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The capacity factor of a power plant is the ratio of generation over its potential generation. It is an important measure to describe wind and solar resources. However, the fluctuating nature of renewable power generation makes it difficult to integrate all generation at times. Whenever generation exceeds the load, curtailment or storage of energy is required. With increasing renewable shares in the power system, the level of curtailment will further increase. In this work, the influence of the curtailment on the capacity factors for a highly renewable German power system is studied. An effective capacity factor is introduced, and the implications for the distribution of renewable power plants are discussed. Three years of highly-resolved weather data were used to model wind and solar power generation. Together with historical load data and a transmission model, a possible future German power system was simulated. It is shown that effective capacity factors for unlimited transmission are strongly reduced by up to 60% (wind) and 70% (photovoltaics) and therefore of limited value in a highly renewable power system. Furthermore, the results demonstrate that wind power benefits more strongly from a reinforced transmission grid than photovoltaics (PV) does.

In renewable energy based systems Grid-Connected Voltage Source Converters (GC-VSC) are used in many applications as grid-feeding converters, which transfer the power coming from the renewable energy sources to the grid. In some cases, the operation of GC-VSC may become unstable or uncontrollable due to, among others: a grid fault or an inappropriate current-power reference, that give rise to fast electrical transients or a saturation of the controller. In this paper, an improved control scheme is proposed to enhance the controllability of GC-VSC in all these situations. This solution consists of two parts, on the one hand a new Proportional-Resonant (PR) controller with anti-windup capability to be used as current controller, and secondly a new current/power reference modifier, which defines the suitable reactive current/power reference to keep the system stable. It is worth to mention that the proposed scheme does not need information about the grid parameters as it only uses the converter current, and the voltage at the capacitors of Inductor-Capacitor (LC) output filter.

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user’s energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances’ operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household’s energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods.

The global climate protection policy aimed at achieving a zero greenhouse gas emissions target has led to the fast incorporation of large-scale photovoltaics into the power network. The conventional AC grid was not modeled to be incorporated with large-scale non-synchronous inverter-based energy resources (IBR). Incorporating inertia-free IBR into the grid leads to technical issues such as the degradation of system strength and inertia, therefore affecting the safety and reliability of the electrical power system. This research introduced a new solution to incorporate a flywheel in the rotor of a synchronous machine to improve the dynamic inertia control during a system disruption and to maintain the constancy of the system. The objective of this work is to enhance large-scale photovoltaic systems in such a way that they can avoid failures during a fault. A model of transient constancy with two synchronous generators and a LSPV is established in PowerWorld modeling software. A line-to-ground and three-phase fault are simulated in a system with up to 50% IBR penetration. The outcomes showed that the power network was able to ride through faults (RTFs) and that the stability of frequency and voltage are enhanced because of a flywheel that improved grid inertia and strength.

In fusion devices, such as European Demonstration Fusion Power Reactor (EU DEMO), primary neutrons can cause material activation due to the interaction between the source particles and the targeting material. Subsequently, the reactor’s inner components become activated. For safety and safe performance purposes, it is necessary to evaluate neutron-induced activities. Activities results from divertor reflector and liner plates are presented in this work. The purpose of liner shielding plates is to protect the vacuum vessel and magnet coils from neutrons. As for reflector plates, the function is to shield the cooling components under plasma-facing components from alpha particles, thermal effects, and impurities. Plates are made of Eurofer with a 3 mm layer of tungsten, while the water is used for cooling purposes. The calculations were performed using two EU DEMO MCNP (Monte Carlo N-Particles) models with different breeding blanket configurations: helium-cooled pebble bed (HCPB) and water-cooled lithium lead (WCLL). The TENDL–2017 nuclear data library has been used for activation reactions cross-sections and nuclear reactions. Activation calculations were performed using the FISPACT-II code at the end of irradiation for cooling times of 0 s–1000 years. Radionuclide analysis of divertor liner and reflector plates is also presented in this paper. The main radionuclides, with at least 1% contribution to the total value of activation characteristics, were identified for the previously mentioned cooling times.

Climate change has brought a compelling need for cooling living spaces to the attention of researchers as well as construction professionals. The problem of overheating enclosures is now exacerbated in traditionally affected areas and is also affecting countries that were previously less prone to the issue. In this paper, we address measurements of thermal comfort and cooling emission efficiency parameters for different devices: ceiling panels, underfloor cooling, fan-assisted radiators, and fan coil. These devices were tested in low and high cooling capacities of up to 40 W/m2 while also featuring heating dummies to imitate internal heat gains. Air temperatures were measured at different heights, allowing to evaluate the thermal stratification with high accuracy. Thermal comfort differences of the tested systems were quantified by measuring both air velocities and operative temperatures at points of occupancy. In summary, the best-performing cooling devices for the studied cooling applications were the ceiling panels and fan radiators, followed by underfloor cooling, with a limitation of stratification. Because of the strong jet, fan coil units did not achieve thermal comfort within the whole occupied zone. The results can be utilized in future studies for cooling emission efficiency and energy consumption analyses of the different cooling devices.

The yeast Yarrowia lipolytica is an industrially relevant microorganism, which is able to convert low-value wastes into different high-value, bio-based products, such as enzymes, lipids, and other important metabolites. Waste cooking oil (WCO) represents one of the main streams generated in the food supply chain, especially from the domestic sector. The need to avoid its incorrect disposal makes this waste a resource for developing bioprocesses in the perspective of a circular bioeconomy. To this end, the strain Y. lipolytica W29 was used as a platform for the simultaneous production of intracellular lipids and extracellular lipases. Three different minimal media conditions with different pH controls were utilized in a small-scale (50 mL final volume) screening strategy, and the best condition was tested for an up-scaling procedure in higher volumes (800 mL) by selecting the best-performing possibility. The tested media were constituted by YNB media with high nitrogen restriction (1 g L−1 (NH4)2SO4) and different carbon sources (3% w v−1 glucose and 10% v v−1 WCO) with different levels of pH controls. Lipase production and SCO content were analyzed. A direct correlation was found between decreasing FFA availability in the media and increasing SCO levels and lipase activity. The simultaneous production of extracellular lipase (1.164 ± 0.025 U mL−1) and intracellular single-cell oil accumulation by Y. lipolytica W29 growing on WCO demonstrates the potential and the industrial relevance of this biorefinery model.