Battery Energy Storage System Power Research Articles

Deterministic power management strategy for fast charging station with integrated energy storage system

With the increasing expansion of fast-charging stations (FCS) and the emergence of high-power electric vehicles (EVs), the development of management strategies to address potential grid disturbances becomes necessary. In this context, this paper proposes an optimized power management strategy for an FCS with integrated battery energy storage systems (BESS). The proposed strategy aims to monitor the variation in AC voltage at the point of common coupling (PCC) and the state of charge (SOC) of the BESS, with the objective of establishing a deterministic formulation to find the optimal instantaneous BESS power level. Therefore, by using the proposed approach, it is possible to compensate for potential fluctuations in AC voltage while maintaining the BESS SOC at appropriate levels. The results are obtained through a real-time hardware-in-the-loop (HIL) platform to verify the system behavior under distinct parameters or operating conditions. These results demonstrate the control flexibility by prioritizing the AC voltage regulation or the BESS charging. Furthermore, the impact of the proposed power management strategy is investigated by performing a comparison with the system without BESS integration. It is observed that the proposed control strategy effectively regulates the PCC voltage and maintains it within acceptable limits.

Optimal placement of battery energy storage system considering penetration of distributed generations

This paper proposes the optimal problem of location and power of the battery-energy-storage-system (BESS) on the distribution system (DS) considering different penetration levels of distributed generations (DGs). The objective is to minimize electricity cost of the DS in a typical day considering the power limit of DG fed to the DS. Growth optimizer (GO) is first applied to search the BESS’s location and power for each interval of the day. The considered problem and GO method are evaluated on the 18-node DS with two penetrations levels of photovoltaic system and wind turbine. The results demonstrate that the optimal BESS placement significantly reduces electricity cost. Furthermore, the optimal BESS location and power also help to reduce the cut capacity of DGs as their power greater than the load demand. The compared results between GO and particle swarm optimization (PSO) method have shown that GO reaches the better performance than PSO in term the optimal solution and the statistical results. Thus, GO is an effective approach for the BESS placement problem.

Operation scheduling strategy of battery energy storage system with the integration of differenced power constraint factor

The battery energy storage system (BESS) as a flexible resource can effectively achieve peak shaving and valley filling for the daily load power curve. However, the different load power levels have a differenced demand on the charging and discharging power of BESS and its operation mode. For further improving the efficiency of BESS in a demand response process, the differenced power demand should be incorporated into the BESS operation schedule. Therefore, this paper proposes a BESS operation scheduling strategy to satisfy the differenced demand based on the control of the power constraint factor. Firstly, a boundary moving method is used to solve the charging and discharging thresholds of BESS to determine the optimal charging and discharging periods. Secondly, a charging and discharging demand degree model is constructed based on the hourly charging and discharging demand. Thirdly, a BESS scheduling model with a minimum standard deviation of daily load curve is established for enhancing the peak shaving and valley filling. Finally, the case study shows that the proposed strategy can further improve the efficiency of BESS participating in demand response, improve the smoothness of the load power curve, and decrease the peak-valley difference.

Wind generator and storage system scheduling for customer benefit and battery life

Due to increased fossil fuel use and fossil fuel limitations, the Indian energy industry is migrating to non-conventional energy resources such as solar power, wind production, and fuel cells, among others. The unpredictability of non-conventional energy sources makes it difficult to balance an electrical system when they are incorporated, necessitating the incorporation of a storage device into the grid. In a microgrid system with wind turbine generation (WTG) and a battery energy storage system (BESS), the BESS may reserve energy during periods of surplus generation and release it to the grid during times of peak demand. The suggested technique establishes the state of charge (SOC) schedule for the BESS by employing an artificial rabbit optimisation (ARO) algorithm that minimises energy costs for customers. The state of health (SOH) of the energy storage is incorporated as an ageing coefficient, which causes the BESS to behave conservatively in order to retain its lifespan. Using a time of use (TOU) tariff, simulation results suggest a substantial possibility to boost the savings of consumers in a grid-connected micro-grid. The simulation findings indicate that by efficiently scheduling the BESS power management technique, the proposed method improves a number of distribution system efficacy.

