decentralised energy paradigm [49]. As an emerging field of study, research on EC can help address many remaining doubts in the technical and economic perspectives of energy networks [50], such as the effects of implementing new policies at a community scale.

Smart energy management will be crucial for guiding energy planning in future urban areas by providing knowledge and awareness to relevant stakeholders [51]. The integration of smart grids into district-level models could lead to significant improvements in how cities are built and managed, as they can be used to identify interactions and conflicts between grid participants [52]. In addition, the design of intelligent energy systems in districts must consider the use of diverse storage technologies to meet energy demand across various temporal periods. Unfortunately, since current electric storage technologies are still unavailable on a large scale, new ways to ensure the dynamic balance between generation and consumption must be evaluated [47]. Methods such as seasonal energy storage can offer great value in the decarbonization of sectors like buildings and mobility [53]. Among EC configurations, community energy storage is a modular, scalable, virtual energy storage system composed of a network of distributed storage units owned by local community members [17]. Future electric networks, combining various RES, can help homeowners become self-sufficient by matching local generation with storage capacity [3], and gain other financial benefits. This includes producing and trading energy locally at a lower price, pooling investments in energy efficiency measures, and selling flexibility services [54].

UBEM has an untapped potential in supporting energy communities, as simulations on urban models offer evidence-based decision support to lower risks, engage, motivate, and guide actors, and promote broader policy goals and regulatory requirements [17]. Since UBEMs can use and provide valuable streams of data for smart-city projects [22], they can create an ideal environment to help bridge the gap between EC research and implementation [55]. Implementing future UBEM projects in smart cities, if supplied with digital twin abilities (real-time data), could enable the timely and optimal control and management of the building’s energy system in response to grid dynamics [22]. Well-developed and validated UBEM tools can be systematically applied to support better-grounded prescriptive provisions [5]. Since some studies have already used UBEM to assess the potential of EC at the urban level [55,56,57], this methodology shows promising benefits for future applications in tool development. The development of tools that scale energy flows can give grid operators the means to maintain grid balance, forecast and manage grid congestion, and ensure the supply of energy [17].

Focusing on the goal of the present research, UBEM can be used to model district energy systems and infrastructure to simulate building-integrated technologies at a city-wide level, such as PV rooftop power, EV charging needs, and district-scale load matching strategies [36]. With these capabilities, UBEM tools can provide transparency, existing knowledge and skills, market-specificity, self-identity, active involvement, embeddedness, and a robust network [17]. With accurate replication of modern technologies, UBEMs could integrate them into their models (e.g., distributed and/or centralised renewable energy generation and storage) to examine the impacts of sharing configurations on local electric microgrids.

Given the limitations of physics-based and data-driven methods, an increasing number of studies have aimed to combine the two approaches to leverage their respective strengths and produce more comprehensive simulation results in UBEM [58]. Combining data-driven and physics-driven methods is vital for tackling real-world challenges in the energy transition. This involves integrating forward and inverse modelling practices more closely to continually improve performance [31]. These hybrid methods offer more accurate estimates of energy performance in building stocks without precise information than physics-based methods, maintain the physical description of each building, and address the data gaps present in purely data-driven methods [30]. Future research can combine concepts from both measurement and verification of hybrid building models to achieve multiple goals while preserving physical interpretation and transparency, thereby enabling human oversight [31]. SynCity is a tool that brings together data-driven and physics-based simulation methods to create synthetic hourly load curve estimates for each building within a city [59]. This and other UBEM tools, like CityBES, CEA, and SimStadt, can guide policymakers, designers, modellers, and researchers interested in the comparison of different energy conservation measures using the results from these tools [14] to build new modules accurately simulating DT. For this, developers of these platforms, together with the research community, will need to include remote sensing technologies in their frameworks.

