scholarly journals Advantages of Applying Large-Scale Energy Storage for Load-Generation Balancing

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3093
Author(s):  
Dawid Chudy ◽  
Adam Leśniak
Keyword(s):  
Energy Storage ◽  
Power Systems ◽  
Large Scale ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Frequency Regulation ◽  
The Cost ◽  
Further Development ◽  
Proper Operation ◽  
Load Generation

The continuous development of energy storage (ES) technologies and their wider utilization in modern power systems are becoming more and more visible. ES is used for a variety of applications ranging from price arbitrage, voltage and frequency regulation, reserves provision, black-starting and renewable energy sources (RESs), supporting load-generation balancing. The cost of ES technologies remains high; nevertheless, future decreases are expected. As the most profitable and technically effective solutions are continuously sought, this article presents the results of the analyses which through the created unit commitment and dispatch optimization model examines the use of ES as support for load-generation balancing. The performed simulations based on various scenarios show a possibility to reduce the number of starting-up centrally dispatched generating units (CDGUs) required to satisfy the electricity demand, which results in the facilitation of load-generation balancing for transmission system operators (TSOs). The barriers that should be encountered to improving the proposed use of ES were also identified. The presented solution may be suitable for further development of renewables and, in light of strict climate and energy policies, may lead to lower utilization of large-scale power generating units required to maintain proper operation of power systems.

scholarly journals A New Tool to Assess Maximum Permissible Solar PV Penetration in a Power System

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8529
Author(s):  
Dhanuja Lekshmi J ◽  
Zakir Hussain Rather ◽  
Bikash C Pal
Keyword(s):  
Power Systems ◽  
Power System ◽  
Large Scale ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Research Work ◽  
Clean Energy ◽  
Solar Pv ◽  
System Operator ◽  
Pv Penetration

With diminishing fossil fuel resources and increasing environmental concerns, large-scale deployment of Renewable Energy Sources (RES) has accelerated the transition towards clean energy systems, leading to significant RES generation share in power systems worldwide. Among different RES, solar PV is receiving major focus as it is most abundant in nature compared to others, complimented by falling prices of PV technology. However, variable, intermittent and non-synchronous nature of PV power generation technology introduces several technical challenges, ranging from short-term issues, such as low inertia, frequency stability, voltage stability and small signal stability, to long-term issues, such as unit commitment and scheduling issues. Therefore, such technical issues often limit the amount of non-synchronous instantaneous power that can be securely accommodated by a grid. In this backdrop, this research work proposes a tool to estimate maximum PV penetration level that a given power system can securely accommodate for a given unit commitment interval. The proposed tool will consider voltage and frequency while estimating maximum PV power penetration of a system. The tool will be useful to a system operator in assessing grid stability and security under a given generation mix, network topology and PV penetration level. Besides estimating maximum PV penetration, the proposed tool provides useful inputs to the system operator which will allow the operator to take necessary actions to handle high PV penetration in a secure and stable manner.

scholarly journals An Intelligent Battery Energy Storage-Based Controller for Power Quality Improvement in Microgrids

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2112 ◽  
Author(s):  
Jaber Alshehri ◽  
Muhammad Khalid ◽  
Ahmed Alzahrani
Keyword(s):  
Energy Storage ◽  
Power Systems ◽  
Power Quality ◽  
Renewable Energy Sources ◽  
Operating Conditions ◽  
Frequency Regulation ◽  
Battery Energy Storage ◽  
Control Approach ◽  
Battery Energy

Modern power systems rely on renewable energy sources and distributed generation systems more than ever before; the combination of those two along with advanced energy storage systems contributed widely to the development of microgrids (MGs). One of the significant technical challenges in MG applications is to improve the power quality of the system subjected to unknown disturbances. Hence innovative control strategies are vital to cope with the problem. In this paper, an innovative online intelligent energy storage-based controller is proposed to improve the power quality of a MG system; in particular, voltage and frequency regulation at steady state conditions are targeted. The MG system under consideration in this paper consists of two distributed generators, a diesel synchronous generator, and a photovoltaic power system integrated with a battery energy storage system. The proposed control approach is based on hybrid differential evolution optimization (DEO) and artificial neural networks (ANNs). The controller parameters have been optimized under several operating conditions. The obtained input and output patterns are consequently used to train the ANNs in order to perform an online tuning for the controller parameters. Finally, the proposed DEO-ANN methodology has been evaluated under random disturbances, and its performance is compared with a benchmark controller.

