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In recent years, as the penetration rate of renewable energy has become higher and higher, the amount of power electronic equipment access has increased, and the power system has shown obvious “double high” characteristics, this puts forward higher requirements for the safe and stable operation of the power system. Based on the current background and research, this paper starts with the concept of resilient grid and studies the influence of the learning ability of resilient grid on the power system. First of all, this article analyzes the driving factors of learning ability from two perspectives of scientific and technological progress and policy orientation. Among them, scientific and technological progress includes internal driving and external driving, and policy orientation includes macro-level and micro-level; Secondly, this paper studies the influence of various factors on the restoration of the distribution network and the power market, and proves it through calculation examples; Finally, this paper summarizes and looks forward to the significance and role of learning driving factors in the construction of new power systems.

This paper focuses on voltage source inverters used in new energy user power supply and energy storage power supply systems. If it is equipped with linear load, the power quality of output voltage can be easily guaranteed. If it is equipped with a nonlinear load, the system is affected by output impedance, and the output voltage is easy to be distorted. In this paper, the mechanism of output voltage distortion of inverter with a nonlinear load is studied. It is concluded that the distortion is caused by the voltage drop of nonlinear load current in the output impedance of the inverter. We proposed an embedded repetitive control compensator to improve the output voltage quality. In addition, we give out the Bode diagram parameters select method. The simulation results show that the proposed control strategy is feasible and effective.

The DC transformer based on the Modular Multilevel Converter (MMC) structure has the advantages of robust scalability, low output voltage harmonic content, easy realization of back-to-back connection, and high power transmission efficiency. It is widely used in DC distribution networks. However, traditional control methods hardly provide inertia for the DC distribution network, thereby reducing the stability of the distribution network when new energy output fluctuations, DC distribution network failures, or other disturbances occur. Aiming at the problem that the DC bus voltage is easily affected by power fluctuations, the paper proposes a virtual inertia control method applied to the DC transformer with MMC structure. Similar to the introduction of virtual synchronous generators in the AC distribution network to enhance the system inertia, the paper designs a control strategy for dynamically adjusting the virtual inertia with a virtual capacitor added, which is based on the traditional two-phase shift control. The paper also presents the calculation method of the virtual capacitance. When the power in the DC microgrid fluctuates significantly, the strategy can effectively enhance the inertia of the DC distribution network and suppress the voltage fluctuation of the DC bus. The paper finally establishes a DC distribution network model with distributed power sources in the PLECS simulation software, and verifies the effectiveness of the control strategy compared with the traditional strategy.

Distributed Power Flow Controller (DPFC) is a power flow control device based on power electronics technology, which can achieve balanced and optimized power flow distribution, limit overload of power flow section and suppress power oscillations through coordinated control of multiple subunits. This paper analyzes the protection measures of DPFC when the AC line fault occurs, and puts forward the control and protection strategy of DPFC, which makes DPFC have fault crossing ability and improves the reliability of DPFC. The rationality and engineering applicability of the strategy of DPFC proposed in this paper are verified by analyzing the instantaneous fault of AC line during the actual operation of Huzhou DPFC project.

This paper presents the PLC-Based system for controlling the SCR power module which is AC–DC converter proposes to linearity output voltage with trigger signal. The technique converted the trigger signal by arccosine function and analog module in the PLC. Converted signal from PLC system is fed to SCR power module instant the traditional signal. Proposed of this paper describes the PLC hardware configuration, compute/math and trigonometric functions with Studio 5000 software. In addition, the interfacing, parameters configuration, and monitoring are considered for testing. The technique with PLC-based, devices configuration and experimental results have been presented will be useful for electric power supply in the industry with more and more demand to apply the PLC system to develop the traditional industry to semi or automatic control in the industry.

With the continuous deepening of power market reforms, studying the interaction of multi-stakeholder in multi-microgrid (MMG) energy trading is of great significance to improve the economic performance of the distribution network. In order to analyze the economic optimization method of multiple stakeholders in the distributed energy system, this paper proposes a multi-stakeholder benefit optimization method based on Stackelberg game theory (SGT), and establishes a local market energy transaction model. The model is composed of external distribution network, leader-intermediary agent (IA) and follower-microgrids (MGs), and uses the master–slave game method to solve the optimization strategy. Under this mode, the profit of each stakeholder in MMG increased by 2.64%, 4.24%, 1.38% respectively. The case results show that based on the method in this paper, multi-stakeholder in MMG can improve economic performance and can increase the level of energy autonomy of MMG.

In today’s world, Solar Photovoltaic energy sources have accredited robustness and reliability in the context of generation, transmission, and distribution of power for the end-users and utility customers. The accurate parameters analysis of Solar PV modules is necessary for the proper assessment, simulation, and optimization of power from Solar PV systems. The main goal of this paper is to apply a suitable approach to identify unspecified parameters of the PV module and at the same time to minimize (%) relative error and computation time to get better-optimized parameters for modeling of PV module. This paper presents a hybrid of a mathematical and iterative approach to analyze the appropriate parameters of the Solar PV model. Here, Root finding algorithms have been applied to extract the optimal unknown parameters of the PV modules by MATLAB programming. These algorithms are demonstrated on three distinct types of PV modules (Thin film, Monocrystalline, and Multicrystalline type) at standard test conditions and nominal operating conditions. These algorithms provide high precision and consume minimal time with ease of implementation. Extraction of parameters and simulation of the model is performed for validation based on datasheets of distinct types of PV modules using MATLAB/ SIMULINK software. The extracted precise parameters give rise to output characteristics of PV modules under variable environmental and electrical conditions. Relative error analysis has been evaluated to distinguish between different techniques applied for parameter extraction of PV modules. The relative error (%) of the proposed techniques for parameters extraction of PV modules varies from −0.5% to ＋0.5%, which shows the supercilious characteristic of the proposed techniques. Future exploration headings concerning the optimization of power from the Solar PV system are suggested.

