Grid Operating Conditions Research Articles

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network is being expanded and modernised. This contributes to the occurrence of various types of network operating states in practice. The appearance of a significant number of objects with random generation in the power system complicates the process of planning and controlling the operation of the power system. It is therefore necessary to constantly search for new methods and algorithms that allow operators to adapt to the changing operating conditions of the power grid. There are many different types of method in the literature, with varying effectiveness, that have been or are used in practice. So far, however, no one ideal, universal method or methodology has been invented that would enable (with equal effectiveness) all problems faced by the power system to be solved. This article presents an overview and a short description of research works available in the literature in which the authors have used modern methods to solve various problems in the field of power engineering. The article is an introduction to the special issue entitled Advances in the Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering. It is an overview of various current problems and the various methods used to solve them, which are used to cope with difficult situations. The authors also pointed out potential research gaps that can be treated as areas for further research.

Dynamic voltage restorer under different grid operating conditions for power quality enhancement with the deployment of a PI controller using gorilla troops algorithm

Incorporating renewable energy sources (RESs) into the grid influences the system’s power quality (PQ). These sources are unreliable because of their intermittence. To analyze the influence of RESs like PV and wind farm systems on voltage quality and harmonic analysis, a Dynamic Voltage Restorer (DVR) series compensator is one of the most effective alternatives presently offered. In this work, the DVR is employed to enhance the PQ issues related to the RESs (e.g., voltage sag, swell, flickering, and harmonics). The DVR is controlled by the PI controller with the employment of the gorilla troops algorithm (GTA) to get the gain values of the presented controller under a variety of PQ issues. Two operating modes are addressed – the off-grid mode and the on-grid mode with considerable PV, wind, and nonlinear loads. Moreover, two comparative study evaluations of different optimization strategies and an additional controller are provided to accomplish the desired result and power of the offered DVR. The MATLAB/Simulink platform is used in this study to validate the suggested strategy’s efficiency in various situations. The findings show that the suggested control technique performs well in terms of voltage quality response and harmonic rejection when subjected to grid voltage distortions.

Geometric deep learning for online prediction of cascading failures in power grids

Past events have revealed that widespread blackouts are mostly a result of cascading failures in the power grid. Understanding the underlining mechanisms of cascading failures can help in developing strategies to minimize the risk of such events. Moreover, a real-time detection of precursors to cascading failures will help operators take measures to prevent their propagation. Currently, the well-established probabilistic and physics-based models of cascading failures offer low computational efficiency, hindering them to be used only as offline tools. In this work, we develop a data-driven methodology for online estimation of the risk of cascading failures. We utilize a physics-based cascading failure model to generate a cascading failure dataset considering different operating conditions and failure scenarios, thus obtaining a sample space covering a large set of power grid states that are labeled as safe or unsafe. We use the synthetic data to train deep learning architectures, namely Feed-forward Neural Networks (FNN) and Graph Neural Networks (GNN). With the development of GNNs, improved performance is achieved with graph-structured data, and GNNs can generalize to graphs of diverse sizes. A comparison between FNN and GNN is made and the GNNs inductive capability is demonstrated via test grids. Furthermore, we apply transfer learning to improve the performance of a pre-trained GNN model on power grids not seen in the training process. The GNN model shows accuracy and balanced accuracy above 96% on selected test datasets not used in the training. Conversely, the FNN shows accuracy above 85% and balanced accuracy above 81% on test datasets unseen during training. Overall, the GNN model is successful in determining, if one or several simultaneous outages result in a critical grid state, under specific grid operating conditions.

