Sizing Of Energy Storage System Research Articles

Data-driven optimization for seismic-resilient power network planning

Many regions of the planet are exposed to seismic hazards that can cause devastating consequences on power systems. Due to these systems’ crucial role, the evaluation and planning for their safe and reliable operation are paramount. This paper develops a novel data-driven optimization framework to assess the power network’s seismic resilience and plan cost-effective investments for its enhancement. Under a robust optimization scheme, an earthquake attacker–defender model finds the worst-case realization of random earthquake network contingencies within an uncertainty set defined with a large number of scenarios generated by state-of-the-art engineering methods. Moreover, data-driven stochastic-robust optimization is employed in a two-stage seismic-resilient power network planning model, leveraging multiple seismic sources’ distributional information. Transmission line expansions and siting and sizing of battery energy storage systems are decided in the first stage, while the second stage decides operational variables. Experiments on a 281-node Chilean power system provide insights for seismic-resilient planning and demonstrate the efficiency of the proposed approach.

Mitigation of total harmonic distortion and flicker emission in the presence of harmonic loads by optimal siting and sizing of wind turbines and energy storage systems

In distribution networks (DNs), there are significant technical challenges due to the high penetration of renewable energy resources (RERs), especially wind turbines (WTs) and the presence of non-linear electrical loads. One of the most important challenges is power quality. In such networks, to mitigate flicker emission produced by WTs and network voltage harmonics, it is necessary to solve the planning problems considering power quality objectives and standards. For this aim, this paper attempts to improve DN performance by optimal siting and sizing of WTs and energy storage systems (ESSs). In the planning problem, to take into account the power quality issue, flicker emission, total harmonic distortion (THD), and network voltage deviation (NVD) are included in the optimization problem. To effectively solve the planning problem, which is a non-linear and non-convex optimization problem, three variants of genetic algorithm (GA) are used. Simulation results indicate that optimal sizing and siting of WTs not only significantly improve the power quality issue but also improves the network performance.

Diurnal variations in wind power density analysis for optimal wind energy integration in different Indian sites

Wind power is becoming increasingly crucial in the global transition to sustainable energy systems. It is rare to find studies based on diurnal variation of wind energy potential for different sites. Seasonal patterns could lead to overestimating the night-time and underestimating the daytime wind power density (WPD), resulting in load losses and higher generation costs. Present study includes Diurnal variations in WPD, that will estimate energy generation patterns more accurately. Current analysis uses hourly wind speed data collected from IMD for six coastal Indian sites spanning 1969–2007. Seasonal, monthly, and diurnal variations at 30 m and 60 m hub heights are analyzed using Weibull distribution model. The peak diurnal WPD of 714.53 W/m2 is observed in Tuticorin during July at 60 m hub height, and the lowest is found during December. The results show substantial differences between day and night WPD, emphasizing the importance of accounting for diurnal wind speed fluctuations. This study can be helpful for the proper sizing of energy storage systems to ensure uninterrupted energy supply throughout the day and night, installing hybrid energy storage systems. It can also help designing hybrid energy systems for power generation, reducing operational costs and environmental impacts.

Optimal sizing of battery energy storage system for a large‐scale offshore wind power plant considering grid code constraints: A Turkish case study

AbstractIntegration of large‐scale wind farms (WFs) into the grid has to meet the critical constraints set in the national grid code. Wind farm operators (WFOs) are inclined to comply with these constraints and avoid heavy penalty costs for violating such regulations. However, this may result in reduced power sent to the grid. Moreover, the addition of new rules to account for the increased penetration of WFs brings challenges to the profitability of the WFs. A battery energy storage system (BESS), if sized optimally, can be a reliable method to fulfill the grid code requirements without sacrificing profit. This paper provides a techno‐economic model to find the optimal rated capacity and power for a BESS in WFs. This optimization model takes the absolute production and delta production constraints into account. Two approaches are studied for integrating these constraints into the grid code. It is shown that the flexible strategy financially outperforms the strict addition of the new rules. This will be useful, especially to attract investments in wind energy projects despite the abovementioned limitations in the grid code. All the modeling and analysis are done for a potential offshore wind power plant (OWPP) in Turkey. Simulation results show the effectiveness of the optimal BESS in increasing the amount of energy delivered to the grid and improving the profitability of the OWPP.

