Grid Operation Research Articles

Numerical Analysis of Hydrogen-Enriched Natural Gas on Combustion and Emission Characteristics

AbstractThe integration of hydrogen into natural gas infrastructure presents a viable strategy for mitigating greenhouse gas emissions and advancing toward carbon neutrality. This study investigates the combustion characteristics and emissions profiles of hydrogen-enriched natural gas mixtures, specifically focusing on the composition of Russian pipeline natural gas. A comprehensive mathematical model was developed to predict emission concentrations and simulate fuel mixture combustion using MATLAB Simulink software. This versatile model facilitates further analysis within the MATLAB ecosystem. The simulation results demonstrate a significant correlation between the hydrogen content in the natural gas mixture and the resulting heat power output. With a constant fuel consumption rate, a notable decrease in heat power was observed as the hydrogen concentration increased, reaching a maximum reduction of 44.9% at a 45% hydrogen content. These findings underscore the feasibility of partially substituting natural gas with hydrogen, while also highlighting the necessity for increased fuel flow rates to maintain equivalent power output levels. This poses additional challenges for natural gas grid operators, necessitating infrastructure adaptations to accommodate higher fuel demands. The insights gained from this research contribute to the growing body of knowledge surrounding hydrogen integration in the energy sector, offering valuable implications for decarbonization strategies and the optimization of natural gas infrastructure.

Reconfiguration of active distribution networks as a means to address generation and consumption dynamic variability

AbstractThe integration of alternative energy sources, storage systems, and modern loads into the distribution grid is complicating its operation and maintenance. Variability in individual generation and consumption elements dynamically affects voltage profiles, which in turn undermines efficiency and power quality. This study proposes to address this dynamical variability using an online reconfiguration approach that involves opening and closing switches to modify the grid's topology and adjust voltage levels in response to load/generation variations. Other grid optimization techniques, based on reconfiguration, typically focus on static, fully instrumented grids with predictable parameters and homogeneous changes, aiming to minimize power losses but overlooking the dynamics of variable grid elements. This study proposes a testing approach that is dependent on the estimated transient status of the grid only using a limited number of measurement units and considering the individual‐stochastic variations of loads and generators. The proposed approach was tested on the IEEE 33‐bus test feeder with up to five varying distributed generators. The results confirm that the algorithm consistently finds a reconfiguration alternative that could enhance system efficiency and voltage profiles, even in the face of dynamic load/generator behavior, demonstrating its effectiveness and online adaptability for grid operation and management tasks.

Short-Term Optimal Scheduling of Power Grids Containing Pumped-Storage Power Station Based on Security Quantification

In order to improve grid security while pursuing a grid operation economy and new energy consumption rates, this paper proposes a short-term optimal scheduling method based on security quantification for the grid containing a pumped-storage power plant. The method first establishes a grid security evaluation model to evaluate grid security from the perspective of grid resilience. Then, a short-term optimal dispatch model of the grid based on security quantification is constructed with the new energy consumption rate and grid loss as the objectives. In addition, an efficient intelligent optimization algorithm, Dung Beetle Optimization, is introduced to solve the scheduling model, dynamically updating the evaluation intervals during the iterative solution process and evaluating the grid security level and selecting the best result after the iterative solution. Finally, the improvement in the term IEEE 30-bus grid connected to a pumped-storage power plant is used as an example to verify the effectiveness of the proposed method and model.

