Battery Energy Storage System Capacity Research Articles

Mitigation of Photovoltaics Penetration Impact upon Networks Using Lithium-Ion Batteries

The paper conducts a comprehensive analysis of the impact of very large-scale photovoltaic generation systems on various aspects of power systems, including voltage profile, frequency, active power, and reactive power. It specifically investigates IEEE 9-bus, 39-bus, and 118-bus test systems, emphasizing the influence of different levels of photovoltaic penetration. Additionally, it explores the effectiveness of battery energy storage systems in enhancing system stability and transient response. The transition to PV generation alters system stability characteristics, impacting frequency response and requiring careful management of PV plant locations and interactions with synchronous generators to maintain system reliability. This study highlights how high penetration of photovoltaic systems can improve steady-state voltage levels but may lead to greater voltage dips in contingency scenarios. It also explores how battery energy storage system integration supports system stability, showing that a balance between battery energy storage system capacity and synchronous generation is essential to avoid instability. In scenarios integrating photovoltaic systems into the grid, voltage levels remained stable at 1 per unit and frequency was tightly controlled between 49.985 Hz and 50.015 Hz. The inclusion of battery energy storage systems further enhanced stability, with 25% and 50% battery energy storage system levels maintaining strong voltage and frequency due to robust grid support and sufficient synchronous generation. At 75% battery energy storage system, minor instabilities arose as asynchronous generation increased, while 100% battery energy storage system led to significant instability and oscillations due to minimal synchronous generation. These findings underline the importance of synchronous generation for grid reliability as battery energy storage system integration increases.

Multi-Timescale Voltage Regulation for Distribution Network with High Photovoltaic Penetration via Coordinated Control of Multiple Devices

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.

Optimizing quasi-dynamic wireless charging for urban electric buses: A two-scenario mathematical framework with grid and PV-battery systems

This paper presents a mathematical optimization framework for the strategic placement of quasi-dynamic wireless charging (QWC) stations within road networks to address the charging needs of battery electric buses (BEBs). This study evaluates two scenarios for powering the buses. In the first scenario, a grid-connected system is considered. The optimization aims to minimize annual costs related to capital, operation, and energy losses of the electric bus fleet. This involves determining the optimal locations for QWC stations, the length of power transmitters, and the corresponding battery capacities for the BEBs. Using MATLAB-based optimization tools Casadi and Yalmip, with solvers Bonmin and Fmincon, the optimal configuration includes a 13 kWh battery capacity and a 300 m power transmitter distributed across five bus stop areas. The second scenario employs a chance-constrained optimization approach for an isolated solar photovoltaic (PV) and battery energy storage system (BESS). This system is designed to reliably meet the BEBs' energy requirements throughout the day, considering different seasonal data (winter, summer, all seasons/year-round). The optimization results for the PV and BESS capacities vary with the seasons: 394.247 kW and 2012.6 kWh using summer data, 1762.1 kW and 2738.2 kWh using winter data, and 1610.8 kW and 2741.9 kWh using year-round data. Additionally, the paper examines the impact of varying bus fleet sizes on the optimal battery size and power transmitter combination using a real-world example of the bus route between Khalifa City and Abu Dhabi Downtown in the UAE. The findings suggest that larger batteries with fewer or no charging stations are more economical for smaller fleets. Conversely, as the fleet size increases, a combination of smaller battery sizes and a greater number (and length) of QWC (power transmitters) becomes more cost-effective. This research offers significant insights into the efficient deployment of QWC stations and the integration of renewable energy and energy storage for sustainable urban electric bus networks. The proposed optimization models provide a systematic approach to designing and operating charging infrastructure, contributing to sustainable urban transportation systems. Moreover, the study highlights the influence of seasonal data on PV system sizing and costs.

Optimization method for capacity of BESS considering charge‐discharge cycle and renewable energy penetration rate

AbstractIn order to achieve the “carbon peaking and carbon neutrality” goals, we must vigorously develop renewable energy power generation. As the penetration of renewables progressively escalates, the corresponding demand for battery energy storage systems (BESS) within the power grid rises concomitantly. This paper presents an innovative optimization approach for configuring BESS, taking into account the incremental variations in renewable energy penetration levels and BESS charge‐discharge cycles. Employing incremental analytical techniques and pivotal metrics such as capacity elasticity, the proposed method determines the optimal penetration rate and corresponding BESS capacity outcomes for deploying energy storage systems. An example analysis of a rural power distribution benchmark is carried out by using the method in this paper, which proves the effectiveness of the method in this paper. This methodology was substantiated through its application to a case study of a rural power distribution benchmark, thereby validating its efficacy. Furthermore, it was compared with the particle swarm optimization, providing a comparative assessment of their relative performance.

