Control Of Battery Energy Storage System Research Articles

Enhancing commercial building resiliency through microgrids with distributed energy sources and battery energy storage systems

Resilience analysis is gaining focus, but no extensive research exists for commercial buildings. This research presents the results of a novel analysis of the resiliency in commercial buildings by examining the relationship between electric microgrids, Distributed Energy Resources (DERs), and Battery Energy Storage Systems (BESS). As energy systems face increasing challenges, including extreme weather events and grid vulnerabilities, integrating microgrids, DERs, and BESS has emerged as a promising solution to strengthen the resilience of commercial buildings. Microgrids can harness renewable energy sources and reduce environmental impacts when integrated with DERs. Most literature studies focus on residential or commercial buildings with peak-valley tariffs and simplified electrical market models. In contrast, this study focuses on BESS’ pivotal role in DER output and ensuring uninterrupted power during grid disruptions and presents an innovative approach to analyzing resilience in commercial building microgrids and an economic optimization of commercial building microgrids with Time of Use tariffs utilizing DERS and BESS. The analysis includes the technical aspects of BESS integration and control strategies that optimize their operation. It also studies the economic and environmental benefits of microgrids, DERs, and BESS, focusing on cost savings, greenhouse gas emission reductions, and grid support services.

Controller design and optimal sizing of battery energy storage system for frequency regulation in a multi machine power system

Frequency regulation is one of the key components needed to keep the power grid stable and reliable in the case of an imbalance between generation and load. This study looks at several control techniques for Battery Energy Storage Systems (BESSs) to keep the frequency stable in the power system during generation/load disruptions. This research aims to build several BESS controllers, including the proportional-integral (PI), proportional integral derivative (PID), and Tilt-Integral Derivative (TID) controllers. Also, the BESS controller parameters are optimized and compared by using metaheuristics based particle swarm optimization (PSO) and the BAT algorithm. However, for practical power systems with high MVA ratings, the size of the battery energy storage systems has to be increased considerably to offset frequency deviations. Additionally, by utilizing PSO to reduce the frequency deviations within prescribed limits, this work presents the optimal BESS sizing for multimachine power systems. Furthermore, the research study examines the impact of BESS connected to various voltage levels, such as LV, MV, and HV subgrids. It also examines BESS's role in frequency management, which is an area of expertise that needs further investigation and is anticipated to have a big influence on the frequency stability of the system. Time domain simulations are carried out, which shows that the PSO based controller design is capable of stabilizing the system frequency with superior performance as well as the BESS's capability to participate effectively in frequency regulation. Additionally, this paper also implements the experimental verification through Hardware-in-the-Loop (HIL) simulations to validate the performance and robustness of the proposed control system. The WSCC 9-bus test system is used to demonstrate the BESS's efficacy. The system is modelled and simulated using the DIgSILENT PowerFactory software.

Optimal sitting, sizing and control of battery energy storage to enhance dynamic stability of low‐inertia grids

AbstractAs inverter‐based resources like wind turbines increase, grid inertia and stability decrease. Optimal placement and control of energy storage systems can stablise low‐inertia grids. This paper investigates how optimal battery energy storage systems (BESS) enhance stability in low‐inertia grids after sudden generation loss. The sitting, sizing and control of BESS are determined simultaneously in each genetic algorithm (GA) population, then voltage and frequency stability is evaluated based on the network simulation. This continues until the optimal solution is found. A network based on Kundur's four‐machine system is modelled for the first study and two of the four synchronous generators (SGs) have been replaced with wind farms. Then, the production of the third SG has been decreased by 13%. According to the results, addition of wind farms causes the frequency drop below 49.6 Hz for more than 5 min, indicating instability. It is also demonstrated that with optimal control parameters and placement, a 60 MW BESS can alleviate the voltage and frequency fluctuations, leading to enhanced stability. This method has also been tested on the IEEE 39‐bus network, where the installation of a BESS with a capacity of 9 MVA could restore the frequency stability.

