Investigation on Grid-Scale BESS Capacity for Frequency Support under various System Inertia

The Hawai‘i Natural Energy Institute (HNEI) is leading a team engaged in the research, development, deployment, and analysis of grid-scale battery energy storage systems (BESS) that are designed for system control and power quality support at the generation, transmission, and distribution levels. The program aims to identify high value BESS applications at various system levels, develop control algorithms that maximize the benefit to the grid/customer and the lifetime of the BESS, and evaluate and optimize those algorithms under real world operating conditions. The focus is to deploy, operate, and validate the performance of four grid-scale BESS for various ancillary service applications on grid systems across the state. Large scale battery energy storage systems will become an important part of the electric grid in the near future and it will be essential to ensure their reliability. The objective of this project is to understand the degradation of the individual batteries to anticipate failures. Laboratory testing of advanced Li-ion battery cells is performed to support life-time analysis of technologies targeted for large-scale grid energy storage applications. HNEI’s battery testing efforts have focused on grid scale deployment lithium ion titanate battery technology which is successfully being used for a variety of purposes including frequency and power regulation of large scale wind and solar energy generation. There is currently a lack of understanding of long term performance of battery technology in general, and specifically lithium ion titanate, used under large scale, grid conditions. This work aims to fill that gap in knowledge through laboratory scale battery testing and the development predictive lifetime models of performance of grid battery technology. Accelerated testing of identical lithium ion titanate battery technology was performed in the laboratory and those results will be used to develop predictive performance models. This model will combine existing modules [1-4] that account for single cell asymmetric degradation, single cell heat generation, string imbalance, and cell paralleling. As real world data is collected from the grid batteries, the predictive models will be compared and assessed for accuracy and ability to predict performance. This work will present preliminary results of the cycle and calendar aging long-term studies. [1] M. Dubarry, N. Vuillaume and B. Y. Liaw, J. Power Sources 186(2), (2009) 500-507. [2] M. Dubarry, C. Truchot and B.Y. Liaw, J. Power Sources 219 (2012) 204-216. [3] https://www.soest.hawaii.edu/HNEI/alawa/[4] M. Dubarry, C. Truchot, A. Devie and B. Y. Liaw, J. Electrochem. Soc 162(6), (2015) A877-A884.

The increasing penetration of non-synchronous generators, accompanied by retiring/displacement of synchronous generators has created a number of new operational issues in the Australian National Electricity Market (NEM) network, such as low-inertia conditions. Low-inertia power systems are likely to experience relatively large frequency variations following a generation/load mismatch. This is evidenced by a recently occurred event in the Australian National Electricity Market (NEM) grid, which led to cascading failures and electrical separation of the two states of Queensland and South Australia from the rest of the NEM. This paper first provides an extensive description of the event occurred on 25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> August 2018, whose high-level dynamic behaviour was simulated in a NEM reduced test system. Secondly, the use of grid-scale battery energy storage system (BESS) in providing primary frequency response (PFR) is investigated as a potential countermeasure to cascading failures. Simulation results show BESS could have potentially avoided under-frequency load shedding (UFLS).

The integration of battery energy storage systems (BESS) with renewable energy is a potential solution to address the disadvantages of renewable energy systems, which is irregular and intermittent power. In particular, residential BESS is advancing in numerous countries. The residential BESS connected to the photovoltaic system (PV) can store the PV power in the battery through charging, and supply the PV power, which was stored in the battery, to the load through discharging when there is no PV power. Therefore, the utilization of residential BESS with PV reduces the daily electric power consumption and the electricity bills that households have to charge. However, it is understood that there is no case of installing and using residential BESS in Korea yet. Most residential houses in Korea are apartment houses, and thus residential BESS can be used with balcony PV. This paper presents operation modes of residential BESS with balcony PV for apartment houses. The BESS capacity was estimated by considering the balcony PV capacity, which can be installed in households, and power consumption. The applicability of the residential BESS was analyzed through performance and economics evaluation under current and various conditions. The operation modes of BESS were divided into four types according to PV power supply priority and battery charging source, and a test took place in a demonstration house. The risk of fully discharging the battery has been discovered when PV power is first charged to the battery or when only PV power is charged with the battery. As a result, preferential charging of the battery with PV power and then with PV and grid power was found to be the most optimal operation mode. In addition, additional functions were proposed for residential BESS in apartment households. The results will contribute to effective application of residential BESS with balcony PV in the near future.

