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With the complete implementation of the “Replacement of Coal with Electricity” policy, electric loads borne by urban power systems have achieved explosive growth. The traditional load forecasting method based on “similar days” only applies to the power systems with stable load levels and fails to show adequate accuracy. Therefore, a novel load forecasting approach based on long short-term memory (LSTM) was proposed in this paper. The structure of LSTM and the procedure are introduced firstly. The following factors have been fully considered in this model: time-series characteristics of electric loads; weather, temperature, and wind force. In addition, an experimental verification was performed for “Replacement of Coal with Electricity” data. The accuracy of load forecasting was elevated from 83.2 to 95%. The results indicate that the model promptly and accurately reveals the load capacity of grid power systems in the real application, which has proved instrumental to early warning and emergency management of power system faults.

Photovoltaic (PV) power generation has developed rapidly in recent years. Owing to its volatility and intermittency, PV power generation has an impact on the power quality and operation of the power system. To mitigate the impact caused by the PV generation, an energy storage (ES) system is applied to the PV plants. The capacity configuration and control strategy based on the stochastic optimization method have become an important research topic. However, the accuracy of the probability distribution model is insufficient and a stochastic optimization method is rarely used in a control strategy. In this paper, a stochastic optimization method for the energy storage system (ESS) configuration considering the self-regulation of the battery state of charge (SoC) is proposed. Firstly, to reduce the sampling error when typical scenarios of PV power are generated, a time-divided probability distribution model of the ultra-short-term predicted error of PV power is established. On this basis, to solve the problem that SoC reaches the threshold frequently, a self-regulation model of the SoC based on multiple scenarios is established, which can regulate the SoC according to rolling PV power prediction. A stochastic optimization configuration model of the energy storage system is constructed, which can reduce the impact of PV uncertainty on the configuration result. Finally, the proposed stochastic optimization method is validated. The fitting error of the time-divided probability distribution model is 15.61% lower than that of the t-distribution. The expected revenue of the optimal configuration in this paper is 8.86% higher than the scheme with a fixed probability distribution model, and 16.87% higher than without considering the stochastic optimization method.

Global warming and climate change are becoming a global concern. In this regard, international agreements and initiatives have been launched to accelerate the use of renewable energy and to mitigate greenhouse gas (GHG) emissions. Yemen is one of the countries signed on these agreements. However, Yemen is facing the problem that the structure of the power grid is fragile and the power shortage is serious. Accordingly, this paper aims to study the potential for renewable energy in Yemen and assess the technical and economic feasibility of hybrid energy systems. Firstly, this paper introduces the status and challenges of Yemen&rsquo, s electricity sector, the status of renewable energy, and the status of GHG emission. Secondly, this study proposes the method of optimizing different configurations of off-grid hybrid (solar/wind/diesel engine) energy systems for electrifying various consumers in Taiz province, Yemen under three scenarios of energy strategies. The objective function is to seek the most optimal hybrid energy system that achieves the least cost and most advantageous technical performance, while instigating the best economic scenario of energy strategies. Finally, Homer pro software is used for simulation, optimization, and sensitivity analysis of the designed energy systems. The results found the best economically feasible scenario, the hybrid PV/wind/diesel energy system, among the other scenarios. A photovoltaic (PV)/wind energy system achieved the best technical performances of 100% CO2 reduction, with a 54.82% reduction in the net present cost (NPC) and cost of energy (COE), while the hybrid energy system (PV/wind/diesel engine) achieved the best economic cost of 61.95% reduction in NPC and COE, with a 97.44% reduction of CO2 emission.

Blockchain-based peer-to-peer (P2P) energy trading is one of the most viable solutions to incentivize prosumers in distributed electricity markets. However, P2P energy trading through an open-end blockchain network is not conducive to mutual credit and the privacy protection of stakeholders. Therefore, improving the credibility of P2P energy trading is an urgent problem for distributed electricity markets. In this paper, a novel double-layer energy blockchain network is proposed that stores private trading data separately from publicly available information. This blockchain network is based on optimized cross-chain interoperability technology and fully considers the special attributes of energy trading. Firstly, an optimized ring mapping encryption algorithm is designed to resist malicious nodes. Secondly, a consensus verification subgroup is built according to contract performance, consensus participation and trading enthusiasm. This subgroup verifies the consensus information through the credit-threshold digital signature. Thirdly, an energy trading model is embedded in the blockchain network, featuring dynamic bidding and credit incentives. Finally, the Erenhot distributed electricity market in China is utilized for example analysis, which demonstrates the proposed method could improve the credibility of P2P trading and realize effective supervision.