4.3. Positive Energy Districts

Positive Energy Districts (PED) refer to urban areas or groups of connected buildings that are energy-efficient and flexible, with an annual surplus production of RES and net-zero greenhouse gas emissions [60]. Currently, PED is considered a pioneering strategy to guide cities through the process of achieving climate neutrality [61], combining energy efficiency and renewable energy production to foster the development of sustainable smart cities [62]. The PED concept, derived initially from Nearly/Net/Positive Zero Energy Buildings, was extended from individual buildings to the district level [63]. Among the different concepts existing to refer to local-level energy transition in cities, PED currently constitutes a reference terminology within EU policies and programmes [64]. Since cities are responsible for most global greenhouse gas emissions [65], this topic has become highly relevant in the European Union, as it has set a target to simultaneously transform 100 cities into PEDs before 2025, thereby setting a benchmark for the continent [66], motivated through the investment of programmes such as the H2020 framework [67]. At present, numerous European cities are developing, refining, and implementing policies that will shape their strategies and actions towards energy citizenship [68]. This confirms that PED models can play a pivotal role in climate change mitigation strategies aimed at achieving carbon neutrality in urban areas, providing the tools needed to understand the benefits and advantages of these systems and informing stakeholders to adopt best practices in energy planning [51].

The multidisciplinary aspect of PEDs is firmly embedded in urban planning frameworks, highlighting the need to integrate diverse systems and infrastructures [63]. For this reason, their design must be capable of engaging multiple stakeholders dynamically [69]. The implementation of PED models in cities could offer several advantages in the market, including economies of scale and the opportunity to share energy resources between buildings [70], promoting investment and employment growth, as well as the rehabilitation of the outdated building stock [61]. The development of a PED demands the integration of innovative solutions to be applied both indoors and outdoors within the district. Therefore, the success of PED implementation depends on numerous factors and can be affected by technical, socio-economic, administrative, cultural, and legislative aspects of the existing environment [64]. Approaches that involve multiple stakeholders need policies and tools based on data to balance various environmental factors, including energy, carbon emissions, and both indoor and outdoor environmental quality, increasing the debate about PEDs as a symbol of decarbonised urban design [69].

Similarly to the EC concept, to achieve a complete transition toward PED, it is crucial to model the energy sharing among buildings, including physical or virtual distribution, peer-to-peer topologies and infrastructure [71]. Additionally, they must be able to mitigate adverse impacts on existing power networks by incorporating energy flexibility technologies [72]. Physics-based UBEM methods and tools can lay the base for simulating PEDs to assess their effects in local and regional markets. UBEM tools represent the starting point for modelling complex district energy systems, as they are often used to understand the energy demand of building clusters and energy systems at the urban domain [73]. PED focus on long-term climate mitigation using energy efficiency, clean energy production and flexibility based on building on-site production, where many UBEM applications have been implemented to simulate energy performance in urban settings [74]. Initial studies using UBEM tools, in Spain [75], Greece [76], the Netherlands [68], and Japan [77], suggest that achieving PED may be possible through a combination of RE production and minimising the significant demand for heating by utilising more efficient sources [65]. These limited PED examples illustrate a fragmented design environment where incompatible tools and performance standards support various design goals without coherence or a clear grasp of the mutual impacts of variables and environmental performance criteria [69]. Still, among these pioneering PED projects, we can see that combining efforts in physical energy transition investments with societal engagement offers incredible innovative potential, which is a genuinely groundbreaking lesson [68].

Although there is a global motivation to promote PEDs, a gap remains in their integration with computational urban design tools, which are critical for their multifaceted design [69]. Even though they offer several technical and economic benefits, they are still in the early stages of development, and implementing a fully functional PED remains an objective rather than a current reality [76]. Varied tool capabilities suggest a broad range of use cases, spanning detailed urban planning, wider policymaking, and energy system analysis, and catering to different user needs and levels of expertise [78]. However, Current tools are fragmented, significantly restricting their capacity to support such a complex design process, lacking the essential interconnectivity between tools and a comprehensive framework needed to address the multi-dimensional challenges inherent in the design process of PEDs [69]. New platforms created to facilitate peer-to-peer economic activities could have a significant impact on the transport and housing sectors in many cities and considerably influence energy systems within districts and urban areas, including the further growth of zero- and positive energy districts [79].

