Grid Control Research Articles

Intermittent power smoothing control for grid connected hybrid wind/PV system using battery-EDLC storage devices

Wind and solar radiation are intermittent with stochastic fluctuations, which can influence the stability of operation of the hybrid system in the grid integrated mode of operation. In this research work, a smoothing control method for mitigating output power variations for a grid integrated wind/PV hybrid system using a battery and electric double layer capacitor (EDLC) is investigated. The power fluctuations of the hybrid system are absorbed by a battery and EDLC during wide variations in power generated from the solar and wind system, subsequently, the power supplied to the grid is smoothened. This makes higher penetration and incorporation of renewable energy resources to the utility system possible. The control strategy of the inverter is realized to inject the power to the utility system with the unity power factor and a constant DC bus voltage. Both photovoltaic (PV) and wind systems are controlled for extracting maximum output power. In order to observe the performance of the hybrid system under practical situations in smoothing the output power fluctuations, one-day practical site wind velocity and irradiation data are considered. The dynamic modeling and effectiveness of this control method are verified in the MATLAB/Simulink environment. The simulation results show that the output power variations of the hybrid wind/PV system can be significantly mitigated using the combination of battery and EDLC based storage systems. The power smoothing controller proposed for the hybrid storage devices is advantageous as compared to the control technique which uses either battery or ultracapacitor used for smoothing the fluctuating power

Continuous Optimization of Business Environment for Power Grid Timing Control

How to optimize the business environment is a very difficult issue. Now the stability of the timing control of the power grid is poor and the energy cost is high. This paper uses literature reviews and case studies to study the optimization of the business environment of the timing control of the power grid. Through collecting and analyzing relevant literature and combining actual cases, the current business environment problems of timing control of power grids are discussed, and corresponding solutions are proposed. The research results show that the stability of the power grid of this technology is up to 99%, and the continuous optimization of the business environment of the power grid timing control can reduce energy costs. It is necessary to conduct business interconnection and share timing signals, and through the formulation of unified technical standards, data security protection, management and supervision mechanism improvement, and fair investment and cost sharing mechanism, enhance the efficiency of power grid timing control and energy exploitation.

Research on robust adaptive control of strong nonlinear complex large power grids.

In this paper, a Multi-Index Nonlinear Robust Adaptive Control (MINRAC) method was proposed for ultra-complex multi-input multi-output power systems with uncertain parameter factors and external disturbances. The controller designed by this method has excellent static and dynamic characteristics. Under the condition of the uncertainty of system parameters and external disturbance at the same time, the MINRAC method can ensure that the multiple indexes concerned by ultra-complex power systems can be controlled at their expected values. The simulation results showed that the control mechanism of MINRAC method was consistent in both single-machine infinite bus system and multi-machine interconnected coupling system. The output function chooses power angle, angular frequency and terminal voltage as constraints. When the system has parameter uncertainty and external interference, the uncertain parameter values are adjusted by adaptive control to force these indicators to tend to the given expected value. For three-phase short circuit, which is the most serious fault in power system, the use of multi index nonlinear robust controller can ensure that the system is stable in a wide range and has better dynamic performance.

Coherency-Aware Learning Control of Inverter-Dominated Grids: A Distributed Risk-Constrained Approach

Coherency-Aware Learning Control of Inverter-Dominated Grids: A Distributed Risk-Constrained Approach

Intelligent ANFIS-Based Distributed Generators Energy Control and Power Dispatch of Grid- Connected Microgrids Integrated into Distribution Network

