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1
Tuzikova, Valeriya, Josef Tlusty, and Zdenek Muller. "A Novel Power Losses Reduction Method Based on a Particle Swarm Optimization Algorithm Using STATCOM." Energies 11, no. 10 (2018): 2851. http://dx.doi.org/10.3390/en11102851.
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In the modern electric power industry, Flexible AC Transmission Systems (FACTS) have a special place. In connection with the increased interest in the development of “smart energy”, the use of such devices is becoming especially urgent. Their main function is the ability to manage modes in real time: maintain the necessary level of voltage in the grids, control the power flow, increase the capacity of power lines and increase the static and dynamic stability of the power grid. The problem of system reliability and stability is related to the task of definitions and optimizations and planning indicators, design and exploitation. The main aim of this article is the definition of the best placement of the STATCOM compensator in case to provide stability and reliability of the grid with the minimization of the power losses, using Particle Swarm Optimization algorithms. All calculations were performed in MATLAB.
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Wen, Huixian. "Power flow analysis of 110kV power supply system based on PowerWorld." Journal of Physics: Conference Series 2495, no. 1 (2023): 012025. http://dx.doi.org/10.1088/1742-6596/2495/1/012025.
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Abstract With the growing global interest in various economic and environmental benefits of the power system, it is essential to ensure the safe and stable operation of the power grid. The optimal power flow calculation method is studied using the PowerWorld and Newton-Ralfsnn methods. The results calculated by the Simulator LP OPF function are compared with the manual calculation of the 110 kV power supply system grid. After optimization, the total cost is saved by 623.05 ¥/h. The simulation results can directly and vividly reflect the power distribution and optimize the power supply and distribution power flow.
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Peng, Dong, Yawei Xue, Tianzhi Li, Lina Wang, and Jinchao Li. "Research on Operation Benefit Evaluation of Power Network Project Based on Combination Weighting Method." E3S Web of Conferences 118 (2019): 01055. http://dx.doi.org/10.1051/e3sconf/201911801055.
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Firstly, on the basis of economic efficiency, equipment and operational safety, public interest and other dimensions, this paper constructs an evaluation index system for operational benefit of power grid projects, and then a combined weighting method based on Analytic Network Process (ANP) and entropy weight method is established. Finally, an empirical study is carried out based on the actual data of power grid projects, and the research results prove the effectiveness of the model.
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Joseph, C. Attachie, K. Amuzuvi Christian, and Diamenu Godwin. "Large-scale wind power grid modelling and stability evaluation using stochastic approaches." International Journal of Applied Power Engineering 11, no. 3 (2022): 237~250. https://doi.org/10.11591/ijape.v11.i3.pp237-250.
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Due to dwindling fossil fuel reserves, there is a global demand to increase the level of low-carbon based renewable energy resources (RERs) for electric power generation. Coupled with concerns that emissions from fossil fuels is leading to climate change with possible disastrous consequences, effort is seriously under way to discover the probable usage of RERs on large-scale without being integrated into an existing power grid. It is envisaged that such a large-scale RER power grid will operate solely on its own and expected to be stable and reliable comparable to a conventional power grid. The impact of wind power generation as part of the electric power grid is no longer negligible. Wind energy generation is one of the most established renewable energy resources to help ensure low carbonbased renewable energy (RE) self-sufficiency, yet it is also one of the most volatile RERs. Despite its disadvantages, wind power generation is expected to continue its strong growth in the coming years as result of high interest in clean energy to curb the global warming. Various studies are looking at the prospects for solely RER power grids for usage on large-scale. However, the issue has been the stability and the reliability of such power grids. Variability of power output, intermittency and load mismatch are wind farms’ unique characteristics potentially harmful to grid voltage stability. In response to this problem, A large-scale wind power system was modelled using stochastic approach and the results analyzed using Lyapunov method, matrix exponential and Hurwitz criterion to ascertain the stability behavior of an entirely 100% RE grid being envisaged for the near future.
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Burtsev, Anton V. "Practical application of the method for calculating lightning impact levels on overhead power lines." Transactions of the Kоla Science Centre of RAS Series Engineering Sciences 14, no. 6/2023 (2023): 66–72. http://dx.doi.org/10.37614/2949-1215.2023.14.6.008.
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The article presents examples of the practical application of the method for calculating the level of lightning effects on overhead power lines. The method is based on calculation of distances between coordinates of lightning and overhead line supports. The method was tested on overhead lines of Murmansk region. The proposed method is of interest for power grid companies, as it allows identifying the most vulnerable sections of power lines to lightning.
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Moon, Guk-Hyun, Rakkyung Ko, and Sung-Kwan Joo. "Integration of Smart Grid Resources into Generation and Transmission Planning Using an Interval-Stochastic Model." Energies 13, no. 7 (2020): 1843. http://dx.doi.org/10.3390/en13071843.
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In the power industry, the deployment of smart grid resources in power systems has become an issue of major interest. The deployment of smart grid resources represents an additional uncertainty in the integrated generation and transmission planning that raises uncertainties in investment-related decision making. This paper presents a new power system planning method for the integration of electric vehicles (EVs) and wind power generators into power systems. An interval-stochastic programming method is used to account for the heterogeneous uncertainties attributable to natural variability and lack of knowledge. The numerical results compare the multiple integration scenarios and verifies the effectiveness of the proposed method in terms of cost distribution and regret cost.
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An, Chang-Gyun, Hoon Lee, Tae-Gyu Kim, Junsin Yi, and Chung-Yuen Won. "A Study on Energy Management and Cooperative Control Considering LVRT in a Hybrid Microgrid." Energies 16, no. 11 (2023): 4372. http://dx.doi.org/10.3390/en16114372.
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Recently, the establishment of technical standards for grid connection has gained interest in academia and industry. These standards have focused on the reactive power control function of the grid-connected inverter and maintenance of grid operation, and include detailed information about the grid support function. However, remote control communication and control devices for grid support functions, and other distributed sources, such as wind power and energy storage systems, other than inverters have not been addressed. In this paper, the control of the interlinking converter (ILC) in a hybrid microgrid considering low voltage ride-through (LVRT) among grid support functions is investigated. The proposed method consists of an energy management system considering LVRT and a cooperative control scheme. In the energy management system, an algorithm capable of mode selection was constructed by applying the LVRT curve. Then, considering the LVRT situation, the allowable reactive power range of the ILC was mathematically analyzed through the cooperative control of the energy storage device and the ILC. The proposed method enables us to perform active and reactive power control of the ILC in a hybrid distribution network, considering the power factor under various conditions. This functionality, such as supplying reactive power, significantly contributes to the enhanced grid resilience with distributed power sources, including renewable energy. The proposed strategies were verified through experiments after configuring an experimental set of distributed power sources.
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Liang, Kun, Baoxian Zhou, Yiying Zhang, Yiping Li, Bo Zhang, and Xiankun Zhang. "PF2RM: A Power Fault Retrieval and Recommendation Model Based on Knowledge Graph." Energies 15, no. 5 (2022): 1810. http://dx.doi.org/10.3390/en15051810.
