Stability Of Electric Grid Research Articles

Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour

Photovoltaic (PV) system power supply is characteristically intermittent. Therefore, PV forecasting is crucial for decision makers responsible for electrical grid stability. With forecast models traditionally trained as macro-level solutions, where a single model emulates the entire PV system, there is uncertainty regarding the ability of these macro-level models to capture the low-level power output dynamics of large multi-megawatt PV systems. Instead, an aggregated inverter-level forecasting methodology is proposed to obtain an enhanced forecasting accuracy. These macro-level and inverter-level forecasting methodologies are implemented with state-of-the-art deep learning based Feedforward neural network, Long Short-Term Memory and Gated Recurrent Unit recurrent neural network models. Results are generated for a real-world scenario, with multi-step forecasts delivered 1–6 h ahead for a 75 MW rated PV system. To ensure the scalability of the proposed methodology, a unique inverter-clustering technique is presented, which reduces the effort of optimising multiple low-level forecast models. A heuristic process of systematic hyperparameter optimisation is also proposed, which serves to guide future forecasting practitioners towards unbiased model development. From the deterministic and probabilistic confidence interval evaluations, overall results demonstrate a marginal increase in forecasting accuracy from the proposed aggregated inverter-level forecasts. The best performing macro-level model obtained Mean Absolute Percentage Error (MAPE) values ranging between 1.42%–8.13% for all weather types and forecast horisons. In comparison, the equivalent inverter-level forecasts delivered MAPE values ranging from 1.27%–8.29%. Finally, it is concluded that deep learning based macro-level forecast models have a sufficient ability to capture low-level PV system behaviour.

Energy trade market effect on production scheduling: an Industrial Product-Service System (IPSS) approach

ABSTRACT The demand response monitoring is one key energy-efficient production technique and can reduce costs contributing simultaneously to the reduction of environmental emissions and maintaining the stability of electrical grids following the policies examined from European and International organizations. Although, energy trade market effect has been investigated in the literature, the novelty of this research work lies in the design and development of an Industrial Product Service System (IPSS) business model, aiming to the collaboration between Energy Sales Companies (ESC) and manufacturing companies. ESC through an energy-demand management tool, supported by data acquisition devices connected to the Cloud, calculates and signals energy price forecasts to the manufacturers. Moreover, an adaptive production scheduling approach considering the power usage of manufacturers in response to time-varying energy prices is presented. Particularly, an intelligent algorithm is applied for the generation and examination of multi-criteria alternative assignment scenarios and an evaluation of each planning scenario against a set of user-defined criteria is made for the identification of the most efficient ones. The proposed approach is validated in an industrial case study using production and energy-consumption data from a mold making industry. The results indicate potential savings for the industrial consumer finding both actors to mutually benefit.

Intelligent Optimization Framework for Efficient Demand-Side Management in Renewable Energy Integrated Smart Grid

The implementation of real-time price-based demand response program and integration of renewable energy resources (RESs) improves efficiency and ensure stability of electric grid. This paper proposes a novel intelligent optimization based demand-side management (DSM) framework for smart grid integrated with RESs. In the intelligent DSM framework the artificial neural network (ANN) forecasts energy usage behavior of consumers and real-time price-based demand response program (RTPDRP) of electric utility company (EUC). The smart energy management controller of the proposed intelligent DSM framework adapts forecasted energy usage behavior of consumers using forecasted RTPDRP to create operation schedule. The consumers implement the created schedule to minimize energy cost, peak load, carbon emission subjected to improving user comfort and avoiding rebound peaks. Simulations are conducted using our proposed hybrid genetic ant colony (HGAC) optimization algorithm to create schedule for three cases: EUC without RESs, EUC with RESs, and EUC with both RESs and storage technologies. To endorse the applicability and productivity of the proposed DSM framework based on HGAC optimization algorithm with five existing algorithms based frameworks. Simulation results show that the proposed DSM framework is superior compared with the existing frameworks in terms of energy cost minimization, peak load mitigation, carbon emission alleviation, and user discomfort minimization. The proposed HGAC optimization algorithm reduced electricity cost, carbon emission, and peak load by 12.16%, 4.00%, and 19.44% in case I; by 26.8%, 20.71%, and 33.3% in case II; and by 24.4%, 16.44%, and 37.08% in case III, respectively, compared to without scheduling.

