• Energy Research

Multiloop voltage-controlled virtual synchronous generator (VSG) based control scheme is recently popular in forming stable microgrids. This scheme deploys traditional VSG (TVSG) control for power controllers in the outermost loop and proportional-integral synchronous reference frame-based voltage-current (PIVA) controllers in the inner loop (named TVSG-PIVA scheme). But, this scheme usually exhibits larger deviations and longer settling times in transient response under large active power demands. This poor transient response would further lead to unnecessary tripping which is referred to as nuisance tripping in the literature. This hampers the system’s stability even though there is no genuine fault. Thus, to address this problem, this paper proposes a modified VSG (MVSG) power controller based on adjusting the gain constant of the speed governor in the TVSG through an equalizing factor. From simulations, it is found that this MVSG supported by PIVA controllers (named MVSG-PIVA scheme) successfully avoided undesired trips under large active power demands, but, is susceptible to nuisance tripping under large reactive power demands. Thus, to effectively improve transient response and reduce the chances of nuisance trips under both large active/reactive power loads, this paper proposes a hybrid control scheme by deploying the MVSG power controller and internal model control-based VA controllers (named MVSG-IMCVA scheme). The efficacy of the proposed MVSG-IMCVA scheme is compared with the MVSG-PIVA scheme and conventional TVSG-PIVA scheme under different power factor loadings. From the results, it is proved that the proposed scheme improved the transient response and reduced unnecessary trips. Thus, the proposed modifications demonstrate the empowerment of the multiloop voltage-controlled VSG scheme, thereby ensuring system stability during dominant load changes.

The modern-day urban energy sector possesses the integrated operation of various microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility grid burden. However, these cluster microgrids require a precise electric load projection to manage the operations, as the integrated operation of multiple microgrids leads to dynamic load demand. Thus, load forecasting is a complicated operation that requires more than statistical methods. There are different machine learning methods available in the literature that are applied to single microgrid cases. In this line, the cluster microgrids concept is a new application, which is very limitedly discussed in the literature. Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines, long short-term memory, and artificial neural networks (ANN) to forecast the load demand in the short term. The effectiveness of these methods is analyzed by computing various factors such as root mean square error, R-square, mean square error, mean absolute error, mean absolute percentage error, and time of computation. From this, it is observed that the ANN provides effective forecasting results. In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The effectiveness of these optimization algorithms is verified in terms of training, test, validation, and error analysis. The proposed system simulation is carried out using the MATLAB/Simulink-2021a® software. From the results, it is found that the Levenberg–Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.

Hydropower has been used for many years and is essential to meet the renewable energy ambition of the world at present. In a hydroelectric power plant, voltage and frequency control are required, but, the voltage control could be done on the load side. In the present paper, frequency control using Harris Hawks optimization (HHO) for improved performance has been presented. Simulations are performed on the dynamic model of the hydropower plant and results are compared with the conventional PID that is designed using the Ziegler-Nichols method. The efficacy of the proposed algorithm is also tested at dynamic conditions of the hydropower plant.

Over the years, the automation of traditional power grids has been taking place to overcome the difficulties such as blackouts, outages, demand-side management, load profiling, enhancing customer participation, etc. This automation enables the traditional grids to be transformed into smart grids. Smart homes/buildings are key sub-categories of smart grids. The advanced metering infrastructure connected to them continuously captures and stores the energy consumption data as datasets. Usually, understanding the structure of data and the behaviour of customers from energy consumption datasets is a tedious task. There are some literature works tried to explore various smart home energy consumption datasets as well as investigate customer behaviour, however, most of these methods are complex in implementation. Hence, this paper proposes a simple approach for the comprehensive exploration of the smart home energy consumption dataset. This approach can be used for any similar smart home dataset that contains numerical data. Further, using the exploration results, this paper analyzes the customers’ energy consumption behaviour by identifying peak hours in communication and electrical perspectives. To implement the proposed approach, an energy consumption dataset ‘Tracebase’ is considered as a case study. The exploration of the considered dataset results in 2356 files distributed among various directories. For customer behaviour analysis, the energy consumption data of all 43 appliances (with more than 95 million records) is considered from the “complete” directory of the “Tracebase” dataset. This analysis revealed the peak hours as hour-23 from the communication perspective and hour-9 from the electrical perspective. These represent the customer behaviour in terms of their participation in the power network, which further helps for better grid operations.

