Grid Platform Research Articles

Investigation and application of big data and AI-Powered provincial power grid accurate planning support platform

ABSTRACT This study presents an accurate planning assistance platform for big data and artificial intelligence-based provincial power grids. The platform greatly increases the efficiency of data integration and management by building a planning big database containing fundamental geographic information, thematic data, remote sensing image data, and planning business data. Technically speaking, this paper addresses the issues of accuracy and speed in the processing of remote sensing image data by using deep learning algorithms like convolutional neural networks (CNN) and full convolutional neural networks (FCN) to achieve automatic classification of high-resolution optical remote sensing images and residential area extraction. In the meantime, the intelligent routing approach, which is based on cost surface modelling and multi-criteria decision-making, optimises the routing of power lines by carefully taking into account a wide range of variables, including land use, accessibility for vehicles, and environmental impact. In addition to resolving the primary issues with present power grid planning, this study’s exact planning support platform for provincial power grids greatly enhances real-time, accurate, and scientific planning.

Designing Integrated EV Charging Station to Grid Platform

The increasing fuel prices and global warming have received worldwide attention regarding the importance of environmental-friendly utilization. Indonesia enjoys the sustainable growth of 4-5% annually also supports the growth the transportation vehicles. The incremental of transportation vehicles poses danger to the national budgets, where high subsidy allocation in national budgets is paid for fuels subsidy. To address this issue, the government has introduced many incentives and supported the use of Electric Vehicles (EVs) nationwide. Supporting EVs nationwide is not an easy task since it involves collaborating with electricity suppliers, EV resellers, and maintenance tasks associated in supply chain management. The article takes case study of a state-own energy company, ICP, that is given mandate to support the EV adoption. The outcome of the article is expected to provide a guideline for a utility company to deliver electricity to charging stations nationwide.

SEP Optimization of Power System Location and Timing Module Based on MLE-KNN Synthesis Algorithm

To ensure the safe operation of the power system, it is necessary to monitor and manage all kinds of power facilities. Real-time power monitoring can play a role in the early warning of geographical disasters. At the transmission end, the state detection and fault location of transmission lines are related to the normal operation of the power system. The precise time synchronization of the power system can be guaranteed through the satellite positioning time service, so that the time of various power operation actions is consistent. The accuracy can reach several microseconds, which provides a basis for the analysis of power grid operation. In this paper, by installing BeiDou functional modules in power equipment and developing the BeiDou operation platform for the distribution network, the precise management of power timing and positioning is realized by docking with the geographic information systems platform of the power grid. The three-dimensional sphere error probability (SEP) algorithm is selected to evaluate the positioning error data. Based on the three traditional SEP calculation methods, the maximum likelihood estimation algorithm is optimized and improved, which can achieve nanosecond synchronization accuracy. It can also be applied to other precision of synchronization scenarios of the power system to meet the development needs of the future clock system of the power system. Through the simulation of experimental data and comparison with other algorithms, the accuracy is verified and the reliable estimation of parameters is realized. Considering the information loss caused by dimension reduction evaluation, the simulation analysis in this paper is compared with other methods, and the accuracy is significantly improved. The power system positioning and timing module developed in this paper can not only be used in the power system but also has a certain reference and application value for other devices in the same industry that need timing and positioning services.

A non-unit line protection method for MMC-HVDC grids based on the curvatures of backward traveling waves

The existing protection techniques for high-voltage direct-current (HVDC) grids suffer from several shortcomings such as high sampling frequency, poor robustness, and reliance on simulation for threshold setting. To solve these problems, this paper proposes a non-unit protection method for modular multilevel converter (MMC)-based HVDC grids using the curvatures of backward traveling waves. To this end, the propagation characteristics of traveling waves and the boundary characteristics of DC lines are first studied, then the analytical expressions of backward traveling waves are derived. Moreover, the curvatures of backward traveling waves are analyzed. On this basis, a non-unit protection method is proposed, including zone selection, disturbance identification, and pole selection. At last, with a protection platform and a real-time digital simulator (RTDS) platform of the MMC-HVDC grid, the accuracy and the robustness of the proposed protection method are verified. The results show that the protection method can correctly identify faults with different distances and resistance in 1 ms and has strong robustness against transition resistance, sampling frequency, boundary value, noise, system topology, and line parameters.

