Performance Of Energy Storage System Research Articles

Improved sparrow search algorithm optimization deep extreme learning machine for lithium-ion battery state-of-health prediction

SummaryAccurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation of the battery energy storage system (BESS). Deep extreme learning machine (DELM) optimized by the improved sparrow search algorithm (ISSA) is developed to predict the SOH of LIBs under random load conditions in the paper. Firstly, two indirect health indicators are extracted from the random partial discharging voltage and current data, which are chosen as the inputs of DELM by the Pearson correlation analysis. Then, ISSA is presented by combining the elite opposition-based learning (EOBL) and the Cauchy-Gaussian mutation strategy to increase the diversity of sparrow populations and prevent them from falling into the local optimization. Finally, the ISSA-DELM model is utilized to estimate the battery SOH. Experimental results illustrate the high accuracy and strong robustness of the proposed approach compared with other methods.

Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting.

In the competitive electricity market, electricity price reflects the relationship between power supply and demand and plays an important role in the strategic behavior of market players. With the development of energy storage systems after watt-hour meter, accurate price prediction becomes more and more crucial in the energy management and control of energy storage systems. Due to the great uncertainty of electricity price, the performance of the general electricity price forecasting models is not satisfactory to be adopted in practice. Therefore, in this paper, we propose a novel electricity price forecasting strategy applied in optimization for the scheduling of battery energy storage systems. At first, multiple nonstationary decompositions are presented to extract the most significant components in price series, which express remarkably discriminative features in price fluctuation for regression prediction. In addition, all extracted components are delivered to a devised deep convolution neural network with multiscale dilated kernels for multistep price forecasting. At last, more advanced price fluctuation detection serves the optimized operation of the battery energy storage system within Ontario grid-connected microgrids. Sufficient ablation studies showed that our proposed price forecasting strategy provides predominant performances compared with the state-of-the-art methods and implies a promising prospect in economic benefits of battery energy storage systems.

Ramp-rate limiting strategies to alleviate the impact of PV power ramping on voltage fluctuations using energy storage systems

With the increasing adoption of solar photovoltaics (PVs) in the power grid, the grid authorities are faced with significant challenges in managing PV intermittency, variability and uncertainty. The inherent ramping behaviour of the PVs results in short-term PV power fluctuations which in-turn affects the grid voltage regulation. This has resulted in implementation of new grid codes to limit the ramp-rate (RR) behaviour of PVs allowing the grid resources to curb the power fluctuations. Energy storage system (ESS) is one such fast acting resource that helps in limiting and smoothing PV power fluctuations when coordinated by RR control algorithms. This paper proposes an ESS-based RR control strategy to smoothen and limit PV power fluctuations in the power grid. The proposed RR control strategy has been implemented for two types of smoothing namely, aggregated smoothing that considers the overall PV output, and individual node-level smoothing that considers the individual PV output. The technical feasibility of this RR control strategy to limit the PV power fluctuations is analysed using a standard IEEE test network with both centralized and distributed ESS placements at various PV penetration levels. The effectiveness of the proposed RR control strategy in reducing voltage fluctuations in the power network is also studied. Moreover, cost optimization is carried out with various PV penetration levels to ensure optimal ESS operation at the lowest possible cost while adhering to the optimization and network constraints. The paper further provides detailed techno-economic analysis for various case scenarios.

Optimal ESS size calculation for ramp rate control of grid-connected microgrid based on the selection of accurate representative days

Recently, energy storage system (ESS) is in the spotlight because of its deployment to alleviate high ramp rate in the microgrids; enabling the large-scale penetration of renewable energy resources into the utility grid. A limited number of representative days are often chosen to ensure computational tractability while optimizing the ESS size for ramp rate control. Most developed ESS size optimization approaches focused on optimal ESS operation during the optimization horizon. However, the solution's optimality also depends on the accuracy of selected representative days, as sizing is based on the optimal operation during these days. This study develops a comprehensive methodology for optimal ESS size calculation by incorporating the selection of accurate representative days. A novel representative day selection technique is proposed to select accurate representative days from the data spanning the optimization horizon in a reasonable amount of time. The suitability of the obtained representative days was assessed with regard to the cost of ramp violations. The results indicate that the proposed technique can obtain more accurate representative days. Thus, by employing this technique, optimality of the ESS size can be guaranteed. The significance of representative days’ selection from a larger data set rather than a single year is highlighted. Moreover, we demonstrate how the adoption of worst-case scenario can undermine the solution's optimality.

