scholarly journals Publisher's Note: “Review of adaptive decomposition-based data preprocessing for renewable generation rich power system applications” [J. Renewable Sustainable Energy 13, 062703 (2021)]

2022 ◽  
Vol 14 (1) ◽  
pp. 019901
Author(s):  
Satyabrata Das ◽  
B. Rajanarayan Prusty ◽  
Kishore Bingi
Keyword(s):  
Power System ◽  
Sustainable Energy ◽  
Data Preprocessing ◽  
Renewable Generation ◽  
Adaptive Decomposition

An Inverter can be Used in Hybrid Wind-Solar Grid-Connected Power System

2012 ◽  
Vol 512-515 ◽  
pp. 70-73 ◽  
Author(s):  
Yang Tian ◽  
Ru Cheng Han ◽  
Chen Li
Keyword(s):  
Global Warming ◽  
Power System ◽  
Sustainable Energy ◽  
Future Generations ◽  
Energy Sources ◽  
Grid Security ◽  
Operational Analysis ◽  
Harmonic Content ◽  
The Earth ◽  
Simulation Results

With increasing concern of global warming, many are looking at sustainable energy solutions to protect the earth for the future generations.. This paper presents a new inverter which can be used in hybrid wind/photovoltaic grid-connected power system. This inverter allows the two sources to work separately or simultaneously depending on the availability of the energy sources. Harmonic content is detrimental for the Grid security. The introduction of the inverter can effectively reduce the harmonics generated by the system. Operational analysis of the proposed system will be discussed in this paper. Simulation results are given to highlight the merits of the proposed inverter.

scholarly journals Data Preprocessing for Load Forecasting using Artificial Neural Network

2020 ◽  
Author(s):  
Rafael S. F. Ferraz ◽  
Renato S. F. Ferraz ◽  
Lucas F. S. Azeredo ◽  
Benemar A. de Souza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Demand Forecasting ◽  
Energy Resource ◽  
Data Preprocessing ◽  
Electric Power System ◽  
Percentage Error ◽  
Gradual Change ◽  
Artificial Neural

An accurate demand forecasting is essential for planning the electric dispatch in power system, contributing financially to electricity companies and helping in the security and continuity of electricity supply. In addition, it is evident that the distributed energy resource integration in the electric power system has been increasing recently, mostly from the photovoltaic generation, resulting in a gradual change of the load curve profile. Therefore, the 24 hours ahead prediction of the electrical demand of Campina Grande, Brazil, was realized from artificial neural network with a focus on the data preprocessing. Thus, the time series variations, such as hourly, diary and seasonal, were reduced in order to obtain a better demand prediction. Finally, it was compared the results between the forecasting with the preprocessing application and the prediction without the  preprocessing stage. Based on the results, the first methodology presented lower mean absolute percentage error with 7.95% against 10.33% of the second one.

Parameter Identification of Steam Turbine Speed Governor System

2011 ◽  
Author(s):  
Tao Yang ◽  
Lei Tang ◽  
Yanghai Li ◽  
Wei Gao ◽  
Kun Wang ◽  
...  
Keyword(s):  
Power System ◽  
Parameter Identification ◽  
Steam Turbine ◽  
Data Preprocessing ◽  
Practical Significance ◽  
Static Test ◽  
Nonlinear Part ◽  
Speed Governor ◽  
Speed Governor System ◽  
Turbine Speed

The dynamic characteristics of the steam turbine speed governor system is one of the major factor that influence the security of the power system. It has important practical significance for the security of the power system to establish the detailed dynamic model of the steam turbine speed governor system through parameter identification. This paper starts from the actual needs of the modeling of steam turbine speed governor system, several key issues such as field test of the speed control system, data preprocessing, parameter identification and simulation verification are researched, the solution of the key problems in the field static test and dynamic disturbance test is summarized, a variety of data preprocessing algorithms and parameter identification algorithms are achieved, and a new method for the simulation verification is proposed. On this basis, software based on MATLAB for the parameter identification of the steam turbine governor system is developed, which can perform the data preprocessing, parameter identification and simulation verification. The software offers a variety of parameter identification method to identify the linear and nonlinear part of the system model quickly and efficiently, and provides detailed qualitative and quantitative method for the evaluation of the simulation verification. The parameter identification results of steam turbine governor system of a power plant show that the software is user friendly and feature rich, and also show that it can complete parameter identification of steam turbine speed governor system intelligently and precisely.