Being multivariable in nature, voltage and current control loops have controllers in the forward and cross-coupling paths. Most methods discussed in the literature focus on tuning the controllers in the forward paths to reduce the dq coupling. A modified pole-zero cancellation (MPZC) technique has recently been discussed, which uses the concepts of pole-zero cancellation and particle swarm optimization to effectively tune the forward path controllers. However, given the fixed gains in the cross-coupling paths, it is not possible to realize a superior transient response from this technique. Therefore, to achieve enhanced vector control of VSIs under transient conditions, this paper proposes a hybrid MPZC (HMPZC) method, which incorporates multivariable control along with the MPZC technique for both voltage/current control loops. In the proposed HMPZC method, the MPZC method is used to tune the forward path controllers, and multivariable control-based PI controllers are assigned in the cross-coupling paths of dq-axes loops rather than fixed gains. In this paper, these multivariable control-based PI controllers are designed using direct synthesis method-based internal model control (IMC). From the simulation results, it is verified that the proposed HMPZC method has reduced the coupling between the d- and q-axes loops of the current/voltage, leading to the improved transient response and power delivery capability of VSIs.

Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real‐time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology‐enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES‐ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC‐PSO, and PSO‐COGENT algorithms, the created HES‐ACO algorithm was simulated at CloudSim and found to optimize all parameters.

Steady increase in electricity consumption, fossil fuel depletion, higher erection times of conventional plants, etc., are encouraging the use of more and more onsite renewable energy. However, due to the dynamic changes in environmental factors as well as the customer load, renewable energy generation is facing issues with reliability and quality of the supply. As a solution to all these factors, renewable energy integrated cluster microgrids are being formed globally in urban communities. However, their effectiveness in generating quality power depends on the power electronic converters that are used as an integral part of the microgrids. Thus, this paper proposes the “Fuzzy Hysteresis Current Controller (FHCC)-based Inverter” for improving the power quality in renewable energy integrated cluster microgrids that are operated either in grid-connected or autonomous mode. Here, the inverter is controlled through a fuzzy logic-based hysteresis current control loop, thereby achieving superior performance. System modelling and simulations are done using MATLAB/Simulink®. The performance analysis of the proposed and conventional inverter configurations is done by computing various power quality indices, namely voltage characteristics (swell, sag, and imbalance), frequency characteristics (deviations), and total harmonic distortion. The results reveal that the proposed FHCC-based inverter achieves a better quality of power than the traditional ST-PWM-based multilevel inverter in terms of IEEE/IEC/EN global standards for renewable energy integrated cluster microgrids application.

An increased electricity demand and dynamic load changes are creating a huge burden on the modern utility grid, thereby affecting supply reliability and quality. It is thus crucial for modern power system researchers to focus on these aspects to reduce grid outages. High-quality power is always desired to run various businesses smoothly, but power-electronic-converter-based renewable energy integrated into the utility grid is the major source of power quality issues. Many solutions are constantly being invented, yet a continuous effort and new optimized solutions are encouraged to address these issues by adhering to various global standards (IEC, IEEE, EN, etc.). This paper therefore proposes a concept of establishing a renewable-energy-based microgrid cluster by integrating various buildings located in an urban community. This enhances power supply reliability by managing the available energy in the cluster without depending on the utility grid. Further, a “fuzzy space vector pulse width modulation” (FSV-PWM) technique is proposed to control the inverter, which improves the power supply quality. This work uniquely optimized the dq reference currents using fuzzy logic theory, which were used to plot the space vectors with effective sector selection to generate accurate PWM signals for inverter control. The modeling/simulation of the microgrid cluster involving the FSV-PWM-based inverter was carried out using MATLAB/Simulink®. The efficacy of the proposed FSV-PWM over the conventional ST-PWM was verified by plotting voltage, frequency, real/reactive power, and harmonic distortion characteristics. Various power quality indices were calculated under different disturbance conditions. The results showed that the use of the proposed FSV-PWM-based inverter adhered to all the key standard requirements, while the conventional system failed in most of the indices.