RETRACTED: Multi-level charging stations for electric vehicles by considering ancillary generating and storage units

Forecasting in smart grids is of utmost importance due to the existence of hybrid and electric grid-connected vehicles. The grid flow capacity, especially during peak hours, cannot bear adding a large number of charging stations (CS). Rapid EVCSs are a suitable option for promoting people's interest in using these vehicles, instead of gasoline-burning vehicles. Lack of long and tiresome lines for charging the vehicles and available CSs can greatly impact EVs' penetration. Accordingly, this study aimed to design an EVCS while considering different sources and equipment such as PV system, battery, and diesel for drivers' comfort and taking into account multi-level charge. Thus, the optimal design of the station for the grid-connected mode was converted into an optimization problem and a developed hybrid PSO and gray wolf algorithm were used for solving it. As the result of this optimization, the energy storage system's power and capacity, as well as the diesel generator's and the energy storage system's performance duration can be determined in continuous hours. The results showed that the proposed model successfully used all the options available for designing the EVCS.

Fusion Deconvolution for Reliability Analysis of A Flywheel-Battery Hybrid Energy Storage System

A hybrid flywheel-battery energy storage system is able to smooth the battery charging/discharging; harmful impact can be filtered by the flywheel to reduce battery damage and extend battery life. However, due to extremely high rotating speed of the flywheel, the hybrid storage system is often subject to mechanical failures in the flywheel transmission system. Therefore, it is critical to detect unexpected faults in the flywheel transmission to ensure the normal operation of the hybrid energy storage system. To this end, in this study a new fusion deconvolution is proposed to perform reliability analysis on the hybrid flywheel-battery energy storage system. Firstly, the Deterministic Random Separation (DRS) is used to simplify the sensory signal components to obtain the fault impulse. Then, the multipoint kurtosis (MKurt) of the impulse is used to determine the parameters of the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) model. Lastly, the envelope analysis of the signal filtered by the MOMEDA deconvolution is carried out to obtain the characteristics of the periodic fault pulses. Both numerical and experimental results demonstrate that the proposed fusion deconvolution method produces satisfactory diagnostic performance. Compared with existing popular deconvolution algorithms, the proposed method proposed improves the extraction of the periodic fault pulses.

The multi-stage framework for optimal sizing and operation of hybrid electrical-thermal energy storage system

In order to mitigate the intermittency of renewable energy, the paper proposes a hybrid electrical-thermal energy storage system, which complementarily utilizes the cost-effective two-tank direct molten salt thermal energy storage system and the flexible lead-acid battery. A multi-stage framework is further proposed for optimal sizing and operation of the hybrid energy storage system. First, the renewable energy capacity is optimized considering the minimization of total net load. Then, the typical net load profiles are selected by K-means clustering algorithm. Finally, a bi-level optimization model considers the minimization of levelized cost of storage (LCOS) and power deviation to optimize the sizing and operation strategy of the hybrid energy storage system, and the optimization problem is solved by meta-heuristic algorithm and mixed-integer linear programming. The case studies show that: (1) the hybrid energy storage system is more reliable than single thermal energy storage and more cost-effective than single battery; (2) the multi-stage framework outperforms the commonly-used rule-based operation strategy; (3) demand response strategy can effectively reduce the investment cost of the proposed system.

