Power Grid Control Research Articles

State-of-the-Art of Optimal Active and Reactive Power Flow: A Comprehensive Review from Various Standpoints

Optimal power flow (OPF), a mathematical programming problem extending power flow relationships, is one of the essential tools in the operation and control of power grids. To name but a few, the primary goals of OPF are to meet system demand at minimum production cost, minimum emission, and minimum voltage deviation. Being at the heart of power system problems for half a century, the OPF can be split into two significant categories, namely optimal active power flow (OAPF) and optimal reactive power flow (ORPF). The OPF is spontaneously a complicated non-linear and non-convex problem; however, it becomes more complex by considering different constraints and restrictions having to do with real power grids. Furthermore, power system operators in the modern-day power networks implement new limitations to the problem. Consequently, the OPF problem becomes more and more complex which can exacerbate the situation from mathematical and computational standpoints. Thus, it is crucially important to decipher the most appropriate methods to solve different types of OPF problems. Although a copious number of mathematical-based methods have been employed to handle the problem over the years, there exist some counterpoints, which prevent them from being a universal solver for different versions of the OPF problem. To address such issues, innovative alternatives, namely heuristic algorithms, have been introduced by many researchers. Inasmuch as these state-of-the-art algorithms show a significant degree of convenience in dealing with a variety of optimization problems irrespective of their complexities, they have been under the spotlight for more than a decade. This paper provides an extensive review of the latest applications of heuristic-based optimization algorithms so as to solve different versions of the OPF problem. In addition, a comprehensive review of the available methods from various dimensions is presented. Reviewing about 200 works is the most significant characteristic of this paper that adds significant value to its exhaustiveness.

Wind Power Deterministic Prediction and Uncertainty Quantification Based on Interval Estimation

Abstract With the increasing penetration of wind power into modern power systems, accurate forecast models play a crucial role in large-scale wind power consumption and power system stability. To improve the accuracy and reliability of ultrashort-term wind power prediction, a novel deterministic prediction model and uncertainty quantification with interval estimation were proposed in this study. In consideration of the dynamic characteristics of a generator and conditional dependence, the generator rotor speed and pitch angle were regarded as the indicators of the dynamic characteristics of the generator, and light gradient boosting machine (LGBM) with a Bayesian optimization method was explored to build the deterministic prediction model. Considering the conditional dependence between output power and forecast error, a fuzzy C-means clustering method was used to cluster forecast errors into different clusters, and the best error probability distribution was obtained by fitting the error histogram with nonparametric kernel density estimation. Prediction intervals at different confidence levels were calculated, and the error uncertainty was quantified. A case study was conducted to compare prediction accuracy and reliability by using the present and proposed methods. Results demonstrate that the LGBM deterministic prediction model combined with Bayesian optimization has better prediction accuracy and lower computational cost than the comparative models, specifically when the input features are high-dimensional big data. The nonparametric estimation method with conditional dependence is reliable for interval prediction. The proposed method has a certain reference value for wind turbines participating in frequency regulation and power control of power grid.

Automatic Grid Switch Control between Two Electrical Sources

This paper describes about the grid switch control system. The aim of the project is to provide a facility by which we can control the two power sources in the power grid. The purpose of the project is to provide a flexible system by which we can control the two DC power sources. Now days, there is a lot of requirements to control the appliances in an industry or in a home connected to the power grid when we are working. The main aim of the present project is to provide a facility by which we can control the DC power sources connected to the power grid. The only thing is we need to switch the button for a control board to the power control section. The control board which is attached to the power grid control section in the industry. This receives the commands to the microcontroller whenever there is a power cut from the main source. The microcontroller plays a major role in receiving the commands from the main source module and to switch from a Main DC source or from a Battery DC source which is fed to thee DC generator (Wind energy). The particular source is switched according to the command. Here in this project a power grid is connected to motor control unit. The project implements the design of a system by which we can control the power grid of two DC sources.