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Energy saving and emission reduction have become common concerns in countries around the world. In China, with the implementation of the new strategy of “carbon peak and neutrality” and the rapid development of the new smart grid infrastructure, the amount of data of actual power grid dispatching and fault analysis show exponential growth, which has led to phenomena such as poor supervision effectiveness and difficulty in handling faults in the process of grid operation and maintenance. Existing research on retrieval recommendation methods has had a lower accuracy rate at cold-start due to a small sample of user interactions. In addition, the cumulative learning of user personalization during general retrieval results in a poor perception of potential interest. By constructing a power knowledge graph, this paper presents a power fault retrieval and recommendation model (PF2RM) based on user-polymorphic perception. This model includes two methods: the power fault retrieval method (PFR) and the user-polymorphic retrieval recommendation method (UPRR). First, we take the power grid fault dispatching business as the core and reconstruct the ontology layer of the power knowledge graph. The PFR method is used to design the graph-neighbor fault entity cluster to enhance the polymerization degree of a fault implementation scenario. This method can solve the search cold-start recommendation problem. At the same time, the UPRR method aims to form user retrieval subgraphs of the past-state and current-state and make a feature matching for the graph-neighbor fault entity cluster, and then realize the accurate prediction of the user’s general search intention. The model is compared with other current classical models through the evaluation of multiple recommendation evaluation metrics, and the experimental results show that the model has a 3–8% improvement in the cold-start recommendation effect and 2–10% improvement in regular retrieval. The model has the best average recommendation performance in multiple metrics and has good results in fault analysis and retrieval recommendation. It plays a helpful role in intelligent operation and maintenance of the power grid and auxiliary decision-making, and effectively improves the reliability of the power grid.
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Nazir, Muhammad Shahzad, Fahad Alturise, Sami Alshmrany, et al. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend." Sustainability 12, no. 9 (2020): 3778. http://dx.doi.org/10.3390/su12093778.
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To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.
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Jo, Haesung, Jaemin Park, and Insu Kim. "Environmentally Constrained Optimal Dispatch Method for Combined Cooling, Heating, and Power Systems Using Two-Stage Optimization." Energies 14, no. 14 (2021): 4135. http://dx.doi.org/10.3390/en14144135.
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The reliance on coal-fired power generation has gradually reduced with the growing interest in the environment and safety, and the environmental effects of power generation are now being considered. However, it can be difficult to provide stable power to end-users while minimizing environmental pollution by replacing coal-fired systems with combined cooling, heat, and power (CCHP) systems that use natural gas, because CCHP systems have various power output vulnerabilities. Therefore, purchasing power from external electric grids is essential in areas where CCHP systems are built; hence, optimal CCHP controls should also consider energy purchased from external grids. This study proposes a two-stage algorithm to optimally control CCHP systems. In Stage One, the optimal energy mix using the Lagrange multiplier method for state-wide grids from which CCHP systems purchase deficient electricity was calculated. In Stage Two, the purchased volumes from these grids were used as inputs to the proposed optimization algorithm to optimize CCHP systems suitable for metropolitan areas. We used case studies to identify the accurate energy efficiency, costs, and minimal emissions. We chose the Atlanta area to analyze the CCHP system’s impact on energy efficiency, cost variation, and emission savings. Then, we calculated an energy mix suitable for the region for each simulation period. The case study results confirm that deploying an optimized CCHP system can reduce purchased volumes from the grid while reducing total emissions. We also analyzed the impact of the CCHP system on emissions and cost savings.
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Dao, Tang-Tin, Q. S. Vu, Van-Duc Phan, and Minh Tran. "Novel method for calculating installed capacity of stand-alone renewable energy systems." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (2021): 1256–62. https://doi.org/10.11591/ijeecs.v21.i3.pp1256-1262.
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The use of new energy sources to replace traditional energy sources is the worldwide interest based on its irrefutable advantages, especially in regions where supply systems Power supply cannot reach. The devices installed capacity has a significant effect on the economy as well as on system operation. In this paper, formulate and solve the problem of optimizing installed capacity for devices (generators, charge controllers, storage, inverters ...) that are used in independent renewable energy systems. In illustrating this method of calculation, we apply it on a standalone system, i.e., it is not connected to the power supply grid.
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Deepak, Karanam, Rajib Kumar Mandal, and Vimlesh Verma. "Novel aggregated controller of wind and PV based grid connected charging station for electric vehicle." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 4 (2023): 2319. http://dx.doi.org/10.11591/ijpeds.v14.i4.pp2319-2327.
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<span lang="EN-US">Novel technologies are adopting electric vehicles (EVs) day-by-day due to increasing interest in EVs. The charging process of EVs is a very important aspect when it is connected to the utility grid. Generally charging of an EV can be done at either home or in charging stations were connected to the utility grid. More harmonics and nonlinear currents are injecting into the utility grid during the charging process of the battery due to the existence of converters for power conversion in charging stations of EVs, which generally affects the quality of the power. In this situation, supplying the power to the utility grid from batteries existing in the vehicle through the charging station will provide a better solution and will charge again when there is less demand on the grid. Further using renewable energies in the charging stations can provide reliable power for both vehicles as well as the utility grid. To achieve better performance and maintain power quality at load bus, an aggregated controller is proposed in this paper. Moreover, reloading conditions are also incorporated to renewable energy sources under disconnection of the utility grid to maintain power balance. Hardware-in the–loop (HIL) based extensive results by using OPAL-RT modules are examined in this article under many situations to validate the proposed method.</span>
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Sauhats, Antans, Andrejs Utans, Jurijs Silinevics, Gatis Junghans, and Dmitrijs Guzs. "Enhancing Power System Frequency with a Novel Load Shedding Method Including Monitoring of Synchronous Condensers’ Power Injections." Energies 14, no. 5 (2021): 1490. http://dx.doi.org/10.3390/en14051490.
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Under-frequency load shedding (UFLS) is a classic and a commonly accepted measure used to mitigate the frequency disturbances in case of loss-of-generation incidents in AC power grids. Triggering of UFLS is classically done at frequency thresholds when system frequency collapse is already close to happening. The renewed interest for synchronous condensers due to the global trends on massive commissioning of non-synchronous renewable power generation leading to reduction of system inertia gives an opportunity to rethink the approach used to trigger load-shedding activation. This question is especially relevant for the Baltic states facing a desynchronization from Russian power grid and a necessity to operate in an isolated island mode. The main goal of this paper is to introduce a predictive load shedding (LS) method without usage of either frequency or ROCOF measurements based on the monitoring of active power injections of synchronous condensers and to prove the efficiency of the concept through several sets of case study simulations. The paper shows that the proposed approach can provide a greatly improved frequency stability of the power system. The results are analyzed and discussed, the way forward for the practical implementation of the concept is sketched.
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Bo, Long, Lijun Huang, Yufei Dai, Youliang Lu, and Kil To Chong. "Mitigation of DC Components Using Adaptive BP-PID Control in Transformless Three-Phase Grid-Connected Inverters." Energies 11, no. 8 (2018): 2047. http://dx.doi.org/10.3390/en11082047.
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Transformerless grid-connected inverters, due to their advantages of high efficiency, small volume and light weight, have been the subject of more research and interest in recent years. Due to the asymmetrical driving signal in pulse width modulation (PWM) caused by time-delay, zero-drift of the current sensors and imparities of the power transistors, output of the grid current contains dc component. As a result, power quality of the grid is degraded. In this paper, a dc (direct current) component suppression scheme with adaptive back-propagation (BP) neural network proportional-integral-differential (PID) control is proposed for dc component minimization. Moreover, sliding-window-double-iteration-method (SWDIM) is utilized for fast dc component extraction. Compared with the conventional method, the proposed scheme shows better performance, and the dc component can be attenuated to be within 0.5% of the rated current.
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Dao, Tang-Tin, Q. S. Vu, Van-Duc Phan, and Minh Tran. "Novel method for calculating installed capacity of stand-alone renewable energy systems." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (2021): 1256. http://dx.doi.org/10.11591/ijeecs.v21.i3.pp1256-1262.