Assessment of the E3C corpus for the recognition of disorders in clinical texts

Abstract Disorder named entity recognition (DNER) is a fundamental task of biomedical natural language processing, which has attracted plenty of attention. This task consists in extracting named entities of disorders such as diseases, symptoms, and pathological functions from unstructured text. The European Clinical Case Corpus (E3C) is a freely available multilingual corpus (English, French, Italian, Spanish, and Basque) of semantically annotated clinical case texts. The entities of type disorder in the clinical cases are annotated at both mention and concept level. At mention -level, the annotation identifies the entity text spans, for example, abdominal pain. At concept level, the entity text spans are associated with their concept identifiers in Unified Medical Language System, for example, C0000737. This corpus can be exploited as a benchmark for training and assessing information extraction systems. Within the context of the present work, multiple experiments have been conducted in order to test the appropriateness of the mention-level annotation of the E3C corpus for training DNER models. In these experiments, traditional machine learning models like conditional random fields and more recent multilingual pre-trained models based on deep learning were compared with standard baselines. With regard to the multilingual pre-trained models, they were fine-tuned (i) on each language of the corpus to test per-language performance, (ii) on all languages to test multilingual learning, and (iii) on all languages except the target language to test cross-lingual transfer learning. Results show the appropriateness of the E3C corpus for training a system capable of mining disorder entities from clinical case texts. Researchers can use these results as the baselines for this corpus to compare their own models. The implemented models have been made available through the European Language Grid platform for quick and easy access.

Design and Implementation of Hardware in the Loop Simulation Test Platform for Photovoltaic Inverter

In order to ensure the performance and safety of photovoltaic grid connected inverter, based on hardware in the loop simulation technology, the design and implementation of photovoltaic grid connected inverter simulation and test platform are discussed. The system structure, principle and main functions of the platform are introduced. The simulation and test platform is composed of simulation and monitoring computer, simulation and measurement interface device, programmable power supply and test instruments. In the platform, the programmable DC power supply, the programmable AC power supply and RLC load is used to simulate the characteristics of solar panels, power grid and load respectively. The test process is controlled by LabWindows/CVI software interface, and the test data collection is completed through power quality analyser, power analyser, oscilloscope and simulation and measurement interface device. The simulation test software completes the data analysis and realizes the full-automatic detection of the grid connected inverter. The established hardware in the loop simulation test platform of photovoltaic grid connected inverter has the ability to conduct comprehensive test and detection of photovoltaic grid connected system, which can be used to verify and evaluate the photovoltaic inverter before it is connected to the power grid.

Longitudinal transcriptome profiling of localized hormone-sensitive tumors in treatment-naïve ENACT patients with prostate cancer with and without enzalutamide (ENZA).

5026 Background: The ENACT trial (NCT02799745) randomized men with low- or intermediate-risk prostate cancer to 12 months of ENZA monotherapy followed by active surveillance (AS) for 12 months or to AS alone. Men were followed longitudinally, including by serial genome-wide expression profiling. Of 258 genomic signatures evaluated from baseline samples, increased Decipher genomic classifier score was prognostic for progression on AS; androgen receptor activity (AR-A) and PAM50 subtypes were associated with clinical benefit from ENZA, as previously reported. Here we present transcriptomic changes over time for men undergoing AS and those treated transiently with ENZA. Methods: Gene-expression profiling was performed via the Decipher GRID platform (Veracyte, Inc.) on 131 ENACT patient samples (ENZA, n=57; AS, n=74) collected at prespecified time points (screening, 12 months, and 24 months). Predefined GRID signatures were assessed for correlation with biological and clinical characteristics, including immune activity, treatment sensitivity, metastatic risk, and molecular subtypes. Statistical analysis was conducted by Cox proportional hazards models and logistic/linear regression; longitudinal changes were assessed by linear mixed-effects models. Results: Analysis of 12-month longitudinal samples showed significant transcriptomic changes in patients treated with ENZA, including markers of AR-A downregulation, decreased regulatory T-cell suppression, and increased immune activation. Many of the transcriptomic signature changes that occurred during ENZA treatment, including immune marker changes, were transient and returned to baseline values by 24 months following ENZA treatment discontinuation at 12 months. Conclusions: This exploratory biomarker analysis provides valuable data on transcriptomic changes in patients undergoing AS and those exposed to ENZA followed by cessation of treatment. Longitudinal analysis data provided insights into progression of early disease in the AS arm and possible strategies for extending ENZA activity beyond progression. Immune modulation with ENZA treatment may provide further insights for exploration of immunotherapy strategies. Clinical trial information: NCT02799745 .

