Search Results (19)

Against the backdrop of rapid global urbanization, peri-urban villages universally face the dual dilemmas of landscape homogenization and the imbalance between heritage preservation and functional renewal. As a typical representative, the “Shanghai-style Jiangnan” villages feature an open water–land chessboard pattern and linear water-house parallel organization, which are distinctly different from the closed and introverted texture of traditional Suzhou-Hangzhou water towns. Such villages urgently need to balance the continuation of the original spatial fabric and the adaptation of modern functions. Existing studies on rural landscapes mostly focus on the static vertical identification of single elements, lacking a systematic quantitative analysis of the horizontal topological relationships among multiple elements, making it difficult to accurately define the spatial boundaries between preservation and renewal. This study takes Xinyuan Village in Jinshan District, Shanghai, as an empirical subject to construct a model for the vertical gene decoding of the “Point-Line-Network” and horizontal topology coupling of “Surface Gene.” By introducing a landscape sensitivity assessment combined with the Entropy Weight Method (EWM) and GIS (Geographic Information System) spatial Kernel Density Estimation (KDE), a quantifiable landscape control heat map is generated. The study identifies the nested original fabric structure of the “house-water-field-forest-road” and the spatial landscape differentiation characteristics in Xinyuan Village and delineates three-tier differentiated zoning controls through dual-verified heat maps. Validated based on Xinyuan Village, this method effectively resolves the conflict between rural preservation and renewal and realizes the transformation from static museum-style preservation to refined adaptive zoning. It provides specific practical strategies for the renewal of “Shanghai-style Jiangnan” villages and offers a quantitative morphological reference for enhancing the spatial resilience and living heritage of peri-urban villages, while its cross-regional transferability needs further verification. Full article

Compared to shallow geothermal systems, coaxial medium-deep and deep borehole heat exchangers (MDBHE and DBHE) offer higher temperatures and heat extraction rates while requiring less surface area, making them attractive options for sustainable heat supply in combination with ground-source heat pumps (GSHP). However, existing simulation tools for such systems are often limited in computational efficiency or open-source availability. To address this gap, we propose a rapid modeling approach using the open-source Python package “pygfunction” (v2.3.0). Its workflow was adjusted to accept the fluid inlet temperature as input. The effective undisturbed ground temperature and ground thermophysical properties were weight-averaged considering stratified ground layers. Validation of the approach was conducted by comparing simulation results with 12 references, including established models and experimental data. The proposed method enables fast estimation of fluid temperatures and heat extraction rates for single boreholes and small-scale bore fields in both homogeneous and heterogeneous geological conditions at depths of 700–3000 m, thus supporting rapid assessments of the coefficient of performance (COP) of GSHP. The approach systematically underestimates fluid outlet temperatures by up to 2–3 °C, resulting in a maximum underestimation of COP of 4%. Under significant groundwater flow or extreme geothermal gradients, these errors may increase to 4 °C and 6%, respectively. Based on the available data, these discrepancies may result in errors in GSHP electric power estimation of approximately ±10%. The method offers practical value for GSHP performance evaluation, geothermal potential mapping, and district heating network planning, supporting geologists, engineers, planners, and decision-makers. Full article

Under the dual imperatives of air pollution control and energy conservation, this study proposes an enhanced optimization framework for combined heat and power (CHP) district heating systems based on bypass thermal storage (BTS). In contrast to conventional centralized tank-based approaches, this method leverages the dynamic hydraulic characteristics of secondary network bypass pipelines to achieve direct sensible heat storage in circulating water, significantly improving system flexibility and energy efficiency. The core innovation lies in addressing the critical yet under-explored issue of control valve dynamic response, which profoundly impacts system operational stability and economic performance. A quality regulation strategy is systematically implemented to stabilize circulation flow rates through temperature modulation by establishing a supply–demand equilibrium model under bypass conditions. To overcome the limitations of traditional feedback control in handling hydraulic transients and heat transfer dynamics in the plate heat exchanger, a Model Predictive Control (MPC) framework is developed, integrating a data-driven valve impedance-opening degree correlation model. This model is rigorously validated against four flow characteristics (linear, equal percentage, quick-opening, and parabolic) and critical impedance parameters (maximum/minimum controllable impedance). This study provides theoretical foundations and technical guidance for optimizing secondary network heating systems, enhancing overall system performance and stability, and promoting energy-efficient development in the heating sector. Full article