A Model-Aware Comprehensive Tool for Battery Energy Storage System Sizing

This paper presents a parametric procedure to size a hybrid system consisting of renewable generation (wind turbines and photovoltaic panels) and Battery Energy Storage Systems (BESS). To cope with the increasing installation of grid-scale BESS, an innovative, fast and flexible procedure for evaluating an efficient size for this asset has been developed. The tool exploits a high-fidelity empirical model to assess stand-alone BESS or hybrid power plants under different service stacking configurations. The economic performance has been evaluated considering the revenue stacking that occurs when participating in up to four distinct energy markets and the degradation of the BESS performances due to both cycle- and calendar-aging. The parametric nature of the tool enables the investigation of a wide range of system parameters, including novel BESS control logic, market prices, and energy production. The presented outcomes detail the techno-economic performances of a hybrid system over a 20-year scenario, proposing a sensitivity analysis of both technical and economic parameters. The case study results highlight the necessity of steering BESS investment towards the coupling of RES and accurate planning of the service stacking. Indeed, the implementation of a storage system in an energy district improves the internal rate of return of the project by up to 10% in the best-case scenario. Moreover, accurate service stacking has shown a boost in revenues by up to 44% with the same degradation.

Model predictive control of a microgrid with energy-stored quasi-Z-source cascaded H-bridge multilevel inverter and PV systems

This paper presents a new energy management system (EMS) based on model predictive control (MPC) for a microgrid with solar photovoltaic (PV) power plants and a quasi-Z-source cascaded H-bridge multilevel inverter that integrates an energy storage system (ES-qZS-CHBMLI). The system comprises three modules, each with a PV power plant, quasi-impedance network, battery energy storage system (BESS), and voltage source inverter (VSI). Traditional EMS methods focus on distributing the power among the BESSs to balance their state of charge (SOC), operating in charging or discharging mode. The proposed MPC-EMS carries out a multi-objective control for an ES-qZS-CHBMLI topology, which allows an optimized BESS power distribution while meeting the system operator requirements. It prioritizes the charge of the BESS with the lowest SOC and the discharge of the BESS with the highest SOC. Thus, both modes can coexist simultaneously, while ensuring decoupled power control. The MPC-EMS proposed herein is compared with a proportional sharing algorithm based on SOC (SOC-EMS) that pursues the same objectives. The simulation results show an improvement in the control of the power delivered to the grid. The Integral Time Absolute Error, ITAE, achieved with the MPC-EMS for the active and reactive power is 20 % and 4 %, respectively, lower than that obtained with the SOC-EMS. A 1,3 % higher charge for the BESS with the lowest SOC is also registered. Furthermore, an experimental setup based on an OPAL RT-4510 unit and a dSPACE MicroLabBox prototyping unit is implemented to validate the simulation results.

A universal optimization framework for commercial building loads using DERs from utility tariff perspective with tariff change impacts analysis

To accommodate the changes in the nature and the pattern of electricity consumption with the available resources, utilities have introduced variety of tariffs over the years. This paper develops a comprehensive optimization framework that addresses the challenges associated with the diversity of utility tariffs for commercial loads. Optimization of commercial building net load through Battery Energy Storage System (BESS) and renewable energy resources is modeled and explored for minimizing billing cost for different tariffs. Cost functions are formulated for each possible commercial utility tariff type, engendering the universal cost function format. An algorithm is developed to apply the optimization model and generate the desired optimal outputs (e.g., optimal net load and BESS power, costs and savings, etc.). The results for several building loads and all tariff types are compared and analyzed to present the findings. An impacts analysis is performed to observe how different changes in tariffs affect the optimizations results. Results exhibit that for same building load and renewable generation, the tariff type can have an overwhelming impact on costs and savings. Tariffs without Time of Use (TOU) charges or peak demand charge components offer lower savings opportunity from BESS optimization. Price change sensitivities showed the complicated dynamics among TOU price components on savings and historical trend analysis as an effective tool to predict future savings. Tradeoff between energy and demand charge heavy tariff options are also discussed for user benefit. The recent change in TOU time periods presents limited desired benefits while the modified tariff proposed in this paper exhibit significant improvement in terms of utility operation.