With the development of power energy technology, flexible high voltage direct current (HVDC) systems with high control degree of freedom flexibility, power supply to passive systems, small footprint, and other advantages stand out in the field of long-distance large-capacity transmission engineering. HVDC transmission technology based on a modular multilevel converter has been widely used in power grids due to its advantages such as large transmission capacity, less harmonic content, low switching loss, and wide application field. In the modular multilevel converter (MMC)-based HVDC system, the protection strategy of converter station internal faults is directly related to the reliability and security of the power transmission system. Starting from the MMC topological structure, this paper establishes the MMC mathematical model in a synchronous rotation coordinate system by combining the working state of sub-modules and the relationship between each variable of the upper and lower bridge arms of each phase of the MMC. It provides a theoretical basis for the design of the MMC-HVDC control system. The causes of the AC system faults and the internal faults of the converter station in the MMC-HVDC system are analyzed, and the sub-module faults and bridge arm reactor faults in the converter station are studied. The sub-module redundancy protection and bridge arm overcurrent protection strategies are designed for the faults, and the correctness of the scheme is verified by Matlab/Simulink.

Peer-to-peer (P2P) energy management is one of the most viable solutions to incentivize prosumers in renewable energy microgrids. As the application of blockchain expends from the finance field to energy field, blockchain technology provides a new opportunity for distributed energy systems. However, a distributed energy system based on blockchains allows any node in the whole network to read data. In many application scenarios, user privacy cannot be effectively protected, and there is a security problem that the attack cannot be traced. In this paper, we propose an energy management mode based on a permissioned blockchain for a renewable energy microgrid. The novel permissioned blockchain framework uses entity mapping with a unique identity for each enterprise, natural person, or device, in order to avoid ineligible participants to join the microgrid. Each peer entity keeps the transaction information index of the whole network, but only keeps its own specific transaction information, so they can retrieve the transaction information of other peer entities but cannot obtain the details without permission. Moreover, this model could avoid communication delays and promote plug-and-play due to the distributed nature of the permissioned blockchain. The performance of the proposed method is evaluated with a demonstration program which is designed and deployed on a Hyperledger Fabric permissioned blockchain. Simulation results show the feasibility of the proposed method, and the model is conducive to the protection privacy and P2P energy management for decentralized energy systems.

Solar energy has attracted the attention of researchers around the world due to its advantages. However, photovoltaic (PV) panels still have not attained the desired efficiency and economic mature. PV tracking techniques can play a vital role in improving the performance of the PV system. The aim of this paper is to evaluate and compare the technical and economic performance of grid-connected hybrid energy systems including PV and fuel cells (FC) by applying major types of PV tracking technique. The topology and design principles and technical description of hybrid system components are proposed in this paper. Moreover, this paper also introduces economic criteria, which are used to evaluate the economy of different PV tracking techniques and seek the optimal configuration of system components. In the case study, the results show that the vertical single axis tracker was ranked 1st in terms of highest PV generation, penetration of renewable energy to the grid, lowest CO2 emission, highest energy sold to the grid and lowest purchased, and lowest net present cost (NPC) and levelized cost of energy (LCOE). The study found that the optimal design of a grid-connected hybrid energy system (PV-FC) was by using a vertical single axis tracker which has the lowest NPC, LCOE.