Analyzing at-scale distribution grid response to extreme temperatures

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. This paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform’s efficacy is shown by demonstrating estimation of electrical demand for (1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and (2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit.11These numbers are consistent with state-of-the-art prediction results published in the literature (Gruber et al., 2022). For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively.

Time-space intelligent management and dispatch of electric Beidou based on Beidou and digital twin technology

Aiming at the increasingly complex management and dispatching problems of power system, this paper combines Beidou precise space-time technology with digital twin system theory, and puts forward a method of constructing intelligent management and dispatching platform of power grid. The method combines Beidou and IoT (Internet of Things) sensors to form a digital twin management and scheduling platform which can perform state prediction, fault diagnosis, self-healing and other functions. Firstly, this paper analyzes the overall architecture of Beidou digital power twin system, then discusses the construction method of digital twin in power system from the aspects of digital twin modeling theory, data acquisition intelligent terminal based on Beidou, operation mode of digital twin platform, etc. Finally, the application test analysis is carried out. The research results show that the method proposed in this paper has reference value for improving the management and dispatching capability of smart grid.

Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems

Recent economic growth and development have considerably raised energy consumption over the globe. Electric load prediction approaches become essential for effective planning, decision-making, and contract evaluation of the power systems. In order to achieve effective forecasting outcomes with minimum computation time, this study develops an improved whale optimization with deep learning enabled load prediction (IWO-DLELP) scheme for energy storage systems (ESS) in smart grid platform. The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS. The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature selection. Besides, partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective functions. Moreover, IWO with bidirectional gated recurrent unit (BiGRU) model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO algorithm. The experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.

PMU-based Real-time Distribution System State Estimation Considering Anomaly Detection, Discrimination and Identification

In this paper, a real-time state estimation platform for distribution grids monitored by Phasor Measurement Units (PMUs) is developed, tested, and validated using Real Time Digital Simulator (RTDS). The developed platform serves as a proof-of-concept for potential implementation in an existing 50 kV ring network of the Dutch distribution utility Stedin medium voltage distribution grid located in the southwest (Zeeland area) of the Netherlands. To catch up with the fast sampling rates of PMUs, the platform incorporates computationally efficient techniques for state estimation and detection, discrimination and identification of anomalies like bad data and sudden load changes. Forecasting Aided State Estimation has been utilized to enable measurement innovations needed for fast anomaly detection, discrimination, and identification, whilst the Extended Kalman Filter (EKF) algorithm is selected to provide fast state forecasting and filtering. The platform has been tested under various normal and abnormal operating conditions considering different statistical properties of measurement noise as well as different bad data and sudden load change scenarios. To demonstrate advantages and disadvantages for embedding EKF into the platform, EKF is compared with Unscented Kalman Filter (UKF) in terms of estimation accuracy, computational efficiency, and compatibility with the module for anomaly detection, discrimination, and identification. The results of extensive simulations provide good hints about the feasibility of PMU-based real-time state estimation for the Stedin distribution grid.

An open-source platform for modelling, simulation, and performance analysis of multi-converter, mixed-generation power systems

Existing power system modelling and simulation tools are chiefly developed for analysing conventional grids that operate close to nominal frequency during disturbances; as such they are inadequately suited to analyse modern renewable generation sources, which may transiently operate in off-nominal frequencies and over much broader timescales. This warrants development of improved analytical tools for emerging converter-dominated systems. Existing simulation platforms, such as electromagnetic transient (EMT) and transient stability (TS) simulators, provide accuracy and computational efficiency, but are also limited in terms of analytical tools required for drawing broader general conclusions. This paper presents a systematic method to develop and expand positive-sequence, average-value analytical models of converter-based generation systems. The paper develops a new open-source platform for converter-dominated grids, which enables additional analyses including small-signal stability assessment, state trajectory analysis, and controller optimization. Using the proposed method, performance and stability matrices can be readily obtained to assess multi-converter, multi-machine systems. Due to its implementation in decoupled phasor domain, the developed method provides faster performance than existing time-domain simulators, whilst retaining its accuracy in off-nominal frequencies. The developed platform and its capabilities are exemplified using an IEEE benchmark system.

