Peer-to-peer (P2P) energy trading is an emerging energy supply paradigm where customers with distributed energy resources (DERs) are allowed to directly trade and share electricity with each other. P2P energy trading can facilitate local power and energy balance, thus being a potential way to manage the rapidly increasing number of DERs in net zero transition. It is of great importance to explore P2P energy trading via public power networks, to which most DERs are connected. Despite the extensive research on P2P energy trading, there has been little large-scale commercial deployment in practice across the world. In this paper, the practical challenges of conducting P2P energy trading via public power networks are identified and presented, based on the analysis of a practical Local Virtual Private Networks (LVPNs) case in North Wales, UK. The ongoing efforts and emerging solutions to tackling the challenges are then summarized and critically reviewed. Finally, the way forward for facilitating P2P energy trading via public power networks is proposed.

The increasing use of distributed energy resources changes the way to manage the electricity system. Unlike the traditional centralized powered utility, many homes and businesses with local electricity generators have established their own microgrids, which increases the use of renewable energy while introducing a new challenge to the management of the microgrid system from the mismatch and unknown of renewable energy generations, load demands, and dynamic electricity prices. To address this challenge, a rank-based multiple-choice secretary algorithm (RMSA) was proposed for microgrid management, to reduce the microgrid operating cost. Rather than relying on the complete information of future dynamic variables or accurate predictive approaches, a lightweight solution was used to make real-time decisions under uncertainties. The RMSA enables a microgrid to reduce the operating cost by determining the best electricity purchase timing for each task under dynamic pricing. Extensive experiments were conducted on real-world data sets to prove the efficacy of our solution in complex and divergent real-world scenarios.

With multiple microgrids (MGs) integrated into power distribution networks in a distributed manner, the penetration of renewable energy like photovoltaic (PV) power generation surges. However, the operation of power distribution networks is challenged by the issues of multiple power flow directions and voltage security. Accordingly, an efficient voltage control strategy is needed to ensure voltage security against ever-changing operating conditions, especially when the network topology information is absent or inaccurate. In this paper, we propose a novel data-driven voltage profile improvement model, denoted as system-wide composite adaptive network (SCAN), which depends on operational data instead of network topology details in the context of power distribution networks integrated with multiple MGs. Unlike existing studies that realize topology identification and decision-making optimization in sequence, the proposed end-to-end model determines the optimal voltage control decisions in one shot. More specifically, the proposed model consists of four modules, Pre-training Network and modified interior point methods with adversarial networks (Modified IPMAN) as core modules, and discriminator generative adversarial network (Dis-GAN) and Volt convolutional neural network (Volt-CNN) as ancillary modules. In particular, the generator in SCAN is trained by the core modules in sequence so as to form an end-to-end mode from data to decision. Numerical experiments based on IEEE 33-bus and 123-bus systems have validated the effectiveness and efficiency of the proposed method.

In this paper, a fault-tolerant control method for an input-series output-parallel modular grid-tied pulse-width modulation (PWM) current source inverter is proposed to address the most commonly seen single symmetrical gate-commutated thyristor (SGCT) open-circuit fault problems. This method actively offsets the neutral point of the current space vector to ensure a sinusoidal output of the grid current, and it can achieve the upper limit power of the inverter under the condition of a single SGCT open-circuit fault. In addition, an active damping control method based on grid harmonic current feedback is proposed after analyzing the influence of the transformer ferromagnetic resonance caused by the neutral point offset on the power quality of the grid current. It has been demonstrated that the proposed method effectively suppresses the resonance caused by the transformer and the modified modulation, improving the grid current’s power quality.

Grid-forming converters (GFMs) are faced with the threat of transient inrush current and synchronization instability issues when subjected to grid faults. Instead of disconnecting from the grid unintentionally, GFMs are required to have fault ride through (FRT) capability to maintain safe and stable operation in grid-connected mode during grid fault periods. In recent studies, different FRT control strategies with distinguishing features and that are feasible for different operation conditions have been proposed for GFMs. To determine their application scope, an intuitive comparison of the transient performance of different FRT control strategies is presented in this paper. First, three typical FRT control strategies (virtual impedance, current limiters, and mode-switching control) are introduced and transient mathematical models are established. A detailed comparison analysis on transient inrush current and transient synchronization stability is then presented. The results will be useful for guiding the selection and design of FRT control strategies. Finally, simulation results based on PSCAD/EMTDC are considered to verify the correctness of the theoretical analysis.