The emerging blockchain technology has injected new vitality into the energy market, especially the peer-to-peer power trading of microgrid systems. However, with the increase of energy blockchain projects, the difficulty of data communication and value islands between blockchain networks have become open issues. Thus, in this paper, we propose a dynamic adaptive cross-chain trading mode for multi-microgrid joint operation. The novelty is to design a proof of credit threshold consensus mechanism to achieve effective information verification. This consensus mechanism can ensure the adaptive consistency of cross-chain information without changing the existing blockchain architecture of each system. At the same time, we design a corresponding key management interoperability protocol based on RSA algorithm and Chinese remainder theorem, which can realize data transfer and information consensus for cross-chain transactions. The theoretical analysis verifies that the cross-chain communication information is effective and the system is able to protect against the attack of malicious nodes. Finally, a cross-chain simulation experiment is established to analyze the operation efficiency. The result shows that this cross-chain trading takes place within seconds, which basically meets the response requirements for multi-microgrid joint operation.

The application of integrated energy systems is significant for realizing the comprehensive utilization of various energy sources and improving the utilization rate of renewable energy. At present, the optimal operation of integrated energy systems is a research hotspot. However, shortcomings remain in the stochastic optimization operation and the scenario generation method. This paper proposes a stochastic optimization operation model of an integrated energy microgrid based on an advanced multi-scenario generation method. First, this paper establishes the time-divided probability distribution model of the forecasting error of the uncertain factors, such as photovoltaic (PV) power and load, which provide the basis for generating scenarios. Moreover, the covariance matrix is used to calculate the time correlation of the time-divided probabilistic distributed models, and the parameters of the covariance matrix are optimized. Second, based on multiple typical scenarios, the stochastic optimization operation model of the integrated energy microgrid is established. Finally, the real data is used to verify the proposed method. The results show that the nonparametric kernel density estimation method has the best fitting effect. On this basis, the time correlation and the operation costs are compared with the scenario sets generated by other methods, which proves the advantages of the proposed multi-scenario generation method and stochastic optimization operation model.

Hydrogen filling stations can provide hydrogen by consuming renewable electricity, and are an important part of the electric-hydrogen coupling system. An appropriate pricing mechanism for renewable-dominated hydrogen stations is urgently required. In this paper, we propose a dynamic pricing mechanism for such hydrogen stations enhanced by blockchain technology. First, a two-way decentralized trading mode is constructed in the electricity/hydrogen energy markets. Second, a series of propensity factors are integrated into the two-way pricing mechanism, which is a mathematical characterization of various real-time information in the system. Blockchain cross-chain interoperability technology can ensure seamless and effective performance of the proposed pricing mechanism. A case study is presented, and our simulation results show that our method has obvious advantages in improving trading profit and renewable energy utilization efficiency over the method of a constant energy price. Finally, we developed a double-chain blockchain decentralized application to illustrate how the system can implement a two-way dynamic pricing mechanism in an electric-hydrogen coupling system.

With the increase of the penetration rate of photovoltaic (PV) power plant in the power system, PV power fluctuation has become one of the important factors affecting the power quality. The energy storage system (ESS) is an effective way to smooth short-term PV power fluctuation and has been widely used. The control strategy is a key factor that will influence the smoothing effect and size of ESS. In this paper, by using ESS to smooth PV power fluctuation, we proposed a novel control strategy that can regulate the state of charge (SoC) of the battery and calculate the output power of ESS based on the super short-term prediction of PV power. Besides, the rolling prediction theory is introduced into the control strategy, which makes the regulating process be implemented at every sampling moment. Based on this control strategy, an optimal sizing model of ESS is built to analyze the economy of the combined system. Finally, actual data of a PV power plant are used for simulation and verification. The results demonstrate that the proposed strategy can smooth the PV power fluctuation effectively. The total cost and the power of exceeding limitation both are reduced.

Due to their intermittent nature, the use of renewable energy sources has faced the challenges of power insecurity and low efficiency. Recently, fuel cells (FCs) have become a potential choice for backup-power generation in remote microgrids due to their reduced maintenance needs and long lifecycle. However, the efficiency of hydrogen FCs as backup power needs to be improved. The efficiency is related to system sizing and operational techniques. Thus, this paper proposes an efficient energy management strategy and optimal configuration models based on a hybrid system including photovoltaics (PVs) and hydrogen FCs to achieve a high operational efficiency and optimize the system configuration. The system model is built, and an energy management strategy is proposed first. This strategy determines when the PV and FC supply power to a load under various conditions. Proper sizing of the sub-components in the PV/FC hybrid system also plays an essential role in ensuring continuous power flow. To ensure power quality, the hybrid system configuration is estimated using a systematic method without affecting the power quality to minimize residual power losses. In addition, various essential simulations are performed to verify the proposed model. The results indicate that the total efficiency of the system can reach 47.9%.