This paper explores the innovative application of Geographic Information Systems (GISs) to identify and utilize waste heat sources from railway and highway tunnels for fifth-generation district heating and cooling (5GDHC) systems. Increasing the number of prosumers—entities that produce and consume energy—within 5GDHC networks enhances their efficiency and sustainability. While potential sources of waste heat vary widely, this study focuses on underground car/railway tunnels, which typically have a temperature range of 20 °C to 40 °C. Using GIS software, we comprehensively analyzed tunnel locations and their potential as heat sources in Belgium. This study incorporates data from various sources, including OpenStreetMap and the European Waste Heat Map, and applies a two-dimensional heat transfer model to estimate the heat recovery potential. The results indicate that railway tunnels, especially in the southern regions of Belgium, show significant promise for waste heat recovery, potentially contributing between 0.8 and 2.9 GWh annually. The integration of blockchain technology for peer-to-peer energy exchange within 5GDHC systems is also discussed, highlighting its potential to enhance energy management and billing. This research contributes to the growing body of knowledge on sustainable energy systems and presents a novel approach to leveraging existing district heating and cooling infrastructure. Full article

This paper focuses on the potential for integrating Geographic Information System (GIS) software into Fifth-Generation District Heating and Cooling (5GDHC) systems to promote efficient and sustainable energy management, particularly in Kazakhstan. By reviewing the key literature, we identify three main areas where GIS software enhances the planning of 5GDHC systems: decision-making in the context of energy market regulations, operational data utilization, and modeling/simulations for technical design. The technical design emphasizes the role of heat pumps, ultra-low temperature district heating (ULTDH) networks, and end-user buildings. Previous research has explored various methodologies for integrating network and demand-side strategies, developing sustainable district heating and cooling (DHC) systems, and mapping urban areas suitable for DHC deployment. However, none has presented an open-source model incorporating GIS-based decision-making in designing 5GDHC systems. This study, for the first time, addresses this gap through a case study conducted in the Northern Industrial Zone of Karaganda, Kazakhstan, demonstrating how GIS-enhanced modeling can be effectively applied in a developing, industry-driven economy. We outline the mathematical framework for comparing existing simulation tools and developing a custom model suited to the region’s needs. Additionally, we discuss validation and calibration methods, which remain underexplored in the current literature. The proposed model incorporates waste heat recovery from local sources, including nearby wastewater treatment plants, showcasing a sustainable energy solution for the industrial park. The results indicate that a well-structured 5GDHC system, supported by GIS tools, can markedly enhance energy efficiency and sustainability, presenting a scalable and adaptable approach for other regions in Kazakhstan and beyond. Full article

In order to reach climate protection goals at national or international levels, new forms of combined heating and cooling networks with ultra-low network temperatures (5GDHC) are viable alternatives to conventional heating networks. This paper presents a simulation library for 5GDHC networks as sustainable shared energy systems, developed in the object-oriented simulation framework OpenModelica. It comprises sub-models for residential buildings acting as prosumers in the network, with additional roof-mounted thermal systems, dynamic thermo-hydraulic representations of distribution pipes and storage, time-series-based sources for heating and cooling, and weather conditions adjustable to user-specified locations. A detailed insight into an in-house development of a sub-model for horizontal ground heat collectors is given. This sub-model is directly coupled with thermo-hydraulic network simulations. The simulation results of energy balances and energetic efficiencies for an example district are described. Findings from this study show that decentralised roof-mounted solar thermal systems coupled to the network can contribute 21% to the total source heat provided in the network while annual thermal gains from the distribution pipes add up to more than 18% within the described settings. The presented simulation library can support conceptual and advanced planning phases for renewable heating and cooling supply structures based on environmental sources. Full article

Artificial neural networks (ANNs) can be used for accurate heat load forecasting in district heating systems (DHSs). This paper presents an application of a shallow ANN with two hidden layers in the case of a local DHS. The developed model was used to write a simple application in Python 3.10 that can be used in the operation of a district heating plant to carry out a preliminary analysis of heat demand, taking into account the ambient temperature on a given day. The model was trained using the real data from the period 2019–2022. The training was sufficient for the number of 150 epochs. The prediction effectiveness indicator was proposed. In the considered case, the effectiveness of the trained network was 85% and was better in comparison to five different regression models. The developed tool was based on an open-source programming environment and proved its ability to predict heating load. Full article

