Bulk Power Grid Research Articles

An intelligent analysis method for power flow of bulk power grid based on fusion of indicator system feature and image feature

Abstract At present, the adjustment of power flow non-convergence in large power grid is still mostly based on manual trial and error, which relies heavily on expert experience and is inefficient. Therefore, an intelligent power flow analysis method based on index system and image feature fusion is proposed. Firstly, the power flow index system is constructed according to the actual demand, which can provide data support for the following power flow convergence judgment problems. Secondly, taking into account the constraints of similarity and ternary loss, the image feature information is used to guide the output of the information feature representation and to realize the organic interaction of the two modal features. On this basis, the calculation result of the index is correlated with the actual state of power flow to judge the convergence of power flow. Finally, the proposed method is verified on the CEPRI36 nodes system and a large power grid in China. The simulation results show that the accuracy of power flow convergence judgment is 100% for CEPRI36 nodes system and 99.83% for some domestic large power grid. It can effectively predict the convergence of the power flow before the calculation of the power flow required by the project and can provide the basis and theoretical support for the power flow optimization adjustment based on the indicator data.

Temperature reduction of an HTS transformer under short circuit fault by modifying the cryostat structure: Impact of Perlator and valve location

High Temperature Superconducting (HTS) transformers are one of the potential technologies for power systems connected to offshore wind farms and stand-alone and bulk power grids. In such systems, proper fault performance of any electric device including HTS transformer is a vital factor to ensure a safe, and reliable delivery of electric power as well as power quality in electric grid. Short circuits can increase the risk of developing hot spots in superconducting tapes and as a consequence burning the windings in severe fault current cases. One important way to limit the temperature increase of the superconducting winding during short circuit is to increase the heat transfer of the liquid nitrogen (LN2) during fault. In this paper, the impact of increasing the turbulence of the inlet fluid on the Hot Spot Point (HSP) temperature of superconducting windings of a 120 kVA HTS transformer was investigated during a short circuit fault. To increase turbulence and consequently, heat transfer, a device known as Perlator was used. Then, the impact of the Perlator structure and the location and angle of inlet valves were investigated on the HSP temperature of an HTS transformer, under 65 K and 77 K operating temperatures. The results indicated that by using a Perlator and adjusting valve number and location in the cryostat structure, the HSP temperature of the HTS transformer under the fault current was significantly reduced by about 46.2 K which can be vital to save the transformer from failure.

Cooperation for trans-regional electricity trading from the perspective of carbon quota: A cooperative game approach

Trans-regional electricity trading is a meaningful way to optimize resource allocation and achieve carbon peak and carbon neutrality goals. In this paper, we construct the trans-regional electricity trading cooperative game models considering the impact of local microgrid integration. Based on the cooperative game theory with coalition and graph structures, the optimal mode of trans-regional electricity cooperation and the impact of different carbon quota systems on cooperation profits are studied. The profits obtained through the trans-regional electricity trading are reasonably distributed by the profit distribution scheme based on the Owen graph value. The theoretical and numerical simulation results demonstrate that the local microgrid and the bulk power grid form a priori union, and then participate in the trans-regional electricity trading can obtain more profits. In addition, when the low-carbon policy on the demand side of electricity becomes more relaxed, the implementation of a carbon quota shared responsibility between producers and consumers will be more effective than producer responsibility. Compared with the Myerson value, the profit distribution scheme based on the Owen graph value is more suitable for distributing the profits obtained through trans-regional electricity trading.