Study on the impact of rooftop solar power systems on the low voltage distribution power grid: A case study in Ha Tinh province, Vietnam

Vietnam has developed solar power very quickly in recent years. However, the integration of the solar power system into a distribution power grid can cause a clear effect on the voltage of the grid. Accordingly, the voltage at the nodes increases significantly because of the appearance of photovoltaic (PV) systems, and it can lead to overvoltage at some load nodes near the solar power source. The impact of solar power stations with distribution power grids depends on the location and operating conditions of the utility grid. Therefore, the study of the impact of rooftop solar power on the distribution grid needs to add actual operating data of the utility grid to achieve a more detailed assessment and reliability. This study analyzed the influence of rooftop solar power on a low voltage distribution power grid in Ha Tinh province, Vietnam with the support of ETAP software. The scenario was calculated for 50% of the power (3-phase) penetration gain of the transformer. Besides, a proposed synthetic solution is a combination of reactive power compensation with a 5kVAr minimum compensation level at many points on the grid and adjustment of the load tap at the appropriate level depending on the load situation of the transformer. Thus, the voltage of the nodes on the power grid is improved, and the power loss is also decreased.

Analysis and prediction of the penetration of renewable energy in power systems using artificial neural network

The curtailment of renewable energy worsens with increasing penetration in power systems, so it is necessary to explore the upper limit value of the penetration of renewable energy (PRE). This paper uses artificial neural networks (ANN) to study the historical data of California independent system operator (CAISO), analyze the fluctuation balance strategy of wind and solar power, and predict the upper limit value of the PRE, which will peak at 40.5% in 2025. In addition, this paper also simulates the inclusion of energy storage unit (ESU) with an installed capacity of 3 GWh in the grid to reduce curtailments, and analyzes the grid operating conditions and the upper limit value of the PRE, showing that the storage units with an installed capacity of only 3% of the average daily power output of solar energy recover 58% of the annual curtailments (2021), and the maximum PRE is 41.2%.

Critical Node Identification Method of Power Grid Based on the Improved Entropy Weight Method

It is very important to accurately identify the critical nodes of the power grid for its safe and stable operation. In this paper, a method for identifying the critical nodes of the power grid based on the improved entropy weight method (IEWM) is proposed, and the IEWM corrects issues with the information overlap between evaluation indices and inconsistency between the entropy weight (EW) and entropy value (EV). First, considering the power grid topology and operating conditions, structural factor evaluation indices and state factor evaluation indices are established. On this basis, the IEWM is used to assign weights to nodes with different voltage levels, which strengthens the consideration of node voltage levels in the identification method of critical nodes and makes the results more accurate and effective. Simulation experiments of IEEE 30-bus and IEEE 118-bus systems verify the accuracy of the critical node identification method proposed in this paper.

Fault detection and classification in DC microgrid clusters

With the rising popularity of DC microgrids, clusters of such grids are beginning to emerge as a practical and economical option. Short circuit problems in a DC microgrid clusters can cause overcurrent damage to power electronic devices. Protecting DC lines from large fault currents is essential. This paper presents a novel localized fault detection and classification technique for the protection of DC microgrid clusters. In this paper, a variational mode decomposition (VMD) and artificial neural network (ANN) based technique is proposed for accurate and effective fault detection and classification. This research aims to train an ANN that can detect and classify faults in DC microgrid clusters with multiple sources and loads by applying VMD to extract features of current signals. Different types of short circuit faults such as Pole to Pole and Pole to ground faults are considered under various grid operating conditions. The proposed method is capable of real-time fault detection and diagnosis, which can help prevent system failures and minimize downtime. The results indicate that the proposed approach is efficient and effective in detecting/classifying faults in DC microgrid clusters improving the reliability and system safety. The performance evaluation is carried out through rigorous case studies in MATLAB/Simulink environment to prove the efficacy of the proposed method. The VMD-ANN approach is shown to outperform other traditional signal processing techniques in terms of accuracy and robustness. Moreover, the proposed method is applicable to a wide range of DC microgrid clusters, making it a versatile and valuable tool for future research and development.