X-flex flexibility platform for distribution grids accommodating E-mobility

X-FLEX project develops 4 complementary products that will offer services to all the energy stakeholders, from network operators (Transmission System Operator (TSO), Distribution System Operator (DSO), microgrid operators) to final consumers/prosumers and flexibility providers, including other intermediate players, such as retailers and aggregators. SERVIFLEX is the tool for flexibility managers to take advantage of the value of energy storage along with other demand flexibility resources towards the establishment of a holistic framework for flexibility extraction, profiling, forecasting, classification, clustering and management to serve different market and grid needs. GRIDFLEX is the tool for grid and microgrid operators in order to prevent congestion (voltage and current issues) and power quality problems with the increasing share of intermittent Renewable Energy Sources (RES), giving special attention to the potential grid problems due to the impact of extreme climate events. The tool will use flexibility as an alternative to network reinforcement when it is more cost-efficient than traditional reinforcement of the network. The MARKETFLEX tool enables final consumers and prosumers (generation, Demand Response (DR), flexibility providers) to access the market through an aggregator or through a Balancing Responsible Party – (BRP) – to participate on different markets: wholesale market, local energy market or ancillary services market for TSO or DSO. Finally, X-FLEX platform integrates all the X-FLEX solutions in order to provide services for all the energy actors and ensure more secure, stable and clean energy supply to enable local electricity marketplace leveraging flexibility asset. This paper focuses on the integration of electric vehicles into the grid within the X-FLEX project and presents the testing activities at pilot site Luče.

Research on power load forecasting algorithm of power system based on artificial firefly

Abstract The power system is constantly upgraded with the development of information technology. The digital management of power system effectively improves the operation and maintenance efficiency of power system. Although the current power system algorithms have made great progress, there has been no effective prediction algorithm in the field of load forecasting. Load forecasting is a key operation and planning approach of power system. It affects many decisions of power system, such as economic dispatching, automatic generation control, safety evaluation, maintenance dispatching and energy commercialization [1]. In view of the above problems, the power load forecasting algorithm of power system based on artificial firefly will be studied. In the existing resource management framework of power system digital grid platform, the power load business of power system is studied. The artificial firefly algorithm is used to complete the research of power load forecasting algorithm. Simulation results show that the established algorithm has good time complexity and space complexity, and the accuracy of power load forecasting results is improved by at least 22.8%.

Research on open source platform of digital power grid based on CEPH open source distributed storage technology

In order to ensure the operational quality of open source platform and improve the security of power grid information management, a research method of digital power grid open source platform based on CEPH open source distributed storage technology is proposed. The open source platform structure of digital power grid is optimized by using the operation of object to block mapping and human-computer interaction function of CEPH platform. CEPH open source distributed storage is used to optimize the information management function of digital grid open source platform and improve the effect of grid data and information sharing. An adaptive quantization strategy is designed to dynamically determine the load factor weightthe weighty of load factor of the digital grid open source platform and improve the resource storage performance of the platform. The test results show that the digital grid open source platform based on CEPH open source distributed storage technology has low memory occupancy, data processing of high efficiency and good system load balancing ability, which meets the needs of information security and data processing of the digital grid open source platform.

