Model Of Battery Energy Storage System Research Articles

Battery Energy Storage System Performance in Providing Various Electricity Market Services

The Battery Energy Storage System (BESS) is one of the possible solutions to overcoming the non-programmability associated with these energy sources. The capabilities of BESSs to store a consistent amount of energy and to behave as a load by releasing it ensures an essential source of flexibility to the power system. Nevertheless, BESSs have some drawbacks that pose limitations to their utilization. Indeed, effectively managing the stored and released energy is crucial, considering the degradation of performance associated with these systems over time. The substantial capital expenditure (CAPEX) required to install these systems represents a current constraint, impeding their broader adoption. This work evaluates a techno-economic analysis of a 2MW/2MWh BESS providing multiple services, namely participating in capacity and balance markets. The analysis is based on a BESS model implemented in SIMULINK, adopting online data gathered from a Lithium Iron Phosphate (LFP) battery facility. The model evaluates the auxiliary power consumption, state-of-charge (SoC), state of health (SoH), and the round-trip efficiency (RTE) of the overall system. The analysis is based on three price profiles: 2019 (Business-As-Usual), 2020 (COVID-19), and 2022 (Gas Crisis). Furthermore, this work conducts a case study to analyze the behavior of the BESS. It entails a sensitivity analysis, specifically evaluating the influence of CAPEX and upward bid price on the economic viability of the project. The results show a strong relation between the CAPEX variation and the Internal Rate of Return (IRR) of the project.

Analysis of fast frequency control using battery energy storage systems in mitigating impact of photovoltaic penetration in Ethiopia–Kenya HVDC link

The high penetration of photovoltaic (PV) in power grids typically leads to the displacement of traditional synchronous generators (SGs). However, with a high penetration of PV, fewer SGs are running, and the sharing of responsibility to control the system frequency is reduced and easily exacerbates the problem of reduced inertia response in the power system. This can increase frequency deviation when there is a load-generation imbalance or unexpected disturbances. The limited amount of inertial response from the PV generation means that it cannot provide the same frequency support as SGs. Therefore, this paper suggests a fast frequency control (FFC) technique for the battery energy storage system (BESS) to reduce the instantaneous frequency deviation (IFD) in the Ethiopian grid. The authors specifically provide knowledge of the modeling of droop-type controlled BESS, which can provide additional damping, enhance the inertial ability of the system, and reduce IFD. The simulation results indicate the effectiveness of BESS in mitigating the adverse inertial impact of PV and meeting the necessary grid requirement. The proposed method was tested on the Ethiopian power system model through simulations by using DIgSILENT simulation software. To validate the model, transient frequency simulation studies are performed for various fault conditions, and comparisons are made with and without BESS for different levels of PV penetration.

Economic evaluation of battery energy storage system on the generation side for frequency and peak regulation considering the benefits of unit loss reduction

AbstractThe indirect benefits of battery energy storage system (BESS) on the generation side participating in auxiliary service are hardly quantified in prior works. Nevertheless, the configuration of BESS could be affected by its indirect benefits. In this paper, the authors purpose a quantitative economic evaluation method of BESS considering the indirect benefits from the reduction in unit loss and the delay in investment. First, the authors complete further the cost model of BESS for frequency and peak regulation based on the whole life cycle theory. Second, the authors quantify the indirect benefits of BESS in thermal power plants based on the theory of rotor fatigue life loss and establish a benefits model that considers the unit loss reduction during frequency regulation and the delay in investment during peak regulation. Finally, the authors propose a set of indexes for economic evaluation of the thermal power plant with BESS. The simulation results show that the total benefits of BESS can be improved effectively by considering the indirect benefits from unit loss reduction and the delay in investment, proving the effectiveness of the proposed approach which can be meaningful for the future investment in BESS on the generation side.

Modeling of battery energy storage systems for AGC performance analysis in wind power systems

Battery energy storage system (BESS) is being widely integrated with wind power systems to provide various ancillary services including automatic generation control (AGC) performance improvement. For AGC performance studies, it is crucial to accurately describe BESS’s power regulation behavior and provide a correct state of charge (SOC). In addition, BESS model is required to maintain a sufficiently high simulation speed, since it usually is involved in hour- or day-scale simulations. Different from existing models, this paper develops a new BESS model which considers DC-AC converter control, DC link circuit and switching loss, thus providing an accurate power regulation behavior. In addition, it also considers the nonlinear relationship between open-circuit voltage and SOC of battery so that it provides a correct SOC. As a whole, the BESS is simplified as a controlled current source with fundamental frequency (50/60 Hz) and described by algebraic phasor equations to reduce computational burden. This makes the proposed model capable of finishing hour- or day-scale simulations within a few minutes. Time-domain simulations are used to validate and compare the simulation accuracy with the classical first-order transfer function model and electromagnetic model. Based on the proposed model, this paper also introduces and verifies a new BESS-based strategy to improve the AGC performance of wind farms.