Introducing storage operators for coordinating residential batteries in distribution networks under time-of-use tariffs and adaptive power limits

The transition towards Time of Use (ToU) tariffs has become a promising solution for addressing power system challenges resulting from increased installations of renewable energy systems. ToU tariffs encourage residential Battery Energy Storage System (BESS) adoption to reduce customers' bills through maximizing energy storage during low-price intervals (e.g., middle of the day). However, simultaneous BESS charging affects diversity of load, which may lead to the violation of distribution networks constraints. Traditional network management with conservative fixed and static power limits leads to inefficient network capacity use since they do not consider changes in network operating conditions and status of BESS facilities. Specially, these approaches do not allow higher import limits when proportion of BESS facilities are in idling state. To better allocate the capacity of distribution networks to active BESS facilities (charging/discharging), this work introduces an independent storage operator to coordinate BESS control actions by employing time-varying and adaptive power limits. For this purpose, a Mixed Integer Linear Programming (MILP) algorithm is proposed for storage operator to manage BESS facilities while respecting network constraints and customers' desired bills. At each time step, the algorithm decides power limits for active BESS facilities based on predefined linear functions. These functions are generated offline by using Optimal Power Flow (OPF) to establish relationships between power limits and number of active BESS. The application of the algorithm using a real Jordanian distribution network demonstrates its effectiveness to allow a larger number of customers achieving their desired bills compared to using fixed power limits.

A robust damping control for battery energy storage integrated power systems to mitigate inter-area oscillations

This paper presents the effect of a Battery Energy Storage System (BESS) on the power system inter-area oscillations under changing load conditions. The dynamic interaction between the BESS control modes and synchronous generators, along with BESS control parameters, influence the damping of prominent inter-area oscillations under different loading conditions. Furthermore, H∞ mixed sensitivity scheme-based Wide-Area Damping Controller (WADC) is proposed for BESS. This controller is designed using the Linear Matrix Inequality (LMI) framework to mitigate the prominent inter-area oscillation of the power system. Sensitivity to control parameter variation and the geometric measure of the observability are utilized to select the location and feedback signal for the WADC. The performance of the proposed WADC is compared with an Energy Storage System-based stabilizer and WADC proposed in an existing work on a modified New England ten-machine benchmark system. The Real-Time Digital Simulator (RTDS) platform is used to assess the real-time viability of the proposed WADC. The simulation results demonstrate that the proposed WADC can effectively diminish several inter-area modes. Additionally, it offers robust performance under varying load conditions, contingencies and with communication delays in feedback signals. The proposed controller is also effective under the system integrated with Doubly Fed Induction Generator-based Wind Turbine System.

Stochastic Control of Battery Energy Storage System with Hybrid Dynamics

This paper addresses the control of load demand and power in a battery energy storage system (BESS) with Boolean-type constraints. It employs model predictive control (MPC) tailored for such systems. However, conventional MPC encounters computational challenges in practical applications, including battery storage control, and requires dedicated, mostly licensed solvers. To mitigate this, a solver-free yet efficient, suboptimal method is proposed. This approach involves generating randomized control sequences and assessing their feasibility to ensure adherence to constraints. The sequence with the best performance index is then selected, prioritizing feasibility and safety over optimality. Although this chosen sequence may not match the exact MPC solution in terms of optimality, it guarantees safe operation. The optimal control problem for the BESS is outlined, encompassing constraints on the state of charge, power limits, and charge/discharge modes. Three distinct scenarios evaluate the proposed method. The first prioritizes minimizing computational time, yielding a feasible solution significantly faster than the optimal approach. The second scenario strikes a balance between computational efficiency and suboptimality. The third scenario aims to minimize suboptimality while accepting longer computation times. This method’s adaptability to the user’s requirements in various scenarios and solver-free evaluation underscores its potential advantages in environments marked by stringent computational demands, a characteristic often found in BESS control applications.

Optimal Battery Energy Storage Control for Multi-Service Provision Using a Semidefinite Programming-Based Battery Model

Battery energy storage systems (BESS) capable of simultaneously providing multiple grid services can assist a distribution grid operator in overcoming various challenges due to the high penetration of distributed solar photovoltaics and accelerated electrification. The seamless provision of multiple services can be ensured with BESS control decisions based on an accurate model reflecting the battery characteristics. Thus, this paper presents a novel Li-ion battery model based on linear matrix inequalities along with a semidefinite programming (SDP)-based model to determine the optimal BESS control decisions for the provision of multiple services in distribution systems. Peak shaving, power factor improvement, and electricity cost savings are considered for the services. Moreover, the mathematical analysis reveals that the original nonconvex problem can be equivalently transformed into the SDP model. The effectiveness of using BESS in the distribution system is verified through simulations using real-world data. Ultimately, a comparative analysis with a conventional linear model demonstrates that the proposed battery model reduces the energy losses in the batteries, in the simulation condition, and help maintain the battery states within normal operating limits.

