Heating System Research Articles

Field test of machine-learning based mean radiant temperature estimation methods for thermal comfort-integrated air-conditioning control improvement and energy savings

Advanced control strategies for heating, ventilation, and air-conditioning (HVAC) systems aim to enhance both buildings' energy efficiency and occupant thermal comfort. Despite their potential, real-world demonstration studies on such advanced technologies are still lacking. This study presents a field demonstration of a real-time predicted mean vote (PMV)-based HVAC control strategy in a typical residential building under hot and dry climate conditions. This study introduces an advanced thermal comfort-based controller (TCC) for the PMV-based HVAC control. TCC continually assesses indoor and outdoor thermal conditions and adjusts the HVAC setpoint temperature to optimize real-time energy use while ensuring satisfactory indoor thermal comfort. Machine learning models for the PMV-based HVAC control system are employed to estimate a mean radiant temperature (MRT) point, which is one of the variables used to calculate real-time PMV values. Three machine learning models (i.e., linear regression, regression trees, and artificial neural network) are adopted in this study with non-stationary real-time input values, including times, pre-determined setpoints, and indoor and outdoor temperatures. The developed TCC is installed in a full-scale experimental house in Kuwait, which is under hot and dry climate conditions, to assess the house's indoor thermal comfort and AC energy efficiency performance. Results indicate that the TCC provides better thermal comfort performance compared to the non-TCC case, including up to a 60% improvement in PMV. The proposed TCC controlled by the machine learning methods demonstrates HVAC energy savings potential of over 20% while meeting the desired thermal comfort levels in the experimental building. These findings are expected to be valuable, as they can contribute to reducing cooling energy in residential buildings in hot and dry climate conditions.

Towards a Reduction of Emissions and Cost-Savings in Homes: Techno-Economic and Environmental Impact of Two Different Solar Water Heaters

South Africa currently has the highest carbon emission intensity per kilowatt of electricity generation globally, and its government intends to reduce it. Some of the measures taken by the government include a reduction of emissions in the building sector using solar water heating (SWH) systems. However, there is currently no study in the country that comprehensively assesses the technical, economic, and environmental impact of SWH systems across the country. This study therefore used the System Advisor Model (SAM) to model two different technologies of SWH systems (i.e., flat plate (FPC) and evacuated tube (EPC) SWH) at five different locations (i.e., Pretoria, Upington, Kimberley, Durban, and Cape Town) strategically selected across the country. According to the study, the optimum azimuth for both the evacuated tube and flat plate SWH system in South Africa is 0o. Installing FPC and EPC at the different locations would yield payback periods of 3.2 to 4.4 years and 3.5 to 4.3 years, respectively. Comparably, levelized cost of energy for the FPC and EPC will range from 7.47 to 9.62 cents/kWh and 7.66 to 9.24 cents/kWh, respectively, based on where the SWH system is located. Depending on where the facility is located, the annual cost savings for the FPC system would be between $486 and $625, while the EPC system would save between $529 and $638. Using SWHs can reduce CO2 emissions by 75%–77% for the evacuated tube system and 69%–76% for the flat plate system annually, depending on the location.

Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings

In the current context of energy transition and increasing climate change, optimizing building performance has become a critical objective. Efficient energy use and occupant comfort are paramount considerations in building design and operation. To address these challenges, this study introduces a predictive model leveraging Machine Learning (ML) algorithms. The model aims to predict thermal comfort levels and optimize energy consumption in Heating, Ventilation, and Air Conditioning (HVAC) systems. Four distinct ML algorithms Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and EXtreme Gradient Boosting (XGBOOST) are employed for this purpose. Data for the model is collected using a network of Raspberry Pi boards equipped with multiple sensors. Performance evaluation of the ML algorithms is conducted using statistical error metrics, including, Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Results reveal that the RF and XGBOOST algorithms exhibit superior performance, achieving accuracies of 96.7% and 9.64% respectively. In contrast, the SVM algorithm demonstrates inferior performance with a R2 of 81.1%. These findings underscore the predictive capability of the RF and XGBOOST model in forecasting Predicted Mean Vote (PMV) values. The proposed model holds promise for enhancing occupant thermal comfort in buildings while simultaneously optimizing energy consumption in HVAC systems. Further research could explore the practical applications of these findings in building design and operation.