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<span>The use of new energy sources to replace traditional energy sources is the worldwide interest based on its irrefutable advantages, especially in regions where supply systems Power supply cannot reach. The devices installed capacity has a significant effect on the economy as well as on system operation. In this paper, formulate and solve the problem of optimizing installed capacity for devices (generators, charge controllers, storage, inverters) that are used in independent renewable energy systems. In illustrating this method of calculation, we apply it on a standalone system, i.e., it is not connected to the power supply grid.</span>
16
Nirali Upadhyay. "Integrated Passive Anti-Islanding Protection in Micro Grid." Journal of Electrical Systems 20, no. 6s (2024): 2922–37. http://dx.doi.org/10.52783/jes.3297.
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In last two decades due to major interest amongst power system engineers and policy makers penetration of different distributed energy resources (DREs) into power distribution network are significantly increased. Integration of DREs with conventional power grid is a complex issue and will impose some challenges against power engineers. The major challenge is protection of micro grid, which is based on how your protection strategies distinguish islanding and non islanding conditions correctly and timely. Methods used for have strengths and limitations in the areas like speed, accuracy, power quality and Non detection islanding zone. The prime motive of this research is to presents an efficient new integrated anti-islanding protection strategy using passive parameters. The proposed algorithm having a novel islanding detection technique, which discriminate the islanding events from the non islanding events of similar signature accurately with minimum non detection zone and minimum impact on power quality. The proposed strategy is the combination of different conventional and latest passive relays based on single and multiple parameters. Integration of different relays made in such a strategic way that it combines the strengths and benefits of each method and minimizes their limitations. The presented algorithm tested under MATLAB/SIMULINK environment in different network conditions. Finally, the efficiency of the algorithm has checked by testing it on standard microgrid structure. The results received from simulation confirmed that algorithm is detecting islanding conditions reliably and efficiently, followed by generation of trip/block signal correctly.
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Aien, M., R. Ramezani, and S. Mohsen Ghavami. "Probabilistic Load Flow Considering Wind Generation Uncertainty." Engineering, Technology & Applied Science Research 1, no. 5 (2011): 126–32. http://dx.doi.org/10.48084/etasr.64.
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Renewable energy sources, such as wind, solar and hydro, are increasingly incorporated into power grids, as a direct consequence of energy and environmental issues. These types of energies are variable and intermittent by nature and their exploitation introduces uncertainties into the power grid. Therefore, probabilistic analysis of the system performance is of significant interest. This paper describes a new approach to Probabilistic Load Flow (PLF) by modifying the Two Point Estimation Method (2PEM) to cover some drawbacks of other currently used methods. The proposed method is examined using two case studies, the IEEE 9-bus and the IEEE 57-bus test systems. In order to justify the effectiveness of the method, numerical comparison with Monte Carlo Simulation (MCS) method is presented. Simulation results indicate that the proposed method significantly reduces the computational burden while maintaining a high level of accuracy. Moreover, that the unsymmetrical 2PEM has a higher level of accuracy than the symmetrical 2PEM with equal computing burden, when the Probability Density Function (PDF) of uncertain variables is asymmetric.
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Labella, Alessandro, Filip Filipovic, Milutin Petronijevic, Andrea Bonfiglio, and Renato Procopio. "An MPC Approach for Grid-Forming Inverters: Theory and Experiment." Energies 13, no. 9 (2020): 2270. http://dx.doi.org/10.3390/en13092270.
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Microgrids (MGs) interest is growing very fast due to the environment urgency and their capability to integrate renewable energy in a flexible way. In particular, islanded MGs in which distributed energy resources (DERs) are connected to the infrastructure with power electronic converters have attracted the interest of many researchers of both academia and industry because management, control and protection of such systems is quite different from the case of traditional networks. According to their operation mode, MGs that power electronic converters can be divided into grid-forming, grid-feeding and grid-supporting inverters. In particular, grid forming inverters are asked to impose voltage and frequency in the MG. This paper aims to propose a model predictive control (MPC) based approach for grid-forming inverters in an islanded MG. The MPC strategy is implemented because of its capability to define the optimal control actions that contemporarily track the desired reference signals and accounts for equality and inequality constraints. The overall problem formulation (objective function and relevant constraints) is described step by step and considers the specificity of the considered DC source. The proposed approach allows for the obtaining of very good results in terms of readiness against disturbances, even if it requires being fed only by local measurements. In order to validate the developed method, this paper proposes an experimental validation of the designed MPC controller in order to show its correct operation on a real system in a power hardware in the loop set-up using a rapid control prototyping approach.
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Gruosso, Giambattista, Luca Daniel, and Paolo Maffezzoni. "Piece-Wise Linear (PWL) Probabilistic Analysis of Power Grid with High Penetration PV Integration." Energies 15, no. 13 (2022): 4752. http://dx.doi.org/10.3390/en15134752.
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This paper aims at presenting a novel effective approach to probabilistic analysis of distribution power grid with high penetration of PV sources. The novel method adopts a Gaussian Mixture Model for reproducing the uncertainty of correlated PV sources along with a piece-wise-linear approximation of the voltage-power relationship established by load flow problem. The method allows the handling of scenarios with a large number of uncertain PV sources in an efficient yet accurate way. A distinctive feature of the proposed probabilistic analysis is that of directly providing, in closed-form, the joint probability distribution of the set of observable variables of interest. From such a comprehensive statistical representation, remarkable information about grid uncertainty can be deduced. This includes the probability of violating the safe operation conditions as a function of PV penetration.
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Bhutto, Zuhaibuddin, Sikandar Ali Abbasi, Saeed Zaman Jamali, et al. "Evaluation of Drivers and Barriers of Wind Power Generation in Pakistan. SWOT-Delphi Method." International Journal of Energy Economics and Policy 12, no. 2 (2022): 342–48. http://dx.doi.org/10.32479/ijeep.12768.
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In the last two decades electricity shortage has hampered the economic growth of Pakistan. To overcome these crises, thermal power plants were commissioned to bridge the supply and demand gap. Deployment of thermal power generation resulted in an unsustainable energy mix with the higher cost of generation. In the last decade, policymakers have shown considerable interest in deploying renewable energy generally and wind energy particularly. Therefore, this paper evaluates some important drivers and barriers to wind power generation. SWOT-Delphi approach with Relative Importance Index (RII) analysis has been applied. The results show that the deployment of wind power can enhance energy security and environmental sustainability. Major barriers to wind energy are the presence of competitive energy resources, policy implications, and poor grid infrastructure. With this contrasting environment, the evaluation of drivers and barriers of wind power are insightful for formulating sustainable energy planning strategies for future generation mix.
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Sulaiman, Adel, Bharathiraja Nagu, Gaganpreet Kaur, et al. "Artificial Intelligence-Based Secured Power Grid Protocol for Smart City." Sensors 23, no. 19 (2023): 8016. http://dx.doi.org/10.3390/s23198016.