Evaluation of technical and financial benefits of battery energy storage system control strategies

The recent increase in renewable energy generation can balance consumption and reduce carbon emissions. With battery energy storage optimizing supply and demand, it is more important than ever to manage charge control to the benefit of all stakeholders. In this paper, the developed and proposed energy management control methods based on the technical operating criteria of battery energy storage (BESS) and considering self-consumption rate (SCR), self-supply rate (SSR) and curtailment rate are compared in terms of environmental index and economics for daily and annual demand profiles for various household prosumer demand profiles in Istanbul and Antalya. Considering the supply-demand matching based on demand profile, feed-in damping, fixed feed-in, schedule mode, schedule mode with constant charging power and self-consumption control methods are proposed for optimum operation for each prosumer profile. The results show that feed-in damping and fixed feed-in methods can reduce household prosumer costs by up to 22.3% in the daily analysis. Moreover, similar control methods can increase SCR by up to 29.5% and reduce costs by up to 10.62% for higher irradiances in the annual analysis. Proper management of BESS charge control can facilitate sustainable development goals by assisting plans of many stakeholders.

Review of electric vehicle (EV) charging using renewable solar photovoltaic (PV) nano grid

This review article gives a comprehensive review of existing research on renewable solar photovoltaic (PV) nanogrid, which is described from small-scale power system with a single domain for reliability, control, and power quality (PQ) for electric vehicle (EV) charging. A primary feeder on the Microgrid is connected to a nanogrid test bed that includes PV as power source, a battery energy storage system (BESS), smart-inverter multiple and EV charging stations (EVCS). The control algorithms are graded on four metrics: (1) voltage profiles, (2) renewable penetration, (3) PV curtailed and (4) net power flows. To investigate the local power quality, a steady-state power flow model of the nano-grid is created. The control algorithms successfully employ the battery to shift the nano-grid peak load while limiting the nano-grid demand to set level. Furthermore, an increasing emphasis is being placed on commonly used strategies for addressing the characteristics of each renewable system. This review paper characterizes the dynamic operation of 4 distinct BESS control algorithms for solar EV charging nanogrid: (1) peak load shifting, (2) reduce peak period impact, (3) cap demand, and (4) photovoltaic capture. These control modes are executed and analyzed on real-world nano-grid site, and optimal BESS control modes are assessed in terms of (1) solar electric vehicle charging, (2) power quality, (3) grid net demand, (4) photovoltaic curtailment, and (5) solar penetration. Finally, the problems highlight research gaps, and discussions on future trends are critical for enhancing the general technology of the renewable solar photovoltaic nano-grid for EV charging.

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.

Optimal battery energy storage planning and control strategy for grid modernization using improved genetic algorithm

The flexible operation of battery energy storage systems (BESS) to support electricity grid modernization requires optimal planning and an efficient control strategy. This paper proposes the optimal allocation of BESS with photovoltaic systems for microgrids to enhance grid reliability and flexibility. An adaptive BESS control strategy is developed to allow the BESS to operate in both redundant and peak shaving modes. An evolutionary programming-based genetic algorithm implemented with an optimal power flow control is proposed to determine the BESS’s optimal location, sizing, and charge/discharge operation. The objective function is to maximize the benefits of energy loss reduction and peak shaving enhancement while minimizing the installation cost. The proposed approach is conducted with MATLAB and DIgSILENT PowerFactory software, which uses an intelligent automatic data exchange process to solve the optimal solution. A practical 22 kV grid-connected microgrid of Thailand is used as a test system to demonstrate the effectiveness of the planning and control strategy for the BESS installation. The simulation test results show that the proposed optimal BESS allocation and control strategy can significantly reduce the microgrid’s energy loss and peak demand and increase energy shaving leading to enhanced grid resiliency and reliability.

Adaptive centralized energy management algorithm for islanded bipolar DC microgrid

Abstract In this paper, an adaptive centralized energy management algorithm is developed for an islanded bipolar DC microgrid (BDCMG). The proposed microgrid consists of a solar photovoltaic (SPV) system, a wind energy conversion system (WECS), and a battery energy storage system (BESS). The SPV and WECS are operated in maximum power point tracking mode to extract maximum power. The adaptive control algorithm focus on power sharing at a bipolar DC bus based on variations in wind speed, solar irradiance, battery state of charge (SoC), and load. Furthermore, this algorithm maintains voltage balance and voltage regulation on a bipolar DC bus without the use of a communication link and provides effective energy management through coordinated control of renewable energy sources and BESS. The efficacy of the proposed bipolar DC microgrid is simulated and validated on a lab scale prototype to verify its feasibility, and it is observed that the results are found to be satisfactory.