Robust Design of 5th Generation District Heating and Cooling (5GDHC) Systems with Seasonal Thermal Energy Storage via GIS Assessment

This research addresses the need for a method to compare waste heat potential and recovery in 5th Generation District Heating and Cooling (5GDHC) systems. Prosumer buildings, known for enhancing flexibility, are analyzed using Geographic Information Systems (GIS) and image vectorization to evaluate 5GDHC systems. The study predicts the amount of waste heat available with an R2 of 0.96, utilizing Chaikin's algorithm to refine thermal images for automatic recognition of the sources of waste heat. Emphasis is placed on the absence of a method for asset design in 5GDHC, with considerations for annual energy calculations, radial network connections, and energy shares among building types. Recommendations for building connecting in Belgium are proposed, favoring 'B'/'C'-labeled buildings. The research explores the impact of building design on heating and suggests energy savings through regulation strategy changes. Dynamic models for heat pumps aim to reduce errors and emissions. The study connects energy indicators and GIS software, contributing to a top-down design approach in 5GDHC systems. The overall goal is to contribute to decarbonization and reduce CO2 emissions in the energy sector.

Design and Performance Assessment of District Heating Systems in Latvian Region

The energy consumed by buildings for air conditioning accounts for a large percentage of global energy consumption. To promote efficiency and sustainability, the scientific community is making great efforts to develop renewable technologies. A well-known but unfortunately underestimated solution is the development of centralized heating and cooling systems that consistently reduce energy consumption.The work proposes a comparison between three heating configurations covering the demand of a settlement in the Latvian region: 1) centralized district heating (DH) system; 2) 5th generation district heating & cooling (5GDHC) system and 3) individual home heating (HH) systems. Thermal and electrical loads are evaluated by transient simulations of a residential area with 80 buildings for the Riga climate and compared with the same settlement in a Mediterranean region (Milan, IT). The energy plants are based on different technologies: combined heat and power (CHP) plants, gas-fired boilers, and domestic heat pumps.The analysis includes the option of power exchange with the national grid. A transient numerical model has been developed for each solution. Every component is modelled according to performance maps provided by the manufacturers, allowing an accurate simulation in both design and off-design operating conditions. The study includes energy, economic and environmental aspects.The result of the simulation highlights the large difference between the two locations, not only in terms of annual load, but also in terms of load distribution. On an annual basis, the Latvian residential complex requires almost twice as much energy as the Italian one. The thermal losses in the district systems are 4.21% in Milan solution and 5.65% in Riga. The district heating system coupled with heat pump represents the best layout in terms of primary energy consumption in both locations, with energy savings of 50% compared to other solutions. The use of 5GDHC is a good compromise that could increase the use of renewable energy. The adoption of cogeneration plant is a good choice in case of centralized district system that allows the installation of high efficiency genset. On the contrary, for small application as residential, the installation of cogeneration system results expensive and the conversion efficiency does not justify the installation.

Life Cycle Assessment Framework for Diagnostic Imaging

The increasing focus on environmental sustainability is becoming essential in the diagnostic imaging sector, which is accredited for about 10% of the healthcare industry’s carbon footprint. A multitude of research initiatives investigated the environmental impacts of diagnostic imaging, encompassing factors like electricity consumption, carbon emissions, and waste generation from these procedures. Life Cycle Assessment (LCA) stands as a prominent method for structural assessment of environmental impacts, offering a detailed framework for examining the environmental consequences of specific processes. The aim of this study includes analyzing existing LCA approaches in literature to identify their limitations and to suggest an elaborate framework for LCA application in diagnostic imaging.Out of 17 original articles on environmental sustainability in radiology published since 2014, but only a part, 29.4% (5/17), described an LCA approach. The different characteristics of these studies provide valuable insights, enabling the proposal of a more comprehensive LCA research methodology.The reviewed articles did not present a uniform research outcome. According to the GreenHouse Gas (GHG) Protocol Corporate Standard, the optimal outcome for assessing environmental impact is the calculation of greenhouse gas emissions. For a thorough methodological approach in LCA, it is essential to cover all direct and indirect emissions associated with diagnostic imaging. All studies (100%, 5/5) considered the electricity consumption of imaging equipment. Usage of consumables was included in 80% (4/5) of the studies. Only 40% (2/5) of articles considered auxiliary equipment like computers and contrast-medium injectors, as well as heating, ventilation and air conditioning (HVAC) systems. Factors like equipment manufacture, staff travel, and waste generation, though crucial to overall greenhouse gas emissions, were each covered in only 20% (1/5) of the studies. The articles also varied in their LCA versions, with two employing the detailed Cradle-to-Grave approach, while others used partial Cradle-to-Gate and Input-Output LCA methods.The insights from this analysis could lead to a valuable framework for a new LCA methodological approach in diagnostic imaging. This novel approach is designed to overcome the limitations observed in existing research, offering a more comprehensive analysis. By enhancing the LCA framework, it will be possible identify the phases in diagnostic imaging that have the most substantial environmental impact, allowing for the development of more targeted strategies to reduce GHG emissions associated with diagnostic procedures.

