Heating Grid Research Articles

Techno-Economic Feasibility Study of Solar Powered Rural Community Heat Grid System in Langtang

Techno-Economic Feasibility Study of Solar Powered Rural Community Heat Grid System in Langtang

Equilibrium Interaction Strategies for Integrated Energy System Incorporating Demand-Side Management Based on Stackelberg Game Approach

This paper analyzes the balanced interaction strategy of an integrated energy system (IES) operator and an industrial user in the operation process of the IES under the demand-side management (DSM) based on game theory. Firstly, we establish an electric–thermal IES, which includes a power grid, a heat grid and a natural gas grid. Secondly, a two-stage Stackelberg dynamic game model is proposed to describe the game behavior of IES operators and industrial users in the process of participating in DSM. The interactions between the IES operator (leader) and the user (follower) are formulated into a one-leader–one-follower Stackelberg game, where optimization problems are formed for each player to help select the optimal strategy. A pricing function is adopted for regulating time-of-use (TOU), which acts as a coordinator, inducing users to join the game. Then, for the complex two-stage dynamic game model established, the lower user-side constraint optimization problem is replaced by its KKT condition, so that the two-stage hierarchical optimization problem is transformed into a single-stage mixed-integer nonlinear optimization model, and the branch-and-bound method is introduced to solve it. Finally, the equilibrium strategies and income values of both sides of the game are obtained through a case simulation, and the dynamic equilibrium strategy curves under different capacity configurations are obtained through the sensitivity analysis of key parameters. The equilibrium income of the IES is USD 93.859, while the equilibrium income of industrial users in the park is USD 92.720. The simulation results show that the proposed method and model are effective.

Reversible SOCs als Bindeglied zwischen Strom-, Wärme- und Gasnetz und Plus Energie-Quartieren

ZusammenfassungZur Reduktion der CO2-Emissionen im Gebäudeenergiesektor sind alternative Energieerzeugungs- und Energiespeicherkonzepte notwendig. Technologien, die quartiersbezogen zentral sommerliche Überschussenergie aus Erneuerbaren Energien speichern und in weniger ertragreichen Monaten wieder in Strom und Wärme zurückwandeln, sind als Eigenschaften beispielsweise den Elektrolyse- bzw. Brennstoff-Zellensystemen zuzuordnen. Im Projekt Cell4Life wird ein solches System, bestehend aus einer reversibel arbeitenden Festoxidzelle (rSOC) theoretisch und im Labormaßstab im Kontext eines Plus Energie-Quartiers (PEQ) auf dessen funktionale Integrität hin untersucht. Ein Projektziel ist dabei die Optimierung der dynamischen Betriebsweise, für die es zum einen statischer Optimierungsversuche am Prüfstand und zum anderen Gesamtsystemsimulationen bedarf. In dieser Veröffentlichung wird vorgestellt, welche Betriebsweisen statisch optimiert und welche Systemkonzepte innerhalb einer Simulation untersucht werden. Für ein städtisches PEQ hat sich ein rSOC-System, das die Dampfelektrolyse im SOEC-Modus nutzt und im SOFC-Modus mit Wasserstoff und Methan betrieben wird, als am sinnvollsten erwiesen. Darüber hinaus werden reale Betriebsweisen aufbauend auf Wasserstoffbeimischungen in das örtliche Gasnetz in experimentellen Versuchen betrachtet. Basierend auf theoretischen Bedarfsdaten aus dem „PEQ21“ in Wien werden drei Optimierungsszenarien definiert, die neben dem Langzeit- auch den Kurzzeitbetrieb der rSOC-Anlage betrachten.