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Due to the modern power system’s rapid development, more scattered smart grid components are securely linked into the power system by encircling a wide electrical power network with the underpinning communication system. By enabling a wide range of applications, such as distributed energy management, system state forecasting, and cyberattack security, these components generate vast amounts of data that automate and improve the efficiency of the smart grid. Due to traditional computer technologies’ inability to handle the massive amount of data that smart grid systems generate, AI-based alternatives have received a lot of interest. Long Short-Term Memory (LSTM) and recurrent Neural Networks (RNN) will be specifically developed in this study to address this issue by incorporating the adaptively time-developing energy system’s attributes to enhance the model of the dynamic properties of contemporary Smart Grid (SG) that are impacted by Revised Encoding Scheme (RES) or system reconfiguration to differentiate LSTM changes & real-time threats. More specifically, we provide a federated instructional strategy for consumer sharing of power data to Power Grid (PG) that is supported by edge clouds, protects consumer privacy, and is communication-efficient. They then design two optimization problems for Energy Data Owners (EDO) and energy service operations, as well as a local information assessment method in Federated Learning (FL) by taking non-independent and identically distributed (IID) effects into consideration. The test results revealed that LSTM had a longer training duration, four hidden levels, and higher training loss than other models. The provided method works incredibly well in several situations to identify FDIA. The suggested approach may successfully induce EDOs to employ high-quality local models, increase the payout of the ESP, and decrease task latencies, according to extensive simulations, which are the last points. According to the verification results, every assault sample could be effectively recognized utilizing the current detection methods and the LSTM RNN-based structure created by Smart.
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Farnes, Alyssa, Keith Weber, Cassie Koerner, Kathy Araújo, and Christopher Forsgren. "The Power Grid/Wildfire Nexus: Using GIS and Satellite Remote Sensing to Identify Vulnerabilities." Fire 6, no. 5 (2023): 187. http://dx.doi.org/10.3390/fire6050187.
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The effects of wildfire on the power grid are a recurring concern for utility companies who need reliable information about where to prioritize infrastructure hardening. Though there are existing data layers that provide measures of burn probability, these models predominately consider long-term climate variables, which are not helpful when analyzing current season trends. Utility companies need data that are temporally and locally relevant. To determine the primary drivers of burn probability relative to power grid vulnerability, this study assessed potential wildfire drivers that are both readily accessible and regularly updated. Two study areas in Idaho, USA with contrasting burn probabilities were compared. Wildfire drivers were obtained and differentiated between the study areas across the 2018–2021 growing seasons. This study determined that mean wind speed, cumulative growing season precipitation, and the mean Normalized Difference Vegetation Index (NDVI) for an area of interest may be reliable indicators of burn probability on a temporally relevant scale. This assessment demonstrates a method and variables that may be utilized by municipal electric utilities, electric cooperatives, and other power utilities to determine where to harden power grid infrastructure within wildfire-prone areas.
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Liu, Chun Xia, and Li Qun Liu. "Particle Swarm Optimization MPPT Method for PV Materials in Partial Shading." Advanced Materials Research 321 (August 2011): 72–75. http://dx.doi.org/10.4028/www.scientific.net/amr.321.72.
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Today, the large-scale Photovoltaic (PV) power system, connected to grid, is in their advanced development stage and is extremely interest in whole world. The real large-scale PV array can be partially shaded by the shadow of building, cloud, bird and dirt. The output characteristic of PV materials in partially shaded conditions is strong nonlinear, and there are multi local peaks in output power voltage curve, and the only one real peak exists in these local peaks. Certainly, the maximum power point tracking (MPPT) method is very important to extract the as much as possible energy from the costly PV materials. The variant weight Particle Swarm Optimization (PSO) method is proposed to track the real peak by using the excellent multi-peak value optimization characteristic of PSO algorithm. The simulation results shows that the proposed PSO method can improve the response speed and output efficiency of PV materials in partial shading as compared to the conventional Incremental conductance (IC) method.
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Lis, Robert Andrzej. "Decision Tree Algorithm for Real-Time Identification of Critical Voltage Control Areas." Applied Mechanics and Materials 666 (October 2014): 132–37. http://dx.doi.org/10.4028/www.scientific.net/amm.666.132.
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Voltage stability, is driven by the balance of reactive power in a transmission power grid, and depends on the reactive power produced by the power stations and the value generated by the capacitive power lines and static compensators. Of particular interest is to identify those Critical Voltage Control Areas (CVCAs) in a transmission power grid, that may suffer reactive power deficiencies. Since speed of analysis is critical for on-line applications the approach will address the development of a scheme whereby CVCAs can be identified using data mining techniques from on-line power system snapshot (PMU). The database for storing/retrieving result of Modal Analysis can be used to construct decision trees (DTs) for each of the identified CVCAs using key power system attributes. The objective of this paper is to propose an new real-time methodology for identification of CVCAs, which is key function for preventive and remedial actions against instability of the Electrical Power System (EPS). This is carried out under various system operation and contingency conditions by using off-line trained decision trees generated and on-line PMU measurements. Numerical results on the 12-bus test system, shows the suitability and effectiveness of the proposed method.
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Jalali, Ali, Shannon Hicks-Jalali, Robert J. Sica, Alexander Haefele, and Thomas von Clarmann. "A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals." Atmospheric Measurement Techniques 12, no. 7 (2019): 3943–61. http://dx.doi.org/10.5194/amt-12-3943-2019.
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Abstract. Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their respective a priori values or profiles, which can affect the results in the higher altitudes of the temperature and water vapor profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval's averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by repeating the retrieval on a coarser grid where the retrieval is stable even without the use of formal prior information. The averaging kernels of the fine-grid OEM retrieval are used to optimize the coarse retrieval grid. We demonstrate the adequacy of this method for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.
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Zang, Ning, Yong Tao, Zuoteng Yuan, Chen Yuan, Bailin Jing, and Renfeng Liu. "Rasterized Data Image Processing (RDIP) Techniques for Photovoltaic (PV) Data Cleaning and Application in Power Prediction." Energies 17, no. 12 (2024): 3000. http://dx.doi.org/10.3390/en17123000.
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Photovoltaic (PV) power generation has attracted widespread interest as a clean and sustainable energy source, with increasing global attention given to renewable energy. However, the operation and monitoring of PV power generation systems often result in large amounts of data containing missing values, outliers, and noise, posing challenges for data analysis and application. Therefore, PV data cleaning plays a crucial role in ensuring data quality, enhancing data availability and reliability. This study proposes a PV data cleaning method based on Rasterized Data Image Processing (RDIP) technology, which integrates rasterization and image processing techniques to select optimal contours and extract essential data. To validate the effectiveness of our method, we conducted comparative experiments using three data cleaning methods, including our RDIP algorithm, the Pearson correlation coefficient interpolation method, and cubic spline interpolation method. Subsequently, the cleaned datasets from these methods were utilized for power prediction using two linear regression models and two neural network models. The experimental results demonstrated that data cleaned using the RDIP algorithm improved the short-term forecast accuracy by approximately 1.0% and 3.7%, respectively, compared to the other two methods, indicating the feasibility and effectiveness of the RDIP approach. However, it is worth noting that the RDIP technique has limitations due to its reliance on integer parameters for grid division, potentially leading to coarse grid divisions. Future research efforts could focus on optimizing the selection of binarization thresholds to achieve better cleaning results and exploring other potential applications of RDIP in PV data analysis.
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Muhammad, Farhan Chairahman, and Ghina Astri. "The Significance of Stakeholders in the Rooftop Solar Power Plant Ecosystem Industry in Indonesia." International Journal of Current Science Research and Review 07, no. 02 (2024): 1181–86. https://doi.org/10.5281/zenodo.10663941.
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Abstract : Stakeholder analysis in an ecosystem is important for managers to planning the most efficient strategic steps for their organization. On the other hand, there is lack of research that mapping the significance of actors inside the ecosystem of an industry. This study attempts to utilize the network analysis as a method to examine the significance of stakeholders. We demonstrate the usage and effectiveness of this method in the rooftop solar power plant ecosystem industry in Indonesia. This study used a qualitative research methodology. Data were obtained through in-depth interviews with stakeholders who formed the industry. The result of this study are Indonesia’s rooftop solar power plant ecosystem map and power-interest grid of the stakeholders.
