District Energy Research Articles

Exergy-based ecological network analysis for building and community energy systems

Although buildings are transitioning towards complex, dynamic, and interconnected systems, traditional engineering metrics that dominate today don't capture several important whole-network properties. To address this, this paper adopts ecological network analysis (ENA), which has numerous successes for natural ecosystems and socio-technical systems but has yet to be applied for buildings. After translating ENA into comprehensive mathematical models for engineering applications, the novel ENA method is demonstrated with building and community energy systems. For the models to suit building energy systems, which have multiple energy types and non-negligible dynamics, ENA is formulated on an exergy basis, with dynamic flows and balances (i.e., time-varying storages). To demonstrate the proposed approach, ENA is used to redesign the heating and cooling systems for an office building and data center, coupling the buildings together via ambient-loop district energy. The models are implemented using the equation-based Modelica language. Results indicate that the ENA-guided redesign reduces the source energy by 15%. The energy consumed by the heating and cooling systems is reduced by 84% with negligible sacrifice to the thermal performance. Surprisingly, the redesign also reduced the exergy efficiency of the total system from 60% to 34% due to a greater decrease in exergy output relative to exergy input with low-exergy system designs. This indicates that ENA and other network approaches that classify system organization may outperform traditional efficiency-based metrics for building and community energy systems when whole-system perspectives are desired.

Optimal design of aggregated energy systems with (N-1) reliability: MILP models and decomposition algorithms

This work investigates the design optimization of aggregated energy systems (multi-energy systems, microgrids, energy districts, etc.) with (N-1)-reliability requirements. The problem is formulated as a two-stage stochastic Mixed Integer Linear Program which optimizes design (first stage variables) and operation variables (second stage variables) simultaneously considering a set of typical and extreme days. The analysis proposes and compares different approaches to include the (N-1) reliability requirement in the optimization problem. Moreover, the paper proposes two effective decomposition algorithms to solve the large-scale Mixed Integer Linear Program suitable for design problems with and without (N-1) reliability requirements. Depending on the instance, such decomposition algorithms allow reducing the computational time by one or more orders of magnitude (from days to a few hours, in the worst cases tested in this work). The proposed methodology is tested to design the aggregated energy system for a real case study considering both a grid-connected and off-grid installation. Results indicate that the actual reliability of the design solutions depends by the profiles of energy demand and renewable production considered in the failure scenarios included in the design problem. Including N-1 reliability requirements causes an increase in the total annual cost in the range 15–20%, due to the increase in capital costs.

Energetic and exergetic performance analyses of mobile thermochemical energy storage system employing industrial waste heat

With an expanding urban population and increasing energy consumption, the demand for air conditioning is projected to reach 45 % of total energy demand by 2050. Based on projections, there will be a massive demand for air conditioning purposes, and energy consumption for air conditioning will also increase. Mobile thermo-chemical energy storage (MTES) offers an alternative by utilizing waste heat from power plants for heating and cooling via sorption heat storage. MTES proves advantageous due to its portability and phase change material transport capability. This study focuses on an MTES system applied in District Energy Networks (DEN) for cooling application. An analytical model has been developed to evaluate the system performance by considering energy efficiency ratio, round-trip efficiency, coefficient of performance, energetic efficiency, exergy destruction rate, and exergetic coefficient of performance as the performance parameters. Results show that an average round-trip efficiency of 53 %, maximum COP of 1.74, and exergy efficiency of 46.7 % is obtained for the year 2022. Notably, the MTES truck achieves a 50 % exergy efficiency. This study contributes insights into efficient waste heat utilization for sustainable cooling solutions.

District Energy Viewed from the New Bauhaus Initiative Perspective—Sustainable, Inclusive and Aesthetic Heat

(1) Background: To support the energy transition in Europe, the EU has launched multiple initiatives. Supporting the “Green Deal” is the New European Bauhaus (NEB). District heating and cooling (DHC) is an important part of a decarbonized European energy system, and its role in the transition has been stressed by the EU. In this paper, DHC is, for the first time, reviewed assuming the NEB principles. (2) Method: a literature review combined with a review of three cases was used for collecting data. (3) Results: It is confirmed that DHC has strong sustainability values. It is also identified that DHC can become increasingly inclusive by adopting updated digital platforms and new technologies for heat recovery that necessitate close customer interaction whilst recovering waste heat. The least exploited principle is aesthetics. It could sharpen city planning by combining energy system and energy efficiency perspectives, increase the practice of multifunctional buildings (for example energy provision and recreation), and foster a closer interplay between architecture and energy. (4) Conclusions: for both innovating and expanding DHC, the NEB principles can serve as catalysts.