Deep neural network-based hierarchical learning method for dispatch control of multi-regional power grid

Multi-regional power grid with interconnected tie-lines has become an increasingly important structure for current power systems, and can efficiently reallocate power resources on a large scale. The power dispatch of a multi-regional power grid involving multiple resources plays a key role in maintaining system balance and improving operating profit. Current optimisation methods for this dispatch problem need to execute a complete optimisation calculation at each dispatch moment, and lack online decision and optimisation abilities. Therefore, we introduce a deep neural network-based hierarchical learning optimisation method to establish an online approach to focused coordination dispatch problems. The method can realise system optimisation based solely on historical operating data. First, the focused coordination dispatch problem is formulated mathematically. Then, we establish a hierarchical structure suitable for online learning methods. Under this designed structure, we establish a learning optimisation model for each agent, and introduce a deep reinforcement learning algorithm for solving the optimisation problems online. Simulation results based on the IEEE 300-bus system are presented to validate the efficiency and availability of the proposed hierarchical method.

Distributed Virtual Time-Based Synchronization for Simulation of Cyber-Physical Systems

Our world today increasingly relies on the orchestration of digital and physical systems to ensure the successful operations of many complex and critical infrastructures. Simulation-based testbeds are useful tools for engineering those cyber-physical systems and evaluating their efficiency, security, and resilience. In this article, we present a cyber-physical system testing platform combining distributed physical computing and networking hardware and simulation models. A core component is the distributed virtual time system that enables the efficient synchronization of virtual clocks among distributed embedded Linux devices. Virtual clocks also enable high-fidelity experimentation by interrupting real and emulated cyber-physical applications to inject offline simulation data. We design and implement two modes of the distributed virtual time: periodic mode for scheduling repetitive events like sensor device measurements, and dynamic mode for on-demand interrupt-based synchronization. We also analyze the performance of both approaches to synchronization including overhead, accuracy, and error introduced from each approach. By interconnecting the embedded devices’ general purpose IO pins, they can coordinate and synchronize with low overhead, under 50 microseconds for eight processes across four embedded Linux devices. Finally, we demonstrate the usability of our testbed and the differences between both approaches in a power grid control application.

Adaptive Online-Learning Volt-Var Control for Smart Inverters Using Deep Reinforcement Learning

The increasing penetration of the power grid with renewable distributed generation causes significant voltage fluctuations. Providing reactive power helps balancing the voltage in the grid. This paper proposes a novel adaptive volt-var control algorithm on the basis of deep reinforcement learning. The learning agent is an online-learning deep deterministic policy gradient that is applicable under real-time conditions in smart inverters for reactive power management. The algorithm only uses input data from the grid connection point of the inverter itself; thus, no additional communication devices are needed and it can be applied individually to any inverter in the grid. The proposed volt-var control is successfully simulated at various grid connection points in a 21-bus low-voltage distribution test feeder. The resulting voltage behavior is analyzed and a systematic voltage reduction is observed both in a static grid environment and a dynamic environment. The proposed algorithm enables flexible adaption to changing environments through continuous exploration during the learning process and, thus, contributes to a decentralized, automated voltage control in future power grids.

О разработке программно-аппаратного комплекса моделирования электросетевого контроллера и объектов микрогенерации на основе возобновляемых источников энергии в сетях НН 0,4 кВ

The article discusses the issues of connecting microgeneration facilities based on renewable energy sources (RES) to electric networks of low voltage (LV) 0,4 kV. This method of connection will allow the use of microgeneration facilities as network sources of energy, and at the same time unused electrical energy generated by micro-generation facilities can be supplied to a low-voltage network of 0,4 kV. Methods of simulation modeling of the processes of electrical energy distribution in the power supply system with microgeneration objects are proposed; the requirements for the power grid controller used to ensure the connection of micro-generation facilities to the low voltage network are formulated. The problems arising in connection with this are analyzed and the ways of their solution are offered. The proposed methods for the development of software and hardware solutions using modern methods of simulation of electrical energy generation and distribution based on rapid prototyping in the Matlab Simulink environment make it possible to carry out field experiments in order to simulate the processes occurring in 0,4 kV low voltage electrical networks using microgeneration objects prosumers.