Optimal Placement and Capacity of BESS and PV in EV Integrated Distribution Systems: The Tenth Feeder of Phitsanulok Substation Case Study

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the African vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations.

Optimal dispatch strategy of battery energy storage system in utility-scale photovoltaic integrated grid under variability

The frequency response of a photovoltaic (PV) system integrated power grid is severely hampered due to inadequate inertial support. Integrating a battery energy storage system (BESS) can assist in maintaining frequency response by providing a rapid injection of active power into the grid. Nevertheless, instead of typical constant droop settings enabled in BESS, adopting variable PV power dependent operation of BESS can ensure frequency stability with reduced injection, especially in PV-integrated grids. From this viewpoint, this paper proposes a novel frequency control approach of BESS depending on the available PV power in the grid. A gradient descent-based optimization is utilized to evaluate the required maximum injection from the installed BESS to maintain frequency response under different PV generation values. This approach is then tested by modifying the BESS frequency controller to keep the active power dispatch from BESS within the calculated value under a given outage. The whole methodology is analyzed in a customized IEEE 39 bus New England System under different PV penetration and generation scenarios. The results indicate successful convergence to optimal dispatch levels for the BESS across all evaluated scenarios. As PV generation increases, the optimal BESS dispatch as a percentage of rated PV output declines gradually. However, at lower PV generation, this value rises rapidly and reaches the installed BESS capacity. Later, the applicability of the methodology is validated by comparing the simulated outputs with different test grid network and another support technique- Synchronous Condenser (SC). From the standpoint of frequency stability, the proposed technique can assist the grid planners in regulating BESS operations in a renewable integrated grid more effectively.

Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management

As electric vehicles (EVs) continue to rise in popularity, there has been an intensified focus on the distribution of power within hybrid renewable energy systems. In this context, the significance of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) models for mitigating grid pressures has been increasingly recognized. These models help mitigate grid pressures by using EVs as reliable loads. This research delves into the technical and economic aspects of a hybrid microgrid integrated with various components such as photovoltaic panels (PVs), wind turbines (WTs), battery energy storage systems (BESSs), and EV grid connections, situated at a specific latitude of 40°39.2′N and longitude of 29°13.2′E. The methodology employed is grounded in advanced stochastic metaheuristic approaches. The infrastructure accommodates two distinct loads: a primary load and another dedicated to EV charging. The cornerstone of the energy framework is renewables, particularly PV panels and wind turbines. A dynamic rule-based energy management scheme is implemented to ensure consistent fulfillment of energy demands with an emphasis on cost efficiency. In defining the dimensions of the microgrid, various algorithms including the Coati Optimization Algorithm (COA), Driving Training-Based Optimization (DTBO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Turbulent Flow of Water-based Optimization (TFWO), White Shark Optimizer (WSO), Zebra Optimization Algorithm (ZOA), and Flying Foxes Optimization (FFO) were used. This configuration was evaluated in terms of the annual system cost, present-day value, loss of power supply probability (LPSP), and leveled cost of energy (LCOE). Thanks to the strategic deployment of the TFWO algorithm, optimal results were achieved for the system, including a PV capacity of 411.0560 kW, a WT capacity of 327.0229 kW, and a BESS capacity of 561.1750 kW. With this configuration, the annual cost of the system was determined to be $4.0499 × 105, the present value was set at $4.6452 × 106, the nominal LPSP was calculated at 0.0487 %, and the LCOE was established at $0.1597/kWh. Notably, a significant portion of the energy demand, approximately 69.87 %, was met through renewables, with the remaining 30.13 % covered by traditional sources. Comprehensive sensitivity analysis of key parameters enabled forecasting of future trends. All research activities underwent thorough review, confirming the TFWO algorithm's superior efficiency and faster convergence compared to other methods. The optimization algorithms were implemented in MATLAB 2022b, and statistical evaluations performed using the R programming language.

