Power Grid Energy Research Articles

A Coordinated Frequency Regulation Strategy Integrating Power Generation, Energy Storage, and DC Transmission for Offshore Wind Power MMC-HVDC Transmission Systems

With the increasing proportion of renewable energy in power grids, the inertia level and frequency regulation capability of modern power systems have declined. In response, this paper proposes a coordinated frequency regulation strategy integrating power generation, energy storage, and DC transmission for offshore wind power MMC-HVDC transmission systems, aimed at improving the frequency stability of onshore power grids. First, considering the inability of the receiving-end MMC-HVDC converter station under constant DC voltage control to directly respond to AC system frequency variations, a frequency regulation method is developed based on constant DC voltage control. The approach employs DC voltage as a transmission signal to coordinate the responses of wind turbines and energy storage systems. Subsequently, based on the energy storage configuration of the onshore renewable energy aggregation station, a secondary frequency regulation strategy is proposed. This strategy integrates offshore wind power, MMC-HVDC transmission system, and energy storage systems, balancing AC frequency regulation and the recovery of the state of charge (SOC) of the energy storage system. Finally, the proposed method is tested on a modified IEEE 39-bus system, the results demonstrate that the minimum frequency value can be in-creased by 37.5%, the system frequency can be restored to the initial state after secondary FM, and the results demonstrate its effectiveness.

Flexible and adjustable resource backup capacity planning method for port areas with a high percentage of new energy access

In view of the problem of insufficient flexibility in grid regulation caused by the large-scale integration of new energy sources, this paper proposes an interactive regulation method for flexible resources to participate in wind and solar energy consumption and establishes a two-layer optimization model of planning and operation. The planning layer considers the planned capacity of flexible adjustable resources, and the operation layer comprehensively considers economic costs. Flexible resources are coordinated and regulated to achieve multi-objective optimization using a hybrid swarm migration algorithm based on indicators such as the operating costs of each standby unit and the cost of standby resources. The optimization results are then fed back to the planning layer to obtain the optimal regulation scheme. Meanwhile, this paper expands the source of backup resources to include the source-load-storage. It proposes a strategy for coordinating the participation of diversified resources from the source-load-storage in the backup capacity planning. Finally, a simulation analysis is carried out based on the actual power grid of a port. The results show that the reasonable allocation of diversified flexible resources can improve the economy of the power grid, the wind and solar energy consumption capacity, the flexibility of regulation, and the reliable operation capacity, which verifies the rationality and effectiveness of the proposed method.

Employing battery energy storage systems for flexible ramping products in a fully renewable energy power grid: A market mechanism and strategy analysis through multi-Agent Markov games

In high-proportion renewable energy power systems, flexible ramping products (FRPs) are critical for mitigating the volatility of renewable energy outputs and enhancing the adaptability of power systems. This paper aims to explore a fully renewable energy power system, with a battery energy storage system (BESS) as the sole provider of FRPs. An innovative market mechanism and new approaches to market equilibrium analysis are introduced. Initially, a common day-ahead (DA) market is established, covering both electricity and ancillary services with FRPs. The setup allows FRP providers to make bidding decisions and get rewarded based on actual ramping mileages. A bi-level multi-agent Markov game (MG) model is formulated. The upper level integrates the proposed strategies of wind power plants (WPP), photovoltaic power plants (PVP), and BESS, while the lower level encompasses the joint market clearing mechanism. An iterative solution algorithm, using reinforcement learning, is also introduced to address the model, effectively circumventing the privacy concerns associated with traditional mathematical programming methods. The proposed solution models and strategies are validated and demonstrated to be effective through two illustrative examples, highlighting their application and relevance in fully renewable energy power systems.