Distributed Coordination of Networked Microgrids for Voltage Support in Bulk Power Grids

Distributed Coordination of Networked Microgrids for Voltage Support in Bulk Power Grids

Transfer Learning for Transient Stability Predictions in Modern Power Systems Under Enduring Topological Changes

The topological configuration of a bulk power grid is often altered by network investment upgrades, forecasted disasters and random faults, as well as planned operator-triggered transmission line maintenance and controlled switching actions. Such topological variations can drastically change the measurement data distribution from phasor measurement units (PMUs), which may in turn compromise the accuracy of the artificial intelligence (AI)-aided monitoring and control applications using the measurements. For instance, data-driven transient stability assessment (TSA) models that were trained with static network topologies may no longer be accurate for monitoring power grid stability as the network topology changes. Not only would the number of possible topology changes be too vast to train all possible scenarios, but also the training process will render computationally intensive. This paper proposes a model-based transfer learning (TL) approach that integrates a convolutional neural network and a long short-term memory network (ConvLSTM), to efficiently train a new stability prediction model that predicts the system operating states (SOSs) and identify critical generators (CGs) in case of instability when the system undergoes enduring topological changes. Numerical analyses on three test cases including the IEEE 39-bus test system, the IEEE 118-bus test system, and the large-scale 2000-bus synthetic power grid in the state of Texas verify the efficiency of the proposed approach and highlight benefits in training time and accuracy, when compared to the state-of-the-art alternatives. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As the national power grid goes through transitions towards digitalization and modernization with emerging technologies, maintaining its reliability and resiliency against environmental stressors and cyber attacks remains an urgent need. The power system topology is expected to change more frequently, sometimes to accommodate the proliferation of heterogeneous distributed energy resources and tackle their intermittence, sometimes event-driven due to disruptive events (e.g., faults), and sometimes operator-triggered for maintenance activities and responsive control to return the system back to its normal operating condition. This article is motivated by the need for an efficient and computationally-attractive approach for online situational awareness and real-time transient stability monitoring and assessment of the power grid under an enduring topological change, where the main goal is to identify the power system operating states (SOSs) and critical generators (CGs) in case of instability. Instead of training a new model for each topological change, this paper proposes an adaptive power system transient stability assessment (TSA) method that uses transfer learning (TL) which in return reduces the training time yet with less data than a newly-trained model for each topology change scenario.

A novel method of restoration path optimization for the alternating current–direct current bulk power grid after a major blackout

AbstractThe restoration control of the modern alternating current–direct current (AC–DC) hybrid power grid after a major blackout is difficult and complex. Taking into account the interaction between the line‐commutated converter high‐voltage direct current (LCC‐HVDC) and the AC power grid, this paper proposes a novel optimization method of restoration path to reconfigure the skeleton network for the blackout power grid. Based on the system strength, the supporting capability of the AC power grid for the LCC‐HVDC is first analysed from the aspects of start‐up and operation of LCC‐HVDCs. Subsequently, the quantitative relationship between the restoration path and the restoration characteristic of LCC‐HVDC is derived in detail based on the system strength indices of the short‐circuit capacity and the frequency regulation capability. Then, an optimization model of restoration path considering non‐tree paths is formulated and a feasible optimization algorithm is proposed to achieve the optimal path restoration scheme. A modified IEEE 39‐bus system and a partial power grid of Southwest China are simulated to show that the proposed method is suitable for the restoration of AC–DC power grids and can improve restoration efficiency. This research can be an important guidance for operators to rapidly restore the AC–DC power grid.

Model-free inertia estimation in bulk power grids through O-splines

This paper proposes a novel methodology to precisely estimate the generation units’ inertia constant by using the wide-area monitoring systems (WAMS) information of bulk power grids. The method accurately estimates the rate of change of frequency (RoCoF) and the amplitude of active power deviations through the multivariate O-splines-based differentiators of the discrete-time Taylor–Fourier transform (DTTFT). The proposal utilises active power and frequency measurements that are synchronously collected from WAMS after load changes or generation trips. In the first stage, the Teager–Kaiser energy operator (TKEO) detects the right disturbance instant. Then, the first-order and zero-order O-splines differentiators are applied to the frequency and active power deviation measurements to simultaneously estimate the RoCoF and the amplitude of active power deviations in a sole stage. Thus, the inertia constants of all generation units derived from the second Newton law are estimated using all time-synchronised signals and processing in an exclusive step by the O-splines of the DTTFT in a short time. The attained results and their comparison with a state-of-the-art technique confirm the effectiveness and performance of the proposed strategy for inertia estimates, where simulated signals of two well-known benchmark power grids are tested.

