Fundamentals of Energy Modelling for Positive Energy Districts

Bottecchia, Luigi; Gabaldón, Andrea; Castillo-Calzadilla, Tony; Soutullo, Silvia; Ranjbar, Saeed; Eicker, Ursula

doi:10.1007/978-981-16-6269-0_37

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…An online-based guideline survey conducted by the IEA EBC Annex 83 group pointed out three clusters of PED elements: the energy system carriers; procedural, institutional, and governance aspects of PEDs; and environmental and spatial quality and the social fabric of PED areas [35]. Bottecchia et al [36] emphasized thermal and electricity loads in buildings, renewable energy generation, energy management systems, the spatial resolution to identify the boundaries and where to place the generation technologies, temporal resolution, and the objective functions to characterize PEDs, where the multi-objective functions can include energy balance, emissions, cost, and indoor human comfort. Derkenbaeva et al [37] considered four elements of a PED: geographical boundary, interaction with the energy grid, the energy supply method, and the balancing period.…”

Section: Elements Of a Pedmentioning

confidence: 99%

Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

Han,

Canli,

Shah

et al. 2024

Buildings

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The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments.

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Section: Elements Of a Pedmentioning

confidence: 99%

Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

Han,

Canli,

Shah

et al. 2024

Buildings

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“…System boundaries are considered in a spatial, temporal and functional sense, in accordance to the fundamentals of PED energy modelling described in [11]. The definition of system boundaries is required to enable balancing of flows over these defined boundaries.…”

Section: System Boundariesmentioning

confidence: 99%

“…As a consequence, PED definition has been discussed extensively [2][3][4][5][6], and although there is recognition for the need of a common definition, none could be arrived at as of yet [2,6,7]. Many approaches have been put forward that do not or not exclusively employ a quantitative assessment scheme [8][9][10], but many researchers and practitioners agree that the definition of a PED must ultimately entail the evaluation of an energy balance, whose result must be positive [1- 3,5,6,11].…”

Section: Introductionmentioning

confidence: 99%

A Quantitative PED Definition with Contextual Targets

Schneider¹,

Zelger²,

Sengl³

et al. 2023

Preprint

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This paper presents the goals and components of a quantitative energy balance assessment framework to define PEDs flexibly in three important contexts: the context of the district's density and RES potential, the context of a district's location, induced mobility and the context of the dis-trict's future environment and its decarbonized energy demand or supply. It starts by introducing the practical goals of this definition approach: achievable, yet sufficiently ambitious to be inline with Paris 2050 for most urban and rural Austrian district typologies. It goes on to identify the main design parts of the definition: system boundaries, balancing weights and balance targets and argue how they can be linked to the definition goals in detail. In particular we specify three levels of system boundaries and argue their individual necessity: operation, including everyday mobili-ty, including embodied energy and emissions. It argues that all three pillars of PEDs, energy effi-ciency, onsite renewables and energy flexibility can be assessed with the single metric of a prima-ry energy balance when using carefully designed, time-dependent conversion factors. Finally, it is discussed how balance targets can be interpreted as information and requirements from the sur-rounding energy system, which we identify as a "context factor". Three examples of such context factors, each corresponding to the balance target of one of the previously defined system bounda-ries operation, mobility and embodied emissions are presented: Density (as a context of opera-tion), sectoral energy balances and location (as a context for mobility) and an outlook of a person-al emission budgets (as a context for embodied emissions). Finally, the proposed definition framework is applied to seven distinct district typologies in Austria and discussed in terms of its design goals.

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“…Notably, it states that the surplus should be on an annual basis and either at a local or regional level. Hence, it is essential to understand the exchanges between the different grids and buildings in a PED to properly calculate the energy balance (Bottecchia et al, 2022). In this sense, Figure 5 presents the interaction within these entities as well as within storage options in the case of a PED.…”

Section: Energy Balancementioning

confidence: 99%

Economic, social, and environmental aspects of Positive Energy Districts—A review

Casamassima

Bottecchia

Bruck

et al. 2022

WIREs Energy & Environment

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The concept of Positive Energy Districts (PEDs), introduced in the Strategic Energy Technology Plan, is one of the fundamental approaches for a successful, clean and sustainable urbanization by 2025. According to the European Commission, a PED is a set of buildings where the community controls the energy flows and aims at a net positive energy balance over a year by utilizing renewable energy sources. There are a plethora of concepts similar to PEDs, such as Positive Energy Community, Net Zero Energy Neighborhood, Plus Energy Districts, that create a need to establish a structure that can facilitate the definition, development, and precise identification of PEDs. Thus, this paper aims to fill this research gap by comparing these and other related concepts through a critical literature review based on three pillars composing the triangle of sustainability: economic, social and environmental. By doing this, the paper aims to determine the connections between these similar concepts, homogenize the use of terms and avoid the issue of repetitions, which can help draw lessons learnt from other energy-savings concepts. This study shows how PEDs and Nearly Zero Energy Communities have similar bases, aims and omissions. They diverge in a few key concepts, which can become learning points for PEDs.

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Fundamentals of Energy Modelling for Positive Energy Districts

Cited by 7 publications

References 14 publications

Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

A Quantitative PED Definition with Contextual Targets

Economic, social, and environmental aspects of Positive Energy Districts—A review

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