Operation Of Heating System Research Articles

Optimal operation of combined heat and power systems: An optimization-based control strategy

The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany.

Improved Control of Radiator Heating Systems with Thermostatic Radiator Valves without Pre-Setting Function

Low-temperature district heating will play an important role in a future free of fossil fuels. This will only be able to be realized through the low-temperature operation of heating systems in existing buildings. Existing radiator systems can operate with low temperatures for most of the year because they are designed for extremely cold days, but errors have to be corrected and the control of the radiator systems needs to be improved. In this paper, we present a strategy to achieve low-temperature operation from the radiator system of a multi-family building in Denmark without a pre-setting function in the thermostatic radiator valves. The strategy is based on operating the system with a combination of a minimum supply temperature and small temperature differences over the radiators. The operation of the system is analyzed through a thermal-hydraulic model. A minimum supply temperature weather compensation curve was calculated and implemented in the central supply temperature control. Return temperature measurements in the substation, the risers, and several critical radiators were performed before and after the implementation of the strategy. The measurements confirm that a lower supply temperature results in a reduction of the return temperature. However, the system operator needs to be supported by a tool package to correctly maintain the system’s operation.

Energy Flexible Buildings - The impact of building design on energy flexibility

Thermal load management has substantial theoretical potential for energy flexibility. To use the inherent flexibility in buildings, for example, district heating companies could temporarily control the heating system of buildings to switch-off or preheat dwellings in the morning to avoid using peak load gas boilers. The »thermal flexibility« in this study indicates the tolerance of buildings towards the changes of its heating system operation according to an external signal. The focus of this investigation is to give an overview of »thermal flexibility« of residential buildings in Austria from 1920 – 2020 with different envelope qualities, construction types and heating systems. Existing residential buildings in Austria usually have a high thermal mass within their massive brick or concrete primary structure, and therefore their indoor thermal conditions react slowly to operative changes in the supply of thermal energy. Depending on the buildings ability to retain or store heat inside the building envelope, space heating can be used to offer energy flexibility. Among other factors, especially the quality of the thermal envelope, the thermal capacity of the building, the sluggishness of the heat delivery system and passive solar gains are crucial for keeping indoor thermal comfort. Dynamic building simulation in IDA ICE is used to evaluate the potential of selected building typologies to shift heating loads away from peak demand periods. Potentials of various building archetypes according to the EU-Tabula building database to time-shift the operation of the heating system are pointed out respecting occupants’ comfort.

Flexibility Potential of Space Heating Demand Response in Buildings for District Heating Systems

Using an integrated demand-supply optimization model, this work investigates the potential for flexible space heating demand, i.e., demand response (DR), in buildings, as well as its effects on the heating demand and the operation of a district heating (DH) system. The work applies a building stock description, including both residential and non-residential buildings, and employs a representation of the current DH system of the city of Gothenburg, Sweden as a case study. The results indicate that space heating DR in buildings can have a significant impact on the cost-optimal heat supply of the city by smoothing variations in the system heat demand. DR implemented via indoor temperature deviations of as little as +1 °C can smoothen the short-term (daily) fluctuations in the system heating demand by up to 18% over a period of 1 year. The smoothening of the demand reduces the cost of heat generation, in that the heat supply and number of full-load hours of base-load heat generation units increase, while the number of starts for the peaking units decreases by more than 80%. DR through temperature deviations of +3 °C confers diminishing returns in terms of its effects on the heat demand, as compared to the DR via +1 °C.

A decentralized, model-free, global optimization method for energy saving in heating, ventilation and air conditioning systems

To solve the challenges of high labour and maintenance cost while saving energy in engineering, a decentralized agent-based model-free global optimization method for heating, ventilation and air conditioning systems is proposed. In this novel optimization method, each updated smart equipment is connected according to the physical relationships, such that it can communicate and collaborate with adjacent nodes. Furthermore, to achieve the overall optimal operation of heating, ventilation and air conditioning systems, a decentralized evolutionary algorithm is developed. With the decentralized algorithm executed in all smart nodes in parallel with the feedback of sensor measurements instead of accurate device models, the system can achieve optimal coordination and avoid conflicts between correlated devices. The equivalence between the centralized and decentralized methods is proven. This method is confirmed to be effective through hardware testing based on actual engineering. Practical application: The traditional optimization methods for heating, ventilation and air conditioning are based on a centralized structure with a series of deficiencies, such as high maintenance and labour costs, link congestion and operational lag. This study presents a novel decentralized structure constructed using basic physical relations and a model-free method that possesses the advantages of plug-and-play, rapid development, great flexibility and convenience for engineering implementation without having to build a central monitor. The case study results validate the efficiency and effectiveness of the proposed method.

Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm

The optimization of heating, ventilating and air conditioning (HVAC) system operations and other building parameters intended to minimize annual energy consumption and maximize the thermal comfort is presented in this paper. The combination of artificial neural network (ANN) and multi-objective genetic algorithm (MOGA) is applied to optimize the two-chiller system operation in a building. The HVAC system installed in the building integrates radiant cooling system, variable air volume (VAV) chiller system, and dedicated outdoor air system (DOAS). Several parameters including thermostat setting, passive solar design, and chiller operation control are considered as decision variables. Subsequently, the percentage of people dissatisfied (PPD) and annual building energy consumption is chosen as objective functions. Multi-objective optimization is employed to optimize the system with two objective functions. As the result, ANN performed a good correlation between decision variables and the objective function. Moreover, MOGA successfully provides several alternative possible design variables to achieve optimum system in terms of thermal comfort and annual energy consumption. In conclusion, the optimization that considers two objectives shows the best result regarding thermal comfort and energy consumption compared to base case design.

Optimal operation of novel hybrid district heating system driven by central and distributed variable speed pumps

The district heating system with distributed variable speed pumps can provide substantial energy savings, compared with the district heating system with conventional central circulating pump. However, for large-scale district heating systems, using distributed pumps at all substations leads to high pressures of the network terminals, which will increase the risk of pipe bursting and failure of heat supply. In this paper, the dilemma is overcome by combining the central circulating pump with distributed pumps to construct a novel hybrid district heating system, which can improve energy saving and reduce the network pressures of the district heating system. The optimal pressure control strategy for the new type of district heating network is developed to minimize the total pump power. In order to illustrate the feasibility and effectiveness of the proposed configuration, two cases were investigated regarding an existing district heating system with 1 heat source and 112 substations, including (I) varying the flow rate at all substations with the same relative rate, and (II) varying the flow rate at all substations with different relative rates. Analysis results show that compared with the conventional constant pressure difference control strategy, the hybrid system controlled by the optimal pressure control strategy ensures more stable operation conditions for the distributed pumps and can save 57% pump power when flow rate is 0.3.

Joint Commitment of Generation Units and Heat Exchange Stations for Combined Heat and Power Systems

The combined heat and power system introduces a higher efficiency in energy conversion and consumption. By exploiting the flexibility of district heating systems (DHSs), the joint operation of heat and power systems can improve the overall system flexibility, reduce renewable energy curtailment, and decrease system operating costs. In a typical DHS, the heat exchange station (HES) is a key component which can help adjust the heat distribution among heat loads. In this paper, a joint hourly commitment of generation units and HESs is proposed. The DHS model is presented in which thermal storage and inertia of pipelines and heat loads are characterized. In addition, an approximation is applied to the HES model, making the overall joint commitment problem tractable. Numerical simulations are carried out, which demonstrate that the proposed joint commitment solution can introduce additional benefits in reducing wind power curtailment and system operation cost.

District heating systems load forecasting: a deep neural networks model based on similar day approach

ABSTRACT Accurate heat load forecasting is an important issue to ensure the reliable and efficient operation of a district heating system. In this paper, a hybrid model that combines similar day (SD) selection and Deep Neural Networks (DNNs) to construct SD-DNNs model for short-term load forecasting (STLF) is presented. A new Euclidean Norm (EN) weighted by eXtreme Gradient Boosting (XGBoost) is used to evaluate the similarity between the forecasting day and historical days. In this EN, the outdoor temperature, wind force and day-ahead load are simultaneously considered. And eight features are chosen as inputs of the DNNs to predict the heat load. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) are used as accuracy evaluation indexes. The experimental results demonstrate that the SD-DNNs model can accurately forecast the heat load.

A model-based dynamic optimization strategy for control of indoor air pollutants

The American Society for Heating, Refrigeration, and Air Conditioning Engineers (ASHARE) prescribes fixed ventilation rates for the operation of heating, ventilation, and air conditioning (HVAC) systems, which is sub-optimal in terms of controlling indoor air pollutants and energy consumption. This paper relies on a physics-based model of a residential house to predict the concentrations of indoor pollutants such as ozone, formaldehyde (HCHO), and particulate matter (PM) as function of time, outdoor conditions, and indoor emissions. The model captures real-world scenarios such as pollutants entering the house from ambient air through an air handling unit (AHU) and pollutants emitted in the house. A dynamic optimization problem is then formulated and solved to calculate the optimal ventilation rate at each time instant, that minimizes the total energy consumption, while maintaining the indoor pollutant concentrations below or as close as possible to their respective acceptable thresholds. For the considered example day, operation under optimized conditions reduces the peak pollutant concentration by 31%, time of exposure to undesirable concentrations by 48%, and energy consumption of the AHU by 17.7%.