A comprehensive review about machine learning application in power system especially in smart grid, renewable energy sector etc. is summarized in this paper. In the power sector, the power consumption is increased day by day very tremendously. So, it is very important that we have to generate the more power without disturbing the environment and whatever the generated power must be utilize effectively with minimum losses and higher efficiency. This will be possible with effective way of using the modern technology like machine learning (ML), artificial intelligence etc. This paper also describes the different types of machine learning techniques with diagram which will be very useful for many researchers who want to understand the basic fundamentals of machine learnings.

Smart Grid 2.0 is envisaged to automate the operations of the intelligent electricity grid. Blockchain and smart contracts are integrated to facilitate the transformation from DSO-centric operations to consumer-oriented, distributed electricity grid management. The envisaged smart grids, integrated with blockchain would provoke challenges, which would hinder the maximum utilization of Distribute Energy Resources (DERs). This comprehensive review aims at analyzing the applicability of blockchain technology in Smart Grid 2.0, which would facilitate a seamless decentralization process. Further, the paper elaborates the blockchain-based applications of future smart grid operations and the role of blockchain in each scenario. The paper further provides a concise analysis on the blockchain integration challenges, thereby ensure secure and scalable, decentralized operations of future, autonomous electricity networks.

Due to globalization and industrial development, the need of power is increasing day by day. In addition, due to the high demand for clean energy sources, the use of renewable systems has expanded in recent years. Wind systems can take advantage of the copious and free energy provided by the wind, which is a significant benefit. However, changing incident energy from renewables generate harmonics, which may result in poor system performance. The power quality has therefore become an important issue for both the power suppliers and consumers. Power electronic devices and nonlinear loads have been used frequently in industries hence introducing harmonic distortions in the industrial power distribution network. Harmonics have adverse effects on the power quality like degradation of power generation efficiency, higher operational costs, overheating of equipment, saturation of transformer, electromagnetic interference etc. In order to solve these problems, the implementation of suitable filtration techniques and the evaluation of efficiency with harmonic reduction are crucial. Since, utilities are always aiming to improve the power quality with minimum filtering units, this paper compares the performance of two available filter implementation plan, namely the shunt active power filter (SAPF) and the single tuned filter (STF) in a renewable penetrated industrial distribution network. Based on the outcome of the comparison, a genetic algorithm (GA) based approach is proposed in this paper to determine the optimum size and position of the minimum required filtering units that improves the network’s existing harmonic scenarios. Simulation results to validate the proposed methodology are presented. According to the results, the proposed method of incorporating filtering units significantly outperformed in lowering total harmonic distortion (THD) in an industrial power distribution network with a higher renewable penetration.

Today, power generation from clean and renewable resources such as wind and solar is of great salience. Smart grid technology efficiently responds to the increasing demand for electric power. Intelligent monitoring, control, and maintenance of wind energy facilities are indispensable to increase the performance and efficiency of smart grids (SGs). Integration of state-of-the-art machine learning algorithms and vision sensor networks approaches pave the way toward enhancing the wind farms performance. The generating power in a wind turbine farm is the most critical parameter that should be measured accurately. Produced power is highly related to weather patterns, and a new farm in a near area is also likely to have similar energy generation. Therefore, accurate and perpetual prediction models of the existing wind farms can be led to develop new stations with lower costs. The paper aims to estimate the angular velocity of turbine blades using vision sensors and signal processing. The high wind in the wind farm can cause the camera to vibrate in successive frames, and the noise in the input images can also strengthen the problem. Thanks to couples of solid computer vision algorithms, including FAST (Features from Accelerated Segment Test), SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), BF (Brute-Force), FLANN (Fast Library for Approximate Nearest Neighbors), AE (Autoencoder), and SVM (support vector machines), this paper accurately localizes the Hub and track the presence of the Blade in consecutive frames of a video stream. The simulation results show that determining the hub location and the blade presence in sequential frames results in an accurate estimation of wind turbine angular velocity with 95.36% accuracy. (C) 2021 The Authors. Published by Elsevier Ltd.

In this research paper Enhanced Electromagnetic Field Optimization (EEFO) algorithm and Lightning Process Optimization (LPO) algorithm are projected to solve the problem. Real power loss reduction is the main objective of the work. In the algorithm population is alienated into three fields: positive, negative, and neutral. Based on the attraction–repulsion forces among these electromagnetic positive, negative, and neutral fields, a new-fangled electromagnetic particle will be engendered in iterations. In Electromagnetic field optimization algorithm, solution symbolized by Electromagnetic Particle (EMPE) and is made through electromagnets which equal to the total number of variables. Among the neighbor’s every electromagnet will imply the attraction and repulsion which possess’​ positive, negative polarity. Then in this paper Lightning Process Optimization (LPO) algorithm is used to solve optimal reactive power problem. Naturally happening lightning process has been imitated to formulate the algorithm. Test points situated on the cloud surface and the ground area are considered as solutions. Objective function is computed with all test points and taken as electric field. With reference to the function of head tip elevation, cloud altitude, channel charge subsequent bound of upward leader is interrelated to charge of the channel. With considering voltage stability index EEFO and LPO algorithms are verified in IEEE 30 bus system. Then EEFO and LPO algorithms evaluated in standard IEEE 14, 30, 57, 118 and 300 bus test systems without considering the voltage stability index. Minimization of real power loss with voltage stability index enhancement has been attained and voltage deviation also minimized.

Maintaining a generation-demand balance becomes more challenging nowadays due to the limited availability of traditional automatic generation control (AGC) and spinning reserves. In response to this concern, the purpose of the proposed paper is to bridge the gap by introducing battery energy storage system (BESS) control loop in the load frequency control (LFC) system. Furthermore, a minimum variable share of demand response (DR) is utilized as an additional mean to regulate the system frequency. The growing intermittency of controllable loads calls the awareness to incorporate frequency-sensitive loads by considering a variable load-damping coefficient in LFC problem. The presented control scheme demonstrates a stable power operation with the optimal sharing of BESS, dynamic DR, and the supplementary control loop. Moreover, because of the continuous and sudden variations in the demand, adaptive LFC controller is designed using fuzzy logic based on particle swarm optimization (PSO) tuning method. The proposed controller ensures a stable and robust frequency regulation of the system under load fluctuations. In this paper it is revealed that adding the BESS control loop is very effective in enhancing the performance of LFC as it can deliver fast power compensation. The obtained results prove the capability and effectiveness of the proposed method in an isolated power system.