Advances in natural gas-fired technologies have deepened the coupling between electricity and gas networks, promoting the development of the integrated electricity-gas network (IEGN) and strengthening the interaction between the active-reactive power flow in the power distribution network (PDN) and the natural gas flow in the gas distribution network (GDN). This paper proposes a day-ahead active-reactive power scheduling model for the IEGN with multi-microgrids (MMGs) to minimize the total operating cost. Through the tight coupling relationship between the subsystems of the IEGN, the potentialities of the IEGN with MMGs toward multi-energy cooperative interaction is optimized. Important component models are elaborated in the PDN, GDN, and coupled MMGs. Besides, motivated by the non-negligible impact of the reactive power, optimal inverter dispatch (OID) is considered to optimize the active and reactive power capabilities of the inverters of distributed generators. Further, a second-order cone (SOC) relaxation technology is utilized to transform the proposed active-reactive power scheduling model into a convex optimization problem that the commercial solver can directly solve. A test system consisting of an IEEE-33 test system and a 7-node natural gas network is adopted to verify the effectiveness of the proposed scheduling method. The results show that the proposed scheduling method can effectively reduce the power losses of the PDN in the IEGN by 9.86%, increase the flexibility of the joint operation of the subsystems of the IEGN, reduce the total operation costs by $32.20, and effectively enhance the operation economy of the IEGN.

Tradable green certificate (TGC) scheme promotes the development of renewable energy industry which currently has a dual effect on economy and environment. TGC market efficiency is reflected in stimulating renewable energy investment, but may be reduced by the herding behavior of market players. This paper proposes and simulates an artificial TGC market model which contains heterogeneous agents, communication structure, and regulatory rules to explore the characteristics of herding behavior and its effects on market efficiency. The results show that the evolution of herding behavior reduces information asymmetry and improves market efficiency, especially when the borrowing is allowed. In addition, the fundamental strategy is diffused by herding evolution, but TGC market efficiency may be remarkably reduced by herding with borrowing mechanism. Moreover, the herding behavior may evolve to an equilibrium where the revenue of market players is comparable, thus the fairness in TGC market is improved.

Metal, as the indispensable material, is functioning the society from technology to the environment. Niobium (Nb) is considered a unique earth metal as it is related to many emerging technologies. The increasing economic growth exerts an increasing pressure on supply, which leads to its significance in the economic sector. However, few papers have addressed Nb sustainability, which forms the scope of this paper in order to start the process of Nb market forecasting based on some previous data and some assumptions. Therefore, this paper will discuss different thoughts in material substitution and the substance flow of Nb throughout a static flow using Nb global data to have a better understanding of the process of Nb from production to end of life. This shall lead to the identification of the market needs to determine its growth which is around 2.5% to 3.0%. Moreover, due to China’s huge Nb consumption which comes from the continuous development that is happening over the years, it will also briefly mention the Nb situation as well as its growth which according to statistics will grow steadily till 2030 by a rate of 4.0% to 6.0%. The results show that there should be some enhancement to Nb recycling potentials out of steel scrap. In addition, there should be more involvement of Nb in different industries as this would lead to less-used materials which can be translated to less environmental impact.

China’s aluminum (Al) production has released a huge amount of greenhouse gas (GHG) emissions. As one of the biggest country of primary Al production, China must mitigate its overall GHG emission from its Al industry so that the national carbon neutrality target can be achieved. Under such a background, the study described in this paper conducts a dynamic material flow analysis to reveal the spatiotemporal evolution features of Al flows in China from 2000 to 2020. Decomposition analysis is also performed to uncover the driving factors of GHG emission generated from the Al industry. The major findings include the fact that China’s primary Al production center has transferred to the western region; the primary Al smelting and carbon anode consumption are the most carbon-intensive processes in the Al life cycle; the accumulative GHG emission from electricity accounts for 78.14% of the total GHG emission generated from the Al industry; China’s current Al recycling ratio is low although the corresponding GHG emission can be reduced by 93.73% if all the primary Al can be replaced by secondary Al; and the total GHG emission can be reduced by 88.58% if major primary Al manufacturing firms are transferred from Inner Mongolia to Yunnan. Based upon these findings and considering regional disparity, several policy implications are proposed, including promotion of secondary Al production, support of clean electricity penetration, and relocation of the Al industry.

Coal water slurry gasification is a main source of hydrogen in the developing hydrogen economy. Moreover, biomass and waste can be added, making gasification process greener. To expand the application of coal water slurry and gasification process, it is necessary to understand the micro-structure in this large particle suspension system. In this paper, the micro-structure in coal water slurry was studied by extended DLVO (eDLVO) theory and fractal dimension, which is used to explain the mechanism of stability in large particle suspension systems. The interaction between two coal particles was characterized from the interparticle potential and energy barrier based on the eDLVO theory. The rheology and stability between different types of coals are measured and explained by the aggregating structure and fractal dimension in coal water slurry. The results indicated that there would be an aggregating structure in high rank coals, due to the interparticle potential caused by the surface properties, but probably not in low rank coals. This aggregating structure can be described and characterized by fractal dimension. The aggregation of particles is the source of the stability for high rank coals, as the close-packed 3D network structure in large particle suspension can support coal particles from settling down. The results have demonstrated that the combination of the eDLVO theory and rheological measurement is an effective way to investigate the stability of large particle suspension systems.