A Bi-Objective Optimal Scheduling Method for the Charging and Discharging of EVs Considering the Uncertainty of Wind and Photovoltaic Output in the Context of Time-of-Use Electricity Price

With the increasing share of renewable energy generation and the integration of large-scale electric vehicles (EVs) into the grid, the reasonable charging and discharging scheduling of electric vehicles is essential for the stable operation of power grid. Therefore, this paper proposes a bi-objective optimal scheduling strategy for microgrids based on the participation of electric vehicles in vehicle-to-grid technology (V2G) mode. Firstly, the system structure for electric vehicles participating in the charging and discharging schedule was established. Secondly, a bi-objective optimization model was formulated, considering load mean square error and user charging cost. A heuristic method was employed to handle constraints related to system energy balance and equipment output. Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. The simulation results show that the proposed scheduling strategy has a lower charging cost (CNY 11,032.4) and lower load mean square error (12.84 × 105 kW2) than the strategy employed in the comparison experiment, which ensures the economic and stable operation of the microgrid.

Knowledge distillation-based abnormal power consumption pattern detection for edge environment

Abstract Abnormal power consumption pattern detection can effectively reduce the non-technical loss of electric energy, which is of great significance in maintaining the safe operation of the power grid and protecting the rights and interests of normal users. However, for edge environments with limited communication conditions, uploading electricity consumption data to the cloud for centralized computation is prone to problems such as network congestion and transmission delays. For this reason, this paper proposes a knowledge distillation (KD)-based anomalous power consumption pattern detection model that can be deployed at edge computing terminals. First, the ResNet50 network is selected as the teacher model, and the self-attention mechanism (SAM) is introduced to improve the feature extraction capability of long-term dependency. Then, ResNet18 is used as the student model, and the difference between the output of the student model and the output of the teacher model is minimized by optimizing the distillation loss function. Finally, the effectiveness of the proposed method is verified in practice.

Optimal utilization of frequency ancillary services in modern power systems

The widespread global adoption of wind energy sources has established a significant presence in the existing power grid. However, the massive integration of intermittent wind energy poses forecasting errors, prompting the need for supplementary reserves from conventional energy sources with increased operational expenses and carbon emissions. Hence, to facilitate the seamless operation of large-scale wind-integrated power grids, it is imperative to harness the potential of renewable energy sources and leverage flexible loads to deliver power-balancing services. In this research, dynamic real-time power dispatch strategies have been developed for the Automatic Generation Control (AGC) system to integrate the reserve capacities of conventional generation units and wind power plants and utilize the demand response capabilities of flexible loads for power balancing services. A comprehensive power system grid model was developed in DigSilent PowerFactory software, consisting of coal-based energy systems, wind energy systems, gas turbines, and cold storage units as flexible loads. The study is divided into different case studies to assess the impact of each scenario on system operation in mitigating the forecasting errors of wind power plants. Further, a comparative analysis was performed to illustrate the effectiveness of each case study. The analysis showed that Case Study III, where reserves are provided by coal energy systems and cold storage units, yielded the highest reduction in Positive Regulation (PR) and Negative Regulation (NR) errors, at 89.0% and 94.15%, respectively. Conversely, Case Study IV demonstrated the least reduction in errors, with 67.82% in PR and 78.41% in NR. However, it indicates that reserves can be supplied from wind energy systems and flexible loads without the support of conventional power plants.

Feature selection method for future-state power grid operation mode based on semi-supervised learning

Feature selection method for future-state power grid operation mode based on semi-supervised learning

Adaptive Control Strategies for Enhanced Integration of Solar Power in Smart Grids Using Reinforcement Learning

Integrating solar power into smart grids is challenging because of the variable nature of solar energy. This study focuses on implementing reinforcement learning (RL) using a Deep Q-Network algorithm to enhance the stability and efficiency of a grid. A custom environment was designed using OpenAI Gym, in which real-time simulation of grid operations was conducted using real-time data on solar power, weather, and other grid metrics. The trained RL agent exhibited high predictability in optimally distributing the load and managing the battery storage, with R-squared = 0.886, mean average error = 1,173,046.55 Wh, and root mean squared error = 2,075,515.10 Wh. The model effectively captured the seasonality and daily variations in solar power generation. Forecasting using the proposed model provides insights into future energy trends and uncertainties. The reward function will be further refined and scaled for more complex energy systems by incorporating additional variables and hybrid approaches. This study highlights the potential of RL-based adaptive control strategies for developing more efficient and resilient integration of renewable energy sources into smart grids.