Optimal Design of a Hybrid Energy System for the Supply of Clean and Stable Energy to Offshore Installations

This paper presents an innovative hybrid energy system for stable power and heat supply in offshore oil and gas installations. The proposed concept integrates offshore wind power, onsite gas turbines and an energy storage system based on fuel cell and electrolyzer stacks. It is expected to be an effective option to decarbonize the offshore petroleum sector as it allows a more extensive exploitation of the offshore wind resource by means of energy storage. To ascertain its potential, an integrated model was developed. The integrated model allows to simulate the process and electric grid performances. The inclusion of both domains provides a comprehensive picture of a given design operational performance. The feasibility of the proposed concept was first investigated through a parametric analysis where an understanding of its potential and limitations was gained. A rigorous optimization was then implemented to identify the designs resulting in the best performances and ultimately to obtain a comprehensive picture of the suitability of the concept. It is shown that a well-designed system can reduce carbon emissions compared, not only to a standard concept based on gas turbines (almost 1,300 kt less CO2emissions, making up for a relative 36% reduction), but also to the integration of a wind farm alone (more than 70 kt less CO2emissions, making up for a relative 3% reduction, but complying with grid dynamics requirements). Moreover, the energy storage system brings benefits to the electric grid stability and allows the integration of large wind power capacity without overpassing the 2% maximum frequency variation (as it is the case without energy storage). Not least, the optimization showed that the definition of an optimal design is a complex task, with little margin to further gains in terms of carbon emissions, likely due to technological limitations.

Optimized recurrent neural network with fuzzy classifier for data prediction using hybrid optimization algorithm: Scope towards diverse applications

Today, data processing has become a challenging task due to the significant increase in the amount of data collected using various sensors. To put up knowledge and forecast the data, the existing data mining techniques compute all numerical attributes in the memory simultaneously. However, the over-abundance of entire factors in the data makes accurate prediction infeasible. This paper attempts to implement a new data prediction model using an optimized machine learning algorithm. The proposed data prediction model involves four main phases: (a) data acquisition, (b) feature extraction, (c) data normalization, and (d) prediction. Initially, few data from the UCI repository like Bike Sharing Dataset, Carbon Nanotubes, Concrete Compressive Strength, Electrical Grid Stability Simulated Data, and SkillCraft-1 Master Table are collected. Further, the feature extraction process extracts the first-order statistics like mean, median, standard deviation, the maximum value of entire data, and the minimum value of entire data, and the second-order statistics like kurtosis, skewness, energy, and entropy. Next, the data or feature normalization is done to arrange the data within a certain limit. The normalized features are then subjected to a hybrid prediction system by integrating the Recurrent Neural Network (RNN) and Fuzzy Regression model. As a modification, the number of hidden neurons in the RNN and membership limits of the Fuzzy Regression model are optimized by a hybrid optimization algorithm by merging the concepts of Whale Optimization Algorithm (WOA) and Cat Swarm Optimization (CSO), which is called the Whale Updated Seek Mode-based CSO (WS-CSO) algorithm. Then, the efficiency of the optimized hybrid classifier for all-time prediction of data in different applications is confirmed based on its valuable performance and comparative analysis.

Power Quality and Reliability Considerations of Photovoltaic Distributed Generation

Worldwide energy consumption is increasing at a faster pace than energy generation because of enhanced industrialization, growing population and, improved living standards. Using the Distributed Generation (DG) near the end consumers can support the electrical grid stability and enhance the power system quality. The DG is consisting of a small-scale technology to generate electrical power near the end consumers, which can be renewable energy or generators. Solar Photovoltaic (PV) system is considered as one of the best renewable energy sources, due to its low running cost and low environmental affection comparing with traditional power plants. The proposed PVDG units are employing maximum power point tracking (MPPT) system to track the maximum power available in PV arrays. The proposed DG system used Particle Swarm Optimization (PSO) technique to optimally allocate the PVDG units for minimum cost, transmission line losses, and improved voltage profile of busbars under different operating conditions. This paper proposes a smart optimization technique employing PSO in the design, operation, and control of the proposed system. Also, the paper discusses the PSO algorithm for optimal DGs sizing and allocations. The simulation in this paper uses the IEEE-7 busbars system with an extension of IEEE-3 busbars as a new remote load. The simulation has been carried out by the power world simulator software to compare the voltage and the power losses with and without PVDGs connected to the system. The simulation results showed an acceptable reduction in generating cost, transmission line losses, and improvement of the busbars voltage profile which proves the success of the proposed system.