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Ali, Salem Ahmed, Ismail Mohamed Mahmoud, Zedan Honey Ahmed, and Elnaghi Basem Elhady. "Design of a perturb and observe and neural network algorithms-based maximum power point tracking for a grid- connected photovoltaic system." Design of a perturb and observe and neural network algorithms-based maximum power point tracking for a grid- connected photovoltaic system 14, no. 4 (2024): 3674–87. https://doi.org/10.11591/ijece.v14i4.pp3674-3687.
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Integrating photovoltaic systems (PV) into the grid has garnered significant attention due to increasing interest in renewable energy sources. Maximizing the PV systems output power is crucial for improving energy efficiency and reducing operating costs. This paper presents a comparative analysis of two different techniques of maximum power point tracking (MPPT): perturb and observe (P&O) and artificial neural network (ANN) MPPT, focusing on their application in grid-connected PV systems. The paper evaluates their performance under various operating conditions, including changes in irradiance and temperature, that are discussed in three cases. The comparative analysis includes metrics such as voltage regulation and power loss. MATLAB Simulink is utilized to implement P&O and ANN MPPT methods, which include a PV cell connected to an MPPT-controlled boost converter. The simulation demonstrates the power loss of the PV model as well as the voltage regulation in the three cases for the two methods. The results obtained in simulation and implementations show that the ANN method outperforms the P&O in the three cases discussed in terms of power loss, voltage regulation, and efficiency. The results also show that the change in output power from PV is noticeable when compared to changes in radiation, while the change is slight when temperatures change.
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Young, Joseph, David G. Wilson, Wayne Weaver, and Rush D. Robinett. "A Model Predictive Control to Improve Grid Resilience." Energies 18, no. 7 (2025): 1865. https://doi.org/10.3390/en18071865.
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The following article details a model predictive control (MPC) to improve grid resilience when faced with variable generation resources. This topic is of significant interest to utility power systems where distributed intermittent energy sources will increase significantly and be relied on for electric grid ancillary services. Previous work on MPCs has focused on narrowly targeted control applications such as improving electric vehicle (EV) charging infrastructure or reducing the cost of integrating Energy Storage Systems (ESSs) into the grid. In contrast, this article develops a comprehensive treatment of the construction of an MPC tailored to electric grids and then applies it integration of intermittent energy resources. To accomplish this, the following article includes a description of a reduced order model (ROM) of an electric power grid based on a circuit model, an optimization formulation that describes the MPC, a collocation method for solving linear time-dependent differential algebraic equations (DAEs) that result from the ROM, and an overall strategy for iteratively refining the behavior of the MPC. Next, the algorithm is validated using two separate numerical experiments. First, the algorithm is compared to an existing MPC code and the results are verified by a numerically precise simulation. It is shown that this algorithm produces a control comparable to existing algorithms and the behavior of the control carefully respects the bounds specified. Second, the MPC is applied to a small nine bus system that contains a mix of turbine-spinning-machine-based and intermittent generation in order to demonstrate the algorithm’s utility for resource planning and control of intermittent resources. This study demonstrates how the MPC can be tuned to change the behavior of the control, which can then assist with the integration of intermittent resources into the grid. The emphasis throughout the paper is to provide systematic treatment of the topic and produce a novel nonlinear control compatible design framework applicable to electric grids and the control of variable resources. This differs from the more targeted application-based focus in most presentations.
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Giamarelos, Nikolaos, Myron Papadimitrakis, Marios Stogiannos, Elias N. Zois, Nikolaos-Antonios I. Livanos, and Alex Alexandridis. "A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons." Sensors 23, no. 12 (2023): 5436. http://dx.doi.org/10.3390/s23125436.
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The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy.
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Salem, Ahmed Ali, Mohamed Mahmoud Ismail, Honey Ahmed Zedan, and Basem Elhady Elnaghi. "Design of a perturb and observe and neural network algorithms-based maximum power point tracking for a grid-connected photovoltaic system." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 3674. http://dx.doi.org/10.11591/ijece.v14i4.pp3674-3687.
Full textAbstract:
Integrating photovoltaic systems (PV) into the grid has garnered significant attention due to increasing interest in renewable energy sources. Maximizing the PV systems output power is crucial for improving energy efficiency and reducing operating costs. This paper presents a comparative analysis of two different techniques of maximum power point tracking (MPPT): perturb and observe (P&amp;O) and artificial neural network (ANN) MPPT, focusing on their application in grid-connected PV systems. The paper evaluates their performance under various operating conditions, including changes in irradiance and temperature, that are discussed in three cases. The comparative analysis includes metrics such as voltage regulation and powerloss. MATLAB Simulink is utilized to implement P&amp;O and ANN MPPT methods, which include a PV cell connected to an MPPT-controlled boost converter. The simulation demonstrates the power loss of the PV model as well as the voltage regulation in the three cases for the two methods. The results obtained in simulation and implementations show that the ANN method outperforms the P&amp;O in the three cases discussed in terms of powerloss, voltage regulation, and efficiency. The results also show that the change in output power from PV is noticeable when compared to changes in radiation, while the change is slight when temperatures change.
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Han, Haiteng, Hantao Cui, Shan Gao, Qingxin Shi, Anjie Fan, and Chen Wu. "A Remedial Strategic Scheduling Model for Load Serving Entities Considering the Interaction between Grid-Level Energy Storage and Virtual Power Plants." Energies 11, no. 9 (2018): 2420. http://dx.doi.org/10.3390/en11092420.
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More renewable energy resources have been connected to the grid with the promotion of global energy strategies, which presents new opportunities for the current electricity market. However, the growing integration of renewable energy also brings more challenges, such as power system reliability and the participants’ marketable behavior. Thus, how to coordinate integrated renewable resources in the electricity market environment has gained increasing interest. In this paper, a bilevel bidding model for load serving entities (LSEs) considering grid-level energy storage (ES) and virtual power plant (VPP) is established in the day-ahead (DA) market. Then, the model is extended by considering contingencies in the intraday (ID) market. Also, according to the extended bidding model, a remedial strategic rescheduling approach for LSE’s daily profit is proposed. It provides a quantitative assessment of LSE’s loss reduction based on contingency forecasting, which can be applied to the power system dispatch to help LSEs deal with coming contingencies. Simulation results verify the correctness and effectiveness of the proposed method.
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Medyakov, Andrey A., Denis M. Lastochkin, and Aleksey P. Ostashenkov. "Improving a no-failure operation of a PV systems with grid inverters for agricultural regions." E3S Web of Conferences 390 (2023): 06006. http://dx.doi.org/10.1051/e3sconf/202339006006.
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The growing interest to the use of power generating complexes based on renewable energy sources has been witnessed recently. It necessitates the need to improve the no-failure operation of such complexes. The highest failure rate is typical for the string inverter in grid-connected PV systems. The failure of the string inverter induces supply termination of electrical energy to the grid. The paper explores an approach to improve the no-failure operation of the process of electrical energy distribution by a PV system to the grid driven by the use of micro inverters. The authors used the logical-probabilistic method to assess the no-failure operation of the PV system. Thus, the authors obtained the expressions to calculate the probability of failure for PV systems, enclosing a string inverter, and micro inverters.
