District Heating Plants Research Articles

Thermo-economic analysis of a solar district heating plant with an air-to-water heat pump

Solar district heating systems are essential in reducing carbon emissions and alleviating the energy crisis. By integrating with air-to-water heat pumps, it is possible to enhance the system’s efficiency and flexibility. However, the complementary mechanisms and coupling effects between the heat pump and the other components of the system are not clear. To address these issues, it is necessary to investigate the thermo-economic performance of the system, taking into account energy price fluctuations to achieve a more efficient and economically viable operational model. This study focuses on a solar district heating system in the Danish city of Ørum and investigates the impact of integrating a large-scale air-to-water heat pump. Based on energy and economic analyses, the impact of the heat pump on the system efficiency and the levelized cost of heat are investigated for 2020 and 2022. The results show that the heat pump can convert low-cost electricity into heat which will be stored in the tank, increasing the tank’s heat storage content by 1.2 times. The medium to low-temperature water in the tank can preheat the heat pump’s compressor, raising the COP of the heat pump from 3.3 to 3.5 and increasing the annual utilization cycle of the tank by 1.4 times by reducing the return water temperature to the bottom of the tank. Additionally, the system performance has been improved due to the installation of the heat pump, which increased the system COP from 1.22 in 2020 to 2.62 in 2022. Subsequently, the CO2 emission has been decreased from 192 kg/MWh to 74 kg/MWh with a larger share of sustainable energy. Due to the heat pump operation, the system-levelized cost of heat could be reduced to 68.6 EUR/MWh, compared to 94 EUR/MWh if the heat pump is removed. The investigation also revealed that the heat pump improved the flexibility of the heating system in response to energy prices, especially during the 2022 energy crisis in Europe. The findings of the paper provide a good reference for large solar heating applications.

Thermal request optimization of a smart district heating system

This paper proposes a solution approach to manage the heating plans of tenants served by a district heating plant located in Sweden. To do that, the daily temperature request of each household in the associated pilot region is obtained, and the daily temperature profile of each household is optimized with the help of the proposed decision support system and smart valves. The hot water inflow rates of radiators are remotely controlled via smart valves at each flat to minimize the total energy consumption, carbon emission and cost associated with the energy consumption of the district heating plant. We aim to shave the peak demands while fully satisfying the temperature requests of households without violating their thermal comfort. Peak demand shaving is achieved by generating preheating schedules via mathematical optimization and using the thermal storage potential of the insulated flats. The resulting mathematical optimization model presents significant computational challenges that cannot be efficiently solved using optimization solvers within a reasonable time limit. To this end, we develop three genetic algorithm approaches that are computationally scalable for realistically-sized instances and verified to yield near-optimal solutions for the test instances. Extensive numerical analyses show the effectiveness of the proposed approach and the genetic algorithm since we yield significant carbon emission reduction and cost savings compared with the method that the experts of the utility company propose.

Design and operational features of an active condensation system for enhanced energy efficiency in a biomass-fired district heating plant

The study addresses the design and operational features of an active condensation (AC) system aimed at improving the energy efficiency of a 3rd generation district heating (DH) plant. Several similar biomass-utilizing plants have recently been built in the Republic of Serbia. The plant is equipped with biomass-fired boilers totaling 2 MW capacity, utilizing varying qualities of wet wood chips. The goals are: (i) to determine optimal constant and variable quench temperatures; (ii) to calculate the electricity consumption of ancillary devices and assess their impact on AC system performance; (iii) to evaluate the effect of biomass moisture variations on system size and performance; (iv) to assess the economic viability of integrating the AC system; and (v) to establish selection and design criteria for the heat pump (HP). For a given commercial HP, an optimal quench temperature exists, dependent on the minimal temperature lift required by the HP. This temperature can be calculated simply by subtracting the minimal HP lift from the lowest possible temperature that the HP can supply to a DH system at the average outdoor temperature. Consequently, an optimal variable quench temperature exists that should be adjusted to enable the HP to operate with its minimal temperature lift.