City-Scale Building Anthropogenic Heating during Heat Waves

More frequent and longer duration heat waves have been observed worldwide and are recognized as a serious threat to human health and the stability of electrical grids. Past studies have identified a positive feedback between heat waves and urban heat island effects. Anthropogenic heat emissions from buildings have a crucial impact on the urban environment, and hence it is critical to understand the interactive effects of urban microclimate and building heat emissions in terms of the urban energy balance. Here we developed a coupled-simulation approach to quantify these effects, mapping urban environmental data generated by the mesoscale Weather Research and Forecasting (WRF) coupled to Urban Canopy Model (UCM) to urban building energy models (UBEM). We conducted a case study in the city of Los Angeles, California, during a five-day heat wave event in September 2009. We analyzed the surge in city-scale building heat emission and energy use during the extreme heat event. We first simulated the urban microclimate at a high resolution (500 m by 500 m) using WRF-UCM. We then generated grid-level building heat emission profiles and aggregated them using prototype building energy models informed by spatially disaggregated urban land use and urban building density data. The spatial patterns of anthropogenic heat discharge from the building sector were analyzed, and the quantitative relationship with weather conditions and urban land-use dynamics were assessed at the grid level. The simulation results indicate that the dispersion of anthropogenic heat from urban buildings to the urban environment increases by up to 20% on average and varies significantly, both in time and space, during the heat wave event. The heat dispersion from the air-conditioning heat rejection contributes most (86.5%) of the total waste heat from the buildings to the urban environment. We also found that the waste heat discharge in inland, dense urban districts is more sensitive to extreme events than it is in coastal or suburban areas. The generated anthropogenic heat profiles can be used in urban microclimate models to provide a more accurate estimation of urban air temperature rises during heat waves.

Cost-effective flexibilisation of an 80 MWe retrofitted biomass power plants: Improved combustion control dynamics using virtual air flow sensors

As they deliver dispatchable renewable energy, biomass power plants are expected to play a key role in the stability of the future electricity grids dominated by intermittent renewables. Large-scale, biomass-fired power plants are often retrofitted from coal-fired plants. Such a fuel modification combined with decreasing pollutant emission limits and higher requirements in terms of load flexibility can lead to a decrease of the maximum power delivered by the unit. The limiting factors are partly related to the control systems of those plants. In this paper, we present the results of the upgrading of an 80 MWe, retrofitted biomass power plant that was achieved by improving the dynamic control of the combustion process. Thanks to the addition of virtual air flow sensors in the control system and the re-design of the combustion control loops, the undesired effects of a recent 10% power increase on NOx emissions were more than compensated. The accurate control of the local NOx production in the furnace resulted in a decrease of these emissions by 15% with an increased stability. This study will help increasing the cost-effectiveness of such conversions, and facilitate the development of dispatchable, renewable power units able to contribute to the grid stability.

A Modern Data-Mining Approach Based on Genetically Optimized Fuzzy Systems for Interpretable and Accurate Smart-Grid Stability Prediction

The main objective and contribution of this paper was/is the application of our knowledge-based data-mining approach (a fuzzy rule-based classification system) characterized by a genetically optimized interpretability-accuracy trade-off (by means of multi-objective evolutionary optimization algorithms) for transparent and accurate prediction of decentral smart grid control (DSGC) stability. In particular, we aim at uncovering the hierarchy of influence of particular input attributes upon the DSGC stability. Moreover, we also analyze the effect of possible "overlapping" of some input attributes over the other ones from the DSGC-stability perspective. The recently published and available at the UCI Database Repository Electrical Grid Stability Simulated Data Set and its input-aggregate-based concise version were used in our experiments. A comparison with 39 alternative approaches was also performed, demonstrating the advantages of our approach in terms of: (i) interpretable and accurate fuzzy rule-based DSGC-stability prediction and (ii) uncovering the hierarchy of DSGC-system’s attribute significance.