A PV variability tolerant generic multifunctional control strategy for battery energy storage systems in solar PV plants

This paper proposes a multifunctional control strategy for battery energy storage systems (BESSs) in solar photovoltaic (PV) plants to avoid the unacceptable PV-power ramp-rate caused by PV variability. In addition to the PV variability tolerant ramp-rate control, the proposed multifunctional BESS control strategy is capable of maintaining a user-specified charging profile to store excess PV power while providing backup power support and charge maintenance, by accommodating diverse control functions time to time. Four control functions, namely, Rest (RST), Surplus Energy Storage (STR), Back-up Power Support (SPT) and Charge Maintenance (CMT) functions, are proposed in this article. Besides, extendibility of the proposed multifunctional strategy is presented by adding two action strategies – one with the STR function and another with the SPT function. Validation of the proposed control strategy is conducted by using real historical data from a utility-scale solar PV plant located in Queensland, Australia. Numerical analyses show that the strategy can successfully keep the PV ramp-rate within specified limit along with the user specified charge profile. Further, exploiting the real-field data, the proposed strategy is demonstrated through a real-time co-simulation study conducted in a MATLAB interfaced RSCAD-RTDS platform that can be used as Digital Twin (DT) for the PV plant.

Operation Simulation and Economic Analysis of Household Hybrid PV and BESS Systems in the Improved TOU Mode

With the popularization of electric vehicles and electric boilers, household electricity consumption will increase significantly. Household hybrid photovoltaic (PV) systems and battery energy storage systems (BESSs) can supply increasing household electricity consumption without expanding the existing distribution network. This paper validates the technical feasibility of connecting a large number of household power users that contain BESSs and PVs in a distribution line by a simulation in Matlab. In addition to technical feasibility, this article improves the time-of-use (TOU) form to achieve economic feasibility (covering equipment costs). In the past, the TOU was set from the perspective of the load demand of the grid, but the actual user participation would affect this effect. In this paper, based on a social science survey, a new three-level rate TOU is introduced, which has little impact on residents’ lifestyle, to effectively increase the response frequency effectively. Combined with the improved TOU and the state of PVs, the BESS control mode is set for simulation. To compare the three-tier rate TOU with the normal TOU tariff and select the best household BESS size, a MATLAB simulation is used to simulate the common household BESS capacity. The results indicate that the combination of the three-tier rate TOU with a 4 kWh household BESS can afford the investment of household PVs and BESSs. The high cost issue that previously primarily limited the true use of BESSs is expected to be resolved.

Lyapunov Optimization in Online Battery Energy Storage System Control for Commercial Buildings

The high instantaneous discharging capability of battery energy storage systems (BESSs) make them ideal candidates for reducing peak loads in commercial buildings. An efficient online BESS control algorithm can be beneficial for reducing the monthly electricity bill of individual commercial buildings. Conventional model-based BESS control algorithms rely heavily on accurate long-horizon net load forecast, which is difficult to obtain. To address this problem, we develop a Lyapunov optimization-based online BESS control algorithm and derive its theoretical performance bound. Comprehensive numerical study results using real world commercial building smart meter data in southern California show that our proposed Lyapunov optimization-based online control algorithm with time-varying weighting parameters yields higher savings in electricity cost and requires less computation time in comparison to the state-of-the-art baseline algorithms.

Research on the integrated application of battery energy storage systems in grid peak and frequency regulation

To explore the application potential of energy storage and promote its integrated application promotion in the power grid, this paper studies the comprehensive application and configuration mode of battery energy storage systems (BESS) in grid peak and frequency regulation. Based on the performance advantages of BESS in terms of power and energy response, integrated multiplexing of peak and valley filling (PSVF) application on long-time scales and frequency regulation (FR) application on short-time scales are explored. First, the theoretical feasibility of its comprehensive multiplexing application is explored from technical and economic perspectives. Then, the requirements of both models for decoupling and coupling are analyzed, and their specific composite control strategies and implementations are discussed in further detail. Finally, based on a techno-economic evaluation model, the effectiveness of each control strategy was simulated and evaluated to find the optimal BESS control and configuration in the combined application of PSVF and FR. Thus, it provides theoretical and technical support for the functional reuse of BESS in engineering practice.

