Grid Operation Research Articles

Day-Ahead Two-Stage Bidding Strategy for Multi-Photovoltaic Storage Charging Stations Based on Bidding Space

Against the backdrop of a “dual-carbon” strategy, the use of photovoltaic storage charging stations (PSCSs), as an effective way to aggregate and manage electric vehicles, new energy sources, and energy storage, will be an important primary component of the electricity market. The operational characteristics of the aggregated resources within a PSCS determine its bidding space, which has an important influence on its bidding strategy. In this paper, a novel bidding space model is constructed for PSCSs, which dynamically integrates electric vehicles, photovoltaic generation, and energy storage. A two-stage bidding strategy for multiple PSCSs is established, with stage I aiming at achieving the lowest cost for the power purchased by a PSCS to optimize the power generation and power plan and stage II aiming at achieving the lowest cost of the grid operator’s power purchase to optimize the system’s power balance. Thirdly, the two-stage model is transformed into a single-layer, mixed-integer linear programming problem using dyadic theory and Karush–Kuhn–Tucker (KKT) conditions, enabling the derivation of the optimal bidding strategy. Finally, the example analysis verifies that the proposed model can achieve a reduction in the PSCS’s day-ahead power purchase cost and flexibly dispatch each resource within the PSCS to maximize revenue, as well as reducing power consumption behavior during peak tariff hours, to enhance the market power of the PSCS in the electricity market.

Designing of a wide-area power system stabilizer using an exponential distribution optimizer and fuzzy controller considering time delays

In this paper, explore the effectiveness of a new Wide Area Fuzzy Power System Stabilizer (WAFPSS), optimized using the Exponential Distribution Optimization (EDO) algorithm, and applied to an IEEE three-area, six-machine power system model. This research primarily focuses on assessing the stabilizer’s capability to dampen inter-area oscillations, a critical challenge in power grid operations. Through extensive simulations, the study demonstrates how the WAFPSS enhances stability and reliability under a variety of operational conditions characterized by different communication delay patterns. The application of the proposed stabilizer on this specific IEEE model provides a detailed insight into its performance in real-world scenarios, illustrating its adaptability and effectiveness in managing dynamic disturbances. The simulation results reveal that the proposed WAFPSS achieves significant reductions in the Integral Time Squared Error (ITSE), with improvements of 94.1%, 97.02%, and 98.18% in three distinct cases, showcasing its superior damping capability and robustness. The findings indicate that the advanced optimization techniques provided by the EDO algorithm significantly improve the stabilizer’s response, ensuring robust power system performance. This integration of WAMS with sophisticated control systems using fuzzy logic presents a strategic solution to managing the complexities faced by modern power networks, optimizing their stability in the face of increasing renewable integration and fluctuating demand.

Modeling local distributed solar energy potential: a case study from Virginia, USA

ABSTRACT In this paper, we use GIS analysis to estimate potential distributed solar PV capacity and solar electricity generation in a suburban neighborhood in Virginia, United States. Using a combination of LiDAR and solar insolation data, we find that 37% of rooftop space in the study area would receive sufficient solar insolation to support a distributed solar (DPV) system. Applying conservative assumptions, we estimate that nearly 19 MW (AC) of distributed rooftop PV could realistically be installed, providing 28% of the study area’s estimated annual electricity demand. These findings provide evidence of the significant untapped potential of DPV, and support the need for streamlined permitting processes and other incentives to reduce soft costs and facilitate DPV installation. We also discuss opportunities for merging planning and engineering research to support targeted utilization of DPV in locations where it can best support distribution grid operations, such as on commercial sector buildings in particular.

A Machine Learning Feature Descriptor Approach: Revealing Potential Adsorption Mechanisms for SF6 Decomposition Product Gas-Sensitive Materials.

The man-made gas sulfur hexafluoride (SF6) is an excellent and stable insulating medium. However, some insulation defects can cause SF6 to decompose, threatening the safe operation of power grids. Based on this, it is of great significance to find and effectively control the decomposition products of SF6 in time. Gas sensors have proven to be an effective way to detect these decomposition gases (SO2, SOF2, SO2F2, H2S, and HF). Nanomaterials with gas-sensitive properties are at the heart of gas sensors. In recent years, data-driven machine learning (ML) has been widely used to predict material properties and discover new materials. However, it has become a major challenge to establish a common model between material properties derived from various types of calculations and intelligent algorithms. In order to make some progress in addressing this challenge. In this work, 250 data sets were extracted from 52 publications exploring the detection of SF6 decomposition products by nanocomposites based on relevant work over the past 10 years, and the adsorption behavior of SF6 decomposition products can be predictively analyzed. By comparing six different algorithmic models, the best model for predicting the adsorption distance (XGBoost: R2 = 91.94%) and adsorption energy (GBR: R2 = 78.63%) of SF6 decomposed gas was identified. Subsequently, the importance of each of the selected feature descriptors in predicting the gas adsorption effect was explained. This work combines first-principles computational results and machine-learning algorithms with each other to provide a new research idea for evaluating the gas sensing capability of nanocomposites.