Active disturbance rejection control of three-phase LCL power conversion system under non-ideal grid conditions

Under non-ideal grid operating conditions such as unbalanced grid voltage and harmonic grid that are commonly found in microgrid conditions, the negative sequence components of the microgrid voltage interfere with the active and reactive current controls in the power conversion system, leading to an increase in the harmonic content of the grid-side current and affecting the power quality of the microgrid system. To solve these problems, firstly, the mathematical model of the LCL-type power conversion system is analyzed, and a linear active disturbance rejection control based on model compensation is designed. Secondly, the influence of non-ideal power grid conditions on the control of the LCL-type power conversion system is analyzed, and the active disturbance rejection control strategy of the LCL-type power conversion system based on frequency-locked loops with harmonic cancellation modules (HCM-FLL) is proposed, which speeds up the system, improves the system’s robustness, and reduces the harmonic content of the network measurement current under the condition of power grid voltage unbalance and harmonic power grid. Finally, by using the verification of MATLAB/Simulink simulation, the current power quality obviously under the condition of voltage unbalance and harmonic power grid is evidently improved by the proposed control strategy. When compared to the traditional control methods, the control strategy proposed in this study features a simple control structure, making it easy to implement in engineering without requiring high controller performance or additional circuits. This reduces design costs and provides a wide range of controller parameters, ensuring strong anti-interference performance without the need for frequent controller parameter adjustments.

Optimal allocation of energy storage in distribution network considering microgrid cluster and system economic operation

A two-stage optimization model for the location of distribution grid energy storage (ES) configuration and its capacity determination is proposed for different operating conditions of the distribution grid, consider the impact of microgrid cluster operation strategies and sub-microgrid cooperation on the distribution network (DN). In the first stage, location, number of ES devices, the charging/discharging strategy were solved for using GAMS software, the minimum operating cost of the distribution system is used as the target for the daily operating timescale. In the second stage, the minimum annual operating cost is taken as the target for a one-year time scale, consisting of investment, ES equipment’s operation and maintenance in the DN, and is solved using a particle swarm optimisation algorithm with nested ES lifetime calculations in MATLAB software. The calculations show that the optimal allocation method of ES equipment in DN can effectively reduce the operating costs of DN as well as the investment and maintenance costs of ES under different operating conditions, taking into account the economic operation of microgrid clusters and distribution systems. As a result, the economy of the DN can be substantially improved.

Improvement of power transformer fault diagnosis by using sequential Kalman filter sensor fusion

Power transformers are one of the most important and costly equipment for the reliability and continuity of electrical power systems. For this reason, continuous monitoring of power transformers during normal operating conditions of the power grid and early fault diagnosis from existing parameters before a fault occurs is an important task. One of the most common analysis is the Dissolved Gas Analysis (DGA) method. In the DGA analysis, the concentrations of gases formed in the transformer insulating fluid are measured, classified and used to predict failures. It is observed that the classification and diagnosis are performed by classical and artificial intelligence-based methods in the relevant literature and applications. In this study, gas data classified by machine learning method is combined with sensor fusion methods to increase the diagnosis accuracy. It has been determined that the Sequential Kalman filter, which is first used differently from the literature, increases the estimation accuracy over 90% according to the results obtained by the Majority Voting and Dempster Shafer Evidence Theory fusion methods and the other results with IEC-TC-10 dataset mentioned in the literature.

Adaptive power load restoration considering flexible control of air conditioners for resilience enhancement

Frequent extreme hot weather brings a surge of air conditioner load and further increases the probability of outages. Fitting to different power grid operating conditions and outage scenarios, an adaptive load restoration strategy is proposed, which can take full use of flexible control of air conditioners and reliable power sources from the power grid and distributed generations or microgrids. Considering the influence of outage duration and environmental temperature, an air conditioner aggregated power estimation model is established to evaluate the cold load pickup amount. Based on the allowable maximum load pickup amount, a load pickup amount reduction method by flexible control of air conditioners considering the impact degree on the customer is proposed to guarantee secure load pickup operation. Simulation results of the IEEE 14-bus system and a practical power grid demonstrate that the proposed method can restore the power supply of customers rapidly and securely to enhance power grid resilience under extreme hot weather.

