Grid Connection Research Articles

A weighted stabilized lagrange interpolation collocation method for boundary condition identification in 3D electromagnetic inverse scattering

The identification of boundary conditions in electromagnetic inverse scattering is of importance in various engineering applications, ranging from geophysical exploration to wireless communication. Conventional numerical methods solving this problem often suffer from the iterative process, leading to inefficiencies and non-convergence. This paper introduces a weighted scheme of the stabilized Lagrange interpolation collocation method (weighted SLICM) to resolve this problem. Weighted SLICM efficiently integrates governing equations, boundary conditions, and measurement conditions using a weighted least squares approach, offering a straightforward single-step solution and obviating the need for iterative processes in traditional methods like the finite element method. By incorporating regularization techniques, weighted SLICM decreases measurement errors which are unavoidable in engineering problems, thereby ensuring high efficiency and accuracy. In addition, characterized as a strong-form collocation method that relies solely on point information but not on grid connectivity, the weighted SLICM is readily extendible to complex three-dimensional applications in electromagnetic inverse scattering. Extensive simulations of benchmark problems show its ability to achieve accurate and stable results in boundary condition identification in electromagnetic inverse scattering problems including 1D, 2D, and 3D environments, highlighting the effectiveness of the weighted SLICM in navigating complex engineering challenges and substantially enriching research methodologies in this area.

Do indicators of safeguards influence off-grid solar choices? Assessing quality, affordability, and service models in Kenya

Electricity access remains out of reach for about half of the population in Sub-Saharan Africa, and in rural areas, the electricity access gap reaches almost 70%. Overcoming the electricity access gap is also spurring innovative models of distributed electrification. The considerable debate about how households will advance from off-grid solar technologies to larger appliances and grid connections has mainly focused on the economic and technological aspects of these decisions. Using a discrete choice survey experiment of rural Kenyan households, we find that quality and customer support, such as warranties, after-sale service, and local availability of appliances, are just as important or even more important than more studied techno-economic aspects, such as financing, company recognition, and technical system compatibility. These findings imply that building local capacity and mechanisms for recourse are important areas of focus for supporting off-grid solar as a means for more lasting access to electricity in rural contexts.

Enhancing off-grid wind energy systems with controlled inverter integration for improved power quality

Introduction. Off-grid wind energy systems play a pivotal role in providing clean and sustainable power to remote areas. However, the intermittent nature of wind and the absence of grid connectivity pose significant challenges to maintaining consistent power quality. The wind energy conversion system plays a central role in tapping renewable energy from wind sources. Operational parameters such as rotor and stator currents, output voltages of rectifiers and converters, and grid phase voltage variations are crucial for stable power generation and grid integration. Additionally, optimizing power conversion output through voltage gain analysis in boost converters is essential. Moreover, ensuring electricity quality via total harmonic distortion reduction in inverters is vital for grid compatibility. Goal. Enhancing the power quality of grid-integrated wind energy conversion systems. Methods. The proposed topology is implemented in MATLAB/Simulink with optimized control strategies for enhancing power quality in off-grid wind energy systems. Results. Control strategies with a grid-connected wind energy conversion system yields substantial improvements in power quality. This includes effectively mitigating voltage fluctuations and harmonics, resulting in smoother operation and reduced disturbances on the grid. Practical value. The proposed topology has proven to be extremely useful for off grid-integrated wind system. References 18, table 1, figures 11.

Distributed Regional Photovoltaic Power Prediction Based on Stack Integration Algorithm

With the continuous increase in the proportion of distributed photovoltaic power stations, the demand for photovoltaic power grid connection is becoming more and more urgent, and the requirements for the accuracy of regional distributed photovoltaic power forecasting are also increasing. A distributed regional photovoltaic power prediction model based on a stacked ensemble algorithm is proposed here. This model first uses a graph attention network (GAT) to learn the structural features and relationships between sub-area photovoltaic power stations, dynamically calculating the attention weights of the photovoltaic power stations to capture the global relationships and importance between stations, and selects representative stations for each sub-area. Subsequently, the CNN-LSTM-multi-head attention parallel multi-channel (CNN-LSTM-MHA (PC)) model is used as the basic model to predict representative stations for sub-areas by integrating the advantages of both the CNN and LSTM models. The predicted results are then used as new features for the input data of the meta-model, which finally predicts the photovoltaic power of the large area. Through comparative experiments at different seasons and time scales, this distributed regional approach reduced the MAE metric by a total of 22.85 kW in spring, 17 kW in summer, 30.26 kW in autumn, and 50.62 kW in winter compared with other models.

