scholarly journals Methodology for Implementing the State Estimation in Renewable Energy Management Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2301
Author(s):  
Yun-Sung Cho ◽  
Yun-Hyuk Choi
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
State Estimation ◽  
Measurement Errors ◽  
Large Scale ◽  
Test System ◽  
The State ◽  
Inner Function ◽  
Outer Function ◽  
Bad Data

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-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.

scholarly journals A Hybrid Approach to Short-Term Load Forecasting Aimed at Bad Data Detection in Secondary Substation Monitoring Equipment

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3947 ◽  
Author(s):  
Pedro Martín ◽  
Guillermo Moreno ◽  
Francisco Javier Rodríguez ◽  
José Antonio Jiménez ◽  
Ignacio Fernández
Keyword(s):  
State Estimation ◽  
Measurement Errors ◽  
Load Forecasting ◽  
The State ◽  
Short Term ◽  
Monitoring Equipment ◽  
Hybrid Strategy ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Bad Data

Bad data as a result of measurement errors in secondary substation (SS) monitoring equipment is difficult to detect and negatively affects power system state estimation performance by both increasing the computational burden and jeopardizing the state estimation accuracy. In this paper a short-term load forecasting (STLF) hybrid strategy based on singular spectrum analysis (SSA) in combination with artificial neural networks (ANN), is presented. This STLF approach is aimed at detecting, identifying and eliminating and/or correcting such bad data before it is provided to the state estimator. This approach is developed to improve the accuracy of the load forecasts and it is tested against real power load data provided by electricity suppliers. Depending on the week considered, mean absolute percentage error (MAPE) values which range from 1.6% to 3.4% are achieved for STLF. Different systematic errors, such as gain and offset error levels and outliers, are successfully detected with a hit rate of 98%, and the corresponding measurements are corrected before they are sent to the control center for state estimation purposes.

Research of Robust State Estimation Method and Program Implementation Considering Large-Scale Wind Power Integration

2014 ◽  
Vol 672-674 ◽  
pp. 361-366
Author(s):  
Ya Di Luo ◽  
Jing Li ◽  
Zi Ming Guo ◽  
Gui Rong Shi ◽  
Dong Sheng Wang ◽  
...  
Keyword(s):  
State Estimation ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Estimation Method ◽  
Wind Farms ◽  
The State ◽  
Wind Power Integration ◽  
The Impact ◽  
Bad Data

According to the characteristics of the wind farm measuration and the impact of bad data on the state estimation, this paper introduces the reference value of measurement type and the bad data reference factor into the weight function, and then presents the calculation method of state estimation method for solving residual contamination problem caused by large-scale wind power integration. In order to improve the software computing speed and the data section real-time performance of robust state estimation, using parallel algorithms to do Givens transformation. Finally, the simulation tests of a regional power grid to prove that the proposed method can effectively identify telemetry bad data of wind farms eliminate residual pollution caused by it, which improve the speed and accuracy of the State Estimation.

scholarly journals The Flexible Operation of Coal Power and Its Renewable Integration Potential in China

2019 ◽  
Vol 11 (16) ◽  
pp. 4424 ◽  
Author(s):  
Chunning Na ◽  
Huan Pan ◽  
Yuhong Zhu ◽  
Jiahai Yuan ◽  
Lixia Ding ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Power System ◽  
Large Scale ◽  
Renewable Integration ◽  
Flexible Resources ◽  
Feasible Option ◽  
Coal Power ◽  
Simulation Results ◽  
Flexible Operation

At present time, China’s power systems face significant challenges in integrating large-scale renewable energy and reducing the curtailed renewable energy. In order to avoid the curtailment of renewable energy, the power systems need significant flexibility requirements in China. In regions where coal is still heavily relied upon for generating electricity, the flexible operations of coal power units will be the most feasible option to face these challenges. The study first focused on the reasons why the flexible operation of existing coal power units would potentially promote the integration of renewable energy in China and then reviewed the impacts on the performance levels of the units. A simple flexibility operation model was constructed to estimate the integration potential with the existing coal power units under several different scenarios. This study’s simulation results revealed that the existing retrofitted coal power units could provide flexibility in the promotion of the integration of renewable energy in a certain extent. However, the integration potential increment of 20% of the rated power for the coal power units was found to be lower than that of 30% of the rated power. Therefore, by considering the performance impacts of the coal power units with low performances in load operations, it was considered to not be economical for those units to operate at lower than 30% of the rated power. It was believed that once the capacity share of the renewable energy had achieved a continuously growing trend, the existing coal power units would fail to meet the flexibility requirements. Therefore, it was recommended in this study that other flexible resources should be deployed in the power systems for the purpose of reducing the curtailment of renewable energy. Furthermore, based on this study’s obtained evidence, in order to realize a power system with high proportions of renewable energy, China should strive to establish a power system with adequate flexible resources in the future.