Data-driven classification of Urban Energy Units for district-level heating and electricity demand analysis

The building sector is a significant contributor to global energy consumption and accounts for approximately one-third of total greenhouse gas emissions. While building energy analysis has traditionally focused on individual buildings, analyzing larger settlements, such as districts or neighbors, offers additional opportunities. The objective of this study is to define and classify typical urban areas for energy analysis, referred to in this paper as Urban Energy Units (UEUs), which represent geographical regions within a city with specific building’s characteristics, settlement patterns and energy demand. Sixteen different UEUs were classified using literature and open data. The proposed methodology leverages open-source data and uses a random forest model to enhance missing building properties of the building stock such as building age and construction type. It further subdivides the study area into geographically defined sections, and deploys a decision tree model to classify these sections into the sixteen different UEUs. These UEUs enable the creation of energy districts in a modular manner and flexible for its use in any given area. This study demonstrates the practical implications related to the 2023 german municipality heating plan. The methodology was applied in Oldenburg, a mid-sized German city. The city was subdivided into a total of 8249 UEUs, with the detailed results for energy demand presented in this report.

Optimal scheduling of solar-surface water source heat pump system based on an improved arithmetic optimization algorithm

Integrating solar energy into the combined energy supply of surface water source heat pump systems is expected to reduce the electricity consumption and carbon emissions. In this paper, a solar-surface water source heat pump system model is established to maximize system performance and save economic cost. In order to find the optimal operation scheme, an improved arithmetic optimization algorithm (iAOA) is proposed. This algorithm integrates elite opposition-based and nonlinear acceleration functions to solve the model. The effectiveness of the proposed model and algorithm is verified by applying it to a SWSHP district energy system in the central area of Xiangtan city. Experimental results demonstrate that incorporating solar energy into the SWSHP district energy system can improve system performance and reduce operational costs. In comparison with several other optimization algorithms, this algorithm has a faster convergence speed and a higher convergence accuracy. Therefore, it is considered an effective method for solving solar-surface water source heat pump district energy systems.

Технології електрогенерації та теплопостачання для підвищення енергетичної незалежності окремих територіальних громад

Russian attacks on Ukraine's energy system, which led to the blackout of entire cities and the termination of district heating, highlighted the risks and disadvantages of relying on large-scale district energy supply for communities. In this context, it is important to consider the development of the energy supply of communities in the direction of their energy independence. The purpose of the article is to form a list of electricity generation and heat supply technologies for increasing the energy independence of individual territorial communities, which are appropriate for implementation in Ukraine. The article considers the criteria for the analysis and selection of technologies for distributed power generation and heat supply. An analytical overview of the following power generation and heat supply technologies is presented: solar and wind power plants, gas piston and gas turbine plants, generators, batteries, co- and trigeneration plants, biofuel power plants and heat-generating boilers, heat pumps and electric boilers, and their grouping is performed according to the established criteria. All the considered technologies contribute to reducing the external supply of communities with energy resources and reducing losses during the transmission and distribution of electricity. Technologies that use renewable energy sources and local fuels are appropriate for implementation in communities in accordance with the availability of sufficient amounts of resources. Still, in most cases, they are not able to fully provide communities with electrical and thermal energy due to their insufficient quantity and high cost of equipment, such as batteries and heat pumps. Decisions regarding the implementation of a specific technology of distributed generation should be made after a thorough analysis of the conditions and possibilities of the energy supply of each individual community. This article is aimed at discussing the options of increasing energy independence and ensuring the reliability of the energy supply of territorial communities in Ukraine. Keywords: distributed generation, power generation, heat supply, renewable energy sources, cogeneration, biofuels, heat pumps.

