District Heating Research Articles

Static voltage stability analysis of integrated smart energy systems

The analysis of multi-energy flows forms the cornerstone for the study of state estimation, safety assessment, and optimization in integrated smart energy systems (ISES). The interactions between various energy flows and the inherent variability of renewable energy sources often lead to significant challenges to the static stability of ISES. This paper investigates the static voltage stability of ISES under multi-energy coupling conditions through multi-energy flow analysis in electric-gas-thermal energy systems. First, a steady-state model of the ISES is constructed by representing the interconnected energy subsystems as equivalent sources and loads in the power grid. Subsequently, through the coupling elements of ISES, the power flows of the natural gas system (NGS) and the district heating system (DHS) are converted into active power in the electrical power system (EPS), resulting in an equivalent power flow equation for the ISES. Then, using Brouwer’s fixed-point theorem, the analytical sufficient conditions for solving the equivalent power flow equation are derived. Finally, a simulation model based on MATLAB/Simulink is established. The steady-state criterion for ISES is obtained in this paper, and the correctness and effectiveness of the proposed conclusions are verified by the simulation results.

Exergy Analysis of a District Heating System with a Predictive Model Based on Neural Network Algorithms

The article presents an exergy analysis of a district heating system (DHS) that incorporates a predictive model based on neural network algorithms. The study focuses on evaluating the thermodynamic efficiency of the DHS when these methods are applied, enabling the prediction of temperature changes and rapid adjustment of system parameters to enhance efficiency. The impact of increasing the number of connected consumers on the exergy efficiency of the DHS with the predictive model (DHPM) is examined. The use of neural network algorithms, such as LSTM, significantly enhances the system's ability to predict external temperature changes and respond promptly, resulting in optimized energy consumption and improved exergy efficiency, particularly at the beginning and end of the heating season. A comparative analysis of the exergy efficiency of traditional DHS and the system with the predictive model is included. The exergy analysis is based on modeling the system’s performance with varying numbers of consumers. The results indicate that an increase in the number of consumers leads to a rise in exergy efficiency due to better energy distribution management. The potential for significant thermal energy savings and reduced operational costs through the use of predictive methods is discussed. The study confirms that applying neural network algorithms in predictive models of DHS can substantially improve efficiency and reliability, leading to a more rational use of energy resources, cost reduction, and minimized environmental impact. The findings support the adoption of these methods in large-scale district heating systems for optimization and enhanced energy efficiency.

Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach

Outdated, oversized variable speed pump drives (VSDPs) in industry lead to sub-optimal energy efficiency and considerable energy losses. This paper proposes methods to develop 2D efficiency maps for motors, converters, and pumps using polynomial surface fitting, which enables efficiency evaluation in a wide operating range. The method was applied to an oversized VSDP in an industrial chilled water supply system, comparing the original system with five alternative VSDP combinations with high-efficiency motors and pumps. The five VSDP variants demonstrated average energy savings of around 30%, with the synchronous reluctance motor (SRM) configurations outperforming the induction motor (IM) configurations by up to 7 percentage points, particularly at low loads. The high-efficiency SRM-based 252-IE5 variant delivered the best overall energy performance, highlighting the benefits of optimised system sizing and motor selection for energy savings. The proposed method can be used in both industrial and residential applications and offers great advantages in process systems that require variable flow and pressure of water or other fluids during operation, such as HVAC, water supply and wastewater treatment, district heating, etc. The development of a VSDP drive with efficient energy optimisation is an interdisciplinary problem of mechanical and electrical engineering, and without the interaction of engineers from both fields the result will not be optimal. We try to present our method so that it can be a reliable tool for mechanical, electrical, and other engineers or researchers to assist them in finding possible energy savings, performing energy audits, and selecting the most suitable components when modernising existing or developing new systems.

Sizing large-scale industrial heat pump for heat recovery from treated municipal sewage in coal-fired district heating system

Electrification of district heating and deep integration of sectors of national economies are fundamental elements of the future smart energy systems. This paper discusses the problem of optimal sizing of large-scale high-temperature heat pumps using treated sewage water as a heat source in a coal-fired district heating system. The study presents an approach to modelling of heat pump system that enables techno-economic analysis for investment decision making. Such analysis is enabled by a black-box-type identification model of the selected industrial heat pump. The model was developed based on the data generated by physical modelling of the heat pump using Ebsilon Professional software. In addition, it is proposed that the heat pump system is integrated with a dedicated photovoltaic power plant. The case study takes into consideration site-specific technical, economic, ecological, and legal constraints, weather conditions, hydraulic performance of the heat-ing network, and variability of loads within the sewage and the district heating systems. The results revealed that the proposed modelling approach is effective regarding multiple simulations and system optimisation. In addition, it was found that large-scale heat pump projects can be technically feasible and profitable if the heat pump is appropriately sized and operated. In the given case, the optimum size of the heat pump for a city of around 180 000 inhabitants is around 12 MW under maximum winter load.

