Optimization Of District Heating System Research Articles

Multi-objective optimization of district heating systems with turbine-driving fans and pumps considering economic, exergic, and environmental aspects

District heating is an effective way to improve energy efficiency and decrease emissions. In studies, district heating multi-objective optimization only focuses on the fourth-generation system's planning but ignores the operation optimization of the third-generation. The exergy-related multi-objective optimization is usually combined with economy, while the environmental aspects, especially carbon emissions trading is rarely introduced. Auxiliaries consume lots of energy but are seldom considered both in modeling and optimization. Thus, this paper develops a multi-objective optimization model integrating exergy, environment, and economy with careful consideration of auxiliary equipment modeling and carbon emission trading. The model is validated by a special third-generation district heating system with turbine-driving fans and pumps located in Shenyang, Northeast China. The non-dominated sorting genetic algorithm is used to solve the model. No power generation, self-use priority, and on-grid priority are optimized and the exergy efficiency is improved by about 3–4%. Variable speed of auxiliary equipment can reduce power consumption in the cubic form of the variable speed ratio's reciprocal, and four times the carbon trading price of the current can lead to a one-third proportion in expenditure. The outcomes can be referred to make energy policy and operation decisions related to district heating.

Optimization of district heating system considering carbon taxes and subsidies based on energy policy stage goals

Climate change is one of the main issues that humanity will face in the upcoming decades. Low-carbon district heating is an important alternative for decarbonizing the heating sector against unsustainable higher carbon emission heating methods. Most available studies have concentrated on fixed or predetermined policies instead of attempting a numerical optimization of carbon taxes and subsidies in district heating. This study proposes the optimization of a district heating system in Northeast China considering carbon taxes and subsidies based on energy policy stage goals. Research shows that carbon taxes and subsidies have a certain impact on the change in district heating sources. With the increase in carbon taxes, heating from non-fossil energy sources is increasing, an area that can also be promoted with subsidies for non-fossil energy. Considering the total cost of heating enterprises, it is appropriate to set the carbon tax for district heating at $86. Similarly, considering government expenditure, it is suggested that construction subsidies be set at 10.5$/m2, 3.5$/m2, and36.5 $/m2 for air source heat pumps, biomass boilers, and ground source heat pumps. In addition, the obtained results can be used as guidelines for the heating industry to design more efficient district heating and focus research and development efforts on the most promising non-fossil energy heating technologies.

Design optimization of district heating systems: A review

District heating systems offer the possibility of lowering emissions and support the goal of reaching a carbon-neutral energy system by integrating renewable heat sources. Therefore, this work provided a systematic literature review to identify potential research gaps and show the literature distribution over the relevant topics. The focus is on the design optimization with (non-)linear programming of district heating systems in the context of decarbonization. Furthermore, crucial energy balance equations were extracted from the literature for a potential optimization problem. The systematic literature review limited its search to two databases, 10 years timespan, a quality measure, and uses keywords regarding topic and method. Categories were derived based on the subject and literature to cluster the found publications and identify potential research gaps. The results showed potential research gaps in the depiction of different stakeholder decisions, reduction of computational efforts, and their resulting uncertainties. Additionally, they identified gaps in the integration of low-grade heat sources, thermal storage facilities, and energy converters, especially geothermal energy, large-scale heat pumps, and seasonal storages.

Enhancement of a District Heating Substation as Part of a Low-Investment Optimization Strategy for District Heating Systems

In an ongoing project, low-investment measures for the optimization of district heating systems are analyzed. The optimization strategies are collected in a catalog, which is the core of a guideline. The application of this guideline is demonstrated using two concrete district heating networks as examples. In this study, the improvement of an analog controlled district heating substation by an electronic controller is investigated. High supply temperatures and heat losses are often a challenge in district heating networks. The district heating substations have a major influence on the network return temperatures. The comparison of the two substation setups with analog and electronic controllers is carried out by laboratory measurement. It can be shown that the return temperatures can be reduced by an average of 20 K in winter and transition, as well as 16 K in summer. The district heating network losses are calculated for one of both specific district heating networks. They are calculated from the ratio of network losses to generated energy. The generated energy is the sum of network losses and consumer demand. The thermal losses of the network can be reduced by 3%. The volume flow in the heating network can be reduced to a quarter. Therefore, the pumping energy requirement drops sharply since these changes cubically affect the volume flow.

Predictive Control of District Heating System Using Multi-Stage Nonlinear Approximation with Selective Memory

Innovative heating networks with a hybrid generation park can make an important contribution to the energy turnaround. By integrating heat from several heat generators and a high proportion of different renewable energies, they also have a high degree of flexibility. Optimizing the operation of such systems is a complex task due to the diversity of producers, the use of storage systems with stratified charging and continuous changes in system properties. Besides, it is necessary to consider conflicting economic and ecological targets. Operational optimization of district heating systems using nonlinear models is underrepresented in practice and science. Considering ecological and economic targets, the current work focuses on developing a procedure for an operational optimization, which ensures a continuous optimal operation of the heat and power generators of a local heating network. The approach presented uses machine learning methods, including Gaussian process regressions for a repeatedly updated multi-stage approximation of the nonlinear system behavior. For the formation of the approximation models, a selection algorithm is utilized to choose only essential and current process data. By using a global optimization algorithm, a multi-objective optimal setting of the controllable variables of the system can be found in feasible time. Implemented in the control system of a dynamic simulation, significant improvements of the target variables (operating costs, CO2 emissions) can be seen in comparison with a standard control system. The investigation of different scenarios illustrates the high relevance of the presented methodology.