Тhe study of electronic generation effect on statical aperiodic stability of electrical power system

One of the key aspects in the development of power engineering all over the world is the use of distributed small-scale generation. This is both based on fuel carbon resources with a synchronized connection between sources when they are connected to the electric power grids and renewable energy sources operated in the electrical grid via frequency converters (electronic generation). The latter brings an inevitable broad use of inverters in available AC power systems. The objectives of this paper are numerous. First is the desire to study the effect of electronic generation on modes and stability of current electrical grids and electrical power systems. Another objective is to establish requirements for electronic generation control that lets us minimize actions on relay protection coordination and automation upon the integration of electronic generation in power grids. A final objective is to increase the reliability of general electrical modes. This article shows the outcomes of the study on the statical aperiodic stability of the electrical power system upon the integration of electronic generation, requirements for its statical characteristics, and the control when operated within the electrical power system.

Implementation of a D-STATCOM control strategy based on direct power control method for grid connected wind turbine

Distributed generation (DG) is getting increasing relevancy on the electric energy production. In Tunisia, the increase of the installed wind power has an impact on the grid transmission due to the intermittence of production. Grid operators have now enough field experience to adapt the existing grid codes to the new energy mix. DG systems have proved that they can contribute to grid stability using power converters and control algorithms. Therefore, the installation of the FACT devices is necessary to guarantee the high energy quality and electrical grid stability. STATCOMs present the FACTs which are currently used. This paper presents the main effects of the DG connection to a grid and the role of STATCOM to reduce undesirable effects, making the DG fully operable. Thus, a STATCOM control strategy based on DPC is proposed to show the impact of this device on the quality of electrical power system. The advantage of the proposed technique is to enhance the voltage profile in steady state operation and under different working conditions and sags voltage. Moreover, STATCOM improves the transient voltage stability and therefore helps the wind turbine generator system to remain in service during grid faults. The proficiency and functionality of the proposed controller are demonstrated through detailed theoretical analysis. Computer simulation using the Matlab/Simulink software is performed. Accordingly, a hardware prototype employing a dSpace 1104 board -based system is built for final verification test. Experimental results show that the proposed control method is very effective.

Issues associated with the possible contribution of battery energy storage in ensuring a stable electricity system

The purpose of this paper is to provide a consideration of the role of battery energy storage in enhancing electricity grid stability at a time of increased use of non-hydro renewable generation sources, focusing on the Australian case. Besides the creation of a strategic reserve one measure that might reduce this instability that have being increasingly attracted much attention by both the operators of power systems and electricity distribution networks is the use of battery energy storage. Together, the multitude of regulatory and advisory bodies operating in and around the National Electricity Market of Australia (NEM), and the diverse and sometimes contrary opinions as to the best underlying foundational architecture for the NEM, overlaid by ongoing disagreement at the government policy level, point to continuing uncertainty and difficulties for an Australian electricity system in a state of transition.

An Evaluation of Flux-Coupling Type SFCL Placement in Hybrid Grid System Based on Power Quality Risk Index

Due to the expansion of restructured electricity markets and consequently power network operation approaching dynamic and static margins, the probability of transient instability has remarkably been increased. To reduce the transient instability risk or probabilistic cost of power quality, the present study focuses on determining the optimum location of a flux-coupling type SFCL. To this end, the candidate locations with lower related transient instability risk are selected as the best locations for installing flux-coupling type SFCLs. Nowadays, the type of SFCL employed in this study, which enjoys the benefits of both resistive and inductive type SFCLs, is a suitable means for enhancing the transient stability of electrical grids. Despite being another useful approach in transient stability evaluation, the equal-area criterion is merely restricted to the analysis of the stability of a single-machine power system connected to an infinite bus and also a two-machine power system. The online approach employed in this study is based on the corrected transient energy margin (CTEM), some probabilistic factors, and the cost of transient instability. Furthermore, CTEM index is a useful tool for calculating the critical clearing time (CCT). Therefore, in recent years, this index has become more and more useful. The studies carried out on the IEEE New England 39-bus test system ascertain the high applicability of the proposed method.

