scholarly journals Optimal Scheduling of Hydro–Thermal–Wind–Photovoltaic Generation Using Lightning Attachment Procedure Optimizer

2021 ◽  
Vol 13 (16) ◽  
pp. 8846
Author(s):  
Maha Mohamed ◽  
Abdel-Raheem Youssef ◽  
Salah Kamel ◽  
Mohamed Ebeed ◽  
Ehab E. Elattar
Keyword(s):  
Renewable Energy Sources ◽  
Optimization Methods ◽  
Thermal Unit ◽  
Short Term ◽  
Pv System ◽  
Integration System ◽  
Photovoltaic Generation ◽  
Test Systems ◽  
Generation Scheduling ◽  
Different Characteristics

This paper presents an effective solution for the short-term hydrothermal generation scheduling (STHS) problem using an integration of wind and photovoltaic power (PV) system. Wind and PV power are integrated into the power system to minimize the total fuel cost of thermal units. In this paper, the lightning attachment procedure optimization algorithm (LAPO) is employed to solve the STHS problem using the wind and PV power integration system. The proposed method is applied for solving five test systems with different characteristics, considering the valve-point loading impact of the thermal unit. The first and third test systems include hydro and thermal units only, and the rest of the systems consist of hydro and thermal units with integrating wind and PV power-generating units to inspect the effect of renewable energy sources in the selected test systems. The simulation results are compared with other studied methods. It is found that the proposed method is superior, and it has the ability to obtain the best solutions with respect to other optimization methods that are implemented to solve the STHS problem.

scholarly journals Hydrothermal systems generation scheduling using cultural algorithm

2009 ◽  
Vol 11 (1) ◽  
pp. 65-78 ◽  
Author(s):  
Xiaohui Yuan ◽  
Hao Nie ◽  
Yanbin Yuan ◽  
Anjun Su ◽  
Liang Wang
Keyword(s):  
Differential Evolution ◽  
Heuristic Search ◽  
Hydrothermal System ◽  
Selection Criteria ◽  
Optimization Methods ◽  
Hydrothermal Systems ◽  
Cultural Algorithm ◽  
Short Term ◽  
Generation Scheduling ◽  
Simple Selection

This paper proposes an enhanced cultural algorithm to solve the short-term generation scheduling of hydrothermal systems problem, in which differential evolution is embedded into a cultural algorithm and applies two knowledge sources to influence the variation operator of differential evolution and couples with simple selection criteria based on feasibility rules and heuristic search strategies to handle constraints in the cultural algorithm effectively. A test hydrothermal system is used to verify the feasibility and effectiveness of the proposed method. Results are compared with those of other optimization methods reported in the literature. It is shown that the proposed method is capable of yielding higher quality solutions.

Ultra-Short-Term Ahead Generating Power Forecasting for PV System Based on Markov Chain for Error Series

2011 ◽  
Vol 347-353 ◽  
pp. 1498-1505
Author(s):  
Xiang Yu Zhang ◽  
Chang Hong Deng ◽  
Tian Tian Chen
Keyword(s):  
Markov Chain ◽  
Output Power ◽  
Electrical Engineering ◽  
Solar Irradiation ◽  
Generation System ◽  
Short Term ◽  
Pv System ◽  
Stochastic Effects ◽  
Photovoltaic Generation ◽  
Satisfactory Precision

This article presents a proper approach for the prediction of output power for photovoltaic generation system. Using the results of solar irradiation from the sunny-day-model as the base value, which are accurate when the sky is clear, and correcting the forecasting results with the assistance of Markov Chain in order to make them valid for other kinds of weathers. To be specific, by utilizing Markov Chain, the underlying stochastic effects of clouds coverage can be minimized and thus harvest more accurate results. The method is tested on the photovoltaic generation system of Electrical Engineering School, Wuhan University, P.R.China and rendered a satisfactory precision of forecasting. Finally, further measurements for enhancing accuracy are also discussed in this paper.

scholarly journals Emission Constrained Economic Dispatch with PV Energy Penetration

2019 ◽  
Vol 8 (12) ◽  
pp. 3021-3026
Keyword(s):  
Renewable Energy ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Thermal Power ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Optimization Strategy ◽  
Renewable Energy Resources ◽  
Pv System ◽  
Power Stations