HVdc Grids for Large-Scale Offshore Wind Integration: Coordinating Offshore HVdc Grid Design with Onshore ac Grid Operation

HVdc Grids for Large-Scale Offshore Wind Integration: Coordinating Offshore HVdc Grid Design with Onshore ac Grid Operation

The efficacy of a cellular approach to resilience-oriented operation of distribution grids based on eigenvectors of the Laplacian matrix

This study aims to address the following research query: In the event of an imminent disaster poised to impact distribution grids, what constitutes the optimal course of action for the distribution system operators to keep the lights on? To address this challenge, we propose a cost-efficient cellular model for enhancing the resilience of smart distribution grids. This model prioritizes resilience in the face of natural disasters or other disruptions that could impact service delivery. This method benefits both grid operators and consumers by ensuring reliable power supply while minimizing energy costs. Furthermore, the model's scalability allows it to be applied to distribution systems of varying sizes. The proposed method utilizes an innovative approach to form optimal cellular network configurations within the grid. As the first step in the formation of cellular topology for the grid, the eigenvectors of the Laplacian matrix of the grid will be used to decide on the optimal configurations. Subsequently, a bi-level mixed-integer linear programming model is proposed to decrease the network costs while simultaneously consider potential power transfer scenarios between the cells and the upstream network during both normal and emergency conditions. The researchers validated the effectiveness of the proposed method through simulations on an IEEE 33-bus test system. The results demonstrate outstanding performance, with a significant increase in the resilience index (96 %) and a substantial reduction in load-shedding costs (80 %), making the network considerably more robust.

Intelligent Energy Management across Smart Grids Deploying 6G IoT, AI, and Blockchain in Sustainable Smart Cities

In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.

ЗБІЛЬШЕННЯ ПРОПУСКНОЇ СПРОМОЖНОСТІ МАГІСТРАЛЬНИХ МЕРЕЖ УКРАЇНИ

In connection with the projected significant increase in electricity demand by 2030, as well as for the purpose of devel-oping the electricity system and effective interaction of the IPS of Ukraine with the energy systems of the European Un-ion, the article considers the need and feasibility of converting the Ukrainian power grids from 330 kV to 400 kV, com-paring the technical characteristics of grids of different voltage classes in terms of reliability, stability and efficiency of such grids. The paper provides an applied analysis of the efficiency of switching 330 kV rated voltage networks to 400 kV voltage class by the criterion of increasing the capacity of electrical equipment and reducing total power losses in the network. On the example of the Dnipro region of the IPS of Ukraine, the complex of computer programs designed for the design of power grids of power systems " Thesaurus" was used to model and analyze the modes of operation of the power grid at 330 kV and 400 kV. As a result, it was found that an increase in the voltage level will lead to a de-crease in the load of certain line sections by 17 %, while the value of total network losses will decrease by 10.8 %. Ref. 4, fig. 3.

Triboelectric Nanogenerator with the Double-Mass Pendulum Integrated Spacer for Galloping Energy Harvesting of Transmission Lines.

The transmission lines galloping severely threatens the safety operation of the power grid. A reliable operation and maintenance alternative is to monitor the transmission lines by wireless sensing and warning devices. In this work, a triboelectric nanogenerator with the double-mass pendulum integrated spacer (DMPS-TENG) is proposed for harvesting the galloping energy of transmission lines and powering the wireless monitoring devices. Specifically, by introducing a double-mass pendulum system, the response frequency of the DMPS-TENG is reduced, allowing it to harvest energy at lower frequencies in the range of transmission lines galloping (0-3Hz). Hereby, enhancing the energy harvesting bandwidth and the efficiency. The experiments show that with the introduction of the double-mass pendulum, the optimum frequency of the harvester is reduced from 2.4 to 1.9Hz, enhances the harvesting bandwidth by 18%, and enables an average power output of up to 0.32mW. Additionally, to demonstrate the practical value, a prototype is designed and fabricated to perform three different application experiments in the multi-split transmission lines simulation system. This work presents an innovative approach for galloping energy harvesting of transmission lines, which can be used to inform further development of sensor networks and visualization of the power grid.

