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High levels of non-dispatchable photo-voltaic (PV) generation integrated with the grid may disrupt its operation. Aligning the PV panels to follow local building demand can partially elevate this. We investigate the economic viability of alignment for buildings owners and grid utility. A bi-level optimization framework is built to determine the design of a building rooftop PV system. An upper-level optimizer selects panel design parameters to lower either cost or grid electricity imports of the lower-level optimization. The lower-level energy-economical model optimization sizes the panels to minimize building electricity cost given building demand, given grid pricing, available roof area, and other factors. A case study of commercial building archetypes in Vancouver BC finds that at the price of 1.25 $ CAD / W p (50% reduction of current prices) PV is profitable. For all archetypes profitable alternative grid-friendly designs were found, however, for some buildings large increases in peak power are observed. For some archetypes, a profitable alternative was found that lowers the overall electricity imports (between 4 and 7%) and limits impacts on peak power exports (below 125% of original peak import). Our future work will look into the uncertainty and sensitivity of the framework, technology improvements, and alternative economical models.

Computational optimization is gaining popularity in energy-efficient building design. For choosing an algorithm or setting its parameters, often mathematical test functions are employed. Th...

Statistical models can be used as surrogates of detailed simulation models. Their key advantage is that they are evaluated at low computational cost which can remove computational barriers in building performance simulation. This comprehensive review discusses significant publications in sustainable building design research where surrogate modelling was applied. First, we familiarize the reader with the field and begin by explaining the use of surrogate modelling for building design with regard to applications in the conceptual design stage, for sensitivity and uncertainty analysis, and for building design optimisation. This is complemented with practical instructions on the steps required to derive a surrogate model. Next, publications in the field are discussed and significant methodological findings highlighted. We have aggregated 57 studies in a comprehensive table with details on objective, sampling strategy and surrogate model type. Based on the literature major research trends are extracted and useful practical aspects outlined. As surrogate modelling may contribute to many sustainable building design problems, this review summarizes and aggregates past successes, and serves as practical guide to make surrogate modelling accessible for future researchers.

This paper presents a co-simulation framework for the simultaneous optimization of building geometries and multi-energy systems using the energy hub approach. The rationale for such coupling is that building geometry has an impact on energy demands and solar potentials on roofs and facades, thus also altering the corresponding optimal energy system. As a demonstration of this approach, we formulate bi-objective optimization problems for minimizing operational cost and carbon emissions, with decision variables for building geometry and for selection and sizing of energy system technologies. The methodology is applied to a case study in the city of Zurich, Switzerland, involving four office buildings. Different carbon emissions targets are studied to show the impact on cost, densities, sizing of the multi-energy system, and architectural design implications. Results for this case study show a clear relation between emissions targets and densities due to available solar potentials for renewable energy generation, with an optimal density in the strictest emissions target reaching only 10% of the optimal density without emissions target. This indicates that differentiated environmental targets should be defined depending on the location of a building, where rural sites could have stricter targets than dense urban sites to reflect the respective marginal costs for achieving the targets. Our study shows that coupling multiple simulators into a common optimization and design workflow brings together architectural aspects, such as geometry, with engineering aspects, such as the energy system design, and microclimate conditions, such as local solar potentials. Thus, essential interdependencies between the energy supply and demand side can be captured in the design of energy efficient cities.

This study investigates the performance of a wide selection of single objective black-box optimization algorithms (optimizers) when applied to a large set of building energy simulation problems from the literature. Optimizers include randomized, deterministic and model-based algorithms. We also study the impact of tunable hyper-parameters on performance for some of the optimizers. We apply benchmark metrics from the literature and develop a new metric based on ranks, which is especially useful for comparing small differences in optimal cost values. Our extensive experimental setup allows us to draw generalized conclusions on convergence times, stability and robustness of the optimizers in the context of building energy simulations. Main findings include the importance of tuning optimization algorithm parameters, consistently very fast convergence with the model-based optimizer RBFOpt but with a risk of being trapped in local optima, competitive results with classical metaheuristics such as a simple Genetic Algorithm and Particle Swarm Optimization Algorithm when tuned to specific problem dimensions, and consistently good results with CMA-ES when given a very large evaluation budget. It is striking however that no single algorithm dominates the benchmark for all considered performance metrics and/or optimization problems. Another important finding is that many of the discrete design variables in building energy optimization are in fact ordinal, which makes algorithm operators designed for continuous variables more effective than operators designed for combinatorial problems. These findings help users to select an appropriate optimizer for BEO problems and the available evaluation budget. Finally, this study provides an open-source framework for other researchers to benchmark their algorithms on the same test bed, hence enabling traceable and more rigorous performance comparisons.