Applying Deep Learning to the Heat Production Planning Problem in a District Heating System

District heating system is designed to minimize energy consumption and environmental pollution by employing centralized production facilities connected to demand regions. Traditionally, optimization based algorithms were applied to the heat production planning problem in the district heating systems. Optimization-based models provide near optimal solutions, while it takes a while to generate solutions due to the characteristics of the underlying solution mechanism. When prompt re-planning due to any parameter changes is necessary, the traditional approaches might be inefficient to generate modified solutions quickly. In this study, we developed a two-phase solution mechanism, where deep learning algorithm is applied to learn optimal production patterns from optimization module. In the first training phase, the optimization module generates optimal production plans for the input scenarios derived from operations history, which are provided to the deep learning module for training. In the second planning phase, the deep learning module with trained parameters predicts production plan for the test scenarios. The computational experiments show that after the training process is completed, it has the characteristic of quickly deriving results appropriate to the situation. By combining optimization and deep learning modules in a solution framework, it is expected that the proposed algorithm could be applied to online optimization of district heating systems.

Operational optimization of district heating system based on an integrated model in TRNSYS

The time-varying feature of heating loads of public buildings creates a considerable space for exploring control strategies of matching the balance between the supply and demand side of district heating system (DHS). However, the strong coupling relationship between hydraulic conditions of each building has brought great difficulties for the operation regulation of DHS. Therefore, an operation strategy based on real-time indoor temperatures was proposed and a new integrated model based on dynamic hydraulic conditions combining with real-time temperatures were developed. The integration model-based strategy selects the operation costs as the main objective function and then a set of optimum neighborhood points were acquired according to the optimum financial point. Finally, the optimum operation solution was determined with consideration of the thermal comfort factor. By this decision-making process, the approach is available to obtain the operation solution which can guarantee a more comfortable indoor environment from energy point of view with a slight increase of financial expenditure. A university campus in Harbin was chosen as the case study to investigate optimization strategy and to evaluate the optimization results. The results showed that, compared with the current experience-based operation procedure, operation costs of heating system can be maximumly cut down by 38.18% with satisfying the requirement of indoor thermal comfort.

Operational optimization of district heating systems with temperature limited sources

Future district heating systems (DHS) will be supplied by renewable sources, most of which are limited in temperature and flow rate. Therefore, operational optimization of DHS is required to maximize the use of renewable sources and minimize (fossil) peak loads. In this paper, we present a robust and fast model-predictive control approach to use the thermal mass of buildings as a daily storage without violating temperature constraints. The novelty of this paper includes two elements. First, the focus on an operational control strategy that explicitly accounts for temperature-limited renewable sources, like a geothermal source. Secondly, the optimization problem is formulated as a (nearly) convex optimization problem, which is required for adoption of model-predictive control in practice. The examples show that the peak heating demand can be reduced by 50%, if the thermal inertia of the buildings is used and the heating setpoints are adapted. Furthermore, the operational optimization finds the proper balance between benefits of pre-heating using renewable sources with limited capacity and costs of additional heat losses due to pre-heating.

Economical, environmental and exergetic multi-objective optimization of district heating systems on hourly level for a whole year

District heating systems are proven to be an effective way of increasing energy efficiency, reducing the environmental impact and achieving higher exergy efficiency than individual heating solutions. The leaders in district heating integration are Scandinavian countries with more than 50% of the covered total heating demand. Nevertheless, these systems haven’t reached their full potential in most European countries. The reason for this could be that energy planners often study only the economic feasibility of the system, thus neglecting other crucial aspects of the previously mentioned district heating. In research papers, district heating multi-objective optimization usually takes into account the minimization of the total discounted cost and the environmental impact. Most times, these two objectives are studied as a single objective optimization problem through the internalization of the cost related to carbon dioxide emissions. This paper presents the multi-objective optimization method which is capable of optimizing district heating technology supply capacities and their operation, including thermal storage, for a one-year time horizon in order to satisfy the optimization goals. The model was written in the open-source and free programming language called Julia, while linear programming solver named Clp was used to obtain the solution. The solver is part of Julia’s optimization package called JuMP. Three separate objective functions are included in the model: the minimization of the total discounted cost, the minimization of carbon dioxide emissions and the minimization of exergy destruction. Since these three goals are often in conflict, the final result of multi-objective optimization is the so-called Pareto surface which presents the compromise between all possible results. To deal with the multi-objective optimization problem, the weighted sum method in combination with the epsilon-constraint method was used. The most suitable result has been chosen using the knee point method which is a solution the closest to the Utopia solution where all three goals reach their optimal value.