Geospatial Analysis of Energy Poverty and Accessibility to District Heating Systems

This research paper undertakes a comprehensive geospatial analysis to investigate the relationship between energy poverty and district heating accessibility in Estonia. Utilizing ArcGIS Pro and statistical software, the study maps district heating systems, evaluates the energy efficiency of apartment buildings, and examines socioeconomic factors influencing energy poverty. By integrating GIS data with socioeconomic and building energy performance indicators, the research identifies areas where district heating is either absent or inefficient and correlates these findings with instances of energy poverty. Preliminary data may indicate a significant correlation between the lack of accessible district heating and increased energy expenses among lower-income households. The study also evaluates the impact of building energy efficiency on heating needs, revealing that older, less efficient buildings contribute disproportionately to energy poverty. Recommendations include policy interventions to expand and optimize district heating networks, alongside building renovation programs to enhance energy efficiency. This research contributes to the broader understanding of sustainable urban heating solutions and their role in mitigating energy poverty.

Integrating Low Temperature Waste Heat in District Heating Systems. Legal Framework and Pricing Mechanisms

ABSTRACT:Decarbonisation the heating sector is a key challenge for the European Union to achieve its ambitious goals of becoming the first climate - neutral continent in 2050. Decarbonising the heating sector is about reducing fossil fuel consumption and finding new scenarios based on renewables and restructuring the operation of district heating system. Recovering and integration waste heat into the district heating system has an enormous potential to meet the heating needs of households through non – combustion technologies while reducing carbon emissions. The heat from urban sources of waste heat is advantageous because the heat sources are, close to district heating networks and close to areas with high heat demand. However, there is still no legal or regulatory framework for the use of waste heat and no incentives for its use in district heating in the Member States of EU.This paper has two main objectives: firstly, to consider different pricing and cost determination scenarios when the waste heat is integrated into the district heating system. The heat tariff calculation model is based on the approved Latvian heat tariff calculation methodology. Secondly – to consider a mathematical model for the pricing of waste heat. The waste heat integration strategies are tested on the Case study of district heating system in Salaspils, Latvia. Simplified model of a waste heat recovery system that recovers heat energy from a wastewater treatment plant through heat pump integration is developed.

Suitable Software for the Study of Combustion Processes in Boilers

Diversification of energy resources is a current objective that several countries want to achieve, including in northern Europe. Demand for wood fuels is increasing in Latvia, reflected in consumer expenditure. Using low-quality biomass (LQB) to produce fuel pellets for stabilisation and diversification is possible. LQB pellets can theoretically and practically be used in low-capacity solid fuel boilers to provide different types of individual heating systems with an alternative energy source. Before starting mass production of LQB fuel pellets, it is necessary to clarify the properties of the raw materials. Any fuel study shall be divided into two phases: determination of the parameters of the fuel or raw material (calorific values, moisture content, and ash content) and analysis of the combustion process. The combustion process can be studied in two ways: experimentally and by mathematical modelling. Knowing the parameters that would need to be clarified during the study of the LQB fuel pellets combustion process (thermodynamics, gaseous emissions, PM emissions, bottom ash, and slag), the authors have set the goal of clarifying the software applied to mathematical modelling of these parameters. A bibliometric analysis method was chosen to identify the software. The bibliometric analysis was carried out in the Scopus database. As a result, two software were identified: ANSYS Fluent software is suitable for modelling thermodynamic processes and gaseous emission streams. At the same time, XDEM software is ideal for modelling particle streams and ash/slag generation. This software will be used in future studies.