Identification of generator coherency in power systems with wind farm

In the context of the "dual carbon" goal, the penetration rate of new energy represented by wind power is gradually increasing. The large-scale grid connection of wind power makes the power system more complex and uncertain. The output of wind farms changes the system flow, indirectly affecting the power angle characteristics of synchronous generators, and thereby changing the coherence between generators. Affects synchronous generator homology identification. This paper proposes a generator homology identification method for power systems containing wind farms, in response to the problem that the existing methods for identifying unit homology have not taken into account the adverse effects of wind farms on identification results. Translate the influence of wind farms on the homology of synchronous generators into the contraction admittance matrix as a static electrical distance indicator; Select dynamic data reflecting the homology of synchronous generators after disturbance, and form four dynamic indicators to measure the power angle increment curve between each generator: Euclidean distance, Chebyshev distance, grey correlation, and correlation coefficient; By using the combination weighting method to determine the weights of each indicator, a comprehensive similarity matrix is formed, and the optimal clustering results are determined using fuzzy system clustering and F-statistical values. Finally, the effectiveness of the proposed method was verified using EPRI-9 node system, EPRI-36 node system, and IEEE68 node system as examples.© 2017 Elsevier Inc. All rights reserved.

Assessing residential sustainable energy autonomous buildings for hot climate applications

This paper introduces an innovative solution for clean energy autonomous building (AB) sustainability, offering a 5Z concept—zero-carbon, zero-energy, zero grid connections, zero energy bills, and zero emission mobility. This paper focuses on fundamental research to design sustainable, energy-ABs striving for self-sufficiency in arid climates, using Kuwait as a case study. The study highlights the importance of stringent engineering AB modeling, renewable technology integration, and clean energy storage. The technical approaches include characterizing non-thermal and thermal demands, achieving net-zero energy generation, and custom sizing renewable energy and energy storage systems (ESS) for electric vehicle (EV) charging points. The research methodology involves local construction with yearly weather and energy profile data to simulate the actual system using ESP-r building model. The study's findings reveal the need for a large-scale energy system, energy demand reduction (EDR) inclusive of heating, cooling, appliances, and EV, with the corresponding changes in local energy production system size, battery capacity, and the number of photovoltaics (PVs). The results show varying EDR levels ranging from base case to 10% and to 50% leading to proportional changes in energy system size, size of battery from 3415 kWh to 1710 kWh and the number of PVs from 362 to 181, which means EDR not only optimizes space but also proves cost-effective. The significant implication of this study, not only bridging the knowledge gap and the lack of how-know, but also making a significant advancement and forward thinking in sustainable green electric home modernization and environmentally friendly transportation. This approach transforms energy management practices for more sustainable cities and societies, reducing emissions in both urban infrastructure and transportation.

Intelligent distribution network fault monitoring integrating differential evolution and chaos whale optimization algorithm

Faced with rapid development and increasingly complex power grid structure structure of the power grid, it is necessary to accurately locate faults in modern intelligent distribution networks in order to ensure power supply reliability and improve power supply service quality. Aiming at the low positioning accuracy, long time consumption, and limited coverage types in existing positioning algorithms, a new intelligent distribution network fault positioning algorithm is designed by combining two algorithms to complete the monitoring task. Firstly, intelligent distribution network fault location methods under different distributed power grid connection methods are analyzed. Then, considering the distributed power grid connection, a fault location algorithm is designed by combining differential evolution algorithm, Sine chaotic mapping, and whale algorithm. The research results indicated that the designed method had good benchmark performance, with accuracy and recall values as high as 0.98 and 0.97, respectively. During the training process, it only required 175 iterations to reach a stable state. In practical applications, the accuracy of this algorithm in testing three types of faulty power grids was 98.90%, 98.41%, and 98.25%, respectively. The method can effectively improve the fault location accuracy, providing better positioning technology for fault monitoring problems in power systems.