Forecasting of Daily Heat Production in a District Heating Plant Using a Neural Network

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.

Technological and economical analysis of the heat recovery system from flue gas in a thermal waste treatment plant

A heat recovery system from flue gas increases the overall efficiency of a district heating or CHP plant, as the system recovers the waste energy contained in the flue gas at the chimney outlet. This makes it possible to increase thermal energy production with the same fuel consumption. A method for calculating the thermodynamic parameters of municipal waste incineration plants was developed as part of the work carried out. The mathematical models prepared made it possible to analyse the impact of the heat recovery system from flue gas on technical and economic parameters and the environment, including, among other things, an estimate of the annual amount of heat recovered, the amount of additional electricity consumed, the amount of condensate treated, the estimated capital expenditure, operating profits and operating costs. The results of the analyses were the basis for designing and constructing a heat recovery system from flue gas equipped with a condensation heat exchanger for the municipal waste incineration plant in Cracow. The calculation results were compared with the measurement results obtained during the test run of the constructed installation.

Heat pumps to upgrade existing CHP-DHN systems towards new generation thermal networks

District heating networks with combined heat and power systems and renewable energies are one of the most promising solutions for efficient and sustainable energy supply. In many cases, however, especially for district heating networks prior to the 4th generation, significant renovations are required to meet decarbonization targets. In this paper a study is proposed to evaluate the integration of high temperature heat pumps in an existing combined heat and power - district heating plant to reduce fossil fuel consumption and increase the exploitation of renewable energy sources. The plant is currently operating in central Italy and connects more than 1250 users. The identified solution implies lowering the district heating networks operating temperature and supplying power peaks with a high temperature heat pump acting as a booster. Results showed significant improvements in system performance especially in the winter months, due to the greater impact of lowering the temperature level of the district heating network during these months. Overall, the updated scenario allows the overall demand and ground heat losses to be reduced annually by 5.3 % and 13.5 % respectively. This reduces natural gas consumption by 13.3 % and avoids the emission of about 836 tCO2. The analysis provides guidelines for the upgrade of 3rd generation district heating network that can be useful for planning improvements towards newest generation thermal networks.

Exploring Urban Sustainability: The Role of Geology and Hydrogeology in Numerical Aquifer Modelling for Open-Loop Geothermal Energy Development, the Case of Torino (Italy)

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.

Analysis of Sodium–Water Reaction as heat source for district heating and cooling

Finding new alternatives is a need to develop disruptive solutions that could be applied to the decarbonized, sustainable, circular systemic change for the energy system transition. In this case, a complete new approach, sodium as energy resource, is described. Traditionally, sodium has been considered a risky element even if it were proposed as coolant in many applications, as nuclear or solar thermal plants. Such applications have been concerned by the explosive reaction of alkali as Na with water. We analysed the alternative of profiting of such highly exothermic reaction for an energy use. Previously, we experimentally tested the utilization of sodium as propellant, as reported in Guerrero et al. (2019), what produced the correlation between the energy release and the sodium consumption in excess of water. We are now developing a boiler fuelled by sodium and water. Based on these previous and ongoing activities, this paper shows the proposal and the process analysis of a sodium–water boiler applied to heating and cooling networks,as one of the sectors to be decarbonized in the energy system. A tentative configuration for the design of such heater, and the heat exchangers to adapt heated water temperatures will be presented. We present the conceptual design of a 13.5 MW district heating plant for a ΔT=10°C heating water from 60 to 70 °C, consuming 1 kg/s Sodium, reaching an efficiency of 95%, comparable with existing boilers.