Battery thermal performance oriented all-electric ship microgrid modeling, operation and energy management scheduling

Energy storage system (ESS) integrated all-electric ship (AES) is gaining popularity as it renders higher efficiency and emission reduction. Being an isolated system, generation and storage capabilities are limited, and hence network losses, mechanical and electrical load estimation must be modeled accurately to establish a reliable operation strategy. Thus, the presented work proposes a modeling and operation strategy based on the propulsion architecture, mission profile, operation modes, and thermal performance of ESS. The following aspects are incorporated to improve the accuracy. (1) Electrical load demand, and loss modeling at the generation and load side; (2) energy management system (EMS) for optimal energy dispatch; (3) and experiment-based ESS electrical characterization and cooling designs for the thermal management system. Firstly, mathematical models of electrical components and mechanical propulsion units are used to establish the net electrical load demand from the propulsion and auxiliary units. The parameters obtained from load models are used in EMS energy dispatch to minimize the fuel and thermal-performance-dependent ESS operation costs. Finally, generation side losses are included in the final dispatch to meet the net electrical load demand of the vessel. Based on electrical parameters from test setup and different cooling designs, a battery management system is modeled to estimate the ESS pouch’s temperature profile that is used as an operational constraint of EMS to discourage ESS from operating at high temperatures. Simulation results indicate the impact of AES propulsion architecture and ESS thermal performance on its operation. The results suggest the strong influences of mission profile and operation modes in designing a realistic operation schedule for the actual implementation.

Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

Air-exposed lithium metal as a highly stable anode for low-temperature energy storage applications

The demand for cryogenic applications has resulted in higher requirements for the low-temperature performance of energy storage systems. Lithium-metal batteries are the most promising energy storage systems. Lithium-metal anodes have the merits of high capacity and low potential. However, at low temperatures, especially sub-zero, the formation of lithium dendrites seriously hinders their applications. Herein, distinct from the traditional strategies of separating lithium metal from oxygen substances, we propose a new strategy to suppress dendrites by exposing lithium metal to air for short periods to generate a controlled oxidative protective layer in situ that is compact, homogeneous and mainly composed of Li3N, Li2O, LiOH and Li2CO3. Symmetrical and full cells are assembled. The air-pretreated Li metal symmetrical cell exhibits an excellent lifespan of up to 4500 h (1 mA cm-2) at 30 °C and also shows a smaller voltage polarization of 20 mV at 1.0 mA cm-2 at -20 °C. Importantly, the full cell using the air-pretreated Li metal as an anode and NCM811 as a cathode can charge-discharge normally at -20 and -40 °C. This work provides an efficient and facile approach for developing superior lithium-metal batteries for future utilization at a wide range of temperatures.

Rule-Based Dual Planning Strategy of Hybrid Battery Energy Storage System

A reasonable and efficient scheduling strategy does not only help ensure the safe and stable operation of battery energy storage system, but also extend the battery cycling life and reduce the system overall costs. In this paper, a novel rule-based dual planning strategy is proposed to achieve refined management for the hybrid battery energy storage system, including lead-acid battery storage system and lithium iron battery storage system. Aiming at different characteristics and SOC value of different batteries, the upper layer of the proposed scheduling strategy is designed to determine whether the hybrid battery energy storage system is enabled and grid-connected and in the lower layer, the battery scheduling controller will cooperate the operation of different batteries to realize shallow circulation of the lead-acid battery storage. Various simulations are conducted in the environment of Matlab/Simscape, and the simulation results have proved the feasibility and effectiveness of the proposed scheduling strategy.

The Economic Viability of Energy Storage System and Optimal Operation Strategies of Microgrid: Enhancing Hosting Capacity of Renewable Energy Sources

The Economic Viability of Energy Storage System and Optimal Operation Strategies of Microgrid: Enhancing Hosting Capacity of Renewable Energy Sources