Anomaly detection of gas turbines based on normal pattern extraction

The integration of large-scale renewable energy forces the operating conditions of gas turbines to change more frequently to maintain the stability of electricity grid. This makes gas turbines malfunction more easily. In this case, anomaly detection is increasingly important to the safety and reliability of gas turbines. Current research mainly focuses on the case of abundant fault instances. However, fault data are rare or even unavailable in practice, especially for new gas turbines that have just operated for a short time. Thus, this paper proposes a novel anomaly detection method using only normal data based on nonlinear autoregressive with exogenous inputs network and prior knowledge fusion. This paper proposes the concept of gas turbine normal pattern extraction for the first time, extracts the unchanged feature of normal pattern from historical normal data and detects its changes for anomaly detection. Experiment on actual data shows the proposed method obtains 99.96% detection accuracy for fault data and 98.67% detection accuracy for normal data. Meanwhile, comparison between nonlinear autoregressive with exogenous inputs network and other methods including single-hidden layer feedforward network, Elman network and extreme learning machine verifies its superiorities in characterizing dynamic behaviors and normal pattern of gas turbines. Furthermore, comparison between the proposed method and 17 measurable parameter combinations shows it is the optimal parameter combination for anomaly detection. Comparison with three common purely data-driven anomaly detection methods including one-class support vector machine, isolation forest and principle component analysis further verifies its superiorities. This also indicates the superiorities of data-driven methods and prior knowledge fusion to some extent.

How consumers trade off supply security and green electricity: Evidence from Germany and Great Britain

The expansion of renewable energies requires infrastructure investments to at least maintain the stability of electricity grids. Using survey data from residential consumers in Germany and Great Britain, we infer in pecuniary terms the extent to which people are prepared to reward the presence of renewable resources in electricity production and how they trade off this change in the fuel mix against supply reliability. We rely on random-parameter econometric techniques to capture various degrees of heterogeneity among the respondents. Our central finding is that the vast majority of respondents (a) value a higher percentage of renewables in electricity generation and (b) penalize interruptions to supply. The trade-off between capacity expansion and supply security is such that the reported penalties for outages easily crowd out the willingness-to-pay for renewables.

Economic and Global Warming Potential Assessment of Flexible Power Generation with Biogas Plants

Demand-oriented power generation by power plants is becoming increasingly important due to the rising share of intermittent power sources in the energy system. Biogas plants can contribute to electricity grid stability through flexible power generation. This work involved conducting an economic and global warming potential (GWP) assessment of power generation with biogas plants that focused on the Austrian biogas sector. Twelve biogas plant configurations with electric rated outputs ranging from 150–750 kW and different input material compositions were investigated. The results from the economic assessment reveal that the required additional payment (premium) to make power generation economically viable ranges from 158.1–217.3 € MWh−1. Further, the GWP of biogas plant setups was analyzed using life cycle assessment. The results range from −0.42 to 0.06 t CO2 eq. MWh−1 and show that the 150 kW plant configurations yield the best outcome regarding GWP. Electricity from biogas in all scenarios outperformed the compared conventional electricity sources within the GWP. Greenhouse gas (GHG) mitigation costs were calculated by relating the needed premium to the CO2 eq. saving potential and range from 149.5–674.1 € (t CO2 eq.)−1.

Behavior of Francis turbines at part load. Field assessment in prototype: Effects on power swing

Francis turbines are increasingly demanded to work at part load in order to satisfy the electricity generation demand and to compensate the non-constant generation of other renewable sources such as wind or solar. At part load, Francis turbines present complex flow patterns dominated by cavitation and especially by the draft tube vortex rope. The vortex rope phenomenon affects the structural behavior of the machine as well as the hydraulic circuit response and, for certain conditions, it could become unstable. At this situation, the output power can significantly swing endangering the electrical grid stability. This condition has to be well-known and avoided as far as possible. In this paper, the phenomenon of the power swing at part load in prototypes is studied. For this purpose, different Francis turbine prototypes have been selected. The units have different specific speeds and therefore different power, head and geometry. All of them present the power swing phenomenon at part load. Different sensors, such as accelerometers, proximity probes, pressure sensors or strain gauges, have been placed in different positions of all the machines in order to detect and quantify the phenomenon. The relationship between the hydraulic phenomenon and the output power swing is presented for all the turbines studied. Results obtained permit to asses on the on-site detection of power swing as well as the possibility to avoid it in real-time.