Cyber-Resilient Operation of IoT-Enabled Power Grid: A Nodal Local Energy Market Approach

Cyber-Resilient Operation of IoT-Enabled Power Grid: A Nodal Local Energy Market Approach

Evaluation, Calibration, and Application of Probabilistic 100-m Wind Speed Forecasts Produced by the WRF Ensemble Prediction System in Taiwan

Abstract The use of renewable energy is on the rise; however, it brings more challenges for grid operations and unit scheduling due to the intermittent nature and limitations in predicting renewable power generation. High-quality meteorological forecasts play a critical role in the effective application and management of renewable energy resources. This study evaluates the quality and economic benefits of probabilistic forecasts for 100-m wind speeds from the Weather Research and Forecasting Model ensemble prediction system (WEPS). A multiple linear regression (MLR) method for calibration is also used to improve forecast quality. Additionally, this study explores the application of probabilistic hub-height wind speed forecasts in conjunction with an economic value analysis to determine whether wind turbines should be shut down during typhoon periods. The findings reveal that the forecast in the central offshore region of Taiwan exhibits the highest reliability and discrimination ability compared to those in the northern and southern regions. Additionally, employing the MLR calibration method significantly improved the reliability and discrimination ability of the probabilistic forecasts compared to the raw forecasts. Furthermore, during the typhoon seasons, almost all users, regardless of their cost–loss ratio, can benefit from basing their decisions on WEPS forecasts compared to using a single deterministic forecast. Importantly, decision-makers can benefit more from probabilistic forecasts than the ensemble mean forecasts when both are derived from the same ensemble prediction system, since the former incorporates forecast uncertainty. Significance Statement High-quality meteorological forecasts significantly influence the efficient utilization and management of renewable energy resources. Therefore, this study uses a simple yet effective approach to correct systematic bias in probabilistic wind speed forecasts over Taiwan, aiming to provide decision-makers with more reliable forecasts for hub-height wind speeds. Given that strong winds during typhoon periods pose safety concerns for wind turbine operation, we use an economic value analysis to assist turbine operators in deciding whether to shut down wind turbines. Furthermore, our research demonstrates that ensemble probabilistic forecasts can yield greater economic benefits for decision-makers when compared to the commonly used ensemble mean forecasts.

Implications of Large-Scale PV Integration on Grid Operation, Costs, and Emissions: Challenges and Proposed Solutions

This study examines integrating large-scale photovoltaic (PV) systems into the power grid to achieve a 30% PV share, addressing operational and economic challenges such as backup generation, storage, and grid stability. Applying an electricity dispatch model to the test case of Israel, it highlights significant impacts on fuel consumption, cost, and carbon emissions. Key findings include an 8% drop in the capacity factor of natural gas combined cycle (NGCC) plants, leading to increased starts, stops, and higher fuel consumption. Annual power generation will grow from 81 to 104 TWh, with PV generation increasing from 8.1 to 31.1 TWh. Open cycle gas turbine (OCGT) output will grow from 2.4 to 10.2 TWh, increasing OCGT’s market share from 3% to 10%. NGCC operations’ intermittency will double annual starts from 3721 to 7793, causing a 1.1% efficiency drop and a 2% rise in natural gas consumption. 3.45 GWh of Li-ion battery capacity will be needed. The LCoE is expected to increase from 6.6 to 7.0 c$/kWh without a carbon tax and from 8.7 to 8.8 c$/kWh with a $40/t carbon tax. Annual emissions will rise from 41.8 to 46.5 Mt. This study provides insights for sunny Mediterranean countries with similar renewable energy goals.