Optimal sizing and optimization of financial cost for EVs using SPOA-RBFNN technique

This study proposes a hybrid technique for optimal sizing and cost optimization of hybrid energy storage systems (HESS) integrated into electric vehicles (EVs). The proposed technique, SPOA-RBFNN, combines a student psychology-based optimization algorithm (SPOA) and a radial-basis function neural network (RBFNN). The study aims to minimize the overall cost of the HESS by evaluating two design variables: the super-capacitor (SC) and battery pack size. SPOA is employed to optimize the hybrid HESS design variables, ensuring efficient exploration of solution spaces. The RBFNN method is then used to predict the relationship between these design variables and the overall cost of the HESS in electric vehicles. The results show that the proposed technique is more effective than existing techniques, with an efficiency of 97.99039% compared to 82.137% for GA and 77.26589% for particle swarm optimization (PSO). This work offers a comprehensive and innovative approach to optimizing HESS sizing in EVs, connecting the gap between performance optimization and financial cost analysis.

On sizing of battery energy storage systems for PV plants power smoothing

The high variability of solar irradiance causes fluctuations in the generation of photovoltaic (PV) power plants. This characteristic affects power system operation, thus, strategies are necessary to mitigate the intermittent characteristics of PV power plants. Several countries adopt grid codes incorporating ramp rate (RR) limitations for PV power injection into the grid, also known as power smoothing control. In this sense, battery energy storage systems (BESS) with coordinated RR control algorithms are commonly applied to mitigate current fluctuations from the PV system to the grid. Therefore, studies on the effect of local solar irradiance on BESS requirements for PV power smoothing applications are very important. It is also worth highlighting that in the literature there is a gap in the evaluation of aspects of battery lifetime, BESS volume, price and BESS oversizing for selecting an ideal battery model, among a catalog of batteries, constituting an industrial concern. Therefore, this work presents a detailed BESS sizing methodology, evaluating aspects of the energy requirements, power, and discharging rate (C-rate), based on the mission profile (featuring a customized solution for each project). In addition, the effect of the battery temperature control on the BESS sizing is evaluated. The case study is based on a 1 MW PV system, assessed using a one-year mission profile of solar irradiance and ambient temperature from Goiânia-Brazil. The PV plant is connected to a 440 V grid associated with a BESS that mitigates oscillations of 10%/min. The BESS sizing is evaluated in terms of BESS volume, lifetime, and oversizing factor for power and energy. The proposed methodology reduces the BESS volume by 57.14% in relation to the traditional BESS design of literature.

Optimal Sizing and Energy Management of Electric Vehicle Hybrid Energy Storage Systems With Multi-Objective Optimization Criterion

Optimal Sizing and Energy Management of Electric Vehicle Hybrid Energy Storage Systems With Multi-Objective Optimization Criterion

Bi-level Optimization of Sizing and Control Strategy of Hybrid Energy Storage System in Urban Rail Transit Considering Substation Operation Stability

Bi-level Optimization of Sizing and Control Strategy of Hybrid Energy Storage System in Urban Rail Transit Considering Substation Operation Stability

Probabilistic Planning for an Energy Storage System Considering the Uncertainties in Smart Distribution Networks

Today, many countries are focused on smart grids due to their positive effects on all sectors of a power system, including those of operators, utilities, and consumers. Furthermore, the usage of renewable energy sources for power production is quickly expanding due to the depletion of fossil fuels and the emissions caused by their use. Additionally, intermittent power generation from renewable energy sources, such as wind and solar, necessitates the use of energy storage devices with which to ensure a continuous power supply to meet demand. This can be accomplished by employing an appropriate storage device with a sufficient storage capacity, thus enabling a grid-connected solar PV and wind system to have enhanced performance and to reduce adverse effects on the power quality of the grid. In this study, a probabilistic planning model that takes the intermittent natures of solar irradiances, wind speeds, and system demands into account is introduced. A novel criterion is also adopted to map the three-dimensional spaces of intermittency with the proposed model for optimizing BESS charging/discharging decisions. This planning model is intended to minimize the economic costs of investment and operation of a battery energy storage system (BESS) for a planning period. Moreover, the substation and feeder upgrade costs, as well as the overall system loss costs, are included in the proposed model. Particle swarm optimization (PSO) is utilized to find the optimal sizing, location, and operation of energy storage systems. The proposed methodology was validated using a 69-bus distribution system.