A Showcase for Resilient and Sustainable District Heating in Denmark

This presentation gives a description of the Danish district heating company, Hvide Sande District Heating, which has become independent of fossil fuels by using wind and solar energy. This has resulted in lower consumer heating prices in a time when other fossil fueled district heating plants are raising their heating prices due to higher fossil fuel prices.Besides participating in the Day-ahead market it also participates in the balancing markets.Hvide Sande is a small fishing town. The district heating plant provides heat to 1,700 consumers. From being a natural gas fired Combined Heat and Power plant, it has in recent years become more resilient by investing in a solar collector, wind turbines, a heat pump, an electrical boiler as well as more large thermal storages, and is now independent of natural gas.The two thermal storages of 2,000 m3 and 1,200 m3, respectively, can store around 200 MWh-heat, which allows flexible market-based productions of the different production units. The heat delivered to consumers can thus be produced many hours or days before delivery.Even in the Day-ahead market, the market-based productions are a challenge to plan. Because of the large thermal storages, the manager must look more days ahead as well as consider the heat amount in the thermal storages, when deciding the hourly bids for tomorrow. Biddings are based on forecasts more days ahead of wind velocity, solar radiation, ambient temperatures, and Day-ahead prices.Besides participating in the Day-ahead market it also participates in the balancing markets. However, to take advantage across more electricity markets of this flexibility, a vast digitalization of the plant using advanced bidding methods has been required. This presentation illustrates examples of the daily earnings the plant has had participating across more electricity markets. It is also important that the manager maintains a digital twin of the plant. The digital twin of the plant will in this presentation be used for simulating the resilience of the plant against large changes in electricity prices and fuel prices.

Holistic non-linear optimization of the layout, sizing, and operation of a district heating plant

A large fraction of district heating networks in Europe are still operating without thermal energy storage. While offering operational flexibility, the presence of energy storage complicates the problems of optimal sizing and control of the heat production plant. This necessitates a novel approach to system sizing based on non-linear programming in order to capture the system’s thermal response and inertia. Therefore, the present study focuses on such a typical plant in France and describes a holistic approach to optimize the capacities/parameters of system components, positions of the energy sources, as well as hourly flow rates. Such a simultaneous tuning of the system parameters and operating variables is essential to maximize the cost savings over the system's lifetime. The proposed framework here maintains the original non-linear form of the objective function and the constraints for more accuracy while allowing precise modeling of the thermally stratified storage tank by decoupling its calculations at a lower simulation level. Hence, the computation time in MATLAB® R2020A is mostly below 20 min on a typical personal computer. The lowest levelized cost of heat (57.259 EUR/MWh) is achieved when placing the gas boiler on a parallel line between the biomass boiler and the storage tank while sizing the two boilers using fractions of 0.33 and 0.73 of the maximum hourly demand, respectively, as well as a storage volume of 318.9 m3. Such relatively small storage reduces the lifecycle costs by a maximum of 1.34 % while reducing the specific carbon emissions by 17.33 %. Besides, it dampens the fluctuations in supply flow rates, minimizes unnecessary flow recirculation, and ensures a stable heat supply. Overall, thermal storage has a reasonable positive impact from an economic perspective, but a substantial impact on operational flexibility and mitigated carbon emissions.

Expanding Building Archetypes to Estimate the Indoor Environment Thermal Storage Capacity in the Danish Building Stock when Performing Demand Response

Short-term demand response leveraging the energy flexibility of the building stock is a key solution to decarbonise and ensure the reliability and sustainability of smart energy grids dominated by intermittent renewable energy sources. Most of the building demand response capacity depends on different forms of energy storage in the built environment. In addition to electric batteries, hot water storage tanks, ground source heat exchanger or dispatchable on-site renewable energy sources, buildings possess a tremendous thermal storage capacity within the thermal inertia of their structure and indoor environment. The latter can easily be harnessed with heating/cooling indoor temperature setpoint modulation strategies. However, there is a clear lack of large-scale estimates of this indoor environment thermal storage capacity in the different building stocks. To tackle this issue, detailed dynamic numerical models of the different building typologies in European countries are generated to assess the potential of demand response strategies at nationwide levels. The current paper presents the preliminary results on the thermal storage capacity in the Danish building stock when performing heating temperature setpoint modulation. These results are based on estimates of the effective thermal inertia of the different building archetypes in Denmark. They show that the Danish building stock contains immense thermal storage capacity, which is comparable to all combined batteries in a large fleet of electric vehicles or all industrial-size storage tanks in district heating plants.