Leveraging campus-scale Wi-Fi data for activity-based occupant modeling in urban energy applications

The widespread availability of open datasets in urban areas is transforming how urban energy systems are planned, simulated, and visualized. Urban energy models, however, require an understanding of urban dwellers, as their activities create the demands for energy in buildings. In this paper, we explore using campus-scale Wi-Fi data to identify typical occupant activity patterns as an input to an agent-based model of building occupants at the district scale. The data is taken from a Singaporean university’s Wi-Fi network at high resolution. Each record comprises a timestamp, a device identifier, the location of the device within the campus, and the access point to which it is connected. The Wi-Fi dataset contains 120 different buildings on campus and 10,300 anonymized individual devices. Activities are then assigned to each location on campus according to the building use type. In order to test the methodology, the activity plans of 27,604 undergraduate students, 8,304 graduate students, and 12,018 employees were simulated over a workweek. The results show the model’s ability to produce plausible activity plans but could be improved by implementing sampling rules and expanding the source dataset to include off-peak dates. Nevertheless, using such an agent-based modeling approach at the district scale appears to be a promising methodology to assess the impacts of different planning strategies on occupant behavior and district energy demand.

Energetic performance of a smart neighborhood of existing multifamily buildings with heat pumps, PV and CHP focusing on energy balance and CO2 emissions

A smart energy concept was designed and implemented for a cluster of 5 existing multi-family houses, which combines heat pumps, photovoltaic (PV) modules and combined heat and power units (CHP) to achieve energy- and cost-efficient operation. Measurement results of the first year of operation show that the local power generation by PV modules and CHP unit has a positive effect on the electrical self-sufficiency by reducing electricity import from the grid. In winter, when the CHP unit operates continuously for long periods, the entire electricity for the heat pump and 91 % of the total electricity demand of the neighborhood are supplied locally. In summer, only 53 % is generated within the neighborhood. The use of a specifically developed energy management system EMS is intended to further increase this share. CO2 emissions for heating and electricity of the neighborhood are evaluated and amount to 18.4 kg/(m2a). Compared to the previous energy system consisting of gas boilers (29.1 kg/(m2a)), savings of 37 % are achieved with electricity consumption from the grid being reduced by 65 %. In the second construction stage, an additional heat pump, CHP unit and PV modules will be added. The measurement results indicate that the final district energy system is likely to achieve the ambitious CO2 reduction goal of -50% and further increase the self-sufficiency of the district.

Review of natural language processing techniques for characterizing positive energy districts

The concept of Positive Energy Districts (PEDs) has emerged as a crucial aspect of endeavours aimed at accelerating the transition to zero carbon emissions and climate-neutral living spaces. The focus of research has shifted from energy-efficient individual buildings to entire districts, where the objective is to achieve a positive energy balance over a specific timeframe. The consensus on the conceptualization of a PED has been evolving and a standardized checklist for identifying and evaluating its constituent elements needs to be addressed. This study aims to develop a methodology for characterizing PEDs by leveraging natural language processing (NLP) techniques to model, extract, and map these elements. Furthermore, a review of state-of-the-art research papers is conducted to ascertain their contribution to assessing the effectiveness of NLP models. The findings indicate that NLP holds significant potential in modelling the majority of the identified elements across various domains. To establish a systematic framework for AI modelling, it is crucial to adopt approaches that integrate established and innovative techniques for PED characterization. Such an approach would enable a comprehensive and effective implementation of NLP within the context of PEDs, facilitating the creation of sustainable and resilient urban environments.

Modeling uncertainty in positive energy districts through a non-probabilistic approach

Positive Energy Districts (PEDs) transcribe an emerging energy transition paradigm for rapid upscaling of building energy efficiency programs to match the urgency of climate mitigation and adaptation. PEDs facilitate pro-active sharing of information and experiences, responsive learning and dissemination, and cooperation across sectors and disciplines. The rapid evolution of technologies for energy efficiency and integration of renewable energy in buildings requires a research-to-design approach. Therefore, it is imperative that a designer makes informed decisions at an early stage of design from an energy-efficiency perspective. However, since PED design involves a significant number of stakeholders, the design parameters could be highly uncertain, which eventually affect the quality of the design solution space. This study uses a bilateral process workflow to quantify risks using a nonprobabilistic technique and a PED roadmap structure. The workflow evaluates different PED scenarios and quantifies the PED potential for the city of Sint-Niklaas in Belgium. The results indicate that risk quantification significantly improves PED decision-making when evaluating collective solutions (district heating) for a district.

