Search Results (1,114)

Integration of renewable energy sources into existing residential and communal district heating systems requires technical adjustments and corrections. Measures aimed at reducing heat consumption at the points of delivery have a similar impact. This study aims, through simplified partial models (in heating mode), to present the relationships between these modifications and their potential effects on operational problems and deficiencies. The main parameters assessed in the design and correction of systems are temperature differentials, derived flow rates, pumping work, and control methods. Within the chain of heat source–primary distribution–secondary distribution–consumers, the analysis focuses on secondary circuits with consumers. A simplified multi-building network model was used to compare static and dynamic control strategies under temperature regimes of 70/50 °C, 60/40 °C, and 40/30 °C. The results show that dynamic control based on variable-frequency pumps, weather-compensated supply regulation, and optimized temperature differences between supply and return lines (ΔT) reduces pumping energy by 30–40% and increases heat delivery efficiency by up to 10%. A significant reduction in CO₂ emissions is also observed due to decreased pumping work, reduced heat losses in the distribution network, and the integration of renewable energy sources. The savings depend on the type and extent of RES utilization. The implementation of dynamic control in these systems significantly improves exergy efficiency, operational stability, and the potential for low-temperature operation, thus providing a practical framework for the modernization of district heating networks. Full article

This paper presents a comprehensive economic comparison between renewable and fossil-fuel-based heating systems for a newly constructed residential building in Kraków, Poland, over the period 2022–2030. The analysis introduces the concept of Corrected Final Energy Consumption (CFEC) as a harmonized measure for comparing various energy sources and applies the Present Value of Total Lifecycle Cost (PVTLC) as an appropriate financial metric for non-commercial residential investments. Four heating options were examined: district heating system (DHS), gas boiler, air-to-water heat pump, and heat pump combined with photovoltaic (PV) panels. Based on real tariffs and standardized data from the Energy Performance Certificate (EPC), the DHS option demonstrated the lowest lifecycle cost, while the air-to-water heat pump—despite environmental advantages—proved the most expensive without substantial subsidies. Sensitivity analyses confirmed the strong influence of investment subsidies and fuel price fluctuations on the competitiveness of alternative systems. The findings highlight the methodological shortcomings of conventional annual-cost approaches and propose PVTLC as a more reliable decision-making tool for residential energy planning. The study also discusses regulatory, climatic, and behavioral factors affecting investment outcomes and emphasizes the need to integrate financial, environmental, and social criteria when evaluating household-level energy solutions. Full article

This study explores the decarbonization of the district heating network in Riva del Garda. The existing system (baseline) was modeled in EnergyPLAN, and future configurations were optimized using a Multi-Objective Evolutionary Algorithm (MOEA) to minimize both CO₂ emissions and annual costs. Nine decision variables were assessed under defined boundary conditions to generate alternative future scenarios grouped into five types. In Type A, a large deep geothermal cogeneration plant combined with a small biomass boiler achieved the only zero-emission solution, with lower annual costs than the baseline but high capital needs. Excluding deep geothermal cogeneration (Type B) led to dominance of the biomass boiler and waste heat recovery from the Alto Garda Power (AGP) plant; full decarbonization remained possible only with extensive biomass use at a higher cost. Removing biomass (Type C), the solar thermal plant, and the shallow geothermal heat pump enabled deep but costly decarbonization, including grid electricity dependence. Types D and E, dominated, respectively, by shallow geothermal heat pump and electric boiler, provided moderate emission reductions and further increase in costs. Across all types, thermal storage improved operational flexibility. These analyses were also extended to assess potential district heating network expansions within Riva del Garda and into the neighboring municipality of Arco. Full article

The urban heat island (UHI) has been a critical social problem as urbanization intensifies worldwide, significantly impacting human life by exacerbating heat-related health issues, increasing energy demand for cooling, and resulting in associated environmental problems. However, the fine-scale diurnal and spatial characteristics of UHI remain poorly understood due to the limited resolution of traditional satellite datasets. This study aims to quantify the diurnal and spatial dynamics of surface urban heat islands (SUHI) in Busan and Daegu—the two hottest metropolitan cities in Korea—by integrating high-resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) (70 m) and Geostationary Korea Multi-Purpose Satellite-2A (GK-2A) (2 km) land surface temperature (LST) data. Using the combined datasets, season-representative diurnal LST variations were characterized, and locational heat intensification (LHI) was evaluated across land use types and densities at sub-district scales. The results show that the maximum SUHI intensity reached 10 °C in Daegu and 7 °C in Busan during summer, up to 8 °C higher than estimates from coarse-resolution data. Industrial areas recorded the highest LST (47 °C in Daegu and 43 °C in Busan) with rapid morning intensification rates of 2.0 °C/h and 1.9 °C/h, respectively. Dense urban land uses amplified LHI by nearly twofold compared to less dense urban areas. These findings emphasize the critical role of land use density and industrial heat emissions in shaping urban thermal environments, providing key insights for use in urban heat mitigation and climate-adaptive planning. Full article