Design and performance comparisons of power converters for battery energy storage systems

SummaryThe use of grid‐connected battery energy storage systems (BESSs) has increased around the world. In the scenario of high penetration level of renewable energy sources in distributed generation, BESS plays an important role to combine a sustainable power supply with a reliable dispatched energy source. Different power converter topologies are employed to connect the batteries to the grid, generally using single‐stage converters. However, the battery voltage changes according to its state of charge, which can introduce great variations in the dc‐link voltage. Therefore, the dc/ac stage converter must have its power switches designed to support this voltage variation. A controllable dc‐link voltage is achieved by inserting a dc/dc converter stage to connect the batteries to the dc/ac stage. Among the dc/dc converters, the interleaved bidirectional converter has been widely used due to the low input current ripple. However, the trade‐off of the insertion of the dc/dc stage is barely explored in the literature. Therefore, this work presents a methodology of analysis, considering the battery voltage variation and its influence on BESS power conversion system design and efficiency. Furthermore, it is proposed a power conversion system design oriented toward the voltage class of power switches and efficiency. The analysis in this study are conducted through the use of a case study approach, incorporating both simulated and experimental results.

Battery Scheduling Control of a Microgrid Trading with Utility Grid Using Deep Reinforcement Learning

Managing microgrids (MGs) with variable renewable energy (VRE) is challenging because of uncertainties of electricity production, loads, and energy price, so we need flexible control strategies for battery energy storage system (BESS) to handle those challenges. Model‐based approaches require precise models of the MG to give accurate results but having an accurate model can be difficult in continually changing environments. We introduced a new day‐ahead optimization method to control BESS scheduling and power exchange between the utility grid and an MG with a load, photovoltaics, and BESS with the aim of energy cost minimization. Deep reinforcement learning (DRL) was used for the optimization of sequential actions of BESS over a time horizon. A theoretical optimum scheduling was derived using a linear programming optimization to be compared with the DRL agent. Both no‐battery and greedy control algorithms were used as baselines. It was shown that the results of the proposed technique were better than the baselines through numerical simulations using whole year's data. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Optimal configuration and operation for user-side energy storage considering lithium-ion battery degradation

Battery energy storage systems (BESSs) have been widely employed on the user-side such as buildings, residential communities, and industrial sites due to their scalability, quick response, and design flexibility. However, cell degradation is caused by the charging and discharging of batteries, which reduces the economy of BESSs. For the optimal configuration and operation of BESSs, the battery degradation process should be integrated into the optimal problem by considering the equivalent full cycles over the optimization time horizon. This paper proposes a two-layer optimization frame to estimate and improve the net profit of BESSs in the whole life cycle, the outer layer optimizes the rated capacity and power of BESSs, and the inner layer optimizes the daily operation strategy of BESSs. To this end, the semi-empirical degradation model of lithium-ion batteries and economic models of BESSs are embedded into the optimization frame. Particle swarm optimization (PSO) algorithm and fmincon toolbox of MATLAB are adopted to solve the two-layer frame to maximize the net profit of BESSs. Simulation results of the BESS for a typical industrial user in China demonstrate that the proposed frame can effectively improve the net profit of BESSs.