Operational characteristics of the combined heat and power plant in the district heating system of Belgrade

The combined production of electricity and thermal energy (combined heat and power or co-generation) is the most efficient and convenient approach to reduce costs for energy at industrial power plants and district heating plants that use natural gas as fuel to produce thermal energy for various needs. This paper analyses the operational characteristics of the combined heat and power plant, which has been operating since January 1, 2021, at the Vozdovac Heating Plant as part of the Belgrade district heating system. The combined heat and power plant consists of three gas engine units with a total nominal electric power of 10 MW and thermal power of 10.1 MW, which use natural gas as fuel. The combined heat and power plant is used for district heating and preparing domestic hot water while electricity is sold to the local electric grid. The analysis in this paper focuses on the plant?s operational characteristics: the number of working hours, the total energy consumption and energy production, the efficiency as well as the operational and maintenance costs. Also, the impact of the drastic changes in the prices of natural gas, electricity, and maintenance costs in the last year on the financial profitability of the combined heat and power plant was analysed in particular.

Monitoring ammonia slip from large-scale selective catalytic reduction (SCR) systems in combined heat and power generation applications with field effect gas sensors

Abstract. Following tightened regulations, selective catalytic reduction (SCR) of nitrogen oxides (NOx) by ammonia (NH3) has over the last couple of decades found wider adoption as a means of reducing NOx emissions from e.g. power production and district heating plants. As in the SCR process NH3 injected into the flue gas reacts with and reduces NOx to nitrogen (N2) and water (H2O) on the surface of a specific catalyst, the NH3 injection has to be dynamically adjusted to match both instant and long-term variations in flue gas nitrogen oxide concentration in order to minimize NOx and NH3 emissions. One possibility of realizing such NH3 dosing control would be the real-time monitoring and feedback of downstream flue gas NOx and NH3 concentrations to the NH3 injection control unit. In this study the sensing characteristics and performance of SiC-based Metal Oxide Semiconductor Field Effect Transistor (MOSFET) sensors with a structurally tailored gas-sensitive gate contact of iridium (Ir) for in situ NH3 monitoring downstream from the SCR catalyst in a combined heat and power (CHP) plant have therefore been investigated and evaluated. The sensor's NH3 sensitivity and selectivity as well as the cross-sensitivity to common flue gas components – oxygen (O2), water vapour (H2O), nitric oxide (NO), nitrogen dioxide (NO2), carbon monoxide (CO), and a model hydrocarbon, ethene (C2H4) – were thereby investigated for relevant concentration ranges under controlled conditions in the laboratory. While, at the prescribed sensor operation temperature of 300 ∘C, the influence of H2O, CO, and C2H4 on the sensor's NH3 concentration reading could be regarded as practically insignificant, a moderate cross-sensitivity was observed between NH3 and NO2 and, to a lesser extent, between NH3 / NO and NH3 / O2. As the NOx concentration downstream from the SCR catalyst under normal SCR and power plant operation is expected to be considerably smaller than the NH3 concentration whenever any appreciable ammonia slip occurs, the observed NH3 / NOx cross-sensitivities may, however, be of less practical significance for ammonia monitoring in real flue gases downstream from the SCR catalyst. Furthermore, if required, the small influence of O2 concentration variations on the sensor reading may also be compensated for by utilizing the signal from a commercially available oxygen sensor. Judging from in situ measurements performed in a combined heat and power plant, the structurally tailored Ir gate field effect sensors also exhibit good NH3 sensitivity over the relevant 0–40 ppm range when directly exposed to real flue gases, offering an accuracy of ±3 ppm as well as low sensor signal drift, the latter most likely to further improve with regular zero-point calibration and thereby make the Ir gate MOSFET ammonia sensor a promising alternative for cost-efficient real-time ammonia slip monitoring or SCR system control in heat and/or power production plants.