Power system planners are forced to consider the alarming rate of environmental pollution and rapiddepletion of fossil fuels andutilize renewable energy resources to mitigate the environmental effects of thermal power stations. Combined Economic Emission Dispatch(CEED)offers an effectivesolution to reducefossil fuel emissions as well ascost.Since 1985, CEED is considered to be a common optimization strategy. Literature contains lot of optimization methods for the strategy.In the recent times, using PV energy has proved to be a feasible and dependable alternative for electricity generation systems based on fossil fuels. In the developing countries, the dependency on fossil fuels has been seen as inevitable. At present,the use of renewable energy sources is rapidly increasing in inconventional power generation systems. The present paper puts forwardan approach of combining PVenergy-based grid integrated PV system with fossil fuel based thermal power plant using evolutionary programmingbased optimization technique.Among the various optimization techniques, the Particle Swarm Optimization (PSO) is considered to be the most suitable technique for the problem is explained in detailed manner.The proposed method is to combine CEED with the PV energy and thereby reduces the use of conventional energy resources.It also permits an effective utilizationof abundantlyavailable PV energy.It is tested on standard IEEE 30 bus system with the real time ratings of proposed PV plant situated in Tamilnadu.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

scholarly journals Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2151
Author(s):  
Feras Alasali ◽  
Husam Foudeh ◽  
Esraa Mousa Ali ◽  
Khaled Nusair ◽  
William Holderbaum
Keyword(s):  
Renewable Energy ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
Golden Ratio ◽  
Clean Energy ◽  
Energy Sources ◽  
Percentage Error ◽  
Ann Model ◽  
Demand Forecast ◽  
Pv System

More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.

Three Gorges Cascade Hydropower System Joint Simulative Optimal Operation Research in Power Market

2011 ◽  
Vol 347-353 ◽  
pp. 1370-1373
Author(s):  
Jiao Zheng ◽  
Kan Yang ◽  
Ran Zhou ◽  
Yong Huai Hao ◽  
Guo Shuai Liu
Keyword(s):  
Power Market ◽  
Optimal Operation ◽  
Adaptive Genetic Algorithm ◽  
Three Gorges ◽  
Short Term ◽  
Roulette Wheel Selection ◽  
Generation Scheduling ◽  
Hydropower System ◽  
Selection Operator ◽  
Hydropower Stations

The short-term joint optimal operation simulation of Three Gorges cascade hydropower system aiming at maximum power generation benefit is proposed. And a new method for optimizing cascade hydropower station based on Adaptive Genetic Algorithm (AGA) with trigonometric selective operators is presented. In this paper, the practical optimal operation in power market is described. The temporal-spatial variation of flow between cascade hydropower stations is considered, and time of use (TOU) power price is also taken into account. Moreover, a contrast between Tangent-roulette selection operator and traditional one is made. To a certain degree, the results of simulative optimal operation based on several representative hydrographs show that Tangent-roulette wheel selection operator can find a more excellent solution, because the Tangent-roulette one can overcome the fitness requirements of non-negative. The research achievements also have an important reference for the compilation of daily generation scheduling of Three Gorges cascade hydropower system in the environment of power market.

scholarly journals Optimisation of Energy Accumulation for Renewable Energy Sources

2021 ◽  
Vol 19 ◽  
pp. 205-210
Author(s):  
Milan Belik ◽  
Keyword(s):  
Renewable Energy ◽  
Model Building ◽  
Residential Buildings ◽  
Renewable Energy Sources ◽  
Photovoltaic System ◽  
Energy Sources ◽  
Pv System ◽  
Energy Accumulation ◽  
Wind Power Station ◽  
Future Production

This project focuses on optimisation of energy accumulation for various types of distributed renewable energy sources. The main goal is to prepare charging – discharging strategy depending on actual power consumption and prediction of consumption and production of utilised renewable energy sources for future period. The simulation is based on real long term data measured on photovoltaic system, wind power station and meteo station between 2004 – 2021. The data from meteo station serve as the input for the simulation and prediction of the future production while the data from PV system and wind turbine are used either as actual production or as a verification of the predicted values. Various parameters are used for trimming of the optimisation process. Influence of the charging strategy, discharging strategy, values and shape of the demand from the grid and prices is described on typical examples of the simulations. The main goal is to prepare and verify the system in real conditions with real load chart and real consumption defined by the model building with integrated renewable energy sources. The system can be later used in general installations on commercial or residential buildings.

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