Thermal load prediction in district heating systems

Optimal operation of district heating (DH) systems usually relies on the forecast of thermal demand profiles of the connected buildings. Depending on the purpose of the analysis, thermal request can be required at various levels, from building level to thermal plant level. In the case of demand response for example, thermal request is necessary at a building level to evaluate its applicability and at a plant level to determine the effects. Thermal request profiles are quite different, depending on the observation point. Total requests are not just the summation of the downstream requests, mainly because of the thermal transients. The heat losses also contributes to modify the curves, although generally in a smaller way. In this work, a multi-level thermal request prediction is proposed. This approach has the aim of evaluating the thermal request in the various sections of DH network with reduced computational resources. This includes a compact model for the prediction of building demand and a network model in order to compose together the requests at the various levels. The application to a portion of the Turin district heating network is proposed. This shows that the network dynamics significantly affects the evolution, especially at peak load.

Integrated Heat and Electricity Coordinated Dispatch Considering Multiple Time-Scale Flexibility of CHP Based on Thermal Energy Storage of DHS

With the large-scale installation of combined heat and power (CHP) units and wind farms in China, the amount wind curtailment and system frequency stability problems have arisen. In this paper, from the perspective of coordinated operation of electric heating system, the multiple time-scale operation mode of CHP unit is studied, and the flexibility of CHP unit is released, thus alleviating the pressure of power system dispatching and operation control. Based on the analysis of thermal energy storage of district heating system (DHS), coordination model of heating network system and CHP unit are proposed, which effectively unlock the multi-time scale flexibility of the CHP unit (MTF-CHP). Based on the framework of the combined heat and power coordinated dispatch, an adjustable robust heat dispatch model in DHS and a power system scheduling model based on the allowable heat range of CHP (AHR-CHP) are proposed to realize the coordinated configuration of CHP unit scheduling flexibility and regulating flexibility in power system output and reserve.

Economic model predictive control for demand flexibility of a residential building

Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy. In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from −0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility.

Experimental and numerical analysis of seasonal solar‐energy storage in buildings

This paper presents seasonal-energy storage of solar energy for the heating of buildings. We distinguish several types of seasonal storage, such as latent, sensible, and chemical storage, among which the thermochemical storage is used and analysed in this research. In the first part, a laboratory heat-storage tank, which was made in the laboratory for heating, sanitary, and solar technology and air conditioning from the Faculty of Mechanical Engineering, University of Ljubljana, Slovenia, was presented. The experimental model was tested for charging and discharging mode. Two types of numerical models for sorption thermal-energy storage exist, which are microscale and macroscale (integral). For microscale analysis, the analysis system (ANSYS) model can be used to simulate the behaviour in the adsorption reactor. On macroscale or integral scale, TRaNsient SYStem (TRNSYS) model was used to perform the operation of the storages on the yearly basis. In the second part the simulation of the underfloor heating system operation with a built-in storage tank was carried out for two locations, Ljubljana and Portorož. Furthermore, the comparison between a thermochemical and sensible-heat storage was performed with TRNSYS and Excel software. In this comparison, the focus was on the surface parameters of the SCs and volume of the thermal-storage tank for the coverage of the energy demand for selected building. With this analysis, we would like to show the advantage of the thermochemical storage system, to provide greater coverage of the energy demand for the operation of the building, compared with the seasonal sensible-heat storage (SSHS). Such a heat-storage technology could, in the future, be a key contributor to the more environmentally friendly and more sustainable way of delivering energy needs for buildings.

Optimization of energy expenses at convective and conductive heating of the liquid in cylindrical capacity

The mathematical model (MM) is developed for the description of evaporation process of model liquid with a surface of the cylindrical capacity (CC) for application of power expenses optimization at convective and conductive influence. Also processes of the changes of liquid and steam-gas mixture (SGM) temperature were considered in the MM. The optimization of power expenses was based on the Pontryagin maximum principle with application to process of liquid evaporation. For the modeling of heat and mass transfer processes with evaporation in cylindrical capacity at the combined influence, the system of the ordinary differential equations was used. At some admission, the MM allows to perform optimization of the cyclogram of a convective expulsion (and heating systems operation and to define a tendency of power expenses reduction at consecutive inclusions and switching off the SGM expulsion and heating of walls of cylindrical capacity with a liquid film on its walls. Power expenses of conductive heating of liquid and a expulsion of SGM from cylindrical capacity are accepted for an optimality criterion. Time intervals of heating the walls with liquid and expulsions of SGM providing minimization of power expenses for evaporation of the set mass of liquid are defined with the help of numerical modeling. The result of calculations without optimization show the increased power expenses are carried out.