We investigate the impact of local climatic variations on the energy performance of buildings by conducting simulations using weather files generated from high-resolution measurements cover...

For effective integration of large amounts of renewables and high-efficiency energy technologies, their benefits have to be quantified. Network-level energy optimisation approaches can determine the optimal location of generation technologies within a region and the optimal layout of energy distribution networks to link them. Mixed-integer linear programming (MILP) formulations are generally employed and this is often a burden for large scale models as the computational time drastically increases with the problem size. Most methods used to reduce the complexity of MILP problems focus on the temporal scale or use aggregated demand profiles for the spatial dimension. There is a lack of a method addressing the spatial complexity to assess the potential of interlinked energy networks at large scale. Therefore, this paper introduces a new combined clustering schema enabling quantification of the potential of district heating networks based on results from building scale energy optimisation problems and taking into account building characteristics. A city-scale case is divided into multiple districts based on the output of a density based clustering algorithm. The parameters taken into account by the clustering method are the cluster density, homogeneity index and load magnitude. The analysis of the clustering map along with building characteristics of each cluster reveals the required characteristics for the installation of a district heating network or distributed energy systems.

As energy systems grow more complex, modeling efforts spanning multiple scales, disciplines and perspectives are essential. Improved methods are needed to guide the development of not just individual models, but also multi-model ecologies – systems of interacting models. Currently there is a lack of knowledge concerning how multi-model ecologies can and should be designed to facilitate adequate understanding of energy system complexity and its consequences. Via an analysis of twelve multi-model initiatives both within and outside the energy domain, this paper elucidates possible design patterns and development paths for multi-model ecologies. The results highlight two broad paths to developing energy system multi-model ecologies, one prioritizing interoperability and the other prioritizing diversity. The former path facilitates the efficient development of models spanning multiple scales and (to a degree) disciplines, and can ease systematic testing of assumptions. The latter is suited to bridging traditional disciplines and perspectives and advancing knowledge within the interstices of different knowledge communities. It is furthermore suggested that a combination of diversity, connectivity and hierarchy in multi-model ecology composition is central to enabling the development of complex webs of models capable of addressing the complexity of real-world energy systems.

Machine learning-based surrogate models are trained on building energy simulation input and output data. Their key advantage is their computational speed allowing them to produce building performance estimates in fractions of a second. In this work we showcase the use of deep convolutional neural network surrogate models embedded into a web application, allowing users to rapidly explore building performance at high spatio-temporal resolution. Users can pick any climate on an interactive map, customize a building design with thirteen decisive design parameters, and the surrogate model allows them to retrieve hourly heating and cooling load time series data in fractions of a second. In this work, we further show that the surrogate model reaches an accuracy of R 2 > 0.93 (MAE < 0.27 kWh) for unseen design specifications and climates. These results motivate the use of computationally cheap surrogate models to replace building energy simulation for a wide variety of tasks in the future.

The benefits of decentralised energy systems can be realised through the optimal siting of distributed energy systems and the design of highly interlinked district heating networks within existing electrical and gas networks. The problem is often formulated as a Mixed Integer Linear Programming (MILP) problem. MILP formulations are efficient and reliable, however the computational burden increases drastically with the number of integer variables, making detailed optimisation infeasible at large urban scales. To tackle complex problems at large scale the development of an efficient and robust simplification method is required. This paper presents an aggregation schema to facilitate the optimisation of urban energy systems at city scale. Currently, spatial and/or temporal aggregation are commonly employed when modelling energy systems at spatio-temporal resolutions from plant scheduling up to national scenarios. This paper argues for solving different scales separately using a bottom-up approach, while keeping track of the error made by reducing the resolution when moving from building to urban scale. Novel modelling formulations and optimisation techniques are presented. They enable drastic reduction of the computational time (by up to a factor of 100) required to find an optimal solution in reasonable time without sacrificing the quality of the results (no more than 1% loss in accuracy). A density-based clustering algorithm enables intelligent division of a large city-scale problem into sub-optimisation problems by creating clusters of different density. In each cluster, the trade-off between centralised and decentralised energy systems and the associated district heating network design is evaluated. A solution is selected based on a local optimisation of the network costs. Demand profiles of each building are assigned appropriately, then at an upper level the energy optimisation problem is solved considering the network losses at lower levels. This method enables large-scale modelling of urban energy systems while taking into account building-scale levels of detail. The clustering method enables assessment of the potential of district heating networks on city scale based on building characteristics and available urban energy systems.