This research examines the integration of geological and hydrogeological data in numerical aquifer model simulations, with a particular focus on the urban area of Torino, Italy. The role of groundwater resources in urban sustainability is analysed. The objective is to integrate open-loop geothermal plants into the district heating network of IREN S.p.A. Two case studies are examined: the Torino Nord area and the Moncalieri area, both of which host district heating plants. The work entails the collection and analysis of data from a variety of sources, including geognostic surveys and permeability tests, in order to construct a three-dimensional numerical model of the surface aquifer. Models were built using the public MODFLOW 6 (model of groundwater flow) code and calibrated using PESTHP (High Performance of Model Independent Parameter Estimation and Uncertainty Analysis). Results indicate the potential of urban aquifers as renewable energy sources and the necessity of comprehensive geological and hydrogeological assessments for optimal ground water heat pump (GWHP) system installation. This paper emphasises the significance of sustainable water management in the context of climate change and urbanisation challenges. Full article

Dynamic thermo-hydraulic simulations of district heating networks (DHN) are essential to investigate novel concepts for their sustainable design and operation. To develop solutions for a particular case study, numerous long-term simulations are required. Therefore, computational effort for simulation is critical. Heat consumers (HC) are numerous and determine the dynamics of mass flows and return temperatures in the DHN. Thus, the way in which HCs are modeled has significant impact on the computational effort and the results of the simulation. This article presents a novel Modelica-based model for HCs that builds on an existing simplified modeling approach (open-loop design). The calculation of mass flow and return temperature is improved in terms of robustness, plausible behavior and low computational effort. In particular, the model reacts to limited differential pressure and supply temperatures to ensure plausible behavior across all operating conditions, including undersupply situations. The model is successfully tested using an exemplary DHN. The analysis proves that the HC model itself requires little time to simulate. Nevertheless, it significantly influences the simulation time for the entire DHN, which varies by a factor of five for the investigated system depending on the HC model. Fast dynamics, including a bypass in the model and correction of deviations between set point and actual heat load increase the simulation time, so users should sensibly choose how to use these options. HC models triggering many state events result in high computational effort. Compared to other simple HC models, the proposed model produces more plausible results while maintaining at least equal simulation performance (for models without bypass) or even improving it (for models with bypass, CPU time is reduced by at least 35%). Full article

Due to an urgent need to reduce the fast-progressing climate changes, a rapid, standardized and replicable solution for the infrastructure restructuring of the heat demand coverage of buildings at the district and city levels must be developed. As more and more communities in Europe, especially in Germany, are obligated to have plans for coverage of the heat demand, city planners and energy agencies seek a tool that will help them to design the first drafts of heating network routes, which can supply whole districts and cities with renewable energy. ArcGIS Pro Tools like Least-Cost-Path Analysis (LCPA) and Closest Facility (CF) allow us to find the shortest and “cheapest” way between the heat source and heat consumers in the analyzed areas. Starting from the community level, through the district, county, voivodeship and whole-country levels, the replication of the methods for generating district heating (DH) network infrastructure can be achieved. The proposed LCPA- and CF-based methods help to design the most suitable and efficient DH networks in the analyzed areas. As only a few open-source data inputs, like street networks and building footprints, are needed, the methods can be implemented in all communities in the country of Poland. In this paper, one example of one community in Wroclaw county, called Siechnice, and its surroundings is presented. Full article

Urban living space (ULS) is known to be a significant contributor to carbon emissions. However, there is a lack of studies that have considered the impact of spatial organization indexes (SOIs) of various scales on urban living space carbon emissions (ULSCE), and so far, no definitive conclusions have been reached. To address this gap, taking Tianjin as an example, the measurement methods of ULSCE and SOI at different scales were proposed, and a random forest model was constructed to explore the effects of SOI on ULSCE. The results indicated that on the district scale, Beichen had the highest carbon emissions and absorption in 2021, with carbon emissions reaching 1.43 × 10⁸ t and carbon absorption at 7.29 × 10⁵ kg. In terms of area scale, the comprehensive service area had the highest carbon emissions at 3.57 × 10⁸ t, accounting for 47.70%, while the green leisure area had the highest carbon absorption at 5.76 × 10⁵ kg, accounting for 32.33%. At the block scale, the industrial block had the highest carbon emissions at 1.82 × 10⁸ t, accounting for 54.02%, while the forest block had the highest carbon absorption at 1.25 × 10⁶ kg, accounting for 91.33%. Each SOI had varying impacts, with the industrial land ratio (ILR) having the highest order of importance at the area scale, followed by road network density (RND), residential land ratio (RLR), bus station density (BSD), public service facilities land ratio (PLR), land mixing degree (LMD), open space ratio (OSR), and commercial land ratio (CLR). ILR, RND, and RLR were particularly important, each exceeding 10%, with importance values of 50.66%, 17.79%, and 13.17%, respectively. At the block scale, building area (BA) had the highest importance, followed by building density (BD), building height (BH), land area (LA), and floor area ratio (FAR). BA and BD were particularly important, with values of 27.31% and 21.73%, respectively. This study could serve as both theoretical and practical guidance for urban planning to aid the government in developing differentiated carbon emissions reduction strategies that can mitigate the heat island effect and promote low-carbon healthy urban planning. Full article