Optimal sizing of battery energy storage systems and reliability analysis under diverse regulatory frameworks in microgrids

The integration of battery energy storage systems (BESS) with microgrids (MG) is crucial to improve the reliability and flexibility of renewable energy sources (RES) integration. However, the reliability and regulatory policies are critical factors that affect the optimal operation of MGs in the market. This study aims to enhance the reliability of MGs integrated with RES and BESS by evaluating their performance under different regulatory frameworks, namely feed-in tariff (FiT), net metering (NM), and energy storage incentive (ESI). Also, a dynamic FiT (D-FiT) framework is utilized to improve the reliability of the MG. An artificial bee colony optimization algorithm is utilized to optimize the size of BESS for each regulatory policy to minimize the total cost of the MG. Each policy is formulated based on its specific constraints in the problem. Subsequently, the reliability indices of Loss of Load Expectation (LOLE) and Expected Energy not Supplied (EENS) are calculated for each optimized solution. Moreover, we have integrated the dynamic thermal rating (DTR) system into our proposed model, focusing on the safe augmentation of system component ratings. The study finds that the D-FiT and standard FiT frameworks provide the best reliability level, whereas the reliability improvement under the ESI policy is not significant, as most of the MG's demand is supplied by the main grid. Furthermore, the study shows that the improvements in EENS are higher than LOLE, indicating that installing BESS reduces the loss of energy rather than the number of interruption hours. D-FiT framework has a significant positive impact on both reliability indices, unlike the other frameworks that have a greater effect on EENS. Furthermore, we have noticed a substantial improvement in reliability indices when the DTR system is taken into account, as compared to the static thermal rating (STR) system.

Design and techno-economic analysis of an off-grid integrated PV-biogas system with a constant temperature digester for a cost-effective rural application

This study addresses the issue of fluctuating output in cow dung-based biogas (BG) production due to temperature dependence. A thermal model is proposed and implemented in an area with abandoned BG resources, utilizing typical meteorological year (TMY) data. The model aims for constant BG output by maintaining digester temperature at 39 °C throughout the year. It is found that integrating heating with a 3100 CUM cow dung-based biogas digester (BD) increase the energy production capacity from 2.25 GWh/year to 3 GWh/year. Subsequently, three different cases are studied: standalone PV, PV-BG system, and PV-BG system with the heating arrangement, and they are scheduled using the area's TMY data, according to a strategy that can fulfil highly dynamic load curves with 690 kW peak and 264 kW average demand, as reported by the Government of India, with zero loss of power supply probability (LPSP). In the proposed case, the integration of heating reduced the PV and energy storage system (ESS) sizes by 65 % and 52 %, respectively. The current system imposes a COE of INR 2.1/kWh, which is 70 % cheap than grid electricity in the surrounding areas, with the help of proper resource estimation utilizing TMY data, integration of heating, and compressed biogas (CBG) production with energy.

Integrated Optimal Energy Management and Sizing of Hybrid Battery/Flywheel Energy Storage for Electric Vehicles

This paper presents an integrated optimal Energy Management Strategy (EMS) and sizing of a high-speed Flywheel Energy Storage System (FESS) in a battery electric vehicle (EV). The methodology aims at extending the battery cycle life and drive range by relegating fast dynamics of the power demand to the FESS. For the EMS, the battery power and FESS energy are considered as weighted objectives of an optimization problem that are established using Pontryagin's Minimum Principle (PMP). In order to derive the optimal FESS size, the sizing algorithm is targeted to minimize the battery degradation level and increase the FESS energy interaction with the system. The performance of the proposed methodology is assessed using some driving cycles including ECE-15, UDDC, HWFET, and US06. Comparing the proposed battery/FESS to the battery-only topology, the battery SoH and life cycle are improved considerably for different driving cycles.

Optimal sizing and scheduling of battery energy storage system with solar and wind DG under seasonal load variations considering uncertainties

Battery energy storage systems (BESS) are integrated with renewable distribution generators (DG) within the distribution network (DN) to mitigate active power loss and improve the bus voltage profile through optimal placement and sizing. This work identifies the optimal location for BESS and DGs placement by deriving a loss sensitivity factor by considering the power flow through the branches and bus losses. With optimal location, a novel approach for selecting an optimal charging/discharging time schedule is proposed. This method aims to determine the optimal size and scheduling of BESS through the minimization of the voltage deviation and real power loss in the DN. Following the installation of BESS, the wind and solar DG optimal sizes are determined at maximum power generating hours. To address the uncertainties present in DN, Hong’s (2m+1) point estimation method is utilized to optimize the network operation incorporated with BESS and DGs. Furthermore, a techno-economic analysis is conducted to examine the energy savings resulting from integrating DGs and BESS in the DN. The power losses are further reduced by implementing network reconfiguration to reduce the energy dependency on the grid. The effectiveness of the proposed approach is tested on a 33-bus, 69-bus, and 118-bus DN under seasonal load variations. Incorporating the renewable DG with BESS into the DN reduces the grid energy supply by 30%–40%. The results of this study pave the way for more sustainable and efficient power systems by contributing to the development of techniques for integrating renewable energy sources into DNs.

