Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models (original) (raw)

Optimal Reactive Power for Voltage Stability in Distribution Systems Connected Wind Turbine Farm using Particle Swarm Optimization

The Proceedings of the 5th International Conference on Industrial Application Engineering 2017, 2017

This paper illustrates optimal reactive power setting for voltage stability in power distribution system affected by wind turbine farm high terminal voltage using particle swarm optimization to inquire optimal solution. It involves the implementation of disturbance analyzing device to compare the result of setting reactive compensator. In this research, to optimize the overall voltage limitation, three decision variables were considered, which are i)active/reactive power generated from wind turbine farm plants, ii) specified voltage magnitude and all node limitation and iii) power factor control. Particle swarm optimization (PSO) is well-known and widely accepted as a potential intelligent search method for solving optimization problems ."WA-YU" wind turbine farm project rated 8.0 MW, 22kV of Provincial Electricity Authority (PEA) in Nakhon Ratchasima, Thailand at feeder ten was employed as a case study. Results showed that PSO can search optimal reactive power to solve power flow problems efficiently related the best power factor. The voltage limitation was controlled in ±5% range of nominal voltage (22 kV) based on PEA's regulation grid terms connected with a network of 2008. The controlled voltage provided a benefit which did not affect other power users connected in the same feeder circuit.

Reactive and Active Power Output Optimization in a Wind Farm Using the Particle Swarm Optimization Technique

—In the recent years, the contribution of the wind power to energy supply has increased considerably; hence, the wind farms have to be able to participate to the grid power stability. In this paper, an optimization algorithm allows obtaining the reactive and active power dispatch in a wind power plant is presented. The aim of the proposed algorithm is to minimize the power losses and the difference between the reactive power obtained and required by the transmission system operator at the point of common coupling. The simulation results show the validity and the performance of the proposed algorithm. Keywords—wind farms; grid stability; optimization algorithm; the reactive and active power dispatch; transmission system operator; point of common coupling.

A Simple and Reliable Photovoltaic Forecast for Reliable Power System Operation Control

IFAC-PapersOnLine, 2018

Recently various forecasting methods for photovoltaic (PV) generation have been proposed in the literature. However, these standard methods cannot be successfully and widely used in general due to the fact that they require access to specialized data that are not always and everywhere readily available in practice. Furthermore, prediction accuracy of such methods tends to deteriorate specially due to data scarcity. This paper proposes a simple and reliable PV forecasting method using machine learning and neural networks. Confidence interval (CI) results are specifically provided for the local supply-demand control as well as for the robust power system security. The proposed method uses only weather forecasting data that are provided by the Japan Meteorological Agency (JMA) and which is available to the public. The proposed method maintains a high level of accuracy by using real-time correlation data between the specific target and the neighboring areas. Multiple neural networks are constructed based on a weather clustering technique. It has been confirmed through extensive simulation results that the proposed method demonstrates robustness in prediction accuracy and CI effectiveness.

Photovoltaic Power Generation Forecasting Using a Novel Hybrid Intelligent Model in Smart Grid

Computational Intelligence and Neuroscience

The exponential growth of electrical demand and the integration of renewable energy sources (RES) brought new challenges in the traditional grid about energy quality. The transition from traditional grid to smart grid is the best solution which provides necessary tools and information and communication technologies (ICT) for service enhancement. In this study, variation of energy demand and some factors of atmospheric change are considered to forecast production of photovoltaic energy that can be adapted for evolution of consumption in smart grid. The contribution of this study concerns a novel optimized hybrid intelligent model made of the artificial neural network (ANN), support vector machine (SVM), and particle swarm optimization (PSO) implemented for long term photovoltaic (PV) power generation forecasting based on real data of consumption and climate factors of the city of Douala in Cameroon. The accuracy of this model is evaluated using the coefficients such as the mean squar...

Generation Forecasting Models for Wind and Solar Power

International Journal of Computer and Electrical Engineering

Over the past three decades power demand has increased remarkably due to industrialization and increased demand of automation. On the other hand conventional energy sources like fossil fuel are ever depleting. Therefore, industry and scientist are focusing on renewable energy (RE) sources like wind, solar etc., to address twin challenge of energy security and reduction in pollution caused by excessive fossil fuel usage. Further, number of consumers generating renewable energy in distributed manner and participating in the power network is increasing drastically. This exponential rise in penetration of renewable energy into existing power system has posed challenges to grid stability, reliability and power quality. A precise power demand-generation balance is challenging in smooth and reliable operation network, irrespective of unpredictable demand and intermittent nature of renewable power generation. In this research paper we have discussed and designed time series generation forecast models for wind and solar using historical RE generation data for Maharashtra state of India. Forecast results of designed solar and wind power generation models are compared. The wind and solar power generation forecasts obtained in this paper will help the power system operators; while taking decisions related to energy mix, generation planning, scheduling to maintain reliable and economical operation of power system.

A Hybrid Intelligent Approach for Solar Photovoltaic Power Forecasting: Impact of Aerosol Data

Arabian Journal for Science and Engineering, 2019

The penetration of solar photovoltaic (PV) power in distributed generating system is increasing rapidly. The increased level of PV penetration causes various issues like grid stability, reliable power generation and power quality; therefore, it becomes utmost important to forecast the PV power using the meteorological parameters. The proposed model is developed on the basis of meteorological data as input parameters, and the impacts of these parameters have been analyzed with respect to forecasted PV power. The main focus of this research is to explore the performance of optimization-based PV power forecasting models with varying aerosol particles and other meteorological parameters. A newly developed intelligent approach based on grey wolf optimization (GWO) using multilayer perceptron (MLP) has been used to forecast the PV power. The performance of the GWO-based MLP model is evaluated on the basis of statistical indicators such as normalized mean bias error (NMBE), normalized mean absolute error (NMAE), normalized root-mean-square error (NRMSE) and training error. The results of the developed model show the values of NMBE, NMAE and NRMSE as 2.267%, 4.681% and 6.67% respectively. To validate the results, a comparison has been made with particle swarm optimization, Levenberg-Marquardt algorithm and adaptive neuro-fuzzy approach. The performance of the model is found better as compared to other intelligent techniques. The obtained results may be used for demand response applications in smart grid environment.

A particle swarm optimization for reactive power and voltage control considering voltage stability

… on Intelligent System Applications to Power …, 1999

This paper presents a particle swarm optimization for reactive power and voltage control considering voltage stability. The proposed method determines a control strategy with continuous and discrete control variables such as AVR operating values, OLTC tap positions, and the amount of reactive power compensation equipment. The method also considers voltage stability using a continuation power flow technique. The feasibility of the proposed method is demonstrated on model power systems with promising results.

PV Power Short-Term Forecasting Model Based on the Data Gathered from Monitoring Network

The degree of accuracy in predicting the photovoltaic power generation plays an important role in appropriate allocations and economic operations of the power plants based on the generating capacity data gathered from the geographically separated photovoltaic plants through network. In this paper, a forecasting model is designed with an optimization algorithm which is developed with the combination of PSO (Particle Swarm Optimization) and BP (Back Propagation) neural network. The proposed model is further validated and the experiment results show that the predication model assures the prediction accuracy regardless the day type transitions and other relevant factors. In the proposed model, the prediction error rate is worth less than 20% in all different climatic conditions and most of the prediction error accuracy is less than 10% in sunny day, and whose precision satisfies the management requirements of the power grid companies, reflecting the significance of the proposed model in engineering applications.