Unit commitment in solar‐based integrated energy distribution systems with electrical, thermal and natural gas flexibilities: Application of information gap decision theory

AbstractThe depleting oil reserves, air pollution and increasing energy demand, have overturned the focus of the scientific community to renewable energy sources. Among which the photovoltaic (PV) systems occupy more than half of the market share and are generally installed at the distribution level. The volatile and uncertain nature of these PV productions necessitates flexible resources in energy systems. To this end, the district heating systems have an outstanding flexibility on account of their high thermal inertia. This study investigates the optimal unit commitment scheduling for gas‐fired and non‐gas‐fired distributed generation units (NGU) in an integrated energy distribution system (IEDS) within the physical constraints of the electrical, natural gas and thermal energy distribution networks. Moreover, a planning‐based optimization framework is proposed to investigate the investment of battery storage systems in the electric distribution network under the high penetration of PV systems with the aim of enhancing flexibility and reducing the operating costs of the IEDS. In this framework, the information gap decision theory is deployed under risk‐averse and risk‐seeker strategies to deal with uncertain PV energy production. Additionally, the environmental emissions are considered in a multi‐objective approach. The IEDS is embodied through IEEE 33‐bus EDS, 20‐node natural gas network and an 8‐node district heating systems. Eventually, The proposed approach makes a noteworthy contribution to the advancement of solar energy systems in IEDS.

Improving the potential of fifth-generation district heating and cooling networks through robust design and operational optimization under future energy market and demand uncertainties

Network investments and projected energy prices greatly impact the financial viability and environmental impact of fifth-generation district heating and cooling (5GDHC) networks. Compared to the previous generation, the upfront investment costs in 5GDHC networks are relatively high due to the decentralized energy demand and supply of the participating prosumers. The transition to 5GDHC is also hindered by uncertain energy markets and the fluctuating energy demands of the prosumers. It makes investors hesitant to commit, even for promising business cases. In this work, a framework is presented to assess the economic and environmental performance of a 5GDHC business case, based on a multi-objective robust optimization algorithm (MO-RDO), to identify the optimal trade-offs between environmental and economic designs. The proposed methodology consists of four steps. In the first two steps, a specific business case is modeled, and techno-economic and environmental performances are analyzed against uncertain energy markets. In the third step, the techno-economic and environmental indicator responses are used to develop a data-driven machine-learning model. This model offers better computational efficiency than a high-fidelity nonlinear model and assesses the most influential parameters driving economic and environmental performance via a global sensitivity analysis. This aids in refining the multi-objective robust design optimization (MO-RDO) framework by pinpointing key design variables and objectives amid data uncertainties. Additionally, the formulated MO-RDO provides a mix of environmentally and economically optimal network designs and operational parameters and highlights the trade-offs between them. The designs and trade-offs are evaluated using financial metrics like net present value (NPV), return on investment, and discounted payback period. These metrics are calculated over the project life, considering the initial investment and net savings. For the considered case, the environmental design has a 10% higher investment cost compared to the economically optimal solution but reduces total emissions (TE) by 13% and improves operational cost savings, which is crucial for better financial indicators. The proposed framework aligns with the EU transition plan to incorporate waste energy sources and decarbonization paths, estimating only a 3% increase in NPV as a risk for the environmentally optimal solution on a financial scale.

Investigation of hybrid modeling and its transferability in building load prediction used for district heating systems

In the district heating systems, the historical operation data of the buildings in those areas would be partially or entirely missing. The traditional data-driven model is hard to predict the ground truth results because the historical data is not available for model training. However, utilizing the physics-based methods for load calculation takes a long time to process and encounters low accuracy issues. This paper investigates several hybrid models that integrate the data-driven model and the physics-based models with different fusion methods. The physics-based models calculate envelope load and infiltration load, based on Fourier's law and the grand canonical ensemble theory, respectively. After undergoing load processing, features fusion, and residual connection, the best advanced hybrid models generate 21.35%, 16.35%, and 12.73% better prediction results compared with the data-driven model. Moreover, the advanced hybride models also perform strong transferability across all the data quantity groups. In terms of practical application, the advanced hybrid models could be deployed with effective generalization in limited data scenarios and robust transfer capabilities. The selected best model constructed by hybrid modeling displays the highest performance and saves the total training costs with strong transferability.