An improved adaptive hybrid controller for battery energy storage system to enhance frequency stability of a low inertia grid

Despite the promising dynamic characteristics of battery energy storage system (BESS) for efficient and reliable use in stability enhancement of a low inertia grid due to the large-scale integration of renewable energy sources (RESs), existing BESS controllers are found to be complex, inefficient and less responsive to adapt any changes in frequency of the system. This paper presents a simple improved adaptive Fuzzy-ANFIS hybrid algorithm-based BESS controller. The proposed controller can emulate virtual inertia by controlling the active power flow to improve frequency stability i.e. frequency nadir and rate of change of frequency (ROCOF). Necessary modeling of the controller was carried out and its performance has been analyzed to evaluate the performance of the controller. With different case studies and comparative analysis, the controller has also demonstrated its potentials to improve the frequency nadir by 0.43 % for sudden load variation and 0.61 % for transient short circuit fault. The promising performance of the controller suggests its utility for better grid inertial support to a low inertia grid. Additionally, due to the improvement in ROCOF and settling time, the proposed controller thus can increase the overall inertia in addition to the power balance at point of common coupling (PCC).

Mobile battery energy storage system control with knowledge‐assisted deep reinforcement learning

AbstractMost mobile battery energy storage systems (MBESSs) are designed to enhance power system resilience and provide ancillary service for the system operator using energy storage. As the penetration of renewable energy and fluctuation of the electricity price increase in the power system, the demand‐side commercial entities can be more profitable utilizing the mobility and flexibility of MBESSs compared to the stational energy storage system. The profit is closely related to the spatiotemporal decision model and is influenced by environmental uncertainties, such as electricity price and traffic conditions. However, solving the real‐time control problem considering long‐term profit and uncertainties is time‐consuming. To address this problem, this paper proposes a deep reinforcement learning framework for MBESSs to maximize profit through market arbitrage. A knowledge‐assisted double deep Q network (KA‐DDQN) algorithm is proposed based on such framework to learn the optimal policy and increase the learning efficiency. Moreover, two criteria action generation methods of knowledge‐assisted learning are proposed for integer actions utilizing scheduling and short‐term programming results. Simulation results show that the proposed framework and method can achieve the optimal result, and KA‐DDQN can accelerate the learning process compared to the original method by approximately 30%.

Effects of Household Battery Systems on LV Residential Feeder Voltage Management

With the advancements of the battery energy storage systems (BESSs), reduction of their manufacturing costs and government subsidies, the BESS uptake is likely to increase rapidly in power distribution networks. This paper investigates the effects of residential BESSs on low-voltage (LV) networks using the actual household load profiles equipped with BESS and solar-photovoltaic (PV) systems. The electricity consumption data collected via smart meters (2200 households with PV/BESS, 1950 households with PV only and 1000 households without a PV or a BESS) at different solar-PV penetration levels and network types are used to simulate real network operating scenarios. A real LV distribution network in Australia is analysed in DIgSILENT PowerFactory under different scenarios, such as, customers with and without a solar-PV/BESS, with a solar-PV but without a BESS, and without a solar-PV, by using both the power flow and quasi-dynamic simulation studies under balanced and the unbalanced network loading conditions. According to the study, customers experience large voltage excursions from solar-PV power exports, which could be resolved by the household BESS, provided that the BESS charging is coordinated with the solar-PV production. Moreover, quasi-dynamic simulation shows that the BESS could reduce the violation of the over-voltage limit during the solar peak hours (midday) by lowering the worst-case feeder voltage by 3%. Finally, extreme-event (high solar PV generation scenario) simulation shows that the implementation of the BESS controller to facilitate charging BESS during afternoon solar-PV export may reduce the negative grid impact and will assist to avoid network upgrades.

Operation strategy of battery energy storage systems for stability improvement of the Korean power system

In accordance with the energy transition roadmap of the Korean government, the Korean power system needs to significantly increase the use of renewable energy sources (RES) in the future. This may cause an amount of increase in converter-interfaced generators (CIG) for integrating RES to the utility grid substituting the conventional synchronous generators, and one can easily expect that a critical decrease in the system inertia will occur in the future power system, leading to some stability issues related to that. Besides, considering the short-term state of the Korean power system, another stability issue may arise due to the delayed reinforcement of the shared network connecting large-scaled generation plants. Several countermeasures such as generator tripping and generation curtailment are proposed to overcome stability issues. A combined action with generator tripping and generation curtailment is applied to stabilize the system after the critical event, however generation curtailment might reduce the economical operation of the system. Using their fast response characteristic, battery energy storage systems (BESS) are regarded as a countermeasure to relieve the curtailment. After adequate transmission network reinforcement for the large power plants is made, their long-term application needs to be examined, which is to maintain the frequency stability for the system in low-inertia situations. This study aims to design the BESS control strategy for improving stability as well as to determine key control parameters of the BESS. The proposed control strategy of BESS for stability enhancement is proven to be effective in overcoming stability issues in the short-term and long-term operation of the Korean power system.