In conventional energy system planning, cost optimisation is usually the decisive factor. The objective of the research, on which this article is based, is to develop alternatives to cost-optimised energy supply concepts that are near optimal cost and also meet the criterion of increased resilience. The methodology presented here thus expands the solution space for the planning of energy systems and the consideration of additional criteria beyond pure cost optimisation. The transition from a fossil fuel-based energy system to one reliant on renewable sources brings significant structural changes and uncertainties. Resilience management offers a guiding concept to address the non-linear complexities and unpredictability of this transformation process and to cope with uncertain and unknown stressors. Thus, a comparative assessment of the resilience of different future energy concepts is crucial to provide a basis for decision making and implementation of resilient energy systems. This research approach entailed the optimisation of a heat supply concept for an urban district and the investigation of near-optimal alternatives in the vicinity of the optimal solution. The resilience of these near-optimal solutions was then analysed. For this purpose, certain resilience-enhancing structures and functionalities (diversity, redundancy, buffer capacity) were evaluated by quantifiable indicators. The analysis of the heat supply scenarios has shown that resilience, measured by the indicators used, could be increased at a low additional cost. In the top-performing alternative-heat-supply scenarios generated, the diversity has been increased by 585%, redundancy by 18% and buffer capacity by 98%. The majority of the generated alternatives that were examined showed that an increase in diversity and redundancy could be achieved at a relatively low additional cost. Full article

The Eastern Mediterranean is a rapidly warming climate change hotspot where heat and air pollution increasingly interact to affect human health. This study quantifies the mortality burden attributed to the synergistic effects of thermal stress and air pollution in Thessaloniki, Greece. Daily mortality data (2001–2019) were analyzed together with pollutant concentrations (PM10, NO₂, O₃) and the modified Physiologically Equivalent Temperature (mPET) using a hierarchical Generalized Additive Model with Distributed Lag Non-Linear terms to capture combined, lagged, and age-specific responses. A refined, count-independent definition of the Attributable Fraction (AF) was introduced to improve stability in small strata. The results show that heat and pollution act synergistically, explaining on average 20–30% of daily mortality during severe co-occurrence events. Seniors were most affected during hot, polluted summers (AF ≈ 27%), while adults showed higher burdens during cold, polluted winters (AF ≈ 30%). Intra-urban analyses revealed stronger simultaneous effects in the western, more industrial districts, reflecting combined environmental and socioeconomic vulnerability. The findings demonstrate that temperature extremes amplify pollution-related mortality and underline the need to integrate air quality and bioclimatic indicators into early warning and adaptation systems in Eastern Mediterranean cities. Full article

Against the backdrop of growing concerns over environmental degradation and fossil fuel harms, geothermal energy—clean, low-carbon, widely distributed, and stably supplied—has gained increasing attention, becoming a key focus of renewable energy research. This study focused on a typical doublet-well system in Decheng District, Shandong Province, China, a region with mature geothermal development and high recharge demand. To investigate the water–rock interaction mechanism and its impact on reservoir properties, we combined indoor high-temperature/pressure static experiments with a hydro–thermo–chemistry coupling numerical simulation using TOUGHREACT V4.13-OMP. Experimental validation was conducted by matching the simulated major ion concentrations and pH values with the experimental results, confirming the reliability of the model parameters. The methodology integrated mineral composition analysis (XRD/XRF), hydrochemical testing of reaction solutions, and long-term numerical simulation of the doublet-well system under 50 heating cycles. The key qualitative results include the following: (1) feldspar minerals (sodium/potassium feldspar) are the main dissolved minerals, while dolomite and illite are the dominant precipitated minerals during recharge; (2) recharge-induced mineral precipitation causes significant near-well pore plugging, leading to continuous attenuation of porosity and permeability; (3) reducing Ca²⁺/Mg²⁺ concentrations in recharge water effectively alleviates permeability reduction, providing a feasible optimization direction for geothermal recharge schemes worldwide. This study enriches our understanding of sandstone geothermal reservoir evolution under recharge conditions and offers practical references for optimizing recharge strategies in similar geothermal fields globally. Full article