To increase the energy utilization efficiency, it becomes fairly promising to convert the surplus electricity from renewable generation to other forms of energy for multi-dimensional consumption. In this paper, we propose a hybrid energy scheduling model for a multi-energy microgrid with the integration of the hydrogen energy storage system (HESS) and the heat storage system (HSS). In our study, the operational uncertainties induced by renewables and loads (including electrical, hydrogen, and heat demand) are comprehensively considered. We investigate such an operating regime that HESS stores the surplus electricity in case of abundant renewable generation and generates electricity through hydrogen fuel cells otherwise. Further, heat units including HESS, combined heat and power (CHP), and external heat suppliers are modeled in this paper. We split the decision-makings of energy scheduling for both the day-ahead stage and real-time stage to tackle the power balancing issues. To effectively solve the aforementioned optimization model, a flexible weighted Model Predictive Control (weighted-MPC) strategy is proposed, in which the receding horizon can be suitably adjusted according to the forecasting accuracy of system uncertainties. The effectiveness of the proposed hybrid model for microgrid energy scheduling is comprehensively verified through extensive case studies.

A new efficient technique for the best selection of the unknown variables in the Solid Oxide Fuel Cell (SOFC) stack models is proposed in this paper. The main concept in this paper is the minimization of the sum of squared error values between the empirical voltage and current profile and the obtained voltage and current profiles from the method. The minimization process is defined by a new improved metaheuristic, which is the Modified African Vulture Optimizer (MAVO). The MAVO algorithm is designed to modify the algorithm and achieve results with better effectiveness as concerns convergence and accuracy. For determining the system consistency, two scenarios based on pressure and temperature variations are investigated. The technique has been finally compared with several other techniques to verify its prominence. The results show the minimum value of the SSE under different temperatures, equal to 1.87 e−4, and the minimum value of the MSE under different temperatures, equal to 1.24 e−3. This indicates promising results for the proposed method as a proper identification system. Final achievements indicate that the suggested approach provides outstanding effectiveness toward the compared methods.

To better control the PEMFC air flow, this paper introduces a distributed deep reinforcement learning-based adaptive proportional integral (PI) controller for controlling air flow in a proton exchange membrane fuel cell (PEMFC). In this paper a novel algorithm for tuning the controller is proposed, termed the multi-role exploration strategy distributed deep deterministic policy gradient (MESD-DDPG), which builds upon the deep deterministic policy gradient (DDPG) by employing various trick including a multi-role exploration strategy. In this strategy, explorers and followers with different exploration principles are used for distributed exploration to enhance the exploration efficiency. Moreover, a probability experience replay mechanism is included to foster the training efficiency. In addition, hot start mechanism of experience pool is applied to deal with the reward sparsity problem. The MESD-DDPG also includes a number of innovations for solving the problem of Q-value overestimation. The proposed MESD-DDPG algorithm considers the PI coefficients naturally in the objective and enables the controller better online coefficient adjusting ability via learning, which leads to improved controller adaptability. The simulation results demonstrate the adaptability and the excellent performance of the MESD-DDPG adaptive PI controller.

This paper innovatively applies fuzzy comprehensive evaluation (FCE) method with analytic hierarchy process methodology (AHP) to quantitatively explore the academic value of literature about model predictive control (MPC) as energy management strategy (EMS) for hybrid electric vehicles (HEVs). Among that, Date of publication, Journal Impact Factor, Journal Cite Score and Times Cited are considered as evaluation factors of literature. Then classifications of MPC framework from six perspectives, such as hybrid types and working scenarios, prediction method, cost function, MPC solver and benchmark EMSs in the comparison, are proposed for the first time. The quantitative academic value of literature is reversely assigned to each perspective of MPC with the aid of above method and further study like benefits and limitations of each method. Finally, the academic contribution of method in each perspective is also obtained to objectively analyze future development trend of kinds of method in MPC framework. This paper can not only provide a new method to summarize and analyze the literature about works of interested area, but also supply a comprehensive and detailly view for relevant scholars with the effective suggestions.

Virtual synchronous generator (VSG) control is a promising control method in microgrid (MG) for the inertia support characteristic. However, the VSG inherits the oscillation feature of synchronous generator (SG), thus the active power and frequency oscillation may be observed when VSG control is applied. In MG, the oscillation may cause the unreasonable dynamic active power sharing among VSGs. Subsequently, the VSGs are prone to overload and even unstable, and the existing works based on consensus control cannot suppress the oscillation effectively. In order to tackle this problem, this paper proposes an enhanced second-order-consensus-based distributed secondary frequency controller by introducing the active power second-order consensus control, and reveals the oscillation suppression capacity of the second-order consensus algorithm in depth. Since there is no specific control requirement for the second-order variables, i.e., the change rate of active power and frequency of MG in steady state, the proposed controller can be simplified by replacing the integral control of second-order variables with the proportional control of first-order variables, i.e., active power and frequency, thus the communication data volume will not increase comparing to the traditional first-order-consensus-based controller. This paper also provides a small-signal model of MG with the proposed controller to evaluate the system dynamic performance and design the parameters. Finally, the simulation results verify the effectiveness of the proposed controller.