Living Lab Long-Term Sustainability in Hybrid Access Positive Energy Districts—A Prosumager Smart Fog Computing Perspective

Living Lab Long-Term Sustainability in Hybrid Access Positive Energy Districts—A Prosumager Smart Fog Computing Perspective

Towards the implementation of Positive Energy Districts in industrial districts: an Italian case study

Owing to the opportunity to provide an annual positive energy balance and net-zero carbon emissions, Positive Energy Districts aim at fostering the energy transition of urban city centres. To fully support the decarbonization of cities, it may be interesting to extend their implementation to other energy intensive districts, such as cities’ industrial areas. In this regard, this paper addresses the opportunity to apply the Positive Energy District concept within the industrial area of a city in the South of Italy. A mixed-use building, the industrial wastewater treatment plant and an office building have been involved as users and equipped with a 250 kW wind turbine and multiple photovoltaic plants installed on the roof of the buildings, in parking areas and in an unused land, for a total peak power equal to 466 kW. The renewable-based plants’ generation has been simulated in HOMER Pro® software, on a quarter-hour basis, and an energy and environmental analysis have been performed using users’ real electric load profiles. The proposed configuration allows to save 55% of primary energy and carbon dioxide emissions compared to the baseline case where users’ electric energy demand is fully met by the power grid. In particular, the primary energy saving is equal to 1 GWh/y and the carbon dioxide emissions reduction is equal to 150 tCO2/y.

Development of an early design tool for the sustainability assessment of positive energy districts: methodology, implementation and case-studies

The concept of Positive Energy District is one of the research ideas that embody the ambitions of decarbonization, renovation (both literal and in a wider perspective) and inclusivity for the urban environment portrayed in the EU activities. In this framework, the paper presents a modeling and simulation tool which allows for an early-design depth to be applied in the field of Positive Energy Districts renovation design and integrated performance assessment. The work aims at creating a tool for stakeholders and designers that would allow them to: a) Calculate carbon impacts along the life cycle for different technical systems and materials used for retrofitting; b) Compute use stage carbon emissions, including import-export of electricity; c) Computations of PED carbon emission balances, along the expected useful life of the district computing both embodied and the use stage carbon emissions. The tool has been created as a spreadsheet including typical profiles of energy use per building archetype, with the inclusion of available and free Life Cycle Assessment data within the life cycle carbon assessment and aims at jointly developing use stage and life cycle considerations. It was tested on a district case studies in the EU.

“oPEN Lab” project as an underpin innovation for Positive Energy District solutions in Pamplona

With the EU’s goal of climate neutrality by 2050, urban areas should move towards Positive Energy Districts (PEDs) as powerful ecosystems of balanced energy. The aim of this research is to present the methodology that facilitates the implementation of technical and social innovations, designed and implemented through co-creation, specifically for the neighbourhoods of Pamplona (Spain) in the framework of the project “oPEN Lab” on the way to PEN and based on a local Living Lab (LL). This project is a positive consequence of the evolution and achievements in urban, energy and mobility city planning made possible by the methodology, solutions and actions presented in the context of research and innovation projects such as STARDUST and Efidistrict Fwd.

Energy assessment of a district by integrating solar thermal in district heating network: a dynamic analysis approach

This work focuses on the energy upgrade of a neighbourhood located in Yverdon-les-Bains, Switzerland. Starting from a potential low-temperature district heating network, the possible integration of new buildings equipped with solar thermal collectors capable of interacting with the network is explored. The methodology proposed in this work focuses on the study of the energy balance of the district at reduced time intervals. Bringing the level of detail to every single hour of the year, the concept of energy balance is superseded by the hourly averaged power balance as a specific tool for a detailed exploration of the energy flows exchanged within the district. Self-sufficiency has also been identified as key performance indicators to better understand the ability of the entire district to satisfy its heating and domestic hot water needs, modelled in CitySim’s urban energy environment. The outcomes have shown that an annual energy balance is ineffective and unrealistic for describing performances of the district. On the other hand, the hourly averaged power balance proves to be a powerful tool for understanding the dynamics of the neighbourhood. The district’s energy flexibility, which relies on energy sharing, is a characteristic that cannot be assessed annually. Using the new proposed methodology to evaluate the district’s thermal energy sharing, it was discovered that the district functions as a Zero Energy District for 28.9% of the year. With the outcomes presented in this study, it is now possible to comprehend how a dynamic performance assessment positively impacts a district’s redevelopment strategies.