Comparative Analysis of Congestion Management Methods with Integrated Renewable Energy Generation

Inclusion of renewable energy in power grid at micro and macro level has imposed numerous challenges in the recent years. Occurrence and managing congestion in the power transmission line due to unpredictable and stochastic nature of Renewable Energy Source (RES) integration has become a challenging task to the grid operators. Transmission lines operate at bottlenecks during a congestion episode adding to the extra congestion cost and risk in grid stability which becomes burden to the generation as well as end users. Different methodologies are applied to detect and manage the congestion to eliminate the congestion cost factor and maintain grid stability. The presented analysis compares conventional methodology Linear Sensitivity Factors (LSF) and Heuristic methodology Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) methods for congestion management in power transmission lines with integrated RES (wind and solar) system. For comparative analysis, a modified standard IEEE 30 Bus system is chosen which is integrated with real time RES generation. Optimal locations of RES generation with minimized congestion cost and percentage of RES curtailment are chosen as objective function for comparison of the methodologies.

Risk-return analysis of clean energy grid project investment based on integrated ISM and Monte Carlo model

To solve the problem of complex and difficult to quantify factors affecting investment returns and risks in clean energy power grids, this study comprehensively applies the interpretive structural model and Monte Carlo model to the analysis of investment risk-returns in clean energy power grid projects. The interpretive structural model is utilized to analyze project investment returns, while the Monte Carlo model is used to analyze project investment risks. The project investment risk is based on the factor analysis of project investment returns, and key risk factors are identified through 1000 simulations, and the impact of these risks on project returns is quantified. By combining the two, the investability of the project is analyzed. The results showed that grid electricity prices, kilowatt hour subsidies, technology learning rates, total annual sunshine hours, and system power generation efficiency were key factors driving investment returns. The average expected value of investment return was about 20%, and the probability of investment return below 6% was close to 0. The overall project is worth investing in. From this, it can be seen that the research designed investment risk-return analysis methods for clean energy grid projects can effectively distinguish the main factors affecting investment returns and risks, and pre simulate the risk situation of returns. This study can provide reference for investor decision-making.

Breaking the CO2 Gridlock: Can Renewables Lead the Way for the OECD?

The use of low-carbon energy in power grids is essential for minimizing negative effects on the environment. Energy consumption causes environmental damage to the OECD’s economy. This study aims to investigate the effect of energy consumption, population, and GDP on CO2 emissions using panel data from 17 OECD countries over the period 2000–2023. We use regression approaches, such as partial least squares and principal components, to study the effects of GDP, urban and total population, oil and nuclear use, renewable energy, and industrialization on CO2 emissions. The regression process in this study reduces the data to a two-dimensional representation using a stochastic model and estimation techniques. The findings of this empirical investigation indicate that the United States, Canada, France, Germany, Italy, Korea, Mexico, and the United Kingdom exhibit higher levels of primary energy consumption in comparison to value-added sectors, renewable–geothermal energy, and nuclear energy. We determined the effects of CO2 emissions, GDP, and energy consumption by considering these as the most significant elements. This has made it possible to reduce CO2 emissions by focusing one’s attention and energy on the development of novel technologies, the use of renewable energy sources, and the execution of strategic plans. Attracting increasing attention are technological shifts that deliver enormous quantities of clean energy to combat climate change. Findings from this study can help environmentalists and policymakers better understand the role of structural change and energy consumption processes in the globalization process.

An optimal grid scheduling method considering coupling degree of nodes and duality of reciprocal inverse mapping

The optimal scheduling method of the power grid considering the node coupling degree and the duality of mutual inverse mapping is studied to effectively establish the random dynamic scene of the power grid and improve its optimal scheduling effect. Considering the duality of the mutual inverse mapping, a random dynamic scene of the power grid is generated. The optimization model of the grid random dynamic scene partition was established considering the coupling degree of the nodes in the region. The anti-prey particle swarm optimization algorithm was used to solve the optimization model, and the scene partition results were obtained. To construct an optimization scheduling model for the regional power grid with the objective functions of minimizing energy abandonment rate and grid loss and the constraints of power flow, output, and climbing power. A predator-prey particle swarm optimization algorithm is used to solve the optimization scheduling model, and the minimum energy abandonment rate, minimum grid loss, and corresponding optimal scheduling strategies are obtained. The experimental results show that this method can effectively generate random dynamic scenes of a power grid and has a better scene partitioning effect. Under different working conditions, this method can achieve optimal dispatching of the power grid and reduce the power grid energy abandonment rate and network loss.

