Efficient Grid Operation Research Articles

Multiple distributed generators islanding detection using GBDT-JS techniques

Photovoltaic arrays and wind Distributed Generators (DG) have become integral to our renewable energy landscape. However, when a DG disconnects from the grid, the risk of islanding arises, necessitating detection within the stringent of two-second timeframe mandated by IEEE standards. Machine-level algorithms play a pivotal role in enhancing power system reliability and safety by swiftly identifying and isolating isolated segments, thereby preventing potential hazards and ensuring efficient grid operation. This study introduces an algorithm-based islanding detection approach for distributed generating systems employing both Solar Photo Voltaic (SPV) and wind systems. The GBDT-JS algorithm, a combination of Gradient Boosting Decision Trees and Jelly Fish Techniques, emerges as an intelligent solution. The focus of this technique is based on the Rate of Change in Phase Angle (RCPP) at the target DG position, offering a characterized approach to islanding detection. In addressing the major difficulties and challenges, the GBDT-JS method proves instrumental in categorizing islanding situations and grid disturbances. This classification aids in determining the system’s adoptability based on various loading and switching capabilities. The achievements in overcoming these challenges lie in the algorithm’s ability to provide a comprehensive solution, ensuring the reliability and safety of distributed generating systems.

An intensive decomposition integration paradigm for short-term wind power forecasting based on feature extraction and optimal weighted combination strategy

Wind power forecasting are crucial for enhancing power grid operation efficiency and ensuring the reliability and safety of the power supply. However, accurate wind power prediction has become a challenge due to the randomness and volatility of wind speed. In this study, an intensive decomposition integration paradigm based on feature extraction and optimal weighted combination strategy is proposed to make accurate predictions for wind power. The proposed method is verified by using two sets of wind power data from Inner Mongolia, China. Empirical results indicated that the proposed method has better prediction ability and stability than other comparative models.

Optimal location for an EVPL and capacitors in grid for voltage profile and power loss: FHO-SNN approach

In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for Electric vehicle (EV) charging stations (EVCS) and capacitors in power grids to enhance voltage stability and reduce power losses, facilitating sustainable and efficient grid operation. Meeting the increasing demand for EV charging stations while simultaneously addressing grid reliability, voltage control, and power loss issues poses significant challenges. A hybrid approach for EV parking lot and optimal location of capacitors in voltage and power loss is proposed in this manuscript. The proposed hybrid method combines the Fire Hawk Optimizer and Spiking Neural Network, hence called the FHO-SNN technique. The objective of the proposed method is used to lessen the active-power-losses that improve system performance. The novelty of this proposed method is electric vehicle parking lot and optimal location of capacitors in voltage and lessens the power loss. The proposed system is done in the MATLAB, and it is validates their performance with existing methods, such as Arithmetic Optimization Algorithm (AOA), Seagull Optimization (SO), and Atomic Orbital Search (AOS). The proposed method shows low convergence time, which is 4 sec, number of feeders, and best function node is found compared with other existing methods. The optimization approach can enhance the stability and efficiency of electric grids by improving voltage profiles and reducing power losses. The implications of this research highlight the potential for optimizing the combination of electric vehicles and capacitors into the grid, resulting in a more sustainable, efficient, and resilient energy and transportation system.

Real-time rolling-horizon energy management of public laundries: A case study in HSB living lab

Energy Management Systems (EMSs) play a vital role in managing energy consumption for both utilities and consumers. By using EMSs, utilities can influence on energy usage and ensure a more reliable and efficient grid operation, while consumers can make informed decisions about their energy consumption, leading to significant cost savings and reduced environmental impact. In this paper, a real-time rolling-horizon model is developed for managing energy consumption in public laundries aiming at minimizing energy costs, peak demand, and CO2 emission under the traditional Energy-Based Tariff (EBT) and the Power-Based Tariff (PBT). The developed model can not only reduce energy costs, peak demand, and CO2 emission by optimal task scheduling for washing machines and tumble dryers but also ensure users' preferences for a comfortable lifestyle. To demonstrate the effectiveness of the proposed EMS, several simulations were performed under different scenarios using real data and by a realistic case study in HSB living lab demonstration site. The simulation results reveal that implementing the proposed EMS can significantly decrease energy costs and peak demand in public laundries by 13.59% and 39.40%, respectively, when using the PBT tariff. However, the reduction in energy costs and peak demand is negligible when using the EBT tariff. Likewise, the results indicate that using the EMS and changing tariffs have a minimal impact on CO2 emissions reduction.

Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

This article presents a review of current advances and prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.

Deep learning-enabled MCMC for probabilistic state estimation in district heating grids

Flexible district heating grids form an important part of future, low-carbon energy systems. We examine probabilistic state estimation in such grids, i.e., we aim to estimate the posterior probability distribution over all grid state variables such as pressures, temperatures, and mass flows conditional on measurements of a subset of these states. Since the posterior state distribution does not belong to a standard class of probability distributions, we use Markov Chain Monte Carlo (MCMC) sampling in the space of network heat exchanges and evaluate the samples in the grid state space to estimate the posterior. Converting the heat exchange samples into grid states by solving the non-linear grid equations makes this approach computationally burdensome. However, we propose to speed it up by employing a deep neural network that is trained to approximate the solution of the exact but slow non-linear solver. This novel approach is shown to deliver highly accurate posterior distributions both for classic tree-shaped as well as meshed heating grids, at significantly reduced computational costs that are acceptable for online control. Our state estimation approach thus enables tightening the safety margins for temperature and pressure control and thereby a more efficient grid operation.

Design and Application of a UAV Autonomous Inspection System for High-Voltage Power Transmission Lines

As the scale of the power grid continues to expand, the human-based inspection method struggles to meet the needs of efficient grid operation and maintenance. Currently, the existing UAV inspection system in the market generally has short endurance power time, high flight operation requirements, low degree of autonomous flight, low accuracy of intelligent identification, slow generation of inspection reports, and other problems. In view of these shortcomings, this paper designs an intelligent inspection system based on self-developed UAVs, including autonomous planning of inspection paths, sliding film control algorithms, mobile inspection schemes and intelligent fault diagnosis. In the first stage, basic data such as latitude, longitude, altitude, and the length of the cross-arms are obtained from the cloud database of the power grid, while the lateral displacement and vertical displacement during the inspection drone operation are calculated, and the inspection flight path is generated independently according to the inspection type. In the second stage, in order to make the UAV’s flight more stable, the reference-model-based sliding mode control algorithm is introduced to improve the control performance. Meanwhile, during flight, the intelligent UAV uploads the captured photos to the cloud in real time. In the third stage, a mobile inspection program is designed in order to improve the inspection efficiency. The transfer of equipment is realized in the process of UAV inspection. Finally, to improve the detection accuracy, a high-precision object detector is designed based on the YOLOX network model, and the improved model increased the mAP0.5:0.95 metric by 2.22 percentage points compared to the original YOLOX_m for bird’s nest detection. After a large number of flight verifications, the inspection system designed in this paper greatly improves the efficiency of power inspection, shortens the inspection cycle, reduces the investment cost of inspection manpower and material resources, and successfully fuses the object detection algorithm in the field of high-voltage power transmission lines inspection.

Short-term PV power forecasting based on time series expansion and high-order fuzzy cognitive maps

Achieving short-term accurate photovoltaic power forecasting is of great significance to improve the efficiency of grid operation, especially in power stations where historical values of meteorological parameters are lagged or not recorded. Therefore, numerous studies have been presented to predict the intricate stochastic properties of photovoltaic power sequence in recent years. However, due to the limitation of algorithm performance and the effect of noise in the time series, previously proposed methods may not extract effective features from power data. Furthermore, most studies have only focused on point prediction, which ignores the uncertain information and unavoidable forecast bias. In this study, a hybrid framework based on fuzzy information granulation algorithm, improved variational mode decomposition technique, and high-order fuzzy cognitive maps is proposed to fill these gaps. Comparison experiments were set up using 5-minute photovoltaic power data from Alice Springs, Australia. The computational results not only demonstrate that the proposed framework significantly improves forecast accuracy of short-term photovoltaic power, but also achieves effective interval prediction by fuzzy information.