Determining the Power and Capacity of Electricity Storage in Cooperation with the Microgrid for the Implementation of the Price Arbitration Strategy of Industrial Enterprises Installation

The growing worldwide costs of energy produced as a result of conventional fuel combustion, the limited capacity of the distribution grid, and the growing number of unstable installations based on renewable energy sources increase the need to implement systems of stabilization and regulate loads for end users. The battery energy storage system (BESS) that operates in the internal microgrid of an enterprise enables the management of the accumulated energy in any time zone of the day. Using a price arbitrage strategy with an electricity storage facility, we can reduce the cost of high electricity prices during peak demand periods. This study aims to determine the most effective method of setting up the capacity and electrical power of an energy storage system operating in a microgrid, in an enterprise to implement a price arbitration strategy. Such a method should include consideration of the characteristics of the demand profile of consumer systems, the charges related to electricity, and electricity storage costs. The proposed deterministic method is based on the use of a defined parameter, “marginal income elasticity”. In this study, the size of energy storage refers to the power and electric capacity of BESS that are used for the implementation of the price arbitrage strategy.

Machine Learning in Operating of Low Voltage Future Grid

The article is a continuation of the authors’ ongoing research related to power flow and voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use Machine Learning (ML) technology in a future grid. Based on supervised learning, a Selectively Coherent Model of Converter System Control for an LV grid (SCM_CSC) is proposed. This represents a fresh, new approach to combining off and on-line computing for DSOs, in line with the decarbonisation process. The main kernel of the model is a neural network developed from the initial prediction results generated by regression analysis. For selected PV system operation scenarios, the LV grid of the future dynamically controls the power flow using AC/DC converter circuits for Battery Energy Storage Systems (BESS). The objective function is to maintain the required voltage conditions for high PV generation in an LV grid line area and to minimise power flows to the MV grid. Based on the training and validation data prepared for artificial neural networks (ANN), a Mean Absolute Percentage Error (MAPE) of 0.15% BESS and 0.51–0.55% BESS 1 and BESS 2 were achieved, which represents a prediction error level of 170–300 VA in the specification of the BESS power control. The results are presented for the dynamic control of BESS 1 and BESS 2 using an ANN output and closed-loop PID control including a 2nd order filter. The research work represents a further step in the digital transformation of the energy sector.

Revenue Stacking for BESS: Fast Frequency Regulation and Balancing Market Participation in Italy

Battery energy storage systems (BESS) are considered a relevant flexible resource for supporting the balancing of a RES-penetrated power grid. Since their cost structure is characterized by very high capital costs, it is of utmost importance to ensure efficient and effective operations from a techno-economic perspective. The possibility of services (and revenues) stacking is one of the most discussed optimization solutions. The present work provides a novel approach for BESS modeling, including the stacking of two diverse ancillary services, a dedicated balancing market bidding model, and a state-of-charge management strategy. Fast frequency regulation is proposed as a power-based service, requiring large ramping capability, but asking BESS activation just for a limited amount of time. For the remainder, BESS power can be traded on balancing market (BM): energy-based services, such as tertiary regulation, could be effectively coupled with power-based, fast regulations, increasing the economic attractiveness of investments in BESS. The case of fast reserve (FR), a new high-speed frequency response service proposed by the Italian TSO in Italy, is assessed in this study. FR provision foresees a capacity-based remuneration (k€/MW/year) and requires to ensure 1000 hours per year of availability. After assessing its cost-effectiveness as a stand-alone service, a sequential multiservice strategy is proposed, where BESS provides FR for 1000 hours, while for the rest of the time it is dedicated to the provision of replacement reserve (RR). Performances of BESS are evaluated considering the reliability of the provision, its operational efficiency, and investment’s economics. Performed tests demonstrate how, within the current Italian regulatory framework, the investment’s rate of return improves thanks to the multiservice approach. In particular, while maintaining a proper reliability, the minimum acceptable remuneration from FR yearly auctions decreases by 13%; at the same time, self-dispatching of energy through BM calls reduces the need to purchase energy on day-ahead market and keeps BESS state-of-charge far from saturation regions, thus also increasing its lifetime.