Prosumer-based district heating networks attract an increasing interest in energy research. There are numerous publications addressing the prosumer integration into district heating networks with a focus on grid side operation. However, the operation of the prosumer side has not been extensively investigated in the literature where bidirectional heat transfer stations, heat generators, consumption and storages can be connected in different ways. These different connections have different influences on the district heating network operation that require deeper analysis and understanding. This paper evaluates the influence of using different prosumer side system configurations as well as their suitability for prosumer-based district heating networks. Beginning with the characteristics of possible prosumer side configurations this paper evaluates the applicability of these configurations according to the number of components and operational flexibility. Subsequently, the most promising subset of the evaluated configurations are simulated in realistic scenarios using SimulationX® software and its Green City toolbox to gain detailed insight into their operation and efficiency. The simulated configurations are analyzed with respect to exportable excess heat, grid temperatures and the overall efficiency of the heat supply. The configurations are studied in various scenarios that differ in heat generation type (heat pump, solar thermal collectors or combustion device) and the necessary supply temperatures on the prosumer and grid side. In conclusion, this paper provides a decision guidance to select the most suitable prosumer side configuration for a desired district heating network and consumption temperatures.

The occurrence of a large spontaneous leakage in a district heating network leads to a pressure wave through the entire network. For safe operation, it is necessary to determine the leakage location and to exclude the affected network part. For optimal evaluation of the pressure wave, pressure sensors must be placed in the network to provide high-quality data. Already installed sensors can be used, but improved transmission technology is needed. For practical application, as few sensors as possible should have to be equipped accordingly. This paper addresses the questions of how many sensors are needed and where they need to be placed for a given network. In a joint research project, a method was developed that allows the evaluation of the pressure wave even with noisy input data. The method used for pressure wave evaluation works in two stages. In the first stage, the pressure drop times are determined from the measurement data. In the second stage, these times are compared with test leakages, for which corresponding pressure drop times are calculated. The second stage provides the test leakage locations at which the leakage can probably be localized. For the placement evaluation, all possible leakages should be included and compared with all test leakages. This paper shows how the resulting large amount of data is consolidated to achieve a suitable metric for the quality of the placement. This procedure is used for the optimal positioning of the sensors and the network is currently equipped accordingly. From the calculations, results for different positioning and with different numbers of sensors are shown. Additionally, it is addressed that some positions in the network might be unsuitable due to unfavorable superposition of traversing waves and resulting difficulties to detect the pressure drop time points. Exemplarily simulation results and measurement data are shown.

Harmonic current detection is a key algorithms for shunt active power filter controller, which generates reference currents to compensate the harmonics of grid current. This paper presents a new harmonics detection method based on the triangle orthogonal principle algorithm. Compared to the conventional harmonic current detection methods, the presented method has the advantages of simple structure, low calculation, and fast execution time. Using the phase signal detected by the phase-locked loop synchronized with the power grid signal, the method based on the triangle orthogonal principle can provide a simple method to detect the reference current. At the same time, because the method proposed in this paper has lower implementation complexity and less microcontroller resource consumption, low-cost microcontrollers can be used to implement the proposed method, which has practical engineering value. Finally, this paper analysis the proposed method in detailly and verifies its great performance through experimental results.

Secondary Lithium-ion batteries are widely used in a variety of sizes from single cells in personal electronics, to large packs in Electric Vehicles (EVs), and very large packs in grid-scale storage. With the number of applications increasing, this brings more laboratory testing of such batteries. A number of papers discuss the safety intrinsic to cells, however, excluding EVs there is little discussion of cycling larger, multi-cell packs. This paper considers safe laboratory testing of commercial high capacity single cells and multi-cell battery packs. Using the hazards and failure modes that can occur, appropriate control measures are proposed, including fuses, contactors and good practice in system design. Suitable enclosures and fire suppression are also recommended should these measures fail. For single cells without a BMS, a separate protection circuit is presented utilizing analogue components for reliability.

This paper proposes an online data splicing power flow analysis method based on sensitivity analysis. Since the step-down transformer in the whole network data is usually equivalent to the 220 kV side, the equipment below 220 kV is not modeled in detail, and the provincial network data is modeled to 110kV and 35 kV voltage level equipment in detail. Different levels of modeling have a greater impact on the accuracy of stability analysis and calculation. Therefore, the purpose of improving data accuracy is achieved by splicing the entire network data (external network) and provincial network data (internal network). This paper is based on the sensitivity analysis method to stabilize the tie-line power by adjusting the output of the external network units, and form the whole network data that can accurately analyze the stability characteristics of the provincial network. The problem of power flow deviation caused by the voltage and angle deviation of the nodes at both ends of the regional tie line during the data splicing process is solved. Through the actual application of splicing the entire network and Hunan Provincial Network data, the effectiveness and necessity of the proposed method have been verified.

In order to improve the reliability of the shunt active power filter(SAPF) system, the fault-tolerant control strategy of the system is studied in this paper. A fault-tolerant control strategy based on improved switch redundancy is designed inside SAPF system, and fault-tolerant is achieved through circuit reconstruction. Then the operating mode and voltage vector characteristics of the fault-tolerant topology are analyzed, and the corresponding SVPWM control algorithm is derived. Finally, experiments verify the practicability and effectiveness of the SAPF fault-tolerant control method proposed in this paper.

The connection relationship of distribution network topology is of great significance for the maintenance and fault diagnosis of distribution network, and scheduled power outage optimization. At present, the verification of topological documents mainly relies on on-site inspection, which consumes a lot of manpower and material resources and is inefficient. Therefore, an efficient method for topology verification of low-voltage substation areas is required. Given this background, a model for error correction and user access phase identification of low-voltage stations based on multi-dimensional voltage data collected by smart meters is presented in this paper, which can provide a certain reference for topology identification and line troubleshooting of low-voltage substations. First, the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm and the Principal Component Analysis (PCA) performs dimensionality reduction on the original load data to solve the problem of redundancy caused by the high dimension of the original voltage data set. Second, the Local Outlier Factor (LOF) algorithm is used to identify abnormal samples in the voltage data set. Then, the spectral clustering method is used to cluster the dimensionality-reduced load data to realize the phase identification of single-phase users in the low-voltage station area. Finally, the real data of a certain area in Haining, Zhejiang Province of China are used as simulation cases for demonstrating. The results of the case studies show that the model proposed in this paper is feasible and effective.