Multiple Functional Bonds Integrated Interphases for Long Cycle Sodium‐Ion Batteries

AbstractSodium‐ion batteries (SIBs) have garnered significant interest as one of the most promising energy suppliers for power grid energy storage. However, the poor electrode/electrolyte interfacial stability leads to continual electrolyte decomposition and transition metal dissolution, resulting in rapid performance degradation of SIBs. In this work, we propose a strategy integrating multiple functional bonds to regulate electrode/electrolyte interphase by triple‐coupling of succinonitrile (SN), sodium hexafluorophosphate (NaPF6) and fluorinated ethylene carbonate (FEC). Theoretical calculation and experiment results show that the solvation structure of Na+ and ClO4− is effectively reconfigured by the solvated FEC, SN and PF6− in PC‐based carbonate electrolyte. The newly developed electrolyte demonstrates increased Na+‐FEC coordination, weakened interaction of Na+‐PC and participation of SN and PF6− anions in solvation, resulting in the formation of a conformal interfacial layer comprising of sodium oxynitrides (NaNxOy), sodium fluoride (NaF) and phosphorus oxide compounds (NaPxOy). Consequently, a 3 Ah pouch full cell of hard carbon//NaNi1/3Fe1/3Mn1/3O2 exhibits an excellent capacity retention of 90.4 % after 1000 cycles. Detailed postmortem analysis of interface chemistry is further illustrated by multiple characterization methods. This study provides a new avenue for developing electrolyte formulations with multiple functional bonds integrated interphases to significantly improve the long‐term cycling stability of SIBs.

Multiple Functional Bonds Integrated Interphases for Long Cycle Sodium-Ion Batteries.

Sodium-ion batteries (SIBs) have garnered significant interest as one of the most promising energy suppliers for power grid energy storage. However, the poor electrode/electrolyte interfacial stability leads to continual electrolyte decomposition and transition metal dissolution, resulting in rapid performance degradation of SIBs. In this work, we propose a strategy integrating multiple functional bonds to regulate electrode/electrolyte interphase by triple-coupling of succinonitrile (SN), sodium hexafluorophosphate (NaPF6) and fluorinated ethylene carbonate (FEC). Theoretical calculation and experiment results show that the solvation structure of Na+ and ClO4 - is effectively reconfigured by the solvated FEC, SN and PF6 - in PC-based carbonate electrolyte. The newly developed electrolyte demonstrates increased Na+-FEC coordination, weakened interaction of Na+-PC and participation of SN and PF6 - anions in solvation, resulting in the formation of a conformal interfacial layer comprising of sodium oxynitrides (NaNxOy), sodium fluoride (NaF) and phosphorus oxide compounds (NaPxOy). Consequently, a 3 Ah pouch full cell of hard carbon//NaNi1/3Fe1/3Mn1/3O2 exhibits an excellent capacity retention of 90.4 % after 1000 cycles. Detailed postmortem analysis of interface chemistry is further illustrated by multiple characterization methods. This study provides a new avenue for developing electrolyte formulations with multiple functional bonds integrated interphases to significantly improve the long-term cycling stability of SIBs.

Transient Synchronous Stability Modeling and Comparative Analysis of Grid-Following and Grid-Forming New Energy Power Sources

New energy power sources can be categorized into grid-following and grid-forming types based on their synchronization characteristics with the grid. Due to the different basic control operation principles of these two types of new energy power sources, they can lead to the instability of the grid-connected system through different paths. A mathematical model is established to describe the dynamic characteristics of the grid-following and the grid-forming new energy power resources, and the control block diagram of the nonlinear dynamic characteristic equation is compared and analyzed. The similarity between grid-following and grid-forming new energy power supply is revealed, and we preliminarily discuss the influencing factors of the stability of new energy sources. Using a single-machine infinite system model, the influence of key control parameters on transient stability is separately investigated for grid-following and grid-forming types. The influence of the converter synchronous loop control parameters on the system is verified, and the factors affecting the stability are discussed in combination with current limiting and frequency limiting. The research will provide reference for the design of a new energy power grid connection system.