GMM clustering for heating load patterns in-depth identification and prediction model accuracy improvement of district heating system

The identification of heating load patterns, also known as load profiles, is of vital importance to effective management and operation of district heating system (DHS). Clustering algorithms have been successfully applied in identifying heating load patterns. In this paper, we propose that the heating load patterns should be analyzed more specifically, and Gaussian Mixture Model (GMM) clustering is selected to extract sub-patterns. The novelty of this paper is that a new GMM clustering is applied to identify temperature related sub-pattern and people behavior related sub-pattern, and the clustering result is further utilized to improve the accuracy of prediction models. An energy station in Tianjin is used as case studies and four typical operation patterns are found with their characteristic of occur time and energy signature, which are defined as working pattern, on-duty pattern, daytime–nighttime pattern and nighttime–daytime pattern. The results reveal that this proposed method can make an in-depth identification of heating load patterns and also proves that the prediction accuracies of regression and artificial intelligence model are significantly improved by utilizing the GMM clustering results.

Numerical analysis and performance assessment of the Thermal Energy Storage unit aimed to be utilized in Smart Electric Thermal Storage (SETS)

Used on a wide scale Electric Thermal Storage (ETS) is one of load balancing method for the electrical power system. ETS is the technology of converting off-peak electricity into heat and using it in household heating 24 h a day. Since the thermal energy is stored in the bed during off-peak hours, and while discharged during peak hours, it results in significant savings in the household heating cost. In the study, the principles of ETS central heating system operation are presented. The proposed ETS central heating system is suitable for Smart Electric Thermal Storage (SETS) when connected with an appropriate charging control system. As the central part of the study, a numerical model of the ETS discharge is presented. The proposed two–dimensional model takes into account the transient temperature distribution within the considered geometry. The governing system of differential equations along with boundary and initial conditions are solved using a Finite Volume Method. The accuracy of the author's code is assessed along with the analytical solution, as well as Ansys Transient Thermal. Furthermore, numerical results are compared with experimental data of the ETS central heating system operation. After the numerical model validation, calculations of the ETS unit performance are carried out including flow distribution, heat transfer coefficients, and heat flow within the system. A preliminary analysis of the operating parameters, the initial air temperature and the air velocity, as well as construction of the ETS unit, on the system performance is also presented.

Experimental Study on Dynamic Heat Release Performance of Water Tank with Phase Change Materials

Solar heating system has raised much attention due to the global energy shortage and environment pollution. The dynamic heat release performance of water tank (WT) with phase change materials (PCM) plays an important role in the operation of solar heating system, while seldom available literatures focused on this research topic. In this paper, an experimental system for heat release of the WT-PCM was established. The temperature variations of the water and the PCM during heat release of the WT-PCM and the traditional water tank were measured respectively, and their dynamic heat release performance were also analyzed based on the measured data. The results are as follows: the heat release of the WT-PCM can be divided into three phases: sensible heat before phase change, latent heat during phase change and sensible heat after phase change, and the water temperature will be temporarily heated after the sudden drop; the water supply temperature decline rate of both tanks will increase dramatically at the initial stage of heat release, when the PCM temperature is reduced to 50℃, the water supply decline rate of the WT-PCM is less than that of the traditional water tank, while when PCM is completely solidified, the water supply temperature decline rate of the WT-PCM is more than that of the traditional water tank. The heat release performance of the WT-PCM is much better than that of the traditional water tank. The research results can provide useful reference for engineering application of the WT-PCM.

Efficiency increasing by development of district heating systems operational modes using graphical analysis

The tasks of analyzing, developing and justifying of the district heating systems (DHS) operation modes arise at different stages of their life cycle. The article provides a brief description of the tasks, steps and methods for calculating and analyzing DHS modes, which are implementing as subtasks of the "ANGARA-HN" software. Special attention is paying to the automation of analysis processes. The developed computational and graphical software tools for parameters analyzing of the DHS operating modes consists of the means: analyzing of the initial information, calculating the values of the mode parameters and analyzing results of the calculation. Various tools allow you to visualize the distribution of selected parameters values on the graphical city plan, highlight bottlenecks, building pressure distribution graphs. The developed software designed to automate the workplaces of regimes group engineers at operational enterprises or for designer workers, when developing and justifying development schemes for urban DHS. Tools for analysis and graphical visualization significantly increase the efficiency of work by reducing the time of analysis, development and justification of operating conditions, as well as improve the quality of the results.

Влияние погодных факторов на работу радиационной системы обогрева

Влияние погодных факторов на работу радиационной системы обогрева