5. Conclusions

This work has reviewed the state-of-the-art of urban building energy models, focusing on physics-based bottom-up approaches and their potential applications for the design and implementation of building-to-grid urban system energy tools. This study has identified the various techniques currently used, the required components to build them, the most used tools for this purpose, and the current applications of these models to date. In addition, it has identified the current limitations, gaps and three potential perspectives that could benefit from developing the proposed methods and tools. As previously evaluated in the literature, this analysis confirms that UBEM can serve as the foundational basis for building-to-grid models in urban energy systems, with the level of granularity necessary to simulate them accurately. Finally, this study proposed three potential research directions where the use of physics-based UBEMs with building-to-grid capabilities could be utilised to support existing or new tool development, urban planning, and policymaking.

This review identified that to develop a comprehensive and reliable urban system energy using UBEM tools, several key considerations and limitations must be addressed first. UBEMs present new challenges and limitations, especially in the type of data needed for their construction (e.g., architecture features and occupancy behaviour), data collection and validation, standardisation of procedures and results, and the inclusion of elements that, to date, have not been considered in the analysis. These and other challenges will be eased once data sensing technologies are globally implemented with the implementation of IoT and smart metering. As some tools already have the framework available to serve as the foundation of district energy systems, enabling the design of future electric power networks and helping decision-makers assess the implications of energy policies worldwide, comprehensive and reliable open-source and user-friendly software tools must be developed and provided to stakeholders as soon as possible to achieve the goals of the energy transition. For this, involved energy organisations and academia must set up the initial necessary guidelines to push tool designers, developers and users towards a desired common transition path.

Novel topics of interest, further research on UBEM-oriented urban district Digital Twins, endowed with Energy Communities and Positive Energy Districts abilities, along with the use of mature open-access and user-friendly tools, can accelerate the design and planning of modern district energy systems. These three perspectives are intrinsically related and can work together in future urban system modelling tools to enhance the applications of urban building modelling and its results in real-life scenarios However, the modelling of future urban energy systems using physics-based UBEM will require their integration with data-driven approaches, like IoT and smart metres, to transform them into virtual matching representations, which can later be used to manage real-life operation of EC and PED scenarios in urban settings. Digital twins (hybrid physics-based and data-driven methods using data sensing techniques directly from buildings) can provide real and accurate information, helping to solve many identified challenges and limitations of UBEMs, such as data acquisition and validation, occupancy prediction and process automation. Like ECs, future tools could evaluate the trading of energy resources between PEDs, using similar programming considerations. Furthermore, similarly to ECs, future PED analysis models could be used to observe the interaction between districts in a region, reducing even more the uncertainties of the grid, providing higher knowledge of their management, and the increasingly need for energy from large-scale power plants. This promotes future research about the commonalities between EC and PED, among other sharing and trading configurations, to identify a roadmap for UBEM tool development.

Although the potential applications of building-to-grid models have already been identified in experimental articles and subsequent reviews, the use of UBEM tools has primarily been limited to analysing the retrofitting and energy efficiency potential in large-scale cities. Even though UBEM modelling has been applied in various countries of the world for a considerable time, there are still many gaps in this field that need to be addressed to accelerate its global implementation, particularly in terms of certain tool features. Still, it seems that UBEM tools are just one step away from implementing these models in their frameworks. To date, the existing literature has proposed existing and novel research that can serve as the basis to develop future UBEM tools with the desired skill to develop future urban environments. The potential use of physics-based UBEMs increases the motivation for future research and technology development, particularly when focusing on the primary goal of this study. Eventually, existing platforms, or new emerging tools, will need to incorporate the perspectives proposed in this research into their frameworks, considering standardised input parameters and well-known established results and analysis procedures, helping to address various existing gaps.