GPU-accelerated state estimation for large-scale power systems

2021 ◽  
Author(s):  
Meng Fu ◽  
Yang Li ◽  
Jimin Hua ◽  
Yanjun Feng ◽  
Yifan Zheng
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Large Scale

Computational development of a practical educational tool for state estimation of power systems using the MATLAB optimization toolbox

2018 ◽  
Vol 56 (2) ◽  
pp. 105-123 ◽  
Author(s):  
EA Zamora-Cárdenas ◽  
A Pizano-Martínez ◽  
JM Lozano-García ◽  
VJ Gutiérrez-Martínez ◽  
R Cisneros-Magaña
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Electric Power ◽  
Smart Grids ◽  
Weighted Least Squares ◽  
Electric Power Systems ◽  
The State ◽  
Estimation Problem ◽  
Educational Tool ◽  
And Control

State estimation is one of the most important processes to perform a reliable monitoring and control of the steady-state operating condition of modern electric power systems; thus, it is currently a fundamental part in the development of research to enhance the monitoring and security of the smart grids operation. This important topic is taught in advanced courses of operation and control of power systems, for graduate and undergraduate power engineering students. However, the most used software packages for simulation and analysis of power systems by researchers, students, and educators have put little attention on the state estimation module. Due to this fact, this paper proposes an approach to develop the computational implementation of a practical educational tool for state estimation of electric power systems using the MATLAB optimization toolbox. In this proposal, the formulation of the state estimation problem consists of developing a general digital code to implement an objective function based on the weighted least squares method. While the lsqnonlin function of the MATLAB optimization toolbox solves the formulated state estimation problem. Simplifying both research and educational processes, this tool helps graduate and undergraduate students to improve learning, understanding, and the times of implementation and development of research in state estimation. Simulations of an equivalent model of the Mexican interconnected power system consisting of 190 buses and 46 machines are used to test and validate the proposal performance.

scholarly journals Distributed State Estimation of Multi-region Power System based on Consensus Theory

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 900 ◽  
Author(s):  
Shiwei Xia ◽  
Qian Zhang ◽  
Jiangping Jing ◽  
Zhaohao Ding ◽  
Jing Yu ◽  
...  
Keyword(s):  
Power Systems ◽  
Power System ◽  
State Estimation ◽  
Solution Method ◽  
The State ◽  
Rapid Expansion ◽  
Estimation Model ◽  
Consensus Theory ◽  
Distributed State Estimation ◽  
Bus System

Effective state estimation is critical to the security operation of power systems. With the rapid expansion of interconnected power grids, there are limitations of conventional centralized state estimation methods in terms of heavy and unbalanced communication and computation burdens for the control center. To address these limitations, this paper presents a multi-area state estimation model and afterwards proposes a consensus theory based distributed state estimation solution method. Firstly, considering the nonlinearity of state estimation, the original power system is divided into several non-overlapped subsystems. Correspondingly, the Lagrange multiplier method is adopted to decouple the state estimation equations into a multi-area state estimation model. Secondly, a fully distributed state estimation method based on the consensus algorithm is designed to solve the proposed model. The solution method does not need a centralized coordination system operator, but only requires a simple communication network for exchanging the limited data of boundary state variables and consensus variables among adjacent regions, thus it is quite flexible in terms of communication and computation for state estimation. In the end, the proposed method is tested by the IEEE 14-bus system and the IEEE 118-bus system, and the simulation results verify that the proposed multi-area state estimation model and the distributed solution method are effective for the state estimation of multi-area interconnected power systems.

Sign in / Sign up

Export Citation Format

Share Document