(IBA Technology Award) Energy Storage Batteries using Mn and Ti Based Elelctrode Materials

Renewable generation, such as solar and wind, is intermittent, which causes sudden and unanticipated changes to power output and contributes to electricity grid instability. The stability and reliability of electricity grids requires a continuous balance between energy supply and demand. The aqueous lithium-ion battery (LIB) may solve both the safety problem associated with lithium-ion batteries that use highly toxic and flammable organic solvents, and the poor cycling life associated with commercialized aqueous rechargeable batteries including lead-acid and nickel-metal hydride systems. Aqueous LIBs have been demonstrated to be one of the most promising stationary power sources for the sustainable energy such as wind and solar power. As early as in 2005, we developed a new concept hybrid electrochemical supercapacitor in which a Li-ion intercalated compound was used as a positive electrode in combination with an activated carbon negative electrode in a Li2SO4 aqueous electrolyte (ZL. 200510025269.6). A hybrid cell consisting of a spinel LiMn2O4 positive and an AC negative electrode shows excellent reversibility with a sloping voltage profile from 0.8 to 1.8 V at an average voltage near 1.3 V, and delivers an 8 Wh/kg of 60000 F practical cell. In order to improve the specific energy, we employed LiTi2(PO4)3, but the cell shows poorer cycling stability compared with the pure AC anode. We found that, in the presence of oxygen, the discharged state of lithium-ion intercalated compounds (LIC) of all negative electrode materials will react with water and O2 despite of the pH value of the electrolyte, which is mainly responsible for the capacity fading of aqueous lithium-ion batteries during charge/discharge cycling. By eliminating the O2 (using a sealed cell), adjusting the pH values of the electrolyte, and using carbon-coated electrode materials; the LIB shows much cycling stability. A commercialized hybrid LIB shows a specific energy of ca. 30 Wh/kg, and exhibits a good cycling stability over 3500 cycles. In 2005, we also for the first time employed carbon coating technology to improve the electrode performance of Li4Ti5O4. By optimizing the graphitization and the coated carbon layer thickness, the nano-thickness carbon layer coated LTO shows excellent rate capability and less gas generation during cycling and storage. A LMO/LTO Li-ion battery show excellent cycling stability, high power and safety as stationary power source for smart-grid systems.

Study of an HTGR and renewable energy hybrid system for grid stability

A hybrid system combining nuclear energy from a high temperature gas reactor (HTGR) and renewable energy from solar and wind is investigated for its ability to compensate the characteristic effects of intermittent and perturbating performance of the renewable energy power generation to provide electric grid stability. The HTGR considered employs a direct cycle gas turbine for power generation while cogenerating heat for industrial use as such hydrogen production. The hybridization with renewable energy takes advantage of two intrinsic design features of the HTGR system, namely, the agile power maneuvering of the gas turbine and the existential massive capacity of heat in the reactor graphite core. The former, performed through a new proposal of using the inventory and bypass control devices already built in the gas turbine, is found to be effective and efficient to compensate for the intermittent power generation including hourly to daily variation of renewable energy. The reactor thermal power remains at constant full power while the heat output is increased or decreased subject to the need of reactor power generation. On the other hand, the massive heat capacity stored the HTGR graphite core is shown to be sufficient to compensate for the perturbating power generation of renewable energy on a time scale of seconds to minutes and up to about 20 percent of the rated power output of the nuclear plant. Similarly, no additional control devices, only a new proposal of using the existing devices, are required to perform this control operation. These findings demonstrate the technical and economic potential of the HTGR system to maintain the stability of a grid being incorporated with significant portfolios of renewable energy power generation.

Model of the impact of use of thermal energy storage on operation of a nuclear power plant Rankine cycle

Increasing electricity production by solar and wind energy is projected to impact the stability of electricity grids and consequently may limit the growth of renewable electricity generation. This issue can be ameliorated in part by increasing the flexibility of baseload power plants. A thermodynamic analysis of thermal energy storage (TES) coupled with a nuclear-powered Rankine cycle as one approach of increasing baseload flexibility is presented. During periods of excess capacity, the high-pressure steam supply is used to charge the TES. When electricity generation above the baseload capacity is required, the TES is discharged to generate steam for expansion in the low-pressure turbine. Pressure, temperature, and enthalpy state points within the cycle are presented over a range of charge and discharge rates. The capacity factor over a charge/discharge cycle is up to 9.8% higher than that of the same plant operated with steam bypass. This benefit increases with increasing charge and discharge power. With TES, the thermal-to-electrical efficiency is stable over a wide range of discharge rates. The results support future development of TES systems for baseload thermal power plants in a power grid in which renewable energy is prioritized.