Optimizing battery energy storage prototypes for improved resilience in commercial buildings: Gaussian mixture modeling and hierarchical analysis of energy storage potential

The increasing energy demand in commercial buildings has led to the adoption of photovoltaic (PV) systems as a viable solution. However, the gap between energy consumption and PV capacity in commercial buildings remains uncertain, and there is limited research on the potential for energy storage in different commercial building types. This study utilizes Gaussian mixture modeling to extract energy consumption and PV capacity patterns from diverse commercial buildings, using datasets provided by the Department of Energy. By employing hierarchical analysis, we investigate the energy storage potential of various commercial buildings based on the disparities between energy consumption and PV capacity patterns. Moreover, this study introduces innovative battery energy storage system (BESS) prototypes tailored to the specific needs of different commercial building types. These prototypes determine the optimal BESS capacity and charging schedules, providing an effective means of improving electrical resilience in commercial buildings. The findings reveal that medium offices, warehouses, and secondary schools require BESS capacities of 410kWh, 838kWh, and 349kWh, respectively, and recommend charging time windows of 9:00–17:00, 8:00–18:00, and 9:00–14:00, respectively. Additionally, the cost recovery period for Li-ion phosphate and ternary Li-ion batteries ranges from approximately 2.5 to 7.5 years and 4 to 8.2 years, respectively. The developed BESS prototypes based on energy storage potential offer resilience to commercial buildings during power outages caused by natural disasters and emergencies, helping to improve their electrical resilience. This study provides valuable insights into the potential for energy storage in commercial buildings and promotes the wider implementation of BESS in the commercial sector, contributing to the achievement of effective demand-side management.

The role of flexible demand to enhance the integration of utility-scale photovoltaic plants in future energy scenarios: An Italian case study

Photovoltaic (PV) will have a prominent role in the future energy mix to achieve the energy and climate targets. However, its variability requires that its exploitation must be improved to minimize its integration costs. The objective of this research is to provide a methodology applicable to any energy system modelling tool for assessing the benefits of flexible demand as an option to better manage the PV variability and to reduce its integration costs. Two strategies are used to shift the flexible demand within three different scenarios based on the Italian energy system and its planned evolution to the year 2030. The effects of flexibility are investigated through an expansion capacity optimization of battery energy storage systems (BESS) and powerlines transport capacities in three different scenarios. Results suggest that flexible demand allows to reduce the BESS capacity of around 2 %–6 %, and powerlines capacity in the range of 2%–10 %, thus the resulting VRES avoided integration costs varies from 758 M€ to 8611 M€. However, it has negligible effects in boosting RES penetration and lowering the CO2 emissions. The estimated DSM remuneration opens up interesting possibilities for applications in the context of energy communities and electricity market aggregators.

Inertia Energy-Based Required Capacity Calculation of BESS for Achieving Carbon Neutrality in Korean Power System

Frequency response performance in power systems is becoming vulnerable due to the transition toward the higher penetration of renewable energy such as achieving carbon neutrality. In particular, reducing power system inertia energy as the asynchronous generation increases could result in violating the frequency stability constraint when a disturbance occurs in the power systems. In order to control the rapidly fluctuating frequency of the power system with low inertia, it is necessary to introduce fast frequency response resources such as a Battery Energy Storage System (BESS). This paper proposes a method to calculate the required capacity of BESS for compensating the frequency control performance of the power system using inertia energy. For calculating the required capacity of BESS, the inertia energy in the critical power system, where frequency control performance marginally satisfies frequency stability constraint, should be calculated. Also, the inertia energy in the evaluated power system having deficit inertia energy should be calculated. By comparing power systems that respond with different dynamics when the same disturbance occurs, the proposed calculation corresponds to the ratio of inertia energy deficiency based on critical power system inertia energy within the power imbalance. Through various case studies employing Korean power systems, the effectiveness of the inertia energy-based calculation method for the required BESS is verified by the fact that the BESS integrated power system marginally satisfies the frequency stability constraint. In these study cases, it is found that the instant response of BESS is very effective for compensating the frequency control performance of the low inertia power system. By applying the proposed method, it is also found that about 840 MW of BESS can achieve carbon neutrality in the Korean power system.