Study of the dynamic performance boundaries of a converter’s energy storage device

Maintaining frequency stability is one of the central objectives of power grid operation. While this task is currently primarily done by employing stored rotational energy, in a converter-dominated grid fed by renewables, sources, such as wind and photovoltaic, must be involved in the frequency control of the power grid in order to maintain a stable, efficient grid operation in the process of achieving carbon neutrality. However, due to the lack of rotational inertia reserves, the converter requires additional energy storage devices to respond to the grid’s frequency regulation requirements. For modeling a converter-dominated grid, the behavior of such additional short-time storage must be modeled properly in order to obtain realistic simulation results, which allow drawing conclusions about the frequency stability behavior of the grid. This paper investigates the boundaries of the dynamic performance of the output current of the energy storage device so that the converter can achieve the function of frequency regulation in a more economical manner. In this paper, a dynamic supporting converter based on a phase-locked loop and a grid-forming converter, as well as the DC link of the converter containing an energy storage system, are modeled. On this basis, the optimal boundaries of the dynamic performance of the output current of the energy storage device are investigated. It is concluded that not only the proper sizing of grid-supporting energy storage devices is important for proper grid operation, but the dynamic behavior also has to be modeled and designed properly in order to guarantee a stable operation under all circumstances.

Deploying e-mobility in the interact energy community to promote additional and valuable flexibility resources for secure and efficient grid operation

The volatility of renewable energy sources and the increasing share of decentralised ones imply significant systemic requirements on the flexibility of Smart Grids. E-mobility can support their greater integration by offering flexibility in shaping the electricity demand curve, thereby reducing system costs and CO2 emissions. This paper deploys e-mobility in the LINK-Energy Community to promote additional and valuable flexibility resources for secure and efficient grid operation. Results show that energy communities are the most vital instrument to motivate customers to provide flexibility through e-mobility actively. They may efficiently coordinate their territory's electricity generation and charging schedules and promote carpooling between the EC members. Developing the harmonised fractal market structure with the power grid based on the holistic solution LINK creates all the conditions for the deployment of successful business models. The use case and the respective business model for the congestion alleviation - emergency driven demand response involving e-mobility are described in detail. This study shows that energy communities developed under the holistic perspective of LINK offer flexibility to the grid, even in the case of electromobility.

Research on supporting mechanism of ancillary service of PV system to grid energy efficiency based on multi-time and space-time operation

Under the background of high-energy penetration of new energy into the power grid, this paper takes the ancillary service capability of photovoltaic energy integrated into the grid as the starting point and builds a photovoltaic system reactive power service impact evaluation model on the grid energy efficiency. This is based on the multi-temporal and spatial scale operation mode, in order to study the supporting principles of photovoltaic system reactive power services on the energy efficiency of grid operation and the law of influence on system energy efficiency changes. In this way, the space for power system energy efficiency improvement and the reactive power service market value of renewable energy are explored to improve the renewable energy auxiliary services participation in the theoretical system of electric power spot market transactions. The research conclusions can provide a decision-making reference for system dynamic energy efficiency management and can assist relevant market entities to make optimal decisions in spot market transactions, and provide empirical data for improving the theory of renewable energy participation in auxiliary service market transactions.

A hybrid chaotic-based discrete wavelet transform and Aquila optimisation tuned-artificial neural network approach for wind speed prediction

Wind speed prediction has received reasonable attention recently because of its clean and promising source of renewable energy. Recent studies have shown that developing efficient model to predict wind speed is a challenging task because of its nonlinear and stochastic characteristics. This paper aims to propose a new hybrid model to predict wind speed. For this purpose, Discrete Wavelet Transform (DWT), Phase Space Reconstruction (PSR) of chaos theory, Aquila Optimization Algorithm (AOA) and Backpropagation Neural Network (BPNN) are hybridised and a novel DWT-PSR-AOA-BPNN is proposed. To ascertain the proposed DWT-PSR-AOA-BPNN model performance, different hybrid model variants (DWT-PSR-GA-BPNN, DWT-PSR-PSO-BPNN, PSR-PSO-BPNN and PSR-AOA-BPNN) were developed for comparison. The comparison was done using statistical model evaluators of Mean Average Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and model efficiency of Loague and Green (ELG). The statistical results showed that the proposed DWT-PSR-AOA-BPNN model performed better and is therefore considered efficient wind speed prediction tool when compared with DWT-PSR-GA-BPNN, DWT-PSR-PSO-BPNN, PSR-PSO-BPNN and PSR-AOA-BPNN hybrid models. That is, the proposed DWT-PSR-AOA-BPNN had the lowest MAE, RMSE and MAPE values for the model testing (MAE = 1.1490, RMSE = 1.4190 and MAPE = 0.2743) and validation (MAE = 0.8122, RMSE = 0.9771 and MAPE = 0.1953). The DWT-PSR-AOA-BPNN also achieved the highest ELG values of 0.9904 (testing) and 0.99738 (validation) respectively. It is therefore concluded that considering the DWT-PSR-AOA-BPNN results, the indication corroborates the fact that this model can be utilized for efficient grid operations.

Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation

Although the recent development of solar power forecasting through machine learning approaches, such as the machine learning models based on numerical weather prediction (NWP) data, has been remarkable, their extreme error requires an increase in the amount of reserve capacity procurement used for the power system safety. Hence, a reduction of the serious overestimation is necessary for efficient grid operation. However, despite the importance of the above issue, few studies have focused on the model design, suppressing serious errors, to the best of the authors’ knowledge. This study investigates a prediction model that can reduce the huge overestimation of the solar irradiance, which poses a risk to the power system. The specific approaches used are as follows: the employment of Support Vector Quantile Regression (SVQR), the utilization of Meso-scale Ensemble Prediction System (MEPS, Meso-scale EPS for the regions of Japan) data, which is based on the forecasts from Meso-scale Model (MSM) as explanatory variables, and the hyperparameter adjustment. The performance of the models is verified in the one day-ahead forecasting for surface solar irradiance at five sites in the Kanto region as the numerical simulation, where their forecasting errors are measured by the root mean square error (RMSE) and the 3σ error, which corresponds to the 99.87% quantile error of the order statistics. The test results indicate the following findings: the SVRs’ RMSE and 3σ error tend to be trade-offs in the case of varying the penalty of the regularization term; by using SVR as a post-processing tool for MSM or MEPS data, both of the score of their metrics can be improved from original data; the MEPS-based SVQR (MEPS-SVQR) could provide superior performance in both metrics in comparison with the MSM-based SVQR (MSM-SVQR) if the parameters are properly adjusted. Although the time period and the type of MEPS data used for the validation are limited, our report is expected to help the design of NWP-based machine learning models to enable short-term solar power forecasts with a low risk of overestimation.

A survey on development and prospect of wind turbines virtual synchronous control technology

The ongoing energy revolution has led to the gradual replacement of traditional synchronous generators by renewable enecorresponding authorrgy. This transformation of power generation mode reshapes power system dynamics and poses many challenges to reliable and efficient grid operation. With the continuous improvement of the penetration level of renewable energy represented by wind power in the power system, the synchronous mode of wind turbine will gradually become one of the main modes of system synchronization. In the case of coexistence of multiple synchronization modes, the synchronization mechanism of the whole power system will be fundamentally changed; at the same time, due to the response characteristics of wind turbine to the frequency and voltage dynamics of power grid and the traditional power supply, the synchronous mode of wind turbine will gradually become one of the main modes of system synchronization The frequency and voltage stability of power system also faces new challenges. The virtual synchronous generation technology can make the grid connected inverter simulate the operation mechanism of step generator and provide an effective method to solve the above problems. In this paper, the main technical routes to realize virtual synchronous control of wind turbines are summarized. From the aspects of stability analysis, control system optimization, performance analysis and evaluation, the research and engineering status of virtual synchronization technology in wind turbines are introduced, and the problems to be solved and technical requirements are prospected.

Nearly optimal demand side management for energy, thermal, EV and storage loads: An Approximate Dynamic Programming approach for smarter buildings

This paper proposes a distributed feedback-based optimization method, based on the principles of approximate dynamic programming, aiming for the optimal management and energy efficient operation of grid connected buildings. Modern building management faces multiple challenges, aiming for, energy efficiency, RES integration, EV operation, cost reduction and of course user comfort. Therefore, this paper adopts a multi-criterion cost objective, where minimum energy costs are required without sacrificing user preferences and satisfaction. Different type of loads are presented, including both thermostatically controllable loads (simulated and modelled in Energy Plus), and controllable electric vehicles (EV) and energy storage systems (ESS). Extensive simulations showcase the effectiveness of the proposed method introducing comparisons with different strategies by exploiting renewable energy integration, varying pricing tariffs and the stored energy of EVs and ESS units. A robustness evaluation of the proposed method is presented, validating the performance under different conditions. Finally, the paper demonstrates that the proposed strategy can be utilized for the management of real-world problems and buildings.