Decentralized Voltage Stability Monitoring and Control With Distributed Computing Coordination

The computing infrastructure to support the power grid monitoring and control evolves with the increasing system complexity and integration of distributed energy resources (DERs). The integration of DERs requires a stability assessment application to monitor and control at a faster rate compared to the legacy system. Distributed computing architecture provides opportunities for managing these key changes by introducing solutions within substation computers and digital devices such as phasor measurement unit. The proposed decentralized voltage stability using a distributed coordination platform is a secondary emergency control mechanism utilizing large amounts of data in real-time for faster monitoring and control with the legacy system. The developed algorithms have been validated using the IEEE 30, 57, 118, and 300 bus systems using offline and real-time hardware-in-the-loop testbed modeling and simulation. To demonstrate the flexibility of the distributed coordination blocks, multiple case studies and implementation are presented. With control decisions required under changing operating conditions, the IEEE 30 bus system is simulated in real-time using a digital simulator and a network simulator (NS-3). The impact of the SYN flood cyberattack and tolerance of the computing infrastructure to resulting missing data have also been illustrated.

Power grid automatic dispatching method considering renewable energy fluctuation

In the process of power supply, the abnormal fluctuation of renewable energy leads to the increase of economic cost of power grid dispatching. Therefore, this paper proposes a power grid automatic dispatching method considering the fluctuation of renewable energy. According to the fluctuation of renewable energy, the power of renewable energy is predicted, and the power balance is judged by combining with other power related parameters in power grid operation. According to the change of peak load regulation, the power grid control center issues dispatching instructions and adjusts power related parameters to achieve the purpose of power automatic dispatching. The experimental results show that the proposed power grid automatic dispatching method considering renewable energy fluctuation has small operation delay and low economic cost, the economic benefit of which is higher than that of the traditional dispatching method.

Analysis Of Typical Power Grid Blackout Accidents And Suggestions For Countermeasures

The prevention and control of power grid blackout has become an important research content of the modern power industry and government departments. This paper collects and compares 160 influential international large-area blackouts since 1960, summarizes and analyzes the main causes, common problems and developing Process. Combined with the operation of China's power grid, the main risk sources of large-scale blackouts and the recovery process are summarized. Based on the lessons of international blackout accidents, some specific suggestions and countermeasures are put forward from four aspects, such as strengthening risk management and operation control of large power grid, which can provide useful reference for the safe operation of large power grids above provincial level.

A multi-source data collection and information fusion method for distribution network based on IOT protocol

With the construction of Global Energy Internet, Intelligent distribution network has more and more characteristics of multi scene, multi service and changeable measurement and control objects. In order to realize data collection and information exchange among the intelligent terminal and energy, power grid, load, energy storage and other control resources in distribution network, we should research an architecture which can adapt to various data sources Based on dynamic self describing protocol.

Power Grid Surveillance and Control Based on Wireless Sensor Network Technologies: Review and Future Directions

In this paper, the concept of the smart grid and the emerging challenges of wireless communication technology for use on the smart grid (SG)is reviewed. One modern-day technology of great significance is wireless sensor networks (WSNs), which has the potential to ensure the smooth operation of smart grid management and increase its energy efficiency, reliability and cost-effectiveness. The advanced electric power grid infrastructure known as smart grid takes advantage of automated control and cutting-edge communication strategies to achieve greater efficiency, reliability and safety, whilst seamlessly combining renewable and alternative sources of energy. Unlike conventional communication technologies, WSNs are more beneficial to modern electric power systems because they are affordable and operate on a cooperative basis. For this reason, WSNs are considered to have good potential for improving the way electric power is produced, distributed and used. Therefore, they are a key element of the smart grid. Nevertheless, the implementation of WSNs in smart grid settings may be inadequately reliable due to the complexity and unfavourable aspects of electric power system environments. The purpose of the current work is to review WSN uses in electric power systems and the opportunities and difficulties offered by this technology, as well as to delineate possible facets of smart grid applications and wireless technologies that future research could explore. Therefore, this work is significant.