In three-phase grid synchronization applications, the performance of traditional phase-locked loops will decrease under non-ideal grid voltage conditions. In order to solve the above problem, moving average filters (MAF) has been widely studied and utilized in PLLs in recently published papers with the expense of slower transient speed. This paper proposes a paralleled filtering stage to improve the dynamic performance of PLL. The suggested filter with smaller time delay is based on MAF and delay signal cancellation (DSC) operator, which is integrated in quasi-type-1 structure based PLL (QT1-PLL). Compared with differential MAF-PLL (DMAF-PLL) and QT1-PLL, the proposed filter can obtain a faster transient response without degrading phase estimation accuracy. The experimental results demonstrate that the proposed method can provide satisfactory performance.

In order to realize the precise management of investment in equipment overhauls technical innovation project, this paper fully analyzed the cost characteristics of equipment in every section of the life cycle and established the actual available Life Cycle Cost (LCC) model and provided a combination forecasting method to forecast the future costs. Then this paper obtained the full life cycle cost of the equipment for overhauls technical innovation project. By comparison of the two average annual LCC costs of overhaul and technical innovation project, the final optimization project with the best economic benefit could be determined. Case analysis verified the economics of the method, which could provide decision support for precise investment in the power grid.

The development of ultra-large-scale energy storage system(ESS) is beneficial to integrate the real-time renewable energy generation with uncertainty and intermittent features and provide effective capacity support for the power grid. Since renewable energy has strongly decentralized feature, it is effective way to develop distributed dispatch strategy for storage devices to integrate them. This paper focuses on built a multi-timescale dispatch model for ESS. The goal of the proposed model is to minimize the day-aheadoperation cost with better charging/discharging operation allocation for each ESS in each dispatch interval during one day, and smooth the real-time fluctuation s of renewable energy. To address this issue, this paper presents a distributed multi-timescale and discrete-time-gradient optimization algorithm by combining consensus and optimization theories. By using this method, each ESS can response to the change of renewable generation and obtain the optimal operation by using local information share, resulting in better flexibility, robustness, scalability and privacy, etc. Thus, it is suitable for distributed model development and optimal dispatch for ultra-large-scale ESS. Finally, the paper is tested on a system with eleven ESSs and five renewable energy generations. The simulation results show the feasibility and effectiveness of the proposed model and algorithm.

Wind conditions are the most critical concerns during offshore wind turbine installation. Unfavorable wind can cause safety issues and a long time waiting, resulting in project delay and huge costs. Offshore wind energy industry utilizes weather forecast reports, however, many times the reports are found inconsistent, particularly in wind speed. The decision for installation work is then hard to make. The present paper provides a solution, by using the concept of time lag in Cross-Correlation and Pearson Correlation Coefficient, to analyze the similarity degree of the two reports, as well as to predict the next opening of weather window and the periodicity of the window. Not only aligning the reports, but also precisely judging of wind types are critical. The paper introduces a new view, in simulating four wind models, and defines their wind characteristics by Fourier Transform in frequency domain, to identify wind types in the anemometer measured data. The results show wind types in the anemometer data are identified. In whole paper, the industrial standard Certificate of Approvals are considered. Moreover, two new Certificate of Approvals specific to the above situations are proposed, for the industry’s reference during offshore wind turbine installation. The purpose of this paper is to assist scientific/engineering frame works and the decision making for offshore wind, in the way of establishing new Certificate of Approvals. All inputs in the present paper are random or measured by author’s personal devices to avoid the infringement of intellectual properties and patents.

The adoption of the droop control provides a feasible scheme for the distributed generation connected to the utility grid. Nevertheless, it does not contribute to the inertia and damping factor necessary for power systems. In order to achieve increased stability of microgrids, some scholars simulate the operating characteristics of traditional synchronous generators through control equations, thus virtual synchronous generator control (VSG) was developed. Firstly, the small signal models of inverter based on droop control and VSG control are built in grid-connected (GC) state and stand-alone (SA) state to compare the dynamic response when load transition and power reference change. And we can deduce that in terms of dynamic response neither of these two control strategies can simultaneously satisfy the stringent requirements of different modes of operation. Then, by integrating the advantages of the traditional droop control and VSG control, inertial droop control is introduced in this paper. The presented inertial droop control not only features virtual inertia but also adds compensators to the output power part of the active power control loop to ensure the dynamic response of the system. In the GC state, inertial droop control helps to restrain power oscillations, shorten power adjustment time, and minimize frequency fluctuations to maintain frequency stability. In the SA state, the inertial droop control enables to achieve high enough inertia and damping properties. Finally, this paper demonstrates the superiority of the inertial droop control by means of simulation results.

This paper researched on the pre-synchronization grid connection method of a virtual synchronous generator (VSG), and proposed a fast pre-synchronization method based on the virtual power loop. According to the self-synchronization characteristics of the VSG power loop, the double power loop design was adopted in this paper. The two power loops had the same structure, which the power loop 1 was used to implement the basic VSG function, and the power loop 2 was used to achieve rapid pre-synchronous control. Therefore, the mathematical model of the VSG was similar to the synchronous generator in the pre-synchronization control. This method did not require a phase-locked loop and VSG has a good inertia and Damping performance. Moreover, this method was suitable for the networking of multiple VSGs.

When grid impedance cannot be ignored, high-frequency harmonic amplification often occurs when Doubly-Fed Induction Generator (DFIG) generation system is connected to a non-ideal grid with background noise. This paper analyzes the mechanism of high-frequency harmonic amplification of DFIG wind power generation system under non-ideal grid, and quantifies the damping through the equivalent RLC one-port impedance model. The concept of voltage amplification factor is proposed to guide the design of virtual resistor for harmonic suppression. Based on the design results, the virtual resistor is realized by an active damper connected in parallel with the DFIG system at the point of common coupling (PCC). Considering the limit of this device, this paper designs the lower limit of the virtual resistor in case of over-modulation problem. Finally, a controller hardware-in-the-loop experiment is carried out based on the RT-LAB platform, and the experimental results verified the correctness of the analysis and the effectiveness of the device.