Energy models based on optimization principles are valuable tools for optimizing the design elements and the operating strategies of multiple distributed energy systems (DES). Such models, commonly formulated as Mixed-Integer Linear Programs (MILP), achieve a good trade-off between model accuracy and computational complexity. However, the latter aspect depends heavily on the number of variables. Hence, problems can become intractable when large spatial or temporal resolutions are considered. In this paper, the focus is placed on the temporal dimension and different representations of it are evaluated. The model is solved for a full year in hourly time-steps, for a set of optimally-selected typical days, and, finally, using a rolling horizon formulation in which the DES operation is optimized sequentially. Results show the possibility of decreasing the computational burden by several orders of magnitude without sacrificing the accuracy of the optimization results, by appropriately selecting the parameters of the temporal reduction method.

Many energy policies set a goal of decreasing the carbon emissions of the energy sector by up to 100%, including the electricity grid. This is a long term and gradual process. Energy systems in cities will likely be the starting point for greenhouse gas emissions mitigation since they account for 80% of global carbon emissions. This paper analyses the impact on urban districts of decarbonizing the electric grid supply. A multi-objective optimization model has been developed that combines the optimal design and operation of distributed energy systems, the design of district heating (DH), electricity grid constraints based on linearized alternating current (AC) power flow and grid upgrade options. A number of scenarios were defined corresponding to different levels of renewable energy share in the electricity grid. For each scenario, we analyse the changes to the design and operation of the urban energy system, the impact on the district heating potential, and the impact on the operation of the distribution grid as well as the grid upgrade potential. The results showed that the renewable share of the grid has a large impact on the optimal solutions obtained. When the renewable share is below 55%, a lot of photovoltaic (PV) electricity has to be used to offset the carbon emissions from the grid. Conversely, when the renewable share is above 70%, the use of PV decreases and heating systems become electrified by producing heat with heat pumps (HP). District heating is used regardless of the renewable share in the grid, but as carbon emissions limits are tightened the potential of DH decreases. Only when the renewable share in the electricity grid is 100% it is possible to have a carbon-neutral district. In the carbon optimal solutions of each scenario there is no need for DH, but the grid has to be upgraded to enable electrification of the heating system.

This paper presents two alternative methods for selecting tuned hyper-parameters of search heuristics for computationally expensive simulation-based optimization problems. The set of available hyper-parameters are obtained by conducting a meta-optimization to a number of mathematical test functions at different problem dimensions. Since a large number of tuned hyper-parameters is obtained from the meta-optimization, we develop methods to select tuned hyper-parameters for our actual simulation-based problems. The first selection method is based on clustering of the tuned hyper-parameters, where the medoids of the largest clusters are chosen. The second method constructs a ranking matrix that cross-checks the expected cost value for each tuned hyper-parameter configuration when solving the other training functions in the set, and that configuration is selected which has the minimal sum of ranks in the matrix. We apply our methods to custom particle swarm optimization and evolutionary algorithms. The feasibility of our approach is demonstrated by benchmarking the tuned search heuristics to a number of other search heuristics from open-source libraries on continuous building energy simulation problems. Our results show that our tuning and selection methods are successful in identifying hyper-parameters that result in competitive performance to other popular or recent algorihtms such as CMA-ES or RBFOpt. Future work should apply the methods developed to more recent search heuristics and make use of synthetic training functions in the meta-optimization that better resemble building simulation problems, instead of the mathematical test functions currently employed.

Reliable data-driven models that estimate building envelope properties are indispensable for achieving emissions reduction targets. An extensive body of existing research investigates such methods, but benchmarking is limited and it is often unclear whether the approaches are scalable and robust to diverse building properties. Machine learning approaches, which natively handle complex, multivariate datasets, are rarely applied in this domain. This paper benchmarks seven different methods for characterization of the whole-building heat loss coefficient, including traditional gray box and novel black box approaches. To do so, a dataset of 16,000 simulated buildings is created. The models are benchmarked against ground truth, including an assessment of robustness to climate, construction materials, air-infiltration rate and occupant behaviour. We show the deep learning methods outperform other approaches in terms of accuracy and robustness, but that all of the approaches have limitations that restrain their practical usage. Based on this result, we suggest that further research is required to develop reliable and scalable approaches for the characterization of quantitative envelope properties from sensor data. The model code, data creation pipeline and final dataset used for this work are open-sourced so that future work can expand on this study. We encourage the use of our framework to support innovation in this domain.