Scalable Designs for Reinforcement Learning-Based Wide-Area Damping Control

This article discusses how techniques from reinforcement learning (RL) can be exploited to transition to a model-free and scalable wide-area oscillation damping control of power grids. We present two control architectures with distinct features. Performing full-dimensional RL control designs for any practical grid would require an unacceptably long learning time and result in a dense communication architecture. Our designs avoid the curse of dimensionality by employing ideas from model reduction. The first design exploits time-scale separation in the generator electro-mechanical dynamics arising from coherent clustering, and learns a controller using both electro-mechanical and non-electro-mechanical states while compensating for the error in incorporating the latter through the RL loop. The second design presents an output-feedback approach enabled by a neuro-adaptive observer using measurements of only the generator frequencies. The controller exhibits an adaptive behavior that updates the control gains whenever there is a notable change in the loads. Theoretical guarantees for closed-loop stability and performance are provided for both designs. Numerical simulations are shown for the IEEE 68-bus power system model.

A Multi-Step Unified Reinforcement Learning Method for Automatic Generation Control in Multi-Area Interconnected Power Grid

As the penetration rate of new energies, energy storage devices and electric vehicles increases continuously in power grid, power grid faces strong random disturbances, as well as the issues of downfall in frequency control ability induced by the factors such as system inertia reduction and frequency control under-capacity in traditional power units. Therefore, an algorithm of automatic generation control with DQ(σ, λ) is proposed for the multi-area interconnected power grid. By integrating the eligibility trace, DQ(σ, λ) adopts the principle of double Q-learning to unify double Q(λ) and double Expected-Sarsa(λ), thereby the over-estimation of Q in the algorithm and the phenomenon of over-fitting can be avoided. The simulations are provided to demonstrate the effectiveness of the proposed algorithm, where the improved IEEE standard two-area load-frequency control model and the model of the multi-area interconnected power grid based on Central China Power Grid are adopted. The results show that the phenomena of random disturbances and frequency instability can be eliminated in multi-area interconnected power grid. The convergence is faster and the dynamic performance is better in the proposed algorithm compared with the traditional algorithms.

Framework and technologies of App store in new generation power grid dispatching and control system

In the existing power grid dispatching and control system, the management mechanism of the software version is incomplete; the software quality is difficult to guarantee; the level of automation of the software release is low; the feedback mechanism of the software usage is lacking. So it is difficult to meet the openness requirements of New Generation Power Grid Dispatching and Control System (NGPGDCS). In this paper, we propose a framework of app store for NGPGDCS, and give some specific introductions to key technologies such as app review and release process, app precise recommendation and download, app intelligent upgrade, and app evaluation feedback. This solution has been piloted in some power grid dispatching and control centers at provincial level and above. Practice has proved that this solution helps to improve the quality of apps, enhance the degree of software automation release, and promote the continuous improvement of the quality of apps.

Design and implementation of business access control in new generation power grid dispatching and control system

Abstract This paper analyzes the changes of the new generation power grid dispatching and control system in system architecture, human-computer interaction mode, business organization mode, etc., combs the new requirements of the new generation power grid dispatching and control system for access control and proposes the access control solution for business in the new generation power grid dispatching control system. Key technologies are studied, such as path-based resource identification definition, metadata-based resource management, multi-factor access control based on rule engine, cross-system access control based on upper and lower organizational relationships. The solution is verified in the prototype system and provides multi-dimensional security access control means for business in the new generation power grid dispatching and control system.

