Volume 26, Issue 1

The cascaded H-bridge (CHB) inverters are the prominent inverters used for renewable energy applications. Fault-tolerant operation is one of the foremost concerns to safeguard the continuous operation of the system under a fault on semiconductor switches and DC power supplies. A novel hybrid CHB inverter is proposed by adding a seven-level boost SLB module in cascade with the H-bridge modules. The proposed topology ensures the reliable and dynamic operation of the inverter during the occurrence of a fault. In addition to the fault-tolerant capability of the SLB inverter, the proposed design provides the voltage boosting and self-balancing of the switched capacitors. Extensive simulation results at different operating conditions substantiate the concept of the proposed novel fault-tolerant inverter structure. The prototype system is developed in the laboratory and the experimental results validate the proposed system configuration.

This paper proposes a design method of distribution network micro PMU optimal layout scheme based on information entropy theory, realizes the optimal configuration of distribution network micro PMU, accurately reflects the operation status of distribution network, and ensures the smooth operation of power system. On the premise that the number of PMUs is the least and the distribution network is completely observable, the dynamic characteristics of uncertain nodes such as distributed generation in the distribution network and the existing traditional measurement equipment and measurement information are comprehensively considered, and the information entropy theory is used to measure and evaluate the uncertainty of the distribution network. The distribution network micro PMU distribution point set taking into account the network uncertainty is selected based on the information entropy evaluation method. In the round by round iterative solution, greedy algorithm is used to select the node with the lowest distribution network uncertainty from the existing network nodes in each round as the installation node of the distribution network micro PMU, and obtain the installation node set of the micro PMU, so as to complete the design of the distribution network micro PMU optimal layout scheme. The experiment shows that the optimal distribution network micro PMU layout scheme designed by this method can effectively realize the optimal configuration of PMU, and can realize the full observability of the distribution network with a small number of PMU configurations.

In recent years, model predictive control (MPC) has gained significant traction within the realm of LC -filtered voltage source inverters (VSIs). In order to reduce the number of current sensors in conventional MPC, this paper proposes a MPC scheme without current sensors for LC -filtered VSIs. Firstly, this paper analyzes the mathematical model of LC -filtered VSIs and specifies the predictive control strategy based on capacitive current. On the basis of considering only the fundamental wave components of the capacitance current, this paper establishes a new sliding mode observer to observe the capacitance current. In order to ensure the stability of the system, the stability of the proposed sliding mode observer is analyzed using Lyapunov stability criterion, and the influence of the sliding mode gain on the current and voltage observations is also analyzed according to the closed-loop transfer function, giving the design method of the sliding mode gain. In addition, the parameter sensitivities of the newly proposed method and the conventional method are analyzed in detail in this paper. The results show that the method proposed in this paper does not depend on the filter inductance and the observed capacitance current have smaller errors and stronger parameter robustness than the conventional method. The comparative experimental study verifies the feasibility of the control scheme proposed in this paper.

Today, creating a smart grid that is resistant to cyberattacks is a subject of utmost significance. One of the components of the smart grid that is most susceptible to a cyber-attack is the phasor measuring unit (PMU). The reason is that PMU employs IEEE C37.118.2 communication standards, which specify the structure and sequencing of data packets but offer no security measures. Users must implement the security techniques to ensure the protection of PMU data. Additionally, PMU communicates via a public wide-area network, raising the risk to security. In addition, PMU is a crucial component of the smart grid, enabling different crucial choices for the reliable functioning of the smart grid to be made using its data. This research suggests a chaos-based data encryption solution to close the knowledge gap and reduce the confidentiality assault on PMU data. For this, a brand-new boost converter logarithmic map (BCLM), a one-dimensional (1D) chaotic map, has been presented. The research demonstrates how the suggested chaotic map has better chaotic qualities than conventional chaotic maps. The pseudorandom generator is the chaotic BCLM system. The PMU data are encrypted using the random sequence produced by the BCLM chaotic system. The suggested chaotic map is not computationally demanding, making it simple to implement in a PMU device with limited resources.

This paper explains in detail the design and control of a utility grid-connected bipolar DC microgrid, which consists of a solar photovoltaic system (SPV), a wind energy conversion system (WECS), a battery energy storage system (BESS) at the DC bus, and a three-level neutral point clamped (NPC) converter to connect the DC microgrid to the utility grid. A three-level bidirectional DC–DC converter at the battery energy storage system (BESS) is controlled with model predictive current control (MPCC), and a three-level NPC converter is controlled with a voltage-oriented control based proportional resonant (VOC-PR) controller. The proposed MPCC scheme for the three-level bidirectional DC–DC converter and the VOC-PR control scheme for the three-level NPC converter coordinate with each other to balance and regulate the voltage at the bipolar DC microgrid, control the power flow to the loads at the DC microgrid and the load at the point of common connection (PCC), and also inject or draw power from the utility grid and BESS based on the availability of Renewable Energy Sources (RES). Simulation and experimental results are presented to demonstrate the effectiveness of the proposed MPCC control scheme.