Optimal dispatching rules of hydropower reservoir in flood season considering flood resources utilization: A case study of Three Gorges Reservoir in China

Making full use of flood resources on the basis of ensuring flood control safety plays a positive role in improving the comprehensive benefit of reservoirs during flood season. To improve the utilization rate of flood resources of hydropower reservoirs, taking the Three Gorges Reservoir (TGR) as an example, an optimal forecast dispatching model for flood resources utilization (FRU) in flood season is established first based on the safety requirements of flood control, combining the power demand and the safe operating conditions of the power grid. Then, considering the uncertainty of runoff forecast, the stochastic simulation method of inflow forecast based on the gaussian mixture model (GMM) is used to assess the flood control risk of FRU dispatching. Finally, the optimal dispatching rules of TGR in flood season considering FRU are derived by using the gradient boosting regression tree (GRBT). The experimental results show that the optimal dispatching rules can effectively improve the utilization rate of flood resources and increase the generation benefit of TGR during flood season without an additional increase in flood control risk. Compared with routine dispatching, the hydropower generation of the TGR by rule dispatching considering inflow forecast information of 3, 5, and 7 days can be increased by 3.38%, 5.57%, and 6.93% respectively. This shows that the dispatching rules derived are very reliable and promising, which can provide decision support for reservoirs operation in flood season.

Mitigation of Voltage and Frequency Excursions in Low-Inertia Microgrids

Power systems proliferated by distributed generation sources are becoming increasingly prone to frequency and voltage disturbances. These problems are exacerbated in microgrids since they have fewer intrinsic disturbance-rejecting measures and features. To increase the reliability and stability of emerging power systems, the advanced control structures of the distributed generation sources based on power electronics devices must be deployed during suboptimal operating conditions. The aggravating circumstance is that both voltage and frequency excursion can be transient and long-lasting and consequently can occur simultaneously. The algorithm proposed in this paper integrates voltage support (nominal voltage restoration) and inertia emulation features with the comprehensive current references management scheme, thus securing improved grid operating conditions during several different faults and occurrences. The control algorithm is developed and tested in the context of a small microgrid, but it can be applied with minimal alterations in traditional grids, also. To prove that it is possible to decrease simultaneously voltage unbalances and frequency deviations, a test microgrid consisting of a synchronous generator, photovoltaic system, battery storage system, and controllable balanced and unbalanced loads was developed in a hardware-in-the-loop environment.

Stabilisation of the power grid with a hydrogen energy buffer

Abstract West Pomeranian University of Technology in Szczecin, as a leader, along with the University of Szczecin and Enea Operator LLC, is implementing the R&D project ‘Development of an intelligent and automatic system for stabilising the operation of power distribution networks based on modular installations of a hydrogen energy buffer with the intention of utilising hydrogen’. Its goal is to build the first hydrogen energy buffer in Poland, whose task will be to stabilise the parameters of the power grid. The operating conditions of the power grid, and the impact of the energy buffer system on its stability, have been determined. The nominal parameters of the buffer elements were calculated and the place of its installation and the assumptions for its operation have been indicated. The buffer will be located in the 110 kV / 15 kV substation.

Simulation Analysis of Relay Protection Action and Weak Point Adaptability Under Non Preset Operating Conditions of Complex Large Power Grid

In order to improve the protection performance of relay protection devices in complex large-scale power grid, a simulation analysis method of relay protection action and weak point adaptability under non preset operating conditions of complex large-scale power grid is proposed. In this method, pscad/emtdc simulation software is used to establish the simulation models of zero sequence and negative sequence directional elements, distance elements and longitudinal current differential elements of relay protection devices. Under the non preset working conditions of complex large power grid, the DC voltage imbalance characteristics, AC and DC power distribution characteristics and fault harmonic characteristics are analyzed, and the adaptability and weak points of relay protection action are analyzed according to these characteristics. The experimental results show that this method is more accurate in obtaining the characteristic value of DC fault under non preset conditions of complex large power grid, and can effectively obtain the action of relay protection devices at different times when non preset conditions occur in complex large power grid and when the setting value of DC low voltage is different. It is concluded that its action adaptability is better when the setting value of DC low voltage is 1.5 kA, and when the setting value of DC low voltage is 5.5 kA and 15 kA, The fault time is short, and the relay protection action has weak points.

Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. In this paper, we mitigate these limitations by developing a novel deep meta-reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method, which achieves superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.