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Pokushko, M., A. Stupina, I. Medina-Bulo, et al. "Evaluating the Efficiency of Heat and Power Systems by the Data Envelopment Analysis Method." WSEAS TRANSACTIONS ON POWER SYSTEMS 16 (September 2, 2021): 185–94. http://dx.doi.org/10.37394/232016.2021.16.19.
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The article describes the Data Envelopment Analysis (DEA) method and the main features of its application. The main problems of heat and power systems are described, which are addressed by the DEA method of efficiency assessment presented in the article. The approbation of this method is presented at the objects of the centralized municipal heat supply system of the fuel and energy complex: boiler houses and heat and power plants. 9 objects were analyzed according to four input indicators: available heat capacity, installed heat capacity, heat consumption for own needs, fuel consumption. Also, the efficiency of the system was evaluated according to two output indicators: the release of thermal energy to the grid and the mass of the emission. As a result of the analysis and calculations made, it was revealed that 5 objects have the maximum possible efficiency indicator equal to 1, that is, they function as efficiently as possible. 4 objects of the centralized municipal heat supply system have an efficiency indicator less than 1. Accordingly, improvements are required for the operation of the above Decision-Making Units (DMU)s. These objects have deviations in terms of the inputs and outputs of the actual data and those obtained using the DEA method. Based on the calculations obtained for these 4 objects, the article provides recommendations for changing the quantitative values of their input and output indicators. For example, for object number 2, it is recommended to reduce the installed heat capacity in the grid by 72.57%, without changing the available heat capacity and fuel consumption. Reduce the heat consumption for your own needs by 69.383%. In addition, it is recommended to increase the supply of thermal energy to the grid by 6,034%, and reduce the mass of emission by 11.5%. Specific measures have also been developed to modernize the studied objects in order to achieve the recommended indicators of inputs and outputs. The research results presented in the article are of scientific and practical interest and can be used to improve the efficiency of heat and power systems facilities
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Kararoui, Imane El, and Mohamed Maaroufi. "Fuzzy sliding mode power control for wind power generation systems connected to the grid." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 1 (2022): 606. http://dx.doi.org/10.11591/ijpeds.v13.i1.pp606-619.
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In recent years we have witnessed a real increase in the production of wind turbines and wind farm installations around the world, in order to improve this own energy, several studies have focused on the interest of controlling the active and reactive power of the system. Wind power, and at the same time on the quality of the energy produced and its connection in order to ingest suitable electrical energy into the distribution network. This article studies a new control technology to meet the various constraints in the field. The objective for work is to develop and study the sliding mode control method applied to a wind power system based on doubly fed induction generation (DFIG), as well as an optimization using the fuzzy logic technique. Ensuring the stability of the system is one of the objectives of using the Lyapunov nonlinear technique in the sliding mode control strategy which will be applied to the two converters (machine side and network side). The proposed solution was to validate a simulation on MATLAB/Simulink, tracking test (true wind speed) and also the robustness of the system.
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Imane, El Karaoui, Maaroufi Mohamed, and Bossoufi Badre. "Fuzzy sliding mode power control for wind power generation systems connected to the grid." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 1 (2022): 606–19. https://doi.org/10.11591/ijpeds.v13.i1.pp606-619.
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In recent years we have witnessed a real increase in the production of wind turbines and wind farm installations around the world, in order to improve this own energy, several studies have focused on the interest of controlling the active and reactive power of the system. Wind power, and at the same time on the quality of the energy produced and its connection in order to ingest suitable electrical energy into the distribution network. This article studies a new control technology to meet the various constraints in the field. The objective for work is to develop and study the sliding mode control method applied to a wind power system based on doubly fed induction generation (DFIG), as well as an optimization using the fuzzy logic technique. Ensuring the stability of the system is one of the objectives of using the Lyapunov nonlinear technique in the sliding mode control strategy which will be applied to the two converters (machine side and network side). The proposed solution was to validate a simulation on MATLAB/Simulink, tracking test (true wind speed) and also the robustness of the system.
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Lee, Jinyeong, Kyungcheol Shin, and Young-Min Wi. "Decentralized Operations of Industrial Complex Microgrids Considering Corporate Power Purchase Agreements for Renewable Energy 100% Initiatives in South Korea." Sustainability 16, no. 13 (2024): 5440. http://dx.doi.org/10.3390/su16135440.
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With the rise of environmental policies and advanced technologies, power systems are transitioning from centralized to decentralized systems, incorporating more distributed energy resources (DERs). This shift has increased interest in the operational functions of microgrids (MGs). The “Renewable Energy 100%” (RE100) campaign is pushing companies to adopt renewable energy. In South Korea, industrial complex microgrids (ICMGs) aim to achieve RE100 through corporate power purchase agreements (PPAs) with renewable energy providers. ICMGs need to operate in both grid-connected and islanded modes, facing challenges in power transactions due to different operating agents. This study proposes a decentralized optimal power flow (OPF) method using the separable augmented Lagrangian relaxation (SALR) algorithm to solve these power transaction problems without disclosing internal information. The proposed method decomposes the centralized OPF problem into subproblems for each ICMG and solves them in a distributed manner, sharing only transaction prices and amounts. Numerical results from the case study validate the effectiveness of the proposed method.
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Mehrdad, Moradi, and maghouli Pouria. "Wide Area Oscillation Damping using Utility-Scale PV Power Plants Capabilities." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 2 (2017): 681–91. https://doi.org/10.11591/ijece.v7i2.pp681-691.
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With increasing implementation of Wide Area Measurement Systems (WAMS) in power grids, application of wide area damping controllers (WADCs) to damp power system oscillations is of interest. On the other hand, it is well known that rapidly increasing integration of renewable energy sources into the grid can dangerously reduce the inertia of the system and degrade the stability of power systems. This paper aimed to design a novel WADC for a utility-scale PV solar farm to damp out inter area oscillations while the main focus of the work is to eliminate the impact of communication delays of wide-area signals from the WAMS. Moreover, the PV farm impact on inter area oscillation mitigation is investigated in various case studies, namely, with WADC on the active power control loop and with WADC on the reactive power control loop. The Quantum Particle Swarm Optimization (QPSO) technique is applied to normalize and optimize the parameters of WADC for inter-area oscillations damping and continuous compensation of time-varying latencies. The proposed method is prosperously applied in a 16-bus six-machine test system and various case studies are conducted to demonstrate the potential of the proposed structure.
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Serrano, Javier, Javier Moriano, Mario Rizo, and Francisco Dongil. "Enhanced Current Reference Calculation to Avoid Harmonic Active Power Oscillations." Energies 12, no. 21 (2019): 4075. http://dx.doi.org/10.3390/en12214075.
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Energy storage systems play a key role in the rise of distributed power generation systems, hence there is great interest in extending their lifetimes, which are directly related to DC current ripple. One of the ripple sources is the low-frequency active power fluctuations under unbalanced and distorted grid voltage conditions. Therefore, this paper addresses a multifrequency control strategy where the harmonic reference currents are calculated to reduce harmonic active power oscillations. The stationary reference frame (StRF) approach taken here improves the precision and computational time of the current reference calculation method. Additionally, in order to ensure safe converter operation when a multifrequency reference current is provided, a computational efficient peak current saturator is applied while avoiding signal distortion every time step. If the injected current harmonic distortion is to be minimized, which is a feature included in this work, the peak current saturator is a necessary requirement. Active power ripple is reduced even with frequency variations in the grid voltage using a well-known frequency-adaptive scheme. The simulation and experimental results prove the optimized performance for the control objective: power ripple reduction with minimum current harmonic distortion.