Dynamic load is one of the significant factor affecting the stability of hybrid microgrid (MG), owing to their frequency and voltage dependency. As compared with the static load, a small variation in voltage or frequency significantly influences the stability of MG having dynamic load. Therefore, it necessitates considering the dynamics of load for stability analysis of an MG. Moreover, the stability issues become more critical for the islanded operation. This paper investigates the small signal (SS) stability of a hybrid MG utilizing composite load model (CLM) to include the load dynamics. This paper also investigates the critical universal droop control parameters and other internal controller parameters that influence MG stability. Eigenvalue analysis is used to identify the respective stability domain of different control parameters. The effectiveness of the proposed approach is tested by comparing the dynamics of CLM with constant power and impedance load. In addition, a memetic algorithm and SQP-GS is used to find the optimal values of different key parameters. The simulation results confirmed the effectiveness of the proposed SS dynamic model of hybrid MG and the efficacy of the optimization algorithm for tuning control parameters, which improved the dynamic performance of the hybrid MG.

The high cost and complexity of Proton Exchange Membrane (PEM) electrolyzers pose substantial challenges for their integration and testing within smart grid emulators. Addressing this, our research offers two pivotal contributions. First, we introduce an innovative Equivalent Electrical Circuit (EEC) for PEM electrolyzers. This electrical model serves as an essential tool for evaluating the performance of PEM electrolyzers, especially within the framework of renewable energy systems in smart grids. Our second major contribution is the design and implementation of a hardware PEM electrolyzer emulator based on a DC/DC boost converter. Unlike conventional approaches, our emulator accurately reproduces the nonlinear polarization curve and dynamic behavior of PEM electrolyzers under a large range of operating conditions. By seamlessly integrating into a smart grid emulator, it provides an environment for efficient and cost-effective testing of PEM electrolyzers. Experimental results offer strong validation of the emulator’s ability to replicate the characteristics of the PEM electrolyzer, establishing it as a practical alternative for evaluating and improving smart grid emulators and their management strategies.

In this paper, a selective harmonic elimination (SHE) method for an asymmetric cascade H-bridge multi-level inverter (ACHB-MLI) has been performed by using hybrid search space reduction and Newton–Raphson (SSR-NR) algorithm. Three H-bridges are cascaded in a single phase configuration, while each of the bridge is fed with V dc , 3 V dc and 6 V dc respectively. This particular asymmetric combination of dc sources will produce 10 possible switching combinations and 21-levels in the output voltage ranging from −10 V dc to +10 V dc . Hence, there will be 10 SHE equations corresponding to fundamental and harmonics, and the solution will be the 10 switching angles which satisfy those SHE equations. While traditional gradient-based techniques like the Newton–Raphson method can yield the global optimal solution for the SHE equations, they necessitate an initial guess. Conversely, meta-heuristic methods often face challenges related to sub-optimal convergence. To address these issues, a novel the hybrid SSR-NR algorithm has been introduced in this paper. This method combines stochastic and deterministic elements, offering a promising solution for solving the SHE equations. In the first step, the evolutionary SSR algorithm will be used and the output of the SSR algorithm is given to the NR method as an initial guess to converge to the final solution, this way the proposed hybrid SSR-NR algorithm is able to address the drawbacks of both the algorithms such as initial guess requirement for NR method and sub-optimal convergence characteristics of evolutionary algorithms. The simulations were carried out in Matlab/simulink environment for a 21-level ACHB-MLI. The results obtained showcase the efficacy of the proposed SSR-NR algorithm in solving the SHE equations. For demonstrating the effectiveness of SSR algorithm the results were also compared with other algorithms such as particle swarm optimization and grey wolf optimizer as well. Further, the experimental results on a laboratory prototype using dSPACE DS1104 R&D controller board were also presented in the paper, to validate the proposed methodology.