In recent years, deployment of low carbon energy systems to supply electricity in residential buildings has increased. These energy systems typically integrate different renewable energy resources with energy storage systems to meet electrical energy demand. This paper applies the ”energy hub” model to various energy systems for residential buildings in British Columbia considering several scenarios. We explore the energy system changes in on-grid, off-grid and 100% renewable scenarios. In on-grid systems, the trade off between grid connection and energy storage is explored; the results shows that even the cheapest energy storage system is not feasible with the current cost of grid electricity. For off-grid systems with a diesel generator, storage technologies are used in some energy systems, however the systems still have carbon emissions. Finally, for 100% renewable off-grid systems, both Li-ion and pumped hydro storage systems are used to handle diurnal and seasonal intermittency.

Electricity and transport sectors have to be decarbonised in order to mitigate climate change effects leading to increased penetration of distributed energy systems (DES) and electric vehicles (EV) which can threaten the security of distribution grid operation. Proper design and operation of such systems are crucial if the adverse effects on the grid are to be avoided. Moreover, EVs represent a high load and should not be considered merely as passive assets since they can provide various flexibility services for maintaining the grid stable. This paper presents a multi-domain optimisation framework for minimising carbon emission in low-voltage distribution grids with high share of distributed energy resources and electric vehicles. The framework determines optimal EV flexibility usage (both active and reactive) while satisfying electric and thermal building demands, and maintaining the distribution grid in the stable operation. The model was applied to a real low-voltage Danish distribution grid where measurement data is available on hourly basis in order to determine EV flexibility impacts on carbon emissions, as well as the benefits of optimal DES design. The influence of EV reactive power control on the grid operation, in addition to coordinated charging, is analysed. Results showed that when the system can be optimally designed, emissions decrease by 64% and additionally 32% with proactive EV integration, whereas EV reactive power control enables integration of larger EV amounts and provides significant voltage support without affecting the user comfort.

Distributed and decentralised energy systems coupled with district heating networks are promising concepts for achieving less carbon-intensive urban energy systems. This paper investigates the optimal design and operation of distributed energy systems as well as optimal heating network layouts for different economic and environmental objectives. A mixed integer linear programming model was used for multi-objective optimisation to minimize total cost and carbon emissions. Improvements include the coupling of equipment and network modelling across many buildings, detailed CHP operational constraints, the inclusion of network heat losses, and improved modelling of thermal storage. The model was used on a case study consisting of eleven residential buildings and one commercial building for various design scenarios (available technologies, network layout limitations, operating constraints). Design results (capacities, network layouts) and operational results (energy supply breakdowns) are presented. The results show that district heating can deliver emissions savings of 23% over a standard solution for the same cost. The optimal number of heating network links increases as more stringent carbon emissions targets are implemented. Limits on possible network routes have significant impacts on the optimal technology capacities and on operational schedules. Also, commonly neglected constraints on CHP operation cause noticeably more reliance on district heating networks.

Surrogate models can emulate physics-based building energy simulation with a machine learning model trained on simulation input and output data. The trained model is extremely fast to run, allowing us to estimate simulation outcomes for thousands of different building designs in seconds. Recent studies have shown the diverse benefits for sustainable building design. Surrogates were applied to provide rapid feedback at the early design stage, to accelerate sensitivity analysis, uncertainty analysis and design optimization, or to improve building model calibration. However, the current process of surrogate modelling offers much room for improvement. In particular, a surrogate model is bound to the specific building design problem it has been trained for. This includes a specific site, requiring time-intensive retraining if the building performance at another location is to be analysed. In this paper, we develop a single surrogate model that spans arbitrarily many locations. For that purpose, we are among the first to use a deep temporal convolutional neural network to process annual multivariate weather data with hourly resolution ( ≈ 150,000 inputs). The network learns features relevant to estimate heating or cooling demand. We combine these location-specific weather features with building design parameters to serve as input to a single surrogate model (feed-forward neural network). In a case study with 569 weather files from locations in Canada, we show that the surrogate model deviates by less than 3% when predicting annual heating demand for new building designs at locations outside of the training data set.