Power Grid Violation Action Recognition via Few-Shot Adaptive Network

To address the performance degradation of violation action recognition models due to changing operational scenes in power grid operations, this paper proposes a Few-shot Adaptive Network (FSA-Net). The method incorporates few-shot learning into the network design by adding a parameter mapping layer to the classification network and developing a task-adaptive module to adjust the network parameters for changing scenes. A task-specific linear classifier is added after the backbone, allowing the adaptive generation of classifier weights based on the changing task scene to enhance the model’s generalizability. Additionally, the model uses a strategy of freezing the backbone network and iteratively updating only certain module parameters during training in order to minimize training costs. This approach addresses the challenge of iteratively updating difficulties in the original model, which are caused by limited image data following scene changes. In this paper, 2000 samples under power grid scenarios are used as the experimental dataset; the average recognition accuracy for violation actions is 81.77% for images after scene changes, which represents a 4.58% improvement when compared to the ResNet-50 classification network. Furthermore, the model’s training efficiency is enhanced by 40%. The experimental results show that the method enhances the performance of the violation action recognition model before and after scene changes and improves the efficiency of the iterative model by updating with a smaller sample size, lower model design cost, and lower training cost.

Justification of the scheme of melting ice on the wires of overhead power lines

The article discusses an urgent problem related to the formation of ice and frost deposits on the wires of overhead power lines, which causes significant technical difficulties in ensuring the reliability of power supply, especially in winter. The accumulation of ice and frost on the wires leads to an increase in the weight of the wires, which can cause them to sag, damage insulators, destroy poles, and, as a result, serious accidents on power lines. The causes of accidents in power grids due to ice and frost deposits are analyzed and it is found that their elimination will reduce the probability of damage to overhead power lines from the action of ice loads. However, existing methods of dealing with this problem have a number of disadvantages, such as high cost, low efficiency in certain conditions, and negative environmental impact. The article presents a scheme for melting ice on the wires of overhead power lines by the method of a three-phase short circuit when powered by a power source with a solidly grounded and isolated neutral. This scheme makes it possible to locally increase the temperature of wires to a level sufficient to melt ice and frost, which reduces the risk of emergencies and ensures the uninterrupted operation of the electrical grid. The article provides theoretical justifications for the scheme for melting ice with three-phase short-circuit currents, which confirm its effectiveness. The analysis has shown that the proposed scheme is economically feasible, as it reduces the cost of maintenance and repair of overhead power lines. In addition, it is environmentally friendly, as it does not involve the use of chemicals. Thus, the proposed scheme for melting ice on the wires of overhead power lines is a promising solution for improving the reliability and safety of power supply in winter weather conditions. Its application can significantly increase the efficiency of power grid operation, reduce the number of accidents, and improve the stability of power supply to consumers.

Mechanism and suppression of breaking overvoltage of DC circuit breakers

AbstractThe occurrence of breaking overvoltage in a DC circuit breaker (DCCB) poses a potential threat to the safe operation of a DC grid. Based on the structure of the ±10 kV three‐terminal DC distribution network, this paper sets up the medium‐voltage DCCB and formulates a fault protection strategy. Additionally, a simulation model of the system is developed to analyse the overvoltage characteristics within the DC distribution network across different nodes and fault conditions. By examining the factors that influence breaking overvoltage, this paper unravels the fundamental mechanisms associated with DCCB‐related overvoltage generation. Furthermore, the paper suggests measures to alleviate DCCB overvoltage. These insights provide a theoretical and technical basis for the design and operation of DCCBs within DC distribution networks.

Short time solar power forecasting using P-ELM approach

Accurately predicting solar power to ensure the economical operation of microgrids and smart grids is a key challenge for integrating the large scale photovoltaic (PV) generation into conventional power systems. This paper proposes an accurate short-term solar power forecasting method using a hybrid machine learning algorithm, with the system trained using the pre-trained extreme learning machine (P-ELM) algorithm. The proposed method utilizes temperature, irradiance, and solar power output at instant i as input parameters, while the output parameters are temperature, irradiance, and solar power output at instant i+1, enabling next-day solar power output forecasting. The performance of the P-ELM algorithm is evaluated using mean absolute error (MAE) and root mean square error (RMSE), and it is compared with the extreme learning machine (ELM) algorithm. The results indicate that the P-ELM algorithm achieves higher accuracy in short-term prediction, demonstrating its suitability for ensuring accuracy and reliability in real-time solar power forecasting.

