scholarly journals Hybrid Wind–PV Frequency Control Strategy under Variable Weather Conditions in Isolated Power Systems

2020 ◽  
Vol 12 (18) ◽  
pp. 7750 ◽  
Author(s):  
Ana Fernández-Guillamón ◽  
Guillermo Martínez-Lucas ◽  
Ángel Molina-García ◽  
Jose-Ignacio Sarasua
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Control Strategy ◽  
Rotational Speed ◽  
Power Plants ◽  
Mean Squared Error ◽  
Frequency Control ◽  
Weather Conditions ◽  
Squared Error ◽  
Variable Weather

Over the last two decades, variable renewable energy technologies (i.e., variable-speed wind turbines (VSWTs) and photovoltaic (PV) power plants) have gradually replaced conventional generation units. However, these renewable generators are connected to the grid through power converters decoupled from the grid and do not provide any rotational inertia, subsequently decreasing the overall power system’s inertia. Moreover, the variable and stochastic nature of wind speed and solar irradiation may lead to large frequency deviations, especially in isolated power systems. This paper proposes a hybrid wind–PV frequency control strategy for isolated power systems with high renewable energy source integration under variable weather conditions. A new PV controller monitoring the VSWTs’ rotational speed deviation is presented in order to modify the PV-generated power accordingly and improve the rotational speed deviations of VSWTs. The power systems modeled include thermal, hydro-power, VSWT, and PV power plants, with generation mixes in line with future European scenarios. The hybrid wind–PV strategy is compared to three other frequency strategies already presented in the specific literature, and gets better results in terms of frequency deviation (reducing the mean squared error between 20% and 95%). Additionally, the rotational speed deviation of VSWTs is also reduced with the proposed approach, providing the same mean squared error as the case in which VSWTs do not participate in frequency control. However, this hybrid strategy requires up to a 30% reduction in the PV-generated energy. Extensive detailing of results and discussion can be also found in the paper.

scholarly journals Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5509 ◽  
Author(s):  
Foster Lubbe ◽  
Jacques Maritz ◽  
Thomas Harms
Keyword(s):  
Renewable Energy ◽  
Solar Radiation ◽  
Power Systems ◽  
Gaussian Process ◽  
Solar Power ◽  
Mean Squared Error ◽  
Gaussian Process Regression ◽  
Root Mean Squared Error ◽  
Radiation Data ◽  
Squared Error

The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that contains a significant variable renewable energy component. It is furthermore important to consider the inherent uncertainty in the data when modeling such a complex power system. Gaussian process regression has the potential to address both of these concerns: the probabilistic modeling of solar radiation data could assist in managing the variability of solar power, as well as provide a mechanism to deal with uncertainty. In this paper, solar radiation data was obtained from the Southern African Universities Radiometric Network and used to train a Gaussian process regression model which was developed especially for this purpose. Attention was given to constructing an appropriate Gaussian process kernel. It was found that a carefully constructed kernel allowed for the successful interpolation of global horizontal irradiance data, with a root-mean-squared error of 82.2W/m2. Gaps in the data, due to possible meter failure, were also bridged by the Gaussian process with a root-mean-squared error of 94.1 W/m2 and accompanying confidence intervals. A root-mean-squared error of 151.1 W/m2 was found when forecasting the global horizontal irradiance with a forecasting horizon of five days. These results, achieved in modeling solar radiation data using Gaussian process regression, could open new avenues in the development of probabilistic renewable energy management systems. Such systems could aid smart grid operators and support energy trading platforms, by allowing for better-informed decisions that incorporate the inherent uncertainty of stochastic power systems.

scholarly journals Application of Battery Energy Storage Systems for Primary Frequency Control in Power Systems with High Renewable Energy Penetration

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1379
Author(s):  
Md Ruhul Amin ◽  
Michael Negnevitsky ◽  
Evan Franklin ◽  
Kazi Saiful Alam ◽  
Seyed Behzad Naderi
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Power System ◽  
Storage Systems ◽  
Frequency Control ◽  
Battery Energy Storage ◽  
Battery Energy Storage Systems ◽  
Battery Energy ◽  
Primary Frequency ◽  
Primary Frequency Control