Week-ahead dispatching of active distribution networks using hybrid energy storage systems

This paper presents a week-long scheduling approach to address the issues associated with uncertain stochastic generation. Specifically, the method is designed for active distribution networks (ADNs) hosting hybrid energy storages, composed by a hydrogen energy storage system (HESS) and a battery energy storage system (BESS). The inclusion of a pressurized HESS allows to balance energy over longer time periods, as opposed to methods considering only BESSs. To this end, this paper combines linearized models for the electricity grid with linearized models of the HESS to solve a tractable scheduling problem. The proposed optimal schedule consists of an active power trajectory at the grid connection point (GCP), called the dispatch plan, and the unit commitment schedule of a PEM fuel cell and electrolyzer system interfacing the electricity network with the HESS. Additionally, a bilevel model predictive control strategy is proposed, where the upper layer MPC computes a storage target accounting for the full horizon, while the lower layer computes the controllable resource setpoints to minimize the dispatch tracking error in each period. A numerical experiment shows the effectiveness of the proposed scheduling and control to accurately compute and track a dispatch plan over a full week. The results clearly show the benefits of combining a HESS with a BESS especially in periods where the prosumption is highly uncertain. Finally, we discuss the computational challenges associated with the weekly horizon and the use of a HESS that exhibits different dynamics than a BESS and propose an approach to mitigate the computational cost.

An optimisation method for planning and operating nearshore island power and natural gas energy systems

As the development and use of offshore islands increase, so do the diversity of their energy needs, the cost of utilising the mainland for resource replenishment, and the instability of island energy systems. To address this challenge, this paper introduces an innovative system integrating fossil and renewable energy tailored for these islands, considering seasonal variations and external energy fluctuations. Leveraging a P-graph, an optimisation framework is designed to assess different optimised solutions. A comprehensive analysis is also performed to compare the solutions, while sensitivity assessments evaluate factors including electricity and gas price fluctuations, renewable energy availability, and natural gas demand. Additionally, the carbon emissions resulting from different power generation scenarios are visually represented and thoroughly analysed. The results support two key findings: 1) the cost of combined energy supply is lowest when wind energy accounts for 59 % of the total energy, mainland grid connection accounts for 3 %, fossil fuel energy accounts for 28 %, and natural gas accounts for 10 % of the energy mix; and 2) the price of electricity and natural gas supplied to the islands and the variability in the energy sources of wind and natural gas can significantly affect the cost of the energy system.

English Translation of New Vocabulary for Text Analysis of New Energy Industry and International Evaluation of Power Quality

With the advancement of global energy transformation, the rapid rise of the new energy industry has become a strategic highland that countries are eager to seize. The inherent instability and unpredictability of renewable energy production pose significant obstacles to the seamless operation of the power system. To address these challenges, this article employs text analysis, data-driven methods, and artificial intelligence technology in neologisms to conduct an in-depth international evaluation and comparative research on the power quality of the new energy industry. Using text mining techniques to conduct an in-depth analysis of new energy policy texts in various countries, revealing their policy characteristics and trends. At the same time, the combination of data-driven and artificial intelligence technology enables more accurate prediction and decision analysis. Based on the findings of this study, the United States has achieved a significant 35% enhancement in the quality of new energy electricity over the past decade, attributed to its consistent monitoring and improvement initiatives. Meanwhile, Germany, owing to its robust monitoring system, has witnessed a 25% improvement in electricity quality in the last five years. However, in developing nations like India and Brazil, approximately 60% of new energy projects encounter challenges related to grid connectivity and scheduling, resulting in a deterioration of electricity quality. In contrast, only 20% of developed countries have issues with grid access and scheduling. This phenomenon reflects that developing countries still need to increase investment and improve the layout of new energy industries and the construction of power grid infrastructure. Developing countries should actively learn from the successful experiences of developed countries in the new energy industry and power grid construction, formulate appropriate policy measures based on their national conditions, improve the quality of new energy electricity, and promote energy transformation and sustainable development.