Reliability Assessment of Renewable Power Systems Considering Thermally-Induced Incidents of Large-Scale Battery Energy Storage

The battery energy storage system (BESS) has been envisaged as an effective solution for renewable energy accommodation in power systems. However, the residual capacity and maximum power of large-scale BESS are highly affected by thermally-induced incidents such as battery degradation and Thermal Runaway (TR) propagation. In the prior-art studies, the impacts of thermally-induced incidents on the BESS service performance have not been well modeled, resulting in relatively over-optimistic reliability estimation of power systems. In this paper, the reliability of large-scale grid-connected BESSs as well as its impacts on the overall reliability of power systems are investigated considering the battery degradation and TR propagation. To quantify the time-varying performance of the BESS, a multi-state model is constructed. The proposed model describes the aging process of batteries inside the BESS, incorporating the combined effects of sequential TR and the performance degradation of the surrounding batteries due to heat absorption. Based on the Monte Carlo method, scenarios that reflect the uncertainties of the intermittent wind generation and fluctuating loads are simulated. An optimal scheduling model is deployed, and a solution algorithm is proposed to calculate the scheduling results of the BESS in the real-time performance range subject to its thermal conditions. Case studies are conducted to validate the effectiveness of the proposed model and technique.

Effect of State of Charge Uncertainty on Battery Energy Storage Systems

Battery energy storage systems (BESSs) provide many benefits to the electricity grid, including stability, backup power, and flexibility in introducing more clean energy sources. As BESS penetration grows, knowledge of the uncertainty in the battery's state of charge (SOC) estimate is crucial for planning optimal BESS power injection trajectories. This paper proposes a framework for quantifying SOC estimation uncertainty based on battery rest periods. An uncertainty analysis is presented for a BESS participating in the frequency regulation market.

Whole-lifetime Coordinated Service Strategy for Battery Energy Storage System Considering Multi-stage Battery Aging Characteristics

One battery energy storage system (BESS) can be used to provide different services, such as energy arbitrage (EA) and frequency regulation (FR) support, etc., which have different revenues and lead to different battery degradation profiles. This paper proposes a whole-lifetime coordinated service strategy to maximize the total operation profit of BESS. A multi-stage battery aging model is developed to characterize the battery aging rates during the whole lifetime. Considering the uncertainty of electricity price in EA service and frequency deviation in FR service, the whole problem is formulated as a two-stage stochastic programming problem. At the first stage, the optimal service switching scheme between the EA and FR services are formulated to maximize the expected value of the whole-lifetime operation profit. At the second stage, the output power of BESS in EA service is optimized according to the electricity price in the hourly timescale, whereas the output power of BESS in FR service is directly determined according to the frequency deviation in the second timescale. The above optimization problem is then converted as a deterministic mixed-integer nonlinear programming (MINLP) model with bilinear items. McCormick envelopes and a bound tightening algorithm are used to solve it. Numerical simulation is carried out to validate the effectiveness and advantages of the proposed strategy.

Power and energy constrained battery operating regimes: Effect of temporal resolution on peak shaving by battery energy storage systems