Battery and Flywheel hybridization of a reversible Pumped-Storage Hydro Power Plant for wear and tear reduction

Energy security and environmental challenges are some of the drivers for increasing the electricity generation from non-programmable Renewable Energy Sources (RES), adding pressure to the grid, especially if located in weakly connected (or isolated) islands, like Sardinia. Variable-speed Pumped Storage Hydro Power (PSHP) can offer a high degree of flexibility in providing ancillary services (namely primary and secondary regulations), but due to the hydro-mechanical nature of the equipment, sudden variations in the power output cause wear and tear. Other energy storage devices cannot compete with PSHP in terms of energy and power availability. The aim of this research is to assess the benefits derived from the hybridization of a PSHP with Battery Energy Storage System (BESS) and Flywheel Energy Storage System (FESS), to be installed in the Sardinia island (Italy). A dynamic model of the hybrid plant was made in MATLAB–Simulink® environment. A detailed model of the variable-speed pump-turbine was obtained from experimental data, and a simplified model of a fixed-speed turbine was produced. A detailed FESS model was provided by CIEMAT (Madrid, Spain) and a simplified BESS model was included. A dedicated control strategy to manage the power flows and accounting for State Of Charge (SOC) control, was implemented. A total of 100 combinations of BESS and FESS powers were taken into account, and the control strategy was calibrated for each one of them. The plant was simulated open-loop over a 3600s time period, feeding historical frequency and Automatic Generation Control (AGC) data. The simulations covered three PSHP operation modes: variable/fixed-speed turbine and variable-speed pump, and with/without hybridization. The performances of the hybridization were evaluated with wear and tear indicators for the PSHP (distance travelled by and number of movements of the wicket gate for turbine, fluctuations of the shaft torque for the pump) and capacity loss (life consumption) for the BESS. The results show that all the combinations of BESS and FESS powers result in the reduction of both the travelled distance and number of movements of the guide vanes. The best hybrid combination for the PSHP does not affect the BESS life consumption, which still is always in an acceptable range. A comparison between the non-hybrid variable-speed turbine and the hybrid fixed-speed counterpart shows that the electric powers do not differ substantially, but the hybridization smooths the movement of the guide vanes. The pump torque fluctuations sharply decrease with the hybridization, but more research is needed to validate that the change in the fluctuation index corresponds to a physical phenomenon. Overall, the hybridization improves the plant performances in terms of wear and tear reduction, and the presence of an additional FESS benefits both the BESS and the PSHP. The results also highlight the necessity for more research in variable-speed pumps providing ancillary services, and their impact.

Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources

The reasonable allocation of the battery energy storage system (BESS) in the distribution networks is an effective method that contributes to the renewable energy sources (RESs) connected to the power grid. However, the site and capacity of BESS optimized by the traditional genetic algorithm is usually inaccurate. In this paper, a power grid node load, which includes the daily load of wind power and solar energy, was studied. Aiming to minimize the average daily distribution networks loss with the power grid node load connected with RESs, a site selection and capacity setting model of BESS was built. To solve this model, a modified simulated annealing genetic algorithm was developed. In the developed method, the crossover probability and the mutation probability were modified by a double-threshold mutation probability control, which helped this genetic method to avoid trapping in local optima. Moreover, the cooling mechanism of simulated annealing method was presented to accelerate the convergence speed of the improved genetic algorithm. The simulation results showed that the convergence speed using the developed method can be accelerated in different number BESSs and the convergence time was shortened into 35 iteration times in view of networks loss, which reduced the convergence time by about 30 percent. Finally, the required number of battery system in BESS was further built according to the real batteries grouping design and the required capacity of BESS attained using the developed method.