Resource Efficiency and the Role of Renewable Energy in Miskolc: The City’s Journey Towards Becoming a Smart City

Miskolc, which is the focus of our investigation, is the fourth most populous city in Hungary and the center of one of the most underdeveloped NUTS2 (basic territorial category for the regional policy of the European Union) regions in the European Union. The socialist heavy industry played a decisive role in the development of the city, the decline of which also left deep traces in the city. In its current position, the city tries to manage its available resources as efficiently as possible, and the city management is open to the use of modern urban development tools. This is supported by the fact that Miskolc was the first Hungarian city to join the Green Cities for Sustainable Europe movement in 2011, and then in 2015, it joined the Triangulum project of the EU Smart Cities and Communities program as a follower city. In the process of becoming a smart city, the dimensions of environmental sustainability and energy efficiency were given a prominent role, which should not be surprising considering the traditions of the city. Within this, we must first mention the construction of the geothermal central heating system, with which the city really took significant steps in this field. The main goal of the study is to develop a new smart local concept closely linked to regional development and the key energy sector, through which the local adaptation of the defining elements of the internationally defined smart city in several forms for the city of Miskolc will be presented. In our study, we review how the results achieved by Miskolc so far and the development plans for the future fit in with the smart energy developments of smart cities. Before exploring the processes in Miskolc, we will deal in more detail with the possibilities inherent in district heating and geothermal energy utilization and Hungary’s capabilities.

Optimal control of a model for dynamic district heating networks

In the present paper an optimal control problem for a concrete system of differential-algebraic equations (DAEs) is considered. This problem arises in the dynamic optimization of unsteady district heating networks. Based on the Carathéodory theory existence of a unique solution to the specific DAE system is proved using specific properties of the considered district heating network model. Moreover, it is shown that the optimal control problem possesses optimal solutions. For the numerical experiments different networks are considered including also data from a real district heating network.

Study on steam energy efficiency enhancement, cost management and CO2 emissions in the food industry

Malaysia’s energy intensity per unit GDP has been increasing since the 1990s, leading to the government’s efforts to promote energy efficiency via policies and initiatives such as Energy Performance Contracting (EPC). Additionally, the industrial sector in Malaysia consumed 28% of total final energy in 2018, placing it as the country’s second-largest final energy consumer [1]. This case study focuses on improving energy efficiency in the steam system of an existing plant in the food industry in Malaysia. This work was performed in collaboration with a local Energy Service Company (ESCO) to study the existing steam system, recognize its process requirements and constraints and consequently propose and evaluate retrofit opportunities. These opportunities were evaluated across 3 performance parameters (power output or energy savings, cost savings and CO2 emissions savings). Simulations were performed using the Steam System Modeler Tool (SSMT). The study found that Combined Heat and Power (CHP) was not feasible due to a combination of low steam pressure and flowrate available in the system. However, the study also found that improving condensate recovery coupled with condensate flashing from the high pressure to low pressure header offered the highest increments in overall performance.

Dynamic thermal simulation of a tree-shaped district heating network based on discrete event simulation

The computational complexity involved in modelling district heating (DH) networks impedes the integration of network operations into comprehensive DH system studies. We developed a flexible, accurate, and fast dynamic thermal simulation model utilising discrete event simulation (DES). This model is versatile, suitable for any tree-shaped DH network with a central heating plant and can estimate node temperatures and calculate pipe heat losses. The speed of the model is improved via using variable time steps and by incorporating two advanced techniques: lazy evaluation and a customised priority queue. To further improve the computational speed, we developed a technique to eliminate redundant sampling points. This model was tested and demonstrated excellent consistency with actual measurements. Remarkably, reducing sampling points can speed up the simulation by a factor of three without compromising the temperature accuracy. A 72-day simulation of a network with 102 pipes was completed within 0.219 s. Our findings highlight the significant potential of the DES model for large-scale dynamic network simulations and offer a promising solution for DH network simulations and system optimisation.

Source apportionment of PM2.5 using dispersion normalized positive matrix factorization (DN-PMF) in Beijing and Baoding, China

Fine particulate matter (PM2.5) samples were collected in two neighboring cities, Beijing and Baoding, China. High-concentration events of PM2.5 in which the average mass concentration exceeded 75 µg/m3 were frequently observed during the heating season. Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM2.5 as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities. Secondary nitrate had the highest contribution for Beijing (37.3 %), and residential heating/biomass burning was the largest for Baoding (27.1 %). Secondary nitrate, mobile, biomass burning, district heating, oil combustion, aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period (2019.01.10–2019.08.22) (Mann-Whitney Rank Sum Test P < 0.05). In case of Baoding, soil, residential heating/biomass burning, incinerator, coal combustion, oil combustion sources showed significant differences. The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients. Conditional Bivariate Probability Function (CBPF) was used to identify the inflow directions for the sources, and joint-PSCF (Potential Source Contribution Function) was performed to determine the common potential source areas for sources affecting both cities. These models facilitated a more precise verification of city-specific influences on PM2.5 sources. The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.