This paper introduces a combined framework for the analysis of building-related and transport-related emissions, to allow the comparison of a new high-density mixed-use development concept with traditional urban development patterns. The framework accounts for the energy and emissions arising from the operation of the building, the materials needed to build it, and the transportation of the residents. This provides a novel means to compare the impact of urban form on both building-related and transport-related emissions simultaneously. The building analysis performs detailed building energy simulations and embodied energy calculations. The transportation analysis estimates emissions reductions from eliminating personal vehicle trips through the mixed-use nature of the building, provision of an electric vehicle car sharing fleet, and a public transit hub with a rapid transit line. The framework is used to assess the new concept, referred to as a ‘Mothership’, which provides a potential holistic solution to urban sprawl through high-density mixed-use development that meets the needs of 10,000 people. It includes a high-performance building envelope, advanced energy systems, a public transportation hub and electric car sharing fleet. The analysis compares this method of development to the ‘urban sprawl’ that has characterized new development in North America. The comparison is performed for two case studies in British Columbia, Canada. Results show that the Mothership could reduce operational and embodied emissions by 73% and transportation emissions by 58% compared to low-density suburban development. A key takeaway is that transportation and embodied emissions become much more significant as operational emissions are reduced through high performance building design and clean electricity.

A high-quality building energy retrofit analysis requires knowledge of building characteristics like the type of installed heating system. This means auditing the building in person or conducting a detailed survey, which is not readily scalable for many buildings. This paper presents a data-driven methodology to identify building characteristics from raw smart meter data sets to allow large scale, high-quality building retrofit analysis. We use the concept of energy signatures, a scatter plot with outside air temperature on the x-axis and electricity consumption on the y-axis, which condenses each building’s electricity use into one highly informative graph. Using a Support-Vector Regression model we extract the shape of each signature and cluster them subsequently. Dynamic time warping is used to align the signature shapes of all buildings. In two case studies, consisting of smart meter data sets from 408 and 480 buildings respectively, we show that our clusters correlated well to the heating system type and the building type by comparing to building-level metadata or demographic data.

Scarcity of affordable energy efficient dwellings is a defining characteristic of the global housing crisis. In many countries this problem has been exacerbated by single objective cost-models which favour the homogeneous development of market tenures at the expense of delivering high-quality affordable homes. Despite the obvious environmental and fuel-poverty alleviation benefits of advanced energy performance standards, such as Passivhaus, they are often dismissed as an affordable housing solution due to elevated build-cost premiums. The present work attempts to reconcile this housing affordability – energy performance nexus by establishing a novel decision support framework for Passivhaus design using genetic multi-objective optimization. The use of constrained genetic algorithms coupled to the Passive House Planning Package software is shown to produce cost optimal designs which are fully compliant with the Passivhaus standard. The findings also reveal that the precise choice of Passivhaus certification criteria has significant impacts on overheating risks using future probabilistic climate data. This means that the design implications of using either the peak heating load or annual heating demand certification criteria must be temporally evaluated to ensure resilient whole-life design outcomes. In a typical UK context, the findings show that affordable Passivhaus dwelling construction costs can be reduced by up to £ 366/m2 (or 22% of build cost). Use of this evidence-based decision support tool could thereby enable local authorities and developers to make better-informed decisions in relation to cost optimal trade-offs between achieving advanced energy performance standards and the viability of large affordable housing developments.

We present a comprehensive review of modelling approaches and associated software tools that address district-level energy systems. Buildings play an important role in urban energy systems regarding both the demand and supply of energy. It is no longer sufficient to simulate building energy use assuming isolation from the microclimate and energy system in which they operate, or to model an urban energy system without consideration of the buildings that it serves. This review complements previous studies by focussing on models that address district-level interactions in energy systems, and by assessing the capabilities of the software tools available alongside the theory of the modelling approaches used. New models and tools that address these district-level interactions are reviewed and their competences assessed. These are divided into the following sections: district energy systems (including heat networks, multi-energy systems and low-temperature networks), renewable energy generation (including solar, bioenergy, wind and the related topic of seasonal storage), and the urban microclimate as it relates to energy demands. The scope and detail covered by twenty cross-disciplinary tools is summarised in a matrix; many other tools that focus on specific areas are also discussed. We end by summarising the current state of district-scale urban energy modelling as it relates to the built environment, along with our perspective on future challenges and research directions.