Evaluation Index System of Power Grid Operation Efficiency and Investment Benefit

With the deepening of reform and opening up, and the continuous development of social economy, the operating efficiency and investment benefits of traditional power grids have gradually fallen behind the changes of the times, and further reforms are urgently needed. In response to this situation, a series of detailed and sufficient investigations and studies have been carried out, and a comprehensive and detailed investigation and analysis of the current grid efficiency and benefit evaluation index system have been conducted, and then the current grid efficiency and efficiency individual indicators have been carried out in accordance with the actual situation. It also conducted a comprehensive evaluation of the efficiency and benefit of the power grid.

Monitoring area screening of AC system to improve embedded VSC‐HVDC operation efficiency

With the increasing number of embedded high-voltage DC (HVDC) projects in AC systems, there is growing research interest in controlling the operating point of the HVDC in real time. This real-time operation system requires the AC system information to be continuously updated, which involves practical challenges such as the burden of communication. To mitigate this problem, this paper proposes a novel technique to screen the AC system monitoring area for the efficient operation of the embedded HVDC. The screening algorithm determines the monitoring area by HVDC sensitivity analysis using the power transfer distribution factor. Then, a reliable and efficient grid operation is enabled using the limited data obtained from the determined area. The proposed methodology is verified using a Korean power system with multiple HVDCs. The effectiveness and advantages of the proposed technology are verified by comparing the results obtained using the system-wide data with those obtained from the monitoring area.

Multi level Transformer less PV Inverter with Reduced Switching Losses and Elimination of CM leakage Current

In order to have efficient operation of grid connected Photovoltaic (PV) inverter, the issue of leakage current must be completely eliminated. For this new transformerless multilevel inverter is proposed in this paper with (k-1) levels in which k denotes the number of switches. The inverter provides zero common mode leakage current and also capable of operating under half of rated PV voltage, hence make this proposed inverter topology as LVRT (low voltage ride through) capable. Different modes are employed for the stable operation of the proposed inverter with varying input PV voltage. Simulation work is carried out for three, five and seven level proposed inverter topologies in MATLAB/Simulink software which determines its maximum power point tracking performance. Here the THD (Total Harmonic Distortions) of 3, 5 and 7 levels are compared.

Development and comparison of migration paths for smart grids using two case studies

Due to the energy turnaround in German politics, it is necessary to integrate more and more wind and solar energy into the existing energy system. In particular, power generation is changing from a previously centralized to a decentralized structure, which also has consequences for requirements for safe, reliable and efficient grid operation. Generation and utilization characteristics will become more dynamic and flexible in the future. Increased demand for the measurement, control and automation of voltage and electricity will require the further development of grid infrastructure, the expansion of storage capacity and the introduction of information and communication technology (ICT)-based energy management (Appelrath et al., 2012). Utilities therefore need to know what migration paths into the future of a smart energy grid could look like. And this against the background of which technologies have to be installed, in which order this can happen and which dependencies have to be considered. The aim is to create roadmaps to the modern Smart Grid for two case studies. Within the framework of the Green Access project (Projekt Green Access, 2019), and (Flore & Kumm, 2020), a maturity model and, based on this, migration paths were developed for this purpose, which describe a path from one development stage to the next. It describes the necessary development steps that have to be implemented in the context of migration paths. These migration paths have been developed for a specially designed maturity model and describe the technologies used to move from one maturity level to the next. Finally, there will be a comparison of the developed migration paths of the two case studies.

Research on power grid technical reform planning model based on overall cost constraint

For a long time, the technical reform of the power grid has improved or updated the existing power grid facilities to achieve the effect of low-cost and improved grid operation efficiency. In recent years, the development of power grid has entered to a high-quality development stage, the technical reform of the power grid has been continuously improved. In the actual compilation, the grid technical reform plan usually uses historical data for qualitative analysis, which lacks quantitative optimization analysis considering power constraints and improvement indicators. Based on the power grid planning mathematical optimization model, this paper establishes a technical reform optimization model that comprehensively considers the power grid technical reform planning cost and load shedding cost, and uses the improved IEEE 30-node example to verify the model. The verification results show that the model can obtain the optimization of the technical reform route planning and can effectively guide the preparation of power grid technical reform planning, which has practical significance.