Battery energy storage systems (BESSs) are often used for demand charge reduction through monthly peak shaving. However, during economic analysis in the feasibility stage, BESSs are often sized, and BESS revenue is quantified based on 1 h load and/or solar output data for one year. To quantify the error in the demand charge from coarse-resolution modeling, the effect of two temporal resolutions, 15 min and 1 h, on peak load reduction is compared across a battery rating space defined by the power capacity and energy capacity. A linear program of the system optimizes the peak of the net load and the associated demand charge assuming perfect forecasts. Based on the 15 min load profile of a particular day, a critical power (CP) and critical energy (CE) can be defined, yielding a critical point in the power-energy space. Based on the difference of demand charge (DoDC) across the two load profiles at different temporal resolutions for a real building, the battery rating space is divided into three different regions: oversized region, power-constrained region, and energy-constrained region, which are separated by CP and CE. The DoDC in the power-constrained and energy-constrained regions is explained by time averaging effects and the load sequence at high resolutions. In the power-constrained region of the battery rating space, the difference between the original 15 min peak and the 1 h average peak persists in the optimized net load until the battery power capacity is sufficiently large. In the energy-constrained region, averaging may change the peak period duration, which depends on the sub-hourly sequence of the original load data. Through artificial load data and reordering of real load data, we demonstrate that the sequence effect causes energy-constrained batteries to underestimate peak shaving and demand charge reduction. Demand charge savings were especially sensitive to the BESS power capacity: for a ≈50 kW load, demand charge errors were up to $53 for power-constrained batteries and were an order of magnitude smaller for energy constrained batteries. The power capacity of the battery should be carefully considered when interpreting results from optimizations at low resolutions.

A Learning-Based Bidding Approach for PV-Attached BESS Power Plants

Large-scale renewable photovoltaic (PV) and battery energy storage system (BESS) units are promising to be significant electricity suppliers in the future electricity market. A bidding model is proposed for PV-integrated BESS power plants in a pool-based day-ahead (DA) electricity market, in which the uncertainty of PV generation output is considered. In the proposed model, we consider the market clearing process as the external environment, while each agent updates the bid price through the communication with the market environment for its revenue maximization. A multiagent reinforcement learning (MARL) called win-or-learn-fast policy-hill-climbing (WoLF-PHC) is used to explore optimal bid prices without any information of opponents. The case study validates the computational performance of WoLF-PHC in the proposed model, while the bidding strategy of each participated agent is thereafter analyzed.

Power Configuration Scheme for Battery Energy Storage Systems Considering the Renewable Energy Penetration Level

With the increase of the renewable energy penetration (REP) level in the interconnected power grid, the proportion of the grid-connected conventional synchronous generators reduces continuously, resulting in the decrease of the system inertia. The insufficient system inertia brings challenges to the system frequency stability. Battery energy storage systems (BESSs), regarded as the high-quality frequency regulation resource, play an important role in maintaining the frequency stability of the system with the high REP level. To configure the proper power of BESSs in system frequency regulation, a BESS power configuration scheme (PCS) considering the REP constraint is proposed in this paper. In particular, the process to obtain the REP boundary of the interconnected grid on the premise of system frequency stability is included in the PCS, and the optimal power configuration of the BESS is further determined on the analysis of the BESS impact on the REP boundary. Furthermore, a simulation model of the Australian five-area interconnected power grid is built in MATLAB/Simulink, and the proposed REP-constrained PCS is verified and analyzed. At last, the promising results show that the PCS can take full advantages of the BESS in frequency regulation and meet the system requirement of the frequency stability at a particular REP level.

Rule-based adaptive control strategy for grid-forming inverters in islanded power systems for improving frequency stability

Operating isolated power systems with increasing shares of renewable energy sources requires the integration of battery energy storage systems in order to assure enhanced frequency regulation capabilities. The control mode of power converters interfacing battery energy storage systems to the grid can be based on grid-forming type structures given its superior performance with respect to the mitigation of network frequency disturbances. Nevertheless, in case of network faults, the interactions between existing synchronous units and the grid-forming type converters may adversely affect the global system behavior. Therefore, this paper addresses the study-case of a MW-scale isolated power system with large shares of converter-interfaced renewable generation, operating with both synchronous machines and a grid-forming type power converter. An optimal grid-forming control parameter tuning procedure considering different disturbances is presented, aiming to reduce the associated battery energy storage system power regulating effort following the disturbances. Moreover, it is proposed and discussed the need of a novel rule-based adaptive control solution to switch between different sets of control parameters used in grid-forming type converters depending on the network status following a fault-type disturbance. Extensive numerical simulations performed over different operating scenarios illustrate the performance of the proposed solution.