Improved sequential impedance modeling and stability analysis of virtual synchronous generators considering frequency coupling effects

With the increasing penetration rate of distributed power supply, the interaction between grid-connected inverters and power grid is prone to harmonic oscillation, which will seriously threaten the stable operation of power grid. At present, impedance analysis has been proved to be an effective tool to study the stability of grid-connected systems. However, few studies have considered the influence of frequency coupling effect on the output impedance characteristics of grid-connected inverters. To solve this problem, the sequence impedance model of a three-phase grid-connected inverter controlled by a virtual synchronous generator is established by harmonic linearization method based on the frequency coupling effect. In addition, an improved method of sequence impedance modeling based on grid impedance is proposed to effectively eliminate the influence of coupling terms. Finally, the correctness and practicability of the proposed simplified sequence impedance model are verified by simulation and experiment.

Linear Time-Periodic theory-based novel stability analysis method for voltage-source converter under unbalanced grid conditions

Small-signal stability analysis is essential for the safe operation and parameter design of power electronic-dominated grids. Unbalanced conditions introduce more frequency-coupling terms, which are unneglectable in the stability assessment. This paper proposes a novel Linear Time-Periodic (LTP) theory-based stability analysis method and focuses on grid-connected voltage-source converters (VSCs) without improved unbalancing controls. Firstly, the analysis of fundamental solutions of the LTP system reveals that each LTP mode is characterized by a unique damping factor and multiple oscillation frequencies, defined by LTP eigenvalues and related transformation vectors, i.e., (λi,Vi(t)). Then, a method for calculating (λi,Vi(t)) is proposed using the characteristic matrix of the Harmonic State-Space (HSS) model. An iterative sorting method based on the time-domain interpretation of HSS eigenvalues/eigenvectors is proposed to determine accurate (λi,Vi(t)) in the case of the minimum truncation order. Finally, generalized definitions and calculation methods of the widely used indicators for modal analysis are presented to assess system stability and guide the parameter design. Numerical and simulation results verify the proposed stability analysis method and indicate its advantages compared to the existing Floquet and HSS methods.

A Fast and Accurate Mapping Method for an OPGW Tower Based on Hybrid Distributed Optical Fiber Sensing.

The combination of the dark fiber in existing Optical Fiber Composite Overhead Ground Wire (OPGW) with Distributed Optical Fiber Sensing (DOFS) technology can be used to enable online monitoring and provide early warnings of anomalies in high-voltage transmission lines. Accurate mapping of the optical cable length to the geographic coordinates of actual towers is a key factor in achieving this goal. This paper discusses the principle of using a DOFS system for transmission line tower positioning and presents four available positioning features. To overcome the limitations of single physical parameter positioning, this paper presents a self-developed hybrid DOFS that simultaneously captures Rayleigh backscattering and Brillouin scattering signals. Several physical parameters, including temperature, strain, and vibration, are acquired synchronously. Through hybrid multi-parameter analysis, the rapid and accurate positioning of OPGW line towers is achieved. Experimental results have shown that the proposed method, based on the hybrid DOFS system, can locate up to 82 towers, while the traditional method could only identify 12. The hybrid system was able to complete 80% of the tension towers in 40 h. This paper presents a novel multi-parameter localization method that has the potential to significantly improve the efficiency and reliability of grid operation and maintenance.

Innovative protection schemes through hardware-in-the-loop dynamic testing.