Planning battery energy storage system in line with grid support parameters enables circular economy aligned ancillary services in low voltage networks

Efficiency analysis for a grid-connected battery energy storage system

In this paper, an optimizing model of battery energy storage system (BESS) capacity configuration for active distribution networks (ADNs) is proposed to mitigate the negative effect of distribution systems with distributed generations (DGs), in stability, economy and reliability perspectives. In details, this model involved the charging and discharging limitation and the operation constrains of BESS, as well as the power flow balance of distribution system. Moreover, three optimizing objectives have been presented for the optimal power profile of BESS, including minimizing the voltage fluctuation, reducing the feeder loss and maximizing the consecutive supplied power of ADN, respectively. After the optimization, the BESS capacity is calculated by estimation of the maximum charging and discharging energy. Finally, the simulation results are shown to illustrate the procedure of capacity configuration.

The International Renewable Energy Agency predicts that with current national policies, targets and energy plans, global renewable energy shares are expected to reach 36% and 3400 GWh of stationary energy storage by 2050. However, IRENA Energy Transformation Scenario forecasts that these targets should be at 61% and 9000 GWh to achieve net zero carbon emissions by 2050 and limit the global temperature rise within the twenty-first century to under 2 °C. Despite widely known hazards and safety design of grid-scale battery energy storage systems, there is a lack of established risk management schemes and models as compared to the chemical, aviation, nuclear and the petroleum industry. Incidents of battery storage facility fires and explosions are reported every year since 2018, resulting in human injuries, and millions of US dollars in loss of asset and operation. Traditional risk assessment practices such as ETA, FTA, FMEA, HAZOP and STPA are becoming inadequate for accident prevention and mitigation of complex energy power systems. This work describes an improved risk assessment approach for analyzing safety designs in the battery energy storage system incorporated in large-scale solar to improve accident prevention and mitigation, via incorporating probabilistic event tree and systems theoretic analysis. The causal factors and mitigation measures are presented. The risk assessment framework presented is expected to benefit the Energy Commission and Sustainable Energy Development Authority, and Department of Standards in determining safety engineering guidelines and protocols for future large-scale renewable energy projects. Stakeholders and Utility companies will benefit from improved safety and reliability by avoiding high-cost asset damages and downtimes due to accident events.

This paper presents a novel power flow optimization strategy for a Grid Connected microgrid (MG) equipped with a Battery Energy Storage System (BESS), namely a Li-Ion battery pack. A BESS can be employed to perform several functionalities, related to different user requirements, such as power stability, peak shaving, optimal energy trading, etc. In the proposed system the MG is composed by an aggregation of distributed power generators and loads and a BESS is adopted to manage the power over-production/over-demand in real time, in order to maximize the prosumer profit looking at the current energy prices and the BESS State of Charge (SOC). The Energy Management System (EMS) is based on a Fuzzy Logic Controller (FLC) with a suitable rule inference system designed by an Expert Operator (EO). The control strategy is tested with different power profiles and BESS capacities in order to verify its effectiveness and limits. Furthermore, the FLC has been optimized by a Genetic Algorithm to increase the total profit exploiting the BESS as energy buffer. The optimization results have been compared to the initial FLC designed by the EO, taking into account both the profit and the deterioration of the BESS measured through a suitable battery stress index.

This paper proposes a wind power generation system based on permanent magnet synchronous generator (PMSG) with a distributed battery energy storage system (BESS). After introducing a BESS, the PMSG system can mitigate wind farm fluctuations and provide inertial response. After considering the state of charge (SOC) of BESS, the degree of frequency and voltage support will be modified and the working operation of three converters will be automatically switched. This paper optimizes the capacity of BESS after considering the benefits of virtual synchronization. The cost can be reduced after appropriately increasing the capacity of the BESS due to the benefits of actively supporting the power grid.

To address the instability of solar energy production and users’ electricity demand, the integration of a battery energy storage system (BESS) can mitigate the issue of electricity consumption without altering users’ electricity habits. However, the addition of BESS increases the initial investment in the system, and different electricity pricing models also influence the system’s operational strategy. Therefore, conventional and mixed operation modes were established under tiered and time-of-use pricing. Trends in net present value (NPV) and self-consumption rate (SC rate) were analyzed for four storage prices (138.50–346.25 USD/kWh) as BESS capacity increased. Results show SC rate is highly influenced by BESS capacity in conventional mode. NPV initially correlates positively with installed PV capacity but turns negative as BESS capacity grows. The mixed mode achieves the highest NPV faster, reducing the BESS capacity needed for economic optimality. Based on these findings, NSGA-II and TOPSIS were used to evaluate system performance and economy. The variation trend of optimal capacity under different weightings offers valuable guidance for selecting storage equipment in similar building applications.