Energy trading in the multi-energy market is affected by many uncertainties, especially the fluctuation of renewable energy sources and intermittent demand behavior of customers. The real-time energy management can effectively solve the impact of various uncertainties, ensure the instantaneous balance of energy and improve trading returns. A bidding strategy for multi-energy market is presented, in which reserve price adjustment and dynamic compensation mechanism is innovatively integrated into adaptive learning process. All energy trading participants conduct adaptive learning bidding adjustment based on real-time information in order to obtain higher transaction rate and transaction income. Meanwhile, adding dynamic compensation to the quoted price of fossil energy increases the consumption rate of renewable energy and reduces the emissions of pollutants. Furthermore, blockchain technology can ensure the seamless and effective performance of the presented bidding strategy. In the case study, the results show that our bidding strategy has an obvious advantage in social welfare and allocation efficiency than existing bidding strategies. Moreover, the problem of environmental pollution can be solved to a certain extent through flexible dynamic compensation. Finally, a decentralized application of blockchain is developed to demonstrate how the system could realize real-time energy management and dynamic trading in practice.

The optimum multilayered anisotropy distribution in [CoX/Pt]4 media is studied for microwave-assisted magnetic recording by micromagnetic simulations. The model of the magnetic CoX layer was built upon a regular mesh, including a Voronoi-tessellation polycrystalline structure. If each CoX layer has the same anisotropy, it is found that the moments of grains at the same in-plane position but in different layers may be in the opposite directions, which damages the signal-to-noise ratio (SNR) performance and the thermal stability of recording media. In the optimum media design, the anisotropy field constant $K_{n}$ decreases from the top layer to the bottom layer, where the magnetic moments of grains in each bits are switched nearly uniformly. A medium SNR gain of $1.5\sim 6.8$ dB assisted by spin-transfer oscillator field is confirmed for media with average $\bar H_{k}$ in a range of 2–2.4 T. A best SNR performance of 15.8 dB is found in media with an average $\bar H_{k} =2$ T, with ( $K_{1}-K_{4})$ of $2\times 10^{6}$ erg/cc and an interlayer exchange field constant of 2 kOe.

A micromagnetic model based on a regular mesh is built to simulate CoX/Pt magnetic recording media containing a polycrystalline structure model using the Voronoi tessellation method. The crystal grains have to be segregated in media; thus, the two phases in crystalline grain and at grain boundary are treated separately. The demagnetizing field in a cell is calculated by the fast Fourier transform method, even if the $M_{s}$ distribution is highly nonuniform: in cells at grain boundary, $M^{\prime } _{s}$ is much smaller. The $M$ – $H$ loops are simulated with different media parameters, such as interfacial anisotropy field $H_{k}$ , grain boundary parameters, and magnetoelastic field in plane. The orientation distribution of $H_{k}$ has to be controlled to keep high perpendicular magnetization before flipping.

A two-particle model is utilized to study the switching phase diagram of ECC media in the microwave-assisted magnetic recording, by applying an in-plane AC magnetic field for microwave and a DC magnetic field for write head field. An interesting result is found that, by introducing two mixed linearly polarized AC fields when the ratio of frequencies f2/f1 is in a certain range, the minimum switching DC field can be almost zero when Hac is 20% of Hk1 of hard magnetic layer. This phase diagram is also checked by a 3D micromagnetic model of ECC media with polycrystalline structure included; with two linearly polarized Hac applied, the coercivity Hc can be reduced to almost zero when Hac = 2.0 Hk1, and Hc still decreases from 10.5 kOe to 7.7 kOe when Hac = 0.02 Hk1.