Optimizing microgrid efficiency: Coordinating commercial and residential demand patterns with shared battery energy storage

The optimization of energy systems within a multi-microgrid framework, enriched by shared Battery Energy Storage Systems (BESS), has emerged as a compelling avenue for enhancing the efficiency of distributed energy networks. In response to the increasing integration of BESS in modern energy systems, this study investigates the implications of incorporating BESS within connected residential-commercial Microgrids (MGs). Unlike previous studies that primarily focused on cost and reliability, this research fills a significant gap in the literature by investigating the optimization of load demands patterns. Specifically, we explore the impact of shared BESS on load demand patterns in commercial-residential MGs. The research introduces two innovative critical load metrics, peak-to-average ratio (PAR) and demand profile smoothness (DPS), to assess the influence of BESS on demand profiles. In addition, the study explores the integration of a Dynamic Thermal Rating (DTR) system, compared to traditional fixed thermal rating systems, to further optimize the performance and efficiency of connected residential-commercial MGs enriched by shared BESS. Three distinct case studies, each comprising a commercial MG (shopping mall, hotel, and office building) paired with a residential MG, were considered. Utilizing a Firefly Algorithm (FA) for optimization, the study determines the optimized BESS capacity for minimum total cost. The results highlight that the implementation of shared BESS, especially in collaboration between commercial and residential MGs, significantly reduces imported energy from the main grid, enhancing MG flexibility and resilience. While the economic benefits of shared BESS may not be substantial (up to 3.25 % cost reduction), the study underscores its contribution to more balanced and smoother load demand curves, with improvements in PAR (up to 15.63 %) and DPS (up to26.05 %). Moreover, considering DTR for transmission lines, instead of fixed thermal rating, improves the PAR and DPS up to 28.52 % and 41.06 % respectively. The findings of this study highlight the importance of considering load demand patterns in the design and operation of MGs and underscore the multifaceted benefits of shared BESS beyond economic considerations.

Battery Energy Storage System for Building Integrated Photovoltaic Applications

Human pursuits’ daily energy needs are consistent; however, renewable energy sources are intermittent in nature. Thus, an energy storage system is required to bridge the generation-demand gap. Electrical energy storage (EES) is a system that converts electrical energy into a form that can be easily stored in various devices and converted back into electrical energy as needed. The battery energy storage system (BESS) is one of the most well-known and promising EES technologies for storing renewable energy. Today, various BESS types are in use; some are established technology, while others are still in R&D. The selection of BESS capacity for building integrated photovoltaic (BIPV) systems necessitates a trade-off between critical criteria, including power vs. energy, design voltage, and operating temperature. This research analyzed various BESS technologies and presented optimal capacity selection and design criteria. Hence, the best BESS design approach takes into account climatic data, PV panel specifications, and budget constraints. While BESS technology varies depending on project requirements and other considerations, lithium-ion batteries are the most commonly used due to their high energy density, efficiency, and extended cycle life. For cost-sensitive BIPV systems, lead-acid batteries are preferred due to their low capital cost, technological maturity, and relatively good cycle efficiency. Thus, the correct quantity of batteries in a BESS bank is determined by energy storage goals, BESS type and size, and service load. Finally, the paper concluded with recommendations for best practices for BESS design, appropriate operation, and maintenance conditions for Nigeria’s prevalent BESS technology for BIPV applications

Power quality enhancement for Thailand's wind farm using 5 MWh Li-ion battery energy storage system

Several studies have stated that an increase in wind power plants and unpredictable wind energy generation face several issues during over-voltage. In this study, an operational 8 MW wind farm in Nakhon Ratchasima province faced severe annual feeder trips 146 times, which is not permissible according to the manufacturers. The feeder trips are mainly attributed to high grid voltage caused by low load demand. This study developed a Battery Energy Storage System (BESS) to minimise feeder trips using DigiSILENT and utilized the higher feeder trip period of September to optimise BESS capacity with a 10 % grid load. Notably, a 5 MWh BESS maintained the grid voltage within an allowable range compared to the 1–4 MWh (increment of 1 MWh) throughout the operational period. Furthermore, a 5 MWh BESS was tested during low and high wind periods to assess the stability of over-voltage management. We found that the 5 MWh BESS controlled over-voltage and prevented feeder trips, resulting in enhanced power generation on selective days with high feeder trips, low wind speed, and high wind speed, generating 28.34 MWh, 0.33 MWh, and 76.67 MWh, respectively, compared to conditions without BESS. Additionally, we recommend that the 5 MWh BESS can enhance wind farm power stability and uphold the manufacturer's warranty.