The presence of distributed generation (DG), represented by photovoltaic generation and wind generation, brings new challenges to distribution network operation. To accommodate the integration of DG, this study proposes a bi‐level optimisation model to determine the optimal installation site and the optimal capacity of battery energy storage system (BESS) in distribution network. The outer optimisation determines the optimal site and capacity of BESS aiming at minimising total net present value (NPV) of the distribution network within the project life cycle. Then optimal power flow (OPF) and BESS capacity adjustment are implemented in the inner optimisation. OPF optimises the scheduling of BESS and network losses. On the basis of optimal scheduling of BESS, a novel capacity adjustment method is further proposed to achieve the optimal BESS capacity considering battery lifetime for minimising the NPV of BESS. Finally, the proposed method is performed on a modified IEEE 33‐bus system and proven to be more effective comparing with an existing method without BESS capacity adjustment.

With the increasing penetration of wind power and other distributed renewable generation (DRG), its inherent intermittency and variability impose operational challenges on distribution systems. In order to alleviate this problem, this paper proposes an optimization framework to determine the optimal installation location, capacity and operation strategy of the battery energy storage system (BESS) in the 33-node distribution system with wind power access. The method first screens the BESS candidate installation location through power loss sensitivity analysis, and then adopts the particle swarm optimisation (PSO) algorithm to optimize the capacity and location of BESS with the goal of minimising network loss, capital expenditure (CAPEX) and operating expenditure (OPEX). This framework was systematically tested across three cases: the original 33-bus distribution network system, the system configured with the optimal capacity BESS, and the joint operation system integrating wind power and BESS. The results indicate that the coordinated operation scheme reduces the active power loss by nearly 27% while improving the reliability and operation flexibility.

The high penetration of distributed photovoltaics (PV) in distribution networks (DNs) results in voltage violations, imbalances, and flickers, leading to significant disruptions in DN stability. To address this issue, this paper proposes a multi-timescale voltage regulation approach that involves the coordinated control of a step voltage regulator (SVR), switched capacitor (SC), battery energy storage system (BESS), and electric vehicle (EV) across different timescales. During the day-ahead stage, the proposed method utilizes artificial hummingbird algorithm optimization-based least squares support vector machine (AHA-LSSVM) forecasting to predict the PV output, enabling the formulation of a day-ahead schedule for SVR and SC adjustments to maintain the voltage and voltage unbalance factor (VUF) within the limits. In the intra-day stage, a novel floating voltage threshold band (FVTB) control strategy is introduced to refine the day-ahead schedule, enhancing the voltage quality while reducing the erratic operation of SVR and SC under dead band control. For real-time operation, the African vulture optimization algorithm (AVOA) is employed to optimize the BESS output for precise voltage regulation. Additionally, a novel smoothing fluctuation threshold band (SFTB) control strategy and an initiate charging and discharging strategy (ICD) for the BESS are proposed to effectively smooth voltage fluctuations and expand the BESS capacity. To enhance user-side participation and optimize the BESS capacity curtailment, some BESSs are replaced by EVs for voltage regulation. Finally, a simulation conducted on a modified IEEE 33 system validates the efficacy of the proposed voltage regulation strategy.

Fast-frequency regulation (FFR) is becoming a key measure to enhance the frequency stability of power systems as the penetration of renewables and power electronics continues to grow and the system inertia declines. Although different control methods have been proposed to provide a wind turbine generator (WTG) with a limited capability of virtual inertia and frequency support, the coordination between the WTG and a battery energy storage system (BESS), as well as the potential optimization benefits, have not been fully studied. This study proposes a coordinated control of WTG and BESS that provides FFR to the AC system and at the same time extends the cycle life of the battery. First, a cost effective and SOC-based FFR strategy of BESS alone was proposed. Then, a coordinated FFR method for the WTG–BESS hybrid system under all wind speeds was proposed by analyzing the operational characteristics of WTG. The proposed coordinated strategy improves the FFR performance with a longer cycle life and lower cost of battery under different operating conditions. Simulation results based on varying wind speeds indicate that the proposed FFR strategy raises the frequency nadir and avoids the frequency secondary dip.

The regulation of the grid voltage within operational limits becomes increasingly challenging as residential photovoltaic (PV) adoption rises. Therefore, this study proposes a method for the efficient planning of multiple community battery energy storage systems (BESS) in low voltage distribution systems embedded with high residential rooftop PV units. A bi-level optimization method based on a Neural Network Optimization Algorithm is developed to regulate the voltage in grid-connected solar PV. Since BESS characteristics are crucial for the reliable operation of the distribution networks, the objective of the bi-level optimization problem is to optimally place and operate BESS collectively at a distributed system level. The charging/discharging protocol of the batteries management system is obtained utilizing linear programming that minimizes the daily voltage signal. Simulations were carried out on a modified IEEE low voltage test feeder to examine the impact of PV integration and BESS installation on the voltage profile. Experimental results show the efficacy of the proposed method in enabling the utility to determine the optimal location, capacity, and number of BESS in the distribution system to keep the network voltage magnitude within acceptable bounds. In addition, results demonstrate that the network topology, load profiles, and amount of PV power highly influence the BESS characteristics.