A database for positive energy districts (PED)

The development of Positive Energy Districts (PEDs) is a complex process that involves the integration of various technologies, stakeholders, and policies. To facilitate this process, a database for PEDs has been developed as a joint effort of COST Action ‘PED-EU-NET’, IEA EBC Annex 83, and JPI Urban Europe. This paper reports on the realization of the PED-Database framework and its online implementation as a modular web interoperable platform, giving details on the development life cycle since the scoping phase up to the testing phase. The PED-Database offers a variety of implementation strategies and conceptualizations for the PED concept, making it a valuable resource for urban planners, policymakers, and researchers. The testing phase has shown that there is no one-fits-all solution for PED implementation, and the overall PED framework definitions require further detailing in the local context. However, the database allows users to visualize and compare different PED scenarios by customizing their selection, accessing to the information provided by real PED cases that best meets their expectations and goals. Overall, the PED-Database provides a valuable tool for the development of sustainable and energy-efficient urban areas.

Exploring tools and indicators to support collaborative planning, design, implementation, operation, and evaluation of Positive Energy Districts

The study defines an integrated parametric workflow to support PED collaborative design and implementation. Through a preliminary detailed analysis, existing tools for urban building performance simulation and microclimatic analysis were identified, tested, and selected, aiming at maximising their interoperability potential and overall support to collaborative PED planning practices. Subsequently, it is illustrated how to conduct an ex-post simulation analysis of medium and long-term interventions, with specific projections of different scenarios considering climate change impacts on energy demand. The evaluation of the proposed climate-adaptive interventions takes place using tools for the optimization of systems and technical solutions at the basis of the energy surplus and decarbonization of districts and neighbourhoods. The aim of this research is twofold: i) shading light on the absence of data management, especially at the initial stages of the PED project, resulting in uncertainty and slow-down of the whole implementation process, as well as associated difficulties to trigger virtuous replicability processes towards inclusive urban energy transitions; and ii) promoting the adoption of tools and integrated climate adaptation and mitigation frameworks to support collaborative planning and evaluation of Positive Energy Districts.

Optimizing positive energy districts

A planning tool was developed which is able to integrate renovation strategies on district level as a combination of energy efficiency upgrades for buildings and the use of renewable energy deliver positive energy districts. It combines elements of energy master planning, district development and optimization in a Modelica environment by combining energy demand, circularity and stakeholder engagement on the demand side and life cycle costs in multi-objective optimisation on the supply side. Thus, the tool consists of six dedicated modules for optimizing positive energy districts (PED).

A two-stage optimization method for improving the load flexibility of existing district energy systems

Decarbonization of district energy systems is essential for China to meet its carbon neutrality goal by 2060. Most existing district energy systems are missing historical load data and have incomplete information, resulting in a lack of data support for the low-carbon transition. Moreover, demand-side load flexibility has not been fully exploited in the planning stage. In this paper, we developed a two-stage computational approach to optimize district loads. We first established an integrated Python framework, incorporating the TEASER simulation tool and AixLib model library, to efficiently calculate baseline loads through the bottom-up modeling and simulation of district buildings. Then, a price-based integrated demand response strategy was introduced. A mixed-integer nonlinear programming model was formulated to optimize the energy pricing strategy with the objective of minimum load fluctuations. Finally, a case study was employed to illustrate the feasibility of the calculation method, showing a normalized mean bias error of 7.17%. The results further demonstrated that the strategy could reduce the peak electric and heat loads by 3.55% and 9.57%, and increase load rates by 3.85% and 9.48%, respectively. The strategy could assist district energy service providers to optimize equipment capacity configuration and enhance the low-carbon planning potential of energy systems from the demand-side.