A heat grid-driven method for generation of satellite observation tasks

Managing numerous observation requirements on a large-scale satellite constellation is arduous for satellite control departments. Concurrently, achieving fast and effective task scheduling necessitates autonomous observation task generation. To address this issue, we proposed a novel method combined with Observation Requirements Heat Grid Model (ORHGM) and a Grid Aggregation Algorithm Based on Field of View Exploration (GAAFVE). Firstly, we define a uniform description of the observation requirements for various target types by devising a heat grid model predicated on geographic grid segmentation. Experimental outcomes reveal that our uniform model saves up to 31.93% of the observation tasks for fulfilling identical requirements by independent task generation for various target types. Secondly, based on this model, an autonomous task generation is completed by GAAFVE. Under the constraints regarding satellite imaging width, our algorithm facilitates heat grid search for non-connected regions and demonstrates superiority in computing time compared to other clustering-based task algorithms. The proposed method can allow managing large-scale observation requirements to be completed relatively quickly for satellite control departments. This method can also be extended to task generation in other multi-agent systems beyond satellites, such as the automatic generation of multi-UAV disaster reconnaissance tasks.

The long term price elastic demand of hydrogen – A multi-model analysis for Germany

Hydrogen and its derivatives are important components to achieve climate policy goals, especially in terms of greenhouse gas neutrality. There is an ongoing controversial debate about the applications in which hydrogen and its derivatives should be used and to what extent. Typically, the estimation of hydrogen demand relies on scenario-based analyses with varying underlying assumptions and targets. This study establishes a new framework consisting of existing energy system simulation and optimisation models in order to assess the long-term price-elastic demand of hydrogen. The aim of this work is to shift towards an analysis of the hydrogen demand that is primarily driven by its price. This is done for the case of Germany because of the expected high hydrogen demand for the years 2025–2045. 15 wholesale price pathways were established, with final prices in 2045 between 56 €/MWh and 182 €/MWh. The results suggest that – if climate targets are to be achieved - even with high hydrogen prices (252 €/MWh in 2030 and 182 €/MWh in 2045) a significant hydrogen demand in the industry sector and the energy conversion sector is expected to emerge (318 TWh). Furthermore, the energy conversion sector has a large share of price sensitive hydrogen demand and therefore its demand strongly increases with lower prices. The road transportation sector will only play a small role in terms of hydrogen demand, if prices are low. In the decentralised heating for buildings no relevant demand will be seen over the considered price ranges, whereas the centralised supply of heat via heat grids increases as prices fall.

Peak shaving at system level with a large district heating substation using deep learning forecasting models

The decarbonisation of urban district heating (DH) systems requires increased heating grid flexibility. Therefore, this article examines the optimised operation of a tank thermal energy storage (TTES) on the secondary side of a new DH substation for an industrial site in a German city, in order to shave the peaks of the whole DH system and thus reduce the need for heat-only boilers (HOB). The accuracy of heat load and return temperature forecasts for both the industrial consumer and the DH grid is critical to the performance of the optimisation-based operating strategy of the TTES. Therefore, long short-term memory neural networks are used in combination with continuous model updates through incremental learning to create two forecasting scenarios, one using only preceding data for the forecasts and the other including future weather data. The results show that high forecasting accuracy is most relevant for reducing the annual maximum peak, with a reduction of 2.8% in the preceding data scenario, 4% with future weather data and 7% in a benchmark with perfect forecasts. The economic viability of the storage through HOB heat savings is primarily affected by lower forecasting accuracy when the additional cost of HOB heat is less than 60 €/MWh.

Assessment of Grid and System Supportability Based on Spatio-Temporal Conditions—Novel Key Performance Indicators for Energy System Evaluation

The energy transition introduces new technical standards, laws and regulations regarding the stability and reliability of energy grids and systems. Due to the non-existence of a measuring standard, key performance indicators (KPIs) were developed to enable the measurement and comparison of individual energy grid (namely electricity, heat and gas grid) and system supportabilities while also promoting well-founded decision-making and optimization efforts. Inconsistencies in definitions concerning fundamental energy terms and the correlations between them inhibit the effective usage of the KPIs. Therefore, the overarching issue of the security of energy supply and its related subjects were also approached. The primary subject of this paper is the development of two new KPIs to measure and compare the energy grid and system supportability. These KPIs are based on spatio-temporal conditions in their respective grids. The usage and benefits of the developed KPIs are exemplarily highlighted by analyzing the impact of a scenario with the integration of a large-scale heat pump into the electricity and heat grid. The energy grid supportability is determined for each grid, whereas the energy system supportability takes the interactions of the electricity and heat grid into account. The developed KPIs are intended to enable stakeholders to identify areas with optimization potential in energy grids and systems. Moreover, the KPIs can be used to create a standardized evaluation method for regulatory requirements.