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Partanen, Tero M., and Mikhail Sofiev. "Forecasting the regional fire radiative power for regularly ignited vegetation fires." Natural Hazards and Earth System Sciences 22, no. 4 (2022): 1335–46. http://dx.doi.org/10.5194/nhess-22-1335-2022.
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Abstract. This paper presents a phenomenological framework for forecasting the area-integrated fire radiative power from wildfires. In the method, a region of interest is covered with a regular grid, whose cells are uniquely and independently parameterized with regard to the fire intensity according to (i) the fire incidence history, (ii) the retrospective meteorological information, and (iii) remotely sensed high-temporal-resolution fire radiative power taken together with (iv) consistent cloud mask data. The parameterization is realized by fitting the predetermined functions for diurnal and annual profiles of fire radiative power to the remote-sensing observations. After the parametrization, the input for the fire radiative power forecast is the meteorological data alone, i.e. the weather forecast. The method is tested retrospectively for south-central African savannah areas with the grid cell size of 1.5∘×1.5∘. The input data included ECMWF ERA5 meteorological reanalysis and SEVIRI/MSG (Spinning Enhanced Visible and Infra-Red Imager on board Meteosat Second Generation) fire radiative power and cloud mask data. It has been found that in the areas with a large number of wildfires regularly ignited on a daily basis during dry seasons from year to year, the temporal fire radiative power evolution is quite predictable, whereas the areas with irregular fire behaviour, predictability was low. The predictive power of the method is demonstrated by comparing the predicted fire radiative power patterns and fire radiative energy values against the corresponding remote-sensing observations. The current method showed good skills for the considered African regions and was useful in understanding the challenges in predicting the wildfires in a more general case.
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Lee, Donghyeon, Seungwan Son, and Insu Kim. "Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization." Energies 14, no. 11 (2021): 3112. http://dx.doi.org/10.3390/en14113112.
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Widespread interest in environmental issues is growing. Many studies have examined the effect of distributed generation (DG) from renewable energy resources on the electric power grid. For example, various studies efficiently connect growing DG to the current electric power grid. Accordingly, the objective of this study is to present an algorithm that determines DG location and capacity. For this purpose, this study combines particle swarm optimization (PSO) and the Volt/Var control (VVC) of DG while regulating the voltage magnitude within the allowable variation (e.g., ±5%). For practical optimization, the PSO algorithm is enhanced by applying load profile data (e.g., 24-h data). The objective function (OF) in the proposed PSO method considers voltage variations, line losses, and economic aspects of deploying large-capacity DG (e.g., installation costs) to transmission networks. The case studies validate the proposed method (i.e., optimal allocation of DG with the capability of VVC with PSO) by applying the proposed OF to the PSO that finds the optimal DG capacity and location in various scenarios (e.g., the IEEE 14- and 30-bus test feeders). This study then uses VVC to compare the voltage profile, loss, and installation cost improved by DG to a grid without DG.
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Wu, Juntao, Junjie Hu, Songsong Chen, Wentao Xu, Bihong Tang, and Xuan Wen. "Demand-side adjustable resources unified data model study." Journal of Computational Methods in Sciences and Engineering 23, no. 5 (2023): 2275–89. http://dx.doi.org/10.3233/jcm-226944.
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With the large-scale grid integration of new energy sources, the power relationship between source and demand side is becoming more and more complex, the flexibility requirements of the distribution network are increasing, and new requirements for system safety and reliability are put forward, and it can’t meet the peak regulating requirements of grid by relying solely on source side to cope with variable loads. In view of this, to make the grid operation flexibility improved by using an adjustable load capacity, the study first constructs a load classifying method on the foundation of fuzzy style K-plane clustering method to understand the interaction information of the load devices. Then the adjustable value of the load is analyzed from the demand response and standby perspectives, respectively, and an improved dynamic time-bending-based source-load similarity inscription method is proposed, which aims to unify the multiple load information obtained by clustering. The proposed clustering algorithm takes the highest value of 0.096 for the Davies-Bouldin index index, which is 0.012 and 0.014 higher than the K-plane clustering algorithm and the K-means algorithm, respectively. In addition, the load demand response regulation with the improved dynamic time bending method has a higher capacity for new energy consumption than the variance method, with a difference of 2.0 kW. This indicates that using load regulation to consume new energy to exploit the load curve will stimulate the active participation of customer-side load in maintaining power balance between the electricity consumption side and the demand side, and form an interest community between power supplying and demanding sides.
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Li, Du, Rose Zhou, and Rob Zanoya. "Cross-Sectional Transmission Electron Microscopy Sample Preparation Using Focus Ion Beam Machine and Wedge Technique." Microscopy and Microanalysis 5, S2 (1999): 894–95. http://dx.doi.org/10.1017/s1431927600017797.
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As features on an IC chip become smaller than the resolution power of an optical microscope and of the size of the grinding particles, the trend for preparing cross-sectional transmission electron microscopy (TEM) samples at specific locations (bits) is moving towards using a focused ion beam (FIB) machine. Details on how to use a FIB machine to prepare cross-sectional TEM samples have been outlined in many references.The general procedure is to first mark the specific location (bit) in the FIB machine and then grind the sample down to about 20 microns, 10 microns on each side of the feature of interest. After grinding, the sample is mounted on a pre-cut TEM grid and thinned with the FIB to about 0.1 micron in the region containing the feature of interest. There are several disadvantages to this method. First, the sample goes into the FIB machine at least twice—once for FIB marks on the location and once again for the final thinning.
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Gauglitz, Philip, David Geiger, Jan Ulffers, and Evamaria Zauner. "Modeling public charging infrastructure considering points of interest and parking potentials." Advances in Geosciences 56 (September 8, 2021): 1–12. http://dx.doi.org/10.5194/adgeo-56-1-2021.
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Abstract. Considering climate change, it is essential to reduce CO2 emissions. The provision of charging infrastructure in public spaces for electromobility – along with the substitution of conventional power generation by renewable energies – can contribute to the energy transition in the transport sector. Scenarios for the spatial distribution of this charging infrastructure can help to exemplify the need for charging points and their impact, for example on power grids. We model two kinds of demand for public charging infrastructure. First, we model the demand for public charging points to compensate for the lack of home charging points, which is derived from a previously developed and published model addressing electric-vehicle ownership (with and without home charging options) in households. Second, and in the focus of the work presented here, is the demand for public charging infrastructure at points of interest (POIs). Their locations are derived from OpenStreetMap (OSM) data and weighted based on an evaluation of movement profiles from the Mobilität in Deutschland survey (MiD, German for “Mobility in Germany”). We combine those two demands with the available parking spaces and generate distributions for possible future charging points. We use a raster-based approach in which all vector data are rasterized and computations are performed on a municipality's full grid. The presented application area is Wiesbaden, and the methodology is generally applicable to municipalities in Germany. The model is compared with three other models or model variants in a correlation comparison in order to determine the influence of certain model assumptions and input data. The identification of potential charging points in public spaces plays an important role in modeling the future energy system – especially the power grid – as the rapid adoption of electric vehicles will shift locations of electrical demand. With our investigation, we would like to present a new method to simulate future public charging point locations and show the influences of different modeling methods.
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Salam, S. M., N. Mohammad, and I. A. Chowdhury. "Impact Assessment of an Intensive Base Demand Side Management Program for Telecommunication Load with Energy Storage Device in a Test Grid System Based on Bangladesh Perspective." Journal of Advanced Engineering and Computation 7, no. 1 (2023): 21. http://dx.doi.org/10.55579/jaec.202371.396.