In nuclear power plant (NPP), the digital control system introduced nuclear safety software, whose fault tolerance and reliability directly affect the safety of nuclear power plant facilities. Current manual, simulation and testing strategies all have limitations. An effective method to test the system with software plays an important role to ensure the safe operation of NPP. In this paper, a digital control system software test strategy based on real platform is proposed. Taking the RPS system as an example, this strategy analyzes the system function and software logic path characteristics, then identifies the system state by threshold boundary analyzing, and designs a test method based on the random combination of logical combination paths and parameter value intervals, and builds a real platform test environment. This paper has tested the typical functions of Pressurized water reactor (PWR) protection system. The test results show that the test strategy has significantly improved the coverage of paths and parameters. Compared with manual testing, test efficient has been improved obviously in this test platform. At present, this strategy solves the problem of the low efficiency in manual testing, low real-level in simulation testing, and the insufficient coverage in simple automated testing strategy. This strategy also provides good practice for the control system testing and lays the foundation for the reliability evaluation of the system and software.

In order to effectively solve the problems of resource waste and environmental pollution caused by the gradual increase of power battery decommissioning scale, retired power batteries used in active distribution network (ADN) is one of the solutions. Considering the difference of initial state of each cell, a capacity allocation method of energy storage system(ESS) for ADN considering health risk assessment is proposed in the paper. Firstly, the assessment method of health risk for retired power battery based on state of health (SOH) is in the paper studied considering the influence of different discharge depth and times on the life loss. Secondly, the ESS capacity requirements of different time levels are determined based on the input and output characteristics of ADN. The state of charge (SOC) dynamic safety margin of decommissioned power batteries is set up taking the health risks during the operation of decommissioned power batteries into account. Finally, the location and capacity of ESS is determined considering the balance of supply and demand characteristics of ADN and echelon utilization ESS of residual life cycle cost. Simulation analysis and experimental results show that the configuration method proposed in the paper can effectively reduce the configuration cost and improve the service life of power battery.

In recent years, with continuous focus on clean energy and environmental protection, the scale of photovoltaic generation industry in China has been gradually expanded, making great achievements. However, it also faces huge challenges and problems such as fierce market competition, serious photovoltaic curtailment, high cost, etc. In order to promote the sustainable development of photovoltaic industry, this paper constructs an energy storage-involved photovoltaic value chain (ES-PVC) consisting of three nodes for upstream, midstream and downstream, in which photovoltaic power suppliers, battery energy storage business and electric vehicle manufacturers locate respectively. The coupling problem for node enterprises in the value chain is studied by multi-objective optimization and G1 method, which is measured eventually via a single value transformed by ideal point method. Considering the particularity of issue, the paper proposes an improved genetic algorithm (IGA) to fit and solve the model. A best value chain is acquired via proposed approach whose feasibility and applicability are verified through comparative analysis with analytic hierarchy process (AHP) and linear assignment method (LAM) and sensitivity analysis. The implications and limitations are presented in the conclusion, aiming to provide theoretical reference for value management of photovoltaic enterprise and to get further research.

The harmonic injection method is often used to measure the impedance of grid-connected inverter, which is necessary for impedance-based stability analysis of grid-connected systems. This paper proposes an online impedance measurement range analysis method that takes into account the grid impedance, electrical signal measurement range, and the upper limit of safety response of the equipment under disturbance in online measurement. This method provides a basis for the verification of impedance measurement results. It also can guide the design of the amplitude of the injected disturbance. The method helps to provide more accurate impedance measurement results, which is important for impedance-based stability analysis. Finally, this paper verifies the proposed impedance measurement range through simulation.

The aim of this paper is to present a comprehensive literature review on the application of decomposition methods of time series forecasting in power systems. A comprehensive search is conducted, relevant publications are identified and summarised in terms of the aim of the forecast, decomposition forecasting method used, and a comparison is made with other forecasting techniques. Moreover, publications are analysed by number of publications per year, number of papers by the decomposition method used, and number of publications by area of application of the method in power systems. It is shown that the number of publications per annum grew substantially after 2014 due to studies on the application of decomposition methods in forecasting distributed generation output. Of the methods of forecasting, most publications have used multiplicative decomposition, a lower number has used additive decomposition, with the balance of the papers only using decomposition of the time series as an initial phase towards forecasting using other techniques. The analysis of the papers also shows that decomposition methods have been used in power systems mainly for load, price and distributed generation forecasting. The review contributes to literature by providing an extensive overview of the papers and a repository of references to researchers interested in decomposition forecasting techniques.

Capacitor banks used in substations cause a maintenance problem, which consume time for technicians to identify the root cause of the problem which can result in voltage control issues. Therefore, aim of this project is to identify either the unit or element fails within the capacitor bank using the dedicated voltage differential protection function. The voltage differential across the capacitor bank is calculated using the Capacitor Bank Assistant (CBA) tool in AcSELerator quickset. There are two modes of operation are considered (a) Alarm and (b) Trip. The alarm mode is used to notify the system operator for small change in system voltage due to element fail within capacitor bank, whereas in trip mode operation the change in voltage reaches the maximum threshold value and send the trip signals in order to disconnect the faulty banks from rest of the power system. The paper designed the voltage differential logic in AcSELerator quickset for both alarm and trip modes respectively. The system-based voltage differential protection function testing for shunt capacitor banks is introduced in this paper. The reason for introducing the system-based testing is because two CMCs are used to make a connection for VZ and VY channels of SEL487V IED in order to test the voltage differential function. The two CMCs are synchronized in Relay SimTest software using GPS signals from SEL satellite clock (SEL-2407) and Antenna. Relay SimTest software has a provision to model the capacitor bank and simulations result are analyzed for alarm and trip mode operations respectively. In addition to the main protection (voltage differential), this manuscript provides the protection setting and simulation results for the overload backup protection for SCB’s. However, it is observed that voltage differential scheme for SCB’s is more sensitive in comparison with overcurrent backup protection. The cost of the voltage differential is more expensive than the backup overload protection because of using only with CT signals. In summary, it is a good practice to implement both Main (Voltage differential scheme) and Back-up (Overload) protection schemes in order to effectively protect the SCB’s from any kind of internal faults in the system. Finally, the IEC61850 GOOSE message application is considered for SCB’s. The engineering configuration is performed for two logical nodes A87PDIF1 (alarm mode) and T87PDIF1 (trip mode). The IEC61850 GOOSE simulation results are analyzed using IED Scout. Finally, hardwired and IEC61850 GOOSE simulation results are compared. The GOOSE operation is faster than hardwired signals for shunt capacitor banks voltage differential function.