Optimizing Solar Energy Integration in Tallinn's District Heating and Cooling Systems

Optimizing Solar Energy Integration in Tallinn's District Heating and Cooling Systems

Multi-objective optimization and multi-attribute decision-making support for optimal operation of multi stakeholder integrated energy systems

To efficiently tackle the optimal operation problem of multi-stakeholder integrated energy systems (IESs), this paper develops a multi-objective optimization and multi-attribute decision-making support method. Mathematically, The optimal operation of IESs interconnected with distributed district heating and cooling units (DHCs) via the power grid and gas network, can be formulated as a multi-objective optimization problem considering both economic, reliability and environment-friendly objectives with numerous constraints of each energy stakeholder. Firstly, a multi-objective group search optimizer with probabilistic operator and chaotic local search (MPGSO) is proposed to balance global and local optimality during the random search iteration. The MPGSO utilizes a crowding probabilistic operator to select producers to explore areas with higher potential but less crowding and reduce the number of fitness function calculations. Moreover, a new parameter selection strategy based on chaotic sequences with limited computational complexity is adopted to escape the local optimal solutions. Consequently, a set of superior Pareto-optimal fronts could be obtained by the MPGSO. Subsequently, a multi-attribute decision-making support method based on the interval evidential reasoning (IER) approach is used to determine a final optimal solution from the Pareto-optimal solutions, taking multiple attributes of each stakeholder into consideration. To verify the effectiveness of the MPGSO, the DTLZ suite of benchmark problems are tested compared with the original GSOMP, NSGA-II and SPEA2. Additionally, simulation studies are conducted on a modified IEEE 30-bus system connected with distributed DHCs and a 15-node gas network to verify the proposed approch. The quality of the obtained Pareto-optimal solutions is assessed using a set of criteria, including hypervolume (HV), generational distance (GD), and Spacing index, among others. Simulation results show that the number of Pareto-optimal solutions (NPS) of MPGSO are higher by about 32.6 %-62.1%, computation time (CT) are lower by about 2.94 %-46.1 % compared with other algorithms. Besides, to further evaluate the performance of the proposed approach in addressing larger-scale issues, the study employs the modified IEEE 118-bus system of greater magnitude. The proposed MPGSO algorithm effectively handles multi-objective and non-convex optimization problems with Pareto sets in terms of better convergence and distributivity.

Multi-software based dynamic modelling of a water-to-water heat pump interacting with an aquifer thermal energy storage system

Aquifer thermal energy storage systems may support the decarbonization of heating and cooling energy needs of urban areas, not only in heating-dominated countries but also in Southern Europe. In this framework, this work investigates the adoption of a water-to-water heat pump interacting with two aquifers and activating a small-scale district heating and cooling network serving a mixed-use district of eight residential and office buildings in Rome (Italy). The dynamic behaviour of aquifers has been replicated using the GeoSIAM software. Energy conversion systems and users’ thermal and cooling loads have been simulated in TRNSYS 18. The dynamic models have been integrated using an iterative approach based on conditions regarding plant operation and injection temperature in wells. The proposed solution has been then compared from the energy and environmental perspective with a traditional system, including an air-to-water heat pump. In addition, the balance between heating and cooling mode operation has been assessed. The results obtained encourage the adoption of aquifer thermal energy storage systems in Central Italy. Indeed, the primary energy saving, and the carbon dioxide emissions avoided are equal to 18%, whereas the imbalance between cooling and heating loads is limited to -5.2%.

Pipe Size Calculation for Steam Supply

Pipe designers and engineers often encounter complex mathematical equations to determine the diameter required for adequate steam discharge capacity at specific flow velocities. This article aims to simplify the calculation of steam pipe sizes. The formulas and factors derived herein can be utilized for determining pipe diameters in steam applications. Ultimately, the findings provide engineers and designers with practical tools to enhance the reliability and efficiency of steam system.

