Location Of Battery Energy Storage System Research Articles

A simulation-metaheuristic approach for finding the optimal allocation of the battery energy storage system problem in distribution networks

This paper proposes a simulation study to solve the optimal allocation of the Battery Energy Storage System (BESS) problem in distribution networks. The effect of BESS’s installation in the selected distribution networks is surveyed for a 24-hour period, where time-of-use electricity charges are divided into three periods: standard, peak, and off-peak hours. This study will use Teaching Learning-Based Optimization (TLBO) as the main optimizer for the problem simulation. The objective function is to minimize the combined cost of purchasing electricity and energy loss, where the optimal location of BESS and its operated power at each hour are treated as the control variables to be optimized. Two distribution systems are utilized, viz. 18-node and 33-node systems are considered to assess the performance of TLBO in solving the mentioned problem, where a comparison with other recent metaheuristic algorithms also have been conducted. The study’s findings demonstrated the promising results of TLBO in terms of minimizing the energy cost and significantly reducing the peak loads during peak hours in the 24 h. The simulations also show that TLBO can be used as an effective tool for position and power of BESS optimization solution, where for the 18-node system, there is about 3.7 % cost reduction and for the 33-node system, about 12% cost saving for power purchased for the surveyed 24-h period.

Best Practice in Battery Energy Storage for Photovoltaic Systems in Low Voltage Distribution Network: A Case Study of Thailand Provincial Electricity Authority Network

This research investigated the increases of the voltage profile on the Provincial Electricity Authority (PEA)’s low voltage (LV) network due to the solar photovoltaic (PV) penetration. This study proposed the solution to maintain the voltage profile within the PEA’s standard limitation by using battery energy storage system (BESS) application. The algorithm using bisection method to figure out the optimal size and location of BESS was examined and simulated in different scenarios such as summer/winter and weekend/weekday behaviors. Furthermore, the allocation of a battery in various locations was also considered. DIgSILENT power factory with DPL script and Python are the tools used to cover diverse scenario cases. The results showed that the best practice of how to implement BESS to solve the voltage rise problem was the BESS installation at the distribution transformer and the BESS installation separately at the end of each feeder near the loads. However, the optimal size of BESS installation at the distribution transformer was almost double that of installation at the end of each feeder.

Sizing and Sitting of Battery Energy Storage Systems in Distribution Networks with Transient Stability Consideration

In this study, the capacity and location of battery energy storage systems (BESSs) in a distribution network were evaluated to increase the stability and reliability of power systems by applying the proposed transient stability indicators. The search capability of particle swarm optimization (PSO) combined with Pareto optimality in MATLAB R2019b was used to solve multi-objective problems in steady-state conditions, including active power loss, voltage deviation, and total operating cost. The BESS capacity and location were then derived using the Manhattan distance method. Subsequently, the BESS control model was constructed using the commercial software DIgSILENT PowerFactory 2021 to evaluate generator power smoothing and short-term voltage stability index during the transient response and determine the final capacity and location of BESS in distribution networks. Finally, the accuracy of the proposed method was verified by considering the daily load and solar power generation curves using the IEEE 33-bus radial power distribution network.

Optimal Battery Energy Storage System Based on VAR Control Strategies Using Particle Swarm Optimization for Power Distribution System

We designed a battery energy storage system (BESS) based on the symmetrical concept where the required control is by the symmetrical technique known as volt/var control. The integration of BESS into the conventional distribution has significantly impacted energy consumption over the past year. Load demand probability was used to investigate optimal sizing and location of BESS in an electrical power system. The open electric power distribution system simulator (OpenDSS) was interfaced with MATLAB m-file scripts and presented by using time series analysis with load demand. The optimal BESS solution was adapted by using a genetic algorithm (GA) optimization technique and particle swarm optimization (PSO). The simulation results showed that the BESS was directly connected to the power grid with GA and PSO, and it was observed that BESS sizing also varied for these two values of 1539 kW and 1000 kW, respectively. The merit of those values is the power figure of the system, which is necessary for installation. Therefore, optimal sizing and location of the BESS are helpful to reduce the impact from the load demand to the total system loss and levelling of the energy demand from the power system network. The integration of the BESS can be applied to improve grid stability and store surplus energy very well. The grid increased the stability of the power system and reduced the impact from the large scale of BESS penetration.