Exploring the Efficacy of Random Linear Parameter Models for Forecasting Heating Demand in District Heating Networks

The heating and cooling sector accounts for approximately half of global energy consumption, making it a pivotal focus for energy transition efforts. District heating and cooling networks have emerged as efficient, innovative, and dominant technologies in various regions. Accurate forecasting of heating demand within these networks is imperative for effective energy transition and responsive demand management. While existing forecasting techniques primarily concentrate on limited parameters such as ambient temperature and time of day, this paper explores the multifaceted impact of various factors, including ambient temperature, energy prices, number of consumers, building types, and solar radiation, on heating demand. Through a comprehensive study of these determinants, a robust and efficient forecasting model is developed in this research. Specifically, a novel Random Linear Parameter Model (RLPM) is introduced for hourly heating demand forecasting within district heating networks. The model's performance is assessed and compared with existing forecasting models, namely Linear Regression, ARIMA, and RLPM. A notable advantage of the Random Parameter Linear Regression model lies in its capability to recognize unobserved parameters influencing demand and account for unobserved heterogeneity, providing a robust framework for incorporating these factors into the forecasting process. This study aims to underscore the proposed approach's advantages in terms of accuracy and its ability to estimate the determinants of heating load, thereby offering reliable future forecasting. The findings have the potential to significantly contribute to optimizing district heating systems by delivering more nuanced and dependable predictions of heating demand.

Comparing Numerical and Analytical Methods for Heat Loss Determination of District Heating Systems

Energy efficiency assessment considers the heat losses in district heating systems. For instance, life cycle assessments of such engineering structures require knowledge of the heat losses during their use phase. Therefore, it is essential to have the most accurate knowledge of the heat losses of a district heating system. In this study, three different methods for the determination of specific heat losses for buried preinsulated steel pipes are compared. The first method involves an analytical calculation in accordance with EN 13941, while the second utilizes an equivalent circuit approach. The third method employs finite element analysis. The objective was to evaluate the accuracy of the methods, the achievable range of results, and the effort required to solve the respective calculation algorithms. Therefore, typical 2-dimensional cross sections including different pipe diameters were selected. In situ measurements were not part of this study. Consequently, the analysis centres on the deviation between the methods. All three methods determine the heat loss in both the supply and return pipes. While the analytical calculation method cannot determine temperatures in the soil, the equivalent circuit method can handle more complex tasks and gives detailed results at predefined points in the model. With the finite element method, a high degree of detail can be achieved, but the requirements for solving the algorithms increase. An emerging trend in district heating involves reducing operational temperatures in both new and existing networks. This will change the relation between heat losses and heat delivered to the customers. Subsequently, an increasing interest in the actual heat losses and the precision of calculation is expected within this development. Therefore, it remains essential to evaluate the performance of different models

Attracting Customers to District Heat Supply: The Case of Riga

District heating will be important in achieving future climate goals. Possibilities of using waste heat from different sources, e.g. subways, hospitals, shops, data centers, rivers are often discussed. Many district heating companies face the challenge of sufficient coverage of connected consumers in a city or region. To expand the operating area, companies should initially attract objects which are close to heat networks to lower the connection costs. The research question is how to attract existing buildings under construction to the district heating system. The present work uses system dynamics modeling for studying the possibilities of the Riga district heat supply company to increase consumer network. Modeling is based on historical data of residential buildings. The results show that old buildings choose to connect to the district heat supply when these are being renovated, or the individual heat supply equipment is out of order. The older the buildings, the more likely these will be connected to the district heating, however this decision may take at least 70 years. Renovation increases the probability of connection to the district heating, so the impact of subsidies for renovation is important. Regulation that requires connection to the district heating as a priority choice in case of renovation is also important.

Techno-Economic Model of District Heating Energy Hub: The Case of Latvia

Up to 60% of Latvia’s energy resources are used as fuel for district heating systems. Heating sector is an important emitter of greenhouse gases – especially carbon dioxide. Therefore, reduction of carbon emissions of district heating systems are crucial in Latvia’s decarbonization efforts. One of the solutions is integration of energy hub in the district heating network. There are many types of energy hubs, and the most suitable solution must be found in each case. A goal of this study is to find the best solution for Latvia’s district heating systems to achieve decarbonization and use of green (renewable) hydrogen. This coincides with European Union’s plans to increase the use of green hydrogen not only in industrial and residential application, but in heat production as well.To facilitate the plans of the European Union and Latvia’s decarbonization needs a mathematical model made in Microsoft Excel is used as a method to explore the techno-economic aspects of the energy hub and its integration in the district heating system. Several alternatives are considered, and green hydrogen is used in all of those. Results show the economically most feasible alternative. Calculations are made on an annual term basis, considering such factors as electricity and district heating price, required capacity of electrolysis apparatus for hydrogen production etc.From the model some results are apparent – the price of electricity has a sizable impact on the economic feasibility of the project, and the best use for green hydrogen economics-wise may differ throughout the year as the price of electricity, hydrogen, district heating and methane gas change.