scholarly journals Biobjective Optimization-Based Frequency Regulation of Power Grids with High-Participated Renewable Energy and Energy Storage Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Tingyi He ◽  
Shengnan Li ◽  
Shuijun Wu ◽  
Chuangzhi Li ◽  
Biao Xu
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Large Scale ◽  
Frequency Control ◽  
Renewable Energy Sources ◽  
Automatic Generation ◽  
Load Frequency Control ◽  
Total Power ◽  
Frequency Regulation ◽  
Entropy Weight

Large-scale renewable energy sources connected to the grid bring new problems and challenges to the automatic generation control (AGC) of the power system. In order to improve the dynamic response performance of AGC, a biobjective of complementary control (BOCC) with high-participation of energy storage resources (ESRs) is established, with the minimization of total power deviation and the minimization of regulation mileage payment. To address this problem, the strength Pareto evolutionary algorithm is employed to quickly acquire a high-quality Pareto front for BOCC. Based on the entropy weight method (EWM), grey target decision-making theory is designed to choose a compromise dispatch scheme that takes both of the operating economy and power quality into account. At last, an extended two-area load frequency control (LFC) model with seven AGC units is taken to verify the effectiveness and the performance of the proposed method.

scholarly journals Sizing and Management of Energy Storage Systems in Large-Scale Power Plants Using Price Control and Artificial Intelligence

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3296
Author(s):  
Carlos García-Santacruz ◽  
Luis Galván ◽  
Juan M. Carrasco ◽  
Eduardo Galván
Keyword(s):  
Energy Storage ◽  
Power Plants ◽  
Large Scale ◽  
Storage Systems ◽  
Renewable Energy Sources ◽  
Ancillary Services ◽  
Energy Sources ◽  
Energy Storage Systems ◽  
Electric System ◽  
Fundamental Part

Energy storage systems are expected to play a fundamental part in the integration of increasing renewable energy sources into the electric system. They are already used in power plants for different purposes, such as absorbing the effect of intermittent energy sources or providing ancillary services. For this reason, it is imperative to research managing and sizing methods that make power plants with storage viable and profitable projects. In this paper, a managing method is presented, where particle swarm optimisation is used to reach maximum profits. This method is compared to expert systems, proving that the former achieves better results, while respecting similar rules. The paper further presents a sizing method which uses the previous one to make the power plant as profitable as possible. Finally, both methods are tested through simulations to show their potential.

scholarly journals Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

2020 ◽  
Vol 34 (01) ◽  
pp. 630-637 ◽  
Author(s):  
Ferdinando Fioretto ◽  
Terrence W.K. Mak ◽  
Pascal Van Hentenryck
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Practical Applications ◽  
Fundamental Building Block ◽  
Optimal Power

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.

scholarly journals The cost-effectiveness of an energy storage system using hydrogen and underground gas storage

2019 ◽  
Vol 137 ◽  
pp. 01007 ◽  
Author(s):  
Sebastian Lepszy
Keyword(s):  
Cost Effectiveness ◽  
Energy Storage ◽  
Historical Data ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Energy Storage System ◽  
Electricity Production ◽  
Energy Market ◽  
The Cost ◽  
Random Nature

Due to the random nature of the production, the use of renewable energy sources requires the use of technologies that allow adjustment of electricity production to demand. One of the ways that enable this task is the use of energy storage systems. The article focuses on the analysis of the cost-effectiveness of energy storage from the grid. In particular, the technology was evaluated using underground hydrogen storage generated in electrolysers. Economic analyzes use historical data from the Polish energy market. The obtained results illustrate, among other things, the proportions between the main technology modules selected optimally in technical and economic terms.

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