A bi-level reinforcement learning model for optimal scheduling and planning of battery energy storage considering uncertainty in the energy-sharing community

Sharing of battery energy storage systems (BESS) in the energy community by reflecting the real world can play a significant role in achieving carbon neutrality. Therefore, this study aimed to develop a bi-level reinforcement learning (RL) model of BESS considering uncertainty in the energy-sharing community for the following optimization strategies: (i) short-term scheduling model for optimal electricity flows considering operational objectives (i.e., self-sufficiency rate (SSR), peak load, and economic profit); and (ii) long-term planning model for optimal BESS plan (i.e., install, replace, and disuse) along with battery types (new or reused batteries). A case study in the South Korea Nonhyeon neighborhood was conducted to evaluate the developed bi-level RL model feasibility based on future scenarios considering the time-dependent variables. The developed model increased economic profit by up to 18,830 USD compared to the rule-based model. Compared to the case where BESS was not installed, SSR increased by up to 7.79% and peak demand decreased by up to 1.31 kWh. These results show that the developed model could maximize the economic feasibility of community-shared BESS by reflecting the uncertainty in the real world, ultimately benefiting participants in the energy-sharing community.

An adaptive VSG control strategy of battery energy storage system for power system frequency stability enhancement

With the steady deployment of intermittent generation, the inertia of the grid decreases, and the stability problem is becoming increasingly critical within system operation. The virtual synchronous generator (VSG) control is a means to control battery energy storage systems (BESS) to retain the dynamics of conventional synchronous generators and ensure a smooth transition toward converter-dominated power systems. Since the parameters in the VSG control can be varied, it can be turned to be an alternative to obtain an optimal response for the overall performance of frequency when the operating conditions change. In this paper, the adaptive VSG control is proposed to improve the dynamic characteristic of active power at a certain capacity. For this purpose, firstly, the electromechanical transient model of BESS based on VSG control is built. Afterward, the effect of inertia under grid disturbance by transfer function is analyzed, and the influence of key parameters on dynamic stability is identified. Finally, the proposed adaptive VSG control is verified in the modified IEEE 4-machine 2-area system and an actual power grid example. The simulation results show the effectiveness and great potential of this model in improving frequency stability.

Modeling and Control-Tuning of a Single-Stage MMC-Based BESS

In recent years, the integration of battery energy storage systems (BESSs) with multilevel modular converters (MMCs) has received interest in power system applications. In this work, this configuration is called a MMC-based BESS. The batteries are connected directly to the MMCs on submodules (SMs), called the single-stage approach. Several control strategies have been proposed to guarantee the proper operation of a MMC-based BESS. This system is complex due to the control strategy. Another challenge is in obtaining the controller gains for a MMC-based BESS converter. In this sense, there is a gap in the methodology used to calculate the controller gains. Thus, this work aimed to tune the analytical expressions of a MMC-based BESS by considering the single-stage approach. The methodology is validated through detailed simulation models of 10.9 MVA/5.76 MWh connected to a 13.8 kV power system. Finally, to validate the dynamics of the controllers, the simulation results in the PLECS software for the charging and discharging processes are presented.

Robust bidding strategy of battery energy storage system (BESS) in joint active and reactive power of day-ahead and real-time markets

The most important applications of an Energy Storage System (ESS) in power systems are energy arbitrage along with procurement of Ancillary Services (ASs). In addition to economic benefits, ESS also improves network reliability and stability. In this paper, a bidding strategy model of a Battery Energy Storage System (BESS) in a Joint Active and Reactive Power Market (JARPM) in the Day-Ahead-Market (DAM) and the Real-Time-Market (RTM) using a robust framework is presented. In this study, the BESS model is considered a price-taker, with the private owner trying to maximize its profit while facing price uncertainty. In the first stage of the proposed model, JARPM is cleared in a deterministic way without the presence of BESS to determine hourly prices of active and reactive power. In the second stage, by determining the prices uncertainty interval and the robust budget for DAM and RTM, the robust bidding model aims to maximize the BESS owner's profit, using the robust counterpart formation of the objective function and dual theory. The proposed robust formulation based on a Mixed Integer Non-Linear Programming Model (MINLP) is implemented in the GAMS software environment on the IEEE-24-RTS test system. The result shows that by the participation of BEES in the market, the average price of active and reactive power is reduced by 2 % & 3 % respectively. In addition, the overall cost of DAM execution is reduced markedly. On the other hand, the proposed framework in three case studies also guarantees a suitable profit level for the private owners of BESS (30–35 $/hour).

Linear battery models for power systems analysis

Mathematical models are just models. The desire to describe battery energy storage system (BESS) operation using computationally tractable model formulations has motivated a long-standing discussion in both the scientific and industrial communities. Linear BESS models are the most widely used so far. However, finding suitable linear BESS models has been controversial. This paper focuses on the description of linear BESS models. Four linear BESS formulations are presented, among the most popularly used. A new formulation is also proposed. The 5 BESS models are tested in 100 random BESS and 1.450 random samples of daily profiles of renewable generation. Two classical problems of power systems, namely, the set-point tracking problem and the transmission expansion planning problem, are selected for numerical analysis. Five thousand simulations are used to draw a better interpretation of each linear formulation presented and showcase specific challenges of BESS models. Practical recommendations are provided based on the findings.