Probabilistic state estimation in district heating grids using deep neural network

Probabilistic state estimation is critical for operating and controlling district heating grids efficiently. However, computational bottlenecks of traditional solvers limit the feasibility of uncertainty-aware Bayesian estimation. This paper proposes using deep neural networks (DNNs) to enable fast and accurate posterior estimation. Fully-connected neural networks (FCNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) are evaluated as candidate approximators of the physical model. Markov chain Monte Carlo sampling in the heat exchange space is leveraged to generate posterior samples. Experiments on a benchmark heating grid demonstrate FCNNs can efficiently learn the mapping from heat exchanges to network states. A FCNN trained on 20 training epochs after hyperparameter optimization provides the best approximation accuracy and uncertainty estimates, outperforming prior methods based on Deep Neural Networks. The results highlight the potential of data-driven deep learning models for probabilistic state estimation. The proposed framework could enable real-time uncertainty-aware control and decision-making for future intelligent district heating grids.

Long-term heat storage opportunities of renewable energy for district heating networks

This study compares different thermal heat storage solutions existing in the market, fuelled with energy from different renewable energy sources with a focus on integrating thermal heat storage into the district heating grid. The paper is based on a case in the municipality of Silkeborg, Denmark, which has the largest solar thermal panel plant in Northern Europe. A theoretical approach was used to compare with assumed excess power from wind and solar in the DK1 area, with Silkeborg's allocated excess power at 0.01%. This yielded overall efficiencies between η = 0.739–0.765 and η = 0.864–0.895 for the Silkeborg solar thermal plant. Four different thermal heat storage solutions were compared: tank thermal energy storage, pit energy storage, aquifer thermal energy storage and borehole energy storage (BTES). The analysis showed that, of the four solutions compared, BTES was the best for storing thermal energy for a longer period of time, with the lowest heat loss rate of 0.6% and the highest efficiency of up to 89.5%. However, some complications make it difficult to establish a BTES storage solution, since it is very much dependent on earth conditions and initial capital.

Flexible energy management of storage-based renewable energy hubs in the electricity and heating networks according to point estimate method

Hubs contain sources and storages that can transfer and store energy. It is predicted that the energy management of hubs enhances the network's economic and technical status. Therefore, the paper presents flexible energy management of energy hubs linked with electrical and thermal grids. In the energy hub, wind and photovoltaic systems generate electricity, and bio-waste units are utilized to concurrently produce electricity and heat power. Compressed air and thermal energy storage control the flexibility of the hubs. The objective function minimizes the expected cost of energy injected by the upstream grid. Constraints are network optimal power flow equations, operation model, and flexibility limit of the hub. The design has uncertain parameters caused by the load, energy price, and unpredictable renewable power. The point estimation technique helps model these uncertain variables to overcome the high volume of the problem and accurately evaluate the flexibility. Contributions include evaluating the performance of compressed-air storage and bio-waste units equipped with combined heat and power technology in the energy hub, considering the flexibility limit of hubs in both electrical and thermal sectors due to uncertain renewable power, using the point estimation method for modeling the uncertainties of hubs’ energy management to reduce computation time and provide accurate calculation of flexibility. The scheme is tested using a standard case, where the results show the ability of energy hubs to enhance and promote the economic and operation status of electricity and heating grids by about 36.5% and 36%− 43%, respectively, in comparison with the power flow method. Storages can extract 100% flexibility conditions for hubs. The scheme has succeeded in providing high flexibility conditions by proper energy management of compressed-air and thermal storage. Furthermore, the presence of the storage equipment beside wind, solar, and bio-waste renewable sources in the form of a hub, and energy management of the hubs have led to a more suitable economical and operational state of electrical and thermal networks compared to power flow studies.

