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The challenges of modern power systems with a high level of renewable generation penetration will impose increased ancillary service on Photovoltaic (PV) systems, including frequency regulation. In this paper, a frequency regulation strategy for PV systems without energy storage is proposed. The proposed strategy is divided into two layers: the frequency regulation layer and the power reserve control layer. In the power reserve control layer, a novel power reserve control is proposed as the core element and novel feature. The existing power reserve control for PV systems must estimate the maximum available power, which is difficult because of the variation in environmental conditions and complex modeling of PV systems. Instead, a power tracking control method based on a curtailment power-current curve is proposed. It can automatically track a given reserve ratio under any environmental condition without an irradiance sensor. In the frequency regulation layer, the combination of droop control and virtual inertia control is used to adjust the given power reserve ratio. Simulation results show that the proposed strategy offers good accuracy and tracking speed and can significantly improve the frequency response of the power system.

Accurate estimation of lithium battery state of charge is very important for ensuring the operation of battery management system, realizing the energy management strategy of electric vehicles, reducing mileage anxiety and promoting the sustainable development of electric vehicles. In this paper, several studies are carried out for state of charge estimation of lithium-ion battery: (1) Aiming at the problem of parameter identification of battery model, an optimal identification method of model parameters based on ant lion optimization algorithm is proposed. (2) An adaptive weighting Cubature particle filter (AWCPF) method is proposed for SOC estimation. The proposed AWCPF method is based on particle filter (PF) algorithm, while the Cubature Kalman filter (CKF) algorithm is utilized to generate the proposal distribution for PF algorithm, which can retrain the particles degradation problem in PF algorithm. To solve the problem that the CKF algorithm is sensitive to noise, comparing with fixed sigma point weights of the conventional CKF, the weights of sigma points are adaptively adjusted based on state and measurement residual vectors. Furthermore, the process noise and measurement noise are estimated iterative. In this paper, experimental verification of different initial values of SOC under various working conditions is carried out. The results show that the proposed AWCPF algorithm based SOC estimation method has high estimation accuracy, strong robustness, fast convergence speed, with the maximum SOC estimation error is less than 1%.

This paper considers the off-line roadside unit (RSU) ON/OFF scheduling problem in a sparse vehicular ad hoc network (VANET). We formulate the problem as a mixed integer non-linear programming problem and propose a heuristic scheduling algorithm called Multi-Level Greedy (MLG) algorithm to find a suboptimal approximation. In the proposed MLG algorithm, we introduce the concepts of optimal service location and suboptimal service location. Then, we obtain the ON-OFF state matrix of RSUs. Simulation results show that the MLG algorithm can significantly reduce the energy consumption of RSUs deployed and can provide a reference basis for energy efficient scheduling in VANETs.

This paper investigates a linear generator (LIG) for application in high speed maglev train. Its configuration and working principle are introduced in detail. The mathematic models of magnetic field and induced voltage are established by the equivalent magnetic circuit method and the law of electromagnetic induction respectively, through which the characteristics of LIG and impact factors are analyzed preliminarily. An analytical and numerical method is proposed to analyze the characteristics of LIG further, in which the finite element model is applied to calculate the magnetic field accurately and the analytical model is used for the induced voltage calculation in a discrete way. The relationships of induced voltage amplitude and speed, induced voltage frequency and speed are clarified, providing the basis for the design of power supply system on vehicle. Finally, the experiments for the magnetic field and induced voltage are carried out on the performance test bench of magnet and real vehicle respectively to validate the analysis results.

Hydraulic transformer is a significant component to develop the future energy saving hydraulic systems; however, the speed stability is one of the problems in such a machine because the rotor is unconstrained and the rotary inertia of the rotor is small. In this paper, aiming at the rotating speed of hydraulic transformer, a hydro-dynamic model of is proposed considering the nonlinear friction, oil compressibility and leakage. Both simulation and experiment are carried out to validate the hydro-dynamic model. Moreover, this paper theoretically analyzes the low-speed stability of hydraulic transformer based on the hydro-dynamic model for the first time. The mechanism of creep is discussed and the creep judgment method is presented. The influence of each parameter on creep is investigated, and the possible ways to avoid creep are explored. The calculations of the lowest stable rotating speed and the lowest stable flow rate without creep are given. This study is significant for the design and practical application of hydraulic transformer in the future.

The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).

Electricity theft is one of the main causes of non-technical losses and its detection is important for power distribution companies to avoid revenue loss. The advancement of traditional grids to smart grids allows a two-way flow of information and energy that enables real-time energy management, billing and load surveillance. This infrastructure enables power distribution companies to automate electricity theft detection (ETD) by constructing new innovative data-driven solutions. Whereas, the traditional ETD approaches do not provide acceptable theft detection performance due to high-dimensional imbalanced data, loss of data relationships during feature extraction and the requirement of experts' involvement. Hence, this paper presents a new semi-supervised solution for ETD, which consists of relational denoising autoencoder (RDAE) and attention guided (AG) TripleGAN, named as RDAE-AG-TripleGAN. In this system, RDAE is implemented to derive features and their associations while AG performs feature weighting and dynamically supervises the AG-TripleGAN. As a result, this procedure significantly boosts the ETD. Furthermore, to demonstrate the acceptability of the proposed methodology over conventional approaches, we conducted extensive simulations using the real power consumption data of smart meters. The proposed solution is validated over the most useful and suitable performance indicators: area under the curve, precision, recall, Matthews correlation coefficient, F1-score and precision-recall area under the curve. The simulation results prove that the proposed method efficiently improves the detection of electricity frauds against conventional ETD schemes such as extreme gradient boosting machine and transductive support vector machine. The proposed solution achieves the detection rate of 0.956, which makes it more acceptable for electric utilities than the existing approaches.

Cell-free (CF) network is a favorable technique against inter-cell interferences to improve the network capacity. However, to further improve the network capacity, a large number of base stations (BSs) are required to be deployed with high cost and power consumption. To tackle this problem, an energy-efficient technique called reconfigurable intelligent surface (RIS)-aided CF network has been recently proposed. By replacing some of the required power-hungry BSs with low-power RISs, the energy efficiency of CF network can be enhanced with guaranteed performance. To achieve this goal, in this paper, we first formulate a joint active and passive beamforming problem to maximize the energy efficiency of RIS-aided CF networks. Then, we propose an alternate optimization algorithm to solve this problem. Specifically, we decompose the original energy efficiency maximization problem into multiple subproblems and solve them alternatively. Particularly, we adopt the zero-forcing (ZF) beamforming scheme to optimize the active beamforming at BSs, while the sequential programming (SP) method is adopted to realize the passive beamforming at RISs. Moreover, a realistic energy-consumption model for wideband RIS-aided CF networks is provided, and the effectiveness of the proposed scheme is evaluated by simulations. Finally, simulation results verify that, the proposed scheme is able to achieve a higher energy efficiency than the existing benchmark solution.