Development of chemistry-specific battery energy storage system models using combined multiphysics and reduced order modeling

Batteries are pivotal towards the decarbonization of energy systems as they address the intermittent nature of renewable energy technologies. The techno-economic feasibility of deploying batteries in microgrids is often analyzed using energy systems modeling tools. These often utilize the idealized battery model, which is simpler but neglects electrochemical phenomena. Reduced-order models, which are derived from continuum-scale physics-based models, have improved accuracy but are yet to be applied in the cost optimization of microgrids. In this work, we proposed a novel methodology for generating reduced-order models of lithium-ion lithium iron phosphate, nickel cobalt aluminum oxide, and nickel manganese cobalt chemistries for use in the techno-economic optimization of microgrids. We simulated previously reported multiphysics models of Li-ion batteries in COMSOL Multiphysics® and reduced them into equivalent circuit models. These were implemented in Island Systems LCOEmin Algorithm (ISLA), our in-house energy systems modeling and optimization tool. We then simulated and optimized renewable energy systems in ISLA to determine the discrepancy between the reduced-order equivalent circuit models versus the idealized battery model. The results revealed that the idealized battery model can miscalculate the SOC by >5 % SOC due to the assumption of constant voltages, while the optimum sizes differed by >5 % when there are sharp peaks in demand or if non-renewable sources are competitive. The idealized battery model is therefore not a valid approximation under these scenarios. This work demonstrated a novel multi-scale framework from the continuum-scale multiphysics modeling of batteries to macroscale energy systems optimization.

Verification and analysis of a Battery Energy Storage System model

Energy Storage System modelling is the foundation for research into the deployment and optimization of energy storage in new and existing applications. The increasing penetration of renewable energy into electrical grids worldwide means energy storage is becoming a vital component in the modern electrical distribution system. In this paper, a model for a Battery Energy Storage System developed in MATLAB/Simulink is introduced and subsequently experimentally verified against an existing 2 MW installation operated by The University of Sheffield (Willenhall). The model presented in this paper achieved average Root Mean Square Error (RMSE) values of 1.29% and 1.05% when comparing the state of charge (SOC) of the model with two different methods of SOC measurement at the Willenhall installation. The methodology behind fine tuning the model to decrease the average RMSE is presented and discussed.

A Comparative Analysis of Peak Load Shaving Strategies for Isolated Microgrid Using Actual Data

Peak load reduction is one of the most essential obligations and cost-effective tasks for electrical energy consumers. An isolated microgrid (IMG) system is an independent limited capacity power system where the peak shaving application can perform a vital role in the economic operation. This paper presents a comparative analysis of a categorical variable decision tree algorithm (CVDTA) with the most common peak shaving technique, namely, the general capacity addition technique, to evaluate the peak shaving performance for an IMG system. The CVDTA algorithm deals with the hybrid photovoltaic (PV)—battery energy storage system (BESS) to provide the peak shaving service where the capacity addition technique uses a peaking generator to minimize the peak demand. An actual IMG system model is developed in MATLAB/Simulink software to analyze the peak shaving performance. The model consists of four major components such as, PV, BESS, variable load, and gas turbine generator (GTG) dispatch models for the proposed algorithm, where the BESS and PV models are not applicable for the capacity addition technique. Actual variable load data and PV generation data are considered to conduct the simulation case studies which are collected from a real IMG system. The simulation result exhibits the effectiveness of the CVDTA algorithm which can minimize the peak demand better than the capacity addition technique. By ensuring the peak shaving operation and handling the economic generation dispatch, the CVDTA algorithm can ensure more energy savings, fewer system losses, less operation and maintenance (O&M) cost, etc., where the general capacity addition technique is limited.

Mitigating Overvoltage in Power Grids with Photovoltaic Systems by Energy Storage

Abstract The rise of photovoltaic (PV) penetration is contributing to the increasing incidence of overvoltage detection in the electrical grid during times of high-power generation. Overvoltage can cause disturbances or (partial) failures in the electrical supply network, since the components used are designed for a certain voltage band. One option to counteract too high voltage levels and thus ensure power quality, grid stability and resilience is the absorption of active power by means of a battery energy storage system (BESS). In this paper, we first built a suitable simulation setup for a typical European network section, including a large-scale PV system connected to the 10 kV level and a BESS model. A suitable charging and discharging algorithm for the BESS with the aim to realize peak shaving for the grid voltage was developed and implemented. Simulations, performed in MATLAB/Simulink®, show the dependence of the battery capacity and power on the grid-serving effect of BESS. By determining appropriate values for these two factors a significant reduction of the voltage level could be achieved.