The impact assessment of energy strategies, more specifically those that promote an integrated approach on resource management in other sectors such as water and food, requires knowledge related to the evaluation of the quality and knowledge that may be estimated by quantitative means. The present paper makes inquiries into those knowledge requirements in addition to review the means used to obtain it—including the required entries and the results they provide. In response to the recognized problems in knowledge, this paper introduces a basic reference structure underlying a system to evaluate the way that a progressive development of inexhaustible energies in a particular geographical region can affect the demand of water and food. Then, the proposed conceptual framework constitutes a novel approach for energy policy makers which only consider partial impacts of the energy management. By considering the nexus of energy, water and food, energy management policies may be redefined and differences with current policies must be investigated.

This paper reports on the different engineering, social and financial challenges behind the building and deploying electric energy monitoring and eco-feedback technology in real-world scenarios, which despite being relevant to the research community are seldom reported in the literature. The objectives of this paper are two-fold: First, discuss the technical and social constraints of real-world deployments. This includes, for example, hardware and software requirements, and issues related to security and intrusiveness of the monitoring solutions. Second, identify and understand the costs associated with developing and deploying such systems. These include hardware costs and consumed energy. To this end, we rely on over five years of experience developing and improving a non-intrusive energy monitoring research platform to enable the deployment of long and short-term studies of eco-feedback technology. During this time, two versions of that platform were deployed in 50 homes for periods that lasted between 6 and 18 consecutive months. By iteratively developing and deploying our sensing and eco-feedback infrastructures, we managed to build upon previous findings and lessons learned to understand how to create, deploy, and maintain such systems. Concurrently, we gained insights regarding what are some of the most relevant costs associated with running such experiments.

This paper presents the importance of load management methodology (LM) and helps in clarifying the difference between load management versus demand-side management (DSM), as LM is a crucial method of controlling the loads especially in peak periods, to prevent tripping. It was found that the previous researches focused on the dual tariff meters to force and motivate the consumer to change the demand time which was a gleam in overcoming the phenomena. However, the dilemma of the DSM was relying mainly on the consumer, who has to change the time of his needs. Therefore, it is difficult to be controlled. Consequently, they cannot find effective feedback as well. On the other hand, LM relies only on the designer or service provider in re-distributing loads according to the load behavior studies (time, location, and type of user) without depending on the consumer in solving the phenomena of extreme fluctuations of loads on the electrical equipment. In this paper, the second methodology is by the service provider, it is going to be handled and discussed by Selecting medium voltage (MV) feeders, re-distribute loads equally, and variously on transformer preventing unifying load which causes overloaded transformer on one period rather than the other. Consequently, it will lead to protect electrical equipment from no-load or overload effects, selecting the exact size. That will improve the economic status and will lead to a stable, balanced system, besides reduce heat island effects and prevent load shedding caused by sudden overloading. The model was created by using DIgSILENT power factory software.

Wind power experienced a substantial growth over the past decade especially because it has been seen as one of the best ways towards meeting climate change and emissions targets by many countries. Since wind power is not fully dispatchable, the accuracy of wind forecasts is a key element for the electric system operators, as it strongly affects the decision-making processes. The planning horizon can be short term (1 -3 months) and long-term (6–12 months) depending on the process.The objective of this paper is to conduct a performance comparison of five deep learning models each combined with three types of data pre-processing and used for short term and long-term multi-variate predictions. The input data are time series of the wind power capacity factor and the temperature. In addition, this paper sets out to demonstrate and review the state-of-the-art deep learning models for prediction with a secondary objective to present the reader a reference point to better understand which model to choose and what factors are significant. The first contribution of this paper is to apply, assess and compare a selection of the novel and cutting-edge deep learning models for multi-variate prediction. Multi-variate predication is achieved through a proposed multiple input and multiple output (MIMO) architecture. Compared to traditional prediction models, machine learning techniques have the advantage of generalization. Among various techniques deep learning is particularly getting more attention due to the applicability to various dataset such as numerical and character. This investigation focuses on five models — Deep Feed Forward (DFF), Deep Convolutional Network (DCN), Recurrent Neural Network (RNN), Attention mechanism (Attention) and Long Short-Term Memory Networks (LSTM). The second contribution is to propose a novel approach to transform the time series dataset to signal for input and reconstruct the model predictions through inverse transformation, by means of the so-called discrete wavelet transformation and fast Fourier transformation. The different models are assessed also by comparing their performance with and without the input dataset manipulation through wavelet and FFT transformation. Beyond that, the model performances are outlined in detail, to give the reader an overview of the models to choose from for short-term or long-term prediction. The results demonstrate that the Attention and DCN perform best with Wavelet or FFT signal, whereas some other models perform better with no data preprocessing. Keywords: Unsupervised machine learning, Multi-variate prediction, Short and long term prediction, Wind power forecasting, Time series to frequency transformation, Performance evaluation and comparison