The Design and Implementation of Blockchain Smart Contract in Hybrid Energy Storage Management of Photovoltaic System

As the main power supply mode of new energy, photovoltaic system has the characteristics of wide distribution and scattered energy collection Traditional management methods cannot realize the effective processing of distributed energy storage. Therefore, finding an effective intelligent method is the focus of current research, and the design of blockchain smart contracts can theoretically realize the centralized processing of photovoltaic energy storage systems. In this paper, the blockchain smart contract system is taken as the research object, and the energy storage management of the photovoltaic system is quantified through mapping, so as to realize the massive quantitative processing of data. Then, the reduction function is used to differentially reduce the distributed functional energy storage system, complete the centralized management of energy storage, and simplify the unnecessary data. Finally, combined with the hybrid energy storage situation, the key indicators in the data are mined, and the completion rate of the contract and the stability analysis of energy storage management are calculated. The results show that blockchain can mine the characteristics of hybrid energy storage, reduce the impact of distributed features on energy storage, improve the energy storage efficiency by 20%, and make the energy storage stability reach more than 90%. At the same time, the completion rate of smart energy storage contracts can be enhanced, the fitting degree can reach 90%, and the grid connection with the thermal grid can be effectively connected, the utilization rate of distributed energy resources will reach 85%, and the cost saving rate will be increased by 10%. Therefore, blockchain can solve the problem of hybrid energy storage in integrated systems, improve the completion rate of smart contracts, and promote the development of new energy.

Optimization of Fuzzy Control Parameters for Wind Farms and Battery Energy Storage Systems Based on an Enhanced Artificial Bee Colony Algorithm under Multi-Source Sensor Data.

With the rapid development of sensors and other devices, precise control for the generation of new energy, especially in the context of highly stochastic wind power generation, has been strongly supported. However, large-scale wind farm grid connection can cause the power system to enter a low inertia state, leading to frequency instability. Battery energy storage systems (BESSs) have the advantages of a fast response speed and high flexibility, and can be applied to wind farm systems to improve the frequency fluctuation problem in the process of grid connection. To address the frequency fluctuation problem caused by the parameter error of the fuzzy membership function in the fuzzy control of a doubly fed induction generator (DFIG) and a BESS, this paper proposes an improved Artificial Bee Colony (ABC) algorithm based on multi-source sensor data for optimizing the fuzzy controller to improve the frequency control ability of BESSs and DFIGs. A Gaussian wandering mechanism was introduced to improve the ABC algorithm and enhance the convergence speed of the algorithm, and the improved ABC algorithm was optimized for the selection of fuzzy control affiliation function parameters to improve the frequency response performance. The effectiveness of the proposed control strategy was verified on the MATLAB/Simulink simulation platform. After optimization using the proposed control strategy, the oscillation amplitude was reduced by 0.15 Hz, the precision was increased by 40%, and the steady-state frequency deviation was reduced by 26%. The results show that the method proposed in this paper provides a great improvement in the frequency stability of coordinated systems of wind farms and BESSs.

Optimized Genetic Algorithms: Study of the Impact on Load Distribution in the Power System

In order to balance the power flow, voltage and current between renewable energy power generation and thermal power generation, this paper takes renewable energy as the research object, analyzes the power flow and voltage after grid connection, and proposes an improved genetic algorithm for research. Firstly, the log function is used to analyze the power flow and voltage data before and after grid connection, reduce the complexity of the data, and calculate the proportion of photovoltaic energy. Then, the load analysis of the power system was carried out by genetic iteration to identify the overloaded nodes, and the identification standard was greater than 80% of the rated load. Finally, a load set is formed, and information such as changes, power flow, and voltage of different power generation nodes is recorded. The results show that the genetic algorithm can maximize the proportion of renewable energy to 50% and reduce the power flow change rate of the power system to 10%. At the same time, improve the load efficiency of the power system, so that the node load is between 75~80%, average the load of each node, especially the photovoltaic matrix and fan, and shorten the load deployment time, so that it is 10s smaller. Compared with the previous algorithms, the genetic algorithm proposed in this paper is better, which can meet the load distribution requirements of the power system and expand the energy proportion of renewable energy.