In power systems, high renewable energy penetration generally results in conventional synchronous generators being displaced. Hence, the power system inertia reduces, thus causing a larger frequency deviation when an imbalance between load and generation occurs, and thus potential system instability. The problem associated with this increase in the system’s dynamic response can be addressed by various means, for example, flywheels, supercapacitors, and battery energy storage systems (BESSs). This paper investigates the application of BESSs for primary frequency control in power systems with very high penetration of renewable energy, and consequently, low levels of synchronous generation. By re-creating a major Australian power system separation event and then subsequently simulating the event under low inertia conditions but with BESSs providing frequency support, it has been demonstrated that a droop-controlled BESS can greatly improve frequency response, producing both faster reaction and smaller frequency deviation. Furthermore, it is shown via detailed investigation how factors such as available battery capacity and droop coefficient impact the system frequency response characteristics, providing guidance on how best to mitigate the impact of future synchronous generator retirements. It is intended that this analysis could be beneficial in determining the optimal BESS capacity and droop value to manage the potential frequency stability risks for a future power system with high renewable energy penetrations.

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

scholarly journals An Adaptive Load Frequency Control for Power Systems with Renewable Energy Sources

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 573
Author(s):  
Mohamed Mokhtar ◽  
Mostafa I. Marei ◽  
Mariam A. Sameh ◽  
Mahmoud A. Attia
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
System Performance ◽  
Frequency Control ◽  
Renewable Energy Sources ◽  
Adaptive Controller ◽  
Load Frequency Control ◽  
Energy Sources ◽  
Load Variations

The frequency of power systems is very sensitive to load variations. Additionally, with the increased penetration of renewable energy sources in electrical grids, stabilizing the system frequency becomes more challenging. Therefore, Load Frequency Control (LFC) is used to keep the frequency within its acceptable limits. In this paper, an adaptive controller is proposed to enhance the system performance under load variations. Moreover, the proposed controller overcomes the disturbances resulting from the natural operation of the renewable energy sources such as Wave Energy Conversion System (WECS) and Photovoltaic (PV) system. The superiority of the proposed controller compared to the classical LFC schemes is that it has auto tuned parameters. The validation of the proposed controller is carried out through four case studies. The first case study is dedicated to a two-area LFC system under load variations. The WECS is considered as a disturbance for the second case study. Moreover, to demonstrate the superiority of the proposed controller, the dynamic performance is compared with previous work based on an optimized controller in the third case study. Finally in the fourth case study, a sensitivity analysis is carried out through parameters variations in the nonlinear PV-thermal hybrid system. The novel application of the adaptive controller into the LFC leads to enhance the system performance under disturbance of different sources of renewable energy. Moreover, a robustness test is presented to validate the reliability of the proposed controller.

scholarly journals Designing a GA-Based Robust Controller For Load Frequency Control (LFC)

2018 ◽  
Vol 8 (2) ◽  
pp. 2633-2639 ◽  
Author(s):  
K. Soleimani ◽  
J. Mazloum
Keyword(s):  
Power Systems ◽  
Power System ◽  
Power Plants ◽  
Frequency Control ◽  
Load Frequency Control ◽  
System Stability ◽  
Robust Controller ◽  
Power Flux ◽  
Pi Controllers ◽  
Multiple Units

Power systems include multiple units linked together to produce constantly moving electric power flux. Stability is very important in power systems, so controller systems should be implemented in power plants to ensure power system stability either in normal conditions or after the events of unwanted inputs and disorder. Frequency and active power control are more important regarding stability. Our effort focused on designing and implementing robust PID and PI controllers based on genetic algorithm by changing the reference of generating units for faster damping of frequency oscillations. Implementation results are examined on two-area power system in the ideally state and in the case of parameter deviation. According to the results, the proposed controllers are resistant to deviation of power system parameters and governor uncertainties.

scholarly journals A Review of Virtual Inertia Techniques for Renewable Energy-Based Generators

2020 ◽  
Author(s):  
Ana Fernández-Guillamón ◽  
Emilio Gómez-Lázaro ◽  
Eduard Muljadi ◽  
Ángel Molina-Garcia
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Power System ◽  
Power Plants ◽  
Control Strategies ◽  
Renewable Energy Sources ◽  
Extensive Discussion ◽  
Renewable Sources ◽  
Virtual Inertia ◽  
Alternative Sources

