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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.

The use of natural gas as an energy source is increasing significantly due to its low greenhouse gas emissions. However, the common methods of natural gas storage and transportation, such as liquefied or compressed natural gas, are limited in their applications because they require extreme conditions. Gas hydrate technology can be a promising alternative to conventional approaches, as artificially synthesized hydrates provide an economical, environmentally friendly, and safe medium to store energy. Nevertheless, the low rate of hydrate formation is a critical problem that hinders the industrial application of this technology. Therefore, chemical promoters are being developed to accelerate the kinetics of gas hydrate formation. In this paper, the effect of new sodium sulfosuccinate compounds, synthesized based on glycerol and pentaerythritol, on methane hydrate formation was studied. Experiments under dynamic conditions using high-pressure autoclaves demonstrated that the conversion of water-to-hydrate forms increased from 62 ± 5% in pure water to 86 ± 4% for the best promoter at concentration 500 ppm. In addition, the rate of hydrate formation increases 2–4 times for different concentrations. Moreover, none of the synthesized reagents formed foam, compared to sodium dodecyl sulfate, in which the foam rate was 3.7 ± 0.2. The obtained reagents showed good promotional properties and did not form foam, which makes them promising promoters for gas hydrate technology.

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.

Background. Laws that enable a circular economy (CE) are being enacted globally, but reliable standardized and digitized CE data about products is scarce, and many CE platforms have differing exclusive formats. In response to these challenges, the Ministry of The Economy of Luxembourg launched the Circularity Dataset Standardization Initiative to develop a globalized open-source industry standard to allow the exchange of standardized data throughout the supply cycle, based on these objectives: (a) Provide basic product circularity data about products. (b) Improve circularity data sharing efficiency. (c) Encourage improved product circularity performance. A policy objective was to have the International Organization for Standardization (ISO) voted to create a working group. Methods. A state-of-play analysis was performed concurrently with consultations with industry, auditors, data experts, and data aggregation platforms. Results. Problem statements were generated. Based on those, a solution called Product Circularity Data Sheet (PCDS) was formulated. A proof of concept (POC) template and guidance were developed and piloted with manufacturers and platforms, thus fulfilling objective (a). For objective (b), IT ecosystem requirements were developed, and aspects are being piloted in third party aggregation platforms. Objective (c) awaits implementation of the IT ecosystem. The policy objective related to the ISO was met. Conclusions and future research. In order to fully test the PCDS, it is necessary to: conduct more pilots, define governance, and establish auditing and authentication procedures.

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.

The purpose of this paper is to uncover how the process of managing electricity in buildings based on the Internet of Things occurs. In particular, the work seeks to depict the factors affecting electricity consumption and management, as well as the application of the Internet of Things in energy management. A systematic literature review is used to examine the breadth of the electric energy management literature, encompassing bibliometric and thematic analysis based on an established procedure. The findings show the evolution of this field within key research networks with a few papers covering important elements of energy management, such as energy use, consumption and monitoring, assessment, and planning, in an integrative manner. Within this field, lacking in theory and practice, the originality of the work is the assembly of electric energy management into a conceptual framework based on real-time consumption and the Internet of Things (IoT). Indeed, the framework brings together the breadth of factors affecting consumption, energy use, and improvements that have been dispersed across the literature into one place. This framework, therefore, represents a stage towards an integrative view of IoT electric energy management and subsequent enhancement of theory and energy efficiency adoption.

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.