This paper briefly introduces the characteristics of Distributed Combined Cooling, Heating and Power (CCHP) System, and indicates that the off-design conditions are the root cause of the performance reduction of system. An active regulation method based on system performance improvement is proposed in this paper, including component regulation method at the unit equipment level and unit equipment regulation method at system level. In addition, the study focusing on the impact of energy storage active regulation at system level on system performance is performed on the 100 kW experimental platform. The experimental results show that: the active regulation method in all conditions of system, compared with conventional generation system, has a primary energy saving rate of 76%, and a relative energy saving rate of 29.5%, and the system performance is significantly improved. Keywords: Distributed energy system, Combined cooling, Heating and power (CCHP), Off-design conditions, Active regulation, Energy storage / cool storage

This paper presents a survey on enhancement of power system performances by optimally placed distributed generations in distribution networks. Thedistribution power system performances such as minimization of real power loss, minimization of reactive power loss, improvement of voltage profile, short circuit current capacity, system average interruption duration index (SAIDI), customers average interruption duration index (CAIDI), power system oscillations, available power transfer capacity of system, loadability of the system, reliability and security of the system, power system stability etc. are consider in this survey paper as major issue in distribution networks. Such power system performances can be enhance by conventional devices as well as planning of DG technologies such as fuel cell, biogas, wind turbine, photovoltaic, solar, geo-thermal, tidal and wave etc. This survey paper is very much useful for scientific persons, engineers, industrial persons and researchers regarding with enhancement of power system performances by optimally placed distributed generations in distribution networks. Keywords: Artificial intelligence techniques, Distributed generation (DG), Distribution networks (DNs), Optimally placed distributed generation, Power system performances

In recent years, power grid enterprises have continuously promoted the construction of intelligent operation inspection system. Pilot applications of various equipment status sensors, online monitoring devices, intelligent wearable devices, mobile terminals and Beidou navigation satellite system have been gradually carried out. The configuration range of intelligent equipment such as UAV and robot has been continuously expanded, and big data analysis technology, artificial intelligence diagnosis technology have also been preliminarily applied. With the continuous development and breakthrough of key technologies such as edge computing and wireless transmission, it provides favorable conditions for the intelligent, diversified and ecological development of the construction and application of the IOT for power transmission and transformation equipment. This paper mainly introduces the overall architecture and functional positioning of the construction of power transmission and transformation IOT, and combined with the business needs of some typical application scenarios in the operation of power grid equipment, to promote the integrated application of IOT technology and power grid equipment operation and inspection business, and also establish an open, shared and symbiotic IOT ecosystem of power transmission and transformation equipment, so as to lay the foundation for intelligent equipment management.

Along with the deepening of the power system reform, the input and output evaluation and analysis of asset became more and more important. Arounding the goal of comprehensive optimization of safety, efficiency and cost throughout the life cycle of assets, this paper constructed a quantitative evaluation model for asset input–output analysis, and put forward an input–output analysis method combining the boston matrix method and the equivalent analysis method. Actual substations’ input cost and output efficiency analysis verified the method feasibility, which could provide decision-making support for optimizing power grid asset management strategies and enhancing asset value operation benefits.

Integration of the pulsed power load (PPL) into the power system leads to high fluctuations in the frequency and voltage at the point of common coupling (PCC). This paper presents an effective solution to overcome these issues related to the PPL using the superconducting magnetic energy storage (SMES) system that has the features of high power capability and fast response. The fuzzy logic control technique (FLC) is employed to control the interfaced DC–DC chopper of the SMES to regulate the active power of the PCC and the state-of-charge (SOC) of the SMES coil. Two scenarios have been addressed with/without integrating the SMES conversion system. The on-time of the studied PPL is 0.1s with a total power of 5.7 MW, which is considered a high power demand in a short period. The simulation results based on MATLAB/Simulink show the high performance of the proposed system based on the FLC-SMES to minimize the voltage and frequency fluctuations during the operation of PPL.

To handle the multiple-objective problem including carbon emission reduction and economic operation of integrated energy system (IES), this paper proposes an optimal scheduling model for IES considering combined weights for low-carbon and economic operation. In the dispatch model of IES, we mainly use the combined cold heat and power (CCHP) to optimize the energy distribution, and use the charge and discharge power of electric vehicles (EVs) to enhance the regulation capability of CCHP. Furthermore, aiming at the lowest carbon dioxide emission and economic cost, and the highest renewable energy accommodation, a multi-objective function considering combined weights is proposed, which combines subjective weights with objective weights. The balance constraints of electricity, heat and cold for the IES are established. Finally, the particle swarm optimization algorithm is used to optimize the multi-objective model based on the energy balance and unit constraints of IES. The proposed method is verified by the case studies. The results show that the proposed model can improve the accommodation of renewable energy and reduce carbon dioxide emissions.

This paper proposes an optimization method for energy planning that will efficiently meet multiple requirements subject to uncertain future projections. A stochastic optimization model is used to identify appropriate energy mixes under various scenarios of uncertainty, and the performance of three different energy policies—a pro-economic policy, a pro-environmental policy, and a governmental plan—is compared. Data envelopment analysis is applied to measure the relative energy efficiency of the optimized energy mixes in providing energy security, energy equity, and environmental sustainability. Thailand’s power development plan for 2032 is used as a case study to illustrate the approach. Analysis of the case study indicates that the pro-environmental policy is the most efficient of the three policies considered. Empirical results from this study provide quantitative support for policy makers seeking to establish an efficient energy policy to satisfy the three requirements while allowing for a range of future uncertainties.

With the development of smart grids and emerging measurement technologies, the massive growth of distribution grid data may impact the reliable, economic, and safe operation of the distribution network. For a large-scale distribution network state estimation, the strategy of measuring data for a distribution grid is critical to its economy and reliability. This paper proposed a distribution network state estimation model based on a graph convolutional neural network. The proposed algorithm uses a genetic algorithm to optimize the sensor locations, frequency of sensors in the distribution network and configures of devices to guarantee the proposed state estimation data accuracy. With minimizing costs of investment and operation, the proposed graph convolutional neural network provides super-resolution state estimation data of the distribution network by using low-resolution measurement data. The proposed method is tested and verified by the IEEE33 distribution network system and the testing result demonstrates the feasibility and effectiveness of the proposed model and algorithm.