Use of Sustainable Energy Systems in Educational Institutions

Educational institutions like other public and private organizations should try to eliminate their carbon footprint for the achievement of the ambitious 2050 target of net-zero carbon emissions. Several mature, reliable and cost-effective sustainable energy technologies can be used in these institutions replacing the use of fossil fuels and reducing their carbon emissions. Solar energy, wind energy and biomass can be used for heat and electricity generation replacing the use of oil, gas and grid electricity. Additionally, various low-carbon emission technologies including heat pumps, co-generation systems, fuel cells, district heating systems and power storage technologies can be also used in them. The combined use of these technologies in schools, colleges and universities can replace the current use of conventional energy sources reducing or completely eliminating their carbon emissions. The abovementioned benign energy technologies are already used commercially in several sectors proving their reliability and cost-effectiveness. It has been indicated that a plethora of green energy technologies can be used in educational institutions eliminating their carbon footprint achieving the desired global 2050 target of net-zero carbon emissions. Our results could be useful to policy makers and to the authorities of schools, colleges and universities who are willing to promote their environmental sustainability, achieving economic benefits and offering valuable educational opportunities to students.

Demand Response Potential of an Educational Building Heated by a Hybrid Ground Source Heat Pump System

Demand response (DR) enhances building energy flexibility, but its application in hybrid heating systems with dynamic pricings remains underexplored. This study applied DR via heating setpoint adjustments based on dynamic electricity and district heating (DH) prices to a building heated by a hybrid ground source heat pump (GSHP) system coupled to a DH network. A cost-effective control was implemented to optimize the usage of GSHP and DH with power limitations. Additionally, four DR control algorithms, including two single-price algorithms based on electricity and DH prices and two dual-price algorithms using minimum heating price and price signal summation methods, were tested for space heating under different marginal values. The impact of DR on ventilation heating was also evaluated. The results showed that applying the proposed DR algorithms to space heating improved electricity and DH flexibilities without compromising indoor comfort. A higher marginal value reduced the energy flexibility but increased cost savings. The dual price DR control algorithm using the price signal summation method achieved the highest cost savings. When combined with a cost-effective control strategy and power limitations, it reduced annual energy costs by up to 10.8%. However, applying the same DR to both space and ventilation heating reduced cost savings and significantly increased discomfort time.

Identifying failure factors due to corrosion erosion on pressured steam pipe elbow in geothermal power plant

This paper presents the findings of a corrosion erosion failure analysis of elbow pipe materials used to flow high-pressure water from underground. The failed elbow pipe material was above the wellhead forming a straight line in the longitudinal direction with a pipe length of 6200 feet below the ground surface. The working fluid in the elbow pipe was 25 % steam and 75 % water, flowing in the elbow pipe with a media flow rate of 180 tons per hour, a pressure of 22 bar, and a temperature of 220 °C. Elbow tubes were made of low carbon steel with Standard ASTM A234 having an outer diameter of 304.8 mm and a wall thickness of 9.271 mm. Macroscopic testing, chemical composition analysis, metallographic testing, hardness testing, X-ray diffraction testing, SEM, and EDS are a few of the test types conducted. The study's findings showed that the elbow tubes experienced a thinning process on the inner wall of the outer curvature side with a rough and wavy surface texture or appearance. This type of failure is known as erosion-corrosion. The level of erosion-corrosion failure that occurs is greatly influenced by the pH of the fluid being flowed reaching 2.67–2.91, this is due to the very high Cl- of 1290 ppm, so the higher the rate of erosion-corrosion that occurs. These materials are the most popular and widely used in the oil and gas sector. However, this pipe has weaknesses because it is susceptible to erosion-corrosion Therefore, it is very important to choose the right material, namely, a material that is resistant to erosion-corrosion

Life cycle cost approach involving steam transport model for insulation thickness optimization of steam pipes

Efficient steam pipe insulation is critical for minimizing heat loss during transportation, ensuring the economic viability of thermal processes or end-user applications. Unlike water-based systems, steam heat loss mechanisms are intricate, predominantly influenced by the transition of steam to condensate which introduces latent heat, thus significantly impacting overall heat loss. This study employs a life-cycle cost approach, integrating a comprehensive steam transport model to evaluate the optimal insulation thickness under varying temperature, flow rates and composite insulation schemes. The steam transport model incorporates a drainage energy loss term, providing a detailed depiction of the energy balance along the steam pipe. Results from the steam transportation simulation conducted on a single long pipe demonstrate a ∼4.6 % deviation between simulated and measured heat loss values. Furthermore, Life cycle cost analysis highlights the economically advantageous insulation configuration, comprising an aerogel blanket as the inner layer and glass wool as the outer layer. With increasing temperature, augmenting the proportion of aerogel blanket yields economic benefit, with the optimum thickness exhibiting a linear growth trend. Conversely, as the flow rate increases, the optimum insulation thickness remains constant.