태양광 발전소의 발전량 예측을 고려한 최적의 ESS 충전 스케줄링에 관한 연구

ESS (Energy Storage System) is being installed as a core equipment to resolve intermittent output characteristics according to weather conditions of photovoltaic power plants (PV). Generally, the ESS installed in the PV is charged from 10:00 to 16:00 and discharged after 16:00. Namely, ESS operation can be seen as maximizing the benefits of REC (Renewable Energy Certificate) weights rather than solving intermittent PV output. Meanwhile, utility line congestion occurs because a large amount of PV output supplied to the utility line at the time that ESS charging is completed. Through participation in the forecast of renewable energy generation, the PV output will be charged with the ESS when it is out of the PV forecast error range (± 6%). By applying the scenario technique and mixed-integer linear programming (MILP) for irregular PV output forecast error, the ESS charging scheduling method is determined to maximize the expected total daily benefit of the ESS operator. As a result of the simulation, it was confirmed that additional benefits of PV operator could be secured through the renewable energy generation forecasting system.

도시철도 변전소의 에너지저장장치 시스템 연계에 따른 기여도 분석 연구

In accordance with global efforts to reduce carbon, energy storage system(ESS) application and research are rapidly progressing in Korea. There are many advantages when ESS is connected to DC electric railway for regenerative braking energy utilization and peak reduction. In this paper, the di/dt and ΔI characteristics of the 50F relay were analyzed according to the presence of failure resistance, ESS operation condition, and train operating condition by connecting the ESS to the DC-link of a DC electric railway substation. To analyze the contribution of ESS, power analysis program, PSCAD/EMTDC was used.

A Flexible Operation and Sizing of Battery Energy Storage System Based on Butterfly Optimization Algorithm

There is a surge in the total energy demand of the world due to the increase in the world’s population and the ever-increasing human dependence on technology. Conventional non-renewable energy sources still contribute a larger amount to the total energy production. Due to their greenhouse gas emissions and environmental pollution, the substitution of these sources with renewable energy sources (RES) is desired. However, RES, such as wind energy, are uncertain, intermittent, and unpredictable. Hence, there is a need to optimize their usage when they are available. This can be carried out through a flexible operation of a microgrid system with the power grid to gradually reduce the contribution of the conventional sources in the power system using energy storage systems (ESS). To integrate the RES in a cost-effective approach, the ESS must be optimally sized and operated within its safe limitations. This study, therefore, presents a flexible method for the optimal sizing and operation of battery ESS (BESS) in a wind-penetrated microgrid system using the butterfly optimization (BO) algorithm. The BO algorithm was utilized for its simple and fast implementation and for its ability to obtain global optimization parameters. In the formulation of the optimization problem, the study considers the depth of discharge and life-cycle of the BESS. Simulation results for three different scenarios were studied, analyzed, and compared. The resulting optimized BESS connected scenario yielded the most cost-effective strategy among all scenarios considered.

Capturing curtailed renewable energy in electric power distribution networks via mobile battery storage fleet

In this paper, a new scheduling model is proposed for the daily operation of a truck-mounted Mobile Battery Energy Storage Systems (MBES) fleet employed in a distribution network. The distribution network is installed with various wind and PV distributed resources wherein a portion of the renewable-based generation capacity is curtailed due to various technical reasons. The MBES fleet scheduling model aims at minimizing curtailed renewable energy by absorbing and releasing excess energy when and where needed. Accordingly, the variable spatial and temporal renewable generation curtailment is recovered by optimal spatio-temporal and power-energy scheduling of the MBES fleet. The required transportation time for the MBES unit transportation comprising detachment, movement, and attachment is considered efficiently. Besides, a detailed breakdown of the MBES transportation cost is presented and modelled via a new formulation. The proposed MBES fleet operation model can be easily integrated into the available commercial distribution optimal power flow packages. Considering linearity, the model can handle very large-scale real-life networks without convergence problems by achieving global optima. The model is numerically tested, and simulation results demonstrate its effectiveness for recovering a considerable share of the curtailed renewable energy irrespective of the resources type, generation time period, or installation location.