Over recent decades, the penetration of renewable energy sources (RES), especially photovoltaic and wind power plants, has been promoted in most countries. However, as these both alternative sources have power electronics at the grid interface (inverters), they are electrically decoupled from the grid. Subsequently, stability and reliability of power systems are compromised. Inertia in power systems has been traditionally determined by considering all the rotating masses directly connected to the grid. Thus, as the penetration of renewable units increases, the inertia of the power system decreases due to the reduction of directly connected rotating machines. As a consequence, power systems require a new set of strategies to include these renewable sources. In fact, ‘hidden inertia,’ ‘synthetic inertia’ and ‘virtual inertia’ are terms currently used to represent an artificial inertia created by inverter control strategies of such renewable sources. This chapter reviews the inertia concept and proposes a method to estimate the rotational inertia in different parts of the world. In addition, an extensive discussion on wind and photovoltaic power plants and their contribution to inertia and power system stability is presented.

scholarly journals Optimization Technique Inspired Load Frequency Controller for Multi Area Power System with Renewable Energy Source

2021 ◽  
Vol 850 (1) ◽  
pp. 012017
Author(s):  
J Shri Saranyaa ◽  
A Peer Fathima ◽  
Asutosh Mishra ◽  
Rushali Ghosh ◽  
Shalmali Das
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Control Method ◽  
Frequency Control ◽  
Renewable Energy Sources ◽  
Optimization Technique ◽  
Optimization Method ◽  
Load Frequency Control ◽  
Load Frequency ◽  
The Individual

Abstract Modern day scenario has an increasing power demand due to the growing development which indeed increases the load on the generation which might cause turbulence in the system and may bounce out of stability. The governor itself can’t handle such frequent load changes and adjust the generation amount to keep the frequency between the margins. This paper proposes an approach towards such predicament to incorporate an optimization method in order to ensure stability of the system despite the drastic changes in demand. Load frequency control is a control method for maintaining the frequency of the system during the change in demand. Use of controllers has proven to be effective in controlling the frequency deviations in the power systems and the response of the controller is further improved using optimization technique for better stability. The PID controller tuned by Particle Swarm Optimization is employed in multi-area system which reduces the time response by a considerable amount and the deviation settles much quicker despite the rapid load changes. The proposed controller is executed further for renewable energy sources connected to the individual areas and demonstration proves that the optimized controller is efficient enough in handling the frequency deviations when wind and solar with sunlight penetration is incorporated.

scholarly journals A Smart Adaptive Switching Module Architecture Using Fuzzy Logic for an Efficient Integration of Renewable Energy Sources. A Case Study of a RES System Located in Hulubești, Romania

2020 ◽  
Vol 12 (15) ◽  
pp. 6084
Author(s):  
Simona-Vasilica Oprea ◽  
Adela Bâra ◽  
Ștefan Preda ◽  
Osman Bulent Tor
Keyword(s):  
Fuzzy Logic ◽  
Renewable Energy ◽  
Power Systems ◽  
Electricity Generation ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Weather Conditions ◽  
Energy Sources ◽  
Fuzzy Logic Systems

Electricity generation from renewable energy sources (RES) has a common feature, that is, it is fluctuating, available in certain amounts and only for some periods of time. Consuming this electricity when it is available should be a primary goal to enhance operation of the RES-powered generating units which are particularly operating in microgrids. Heavily influenced by weather parameters, RES-powered systems can benefit from implementation of sensors and fuzzy logic systems to dynamically adapt electric loads to the volatility of RES. This study attempts to answer the following question: How to efficiently integrate RES to power systems by means of sustainable energy solutions that involve sensors, fuzzy logic, and categorization of loads? A Smart Adaptive Switching Module (SASM) architecture, which efficiently uses electricity generation of local available RES by gradually switching electric appliances based on weather sensors, power forecast, storage system constraints and other parameters, is proposed. It is demonstrated that, without SASM, the RES generation is supposed to be curtailed in some cases, e.g., when batteries are fully charged, even though the weather conditions are favourable. In such cases, fuzzy rules of SASM securely mitigate curtailment of RES generation by supplying high power non-traditional storage appliances. A numerical case study is performed to demonstrate effectiveness of the proposed SASM architecture for a RES system located in Hulubești (Dâmbovița), Romania.

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