A methodology to prevent cascading contingencies using BESS in a renewable integrated microgrid

One of the key features of a microgrid is its capability to operate in an islanded mode when required. However, due to limited resources and bidirectional power flows, a microgrid in islanded mode is susceptible to different contingencies with extensive effects, e.g., cascading loss of distributed generators (also known as Distributed Energy Resources – DERs) due to unacceptable post-fault voltage recovery performance. In a renewable integrated microgrid, conventionally Battery Energy Storage System (BESS) is placed at the point of common coupling of distributed generators. However, such a placement approach does not prevent the cascading tripping of DERs following a fault, especially in the presence of a large share of induction motor loads. To address this challenge, this paper proposes a new placement methodology of BESS based on reactive power margin to prevent cascading contingencies and a subsequent blackout in a microgrid. The proposed methodology deploys an iterative technique. If the voltages at DERs terminals after a fault fail to recover within the stipulated time suggested by the grid code, BESS is placed at the bus with the lowest reactive power margin. It is known as the centralised placement approach of BESS. If this approach does not avert the DER tripping, the location of BESS is changed using the distributed placement scheme. In this scheme, several BESSs are placed on multiple buses, which have relatively lower values of reactive power margin. In a specific microgrid, either the centralised or the distributed BESS placement approach is eventually adopted. To explore the effectiveness of the proposed method, the IEEE 43 bus industrial test system integrated with renewable energy resources is considered as an islanded microgrid for simulations. It is found that the proposed approach results in the most appropriate placement of BESS to make the microgrid immune to cascading tripping of DERs.

Line Loss Reduction by Optimal Location of Battery Energy Storage System for the Daily Operation in Microgrid with Distributed Generations

Distributed generations (DGs) such as solar and wind generations are used as generation source in the microgrid. Due to the fluctuation of renewable energy sources, the battery energy storage system (BESS) is one of the essential components to control and improve the quality of power microgrid. However, a placement for BESS installation with minimizing line loss is one of the most concern problems. This paper aims to determine the optimal location of the BESS by using the analytical method with the line loss sensitivity index. A multi-period optimal power flow (OPF) and line loss analysis with BESS parameters were investigated considering the daily operation of a microgrid in connected mode. The simulation performed on the microgrid that wants to keep total power generation from DGs in BESS and low energy consumption from the utility. Microgrid includes 3 DGs (biomass, solar and wind turbine generators) to assess the robustness of BESS placement. The results confirmed the effectiveness of the presented method. The optimal location of BESS can reduce line power loss and power consumption from the utility.

Optimal Size and Location of Battery Energy Storage Systems for Reducing the Wind Power Curtailments

Both the lack of the downward regulation capacity and the transmission capacity limit have led to considerable wind power curtailments in China. The battery energy storage system (BESS) provides a new solution to reduce the wind power curtailments due to its relatively high energy density and flexible installed location. In this paper, a new bi-level programming model is proposed to optimize the size and location of BESS for reducing the wind curtailments. The revenues of BESS, when it is located either at the wind farm or the load center, are considered comprehensively. Moreover, a numerical optimization algorithm is developed to solve the proposed model. Simulation results demonstrate the validity of the proposed model and developed algorithm. They also reveal that the BESS located at the load center offers more net profit compared to the one located at the wind farm. Subsequently, sensitivity analysis is performed to assess the effect of key parameters on the optimal values of BESS.

Intelligent Determination of a Battery Energy Storage System Size and Location Based on RBF Neural Networks for Microgrids

As an installation of Battery Energy Storage Systems (BESS) at a non-optimum size and location in a microgrid can cause stability issues, increase in cost, system losses and larger BESS size. Thus, the optimum size and location of BESS is a main challenge for combining BESS into a microgrid. For this reason, this paper proposes a novel method to evaluate an optimal size and location of the BESS using the Radial Basis Function Neural Networks (RBFNN). The proposed method consists of a two-stage based on an optimum size process and optimum location process. In the first stage, the optimal size of the BESS is evaluated using the RBFNN based on frequency and voltage control so that frequency and voltage of the microgrid can return to nominal values under the sudden changes in the microgrid. In the second stage, the optimal location of the BESS is determined using the RBFNN based on minimizing power losses in the microgrid. With a suitable RBFNN training, the results indicate that the proposed RBFNN method can achieve the superb performances in predicting the optimum size and location of the BESS with a minor change compared to the measured data based on the simulation. Therefore, it is obvious that if BESS is located at the optimum location and has the optimum size, it can save an enormous amount of power in a microgrid and can avoid the microgrid from instability and system collapse.

Optimal Placement and Control of BESS for a Distribution System Integrated with PV systems

Recently, photovoltaic generation (PV) has attracted a great attention as one of green power generations. Accordingly a large amount of PV systems will be installed in residential areas in the coming years. In such scenario, reverse power flow caused by PV in feeders may cause some problems such as voltage rise over upper limits in the distribution line, and the reverse power flow into the distribution substation. Battery energy storage system (BESS) is a promising option to solve the above both problems at the same time. However, the effect of BESS depends on location of BESS. Therefore, it is necessary to consider an appropriate location in the residential distribution system to install BESS to obtain an optimal effect. This paper investigates an optimal placement pattern of BESS in the residential distribution system with certain patterns of PV installation. It has been made clear that the obtained results can determine the best placement pattern of BESS with the minimum required BESS capacity and power loss.