Search

Energy storage systems are key to propelling the current renewable energy revolution. Accurate State-of-Charge estimation of the lithium-ion battery energy storage systems is a critical task to ensure their reliable operations. Multiple advanced battery model-based SOC estimation algorithms have been developed to pursue this objective. Nevertheless, these battery model-based algorithms are sensitive to measurement noises since the measurement noises affect the accuracy of battery model identification, thus leading to inaccurate battery SOC estimation consequently due to modeling error. The Butterworth low-pass filter has proven effectiveness in measurement noise filtering for accurate parameter identification, while the cutoff frequency design relies on prior knowledge of lithium-ion batteries, making its capability limited to general cases. To overcome this issue, this paper proposes an adaptive cutoff frequency design algorithm for the Butterworth low-pass filter. Simulation results show that the low-pass filter functions properly in the presence of multiple scales of measurement noises adopting the proposed work. Consequently, the parameters of the battery model and the SOC of the battery are both identified and estimated accurately, respectively. In detail, the parameters: R 0 , R 1 , C 1 , and the time constant τ are all identified accurately with low relative identification errors of 0.028%, 11.12%, 6.21%, and 5.94%, respectively, in an extreme case. Furthermore, the SOC of the battery can thus be estimated accurately, leaving a low of 0.081%, 0.97%, and 0.14% in the mean and maximum absolute SOC estimation error and the standard deviation, respectively. [ABSTRACT FROM AUTHOR]

Battery energy storage systems are widely used in energy storage microgrids. As the index of stored energy level of a battery, balancing the State-of-Charge (SoC) can effectively restrain the circulating current between battery cells. Compared with passive balance, active balance, as the most popular SoC balance method, maximizes the capacity of the battery cells and reduces heat generation. However, there is no good solution in the battery management system (BMS) to ensure active balance during distributed charging. In view of this, this paper designs two novel distributed charging strategies based on a kind of smart leader, in which a constant static leader is modified by a dynamic leader. The modified leader is in charge of guiding SoC to converge to the target value and repress SoC imbalance. The maximum and weighed error between the state of the leader and its neighbor cells are used in the two methods, respectively, both in an event triggered manner. When the relevant index exceeds the threshold, the two methods are used to regulate the leader's state. Under this modification, the eigenvalue of the followers' error dynamic system is reduced, and SoCs follow the dynamic leader faster, thus repressing SoC imbalance. Compared with a constant leader, the smart leader pays more attention to improving SoC imbalance. Additionally, to facilitate analysis, a reduced method is applied to transform the system with an unified input time delay into a nondelay system. Several cases are designed to verify the effectiveness of the designed strategies and test it under different parameters and different time delays. [ABSTRACT FROM AUTHOR]

The joint optimization of power systems, mobile energy storage systems (MESSs), and renewable energy involves complex constraints and numerous decision variables, and it is difficult to achieve optimization quickly through the use of commercial solvers, such as Gurobi and Cplex. To address this challenge, we present an effective joint optimization approach for MESSs and power grids that consider various renewable energy sources, including wind power (WP), photovoltaic (PV) power, and hydropower. The integration of MESSs could alleviate congestion, minimize renewable energy waste, fulfill unexpected energy demands, and lower the operational costs for power networks. To model the entire system, a mixed-integer programming (MIP) model was proposed that considered both the MESSs and the power grid, with the goal of minimizing costs. Furthermore, this research proposed a highly efficient deep reinforcement learning (DRL)-based method to optimize route selection and charging/discharging operations. The efficacy of the proposed method was demonstrated through many numerical simulations. [ABSTRACT FROM AUTHOR]

In recent years, the goal of lowering emissions to minimize the harmful impacts of climate change has emerged as a consensus objective among members of the international community through the increase in renewable energy sources (RES), as a step toward net-zero emissions. The drawbacks of these energy sources are unpredictability and dependence on nature, leading to unstable load power supply risk. One way to overcome instability in the power supply is by using a battery energy storage system (BESS). Therefore, this study provides a detailed and critical review of sizing and siting optimization of BESS, their application challenges, and a new perspective on the consequence of degradation from the ambient temperature. It also reviews advanced battery optimization planning that considers battery degradation, technologies, degradation, objective function, and design constraints. Furthermore, it examines the challenges encountered in developing the BESS optimization model and evaluates the scope of the proposed future direction to improve the optimized BESS, especially its battery. [ABSTRACT FROM AUTHOR]

Circular business models for batteries have been revealed in earlier research to achieve economic viability while reducing total resource consumption of raw materials. The objective of this study is to measure the economic performance of the preferred business model by creating different scenarios comparing second life (spent) and new battery investment for seven different European regions and four energy management strategies. Findings reveal levels of economic ability for a total of 34 scenarios simulated, including direct savings per kWh, a total change in energy costs per year, battery charge/discharge cycles, and comparative breakeven analyses. Regional effects are also measured based on day-ahead electricity prices and solar irradiation. The minimum payback time is 7 years before battery system investment costs are covered. The most viable energy management strategies also had the highest number of charge/discharge cycles, which decreases battery lifetime. Investment in a second life battery compared to a new battery reduced the payback time by 0.5 to 2 years due to lower investment costs. However, the estimated lifetime range (3 to 10 years) is lower compared to a new battery (5 to 15 years), which questions the circular business model viability for the scenarios studied. Energy management strategies should be combined and customized to increase economic benefits. [ABSTRACT FROM AUTHOR]

This study seeks to determine a suitable arbitrage strategy that allows a battery energy storage system (BESS) owner to obtain the maximum economic benefits when participating in the Colombian electricity market. A comparison of different arbitration strategies from the literature, such as seasonal, statistical, and neural networks-based models, is performed. To determine BESS’s optimal operation, a Mixed Integer Linear Programming (MILP) optimization problem is formulated, including a battery degradation model based on an upper piecewise linear approximation method. A financial evaluation of the different arbitrage strategies is carried out, resulting, for all the analyzed cases, in a negative net present value (NPV); thus, the results show that the income obtained from BESS when only performing energy arbitrage in the Colombian market do not compensate the investment costs. Results have also shown that strategies based on statistical and prediction models have a better performance than seasonal strategies, especially in atypical circumstances such as COVID-19. [ABSTRACT FROM AUTHOR]

Circular business models for batteries have been revealed in earlier research to achieve economic viability while reducing total resource consumption of raw materials. The objective of this study is to measure the economic performance of the preferred business model by creating different scenarios comparing second life (spent) and new battery investment for seven different European regions and four energy management strategies. Findings reveal levels of economic ability for a total of 34 scenarios simulated, including direct savings per kWh, a total change in energy costs per year, battery charge/discharge cycles, and comparative breakeven analyses. Regional effects are also measured based on day-ahead electricity prices and solar irradiation. The minimum payback time is 7 years before battery system investment costs are covered. The most viable energy management strategies also had the highest number of charge/discharge cycles, which decreases battery lifetime. Investment in a second life battery compared to a new battery reduced the payback time by 0.5 to 2 years due to lower investment costs. However, the estimated lifetime range (3 to 10 years) is lower compared to a new battery (5 to 15 years), which questions the circular business model viability for the scenarios studied. Energy management strategies should be combined and customized to increase economic benefits. [ABSTRACT FROM AUTHOR]