Evaluation of Operation State of Power Grid Based on Random Matrix Theory and Qualitative Trend Analysis

Bulk power grid interconnection and the access of various smart devices make the current grid highly complex. Timely and accurately identifying the power grid operation state is crucial for monitoring the operation stability of the power grid. For this purpose, an evaluation method of the power grid operation state based on random matrix theory and qualitative trend analysis is proposed. This method constructs two evaluation indicators based on the operation data of the power grid, which cannot only find out whether the current state of the power grid is stable but can also find out whether there is a bad operation trend in the current power grid. Compared with the traditional method, this method analyzes the power grid’s operation state from the big data perspective. It does not need to consider the complex network structure and operation mechanism of the actual power grid. Finally, the effectiveness and feasibility of the method are verified by the simulations of the IEEE 118-bus system.

Special Section on Local and Distributed Electricity Markets

Driven by the goals of clean energy and zero carbon emissions, the power industry is undergoing significant transformations. The rapid growth of diverse distributed energy resources (DERs) at grid edge such as rooftop photovoltaics (PVs) and electric vehicles is transforming the traditional centralized power grid management to a decentralized, bottom-up, and localized control paradigm. Establishing local and distribution-level electricity markets provides an effective solution to managing large amounts of small-scale DERs. New regulations such as the recent FERC Order 2222 in the U.S. open the door to DERs in the wholesale markets. Through coordinating the local and distribution-level markets with the transmission-level wholesale market, the DERs and prosumers can trade energy and flexibility locally with each other and meanwhile provide energy, flexibility and ancillary services to the bulk power grid. During this transition, there are many new technical challenges to address, calling for innovative ideas and interdisciplinary research in this promising direction. Advanced information and communication technologies (ICT) are needed, as a key enabler, for the development and practical implementation of local and distribution electricity markets. Research into local and distribution markets is strongly interdisciplinary, involving the state of the art in power engineering, economics, and digital/information technology. A broad spectrum of contributors from universities, industry, research laboratories and policy makers is sought to develop and present solutions and technologies that will facilitate and advance practical applications and implementations of local and distribution-level electricity markets to uncover the values of DERs.

Load Flow Analysis and the Impact of a Solar PV Generator in a Radial Distribution Network

The distribution system acts as a conduit between the consumer and the bulk power grid. Due to characteristics such as a high resistance/reactance ratio, distribution networks cannot be solved using conventional methods, such as the Gauss-Seidel and Newton–Raphson. This research proposes a method for the calculation of the power flow in radial networks that considers their wide range of resistance and reactance values, PV generator characteristics, and radial structure. An iterative methodology is employed, with each iteration beginning with the branch that has the highest accurate power flow solution. The procedure is reliable and effective over various workloads and network configurations. To confirm the effectiveness of the suggested technique on the simple and IEEE 33-bus radial distribution system, simulations were carried out in MATLAB. The implications of including a renewable energy source, such as a PV generator, in the network under consideration are investigated by simulation result comparison. The optimal location of the PV generator was also determined using an Artificial Neural Network (ANN) controller. The results of the identification process improve the already exceptional efficacy and performance of the ANN controller.