Development of a Network-location-model for the Economic Optimization of Local Heating Systems in Urban Chile

Cogeneration of heat and electricity is an important pillar of energy and climate policy. To plan the production and distribution system of combined heat and power (CHP) systems for residential heating, suitable methods for decision support are needed. For a comprehensive feasibility analysis, the integration of the location and capacity planning of the power plants, the choice of customers, and the network planning of the heating network into one optimization model are necessary. Thus, we develop an optimization model for electricity generation and heat supply. This mixed integer linear program (MILP) is based on graph theory for network flow problems. We apply the network location model for the optimization of district heating systems in the City of Osorno in Chile, which exhibits the “checkerboard layout” typically found in many South American cities. The network location model can support the strategic planning of investments in renewable energy projects because it permits the analysis of changing energy prices, calculation of break-even prices for heat and electricity, and estimation of greenhouse gas emission savings.

Optimization of district heating system aided by geothermal heat pump: A novel multistage with multilevel ANN modelling

The aim of this study is to obtain the optimum design of geothermal heat pump aided district heating system (GHPDHS) by using a novel ANN model that composed of multistage with multilevel. For this purpose, the acquiring of the best design were performed by utilizing the back-propagation learning algorithm with three different variants which were Levenberg-Marquardt (LM), Pola-Ribiere Conjugate Gradient (CGP), and Scaled Conjugate Gradient (SCG). In this aim, the proposed ANN model was mainly formed from two stages. The first one has a single level whereas the second one composed of three levels in this new ANN model. According to results, the maximum rate of the error occurred in the Pump 2 as % 3.0092 and the minimum of that was obtained from COPsys with % 0.0018. The best R2 value of the third level of the second stage network structure was calculated as 1 for LM-20. As a consequent, this study showed that the multistage with multi-level ANN model could be easily applied to other energy systems in order to save more time and simplicity.

A method for the simulation and optimization of district heating systems with meshed networks

There are two main methods nowadays for modeling district heating systems, but a key disadvantage of both is that a real network containing loops cannot be described without artificial simplifications in order to eliminate those distinguishing features. However, loops are very common in mature networks that have developed a meshed structure, and make the distribution of mass and heat flows quite characteristic. For this reason, a new process integration method for modeling complex district heating systems containing loops is described in this paper. This method makes it possible to analyze how loops and bottlenecks affect the behavior of the network, as well as the distribution path of the thermal energy in it. The district heating system in the town of Kiruna (located in the north of Sweden) has a complex design with several loops and part of it is used in the paper as an example of application.

Multi-objectives, multi-period optimization of district energy systems: II—Daily thermal storage

District heating is an efficient way of providing heat to urban areas. The use of storage tanks integrated with district heating network would permit to increase the annual utilization of base load equipment, avoiding over estimation of the size of backup equipment, and balancing the energy demand fluctuation during day and night.In the present work a multi-objective optimization model for sizing and operation optimization of district heating systems with heat storage tanks is presented. The model includes process design and energy integration techniques for optimizing the temperature intervals, the volume and the operation strategy of thermal storage tanks.The proposed model is demonstrated by means of two test cases. Results show that the efficiency, environmental impacts and total costs of an urban system can be improved after integrating the thermal storage by 4.7%, 5% and 2% respectively.

Design method for the optimization of district heating systems using mixed integer linear programming

During winter, district heating systems may be used to heat dense built environment in large cities. After several years of use, performance of such a system would change because of either refurbishment or ageing. As a result, air temperatures in heated rooms would be different from the design (thermal comfort) temperatures. It is investigated how the system performance changes owing to ageing and refurbishment of the primary pipeline and envelope of heated buildings. To determine how to recover the design temperatures of air in heated rooms, mixed integer linear programming is used. The objective was to select an optimal scenario that would achieve the minimum difference between the corrected and design temperatures inside the investigated rooms. The scenario refers to installation of new (option) valves and water pumps that would require installing in the primary pipe network of the old district heating system examined to re-establish its optimum performance.

Optimization of district heating systems based on the demand forecast in the capital region

A district heating system (DHS) consists of energy suppliers and consumers, heat generation and storage facilities and power transmission lines in the region. DHS has taken charge of an increasingly important role as the energy cost increases recently. In this work, a model for operational optimization of the DHS in the metropolitan area is presented by incorporating forecast for demand from customers. In the model, production and demand of heat in the region of Suseo near Seoul, Korea, are taken into account as well as forecast for demand using the artificial neural network. The optimization problem is formulated as a mixed integer linear programming (MILP) problem where the objective is to minimize the overall operating cost of DHS. The solution gives the optimal amount of network transmission and supply cost. The optimization system coupled with forecast capability can be effectively used as design and longterm operation guidelines for regional energy policies.