Degradation Processes in Current Commercialized Li-Ion Batteries and Strategies to Mitigate Them

Lithium-ion batteries (LIBs) are now widely exploited for multiple applications, from portable electronics to electric vehicles and storage of renewable energy. Along with improving battery performance, current research efforts are focused on diminishing the levelized cost of energy storage (LCOS), which has become increasingly important in light of the development of LIBs for large transport vehicles and power grid energy storage applications. Since LCOS depends on the battery's lifetime, understanding the mechanisms responsible for battery degradation and developing strategies to increase the lifetime of LIBs is very important. In this review, the latest developments related to the performance and degradation of the most common LIBs on the market are reviewed. The numerous processes underlying LIB degradation are described in terms of three degradation loss modes: loss of lithium inventory (LLI), active positive electrode material loss and degradation, and active negative electrode material loss and degradation. A strong emphasis is placed on the most recent strategies and tactics for LIB degradation mitigation.

Application and Optimization of Multi-Factor Authentication and Biometric Technology in Power Grid Energy Network Access Control

With the sudden development in electronic and network technology, the power grid has emerged in several evolved nations and other areas with faster development. Energy storage can enhance the stability, resiliency and efficiency of the electric smart grid. The power grid transfers energy via bidirectional control of energy and information flow. In the power grid, a smart meter is an important component that is deployed on the user side to determine the consumption of energy regularly. However, the data about the usage of electricity leads to the leakage of the private information of the user, which threatens the privacy of the user. The attackers can change the in-transit message induce wrong information and sometimes cause disastrous action. Hence, it is important to assure data security and privacy of users by conducting authentication in power grid communication. An optimization technique named Puzzle Optimization Algorithm (POA) is designed for the efficient privacy-preserving aggregation approach with multi-factor authentication and a biometric scheme. Also, optimal key-based Elliptic Curve Cryptography (ECC) is used to encrypt the sensitive data that are securely stored. Authentication offers high security and texture analysis. In the registration phase, the privacy information of the person with their biometrics is the important feature point of the individual unique identification. Additionally, the designed approach is compared with other existing optimizations to analyse the functionality of the implemented technique. The simulation outcome defined that the suggested technique enhances user privacy and information security than other standard approaches.

Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization

AbstractLithium‐ion batteries are widely employed in electric vehicles, power grid energy storage, and other fields. Thermal fault diagnostics for battery packs is crucial to preventing thermal runaway from impairing the safe operation and extended cycle service life of batteries. Therefore, a lithium‐ion battery thermal fault diagnosis model based on deep learning algorithms is presented, which includes three parts: autoencoder denoising network, coarse mask generator, and mask precise adjustment. Autoencoder denoising network can reduce data noise during thermal imaging acquisition, improve the anti‐interference ability of diagnostic models, and ensure the accuracy of thermal runaway diagnosis. A two‐stage diagnostic structure is then formulated by the coarse mask generator and mask precise adjustment, which enable quick identification, categorisation, and localisation of thermal fault battery cells. According to the test results, the segmentation boundary is more distinct and is capable of matching the original image's level. The recognition accuracy of the thermal diagnosis model for faulty batteries is close to 100%. After denoising by the autoencoder, the prediction results improved by 22% compared to non‐local mean denoising and by about 32% compared to noisy images.