The signal-to-noise ratio (SNR) gain of microwave-assisted magnetic recording (MAMR) with a spin-transfer oscillator is analyzed by a micromagnetic model simulating the write process on a CoX/Pt media, by including the Voronoi polycrystalline microstructure built upon a regular mesh of micromagnetic cells. With a fixed head of 40 nm-width main pole and a 10.5 kOe maximum perpendicular field, a 2 dB SNR gain is found for low-coercivity media (0 K $H_{c} kOe) and a 4 dB gain is obtained for high-coercivity media (0 K $H_{c}>14$ kOe). Besides, the MAMR performance considering the finite rise time of single-pole-type head field in a bit is also calculated. It is found that the MAMR SNR gain is obtained in the media with lower anisotropy because of the weak writing capacity during the rise time.

The CoX/Pt multilayer (X here represents the doping element to form grain boundary) is considered as the potential future medium owing to its high interfacial perpendicular anisotropy [1] utilized in magnetic assisted magnetic recording (MAMR) [2]. Meanwhile, microwave assisted reversal in Co/ Pt and Co/Pd has been reported in experiments [3,4]. The magnetic property and its writability of the CoX/Pt medium with various anisotropy energies K u are simulated by micromagnetics. The CoX/Pt media are modeled with the Voronoi polycrystalline structure built on a regular mesh, as seen in Fig.1. A micromagnetic cell is 1nm×1nm, with 0.4 nm thickness in CoX layer. The magnetic parameters of the two phases inside crystal grains and at grain boundary are treated separately.

In this work, lattice dynamics is utilized to calculate the phonon spectrum of L10-FePt crystal and molecular dynamic (MD) method is constructed to calculate the phonon density of state (PDOS), which can be compared to the result of lattice dynamics. By the phonon spectrum and PDOS, the optimum mode and frequency of the laser to activate phonons is found in L10-FePt, and the heating rate using different frequency and mode of the laser has been schematically simulated by MD method.

This review is focused on the latest developments in continuous-wave (CW) photomixing for Terahertz (THz) generation. The first part of the paper explains the limiting factors for operation at high frequencies ∼ 1 THz, namely transit time or lifetime roll-off, antenna (R)-device (C) RC roll-off, current screening and blocking, and heat dissipation. We will present various realizations of both photoconductive and p-i-n diode–based photomixers to overcome these limitations, including perspectives on novel materials for high-power photomixers operating at telecom wavelengths (1550 nm). In addition to the classical approach of feeding current originating from a small semiconductor photomixer device to an antenna (antenna-based emitter, AE), an antennaless approach in which the active area itself radiates (large area emitter, LAE) is discussed in detail. Although we focus on CW photomixing, we briefly discuss recent results for LAEs under pulsed conditions. Record power levels of 1.5 mW average power and conversion efficiencies as high as 2 × 10−3 have been reached, about 2 orders of magnitude higher than those obtained with CW antenna-based emitters. The second part of the paper is devoted to applications for CW photomixers. We begin with a discussion of the development of novel THz optics. Special attention is paid to experiments exploiting the long coherence length of CW photomixers for coherent emission and detection of THz arrays. The long coherence length comes with an unprecedented narrow linewidth. This is of particular interest for spectroscopic applications, the field in which THz research has perhaps the highest impact. We point out that CW spectroscopy systems may potentially be more compact, cheaper, and more accurate than conventional pulsed systems. These features are attributed to telecom-wavelength compatibility, to excellent frequency resolution, and to their huge spectral density. The paper concludes with prototype experiments of THz wireless LAN applications. For future telecommunication systems, the limited bandwidth of photodiodes is inadequate for further upshifting carrier frequencies. This, however, will soon be required for increased data throughput. The implementation of telecom-wavelength compatible photomixing diodes for down-conversion of an optical carrier signal to a (sub-)THz RF signal will be required.

The state diagram of spin-torque oscillator (STO) with perpendicular reference layer (REF) and planar field generation layer (FGL) was studied by a macrospin model and a micro-magnetic model. The state diagrams are calculated versus the current density, external field and external field angle. It was found that the oscillation in FGL could be controlled by current density combined with external field so as to achieve a wide frequency range. An optimized current and applied field region was given for microwave assisted magnetic recording (MAMR), considering both frequency and output field oscillation amplitude. The results of the macro-spin model were compared with those of the micro-magnetic model. The macro-spin model was qualitatively different from micro-magnetics and experimental results when the current density was large and the FGL was non-uniform.