In this paper, a dual-layer grid comprehensive resource optimizing allocation model is proposed, which considers power quality controlling and load optimization scheduling under large-scale application of distributed electric heating. The upper-layer planning aims to minimize the active power loss of the distribution network, the minimum voltage deviation, and the minimum investment cost of the power quality control device. The capacity configuration of the management device and the number and location of the commutation switch configuration were determined. The lower layer is load optimization scheduling, with the minimum number of action switches and the minimum three-phase imbalance as the planning goals, and the decision variable is the state of the commutation switch. By co-simulation through Matlab and OpenDSS, the improved particle swarm algorithm and genetic algorithm are used for multi-objective optimization and the solution. In this way, the capacity configuration of reactive power compensation and active filter, as well as the installation position and switch state of the commutation switch are optimized and managed. Finally, taking the rural low-voltage distribution network in the Tongzhou District as an example, simulations considering the variation in the distributed electric heating penetration rate in the range of 20–80% are carried out. The calculation example results show that the proposed algorithm is effective, can effectively improve the power factor, reduces the harmonic content of the distribution network and the three-phase unbalance, and significantly improves the distribution network voltage. Full article

Progressing digitalization and networking of systems and organizations representing Critical Infrastructures opens promising new potentials and opportunities, which on the downside, are accompanied by rising complexity and increasingly opaque interdependencies. The consequently increasing lack of knowledge leads to uncertainties affecting risk assessment and decision-making in case of adverse events. This trend motivated recent discussions and developments in risk science, emphasizing the need to handle such uncertainties. Complementarily, research in the resilience domain focuses on system capabilities to handle surprising hazardous situations. Several frameworks presented in the literature aim at combining both perspectives but either lack the focus on operational management, have a rather theoretical approach, or are designed for specific applications. Based on this observation, we propose an approach that integrates resilience management into the actual operation of Critical Infrastructure Systems and Organizations by providing an operational process that coordinates the fundamental resilience capabilities of responding, monitoring, anticipation, and learning. Furthermore, we tackle the challenge of uncertainties resulting from a lack of knowledge by aligning the concepts of digital twin and resilience management. The proposed framework is extensively discussed, and required processes are presented in detail. Eventually, its applicability and potential are reviewed by means of a complex hazardous situation at a Bavarian district heating power plant. Full article

The aim of the ISORC/OPTIMISER project is to increase and improve the use of solar thermal energy in district heating networks. One of the main tasks of the project is to develop an optimization tool for the sizing and operation of a solar district heating network. This is the first optimization tool using an open-source interface (Julia, JuMP) and solver (Ipopt) to solve nonlinear problems. This paper presents the multi-period optimization problem which is implemented to consider the dynamic variations in a year, represented by four typical days, with an hourly resolution. The optimum is calculated for a total duration of 20 years. First, this paper presents the modeling of the different components of a solar district heating network production plant: district network demand, storage and three sources, i.e., a fossil (gas) and two renewable (solar and biomass) sources. In order to avoid prohibitive computational time, the modeling of sources and storage has to be fairly simple. The multi-period optimization problem was formulated. The chosen objective function is economic: The provided economic model is accurate and use nonlinear equations. Finally the formulated problem is a nonlinear Programming problem. Optimization of the studied case exhibits consistent operating profiles and design. A comparison is made of different types of storage connection at the production site, highlighting the relevance of placing the storage at the solar field outlet. The optimum configuration supplies 49% of demand using solar energy, achieving a renewable rate of 69% in combination with the biomass boiler. Full article

DiGriPy is a newly developed Python tool for the simulation of district heating networks published as open-source software in GitHub and offered as a Python package on PyPI. It enables the user to easily build a network model, run large-scale demand time series, and automatically compare different temperature-control conditions. In this paper, implementation details and usage instructions are given. Tests showing the results of different scenarios are presented and interpreted. Full article