Recent schreef Martien Visser dat voor hem persoonlijk bij vervanging van zijn cv-ketel een volledig elektrische warmtepomp met een aantal straalkacheltjes goedkoper is dan een hybride exemplaar zonder die kacheltjes. Het leverde hem tal van reacties op met als boodschap dat dit al gebeurt of overwogen wordt, omdat dat ook voor anderen de goedkoopste optie is. “Zo zijn we in Nederland blijkbaar bezig. Arme netbeheerders! Vindt u het dan gek dat er enorme net schaarste is, dat er 10.000 wachtende bedrijven zijn en dat de kosten en doorlooptijden de pan uitrijzen?”
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Wind and solar power generation will continue to grow in the energy supply of the future, but its inherent variability (intermittency) requires appropriate energy systems for storing and using power. Storage of possibly temporary excess of power as methane from hydrogen gas and carbon dioxide is a promising option. With electrolysis hydrogen gas can be generated from (renewable) power. The combination of such hydrogen with carbon dioxide results in the energy carrier methane that can be handled well and may may serve as carbon feedstock of the future. Biogas from biomass delivers both methane and carbon dioxide. Anaerobic microorganisms can make additional methane from hydrogen and carbon dioxide in a biomethanation process that compares favourably with its chemical counterpart. Biomethanation for renewable power storage and use makes appropriate use of the existing infrastructure and knowledge base for natural gas. Addition of hydrogen to a dedicated biogas reactor after fermentation optimizes the biomethanation conditions and gives maximum flexibility. The low water solubility of hydrogen gas limits the methane production rate. The use of hollow fibers, nano-bubbles or better-tailored methane-forming microorganisms may overcome this bottleneck. Analyses of patent applications on biomethanation suggest a lot of freedom to operate. Assessment of biomethanation for economic feasibility and environmental value is extremely challenging and will require future data and experiences. Currently biomethanation is not yet economically feasible, but this may be different in the energy systems of the near future.
The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
