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![Figure 5. Connectivity model of three-winding transformer [19].](profile/Yunhyuk-Choi/publication/350987613/figure/fig1/AS:1022285880238083@1620743355529/Connectivity-model-of-three-winding-transformer-19_Q320.jpg)
This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoup...
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... has recently announced the 9th Basic Plan of Long-Term Electricity Supply and Demand, which aims at having 78.1 GW of renewable energy by 2034 [2]. As shown in Table 1, the capacity of coal should decrease from 34.7 GW in 2020 to 29.0 GW in 2034. For the 30 abolished coals, 24 will be converted to liquefied natural gas (LNG). ...Context 2
... Models 1 and 2: The renewable energy generation was calculated using the acquired data because the losses in the transformers were very small. More detailed data are shown in Table A1 [17,18]. ...Context 3
... Models 1 and 2: The renewable energy generation was calculated using the acquired data because the losses in the transformers were very small. More detailed data are shown in Table A1 [17,18]. ...Context 4
... simulation condition of this case is identical to scenario B, except for the location of the wrong data. Table 10 shows the estimated values of the tap positions and the voltage. I † represents the wrong input data of the tap position estimation. ...Context 5
... † represents the wrong input data of the tap position estimation. Table 11 shows the normalized residuals and sensitivity of the bad data detection and replacement for the four cases. From the results, the true values of the power system can be accurately calculated from the wrong data using the proposed process. ...Context 6
... dynamic test in this study was focused on finding differences between the REMS and the measurements in KEPS. Table 12 shows various scenarios for validating the state estimation performance in KEPS. The test system was based on the KEPS in 2016. ...Context 7
... two HVDC systems in the KEPS transmit relatively cheap electric power from the mainland to the Jeju. As shown in Table 13, the penetration of renewable energy used in this paper is approximately 6.6%, which consists of generator with renewable energy of 251. In this scenario, the state estimation was checked to ensure that it meets the originally specified functions, which calculate the bus voltage, branch flow, and generation based on the standard deviation of the measurements. ...Context 8
... state estimation adopting the proposed algorithm in this study converged after nine iterations. The voltage convergence tolerance was 0.005 p.u. Table 14 shows a summary of the state estimation, including the number of bad data processing instances, the value of the largest bus mismatch, and the suspect ratio. Because of the suspect measurements of the HVDC system, the number of iterations was creased. ...Context 9
... the case of >20 MW and 20 MVAR, the number of bus mismatches of the MW and MVAR was 0 and 5, respectively. Table 15 shows the MW and MVAR summary of the generation and load for the state estimation results and measurements. The MW generation between the estimation results and measurements had a small difference because of the different weighting factors, which represent the reciprocal of the standard deviation. ...Context 10
... particular, a thyristor controlled series capacitor (TCSC) is installed and a special protection system (SPS) is operated in preparation for an accident on a 765 kV line. Table 16 shows the performance requirements of the state estimation of ERCOT for the convergence, branch flow, and voltage. Table 17 shows the performance requirements of the state estimation of CASIO [27]. ...Context 11
... 16 shows the performance requirements of the state estimation of ERCOT for the convergence, branch flow, and voltage. Table 17 shows the performance requirements of the state estimation of CASIO [27]. [26]. ...Context 12
... summation of the negative load shall be <100 MW or the ratio between the negative load and the system load shall be <2%. The number of the negative generation units shall be <50, and the summation of the negative generation shall be <50 MW. Table 18 shows the performance of the state estimation adopting the proposed algorithm based on the reference of ERCOT. The maximum differences for the branch flow, critical branch flow, and voltage were 2.17%, 0.4%, and 0.92%, respectively. ...Context 13
... maximum differences for the branch flow, critical branch flow, and voltage were 2.17%, 0.4%, and 0.92%, respectively. As shown in Table 18, although the comparative study was performed with one dataset, the proposed algorithm agrees well with the reference of ERCOT. In order to analyze the telemetry accuracy effect on the proposed algorithm, the state estimation was simulated for different meter suspects ranging from 3% to 10%. ...Context 14
... scenario was assigned as the suspect data from 3% to 10% of 4320. Table 19 shows the average values of the estimation results and measurements for the branch flow and generation, respectively. With the increase in the range of suspects, the average difference between the estimation data and the measurements increased, as shown in Figures 14 and 15. ...Similar publications
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Background/Purpose
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Citations
... The volatility and intermittency of RERs can hinder the stability of the power grid, necessitating a system that monitors and manages power generation and system status in real time. Recently, Renewable Energy Resource Management Systems (RERMSs) have gained attention for their ability to use information and communication technology to manage RERs [6][7][8]. RERMSs collect power generation and environmental data from RERs, visualize and display critical information, record and manage data, trigger alarms when faults occur, and analyze sensor measurements to locate and report faults. ...
Recently, solar and wind power generation have gained attention as pathways to achieving carbon neutrality, and Renewable Energy Resource Management System (RERMS) technology has been developed to monitor and control small-scale, distributed renewable energy resources. In this work, we present an Event-Triggered Transmission (ETT) algorithm for RERMS, which transmits sensor measurements to the base station only when necessary. The ETT algorithm helps prevent congestion in the communication channel between RERMS and the base station, avoiding time delays or packet loss caused by the excessive transmission of sensor measurements. We design a hybrid state estimation algorithm that combines Kalman and Finite Impulse Response (FIR) filters to enhance the estimation performance, and we propose a new ETT algorithm based on this design. We evaluate the performance of the proposed algorithm through experiments that transmit actual sensor measurements from a photovoltaic power generation system to the base station, demonstrating that it outperforms existing algorithms.
Energy is crucial to economic development, but its production usually has a negative impact on the environment. This ambivalence leads to the need for methods to improve energy efficiency. Transportation is one of the largest global energy consumers. Therefore, improving the energy efficiency of transportation is crucial for sustainable development. The aim of this article is to show the limitations of energy management in railways, resulting from the model of market regulation. The question in this context is whether only technological methods can be used in railways to steer its energy efficiency, as is suggested by the existing research. Critical analysis, desk research and a case study of Polish railway undertaking were used to find an answer to the research question. The discussion of the results shows that the European regulatory system leads to greater complications in the field of energy management than in other global regions, where railways are also important for the economy. Due to these limitations, rail operators use indirect methods to measure energy efficiency. Results indicate that although energy efficiency improvements are being achieved, they are mainly due to organizational measures and not technological ones as could be expected based on previous research.