Optimal control of a hybrid microgrid for hydrogen-based heat supply using deep reinforcement learning

Abstract Green hydrogen is considered one of the key technologies of the energy transition, as it can be used to store surpluses from renewable energies in times of high solar radiation or wind speed for use in dark lulls. This paper examines the decarbonization potential of hydrogen for the heating industry. Worldwide, 99% of hydrogen is produced from fossil fuels, because hydrogen derived from renewable energy sources remains prohibitively expensive compared with its conventional counterpart. However, due to the expansion of renewable energy sources and the current energy crisis of conventional energy sources, hydrogen from renewable energy sources is becoming more and more economical. To optimize the efficiency of green hydrogen production and make it more price-competitive, the author simulates a hydrogen production plant consisting of a photovoltaic plant, a power grid, hydrogen storage, an electrolyser, a natural gas purchase option, a district heating plant and households. Using the deep deterministic policy gradient algorithm from deep reinforcement learning, the plant is designed to optimize itself by simulating different production scenarios and deriving strategies. The connected district heating plant is used to map how hydrogen can be optimally used for heat supply. A demonstrable outcome of this paper is that the utilization of deep deterministic policy gradient, over the course of a full year, can result in a competitive production of hydrogen derived from renewable or stored energy sources for the heating industry as a natural gas substitute.

Condition-Based Maintenance of Gensets in District Heating Using Unsupervised Normal Behavior Models Applied on SCADA Data

Increasing interest in natural gas-fired gensets is motivated by District Heating (DH) network applications, especially in urban areas. Even if they represent customary solutions, when used in DH, duty regimes are driven by network thermal energy demands resulting in discontinuous operation, which affects their remaining useful life. As such, the attention on effective condition-based maintenance has gained momentum. In this paper, a novel unsupervised anomaly detection framework is proposed for gensets in DH networks based on Supervisory Control And Data Acquisition (SCADA) data. The framework relies on multivariate Machine-Learning (ML) regression models trained with a Leave-One-Out Cross-Validation method. Model residuals generated during the testing phase are then post-processed with a sliding threshold approach based on a rolling average. This methodology is tested against nine major failures that occurred on the gas genset installed in the Aosta DH plant in Italy. The results show that the proposed framework successfully detects anomalies and anticipates SCADA alarms related to unscheduled downtime.

Thermochemical Energy Storage with Integrated District Heat Production–A Case Study of Sweden

The implementation of electricity-charged thermochemical energy storage (TCES) using high-temperature solid cycles would benefit the energy system by enabling the absorption of variable renewable energy (VRE) and its conversion into dispatchable heat and power. Using a Swedish case study, this paper presents a process for TCES-integrated district heating (DH) production, assesses its technical suitability, and discusses some practical implications and additional implementation options. The mass and energy flows of a biomass plant retrofitted with an iron-based redox loop are calculated for nine specific scenarios that exemplify its operation under electricity generation mixes that differ with respect to variability and price. In addition, the use of two types of electrolyzers (low-temperature and high-temperature versions) is investigated. The results show that for the Swedish case, the proposed scheme is technically feasible and capable of covering the national DH demand by making use of the existing DH plants, with an estimated process energy efficiency (electricity to heat) of 90%. The results also show that for a retrofit of the entire Swedish DH fleet, the required inventories of iron are approximately 2.8 Mt for the intermediate scenario, which represents 0.3% and 11.0% of the national reserves and annual metallurgical production rates of the national industry, respectively. In addition to the dispatchable heat, the process generates a significant amount of nondispatchable heat, especially for the case that employs low-temperature electrolyzers. This added generation capacity allows the process to cover the heat demand while decreasing the maximum capacity of the charging side computed herein.