Harmonic Detection Technology for Power Grids Based on Adaptive Ensemble Empirical Mode Decomposition

Harmonic detection and control for power grids have always been major concerns for researchers. With the application of diverse semiconductor materials in power systems, numerous asymmetrical loads arise, resulting in increasingly poor performance of traditional harmonic detection methods. Ensemble empirical mode decomposition (EEMD) provides a new approach for harmonic detection in power systems. Because the harmonic waves in power systems are indeterminate, optimal decomposition results cannot be achieved by means of artificially configured parameters. For such cases, the development of deep neural networks has provided a new solution for harmonic detection. In this study, particle swarm optimization is combined with a deep neural network to establish an adaptive harmonic separation algorithm. By training an adaptive model in this manner, adaptive EEMD can be realized. Moreover, decomposition parameters can be established based on the harmonic content of signals to effectively separate harmonic waves of diverse orders.

Analysing smart power grid against different cyber attacks on SCADA system

The integration of information and communications technology (ICT) with traditional power grid is transforming the power grid into a more reliable and smarter cyber physical system (CPS). A supervisory control and data acquisition (SCADA) system is used to implement the cyber layer for power grid, which is responsible for real-time monitoring and control of power grid and power delivery network. A SCADA system combined with other devices creates smart power grid environment, but it also makes power grid vulnerable against different cyber-attacks as it requires connection with various kinds of open networks. Smart power grids are a extremely critical infrastructure and attack of any kind can affect socio-economical condition of the region. In this paper we present a non-complex co-simulation environment for analysing smart power grid using OMNeT++ and openDSS, which also includes two different cyber-attack scenarios. The main contribution of the paper lies in the behavioural analysis of the smart power grid environment while various attacks are performed on cyber layer of the grid and respective response of the power grid during these attacks. The findings of the proposed work show the vulnerabilities of smart power grid, as during cyber-attacks power grid was unstable and power generation was dropped significantly.

A Distributed Dynamic Power Flow Algorithm for an Interconnected System Containing Two-Terminal LCC-HVDC Tie-Line

In China, a growing number of regional power grids are interconnected by the line-commutated converter based high voltage direct current (LCC-HVDC) tie-line, forming a large-scale interconnected power grid. As the operation and control of the interconnected power grid are undertaken by multiple control centers, there is a demand for a distributed alternating current (AC)/direct current (DC) power flow algorithm. In this paper, a distributed power flow algorithm considering the LCC-HVDC tie-line is implemented based on the existing work of the author. The paper proposed some technologies to implement the algorithm. In the fixed-point iteration scheme of this paper, the combined boundary bus states are used to calculate the power flow of the DC system, thus the coordination of discrete state variables of the DC system is avoided. Also, the idea that converting the DC tie-line to an AC line is proposed to calculate the extended WARD equivalent of the external network of subsystems. Moreover, the unbalanced power distribution method is improved to make it more in line with the practical situation. Tests were carried out on the IEEE 118-bus system and an interconnected power grid constructed by six IEEE 118-bus systems, the correctness and effectiveness of the algorithm were proved.

Research on Key Technologies of Intelligent Operation Control of Super-large Urban Power Grid Based on Multi-center Structure

Under the background of higher and higher requirements of intelligent, reliable, green and efficient operation of power grid, the traditional operation control technology of urban power grid can no longer fully meet the new requirements of integrated operation and intelligent dispatching. The increasing environmental pollution and the gradual shortage of natural resources promote the rapid development of intelligent technology for distribution network dispatching. Traditional urban distribution network management ideas, especially the concept and mode of distribution network dispatching, can no longer adapt to the new situation, and need to be repositioned and studied deeply. In the construction of intelligent distribution network, intelligent control system is a very important part, which has a very positive significance for the overall operation of the power grid. This paper takes the construction of intelligent distribution network dispatching system as the long-term vision, extends the development and application trend of intelligent distribution network dispatching technology, and puts forward ideas on how to construct the concept and key technology of intelligent distribution network dispatching suitable for China's super large cities based on the city multi center structure.