Enhancing the Evaluation of District Heating System Resilience: A Literature Review

Ensuring an uninterrupted supply of energy, electricity, and heat is becoming an increasingly pressing necessity. Energy provides both social and economic growth therefore, the energy systems should be resilient to different external conditions. Resilience is the ability of a system to recover from adversity [1] and in connection with the critical infrastructure - the ability to prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions. Resilience includes the ability to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents. Several factors influence the resilience of the energy supply system: energy security, reliability, robustness, flexibility, and resilience. If energy resilience has not been well considered or even ignored in the planning, design, and operation stages in district energy communities can lead to system fragility and vulnerability. Therefore, the research assesses the different methods for resilience assessment in energy systems by focusing on district heating. During climate change mitigation and adaptation energy supply system resilience is unclear. Based on current studies, the energy resilience factor for most is considered in the context of the electricity transmission system, and the heat transmission resilience study is limited. During the research of the literature, it has been concluded that there is no consistent quantitative and quality approach to resilience because there is no consistent treatment of the concept of resilience. The available literature sources about energy resilience are limited in their scope and usability and hence are not amenable for use outside the scope where they have been developed. The metric and formula used for calculating resilience, if any, and the input data required for such calculations are also dependent on the research scope. There is a need for a fundamental generic quantitative and quality approach for resilience that would be usable and useful across various scopes or sectors in a consistent manner, which can be used for the development of resilient systems and effective resilience strategies for district heating systems.

Use of an Absorption Heat Pump to Lift the District Cooling Waste Heat Temperature for the District Heating Supply in Tallinn: a Technical and Economic Analysis

Absorption heat pumps can lift a lower temperature waste heat source to a higher temperature useful output. In that process, it uses less electricity as an input compared to heat pumps with electrical compressors. Therefore, in some cases, it can be used to lift a lower temperature waste heat source to a higher temperature useful output with less electricity consumption. Absorption heat pumps are not very widely spread in district heating, mostly because there aren’t many suitable waste heat sources or conditions needed to run them. Tallinn has a district heating system with an annual generation of more than 2 TWh and a goal to make it an entirely carbon neutral district heating system by 2030. Tallinn has also started to develop a district cooling network, and by the end of 2023, the installed capacity was about 6 MW. District cooling capacity is estimated to grow to around 100 MW in 2030. This paper investigates different types of absorption heat pumps and the possibilities of integrating an absorption heat pump to a district cooling plant with the purpose of using waste heat from the cooling plant for renewable heat generation, that can be used in district heating. Different technical aspects are examined to find a suitable production solution, are presented as results. From an economical point of view, the cost of heat to cover peaks with an absorption heat pump is calculated. The effect of reducing fossil fuel use in the Tallinn district heating network with an absorption heat pump is estimated.

Examination of Operational Methods for a Low-Temperature Aquifer Thermal Storage Air Conditioning System Based on Operational Performance and Considerations of Thermal Storage and Pumping Volume Balance

Aquifer Thermal Energy Storage (ATES) systems are garnering attention as high-efficiency air conditioning technologies that contribute to the realization of a carbon-neutral society. This study focuses on an ATES system constructed in Japan, characterized by its complex geological conditions and thin aquifer layers. Detailed actual performance data measured over four years are presented, and performance analysis results show that a COP of 5 was achieved for the overall building cooling and heating system. In addition, the study provides a detailed analysis of the imbalance in heat quantity, remaining heat storage, and other factors based on actual data, identifying operational issues and summarizing specific improvement measures. Finally, as a proposal for a sustainable operational method to balance the cumulative heat storage and pumping volume of cold and hot water, specific operational procedures are summarized, including setting the return water temperature for the next season based on the dimensionless average pumping temperature of the previous season, and a flowchart is presented. There is no prior research that shows such specific operational procedures, and this can be considered an important achievement of this study.

Seasonal toxicity of urban road dust in runoff process-studies in Poland.