Impact of the incorporation of photovoltaics distributed generation in electric distribution grids in Ecuador

The distributed generation (DG) allows electricity production to be closer to consumers, relieving the burden on distribution grid feeders. Interest in DG has increased in recent years due to its close relationship with smart grids and the development of carbon-free generation technologies. The Ciudad del Sol feeder in the city of Machala in Ecuador is one of the feeders with the highest electricity demand. This study evaluates the incorporation of DG in several consumers connected to this feeder. Depending on the range of energy consumption of each customer, different PV systems are proposed to meet their demands. The results of the study show that the installation of PV generation systems allows considerable savings on the electricity bill. In addition, the reduction of grid demand reduces Joule effect losses and improves voltage profiles. The results suggest that the massive incorporation of correctly dimensioned PV systems does not affect the operating conditions of a distribution power grid.

SMU Open-Source Platform for Synchronized Measurements.

The ramping trend of cheap and performant single board computers (SBC) is growingly offering unprecedented opportunities in various domains, taking advantage of the widespread support and flexibility offered by an operating system (OS) environment. Unfortunately, data acquisition systems implemented in an OS environment are traditionally considered not to be suitable for reliable industrial applications. Such a position is supported by the lack of hardware interrupt handling and deterministic control of timed operations. In this study, the authors fill this gap by proposing an innovative and versatile SBC-based open-source platform for CPU-independent data acquisition. The synchronized measurement unit (SMU) is a high-accuracy device able to perform multichannel simultaneous sampling up to 200 kS/s with sub-microsecond synchronization precision to a GPS time reference. It exhibits very low offset and gain errors, with a minimum bandwidth beyond 20 kHz, SNR levels above 90 dB and THD as low as −110 dB. These features make the SMU particularly attractive for the power system domain, where synchronized measurements are increasingly required for the geographically distributed monitoring of grid operating conditions and power quality phenomena. We present the characterization of the SMU in terms of measurement and time synchronization accuracy, proving that this device, while low-cost, guarantees performance compliant with the requirements for synchrophasor-based applications in power systems.

Convolutional neural nets with hyperparameter optimization and feature importance for power system static security assessment

Static security assessment (SSA) is fundamental in electrical network analysis. However, the growing complexity and variability of grid's operating conditions can make it tedious, slow, computationally intensive, and limited or impractical for on-line applications when traditional approaches are considered. Since this may hinder the emerging analytical duties of system operators, data-driven alternatives are required for faster and sophisticated decision-making. Although different machine learning algorithms (MLAs) could be applied, Convolutional Neural Networks (CNNs) are one of the most powerful models used in many advanced technological developments due to their remarkable capability to identify meaningful patterns in challenging and complex data sets. According to this, a CNN based approach for fast SSA of power systems with N-1 contingency is presented in this paper. To contribute to the automation of model building and tuning, a settings-free strategy to optimize a set of hyperparameters is adopted. Besides, permutation feature importance is considered to identify only a subset of key features and reduce the initial input space. To illustrate the application of the proposed approach, the simulation model of a practical grid in Mexico is used. The superior performance of the CNN alternative is demonstrated by comparing it with two popular MLAs.

A Grid Inductance Detection Method Based on the Oscillation Characteristic of Inverter Terminal Voltage

In grid-connected photovoltaic (PV) systems, grid inductance greatly influences the performance of grid-connected inverters. However, the grid inductance usually varies with the changes of the grid operation conditions. Therefore, accurate grid inductance detection is one of the key technologies to achieve an adaptive control of the grid-connected inverters under different operation conditions. In this article, an equivalent circuit model of a grid-connected PV system which includes the controller, filter, and grid impedance is established. The oscillation characteristic of the inverter terminal voltage caused by the sudden change of the inverter output power is analyzed. The oscillation mechanism of the inverter terminal voltage caused by the interaction of the controller, filter, and grid impedance is investigated. A grid inductance detection method based on the oscillation characteristic of the inverter terminal voltage is proposed. The main advantage of the proposed method is that it does not need complicated signal processing and calculation procedures. A 10-kW grid-connected PV system was built for both simulations and the experimental setup. Finally, simulation and experimental results validate the correctness and effectiveness of the proposed method.