Two-Stage Optimization Model of Centralized Energy Storage Participating in Peak Shaving with Maximum Reserve Capacity and Minimum Carbon Emission of the System

As the proportion of renewable energy increases in power systems, the need for peak shaving is increasing. The optimal operation of the battery energy storage system (BESS) can provide a resilient and low-carbon peak-shaving approach for the system. Therefore, a two-stage optimization model for grid-side BESS is proposed. First, the carbon emission model of thermal power units considering BESS is proposed to describe the ability of the BESS in reducing the carbon emissions. Second, in order to deal with the uncertainty of the photovoltaics and wind forecast errors, a certain capacity of BESS is reserved. The model in the first stage takes the lowest carbon emission of the system as the goal, and the model in the second stage determines the BESS reserve capacity with the objective of minimizing the risk cost of the system. The simulation results show that the carbon emission model of thermal power units with BESS can measure the contribution of energy storage to emission reduction. By setting the reserve capacity of energy storage, the peak-shaving resilience of the system is improved, and the risk of photovoltaics and wind forecast error is reduced.

Optimisation of a standalone photovoltaic electric vehicle charging station using the loss of power supply probability

The UK is planning to ban the sale of fuel vehicles entirely by 2035 and electric vehicles will be a potential alternative to fuel vehicles. The increase in electric vehicles will increase the charging demand. Standalone charging stations are a potential solution to alleviate the grid challenges of increased charging demand. In this work, the authors investigate a reliability analysis of a 2 MW standalone photovoltaic electric vehicle charging station (PVEVCS) using the loss of power supply probability(LPSP). The PVEVCS model consists of a PV system, a battery energy storage system (BESS) and a CS, using the climate data from Camborne, UK and classifying it into high and low irradiation sections. Next, four different charging demand profiles are selected to examine the models’ LPSP. Later, the chosen charging demand profiles are optimised using various combinations of PV systems, BESS and CS. It is concluded that the different solar irradiation had a significant effect on the LPSP. Under the same combination, higher PV capacity has a more positive impact on reducing daytime LPSP, higher BESS capacity has a more significant effect on lowering nighttime LPSP and larger CS capacity has a more significant impact on declining hourly LPSP.

Development of Virtual Inertia Control with State-of-Charge Recovery Strategy Using Coordinated Secondary Frequency Control for Optimized Battery Capacity in Isolated Low Inertia Grid

Integrating renewable energy through inverter-based generators has decreased the power system’s inertia. Reduced inertia may lead to frequency instability during power imbalance disturbances, particularly in an isolated power system with limited inertia. The Battery Energy Storage System (BESS) and a virtual inertia (VI) emulation control system have become popular to mitigate this issue. Nonetheless, the BESS utilization for VI emulation is highly dependent on the availability of BESS capacity, which may affect the energy cost. Therefore, developing a VI emulation control strategy that requires less energy and can recover the state of charge (SoC) to a desired level to optimize BESS utilization is required. This paper proposes a VI control with an SoC recovery strategy through coordination with the generators’ secondary frequency control. Instead of relying on the frequency, such as in the conventional approach, the controlled signal of the generators’ secondary frequency control also includes the VI power and BESS SoC. Hence, the generators can contribute to lowering the VI required energy and recovering the BESS SoC. The results show that the proposed method outperforms the conventional method by requiring around 36% lower energy and the ability to maintain the BESS SoC.

Reliability of autonomous solar-wind microgrids with battery energy storage system applied in the residential sector

Residential electricity generation can benefit from the successful deployment of photovoltaic (PV) and wind renewable energy sources. However, their intermittent nature poses a significant limitation. To address this, a hybrid system can be employed to enhance system reliability and efficiency. Reliability analysis plays a crucial role in evaluating the energy production capacity to meet the demand. This study aims to assess the reliability of a hybrid PV/wind microgrid through simulation. Data from a residence were collected every 10 s, and average values were computed on an hourly basis and exported for computer processing. Solar irradiation, wind speed, and temperature data were also utilized. The modeling process involved defining the PV panel, wind turbines, battery energy storage system (BESS), management strategy, and energy autonomy. The results indicate that when PV and wind systems operate independently, they are unable to consistently reduce the residential energy deficit. However, the PV/Wind/BESS configuration significantly improves system operation and proves sufficient to meet the required load in most hours. Through this configuration, only 42.5% of the total PV/wind energy utilized by the residents needs to be dispatched through the BESS. Moreover, the BESS capacity is reduced by 50% when the systems are used separately. The public grid only supplies power when the BESS is insufficient to cover the load. Therefore, the optimal sizing of the BESS plays a critical role in system viability, reducing initial installation costs, regulating microgrid parameters, and contributing to the reduction of the energy deficit. Hence, investigating the fundamental design parameters of the microgrid and BESS is essential for identifying the optimal capacity of the system and ensuring model reliability.