Coordinated voltage control for improved power system voltage stability by incorporating the reactive power reserve from wind farms

The absorption and output characteristics of reactive power of the doubly-fed induction generator (DFIG) greatly influence the voltage stability of PCC (Point of Common Coupling) where the wind farms are integrated into the bulk power grid. This study proposes a reactive power compensation strategy for coordinated voltage control (CVC) of PCC with large-scale wind farms to achieve the expected voltage quality of the power grid through a minimum amount of control actions in emergencies. To this end, the mechanism of reactive power and voltage control inside DFIG is first analyzed. Then, the concept of reactive power reserve (RPR) sensitivity concerning control actions is introduced and an index of voltage stability margin is proposed to evaluate and analyze the distance between the current operating point and the voltage collapse point by analyzing the relationship between reactive power reserve and voltage stability margin. In the event of an emergency, critical reactive power reserves are obtained to reduce the dimension and complexity of the control problem. The sensitivity of reactive power reserve and the control are formulated into a convex quadratic programming problem to optimize the control strategies for voltage stability. The proposed technology has been validated on the IEEE 39-bus system.

An Experimental Approach for Secondary Consensus Control Tuning for Inverter-Based Islanded Microgrids

A microgrid is a group of interconnected loads and distributed energy resources that can fill the gap between the dependence on a bulk power grid and the transition to renewable energies. The islanded mode presents itself as the most interesting scenario, when local controllers should maintain the power quality standards based on several parameters. A tool specifically focused on the process of parameter tuning of the secondary consensus-based control for inverter-based islanded microgrids was proposed in this paper. One often-quoted drawback in this process is the great number of parameters that must be tuned, even for a very simple microgrid structure. To manage such a large number of parameters, the design of experiments was used in this study. The main motivation for this work was to present an optimized way to define the correct parameters for the secondary consensus control for inverter-based islanded microgrids. The study shows how experimental design methodology can be an efficient tool to tune microgrid parameters, which are typically multi-objective-based experiments. From the results, it is correct to state that the design of experiments is able to reach the optimal setting with a minimal number of experiments, which would be almost impossible to obtain with the trial-and-error method.

Transient stability assessment in bulk power grids using sequential minimal optimization based support vector machine with pinball loss

• A support vector machine with pinball loss is developed for TSA. • Quantile parameter is conducted to improve the robustness of TSA. • Sequential minimal optimization is adopted to accelerate the computational efficiency of TSA. Recent studies show that support vector machine is a promising approach for predicting the transient stability of power systems. However, most of the existing support vector machine methods suffer from excessive training time and low robustness, resulting in inefficiency for implementation. In this paper, a novel TSA method using sequential minimal optimization based support vector machine with pinball loss is developed. In the proposed approach, the system operation indices and projected energy function indices are selected to form the initial TSA feature set. Then, a quantile parameter is introduced to reduce the impact of measurement error by controlling the positions of the boundary samples between stable and unstable classes. Finally, a sequential minimal optimization is applied to decompose the large quadratic programming problem into a series of small quadratic programming problems to accelerate the computational efficiency. The cross-validation and grid search methods are introduced to improve the classification stability and accuracy. The IEEE 50-machine test system and the China Southern Power Grid are used to evaluate the performance of the proposed TSA method. Simulation results demonstrate that the proposed TSA method has high accuracy, robustness, and computational efficiency in bulk power grids.

Microgrid Building Blocks: Concept and Feasibility

For power grids with high penetration of distributed energy resources (DERs), microgrids can provide operation and control capabilities for clusters of DERs and load. Furthermore, microgrids enhance resilience of the hosting bulk power grid if they are enabled to serve critical load beyond the jurisdiction of the microgrids. For widespread deployment of microgrids, a modular and standardized Microgrid Building Block (MBB) is essential to help reduce the cost and increase reliability. This paper proposes the conceptual design of an MBB with integrated features of power conversion, control, and communications, resulting in a systemwide controller for the entire microgrid. The results of a feasibility study indicate that, in a utility-connected mode, MBB-based microgrids can exchange power with the hosting power grid while serving regulation and optimal dispatch functions. In a resiliency (islanded) mode when the microgrid is disconnected from the utility system, the MBB control system acts to stabilize the system frequency and voltage under small or large disturbances. The microgrid controller is supported by a communication system that meets the latency requirements imposed by the microgrid dynamics as well as data acquisition time. The extended IEEE 13-node system is used as a microgrid model to validate the proposed MBB design and functionality.