A Bi-Level Optimized Dispatching Method for Grids with High Penetration of New Energy Considering Ancillary Service Costs

To address the issue of high auxiliary service costs caused by the grid connection of new energy generation, a dual layer optimization scheduling method for high penetration new energy power grids is proposed, taking into account the auxiliary service costs of grid connection. The correlation between the output fluctuations and load fluctuations of new energy generation is analyzed, and a calculation model for auxiliary service costs caused by grid connection of new energy generation is established. Taking into account the timescale division requirements for grid energy trading and auxiliary service market transactions, a new high penetration energy grid two-level optimization scheduling model is established. The upper level scheduling model aims to minimize the sum of grid energy procurement costs and auxiliary service costs caused by new energy generation and completes the optimization scheduling of the grid for several hours. The lower level scheduling model aims to closely follow the upper level scheduling plan for new energy power plants/groups. Optimization and scheduling of the power grid for tens of minutes is completed, taking into account the balanced allocation of wind and solar losses among various power plants within the new energy power plant group. The simulation results show that the method proposed in this paper can reduce the energy transaction costs and auxiliary service transaction costs of the power grid during low and peak load periods.

Prediction method for power fluctuations in cross regional consumption and transportation under the integration of new energy

Against the backdrop of the increasing development of the new energy industry, the volatility of new energy output poses significant challenges to regional power grid balance and energy absorption. Therefore, this article proposes a prediction method for cross regional transmission power fluctuations under new energy integration conditions. A comprehensive and representative sample dataset was constructed by comprehensively considering factors such as fluctuations in new energy output, capacity confidence, and peak shaving characteristic parameters, combined with numerical weather forecast data. Normalize the sample data to eliminate dimensional differences between parameters. The sparrow search algorithm is used to optimize the weights and thresholds of the double hidden layer BP neural network, effectively avoiding local optimization problems caused by over training. The experimental results show that this method has significant advantages in predicting power fluctuations in cross regional absorption and transportation of new energy, with a predicted power to power ratio of over 0.85.

Comprehensive testing technology for new energy vehicle power batteries based on improved particle swarm optimization

As the new energy industry continues to progress, the health management of power batteries has become the key to ensuring the performance and safety of automobiles. Therefore, accurately predicting battery capacity decline is particularly important. A battery capacity degradation prediction model combining unscented particle filtering, particle swarm optimization, and SVR is constructed. It optimizes regression parameters through the introduced optimization strategy. Unscented particle filtering is used to improve particle swarm optimization and battery detection model. The study tested four various models of lithium-ion batteries. The model predicted a mean square error of 0.0011 for battery 5, 0.0007 for battery 6, 0.0022 for battery 7, and 0.0013 for battery 18. In the prediction of different battery types, the mean square error of the NIMH battery was reduced by 0.0008 compared with the particle swarm optimization-support vector regression algorithm, and by 0.0005 compared with the unscented particle filtering-regression vector regression algorithm. The mean square error of lithium-iron phosphate battery was reduced by 0.0008 and 0.0004 respectively compared with comparison models. The mean square error value of lithium titanate battery was reduced by 0.0007 and 0.0003 respectively in the research model compared with comparison models. It improves the prediction accuracy in lithium-ion batteries. Its application in battery health management can provide important technical support for improving battery performance and extending service cycles. The proposed method can be used for battery monitoring and management of power grid energy storage system. By accurately predicting the capacity decline of battery, the operation strategy of energy storage system can be optimized to ensure the efficient operation and long life of the system. The battery management system can be used for drones and aviation equipment to predict battery health and capacity decline in real time, ensuring the safety and reliability of flight missions.

Ti3C2Tx (T = F, O, OH) as a sensor for dissolved gas in transformer oil: A theoretical study

Power transformer is the hub of power grid energy transmission, and its safe and reliable operation is very important to power grid. In this paper, based on density functional theory, Ti3C2Tx (T = F, O, OH) material was used as sensing material. The adsorption models of Ti3C2Tx and dissolved characteristic gases (H2, CH4, C2H4, C2H6, C2H2, CO, and CO2) in transformer oil are constructed. The adsorption distance, bond length change, adsorption energy, charge transfer and density of states (DOS) of the adsorption system were analyzed. The results showed that the gases can adsorb on the surface of Ti3C2Tx spontaneously. Specifically, CO2, CO, C2H2 and C2H4 exhibit a strong adsorption effect on Ti3C2(OH)2, forming hydrogen bonds or chemical bonds, accompanied by large charge transfer and adsorption energy, indicating Ti3C2(OH)2 shows a good sensing performance for these gases.