Chance-constrained unit commitment with energy storage systems in electric power systems

The high penetration of renewables has amplified errors in forecasts of wind and solar power generation that are made to support the operation of power systems. To guarantee the security and reliability of the power system, sufficient dispatchable power generation capacity must be reserved. Various studies of the use of risk assessment to improve operational performance have been carried out. However, most methods of so doing are designed to cover a given risk level of uncertainty, which is determined from forecasts rather than actual results. As renewable generation and load capacity increase, defining the risk level without considering unit commitment (UC) may limit scheduling efficiency. In this study, chance-constrained programming is combined with goal programming to optimize risk-based UC. Solar power generation, wind power generation, and demand forecast errors are handled by the optimization of reserve schedules and adjustments of risk by imposing a penalty cost. The proposed risk-based UC model is transformed into a mixed-integer linear programming (MILP) problem using the equivalent deterministic form and piecewise linear functions to find an efficient solution. Case studies of a realistic Taiwan power system in 2025 are considered; these include peak (Summer 2025) and off-peak (Winter 2025) scenarios. Simulation results reveal that the flexible operation of a battery energy storage system (BESS) (upward/downward reserve) reduces risk costs by a significant amount (19.12%∼100%). Sensitivity analysis reveals how the risk preferences of the System Operator (SO) can be represented in terms of predefined risk levels and confidence intervals to capture the uncertainties in load, wind power generation, and solar power generation forecasts. The spinning reserve level is optimized to reduce the risk cost and the addition of BESS in flexible operations further reduces the risk cost. Lastly, pumped storage units as a downward reserve can reduce wind spillage and excess solar power generation costs.

Two-Stage Energy Management for Energy Storage System by Using Stochastic Model Predictive Control Approach

There is a growing tendency for industrial consumers to invest in both photovoltaic (PV) and energy storage systems (ESSs) to meet their electricity requirements. However, the uncertainty of load demand and PV output brings great challenges for ESS operation. In this paper, a stochastic model predictive control (MPC) approach-based energy management strategy for ESSs is proposed. A non-parametric probabilistic prediction method embedded in time series correlation is adopted to describe the uncertainty of load demand and PV output. Then, a two-stage energy management model is proposed aiming at minimizing the total operation cost. The upper stage can generate an hourly operation strategy for ESSs, while the lower stage focuses on a more detailed minute-level operation strategy. The hourly operation strategy is also used as a basis to guide the ESS operation in the lower stage. Besides, a chance constraint was introduced to achieve a win–win solution between PV power consumption and electricity tariff, while the terminal value constraint of the capacity of ESSs to better cope with the uncertainty beyond the prediction time window. Finally, the numerical results showed that the proposed method can achieve an effective ESS energy management strategy.

STATCOM+BESS MODELLING AND HARMONIC ANALYSIS

In this paper, concepts of Static Compensator plus Battery Energy Storage System (STATCOM+BESS) operation, power control and modelling are reviewed. An implementation in Matlab-Simulink, considering a two level VSC based STATCOM+BESS is realized. Details of the implementation are provided and simulation results are presented. A single, aggregated 50 MVA BESS with the STATCOM connected to an external grid, is considered in the assessment. Harmonic propagation analysis is conducted by considering THD values at the point of connection of the STATCOM+BESS station.

Optimal Economic–Environmental Operation of BESS in AC Distribution Systems: A Convex Multi-Objective Formulation

This paper deals with the multi-objective operation of battery energy storage systems (BESS) in AC distribution systems using a convex reformulation. The objective functions are CO2 emissions, and the costs of the daily energy losses are considered. The conventional non-linear nonconvex branch multi-period optimal power flow model is reformulated with a second-order cone programming (SOCP) model, which ensures finding the global optimum for each point present in the Pareto front. The weighting factors methodology is used to convert the multi-objective model into a convex single-objective model, which allows for finding the optimal Pareto front using an iterative search. Two operational scenarios regarding BESS are considered: (i) a unity power factor operation and (ii) a variable power factor operation. The numerical results demonstrate that including the reactive power capabilities in BESS reduces 200 kg of CO2 emissions and USD 80 per day of operation. All of the numerical validations were developed in MATLAB 2020b with the CVX tool and the SEDUMI and SDPT3 solvers.