SGI-YOLOv9: an effective method for crucial components detection in the power distribution network

The detection of crucial components in the power distribution network is of great significance for ensuring the safe operation of the power grid. However, the challenges posed by complex environmental backgrounds and the difficulty of detecting small objects remain key obstacles for current technologies. Therefore, this paper proposes a detection method for crucial components in the power distribution network based on an improved YOLOv9 model, referred to as SGI-YOLOv9. This method effectively reduces the loss of fine-grained features and improves the accuracy of small objects detection by introducing the SPDConv++ downsampling module. Additionally, a global context fusion module is designed to model global information using a self-attention mechanism in both spatial and channel dimensions, significantly enhancing the detection robustness in complex backgrounds. Furthermore, this paper proposes the Inner-PIoU loss function, which combines the advantages of Powerful-IoU and Inner-IoU to improve the convergence speed and regression accuracy of bounding boxes. To verify the effectiveness of SGI-YOLOv9, extensive experiments are conducted on the CPDN dataset and the PASCAL VOC 2007 dataset. The experimental results demonstrate that SGI-YOLOv9 achieves a significant improvement in accuracy for small object detection tasks, with an mAP@50 of 79.1% on the CPDN dataset, representing an increase of 3.9% compared to the original YOLOv9. Furthermore, it achieves an mAP@50 of 63.3% on the PASCAL VOC 2007 dataset, outperforming the original YOLOv9 by 1.6%.

Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR

The accurate short-term forecasting of an electric vehicle (EV) load is crucial for the reliable operation of a power grid and for effectively reducing energy consumption. Due to the fluctuations in EV charging loads, particularly the significant load variation between commercial and non-commercial areas, global models often suffer from prediction errors when forecasting loads. To address this issue, this paper proposes a regional forecasting method based on K-means++ clustering and deep learning algorithms. First, the K-means++ algorithm was used to partition the data into different regions, and an independent load-forecasting model was established for each region. Then, a combination of kernel support vector regression (KSVR) and gated recurrent unit (GRU) models was used to handle nonlinear features and time-dependent data, where particle swarm optimization (PSO) further optimized the model parameters to improve the forecasting accuracy. Finally, a weighted summation method was used to integrate the forecast results from each region, resulting in a more accurate overall load forecast. The experimental results show that the proposed model provided better prediction performance by capturing the spatiotemporal characteristics of the EV charging load, effectively addressing the challenges posed by regional differences, and outperforming the single-model forecasts.

Droughts in Wind and Solar Power: Assessing Climate Model Simulations for a Net‐Zero Energy Future

AbstractUnderstanding and predicting “droughts” in wind and solar power availability can help the electric grid operator planning and operation toward deep renewable penetration. We assess climate models' ability to simulate these droughts at different horizontal resolutions, ∼100 and ∼25 km, over Western North America and Texas. We find that these power droughts are associated with the high/low pressure systems. The simulated wind and solar power variabilities and their corresponding droughts during historical periods are more sensitive to the model bias than to the model resolution. Future climate simulations reveal varied future change of these droughts across different regions. Although model resolution does not affect the simulation of historical droughts, it does impact the simulated future changes. This suggests that regional response to future warming can vary considerably in high‐ and low‐resolution models. These insights have important implications for adapting power system planning and operations to the changing climate.

Low-carbon scheduling strategy for electric vehicles considering carbon emission flow and dynamic electricity prices

As the global environmental pollution problem intensifies, the carbon reduction transformation of the power system is urgent. In order to solve the problem of unclear carbon flow and distribution in the operation of the power grid, as well as the mismatch between static time-of-use electricity prices and peak and valley periods in the scheduling of electric vehicle charging loads, a multiperiod dynamic electricity price guidance strategy based on carbon emission flow theory is proposed. First, based on the accurate power flow results of the power system, a complex power distribution matrix of the power system is constructed to obtain the distribution of the power generated by the generator units in each node of the network. Then, the Monte Carlo random sampling method is used to simulate the load situation of electric vehicles in a disordered charging state. A mathematical model based on the carbon trading model is established to minimize the load difference at the grid end and maximize the cost of charging on the user side. Finally, the proposed ordered charging method with a multiperiod dynamic electricity pricing strategy is compared with unordered charging, and considering the participation of electric vehicles in carbon trading, this strategy effectively reduces the peak valley difference between the power grid and user charging costs.

A photovoltaic power estimation model based on the improved local cloud occlusion index algorithm considering Sun block luminance

Abstract Accurate estimation of photovoltaic (PV) power generation is of great significance to the operation and optimization of the power grid. However, due to the rapid movement of clouds in a short period of time, the changes in PV power are intermittent and fluctuating. Nowadays, sky images have been widely used for PV power estimation, which can effectively extract overall or local cloud information. However, Sun halos are often mistaken for being part of clouds and lead to incorrect cloud coverage calculations. To solve this problem, this paper proposes a method to calculate the local cloud occlusion index based on an improved two-dimensional Gaussian function. In addition, a new parameter, Sun block luminance, is introduced to quantify the lighting conditions in the solar area. On the other hand, this paper constructs a novel hybrid model, which introduces asymmetric convolution and SE attention module to enhance the extraction ability of sky image features. The test results show that, under sunny and cloudy conditions, the RMSE, MAE and R 2 values of the proposed model are 0.562, 0.462 and 0.989 on sunny days, 3.119, 2.248 and 0.774 on cloudy days, respectively, proving that the proposed model is superior to benchmark models.