With rural revitalization and industrial upgrading, a single electrical perspective can no longer meet diversified energy demands. Meanwhile, rapid growth of distributed resources such as photovoltaics, storage, and biomass enables multi-energy complementarity. This paper proposes a hierarchical distributed optimization framework for Rural Distributed Energy Systems (RDES) explicitly considering storage aggregation. First, basic models are developed for diverse resources in the RDES, and Minkowski sum and inner approximation methods are used for storage aggregation. Considering electricity, heat, and cooling, a two-level operation model is built at both the distribution network and transformer area levels. Then, an ADMM-based distributed algorithm coordinates multiple rural energy areas and the distribution network through iterative interactions. Finally, an integrated energy test network based on the IEEE-30 system verifies that the model minimizes overall operational cost while protecting district interests and ensuring thermal–electrical network safety. Full article

District heating is critical for low-carbon urban energy systems, yet most networks remain centralized in both heat generation and data ownership, fossil-dependent, and poorly integrated with digital, customer-centric, and market-responsive solutions. While artificial intelligence (AI), the Internet of Things (IoT), and automation offer transformative opportunities, their adoption raises complex challenges related to business models, regulation, and consumer trust. This paper addresses the absence of a comprehensive synthesis linking technological innovation, business-model evolution, and institutional adaptation in the digital transformation of district heating. Using the PRISMA-ScR methodology, this review systematically analyzed 69 peer-reviewed studies published between 2006 and 2024 across four thematic domains: digital technologies and automation, business-model innovation, customer engagement and value creation, and challenges and implementation barriers. The results reveal that research overwhelmingly emphasizes technical optimization, such as AI-driven forecasting and IoT-based fault detection, whereas economic scalability, regulatory readiness, and user participation remain underexplored. Studies on business-model innovation highlight emerging approaches such as dynamic pricing, co-ownership, and sector coupling, yet few evaluate financial or policy feasibility. Evidence on customer engagement shows increasing attention to real-time data platforms and prosumer participation, but also persistent barriers related to privacy, digital literacy, and equity. The review develops a schematic conceptual framework illustrating the interactions among technology, business, and governance layers, demonstrating that successful digitalization depends on alignment between innovation capacity, market design, and institutional flexibility. Full article

The decarbonization of the heating sector requires the progressive transformation of district heating systems toward low-temperature and renewable-based configurations. In this context, the integration of thermal prosumers, capable of both consuming and producing heat, represents a promising solution to increase network flexibility and support sector coupling through technologies such as heat pumps. This work presents a thermo-fluid dynamic modeling framework developed to analyze the integration of a heat pump-based prosumer into an existing large-scale district heating network in Italy. The model adopts a graph-based, thermo-fluid dynamic model, combining a steady-state hydraulic formulation with a transient thermal analysis, and is complemented by a set of Key Performance Indicators for the evaluation of energy exchanges and self-sufficiency at user and network levels. Different operational configurations are analyzed, including local sharing within the distribution network and heat export to the main transport network, with and without local thermal storage. The study focuses on summer operation, when the network supplies only domestic hot water, a condition in which distributed renewable generation can play a major role in reducing central plant operation. The results highlight the potential of thermal prosumers to enhance energy autonomy and flexibility in existing district heating networks, paving the way for their evolution toward fully renewable and bidirectional systems. Full article

Efficient operational planning in district heating systems (DHSs) is essential for minimizing operating costs and maximizing energy efficiency. However, since practitioners must determine future production plans under unknown future demands and costs in real-world energy systems, it is challenging to solve the production planning problems of DHSs. In this paper, we propose a multi-head attention-based TimesNet (MATN) in which a transformer decoder is incorporated that operates solely on a 24 h lookback window without requiring any future information. Specifically, the model is trained in an end-to-end manner, for which the training dataset was built by solving a mixed integer programming (MIP) model. Experimental results demonstrate that the proposed MATN model significantly outperforms baseline deep learning-based methods. A qualitative analysis of the hourly production plans further indicates that MATN generates robust operational plans that mimic those generated by an MIP model, which suggests the effectiveness of the proposed approach in terms of economic efficiency and operational stability without depending on future information. Full article