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Power consumption by telecommunication industrial loads is increasing day by day as the user of this technology is on the rise. Telecommunication base station towers are consuming twice or more energy than in the past for the implementation of high-capacity devices to serve more users. As a result, there is an extra power requirement for the telecommunication loads which can cause an inadequate power supply and lead to the implementation of additional infrastructure in the power industry. Powering these resources will demand more energy production and introduce various types of new problems in the grid network. The impact analysis of the effect of this extra demand in a regular network system has great interest. Also, most of the base stations are equipped with a backup battery as an essential need in third-world country grids and contribute a portion of the load demand of a power distribution system. All telecommunication industrial towers are considered under industrial load and have a special industrial tariff imposed by the power supply authority. This paper utilizes the optimal power flow method to calculate a proposed schedule base demand-side management system adopted to shift the pattern of charging batteries along with temperature control loads in the telecommunication towers and outlines an analytical study on a test power grid network. To determine the best electricity flow, generation, and locational marginal prices for each hour, an algorithm is created. Following careful evaluation of the appliance status, the constraint and condition are then applied to the load curve. This study indicates there is energy-saving and both supplier and consumer sides can minimize the operation cost.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
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Zhang, Lishuo, Zhuxing Ma, Hao Gu, Zizhong Xin, and Pengcheng Han. "Condition Monitoring and Analysis Method of Smart Substation Equipment Based on Deep Learning in Power Internet of Things." International Journal of Information Technologies and Systems Approach 16, no. 3 (2023): 1–16. http://dx.doi.org/10.4018/ijitsa.324519.
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An accurate perception of the state of smart substation equipment is a strong guarantee for the reliable operation of the large power grid. This article proposes using deep learning for the device condition monitoring and analysis method in a power internet of things cloud edge collaboration mode. The speeded up robust features (SURF) feature detector is used at the edge of the network to accurately collect the interest points from the image data set, providing a reliable and complete sample data set support for the cloud-based deep learning network. Adding the attention mechanism module to the cloud improves the Yolov5 network model, enhance feature extraction, and increase the monitoring and analysis capabilities of the equipment. The simulation results show that the proposed method has achieved a recall rate of 91.21% and an accuracy rate of 90.54% for insulator fault evaluation indicators.
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Al Azze, Qasim, Balasim М. Hussein, and Hayder Salim Hameed. "Intensifications reactive power during of asymmetric network outages in dual-stator winding generators." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 4 (2021): 2451. http://dx.doi.org/10.11591/ijpeds.v12.i4.pp2451-2458.
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<span lang="EN-US">The paper proposes a protection to dual stator generator, reluctance rotor, from asymmetrical fault. Which prevents the dual stator generator, reluctance rotor, from electrical sage through working process in order to avoid any interruption in the generator-grid connection. The procedure consummated with injecting suitable reactive power during the fault period. The proposed method that makes it possible for wind turbine application via dual stator winding generators (DSWRG) synchronous mod to stay connected to the grid during asymmetrical faults. It has been built according to trusted simulating mode considering all tested parameters according to experiment work. The expirment, consider the DC link side stability and care about the behavior and performance of machine side parameter. As well the machineability is evaluated to ride through asymmetrical fault by observing the secondary side current which has a big role in saving grid side converter. The control takes a response within 200 ms after fault trigger recognition. The generator ability of dynamically remaining connected stable and existing in the network, which is sustained a series voltage disturbance by injecting appropriate amount of reactive power. The main interest required in this paper is the capability of a machine to overcome the asymmetrical fault.</span>
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Moradi, Mehrdad, and Pouria Maghouli. "Wide Area Oscillation Damping using Utility-Scale PV Power Plants Capabilities." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 2 (2017): 681. http://dx.doi.org/10.11591/ijece.v7i2.pp681-691.
Full textAbstract:
<span>With increasing implementation of Wide Area Measurement Systems (WAMS) in power grids, application of wide area damping controllers (WADCs) to damp power system oscillations is of interest. On the other hand it is well known that rapidly increasing integration of renewable energy sources into the grid can dangerously reduce the inertia of the system and degrade the stability of power systems. This paper aimed to design a novel WADC for a utility-scale PV solar farm to damp out inter area oscillations while the main focus of the work is to eliminate the impact of communication delays of wide-area signals from the WAMS. Moreover the PV farm impact on inter area oscillation mitigation is investigated in various case studies, namely, with WADC on the active power control loop and with WADC on the reactive power control loop. The Quantum Particle Swarm Optimization (QPSO) technique is applied to normalize and optimize the parameters of WADC for inter-area oscillations damping and continuous compensation of time-varying latencies. The proposed method is prosperously applied in a 16-bus six-machine test system and various case studies are conducted to demonstrate the potential of the proposed structure.</span>
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Dong, Fugui, Xiaohui Ding, and Lei Shi. "Wind Power Pricing Game Strategy under the China’s Market Trading Mechanism." Energies 12, no. 18 (2019): 3456. http://dx.doi.org/10.3390/en12183456.
Full textAbstract:
Wind power has become the main power generation method in China’s clean energy power generation because of its clean and high efficiency, as well as its high power utilization rate. The research on its pricing mechanism has also become the main research focus of the wind power industry. However, wind power pricing is still at the stage of price benchmarking and no market mechanism has been introduced in China. There are still much research on the pricing mechanism of wind power for us to study. In this paper, the Kernel method is used to distribute wind power income. On the basis of the distribution result, considering the contract execution risk of wind power, cooperative game theory and the Shapley value method are used to redistribute the revenue of wind power connected to power grid. Based on the characteristics of alliance members, ANP (Analytic Network Process) was used to modify the apportioned benefits to obtain the benefit distribution method that was more in line with the interest demands of members, and an example was analyzed. The wind power pricing model based on the cooperative game established in this paper can guarantee the smooth operation of the alliance, reach the pareto optimum, and improve the activity of the wind power market. It will effectively shorten the negotiation time, and reduce the transaction cost and the uncertainty of the wind power transaction.
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Munir, Hafiz, Jianxiao Zou, Chuan Xie, and Josep Guerrero. "Cooperation of Voltage Controlled Active Power Filter with Grid-Connected DGs in Microgrid." Sustainability 11, no. 1 (2018): 154. http://dx.doi.org/10.3390/su11010154.
Full textAbstract:
Due to the excessive use of nonlinear loads and inverter interfaced distributed generators, harmonic issues have been regarded as a major concern in power distribution systems. Therefore, harmonic compensation in microgrids is a subject of current interest. Consequently, a novel direct harmonic voltage-controlled mode (VCM) active power filter (APF) is proposed to mitigate the harmonics in a cooperative manner and provide a better harmonic compensation performance of less than 5%. Due to the dispersive characteristics of renewable energy resources, voltage feedback based on a harmonic compensation control loop is implemented for the first time. This system can be smoothly combined with the current control loop. Our method proposes a better performance while mitigating the harmonics in comparison with conventional resistive active power filters (R-APF). Based on direct voltage detection at the point of common coupling (PCC), the proposed VCM-APF can therefore be seamlessly incorporated with multiple grid-connected generators (DGs) to enhance their harmonic compensation capabilities. The advantage of this scheme is that it avoids the need for designing and tuning the resistance, which was required in earlier conventional control schemes of R-APF for voltage unbalance compensation. Additionally, our scheme does not require the grid and load current measurements since these can be carried out at the PCC voltage, which further reduces the implementation cost of the system. Furthermore, the simulation results show the significance of proposed method.