Probabilistic sizing and scheduling co-optimisation of hybrid battery/super-capacitor energy storage systems in micro-grids

While established deterministic capacity planning models for single-component energy storage systems exist, little attention has been given to probabilistic sizing of hybrid energy storage systems (ESSs) using swarm-based meta-heuristic algorithms. This highlights two key research opportunities, namely: (1) studying the impact of preserving model-inherent characteristics and optimising daily system dispatch on narrowing reality gaps in hybrid ESS designs, and (2) the optimal integration of hybrid ESSs into grid-connected micro-grids based on their applications, with potentially significant financial implications for model designs. In response, this paper introduces a novel probabilistic hybrid ESS capacity planning optimisation model based on a state-of-the-art meta-heuristic algorithm. To demonstrate the effectiveness of the model within a community micro-grid scheme, a case study of an eco-village in Aotearoa New Zealand is presented. The simulation results indicate a ∼4 % and ∼36 % premium above the deterministic results respectively in the most likely case and worst-case probabilistic scenarios. On the other hand, the best-case stochastic estimate of the life-cycle cost of the hybrid ESS is found to be ∼39 % lower than that of the deterministic modelling. Additionally, the economics of temporal energy arbitrage using the battery bank is investigated, indicating that at the current capital cost of stationary LiFePO4 batteries and the present fixed feed-in-tariff (NZ$0.08/kWh), it is not economically viable to cycle the storage for arbitrage reasons alone. In conclusion, this paper highlights the critical need to incorporate probabilistic optimisation techniques and emphasises the importance of sizing and scheduling co-optimisation when designing hybrid ESSs for integration into grid-connected micro-grids.

Review of ancillary services and optimal sizing of an energy storage system in a microgrid

The rapid growth of distributed energy generation has brought new challenges for the management and operation of power systems. Voltage fluctuation is one of the primary factors preventing further photovoltaic (PV) penetration in low-voltage networks. The proper sizing and location of any storage system are essential for reducing costs and eliminating losses, which is another element. This review presents an in-depth overview of the different ancillary services that storage systems may offer and a proper sizing of energy storage systems (ESS). Different kinds of ESSs store energy in different ways. Some of these have reached their full potential, while others are still being worked on. Specific storage solutions may be picked depending on how well the application needs to run. Pumped-hydro and thermal energy storage systems are best for large-scale energy storage, while battery energy storage systems are highly suggested for high power and energy needs. Most of the time, supercapacitors, superconducting magnetic energy storage (SMES), and flywheel energy storage (FES) are used for short-term and fast-response uses. Hydrogen and methane are not often used to store energy because they could be more efficient. A few case studies are also given.

Optimal sizing of battery-supercapacitor energy storage systems for trams using improved PSO algorithm

A hybrid energy storage system (HESS) of tram composed of different energy storage elements (ESEs) is gradually being adopted, leveraging the advantages of each ESE. The optimal sizing of HESS with a reasonable combination of different ESEs has become an important issue in improving energy management efficiency. Therefore, the optimal sizing method of battery-supercapacitor energy storage systems for trams is developed to investigate the optimal configuration of ESEs based on a constant power threshold. Firstly, the optimal sizing model of HESS taking size, mass, and cost of ESEs as a comprehensive objective function is established. Then, an improved particle swarm optimization (PSO) algorithm with a competition mechanism is developed for obtaining the optimal configurations of ESEs. Finally, Guangzhou Haizhu tram is taken as an example to verify the developed method. The results show that the optimal HESS with different ESEs has advantages in reducing the size, mass, and cost, and exerting the charging and discharging capacity of ESEs.

Investigation of Low Order Parameters Affecting Tidal Stream Energy Resource Assessments

Tidal stream energy may have the potential to contribute to the baseline energy needs of the UK. There is a significant difference between the existing assessments of the UK’s resource, highlighting the need for a standardised model. The paper investigates some challenges of quantifying tidal stream energy. This study examines the sensitivity of resource assessments to low-order parameters, in order to inform higher order modelling and contribute to defining a standardised model. A combination of channel characteristics, turbine parameters and simulation settings formed the basis of 96+ cases to determine key parameters affecting resource assessments. Two 0-dimensional, idealised channels were modelled using 2-8 constituents from four UK sites spanning the semi-diurnal form factor range. Blockage-corrected blade element momentum theory was used to represent variable-speed, variable-pitch turbines. Three blockage designed rotors and one rotor designed for an unspecified blockage were simulated. Power capping and combined power and thrust capping were implemented.
 
