Battery Energy Storage Management Research Articles

Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm

This paper investigates the synergistic integration of renewable energy sources and battery energy storage systems to enhance the sustainability, reliability, and flexibility of modern power systems. Addressing a critical gap in distribution networks, particularly regarding the variability of renewable energy, the study aims to minimize energy costs, emission rates, and reliability indices by optimizing the placement and sizing of wind and solar photovoltaic generators alongside battery energy storage systems. An improved large-scale multi-objective evolutionary algorithm with a bi-directional sampling strategy is employed.Two scenarios are considered. In the first scenario, six study cases are analyzed to determine the optimal number, location, and size of distributed generators at peak load demand. The proposed algorithm outperforms existing state-of-the-art methods for small-scale distributed resource allocation. In the second scenario, a multi-period load demand across various seasons is evaluated, introducing new opportunities for battery energy storage systems. The problem is modeled with intertemporal constraints, creating a large-scale optimization challenge. Results demonstrate significant improvements: cost savings of up to 51.67 %, power reductions of up to 76.78 %, and a 99.9 % improvement in voltage deviation. Emission rates decreased by 99.92 % in case 1, 95.19 % in case 2, and 97.38 % in case 3, compared to the base case. The proposed algorithm shows superior convergence and performance in solving both small- and large-scale optimization problems, outperforming recent multi-objective evolutionary algorithms.This study provides a robust framework for optimizing renewable energy integration and battery energy storage, offering a scalable solution to modern power system challenges.

Dynamic Multi-Objective Optimization of Grid-Connected Distributed Resources Along With Battery Energy Storage Management via Improved Bidirectional Coevolutionary Algorithm

Dynamic Multi-Objective Optimization of Grid-Connected Distributed Resources Along With Battery Energy Storage Management via Improved Bidirectional Coevolutionary Algorithm

An improved control for a stand-alone WEC system involving a Vienna rectifier with battery energy storage management

This study focused on efficiency improving, the power flow management and control problem of the standalone wind energy conversion system. Specifically, the system under study consists of a Permanent Magnet Synchronous Generator (PMSM) driving by wind turbine, Vienna rectifier, a Li-ion battery and a DC load. Battery life is vulnerable to fluctuations due to the intermittent nature of the wind, as well as to the demand for energy charging, which reduce considerably battery state of health (SOH) and consequently charging capacity. To overcome these shortcomings, a nonlinear battery charge controller is used to regulate the current supplied to the battery and to the load, and thus guarantee best charging performances. Based on the wind power, the load demand and the battery state of charge (SOC), three operating modes are considered. Specifically, MPPT mode, Constant Current (CC) charging mode and Constant Voltage (CV) charging mode. We also design a new energy management method to protect the energy storage system and increase its lifetime. This algorithm ensure a smooth switching between three controllers designed to provide the three operating modes. The high performances and the efficiency of the proposed controller and management algorithm are highlighted using several MATLAB/SIMULINK simulations.

AC microgrid with battery energy storage management under grid connected and islanded modes of operation

The inevitability of energy storage has been placed on a fast track, ensued by the rapid increase in global energy demand and integration of renewable energy with the main grid. Undesirable fluctuations in the output of renewable sources is the main downside that call for manageable energy storage units. This study presents the viability of battery storage and management systems, of relevance to microgrids with renewable energy sources. In addition, this paper elucidates the development of a control algorithm for the management of battery power flow, for a microgrid connected to a mains electricity grid, is presented here. A shunt active filter algorithm for improving the power quality of grid is also implemented with power flow management controller. The overall management system is demonstrated for on grid and off grid modes of microgrid with varying system conditions. A laboratory scale grid–microgrid system is developed and the controllers are implemented.

Adaptive receding horizon control for battery energy storage management with age-and-operation-dependent efficiency and degradation

A receding horizon controller for optimal, adaptive scheduling of a battery energy storage system is proposed. Batteries degrade as a function of their operation and age; operation also affects battery energy storage system efficiency. Many state-of-the-art energy management techniques simplify the relationship between state of charge (SoC) and degradation, assume the efficiency to be constant, and neglect the impact of state of health and variable efficiency on the SoC progression used to quantify the degradation. This results in significant deviations from optimal operation, over-estimates of remaining capacity, and inaccuracies in the calculation of further degradation. The controller developed in this paper adapts the operating schedule of a battery energy storage system to its age, condition, and state of charge. This is achieved by identifying the factors which affect the degradation processes and efficiency in lithium-ion battery systems and quantifying their impacts, resulting in a system-level model which incorporates all the pertinent parameters. Integrating the developed model into the problem formulation of the receding horizon controller enables online evaluation of age and operation-dependant efficiency and degradation, which allows the controller to make adaptive decisions, minimising operating costs. The proposed controller was tested using data from a real-world case study and the results shows the controller outperforms two existing energy management techniques, whilst delivering the same network services.