Implementation of Genetic-Algorithm-Based Forecasting Model to Power System Problems

2018 ◽  
pp. 140-155
Author(s):  
Sajad Madadi ◽  
Morteza Nazari-Heris ◽  
Behnam Mohammadi-Ivatloo ◽  
Sajjad Tohidi
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Power System ◽  
Load Forecasting ◽  
Wind Farms ◽  
Optimization Method ◽  
Forecasting Model ◽  
Renewable Generation ◽  
Forecasting Methods ◽  
Pool Market

Power system includes many types of markets. Such markets are generally cleared at certain times, whereas market participators have to determine their operational plans before meeting the actual conditions. Therefore, forecasting methods can assist market players. Forecasting methods are applied to forecast electricity demand. The unknown conditions in the power system are increased by integration of renewable generation units. Forecasting methods, which are used for the load forecasting, are updated because the output power of renewable generation units such as wind farms and photovoltaic (PV) panels have more deviation than power demand. The pool market can be introduced as other parameter that is forecasted by market players. In this chapter, the authors investigate a mathematical model for forecasting of wind. Then, the forecasting model is proposed. Genetic algorithm is applied as an optimization method to handle delay associated with wind forecasting.

scholarly journals Optimal Sizing and Spatial Allocation of Storage Units in a High-Resolution Power System Model

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3365 ◽  
Author(s):  
Lukas Wienholt ◽  
Ulf Müller ◽  
Julian Bartels
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Power Transmission ◽  
Renewable Generation ◽  
Large Power ◽  
Transmission Grid ◽  
Storage Units

The paradigm shift of large power systems to renewable and decentralized generation raises the question of future transmission and flexibility requirements. In this work, the German power system is brought to focus through a power transmission grid model in a high spatial resolution considering the high voltage (110 kV) level. The fundamental questions of location, type, and size of future storage units are addressed through a linear optimal power flow using today’s power grid capacities and a generation portfolio allowing a 66% generation share of renewable energy. The results of the optimization indicate that for reaching a renewable energy generation share of 53% with this set-up, a few central storage units with a relatively low overall additional storage capacity of around 1.6 GW are required. By adding a constraint of achieving a renewable generation share of at least 66%, storage capacities increase to almost eight times the original capacity. A comparison with the German grid development plan, which provided the basis for the power generation data, showed that despite the non-consideration of transmission grid extension, moderate additional storage capacities lead to a feasible power system. However, the achievement of a comparable renewable generation share provokes a significant investment in additional storage capacities.

scholarly journals Reservoir based learning network for control of two-area power system with variable renewable generation

2015 ◽  
Vol 170 ◽  
pp. 428-438 ◽  
Author(s):  
Iroshani Jayawardene ◽  
Ganesh K. Venayagamoorthy
Keyword(s):  
Power System ◽  
Renewable Generation ◽  
Learning Network

scholarly journals Optimal Coordination of Aggregated Hydro-Storage with Residential Demand Response in Highly Renewable Generation Power System: The Case Study of Finland

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1037 ◽  
Author(s):  
Arslan Bashir ◽  
Matti Lehtonen
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Power System ◽  
Demand Response ◽  
Renewable Energy Sources ◽  
Renewable Generation ◽  
Generation Structure ◽  
Optimal Coordination ◽  
Residential Demand ◽  
Generation Power

Current energy policy-driven targets have led to increasing deployment of renewable energy sources in electrical grids. However, due to the limited flexibility of current power systems, the rapidly growing number of installations of renewable energy systems has resulted in rising levels of generation curtailments. This paper probes the benefits of simultaneously coordinating aggregated hydro-reservoir storage with residential demand response (DR) for mitigating both load and generation curtailments in highly renewable generation power systems. DR services are provided by electric water heaters, thermal storages, electric vehicles, and heating, ventilation and air-conditioning (HVAC) loads. Accordingly, an optimization model is presented to minimize the mismatch between demand and supply in the Finnish power system. The model considers proportions of base-load generation comprising nuclear, and combined heat and power (CHP) plants (both CHP-city and CHP-industry), as well as future penetration scenarios of solar and wind power that are constructed, reflecting the present generation structure in Finland. The findings show that DR coordinated with hydropower is an efficient curtailment mitigation tool given the uncertainty in renewable generation. A comprehensive sensitivity analysis is also carried out to depict how higher penetration can reduce carbon emissions from electricity co-generation in the near future.

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