Integration of Power to X Economy in Existing Energy System – Case Finland

Hydrogen or Power-to-X economy is seen as the most promising way to carry out the energy transition from fossil to renewable carbon free energy system. Electrification of the society and green hydrogen-based energy system require huge capacity increase in renewable energy production and energy transmission. This paper introduces a simulation study about the integration of green hydrogen-based energy system to existing power system structure in Finland. Regional energy balances are studied with different options of locations of renewable energy and hydrogen production plants and needs for energy transportation either as electricity or hydrogen. The model includes sectoral couplings between electricity, hydrogen and heating grids and finds synergies of flexibility between different energy grids.

District heating potential in the EU-27: Evaluating the impacts of heat demand reduction and market share growth

This paper presents a novel approach to modeling the gradual reduction in heat demand and the evolving expansion of district heating (DH) grids for assessing the DH potential in EU member states (MS). It introduces new methodological elements for modeling the impact of connection rates below 100% on heat distribution costs in both dense and sparse areas. The projected heat demand in 2050 is derived from a decarbonization scenario published by the EU, which would lead to a reduction in demand from 3128 TWh in 2020 to 1709 TWh by 2050. The proposed approach yields information on economic DH areas, DH potential, and average heat distribution costs. The results confirm the need to expand DH grids to maintain supply levels in view of decreasing heat demand. The proportion of DH potential from the total demand in the EU-27 rises from 15% in 2020 to 31% in 2050. The analysis of DH areas shows that 39% of the DH potential is in areas with heat distribution costs above 35 EUR/MWh, but most MS have average heat distribution costs between 28 and 32 EUR/MWh. The study reveals that over 40% of the EU's heat demand is in regions with high potential for implementing DH.

Estimating residential space heating and domestic hot water from truncated smart heat data

The EU aims to digitize the building stock across all member states to better understand energy use and achieve energy efficiency goals to address climate change. Smart heat meters are currently used for billing purposes in district heating (DH) grids. Their data is recorded as integer kWh values, which restricts usability for the modeling and analysis of DH networks. Previous research devised a methodology to estimate space heating (SH) and domestic hot water (DHW) energy from total heating data, but the data truncation process reduced accuracy. This study integrates the SPMS (Smooth–Pointwise Move–Scale) algorithm, which estimates decimal values from DH truncated measurements, to improve the accuracy of the DHW and SH disaggregation methods. The study applies these two methodologies to a dataset of 28 Danish apartments and compares the results against full-resolution and truncated data to evaluate performance. Another dataset, named “optimal dataset” is also assessed to determine overall estimation accuracy. Results show that SPMS reduces the disaggregation methodology error of SH and DHW compared to the truncated data. The optimal dataset outperforms the current methodology, indicating a potential for improving and scaling the methodology for larger datasets.

Multi-objective optimization of a hybrid energy system integrated with solar-wind-PEMFC and energy storage

The move towards achieving carbon neutrality has sparked interest in combining multiple energy sources to promote renewable penetration. This paper presents a proposition for a hybrid energy system that integrates solar, wind, electrolyzer, hydrogen storage, Proton Exchange Membrane Fuel Cell (PEMFC) and thermal storage to meet the electrical and heating demands of a student dormitory in Shanghai. The proposed system is optimized to simultaneously account for multiple objectives, including economy, environmental benefits, and grid interaction, measured by Equivalent Annual Cost (EAC) for the life cycle of 20 years, Primary Energy Saving Ratio (PESR) of the heating system and Grid Interaction Level (GIL) of the electrical system. The effectiveness of the optimization results from NSGA-II is verified and compared with MOPSO to determine the optimal installation configuration and operation strategies. The results highlight the significance of energy storage in enabling greater renewable integration and the potential of hydrogen to play a vital role in the transition to a low-carbon economy. The optimal design of the proposed hybrid system can meet the power and heat demand of a student dormitory with a floor area of 2679m2. The Pareto-optimal solutions of PESR and GIL for NSGA-II fall within the range of (89 %, 104 %) and (70 %, 88 %), respectively. A significant number of Pareto-optimal solutions cluster around an EAC of approximately 160 k RMB. The optimization by MOPSO exhibited the similar results. Additionally, the sensitivity analysis provides insights into the sensitivity of objectives to changes in optimal design parameters, facilitating the design and optimization of similar hybrid energy systems integrated with a closed loop for hydrogen production and utilization in the future.

