Research on battery storage system configuration in active distribution networks

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.

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 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

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.

It is normally accepted that Battery Energy Storage Systems improve frequency regulation by providing fast response to the Automatic Generation Control. However, currently available control strategies may lead to early Energy Storage Systems aging given that Automatic Generation Control requirements are increasing due to zero carbon power generation integration. In this sense, it is important to analyze the aging phenomena in order to assess the technical–economical usefulness of Battery Energy Storage Systems towards zero carbon power systems. In order to avoid early aging, various proposals on aging-reducing algorithms can be found; however, it is unclear if those aging-reducing algorithms affect the performance of Battery Energy Storage Systems. It is also unclear whether those effects must be internalized to properly dimension the capacity of Battery Energy Storage Systems to both comply with performance standards and to prevent early aging. Thus, this paper estimates the storage capacity of a Battery Energy Storage Systems to comply with Automatic Generation Control performance standard under aging-reducing operating algorithms by dynamics simulations of a reduced-order, empirically-validated model of the Electric Reliability Council of Texas. The results show the relationship between the required performance of Automatic Generation Control and Battery Energy Storage System capacity, considering a 1-year simulation of Automatic Generation Control dynamics. It can be concluded that the compliance with performance standards is strongly related to the storage capacity, regardless of how fast the device can inject or withdraw power from the grid. Previous results in the state-of-the-art overlook the quantification of this relationship between compliance with performance standards and storage capacity.

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.

Rapid growth in the renewable energy sector supplying to the grid including solar PV and wind has caused the system inertia to reduce. Consequently, instability problems and supply interruption will happen more frequently during power imbalance in a low inertia power system. Hence, the solution of implementing a grid-scale battery energy storage system (BESS) is proposed to provide frequency support to the grid. This paper presents the modelling of the 565 MW Eastern Region of the Peninsular Malaysia power system in MATLAB/Simulink, where the frequency response of the system under power disturbances is studied and the minimum BESS capacity required for frequency support is investigated. The effect of frequency support with and without BESS implementation is also investigated under various system inertia. Comparing the frequency nadir of the system with and without BESS, the results show that under the largest disturbance of 300 MW, which is 53% of the 565 MW system and the lowest inertia of 2s, the 300 kWh BESS supported the frequency by increasing the frequency nadir up to 0.024 Hz. The results show trends in which the BESS will contribute more to systems with lower inertia value and larger disturbance during frequency support.

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.

Distributed generation integrated with thermal unit commitment considering demand response for energy storage optimization of smart grid

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.

This study aims to show methods of determining the installation site and the optimal capacity of a battery energy storage system (BESS) to attain load leveling. The methods are based on the hardware characteristics of 20 kW/100 kWh and 1 MW/4 MWh prototype BESSs, which are already developed and are being developed, respectively. The candidate site of BESS installation is the secondary side of a distribution substation's main transformer (MTR). These tasks are performed by first classifying the load pattern of an MTR and comparing it to the load pattern of a total power system. If the load factor improves, then the location is determined to be a BESS installation site. Subsequently, the optimal BESS capacity for an MTR is determined using the given operation pattern, the charging/discharging characteristics and efficiency. Computational experimentation was performed on an actual distribution system in Korea. After determining the optimal capacity, the authors verified that installing the BESS on the selected MTR improved both daily and nightly load factors.

Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage

On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting

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.