Optimal battery energy storage system deployment from perspectives of private investors and system operators for enhancing power system reliability

Due to their intermittent nature, high penetration of renewable energy resources (RES) can deteriorate power system reliability. A battery energy storage system (BESS) offers an opportunity to reduce the uncertainty associated with RES and hence improve power system reliability. In the last decade, the cost of BESS has declined so its deployment has become increasingly feasible, but the decision ultimately depends on its Net-Benefit to the investor. Depending on the perspective of the investor, the deployment decision involves discovering the most optimal location, capacity, and technology of BESS. A sensitivity analysis based on the Z-bus matrix is used for selecting the optimal location. In this paper, from the narrow perspective of a private investor, the Net-Benefit function is the energy arbitrage value of BESS minus its overall cost. However, the system operator additionally values BESS because it enables more reliable system operation at reduced operating costs. After examining multiple power system reliability indices, the product of Expected Unserved Energy (EUE) and Value of Lost Load (VoLL) is incorporated into the Net-Benefit function of the system operator. MATPOWER Optimal Scheduling Tool (MOST) is used for testing the proposed Net-Benefit functions on a standard reliability test system (RTS) known as RTS-GMLC. Three diverse case studies are examined in three exploration phases; the first phase helps determine search space whereas the second one narrows it down and the third phase finds the optimal BESS solution. Presented research results demonstrate that, over a period of 15 years, optimal BESS deployment can offer as much as $M164.8 and $M33.6 Net-Benefits to the system operator and private investor respectively.

Battery-Based Energy Storage and SolarSolar Technologies Integrated for Power Matching and Quality Improvement Using Artificial Intelligence

Power quality issues, such as abnormal voltage, current, or frequency, can cause equipment malfunctions and failures, resulting in financial losses. With the developing integration of RES into distribution networks through custom power devices like electronic power cassette adapters, there is a need to address the challenge of integrating the variable output of RES into traditional grids. Energy storage systems are a potential solution, but they are costly for RES applications. This study proposes a hybrid solar structure combined with battery energy storage systems (BESS) to optimize power consumption and improve power quality using a meta-heuristic approach. The research methodology involves simulation using real solar PV data. The significance of the proposed approach lies in its potential to enhance power quality while utilizing the maximum capacity of BESS, thereby improving the integration of RES into distribution networks and mitigating financial losses caused by power quality issues.

Operation Simulation and Economic Analysis of Household Hybrid PV and BESS Systems in the Improved TOU Mode

With the popularization of electric vehicles and electric boilers, household electricity consumption will increase significantly. Household hybrid photovoltaic (PV) systems and battery energy storage systems (BESSs) can supply increasing household electricity consumption without expanding the existing distribution network. This paper validates the technical feasibility of connecting a large number of household power users that contain BESSs and PVs in a distribution line by a simulation in Matlab. In addition to technical feasibility, this article improves the time-of-use (TOU) form to achieve economic feasibility (covering equipment costs). In the past, the TOU was set from the perspective of the load demand of the grid, but the actual user participation would affect this effect. In this paper, based on a social science survey, a new three-level rate TOU is introduced, which has little impact on residents’ lifestyle, to effectively increase the response frequency effectively. Combined with the improved TOU and the state of PVs, the BESS control mode is set for simulation. To compare the three-tier rate TOU with the normal TOU tariff and select the best household BESS size, a MATLAB simulation is used to simulate the common household BESS capacity. The results indicate that the combination of the three-tier rate TOU with a 4 kWh household BESS can afford the investment of household PVs and BESSs. The high cost issue that previously primarily limited the true use of BESSs is expected to be resolved.