Optimal Operation Strategy of Combined Heat and Power System Considering Demand Response and Household Thermal Inertia

With the development of energy Internet and renewable energy, the combined heat and power micro-energy grid (CHPMEG) has become an important carrier for the use of clean energy. In order to improve the living comfort of households and the utilization efficiency of wind power in winter, this paper proposes a coordinated optimal operation strategy of CHPMEG considering the load demand side response and the thermal inertia of the user side. First, the load demand side response is introduced into the optimal operation of CHPMEG, and a load demand response model is proposed based on reward mechanism. Then, the household thermal inertia is taken into account in the system operation, and a heat load model considering thermal inertia is established to describe the corresponding characteristics more exactly. On this basis, an optimization model is further developed and solved to minimize the operation cost of the system. Finally, the case simulations demonstrate the effectiveness of the optimal operation strategy of CHPMEG proposed in this paper.

Investigation of the aerodynamic and thermal performance of a Decentralised Air Handling Unit (DAHU) heat recovery using balls charge

Decentralised ventilation solutions are applicable for both new and renovated buildings. This has a positive economic impact and helps ensure that there is sufficient fresh air even in limited indoor spaces. Unfortunately, there are no design guidelines for selecting wall-integrated decentralised air handling units (DAHUs) according to temperature, energy and aerodynamic requirements. The objective of this paper is to study the thermal and aerodynamic performance of an innovative DAHU using a regenerative thermal load. The analysis solves the main design issues and dimensions of the main and additional DAHU components: regenerative heat exchanger, fans, outdoor grille, indoor diffuser, grids and filters. Special attention is paid to the analysis of the thermal charge composed of balls using analytical and numerical methods. The pressure loss, the average heat transfer coefficient, the temperature of the airflow and the thermal charge of the heat exchanger are analysed. The numerical analysis is conducted using COMSOL Multiphysics to evaluate transient processes and the importance of the charge material. A comparison of analytical and numerical results shows that the heat transfer process of the regenerative heat exchanger charge can be estimated with sufficient accuracy in both cases, while differences in aerodynamic results are observed. In the case of the designed DAHU, the diameter of the elements is recommended to be 15 mm, taking into account the requirements for the pressure loss and, at the same time, to achieve a sufficiently efficient heat transfer process, the unit is designed with 60 m3/h fan that produces a pressure of 130 Pa. In general, this study contributes to the development of decentralised ventilation systems and the design of new ones.

Optimal infrastructure in microgrids with diverse uncertainties based on demand response, renewable energy sources and two-stage parallel optimization algorithm

Future electric grids will face severe uncertainties with the unprecedented penetration of renewable energy sources, which may cause problems in grid exploitation. It is essential to evaluate the uncertainty of system performance in this grid; therefore, traditional exploitation methods may not be suitable for distributions grid such as multi-carrier microgrids when considering the electricity and gas grid constraints. This paper proposes an effective method for simultaneously optimizing different types of energy infrastructure in an environment with various uncertainties while considering grid constraints. To address the multi-energy synergy supply optimization problem in the micro energy grid, a coordinated two-stage programming approach is also suggested. A day-ahead and real-time phases are separated in the scheduling cycle to overcome the effects of the unpredictability of wind energy and solar power. The findings of the day-ahead forecast are then taken into account as uncertain variables for the higher-layer model. The demand response programming model and the energy storage revision model are lower-layer models considering the actual value as the realization of uncertain variables. The competitive swarm optimization algorithm is used to solve the problem mentioned above The Cumulative search optimization (CSO) uses global and local systems in particle swarm optimization and adopts a novel learning mechanism for creating competition between particle pairs. Comprehensive numeric examinations show that convergence speed and precision are critical, especially in solving large-scale problems. Therefore, we use chaos theory to improve its performance. Finally, the proposed method is tested and discussed on a system. The results showed that: (1) power-to-gas could convert extra electricity into natural gas; (2) the proposed two-stage optimization model and algorithm achieved different energy optimizations; (3) price-based demand response (DR) can level the energy load curve and maximize multi energy grid (MEG) revenue by using complementary specifications of energy price; and (4) the micro energy grid could communicate with the high-degree energy grid in order to gain more economic benefits. Accordingly, the incentive-based demand response (IBDR) output is reduced. The electricity and heating provided by convention gas turbine (CGT) is increased, which is fed to upper power grid (UPG) and upper heating grid (UHG). Since MEG’s purchasing power from UPG during the peak period generates more total revenue than island operations. Based on the obtained results, it can be seen that the proposed algorithm was about 50% faster compared to other methods and its standard deviation was about 0.0013 with different implementations, which was much better compared to other models.