Operational Value-Based Energy Storage Management for Photovoltaic (PV) Integrated Active Power Distribution Systems

This paper presents an operational cost-based approach for battery energy storage management. In this approach, the operation value is derived to optimally manage one battery application—photovoltaic (PV) capacity firming or PV smoothing. The proposed approach is an optimization framework that augments a PV smoothing algorithm where the battery is used to smooth the output of intermittent PV farm. The objective of the optimization framework is to provide maximum operational benefits for the battery. To this end, first, a formal method is developed that finds the operational benefits which is then demonstrated using a field verified electro-magnetic transient (EMTP) model of a real-world feeder. Second, the positive effect in the operation of the feeder is quantified in terms of monetary value. It is demonstrated that the knowledge of monetary value can provide better operational benefits of energy storage applications such as PV smoothing.

A Comparative Study Based on the Least Square Parameter Identification Method for State of Charge Estimation of a LiFePO4 Battery Pack Using Three Model-Based Algorithms for Electric Vehicles

Battery energy storage management for electric vehicles (EV) and hybrid EV is the most critical and enabling technology since the dawn of electric vehicle commercialization. A battery system is a complex electrochemical phenomenon whose performance degrades with age and the existence of varying material design. Moreover, it is very tedious and computationally very complex to monitor and control the internal state of a battery’s electrochemical systems. For Thevenin battery model we established a state-space model which had the advantage of simplicity and could be easily implemented and then applied the least square method to identify the battery model parameters. However, accurate state of charge (SoC) estimation of a battery, which depends not only on the battery model but also on highly accurate and efficient algorithms, is considered one of the most vital and critical issue for the energy management and power distribution control of EV. In this paper three different estimation methods, i.e., extended Kalman filter (EKF), particle filter (PF) and unscented Kalman Filter (UKF), are presented to estimate the SoC of LiFePO4 batteries for an electric vehicle. Battery’s experimental data, current and voltage, are analyzed to identify the Thevenin equivalent model parameters. Using different open circuit voltages the SoC is estimated and compared with respect to the estimation accuracy and initialization error recovery. The experimental results showed that these online SoC estimation methods in combination with different open circuit voltage-state of charge (OCV-SoC) curves can effectively limit the error, thus guaranteeing the accuracy and robustness.

Integrated PV Capacity Firming and Energy Time Shift Battery Energy Storage Management Using Energy-Oriented Optimization

In this paper, we propose a complete active-power-management scheme for the control of battery energy-storage systems (BESSs) for two main applications: 1) photovoltaic (PV) capacity firming and 2) energy time shift (ETS). In the proposed approach, first two control algorithms are designed to provide active-power set points to BESS for the above applications. Then, an optimization routine for integrating these controllers is designed. The proposed approach uses an energy-conservation method to integrate these two applications of energy-storage system. The designed algorithm was tested on a transient simulation platform and then implemented on a 720-node actual power distribution feeder. The main advantage of the proposed method is that the algorithm can be used to optimize multiple functions and perform simultaneous control of BESS.

An integrated Control Approach and Power Management of Stand-alone Hybrid Wind/PV/Battery Power Generation System with Maximum Power Extraction Capability

The production of electricity from renewable energy sources likewind and photovoltaic energy has increased in recent years, due to en -vironmental problems and the shortage of traditional energy sources.In this article we present a detailed mathematical model and a controlscheme for hybrid wind and PV based DG system with battery and max-imum power extraction capability for isolated mode of operation. Thewind power generation system uses wind turbine (WT), a permanentmagnet synchronous generator (PMSG), a three-phase diode rectifierbridge, DC/DC boost converter with maximum power point tracking(MPPT) controller. The PV generation system uses PV array, a boostconverter with maximum power point tracking controller. Both sourcesand battery are connected to common dc bus with a dc link capacitorand supply power to load through PWM voltage source inverter. Theoverall control system consists of MPPT controller for both Wind andPV power system, a bi-directional dc-dc converter controller for batteryenergy storage management and load side inverter controller for volt -age and frequency regulation. Control strategies for individual systemcomponents of the proposed system are designed with a view to achievean acceptable level of voltage and frequency regulation while extract -ing the maximum power from wind and PV system. The performanceof the developed hybrid system is investigated in terms of voltage andfrequency regulation capability under changing wind, solar irradiation and variable load conditions.

A three-stage decision-making model for selecting electric vehicle battery technology

This study proposes a three-stage decision-making model for the selection of electric vehicle battery technology. Data used for analysis include surveys completed by 45 technology experts from industry, academia, and research throughout Taiwan. A three-stage model that includes developing multiple-criteria during the first stage, integrating the importance of criteria assessment using the fuzzy analytical hierarchy process in the second stage, and using patent analysis tools to further identify the patent portfolio of the technology selected by experts in the third stage are employed. The empirical results indicate that power source management technology and battery module technology are the key technologies for development by the electric vehicle industry. Battery energy storage management and cooling technology are found to be the key for building patent portfolios. When faced with substantial technical and market uncertainty, multiple-criteria for research and development (R&D) selection and stage-wise integration of decision tool must be employed by battery firms to effectively allocate the resources for R&D decisions.