Research on Detection of Icing Cover Transmission Lines Under Different Weather Conditions Based on Wide-Field Dynamic Convolutional Network LDKA-NET

The safety of transmission lines is a crucial guarantee for the operation of the power grid. To address the issue of low detection accuracy for icing transmission line defects with existing models, this paper proposes a defect detection algorithm for icing transmission line defects under different weather conditions based on a Large Dynamic Kernel Aggregation Net (LDKA-NET). First, a wide field of view convolutional network (WFVC Net) is introduced to enhance the network’s perception and generalization capabilities, enabling better adaptation to complex scene targets. Secondly, a full-dimensional dynamic convolutional feature fusion network is proposed, which strengthens the model’s feature extraction ability by learning linear combinations of multiple convolution kernels and their weighted input-related attention. Finally, an Expectation Maximization Dynamic Convolutional Attention (EM-DCA) mechanism is introduced, which focuses on and utilizes important information in the input data to help the model better allocate attention, thereby improving its generalization and robustness. Experimental results show that on the dataset proposed in this paper, the average accuracy of the improved algorithm (mAP@0.5) reaches 99.01%. The model parameters decreased by 47.7 M compared with the baseline model and 91.26 M compared with the Faster region with CNN feature (Faster R-CNN) model. The accuracy is 4.81% and 3.11% higher than the SSD model and YOLOv5-L model, respectively. Compared with the existing models, our model is smaller and has higher detection accuracy. It can accurately detect ice-covered wires and complete the task of the ice-covered detection of transmission lines.

Assessment of transmission network connection solutions based on HVAC for offshore wind

AbstractThe diffusion of offshore wind power plants (OWPPs), useful to fulfil renewable diffusion policy targets, implies several technological challenges for integration in power transmission network. The definition of the OWPP connection to the transmission network affects possible exploitation of the assets, involves an evaluation of active power losses and loadability and points out the need for compensation devices, in order to comply with connection rules fixed by the grid operator. This paper proposes a methodology to determine the most suitable electric connection for an OWPP based on high‐voltage alternating current (HVAC) cables, for providing indications for transmission system operators. In particular, different HVAC connection schemes for OWPPs are analysed, focusing on new 66 kV standard voltage and high‐voltage levels, providing technical insight on transmission cable optimal operation conditions. An economic analysis is carried out by proper estimation of construction costs and production forecast, accounting for active power losses and reliability impact. The most suitable connection solution depending on OWPP size and distance in Italian framework is evaluated by means of synthetic techno‐economic indicators, further analysing possible sensitivities of energy price and capacity factor.

The role of quality assurance in a high-voltage cable market shaped by the energy transition from a grid operator’s perspective

The role of quality assurance in a high-voltage cable market shaped by the energy transition from a grid operator’s perspective

Optimal planning for high renewable energy integration considering demand response, uncertainties, and operational performance flexibility

The operational performance of the grid needs to be considered and incorporated with other verified system flexibility measures, such as energy storage system and demand response, when planning a hybrid renewable energy system with a high fraction of renewable energy sources. Therefore, this study developed an integrated model that considers the effects of time-of-use demand response and risk-based uncertainty analysis on the operational performance of a grid-connected hybrid energy system for high renewable power penetration. A multi-stage approach for optimal scheduling of the energy systems with time-of-use pricing considering uncertainties’ impacts on grid and energy storage systems operations is modeled. Techno-economic metrics are developed to assess the decision-making effectiveness of the energy system planning and grid operation under four scenarios based on the information gap decision theory to quantify the energy system and grid operation performance flexibility. The considered scenarios are without and with demand response using risk-neutral, risk-averse, and risk-seeking strategies. The analysis of the results indicates that the risk-averse approach achieves the best techno-economic tradeoff, guaranteeing improved technical performances compared to the other risk strategies for uncertainty management in renewable power outputs and grid operational performances within the cost deviation tolerance limit. The risk-averse strategy further justified the substantial inclusion of energy storage and the adoption of the demand response to provide better operational flexibility to ensure improved grid performance, as seen in the reduced grid loss and improved voltage profile with higher power contribution from the RES. Significantly, the considered model can help system planners manage the tradeoff for efficient system performance under substantial renewable energy integration, considering the impacts of uncertainties.