In microgrid environments, the behaviour of Distributed Generation (DG) during fault conditions varies significantly based on DG types and penetration levels. Conventional Overcurrent Relays (OCRs) with standard time-current characteristics may exhibit limitations during excessive fault scenarios, leading to OCR operating delays and mis-coordination within the microgrid. This study proposes a novel constraint on the maximum Current Multiplier Setting (CMS) and utilizes Water Cycle Algorithm (WCA) and Particle Swarm Optimization (PSO) techniques to optimize the Time Multiplier Setting (TMS). Comparative dynamic analysis through real-time validation shows that the non-standard OCR approach outperforms the standard IEC scheme across all grid operation modes with different type, size and location of DGs. For instance, under F1 conditions, the tripping time of OCR1 was reduced from 0.0226 seconds (IEC) to 0.000981 seconds (non-standard). HIL results further affirm the efficacy of the proposed scheme. The optimization process, implemented in MATLAB and validated using ATP/EMTP simulations and SIPROTEC 7SJ62 relays, demonstrates enhanced microgrid protection coordination, improving system reliability and performance.

Calculation method of renewable maximum penetration considering frequency response of renewable energy

The calculation of maximum penetration is crucial for planning renewable energy integration and ensuring the safe operation of power systems. This paper proposes a method for calculating maximum penetration under kinetic energy constraints. In scenarios where renewable energy lacks additional frequency control, we construct a frequency response model to reveal the energy change mechanism during active disturbances. By converting the frequency constraint into a kinetic energy constraint, we derive an analytical expression for maximum penetration under these constraints. Key influencing factors were quantitatively analyzed, revealing nonlinear relationships between system capacity, frequency constraints, and maximum penetration, while also calculating the minimum operational generator capacity. When renewable energy participates in frequency control, its frequency regulation can significantly enhance maximum penetration. However, frequency oscillations caused by time delays in current source frequency control become the primary constraint. The proposed method and conclusions are validated using actual sending-end and receiving-end grid data, providing valuable guidance for grid operation planning.

Saturated load forecasting based on improved logistic regression and affinity propagation

Saturated load forecasting (SLF) is a crucial guarantee for stable operation of power grid. However, insufficient data makes SLF collapse. Although logistic regression (LR) can be employed to tackle this problem, its non-linearity may lead to analytically unsolvable maximum likelihood (ML) function. Due to this dilemma, this paper proposes a sequential LR estimator. It depends on minimum variance unbiased estimator (MVUE) and maximum likelihood estimator (MLE) to form an iterative method: in which a linearized statistical model is established by inverse substitution to reduce complexity. Moreover, a new forecasting framework combining LR estimator with clustering algorithms is constructed. In this framework, load data are clustered based on affinity propagation (AP) before LR. Subsequently, SLF result is obtained from corresponding categories by iteration. Simulation results show that the method proposed in this paper has lower computational complexity and higher prediction accuracy.

Study on photovoltaic power prediction with TSO-LSTM-XGBoost coupled model accounting for weather factors

ABSTRACT The explicit prediction of PV power itself is of great significance to the scheduling and operation of the power grid. To ensure the stable operation of the power system, this paper proposes a coupled model for PV power prediction based on TSO-LSTM-XGBoost. This model considers the weather factor using the tuna swarm algorithm(TSO) to optimize the long and short-term memory network model(LSTM) to overcome the shortcomings of blindness and time-consuming in the process of randomly selecting the parameters of the LSTM model. At the same time, using the extreme gradient boosting model (XGBoost), the algorithm is improved and corrected for the large prediction error in cloudy and rainy weather, and the weighted error method is used to couple the model to obtain the final prediction results. Finally, the accuracy of the proposed model is verified by comparing the PV system and meteorological data of a certain region in Shenzhen, China. The results show that the proposed TSO-LSTM-XGBoost coupled model has a value of 71.98 for MAE in cloudy days and 82.09 for MAE in rainy days, and the prediction accuracy is better than PSO-LSTM and LSTM.