From the High Plains and Deserts: A new look at getting renewable energy from remote sources to load centers [View Point

This issue of <i>IEEE Electrification Magazine</i> is full of technology insights for the next generation of inverters to support the explosive growth of variable renewable energy sources. It is becoming widely understood that the electronic interfaces that enable modern wind and solar photovoltaic (PV) generation radically alter the dynamic relationship among these resources and the living organism that is the bulk power grid.

Optimal Planning of Distributed Battery Energy Storage Systems in Unbalanced Distribution Networks

The proliferation of distributed renewable generation poses many challenges to power distribution networks (DN), such as network congestion and voltage violations. Recent advances in battery technologies have made battery energy storage systems (BESS) more economically viable than ever before, which makes them suitable for many grid-scale applications. By providing grid support, distributed BESS can serve as enablers for higher penetrations of renewables at the DN. Distributed BESS can also provide ancillary services (AS), such as frequency regulation and reserves, to the bulk power grid. This article proposes a budget-constrained planning model for optimally sizing and siting distributed BESS in a DN to provide grid support for a renewable-rich DN and provide AS to the electricity market. The proposed model, formulated as a mixed-integer linear programming problem, maximizes the total profit of participating in the AS markets while supporting the operation of an unbalanced DN. Simulation results and sensitivity analyses demonstrate the merits of the proposed planning model. The accuracy of the linearized unbalanced DN model is also validated.

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

A Self-Evolving Agent System for Power System Online Corrective Control

Power system corrective control is a great important strategy to ensure the safety of a grid; however, with the high penetration of distributed energy resources such as solar, wind, hydro, etc., the difficulty of control with active correction of power system dramatically increases. In the mean times, model-free methods such as Deep Reinforcement Learning (DRL) are being extensively studied by many research scholars to solve those decision and control problems with high complexity and uncertainty dimension in smart grids including corrective control area. But, hyperparameter in DRL is hard to find the optimal one. Therefore, we propose a self-evolving agent system for power system online corrective control based on the hyperparameter tuning method. In this paper, we test this algorithm on the 62-bus regional power system of a 300-bus provincial bulk power grid. Numerical simulation validates the excellent performance of this method in effectively eliminating line overload under auto Hyperparameter Optimization (HPO).

Enabling Secure Peer-to-Peer Energy Transactions Through Dynamic Watermarking in Electric Distribution Grids: Defending the distribution system against sophisticated cyberattacks with a provable guarantee

Countries around the world are setting ambitious goals to achieve carbon neutrality by the mid-21st century. Most plans involve the integration of massive renewable energy resources into the electricity grid. In addition to the large wind and solar farms at the bulk power grid level, there is an increasing number of grid-edge distributed energy resources (DERs) in the queue of installation. Therefore, the role of electricity end users is shifting from passive consumers, who can only purchase electricity, to active prosumers, who can both produce and consume electricity.

Optimization Method for Operation Schedule of Microgrids Considering Uncertainty in Available Data

Operation scheduling in electric power grids is one of the most practical optimization problems as it sets a target for the efficient management of the electric power supply and demand. Advancement of a method to solve this issue is crucially required, especially in microgrids. This is because the operational capability of microgrids is generally lower than that of conventional bulk power grids, and therefore, it is extremely important to develop an appropriate, coordinated operation schedule of the microgrid components. Although various techniques have been developed to solve the problem, there is no established solution. The authors propose a problem framework and a solution method that finds the optimal operation schedule of the microgrid components considering the uncertainty in the available data. In the authors’ proposal, the objective function of the target problem is formulated as the expected cost of the microgrid’s operations. Since the risk of imbalance in the power supply and demand is evaluated as a part of the objective function, the necessary operational reserve power is automatically calculated. The usefulness of the proposed problem framework and its solution method was verified through numerical simulations and the results are discussed.