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Decarbonisation of heat generation has become a priority for district heating network operators. In order to avoid the use of fossil-fired boilers, operators need to know peaks in heat demand in advance. Accurate thermal load forecasting is playing an increasingly important role in this respect. This paper presents the final results of the research project “deepDHC” (deep learning for district heating and cooling) funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK). The three-year project focused on systematically benchmarking thermal load forecasts for district heating networks, based on state-of-the-art machine learning methods. The analysis covers a variety of machine learning techniques, such as neural networks – including latest deep learning methods – (e.g. LSTM, TFT, ESN, RC), decision trees (random forests, adaptive boosting, XGB) and statistical methods (SARIMAX). In addition, the impact of combining methods by so-called “stacking” was investigated. Training and validation of the machine learning algorithms was based on historical operating data from the district heating network for the city of Ulm in Germany, in combination with historical weather data, and weather forecasts. Thermal load forecasts – typically for three days ahead – are presented and compared against one another. An automatic tuning routine was developed as part of the project, which enables regular re-training of the machine learning algorithms based on the latest operating data from the heating network. Furthermore, a web interface for real-time forecasting was developed and implemented at the power station.
The use of natural gas has continuously increased and reached 24.7% of the worldwide primary energy supply in 2020. The same trend applies to Liquefied Natural Gas (LNG), which contributed to 52% of overall natural gas trades in the same year. In this context, the recovery of the cold energy available at LNG receiving terminals during the process of regasification is of a critical importance.
This paper addresses the integration of the regasification process with an Organic Rankine Cycle (ORC) in order to exploit the available LNG cold energy, by condensing the organic fluid. In addition, a gas turbine exploits differences between regasification and distribution pressures. The analysis covers different organic fluids and two ORC heating source configurations: a) a low-temperature one, using seawater, and b) a high-temperature one, using exhaust gas. In addition, the integration of a natural gas-fired topping gas turbine, which uses the LNG cold energy by compressor inlet air cooling, was simulated. The performance of a medium size regasification terminal (50 kg/s) was evaluated as a function of both the regasification and the natural gas distribution pressures.
Dedicated models have been developed using Aspen Plus software to simulate the regasification process and the integrated topping cycles (Organic Rankine and Brayton), and their mutual energy integrations.
The analysis shows that ORC power outputs from 2 MW up to 4.5 MW in case a) and from 6 MW up to 9 MW in case b) can be reached. The topping gas turbine benefits from the inlet air cooling and can add a power output of 35 MW to 40 MW. R125 was the best working fluid for a low-temperature ORC, while R600a showed the best performance for a high-temperature application.
Für Fernwärmeversorger spielt die Lastprognose bei der Anlageneinsatzplanung eine zentrale Rolle. Benötigte Fernwärme oder auch -kälte lassen sich umso kostengünstiger, effizienter und emissionsärmer bereitstellen, je exakter die zu erwartende Last abgeschätzt werden kann. Ein neuartiges, an der Hochschule Kempten entwickeltes Verfahren namens »Deep DHC« kann die Genauigkeit dieser Lastprognosen deutlich erhöhen
High temperature fuel cells are considered a promising option for highly efficient distributed power generation. This paper presents a dynamic model of a solid oxide fuel cell (SOFC) system. Detailed models for all process components were developed and validated with experimental data, which is demonstrated using the burner model as an example. Focus of this paper is on SOFC system heat-up. Different strategies are discussed, and dynamic simulation results of heat-up processes are shown.
This paper presents a modeling framework to address the energy, economy, emissions and land use nexus when exploiting bioenergy in developing countries. The modeling framework combines a qualitative and a quantitative element. The qualitative element integrates two components: (1) technology roadmapping to identify long-term technology targets through expert judgment and (2) scenario analysis to investigate different future storylines. The quantitative element comprises four integrated tools, namely the energy system model (ESM), the land use and trade model (LUTM), an economic model, and an external climate model. An overview of the modeling framework, scenario analysis, structure of the models, modeling techniques, mathematical formulations and assumptions is presented and discussed. The modeling framework is applied to the particular context of Colombia, as a case study of a developing country with large bioenergy potential. In this study case, the impacts that an accelerated deployment of bioenergy technologies might cause on the energy demand and supply, emissions and land use until 2030 are evaluated. Results suggest that a plan to exploit bioenergy in Colombia should prioritize the deployment of technologies for biomethane production, power generation & CHP, which can reduce more GHG emissions and more emissions per incremental hectare of land than first-generation biofuels. Moreover, while the share of bioenergy in the primary energy demand decreases in all the analyzed scenarios, it is possible to envision significant increases in the share of bioenergy in road transport energy demand, power generation and natural gas supply for scenarios implementing roadmap goals. In addition, impacts of El Niño oscillation on the dependence of hydro for power generation can be partly mitigated by exploiting the complementarity of hydro and bioenergy, which might result in a reduction of up to 5–6% in the demand for fossil fuels used in power generation in dry years. However, despite the ambitious goals proposed here, bioenergy alone cannot significantly reduce emissions by 2030 (maximum 10% reduction relative to baseline) and effective climate change mitigation requires a portfolio of additional measures.