This paper puts forward the dynamic load prediction of charging piles of energy storage electric vehicles based on time and space constraints in the Internet of Things environment, which can improve the load prediction effect of charging piles of electric vehicles and solve the problems of difficult power grid control and low power quality caused by the randomness of charging loads in time and space. After constructing a traffic road network model based on the Internet of Things, a travel chain model with different complexity and an electric vehicle charging model, the travel chain is randomly extracted. With the shortest travel time as a constraint, combined with the traffic road network model based on the Internet of Things, the travel route and travel time are determined. According to the State of Charge (SOC) and the travel destination, the location and charging time of the energy storage electric vehicle charging pile are determined. After obtaining the time-space distribution information of the energy storage electric vehicle charging pile at different times and in different regions, it is used as the input of the deep multi-step time-space dynamic neural network, and the network output is the dynamic electric vehicle charging pile. The experimental results show that this method can realize the dynamic load prediction of electric vehicle charging piles. When the number of stacking units is 11, the indexes of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the lowest and the index of R 2 is the largest. The load of charging piles in residential areas and work areas exists in the morning and evening peak hours, while the load fluctuation of charging piles in other areas presents a decentralized change law; The higher the complexity of regional traffic network, the greater the load of electric vehicle charging piles in the morning rush hour.

AC–DC hybrid microgrid consists of a variety of energy resources, different types of loads and storage devices, and involves the interconnection between AC and DC power grids. This leads to increased complexity in system design and coordination control, which needs to take into account the interactions and coordination between individual components. Considering the demand response, the power coordination control method of AC/DC hybrid microgrid is studied, which can effectively adjust the power of microgrid in different operation modes and ensure its smooth operation. Combined with the topology of AC/DC hybrid microgrid and its bidirectional converter, the constraints such as AC-side load demand response and AC/DC-side power balance are set on the premise of considering demand response. Under such constraints, the microgrid is divided into two modes: sub-network independent operation and inter-network power exchange. Corresponding control sections are designed for the two modes. On the premise of the set constraints, the microgrid power in each mode is coordinated by coordinating the control methods of the converters. The results show that this method can respond to several mode switches of AC/DC hybrid microgrid in time and effectively coordinate and control the power during the switching process under the constraint conditions such as load demand response. In addition, in the single mode of inter-network power exchange, this method can also quickly respond to the load demand, effectively adjust the exchange power between AC and DC subnets of microgrid, realize the reasonable distribution of power, and make the hybrid microgrid run stably in different modes. During the 8 s to 10 s phase of the experiment, the load power of the DC subnetwork of the experimental microgrid did not change, while the load power of the AC subnetwork increased to 36 kW, the reference power and converter power remained unchanged, and the microgrid voltage remained stable.

In recent years, the demand for photovoltaic (PV) energy has increased parallel to scientific research on PV cells, including electrical modeling, characterization, and extraction of unknown parameters. Moreover, our main contribution in this paper focuses on experimental investigation of the effect of solar radiation and temperature on the performance of a small photovoltaic plant years after its installation in the Ouargla region. As in many parts of the world, this studied area has experienced rising temperatures due to climate change, affecting system outputs. Therefore, in the first phase, we offer to characterize PV modules in various conditions and analyze the electrical parameters’ performance. The results indicate solar radiation and temperature influence PV modules’ electrical parameters. Moreover, the temperature influences the open-circuit voltage, while solar radiation positively impacts the short-circuit current. On the other side, we determine the optimal parameters of these modules and develop an accurate PV model using the bald eagle search algorithm (BES), gradian-based optimizer algorithm (GBO), and whale optimization Algorithm (WOA) based on a single diode model. The achievements show that the BES and GBO algorithms give good results for the optimum estimation of the PV model compared to WOA, where the best fitness was recorded at 0.015608 with the lowest deviation of 0.012565 and 0.039588, respectively. However, the BES has generated the minimum error values and with minimum iteration number, which indicates that this technique is more stable and robust for PV module parameter extraction.

Ancillary services play a significant role in ensuring stable operation of power systems in the growing penetration of renewable energy. Some of the important functions for ancillary support include voltage support, frequency support, and system restoration. This work highlights the possibility of relevant ancillary support from wind power and investigates how such services will impact from an economic point of view. A crucial aspect of ancillary services from wind power relates to tailored and dynamic information about the conditional power production from the turbines and wind parks which could be obtained through accurate wind power forecasting. The study of the impact of forecast accuracy and correlation in errors on the possibility for wind power to provide ancillary services is analyzed. Furthermore, the limitations and challenges associated with the technical capabilities of wind turbines to support such services are reviewed and presented. An initial qualitative assessment of the value of providing such services is carried out and the capabilities combined with electricity market data from Nord Pool taking an example of the western region of Denmark is discussed. The result of this work shows that enhancing ancillary services from wind power combined with more accurate wind forecasting has a positive financial impact on wind power market.