The rapid advancement of technologies such as artificial intelligence, big data, and cloud computing has driven continuous expansion of global data centers, resulting in increasingly severe energy consumption and carbon emission challenges. According to projections by the International Energy Agency (IEA), the global electricity demand of data centers is expected to double by 2030. The construction of green data centers has emerged as a critical pathway for achieving carbon neutrality goals and facilitating energy structure transition. This paper presents a systematic review of the role of waste heat recovery technologies in data centers for achieving low-carbon development. Categorized by aspects of waste heat recovery technologies, power production and district heating, it focuses on assessing the applicability of heat collection technologies, such as heat pumps, thermal energy storage and absorption cooling, in different scenarios. This study examines multiple electricity generation pathways, specifically the Organic Rankine Cycle (ORC), Kalina Cycle (KC), and thermoelectric generators (TEG), with comprehensive analysis of their technical performance and economic viability. The study also assesses the feasibility and environmental advantages of using data center waste heat for district heating. This application, supported by heat pumps and thermal energy storage, could serve both residential and industrial areas. The study shows that waste heat recovery technologies can not only significantly reduce the Power Usage Effectiveness (PUE) of data centers, but also deliver substantial economic returns and emission reduction potential. In the future, the integration of green computing power with renewable energy will emerge as the cornerstone of sustainable data center development. Through intelligent energy management systems, cascaded energy utilization and regional energy synergy, data centers are poised to transition from traditional “energy-intensive facilities” to proactive “clean energy collaborators” within the smart grid ecosystem. Full article

The storage of different forms of energy is becoming increasingly important in the energy system sector, due to the significant fluctuations that renewable energy sources influence on urban energy systems. Nowadays, these sources have been promoted for the transition towards modern energy systems at different scales, due to their reduced emissions of greenhouse gases. Yet, many doubts remain about their efficacy in urban settlements worldwide. For this reason, to promote the fast implementation of renewable energy technologies around the world, it is of great importance to design and develop free-access and user-friendly tools to help stakeholders in the planning and management of urban energy districts. The present study has proposed an evaluation tool to model decentralised energy storage systems using Urban Building Energy Models, including an optimisation method to size the best capacity in each building of a district. The developed models simulate two storage technologies: battery power banks and heated water tanks. To present the outcomes of the tool, these models have been tested in two scenarios of Portugal, located in a densely populated area and the most isolated region of the country. Among the most important findings of the results are their ability to evaluate the performance of individual buildings by group archetype and total district metrics, using different temporal periods in a single model to identify the buildings taking most advantage of storage technologies. In addition, the optimisation algorithm efficiently estimated the ideal size of each storage technology, reducing the need of unnecessary capacity. Full article

The increasing demand for decarbonized urban environments has intensified interest in integrating renewable energy systems within cities. This review investigates the potential of urban wind energy as a promising technology in the development of Positive Energy Districts, supporting the transition toward climate-neutral urban areas. A systematic analysis of recent literature is presented, covering methodologies for urban wind resource assessment, including Geographic Information Systems (GIS)-based mapping, wind tunnel experiments, and Computational Fluid Dynamics simulations. The study also reviews available small-scale wind technologies, with emphasis on building-integrated wind turbines, and evaluates their contribution to local energy self-sufficiency. The integration of urban wind systems with energy storage, Power-to-Heat solutions, and smart district networks is discussed within the PED framework. Despite technical, economic, and social challenges, such as low wind speeds, turbulence, and public acceptance, urban wind energy offers temporal complementarity to solar power and can enhance district-level energy resilience. The review identifies key technological and methodological gaps and proposes strategic directions for optimizing urban wind deployment in future sustainable city planning. Full article

With more hybrid heating systems available, there is a need to optimize energy use intelligently from the end-consumer perspective. This paper focuses on a multi-criteria heating system optimization to optimize cost, carbon emission, and comfort level of building occupants. A discrete Multi-Objective Model Predictive Controller (MO-MPC) algorithm is proposed to optimally utilize two heating sources connected to a building, namely district heating (DH) and a building-integrated electrical heat pump (HP). The model is tested on a real-world building case simulated with a gray box building model. The results are compared to a conventional PID controller as well as the MPC scheme, each with a single heating input, and eight different cases are constructed to make this comparison more visible. The results indicate that, using MO-MPC, a cost saving of up to 10% and emission saving of up to 13% can be reached without additional thermal discomfort, while the potential savings on cost and emission with the hybrid system can be up to 25% and 77%, respectively. Further, a sensitivity analysis on price and emission parameters is conducted to investigate the changes in the provided solution. Full article