Replacing gas boilers with heat pumps is the fastest way to cut German gas consumption

The supply security of fossil gas has been disrupted by the Russo-Ukrainian War. Decisions to relocate the production and transport of gas have become so urgent that new long-term contracts are imminent that undermine the Paris Climate Agreement. Here, we simulate how quickly the addition of renewable electricity and the installation of heat pumps can substitute enough gas to reduce supply risk, while taking a decisive step towards meeting the Paris Agreement. Our bottom-up modelling, using Germany as an example, shows technical pathways on how installing heat pumps is one of the fastest ways to reduce gas consumption, in addition to reducing the load hours of gas-fired power plants. With targeted efforts, maximally 60% of gas from the Russian Federation can be substituted by 2025 with heat pumps and grid expansions, and enough electricity will remain available that the phase-out of coal and the entry into e-mobility will still be practicable.

Can grid-tied solar photovoltaics lead to residential heating electrification? A techno-economic case study in the midwestern U.S.

This study aims to quantify the techno-economic potential of using solar photovoltaics (PV) to support heat pumps (HP) towards the replacement of natural gas heating in a representative North American residence from a house owner’s point of view. For this purpose, simulations are performed on: (1) a residential natural gas-based heating system and grid electricity, (2) a residential natural gas-based heating system with PV to serve the electric load, (3) a residential HP system with grid electricity, and (4) a residential HP+PV system. Detailed descriptions are provided along with a comprehensive sensitivity analysis for identifying specific boundary conditions that enable lower total life cycle cost. The results show that under typical inflation conditions, the lifecycle cost of natural gas and reversable, air-source heat pumps are nearly identical, however the electricity rate structure makes PV costlier. With higher rates of inflation or lower PV capital costs, PV becomes a hedge against rising prices and encourages the adoption of HPs by also locking in both electricity and heating cost growth. The real internal rate of return for such prosumer technologies is 20x greater than a long-term certificate of deposit, which demonstrates the additional value PV and HP technologies offer prosumers over comparably secure investment vehicles while making substantive reductions in carbon emissions. Using the large volume of results generated, impacts on energy policy are discussed, including rebates, net-metering, and utility business models.

Deep learning-enabled MCMC for probabilistic state estimation in district heating grids

Flexible district heating grids form an important part of future, low-carbon energy systems. We examine probabilistic state estimation in such grids, i.e., we aim to estimate the posterior probability distribution over all grid state variables such as pressures, temperatures, and mass flows conditional on measurements of a subset of these states. Since the posterior state distribution does not belong to a standard class of probability distributions, we use Markov Chain Monte Carlo (MCMC) sampling in the space of network heat exchanges and evaluate the samples in the grid state space to estimate the posterior. Converting the heat exchange samples into grid states by solving the non-linear grid equations makes this approach computationally burdensome. However, we propose to speed it up by employing a deep neural network that is trained to approximate the solution of the exact but slow non-linear solver. This novel approach is shown to deliver highly accurate posterior distributions both for classic tree-shaped as well as meshed heating grids, at significantly reduced computational costs that are acceptable for online control. Our state estimation approach thus enables tightening the safety margins for temperature and pressure control and thereby a more efficient grid operation.