Novel approach to remote rural heating: Direct coupled photovoltaic electric heater underfloor heating system with phase change materials

Limited access to grids and the unavailability of traditional energy sources present significant challenges to effective heating in remote rural. This paper proposes a direct coupled photovoltaic electric heater underfloor heating system with phase change materials (direct coupled PVEH-UFHs), which eliminates the reliance on the power grid or conventional energy sources for heating in remote areas. The model of the direct coupled photovoltaic electric heating system (direct coupled PVEHs) and the building model with PCM floor were created and the accuracy of the direct coupled PVEHs model was verified experimentally. The study proposes using the total heating achievement rate (THAR) as the evaluation index and identifies the critical parameters affecting the system performance through sensitivity analysis. The results indicate that the photovoltaic capacity factor (PVCF), PCMs thickness and melting temperature are the key parameters affecting the heating performance of the system. The recommended 1.4 PVCF and PCM melting temperature of 294 K. The minimum payback time for system configuration that achieves 90 % THAR is 16.2 years. In summary, this paper proposes a design method and evaluation criteria for direct coupled PVEH-UFHs suitable for energy-efficient heating in remote areas.

Collaborative optimization model of industrial high‐energy consumption park under green and low‐carbon transformation

AbstractUnder the background of green and low‐carbon transformation of industrial high‐energy‐consuming parks, in order to realize the coordinated development planning of energy and transportation under different peaking capacity requirements, this paper proposes a collaborative optimization model of industrial high‐energy‐consuming parks under green and low‐carbon. Firstly, by studying the energy balance mechanism between industrial load and new energy, an energy topology prior model of energy interaction in the next generation of industrial new energy power grid is constructed. Then, based on the probability characteristics of the equilibrium state of different regions in the high‐energy‐consuming industrial zone, combined with the prior knowledge of industrial load and low‐carbon power system, a collaborative development planning model of high‐energy‐consuming industrial parks is constructed. Finally, the proposed model is simulated. From the simulation results the authors can see the dynamic division model and solution algorithm of energy balance region established in this paper can effectively improve the energy balance ability of power grid, reduce peak shaving demand and use more renewable energy.

BEET: Blockchain Enabled Energy Trading for E-Mobility Oriented Electric Vehicles

Renewable Energy Sources (RESs) are gaining considerable attention to reduce human dependence on fossil fuels and minimize harmful gases in our surroundings. Existing literature on energy trading focused on providing renewable energy to smart homes, smart buildings, and smart offices to fulfill their daily energy demands obtained from RESs. Besides, Electric Vehicles (EVs) use either power grid energy or a battery exchange mechanism to recharge their low EV batteries. The continuous use of power grids to recharge low EV batteries causes a significant load on power grids. Due to this, power grids are inadequate to fulfill the ever-increasing demands of EVs in the future. In this context, we propose a Blockchain Enabled Energy Trading (BEET) framework oriented EV charging. A system architecture of the BEET framework is presented to describe the functioning of each layer and its associated entities. We formulate an optimization problem that maximizes the revenue in the energy trading process using a knapsack optimization. Smart contracts are designed on the consortium blockchain network to sell and buy renewable energy to aggregators and from producers, respectively. Moreover, an EV charging mechanism is designed to intelligently allocate renewable energy to consumers at a low price. A comparative analysis is performed with state-of-the-art works in terms of charging price, revenue, throughput, and latency. The results indicate that the BEET framework outperforms compared to state-of-the-art works to address the renewable energy demand problem to realize E-mobility. It is clarified that the data considered in the experimental analysis were obtained from statistical simulations in realistic E-Mobility environment settings.