Power supply management is a critical problem in the operation of a distribution network, considering the causes of large power losses and interruptions in the main grid caused by the unknown connection of loads and DGs that affect power delivery to customers downstream of a distribution network. The above-mentioned problems can be reduced by the integration of microgrids close to load centers and developing a controller using adaptive control techniques to enhance reliable power supply. For this reason, this paper presents an intelligent method for distributed generators' energy control and power dispatch of microgrids integrated into a distribution network employing an Adaptive Neuro- Fuzzy Inference System (ANFIS). The aim is to control distributed generators energy sources, loads, and power dispatch of grid-connected microgrids among multiconnected power sources to maintain a stable power supply without using any optimization techniques. The proposed intelligent ANFIS system is trained for power-sharing purposes and applied to the microgrid controllers. The mathematical modeling of distributed generators, system design, simulation, and testing of the proposed method were done using MATLAB/Simulink software. The results show that the proposed controller is capable of power dispatch and controls the energy harvest of distributed generators. Additionally, it can assign microgrid power source(s) to additional load(s) connected to the active distribution network without interruptions of power flow. The obtained results outperform similar works that used hybrid ANFISPID (Adaptive Neural Fuzzy Inference System-Proportional- Integral-Derivative), PSO-ANFIS (Particle Swarm Optimization-Adaptive Neural Fuzzy Inference System), and GA-ANFIS (Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System) by automatically connecting and controlling distributed generators energy sources with effective power dispatch in mitigating downtime of grid power operations.

Distributed Optimization Scheduling Consistency Algorithm for Smart Grid and its Application in Cost Control of Power Grid

Distributed Optimization Scheduling Consistency Algorithm for Smart Grid and its Application in Cost Control of Power Grid

Case-Study of an HTS Saturable Core Reactor for Power Flow Control in a Transmission Grid

Case-Study of an HTS Saturable Core Reactor for Power Flow Control in a Transmission Grid

Dynamics Islanding Control for Power Grid with High Penetration of Renewable Energy

Dynamics Islanding Control for Power Grid with High Penetration of Renewable Energy

Review of Challenges and Research Opportunities for Control of Transmission Grids

Review of Challenges and Research Opportunities for Control of Transmission Grids

Multi-DC commutation safety and system stability coordination control evaluation method

Due to the influence of multiple DC commutation failure after AC failure and stable interaction of system frequency, voltage and power Angle, the operation control of power grid is greatly difficult. The control scenario of multi-feed DC commutation failure or system instability is constructed, the comprehensive control performance cost ratio index of emergency control measures to the safety of multi-turn DC commutation and system stability is proposed, and the search method of coordinated control strategy based on global optimal is developed to avoid the risk of chain failure caused by negative control effects. The evaluation of control search path and control target after joint optimization is realized, which is conducive to the construction of system-level defense method and improves the adaptability of strategy.

Fuzzy Controller based Closed Loop Control for Single Stage Single Phase Grid Integrated PV System with Novel Configuration of 7 Level Hybrid Inverter

Fuzzy Controller based Closed Loop Control for Single Stage Single Phase Grid Integrated PV System with Novel Configuration of 7 Level Hybrid Inverter

Analytical review study of the Grid connected Micro grid Energy Management System

As an environmentally friendly method of distributing energy production, the integration of photovoltaic systems into micro grids has drawn in significant focus on. Our goal in doing is to examine features regarding micro grid that is linked to the power grid, with a focus on photovoltaic energy management in particular. Finding the optimal micro grid capacity for the solar system seeks to increase energy efficiency, decrease dependence on main grid, and promote an utilization of green power. The outlined optimization approach evaluates the micro grid’s dynamic interactions using state-of-the-art modelling and simulation tools. These components include photovoltaic panels, energy storage systems, alongside the main grid. The refinement method takes into account crucial factors including patterns of load demand, costs of the grid electricity, and variations in solar irradiation. Finding a happy medium between increasing the amount of power generated by renewable sources and decreasing overall energy costs is the objective. That study takes a multi-scenario approach to determining how various micro grid sizes affect overall system efficiency. Using scenario-based simulations and techno-economic criteria, the appropriate size of the photovoltaic system was determined. Factors like payback time, ROI, and system reliability are taken into account here. The study’s findings provide light on grid-connected micro grids, particularly in regards to photovoltaic energy management, which is crucial for their planning and implementation. In order to make educated decisions towards more robust and ecologically friendly power systems, stakeholders, lawmakers, and decision-makers can use the optimal micro grid size as a benchmark for future renewable power projects. This paper reviews the relevant literature and proposes a division and performance strategy based on its findings. By classifying energy management into three groups according to grid connection, configuration, and control method, this article provides a description of the performance, application, advantages, and disadvantages of algorithms that may be used as a reference for selecting an appropriate algorithm. Also included is a comparison table for the control strategies that were used to regulate a micro grid system that is connected to the grid.