Optimal management and data-based predictive control of district heating systems: The Novate Milanese experimental case-study

This paper addresses the modelling and optimal control of district heating systems connected to the electrical grid, with the goal of maximizing their operational efficiency and enabling the participation to electricity markets. Being these systems governed by nonlinear large-scale dynamical models, a novel procedure is proposed, which enables to obtain suitable models for optimization, and consisting in a combination of physical and identified piece-wise linear models. A two-phase optimization and control scheme is then designed, including an offline scheduling problem for participating to the day-ahead energy market, and an online Model Predictive Control system, minimizing the energy consumption of thermal generators while properly satisfying the users thermal demand. The proposed methodology is developed considering a real district heating plant, owned by the energy company A2A S.p.A. and supplying the city of Novate Milanese (Italy), and different experiments on the plant have been carried out. The experimental results and achieved performances are promising, showing a significant reduction of the operational costs and overall gas consumption.

Process Economy of Alternative Fuel Production from Sewage Sludge and Waste Celluloses Biomass

The treatment and disposal of sewage sludge is one of the most important and critical problems of wastewater treatment plants. 8.7 million tonnes of dry matter of sewage sludge were produced annually in the European Union in the year 2020. Due to the fact that sewage sludge contains a large number of substances that are not beneficial for human health, the conditions for sludge management will be significantly tightened in the EU countries. One option for sludge liquidation is the production of biofuel in a form of granules or pellets from sewage sludge enriched by waste celluloses. The achieved results show that the resulting quality of such alternative biofuel is fully comparable to conventional fossil fuels. The economic analysis is based on the simulation of cash flows associated with the implementation of the project over the lifetime and the calculation of levelised cost (LCOE). Results shows (under the current economic situation) that solar dryer technology ensures the lowest LCOE at the level of 26 EUR/GJ in fuel. If the LCOE of the alternative biofuel includes the price of the saved emission allowance and the future costs of sewage sludge disposal, the resulting price is directly competitive with lignite. The results thus clearly show that there is an ecological and economically competitive substitute for solid fossil fuels, which may be an important step for the future use of local combustion sources such as district heating plants.

Techno-economic performances of active condensation in a medium-scale biomass-fired district heating unit

This work compares the economic viability of active and passive condensation in a medium-scale biomass combustion plant considering the variability of the return temperature and heat demand over the year. A typical District Heating plant with a total installed power of 9.5 MW th is considered. The economic feasibility is evaluated by means of the Net Present Value (NPV), Discounted Payback Period (DPP), and the Modified Internal Rate of Return (MIRR). Compared to passive condensation, the NPV of an active condensation plant is 66% higher and reduces the primary energy consumption by more than 50%. However, a higher initial investment and a higher DPP are calculated. Assuming constant return temperature and average heat demand over the year lead to an overestimation of the NPV by more than 110% for an active condensation plant and by more than 160% for a passive condensation plant. The NPV, DPP and MIRR are strongly impacted by a variation of the return temperature of the network.

Performance assessment of using various solar radiation data in modelling large-scale solar thermal systems integrated in district heating networks

The use of solar radiation data models is widespread in energy system analysis, however a gap exists when assessing their impact in modelling large-scale solar thermal systems integrated in district heating (DH) systems. Therefore, this study presents an analysis of how using satellite-based radiation data models (SARAH), reanalysis models (CFSR, ERA and MERRA2) and other data models (Danish Reference Year) affect the modelling of these systems. Taking three DH plants in Denmark as study cases, the measured radiation between 2016 and 2019 are utilized.Using energyPRO-based mathematical models of the systems, heat outputs are calculated and compared with measured data. Moreover, the yearly DH plant operational cost is calculated to observe the economic impact of using inaccurate models. It is found that heat production assessments based on the SARAH model show a better agreement with measured data than the reanalysis-based ERA5, MERRA2 and CFSR models. The empirically-based DRY shows low errors when observing its yearly values but has a higher inaccuracy on the hourly level, providing inaccurate operation profiles of the plant.Additionally, the satellite-based solar data model SARAH is further analyzed to identify patterns of its inaccuracy. After comparing it with 18 locations in Denmark using month-hourly profiles, no error trend can be identified, supporting the robustness of the model.