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Wave Energy Converters (WECs) need to be deployed in large numbers in an array layout in order to have a significant power production. Each WEC has an impact on the incoming wave field, by diffracting, reflecting and radiating waves. Simulating the wave transformations within and around a WEC array is complex; it is difficult, or in some cases impossible, to simulate both these near-field and far-field wake effects using a single numerical model, in a time- and cost-efficient way in terms of computational time and effort. Within this research, a generic coupling methodology is developed to model both near-field and far-field wake effects caused by floating (e.g., WECs, platforms) or fixed offshore structures. The methodology is based on the coupling of a wave-structure interaction solver (Nemoh) and a wave propagation model. In this paper, this methodology is applied to two wave propagation models (OceanWave3D and MILDwave), which are compared to each other in a wide spectrum of tests. Additionally, the Nemoh-OceanWave3D model is validated by comparing it to experimental wave basin data. The methodology proves to be a reliable instrument to model wake effects of WEC arrays; results demonstrate a high degree of agreement between the numerical simulations with relative errors lower than 5 % and to a lesser extent for the experimental data, where errors range from 4 % to 17 % .

The increasing demand for energy and commodities has led to escalating greenhouse gas emissions, the chief of which is represented by carbon dioxide (CO2). Blue hydrogen (H2), a low-carbon hydrogen produced from natural gas with carbon capture technologies applied, has been suggested as a possible alternative to fossil fuels in processes with hard-to-abate emission sources, including refining, chemical, petrochemical and transport sectors. Due to the recent international directives aimed to combat climate change, even existing hydrogen plants should be retrofitted with carbon capture units. To optimize the process economics of such retrofit, it has been proposed to remove CO2 from the pressure swing adsorption (PSA) tail gas to exploit the relatively high CO2 concentration. This study aimed to design and numerically investigate a vacuum pressure swing adsorption (VPSA) process capable of capturing CO2 from the PSA tail gas of an industrial steam methane reforming (SMR)-based hydrogen plant using NaX zeolite adsorbent. The effect of operating conditions, such as purge-to-feed ratio and desorption pressure, were evaluated in relation to CO2 purity, CO2 recovery, bed productivity and specific energy consumption. We found that conventional cycle configurations, namely a 2-bed, 4-step Skarstrom cycle and a 2-bed, 6-step modified Skarstrom cycle with pressure equalization, were able to concentrate CO2 to a purity greater than 95% with a CO2 recovery of around 77% and 90%, respectively. Therefore, the latter configuration could serve as an efficient process to decarbonize existing hydrogen plants and produce blue H2.

This paper proposes the computation and assessment of optimal tilt and azimuth angles for a receiving surface, using a mathematical model developed at the University of Tomsk, Russia. The model was validated and analyzed for the Nuevo León State, Northeast Mexico, utilizing a set of metrics, comparing against satellite data from NASA. A point of interest in the city of Monterrey was analyzed to identify orientation patterns throughout the year for an optimal solar energy gathering. The aim is providing the best orientation tilt angles for photovoltaic or solar thermal panels without tracking systems. In addition, this analysis is proposed as a tool to achieve optimal performance in sustainable urban development in the region. Based on the findings, a set of optimal tilt and azimuth surface angles are proposed for the analyzed coordinates. The aim is to identify the optimal performance to obtain the maximum solar irradiation possible over the year for solar projects in the region. The results show that the model can be used as a tool to accelerate decision making in the design of solar harvesting surfaces and allows the design of discrete tracking systems with an increase in solar energy harvesting above 5% annually.

It is imperative to increase the connectable capacity (i.e., hosting capacity) of distributed generation in order to decarbonise electricity distribution networks. Hybrid generation that exploits complementarity in resource characteristics among different renewable types potentially provides value for minimising technical constraints and increasing the effective use of the network. Tidal, wave and wind energy are prominent offshore renewable energy sources. It is of importance to explore their potential complementarity for increasing network integration. In this work, the novel introduction of these distinct offshore renewable resources into hosting capacity evaluation enables the quantification of the benefits of various resource combinations. A scenario reduction technique is adapted to effectively consider variation of these renewables in an AC optimal power flow-based nonlinear optimisation model. Moreover, the beneficial impact of active network management (ANM) on enhancing the renewable complementarity is also investigated. The combination of complementary hybrid generation and ANM, specifically where the maxima of the generation profiles rarely co-occur with each other and with the demand minimum, is found to make the best use of the network components.

Based on the existing acknowledgment that space charge modulates AC and DC breakdown of insulating materials, this investigation promotes the related investigation into the situations of more complex electrical stress, i.e., AC-DC combined voltages. Experimentally, the AC-DC breakdown characteristics of oil impregnated paper insulation were systematically investigated. The effects of pre-applied voltage waveform, AC component ratio, and sample thickness on AC-DC breakdown characteristics were analyzed. After that, based on an improved bipolar charge transport model, the space charge profiles and the space charge induced electric field distortion during AC-DC breakdown were numerically simulated to explain the differences in breakdown characteristics between the pre-applied AC and pre-applied DC methods under AC-DC combined voltages. It is concluded that large amounts of homo-charges are accumulated during AC-DC breakdown, which results in significantly distorted inner electric field, leading to variations of breakdown characteristics of oil impregnated paper insulation. Therefore, space charges under AC-DC combined voltages must be considered in the design of converter transformers. In addition, this investigation could provide supporting breakdown data for insulation design of converter transformers and could promote better understanding on the breakdown mechanism of insulating materials subjected to AC-DC combined voltages.

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.