FPGA algorithm implementation of hybrid MMC harmonic injection strategy

Capacitor submodule occupies a large volume in Modular multi-level Converter (MMC) and costs a lot of money. Harmonic injection strategy can often achieve the effect of reducing the voltage fluctuation of the sub-module capacitors. Therefore, it can reduce reduce the construction cost of the HVDC transmission system. On the basis of introducing the basic principle of hybrid MMC harmonic injection and simulation verification on PSCAD platform, this paper introduces the physical platform of four-terminal flexible power grid, including its hardware display and monitoring and testing interface, showing the diversity of real-time data acquisition methods. Finally, through the experimental research on the implementation of the hybrid MMC harmonic injection strategy by FPGA, the modulation wave, the conduction number of the sub-module, the capacitance voltage of the sub-module and other physical quantities before and after the second harmonic injection are compared and analyzed. It is verified that the second harmonic injection can change the shape of the voltage modulation wave. At the same time, the second harmonic injection can reduce the voltage fluctuation of the capacitor of the sub-module and realize the lightening from the perspective of reducing the capacitor demand.

Intelligent Deer Hunting Optimization Based Grid Scheduling Scheme

The grid environment is a dynamic, heterogeneous, and changeable computing system that distributes various services amongst different clients. To attain the benefits of collaborative resource sharing in Grid computing, a novel and proficient grid resource management system (RMS) is essential. Therefore, detection of an appropriate resource for the presented task is a difficult task. Several scientists have presented algorithms for mapping tasks to the resource. Few of them focus on fault tolerance, user fulfillment, and load balancing. With this motivation, this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm (DHOA), called IGSS-DHOA which schedules in such a way that the makespan gets minimized in the grid platform. The IGSS-DHOA technique is mainly based on the hunting nature of humans toward deer. It also derives an objective function with candidate solution (schedule) as input and the outcome is the makespan value denoting the quality of the candidate solution. The simulation results highlighted the supremacy of the IGSS-DHOA technique over the recent state of art techniques with the minimal average processing cost of 31717.9.

Research on security protection strategy of flexible interactive platform between captive power plant and power grid

In order to strengthen the security protection of the flexible interactive platform between the self owned power plant and the power grid, ensure the security of business data, resist the malicious attacks of hackers, improve the robustness of the platform operation, and ensure the safety and reliability of users' power consumption, it is necessary to take a number of security protection technical means to build a comprehensive security defense system of the power grid platform.

A Robust Multi-Stage Power Consumption Prediction Method in a Semi-Decentralized Network of Electric Vehicles

A Virtual Power Plant (VPP) balances the load on a power grid by allocating power generated by various interconnected units during periods of peak demand. In addition, demand-side energy devices such as Electric Vehicles (EVs) and mobile robots can also balance energy supply and demand when effectively deployed. However, the fluctuation of energy generated by renewable resources makes balancing energy supply a challenging goal. This paper proposes a semi-decentralized robust network of electric vehicles (NoEV) integration system for power management in a smart grid platform. The proposed approach integrates an aggregator with EV fleets into a blockchain framework. The EVs execute a multi-stage algorithm to predict the power consumption based on a novel federated learning algorithm named Federated Learning for Qualified Local Model Selection (FL-QLMS). From the evaluation results, the proposed system requires 35% fewer transactions in short intervals and propagation delays than the previous approaches and achieves better network efficiency while maintaining a high level of security. Moreover, NoEV achieves a 5.7% lower root mean square error (RMSE) than the conventional approach for power consumption prediction, which is a significant improvement. In addition, the FL-QLMS approach outperforms state-of-the-art methods in terms of robustness to client-side attacks. The evaluation results also show that the performance of FL-QLMS is not affected when 10% to 40% percent of the models are manipulated.

A Defense Mechanism Based on Improved Allocation Strategy of VMs Co-Resident Attack in Power Cloud Platform

Background: Cloud platform is widely applied in the field of electric power. Virtual machine co-resident attack is one of the fundamental security threats to the existing power cloud platform. Objective: This paper addresses a mechanism so as to defend virtual machine co-resident attack on power cloud platform. Methods: Our defense mechanism wields the DBSCAN algorithm to classify the results through the random forest and adopted improved virtual machine deployment strategy which combines the advantages of random round robin strategy and maximum/minimum resource strategy so as to deploy virtual machines. Results: We made a simulation experiment on power cloud platform of State Grid and verified the effectiveness of proposed defense deployment strategy. Conclusion: After the virtual machine deployment strategy is improved, the coverage of the virtual machine is remarkably reduced which proves that our defense mechanism achieves some effect of defending the virtual machine from virtual machine co-resident attack.