A fair grid connection cost-sharing model for electricity based on the random forest machine learning method

Developing countries should clarify the dedicated grid connection cost-sharing model when reforming electricity user price policy, which could be learned from developed countries’ experiences. We use the random forest method and monthly data from several developed countries or regions, including the United States and the United Kingdom in 2018–2020, to identify key features that influence decision-makers in choosing dedicated grid connection cost-sharing models and simulate the connection cost-sharing models suitable for various regions of China using provincial data. It provides experience and enlightenment for China and other developing countries to implement the connection price policy reform.

A comprehensive approach to evaluate risk mitigation strategies in offshore wind farms using spherical fuzzy decision making analysis

The escalating impact of global warming, primarily exacerbated by carbon emissions from conventional energy sources, has prompted significant advancements in offshore wind energy as a pivotal avenue for sustainable development. Amid the surging interest in renewable energy, offshore wind-farms have emerged as a promising solution. To ensure their efficient and effective operation, it becomes imperative to systematically identify and mitigate potential risks. This study addresses the critical need to systematically prioritize risk reduction strategies for offshore wind farms, a problem that has not been comprehensively explored in the literature. A multi-criteria analysis has been adopted to simultaneously evaluate the contradictory criteria in the evaluation process. The novelty of this study lies in its integration of spherical fuzzy sets with multi-criteria decision-making (MCDM) techniques to handle uncertainties and evaluate contradictory criteria simultaneously. As a result of all this analysis, the most critical risks for offshore wind-farms and risk mitigation strategies that can be adopted within the determined risks have been revealed. The main findings reveal that turbine underperformance, disruption of habitats, and grid connection issues are the most critical risks. Furthermore, a combination of robust design and engineering and collaboration and partnerships emerged as the most effective risk mitigation strategies.

OPTIMAL CONTROL OF A HYBRID PV-WIND POWER SYSTEM FOR ISLANDED HYBRID MICROGRID USING MOGA-FUZZY LOGIC CONTROLLER

The paper presents a comprehensive approach to managing the complexities associated with the integration of photovoltaic (PV) and wind energy systems in islanded microgrid environments. These microgrids, often isolated from the main power grid, face unique challenges due to the variability and intermittency of renewable energy sources, which can lead to instability and inefficiencies in power supply. To address these challenges, the study introduces a novel control strategy that combines a Multi-Objective Genetic Algorithm (MOGA) with a Fuzzy Logic Controller. The MOGA is utilized to explore a broad solution space and identify optimal control parameters that balance multiple objectives, including minimizing power fluctuations, maximizing the utilization of renewable energy, and ensuring a stable and reliable power supply. This optimization is crucial in islanded microgrids, where the lack of a larger grid connection necessitates highly efficient and responsive energy management systems to maintain stability. The Fuzzy Logic Controller, on the other hand, provides a flexible and adaptive control mechanism that responds to the dynamic and often unpredictable nature of renewable energy generation. By interpreting input variables in a way that mimics human decision-making, the Fuzzy Logic Controller can effectively handle the inherent uncertainties and non-linearities in the power system, adjusting the operation of the PV and wind power sources, as well as any supplementary energy storage systems, to optimize performance. This adaptive capability is particularly beneficial in scenarios where rapid changes in weather conditions can significantly impact energy generation and consumption patterns. The integration of MOGA with Fuzzy Logic not only enhances the decisionmaking process but also allows for the simultaneous consideration of multiple objectives, which is a critical advancement over traditional single-objective optimization techniques. This dual approach ensures that the hybrid PV-wind power system operates at its highest efficiency, balancing the need for sustainability with the practical requirements of reliability and economic viability. The results from extensive simulations, which model various operational scenarios and disturbances, demonstrate that the proposed MOGA-Fuzzy Logic Controller significantly improves the stability and efficiency of the islanded microgrid. The system is shown to effectively manage power flows, reduce dependency on fossil fuel-based backup generators, and increase the overall penetration of renewable energy. The proposed MOGA-Fuzzy Logic Controller stands out as a promising solution for the optimal control of hybrid PV-wind power systems, offering a viable pathway for achieving sustainable energy goals in islanded and other decentralized grid settings. This work not only advances academic knowledge but also has practical implications for the design and operation of future energy systems, making it a significant contribution to the field of renewable energy and power system engineering.