Load Forecasting with Hybrid Deep Learning Model for Efficient Power System Management

Aim: Load forecasting with for efficient power system management Background:: Short-term energy load forecasting (STELF) is a valuable tool for utility companies and energy providers because it allows them to predict and plan for changes in energy. Method:: 1D CNN BI-LSTM model incorporating convolutional layers. Result:: The results provide the Root Mean Square Error of 0.952. The results shows that the proposed model outperforms the existing CNN based model with improved accuracy, hourly prediction, load forecasting. Conclusion:: The proposed model has several applications, including optimal energy allocation and demand-side management, which are essential for smart grid operation and control. The model’s ability to accurately management forecast electricity load will enable power utilities to optimize their generation.

Advancements in multilevel inverter technologies for photovoltaic-Z-source based EV applications: A comprehensive analysis and future directions

Global demand for electricity has risen, driving a shift toward sustainable energy sources like Photovoltaic (PV) systems. Despite their efficiency challenges compared to traditional fuels, significant investments and research are advancing the technology. This paper investigates multilevel inverter topologies, with a focus on Z-source technology for high-power PV applications. The study begins with an overview of the growing demand for alternative energy sources and the role of multilevel inverters in enhancing PV system performance. It discusses prominent multilevel inverter topologies, such as Neutral Point Clamped (NPC) and Cascaded H-Bridge (CHB) inverters, as well as control techniques including Pulse Width Modulation (PWM) and Predictive Control. Furthermore, it explores the functionality of Z-source inverters both with and without PV systems, highlighting their ability to provide voltage boosting, fault tolerance, and improved power quality. For load purposes, Electric Vehicle (EV) charging has been incorporated. The paper uses MATLAB/Simulink to compare multilevel inverter configurations and finds that the CHB inverter with Z-source is superior for PV applications due to its lower Total Harmonic Distortion (THD) and reduced semiconductor usage. The study also simulates a hybrid storage system with batteries and supercapacitors. The paper concludes with insights into future research directions, advanced control strategies, optimization techniques, and grid integration methods. These avenues promise further enhancement of efficiency, reliability, and grid compatibility for multilevel inverters in PV systems. Overall, this research contributes to the selection of optimal multilevel converter topologies for improving the performance of PV systems and advancing the integration of renewable energy into the electrical grid. The findings offer valuable insights for researchers, practitioners, and policymakers working toward a sustainable energy future.

TS-Fuzzy Controllers based Novel Control of Grid Connected Fuel Cell Stack System

TS-Fuzzy Controllers based Novel Control of Grid Connected Fuel Cell Stack System

Direct Current Control of Grid Connected Two Level Inverter With LCL-Filter Using Deep Reinforcement Learning Algorithm

Direct Current Control of Grid Connected Two Level Inverter With LCL-Filter Using Deep Reinforcement Learning Algorithm

Modeling and Control of Grid Forming Converters: A Systematic Review

Modeling and Control of Grid Forming Converters: A Systematic Review

Enhancement of Power Quality Improvement by Novel Control of Grid Integrated Fuel Cell

Enhancement of Power Quality Improvement by Novel Control of Grid Integrated Fuel Cell

An abc Reference Frame Hysteresis Current Controller for Grid Connected Converter With Constant Switching Frequency Operation

An <i>abc</i> Reference Frame Hysteresis Current Controller for Grid Connected Converter With Constant Switching Frequency Operation

Extended FOGI-FLL Based Seamless Control of Grid Synchronized BES-PV Fed Water Pump System

Extended FOGI-FLL Based Seamless Control of Grid Synchronized BES-PV Fed Water Pump System