 The study indicates dynamic balance is a key parameter affecting tidal dynamics when turbines are present in a channel. Variability of annual energy production due to the nodal cycle was most significant in the drag dominated channel with increasing blockage. Constituents with large amplitude and phase differences led to greater variability of the resource across the nodal cycle. Results indicate that the maximum deviation from the average annual energy production over the 18.6-year nodal cycle is +/- 8.8%. At a low form factor site, the difference between using 2 or 4 constituents was insignificant. However, for a higher form factor site, that is still semi-diurnal, the peak velocity increases by 0.32 m/s with 8 constituents compared to 2. Random Forest modelling indicates that the most important characteristic point on a variable-speed, variable-pitch turbine performance curve is the rated speed. Comparison of capping strategies highlights that combined power and thrust capping leads to an increase in energy per swept area. Combined capping decreased the variability of the resource over the nodal cycle, which is helpful to maintain a steady supply to the grid and sizing energy storage systems.

Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt®

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale has grown and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or with a variety of distributed energy generation and storage systems. One challenging factor for the adoption of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total cost; properly sizing energy storage systems is difficult in simple cases, where a battery is costed independently of any other systems, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt® is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or inform degradation-aware control strategies to optimize battery dispatch and extend lifetime.This work incorporates a battery life model into REopt. The battery life model is linearized so that it may be solvable within the constrains of a mixed-integer linear optimization problem. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying a state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model are made to ensure validity of lifetime estimates made by REopt.Battery life impacts system cost through three decision variables: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cells reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to inform battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when economically favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries are often operated using ‘just-in-time’ charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, increasing battery capacity to provide more flexibility for dispatch, enabling the extension of useful life beyond 10 years and resulting in greater avoided utility costs per unit for energy storage while providing more energy storage for grid resiliency.

Optimal sizing and feasibility analysis of second-life battery energy storage systems for community microgrids considering carbon reduction

The rapid development of distributed renewable energy has made energy storage essential for efficient operation. However, energy storage is challenging for prosumers due to high investment costs and long payback periods. The second use of batteries has been evaluated as an alternative energy storage after the first service in electric vehicles with the remaining 80% capacity. This study investigates the feasibility of using secondary-use batteries as shared storage for prosumers. Financial and technical break-even points regarding the gradually increasing carbon limit on the way to 100% renewable have been determined. The proposed method's performance has been verified by conducting sensitivity analyses regarding various environmental and economic characteristics. The self-consumption rates have been achieved by 65.9% in Spain (Madrid) and 49.9% in England (London), and 48.5% in Türkiye (Antalya) without incentive. Increasing the carbon tax by 1 $/t for high and low energy tariffs can reduce carbon emissions by 181.3 and 1607 kg/yr. The optimum sizes remain until the carbon tax threshold is 60 $/tCO2eq for high energy tariffs. After 60 $/tCO2eq, it is tended to enlarge the hybrid power system. However, severe carbon emission restriction for zero carbon targets prevents the optimum system design since the carbon reduction cost increases to 232.7–304.8 $/kg. This study encourages investors and prosumers to plan the shared energy storage system sensibly and guides governments to develop realistic incentive mechanisms to implement second-life batteries effectively.

A two-layer optimal configuration approach of energy storage systems for resilience enhancement of active distribution networks

Introducing energy storage systems (ESSs) into active distribution networks (ADNs) has attracted increasing attention due to the ability to smooth power fluctuations and improve resilience against fault disturbances. This paper proposes a methodology for simultaneously optimizing the configuration of battery ESSs and the operation of ADNs, and the goal is to increase the resilience of the ADNs withstanding multi-faults. Firstly, based on random sampling and K-means clustering, a generation strategy of typical N-1 and N-2 fault scenarios is designed for the ADNs. Then, a two-layer optimization model is established, where the inner model is to optimize the fault recovery performance from the operational perspective, and the outer model is to obtain the optimal site and size of ESSs from the economic perspective. Further, the second-order cone relaxing (SOCR) method and the hybrid gray wolf optimal and particle swarm optimal (GWO-PSO) algorithm are applied to solve the optimization model. Using MATLAB, the modified IEEE 33-node and 118-node systems are built to check the proposed approach's performance. Different periods are considered to show the multi-faults' development, and by introducing a resilience assessment system with node voltage deviation, fault recovery rate, and network loss rate, the resilience of the ADNs is analyzed. From the comparative results, the proposed approach can optimally configure the battery ESSs, and adjust the network structure as well as the distributed generation outputs. Following the ESS configuration cost reduction of 53.19% and 9.8%, the resilience of the ADNs against the multi-faults will increase by 13.36% and 8.25% for the 33-node and 118-node systems.