Urban road dust (URD) is one of the most important non-point sources of pollution in agglomerations. The aim of this study was to assess the seasonal toxic effects of URD runoff in two regions of Poland. The concentrations of elements in URD and leachate were studied. The impact of pollutants in URD runoff on water organisms was evaluated using Daphtoxkit F and Rotoxkit F (LC50). The acute toxicity tests for crustaceans and rotifers were selected as the response of these taxa reflects the impact on zooplankton, a key component of aquatic ecosystem and the basis of most food webs. The concentrations of elements were found to vary depending on the site, although URD samples collected in Katowice agglomeration (Upper Silesia) had higher values of elements (Mn, Cu, Zn, As) compared to Wrocław (Lower Silesia). The concentrations of Mn, Zn, As, Cr, and Mg in water-soluble fraction of URD were higher in summer and winter in the Upper Silesia region due to rainwater runoff resulting from traffic, industries, post-industrial waste, and the presence of old heating systems. When comparing the content of elements in the water-soluble fraction between seasons, Zn, As, Cr, and Al concentrations were slightly higher in winter. The highest mortality of Daphnia magna and Brachiouns calyciflorus was observed in URD from both agglomerations in winter. However, the mortality is likely due to the concentration of elements or/and the coexistence of an unknown compound or a synergistic effect of the studied elements. This study highlights the alarming seasonal sources of elements in URD runoff, which will directly enter the food chain and affect the entire ecosystem, and human health.

Comparative analysis of energy efficiency for three heating and cooling supply schemes in a region with hot summers and cold winters in a chemical industrial park

Building energy consumption in China accounts for 45% of the total national energy consumption, with air conditioning energy consumption representing approximately two-thirds of that. Therefore, energy efficiency in buildings is of utmost importance. This study focuses on a chemical industrial park located along the Fujiang River and compares three heating and cooling supply schemes: the river water source heat pump system, which utilizes river water as the heat source and heat sink; the water cooling unit and boiler system, which uses water-cooled electric compression chillers for cooling and an oil-fired boiler system for heating; and the split air conditioning and gas water heater scheme, which relies on refrigerants such as fluorine-containing compounds for cooling and a gas water heater for heating. By calculating the energy consumption of the above three schemes and conducting a comparative analysis, it is found that the river water source heat pump system exhibits significantly higher energy efficiency throughout the year compared to the water cooling unit and boiler system and the split air conditioning and gas water heater scheme. This highlights the notable energy efficiency advantage of the river water source heat pump system.

Analysis of the Hybrid Power-Heating System in a Single-Family Building, along with Ecological Aspects of the Operation

This study evaluates a hybrid heating system in a single-family building in northeastern Poland, which has a temperate continental climate. The analysis covers two heating seasons in 2021/2022 and 2022/2023. The hybrid heating system includes an air heat pump HPA–08 CS Plus with a heating power of 8.2 kW (AHP), a condensing gas boiler VC146/5–5 with a power of 14 kW (GB–Condens.), and a solid fuel boiler with a power of 11 kW for central heating. Additionally, hot water is heated by a Basic 270 (DHW’s AHP) air–water heat pump with a power of 2 kW, utilizing a tank with a capacity of 270 dm3 equipped with two heating coils. The building’s average electricity consumption is around 5400 kWh/year. A 4.96 kWp photovoltaic installation is installed on the building’s roof at a 40° angle towards the south to supplement the hybrid system. The study aims to assess whether the PV installation can adequately cover the energy needs of the hybrid heat source for heating and hot water. Furthermore, the study calculates the emission of pollutants (CO2, SOx, NOx, CO, and PM10) into the atmosphere. The total annual electricity production from PV installations was 5444.9 kWh in 2021/2022 and 5684.8 kWh in 2022/2023. The excess electricity was stored in the PGE power grid as per the Prosumer settlement rules. The installed PV installation is sufficient to power the following devices annually: AHP, DHW’s AHP, and GB–Condens. However, the daily electricity production from the PV installation is not enough to cover the energy needs of the heat pump for heating during the cold months in Poland (I–III, XI–XII). It can meet the power needs of a PC all year round and can also be stored during the summer months, for example, in energy warehouses or by directly storing it in the PGE power grid. The use of the PV installation resulted in an average reduction in pollutant emissions into the atmosphere: CO2—94.1%, SOx—91.8%, NOx—95.6%, CO—9.7%, and PM10—32.1%.

Economic Assessment of Scenarios for Reducing Atmospheric Emissions in Krasnoyarsk due to Gas Infrastructure Development and the Use of Alternative Heating Systems

Economic Assessment of Scenarios for Reducing Atmospheric Emissions in Krasnoyarsk due to Gas Infrastructure Development and the Use of Alternative Heating Systems