The growing concern about the role of man-made CO2 emissions with respect to global arming, in combination with the large increase in energy demand spurred by developing nations and a growing global population that is foreseen over the next 15 years have recently turned attention to potential CO2-neutral energy supply solutions.
Grid-compatible integration of typically fluctuating electrical energy sources, like wind and solar power, will be important in order to support the goal to reduce CO2 emissions. However, this will require substantial adjustments to the grids and power plant systems in order to cope with the upcoming new boundary conditions imposed by substantially increased utilization of renewable energies.
To respond to this imperative, GE and RWE Power have started to investigate new technologies for large-scale storage of electrical energy in Adiabatic Compressed Air Energy Storage power plants.
This concept offers efficient, local zero-emission storage based on compressed air held in underground caverns. The compression and expansion of air with turbomachinery help to balance power generation peaks that are not demand-driven on the one hand and consumption-induced load peaks on the other, allowing the optimal use of both traditional fossil fuels and renewables.
Before this concept can be implemented, however, numerous technical issues must be addressed, mainly in the field of turbomachinery and the heat storage device. This paper describes today’s technical capabilities of turbomachinery equipment, and evaluates the need for further development based on the requirements of advanced CAES technology. Ongoing development activities are described and initial results presented.
Methodology for biomass energy potential estimation: Projections of future potential in Colombia
(2014)
This paper presents a novel method to estimate the future biomass energy potential in countries with domestic markets unable to influence international markets. As a study case, the biomass energy potential in Colombia is estimated for the period 2010–2030.
The prediction model is a scenario-based optimization algorithm that maximizes the yearly profit of locally producing and importing commodities in a country subject to certain constraints (domestic demand, limited area, etc.) as well as to demographic, macroeconomic and market data (e.g. domestic and international prices of commodities). The bioenergy potential associated to the production of commodities is calculated according to a methodology presented by the same authors. In order to provide a modeling framework consistent with other state-of-the-art projections, global scenarios for analysis are selected from the literature rather than formulated. Selected global scenarios highlight the influence of global biofuel use on agricultural prices, production and demand.
Results predict a theoretical bioenergy potential in Colombia 56%–69% larger in 2030 than in 2010 (1.31–1.41 EJ). A sensitivity analysis shows that while a higher global biofuel use leads to a higher local bioenergy potential, its influence is less pronounced than that of agricultural yields, demand and specific energy of biomass resources.
This paper presents a novel approach to address uncertainty and improve reliability of the estimation of the biomass energy potential at a country level, particularly suitable for situations when quality and availability of data are limited. The proposed methodology improves the prediction reliability by following four steps: 1) using a simple accounting framework, 2) using a robust selection of probability density functions, 3) using a probabilistic propagation of uncertainty and 4) using sensitivity analysis to identify key variables contributing to uncertainty as well as a root cause analysis and a set of sub-models to improve estimation of key variables.
The application of the methodology to the energy scenario in Colombia shows that the improved estimation of the theoretical energy potential has an almost identical mean value compared to the preliminary estimate, but the uncertainty is significantly lower (less than 50%). Moreover, the mean value of the technical energy potential obtained through the methodology is 25% lower than the preliminary potential and the uncertainty reduces by one third.
Energy scenarios suggest that CO2 capture and storage (CCS) from power plants might contribute significantly to global greenhouse gas emission reduction. Since CCS from power generation is an emerging technology that has not been demonstrated on a commercial scale, related cost and performance information is still uncertain. This paper presents a detailed analysis of the impact of adding CO2 capture and compression process equipment to fossil-fuelled power plants. For coal-fired power generation, no single capture technology outperforms available alternative capture processes in terms of cost and performance.