Multi-level optimisation divides a problem into sections such that each can be addressed using the most appropriate evaluation and optimisation processes. A methodology is proposed to address the design and operation of a building and its energy system, split into three levels: building design, system design and system operation. The optimisation techniques used are a multi-objective genetic algorithm (design) and mixed-integer linear programming (operation); the evaluation methods used are the building energy simulation program EnergyPlus (building level) and the ‘energy hub’ model (system level). The objective functions used here were annual carbon emissions and initial capital cost (for the multi-objective design problem) and annual running costs (for the single objective operational problem). The methods used are described in detail, and the proposed methodology is applied to a case study concerning an office building. The detailed results presented include the trade-off front of optimal design-level solutions, the convergence of the optimisation, trends in the associated design variable values, derived properties of each solution, the operational variable values, and the run-times of the operational optimisation. Conclusions are drawn regarding the case study and the overall approach, and future directions are suggested.

This paper describes a method of integrating decentralized energy systems at neighbourhood scale. The method is based on the energy hub concept, which describes and manages the relation between input and output energy flows and thus can be used to optimise energy consumption. The original energy hub concept is further developed to include decentralized and local energy technologies such as photovoltaics, biomass, or small hydro power, together with district heating systems, building and district conversion and storage technologies at neighbourhood level. Additionally, input from a building simulation tool for evaluating time-dependent buildings energy demand is included in the method. The proposed approach can be used to evaluate and size urban energy systems according to their energy-autonomy, economic and ecological performance. The advantage is that the energy supply systems and local energy storage systems can be evaluated in a combined way at district scale. The suggested method allows to lower peaks in energy demands of neighbourhoods on the electrical grid and to reduce the overall consumption. The developed method is finally applied on a case study for which future energy scenarios of different implementation scale are suggested. The area is located in a village in the mountains and contains 29 buildings. Suggested scenarios include decentralized and local renewable energy sources and district and small heating networks. Results show a variation of 52–98 tons of CO 2 emissions, and an energy autonomy between 64% and 92%. In case of CO 2 emissions, the best solution is given for a combination of multiple supply technologies and small networks which shows 46% lower emissions than for a scenario with only PV and biomass based systems.

This paper presents the methods and results of a bi-level optimization process used to select and size the components of an energy centre as well as to determine their optimal operation. The optimization process used a single-objective optimization of operational control nested within a two-objective optimization of design parameters that minimized capital costs and carbon emissions. Different cases were examined in which the ‘credit’ assigned for the export of excess electricity to the grid was varied. Results are presented including the overall trade-off front, trends amongst design variables, operational schedules for a particular solution, and aggregated parameters like total energy supplied by each device. Constraints on roof area were critical at the design-level, and complex interactions between demand and supply were identified at the operational level. Comparison of the two cases highlights the impact of the export of excess power to the grid.

Variations in operational use (in the time domain) and in design and use (between buildings) are critical for district systems. The effects on energy use of behavioural (stochastic profiles of occupancy and end uses) and physical variations (size, orientation, insulation and air tightness) amongst many buildings is examined. Rather than investigating just the variability of these factors, the aim is to identify subsequent impacts on building energy use. To achieve this, dynamic building energy simulations in EnergyPlus are performed. Results include total demands and their distributions, and temporal and probabilistic profiles. Very large variations in total heating demand are noted. Temporal profiles show changes in peak loads, load durations and periods of zero load. Probabilistic profiles and cumulative distributions show that a few buildings are responsible for the majority of total loads. Full detailed simulations are identified as critical when assessing temporal effects such as peak loads and stora...

The transition to 100% renewable energy systems is an important factor in the transition to sustainable energy systems. A top-down approach is often used to study the design of such energy systems on a country or continental scale. Results of these studies are useful for developing energy roadmaps but they lack the details about how distributed multi energy systems should be designed and operated at local level. An optimisation model of distributed multi energy systems is applied in order to investigate how urban districts should be optimally designed in the (near) future when the goals of energy roadmaps are achieved. The impact of individual and microgrid approaches on distributed multi energy systems is analysed on an urban district level for scenarios with different levels of renewable energy in the electricity grid supply., Energy Procedia, 122, ISSN:1876-6102, CISBAT 2017 International Conference Future Buildings & Districts – Energy Efficiency from Nano to Urban Scale