Charging rate based battery energy storage system model in wind farm and battery storage cooperation bidding problem

Charging rate based battery energy storage system model in wind farm and battery storage cooperation bidding problem

A Holistic State Estimation Framework for Active Distribution Network with Battery Energy Storage System

Battery energy storage systems (BESSs) are expected to play a crucial role in the operation and control of active distribution networks (ADNs). In this paper, a holistic state estimation framework is developed for ADNs with BESSs integrated. A dynamic equivalent model of BESS is developed, and the state transition and measurement equations are derived. Based on the equivalence between the correction stage of the iterated extended Kalman filter (IEKF) and the weighted least squares (WLS) regression, a holistic state estimation framework is proposed to capture the static state variables of ADNs and the dynamic state variables of BESSs, especially the state of charge (SOC). A bad data processing method is also presented. The simulation results show that the proposed holistic state estimation framework improves the accuracy of state estimation as well as the capability of bad data detection for both ADNs and BESSs, providing comprehensive situational awareness for the whole system.

Dual stage microgrid energy resource optimization strategy considering renewable and battery storage systems

Optimal operation of micro grids (MGs) with high penetration of renewable energy sources (RESs) has become inflexible as a consequence of the uncertainty and intermittency nature of RESs. Energy management along with battery energy storage system (BESS) is thus essential to minimize these uncertainties of MGs and to ensure continuity of energy supply and maintain the reliability of the system. In this paper, a dual-stage MG optimal energy management approach considering the BESS model and other dispatchable resources is introduced to minimize the operating cost. The proposed approach comprises of the schedule and dispatch stages. The mixed-integer linear programming is applied to solve the dual-stage energy management problem and the performance of the suggested technique is compared with the particle swarm optimization technique. The obtained results prove that the incorporation of the suggested BESS plant can significantly reduce the operating costs up to 7% and 6% when committed to supplying power in the scheduling and dispatch stages. The results also show that power loss has a considerable impact on MG energy management. The optimal simulation results confirm the effectiveness of the proposed microgrid energy resources optimization strategy.

Beyond the Diffusion of Residential Solar Photovoltaic Systems at Scale: Allegorising the Battery Energy Storage Adoption Behaviour

Understanding the residential adoption decision of battery energy storage systems (BESSs) is central to the implementation of successful intervention policies. However, when the residential solar photovoltaics (PV) becomes a widely accepted technology across a society, accurately modelling the behaviour shows a higher degree of complexity. In this vein, the uptake pathway of BESS and PV coupled with BESS (PV–BESS) would predictably exhibit similar attitudinal traits to that of PV consumption. This notion implies that the antecedent PV decision can be regarded as the past behaviour of the BESS adopter by creating attitudinal implications. The PV use status also yields a higher degree of heterogeneity through the emergence of four new household groups and the inherent imbalances in the involvement of the interwoven financial, technical, sociodemographic, and psychological predictors. This perspective employs the Reasoned Action Approach (RAA) to allegorise a decision-making model of BESS and PV–BESS adoption behaviour in a mature PV market (Australia). It argues that the particularised background factors will likely shape the individual’s attitudes and perceived norms for intention, and showcases affordability and the use of PV as the two control components that dictate the final decision.

Modeling of Li-ion battery energy storage systems (BESSs) for grid fault analysis

Battery energy storage systems (BESSs) are expected to play a key role in enabling high integration levels of intermittent resources in power systems. Like wind turbine generators (WTG) and solar photovoltaic (PV) systems, BESSs are required to meet grid code requirements during grid disturbances. However, BESSs fundamentally differ from WTG and PV systems because they are not only required to operate as a source (discharging) but also as a load (charging). This paper investigates the system-level behavior of BESSs under grid fault conditions. It presents first a generic electromagnetic transient (EMT)-type model of a two-stage Li-ion BESS that can be configured to comply with grid codes. Compared to previous studies, the proposed model introduces a key step in the characterization of BESSs considering decoupled sequence control (DSC) and the non-linear impact of current limiters under stringent unbalanced faults. The model is used to demonstrate that BESSs behave differently in charging mode, a factor that needs to be accounted in protective relaying practices. The conducted simulations show that the charging mode is more stringent on both dc-link voltage regulation and grid voltage support, and that this is aggravated by the use of DSC scheme compliant with VDE-AR-N 4120 grid code.