The Influence of Ant Colony Algorithm on the Power Distribution Scheme of Thermal Power Plants

The power distribution scheme of thermal power plants can balance the power flow voltage and current in the station area to ensure the stability of power grid operation, and the changes in power distribution scheme such as new energy, dwarf, wind power, wind power, etc., to ensure the quality of power supply of the whole power grid, but in the process of thermal power grid distribution, there are interference factors such as a large amount of data, difficulty in data analysis, and stability of new energy power generation, which affect the effectiveness of the entire distribution scheme In the mediation process of new energy sources such as photovoltaics, the stability of voltage and current is improved, kept between 90% ~ 95%, and the current of the power grid is effectively constrained to avoid the problem of excessive current and voltage, and the accuracy of power distribution is improved, and the improvement rate reaches 10~20%. Compared with the previous algorithms, it is found that the algorithm can shorten the power distribution time of thermal power plants and keep it between 5~6 seconds. Therefore, the ant colony algorithm can optimize the power distribution scheme of thermal power plants and support the grid connection of new energy.

Research on the coupling mechanism of transient overvoltage in large-scale new energy station grid connection

Abstract The large-scale new energy station transmission system is relatively weak, and long-distance transmission is prone to transient overvoltage. The hysteresis of the control loop of the Static Var Generator (SVG) can also contribute to the increase of transient overvoltage. Therefore, this article first establishes a photovoltaic power generation model, SVG model, and photovoltaic grid-connected system. Secondly, the transient overvoltage coupling mechanism of photovoltaic grid-connected systems under various faults such as commutation failure, DC blocking, and DC restart was analyzed through PSCAD simulation. At the same time, the changes in transient overvoltage before and after the installation of SVG were compared, and SVG’s reactive power dynamic behavior was analyzed. Finally, the mechanism of SVG assisting in increasing transient overvoltage was summarized.

Simulation analysis of overvoltage caused by grounded short circuit in the collection system of far-offshore wind farms

Abstract As the distance from the shore of offshore wind farms increases, MMC-HVDC technology is gradually gaining attention in its grid connection schemes. A broadband electromagnetic transient model of offshore wind farms with a total capacity of 800 MW connected to the grid via a flexible direct current transmission system is developed. After three types of grounded short circuit faults occur at different fault positions within the power collection system, the characteristics of overvoltage are obtained and they are analyzed on the basis of considering the low voltage ride-through capability of the direct-drive permanent magnet wind power generator. Combining the two factors of range and amplitude, the results show that the overvoltage phenomenon is the most serious when a two-phase grounded short circuit occurs at the converging busbar, which can be the focus of the study when setting up overvoltage protection.

Enhancing grid hosting capacity with coordinated non-firm connections in industrial energy communities

A significant increase in grid connection requests from industrial customers has lead to long connection queues. Combined with long lead times on grid construction, the result is significant socioeconomic losses due to lack of grid capacity. Distribution system operators (DSO) have therefore introduced non-firm connections as an alternative, where the new grid customer may connect on the condition that the DSO retains the right to disconnect the grid customer if necessary. An option to potential disconnection is for customers to leverage flexibility to stay below an agreed capacity level. Unfortunately, many existing grid customers possess flexibility potential but lack incentives to utilise it. To address this, we propose a “coordinated non-firm connection”, where the new grid customer forms an energy community with existing grid customers to coordinate flexibility and capacity utilisation. To demonstrate technical feasibility and incentive compatibility, we formulate a complementarity model. Applying this model to a case study involving three industrial grid customers, we showcase both technical potential and incentive compatibility. Results illustrate how energy community members engage in capacity trading within a local market, ensuring adherence to grid limitations. The game theory-based model confirms sufficient economic benefit for all members to participate.